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Assignment to disaster

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Friday 23 November 2012

Assignment to disaster (sam durell #1) by edward s. aarons (gold medal / muller 1955/59); feat. charles binger cover art.

7 comments:

I am not so interested in these faux-Bond titles. It seems as revolutionary as the Bond novels and films were they produced an amazing amount of bad rip-offs. I am still waiting for the inimitable Nick Jones review of a Travis McGee novel.

That's great except this is not a faux-Bond and is actually quite superior to any of Fleming's novels.

I really can't agree with that assessment, Dave. Certainly the Bond novels helped to spur a wave (though they were far from alone), but these books stand on their own. Just because something doesn't come first, doesn't make it invalid. Plus, Aarons was a very different stylist than Fleming. You should read one.

Funnily enough, Olman, I was making pretty much the same point in the comments on the previous post. Who's to say if Aarons had even read Fleming before penning Assignment to Disaster; Casino Royale wasn't published in the US until 1954, and didn't sell that well anyway. Dave, I promise you won't have much longer to wait for some thoughts on McGee!

In all fairness to the OP on this thread, he's always been very consistent in his policy that one has every right to pass judgment on any book or film, sight unseen. Oh wait.... ;)

Fascinating--so glad you started this, Nick. http://www.mysteryfile.com/Aarons/Durell.html Research is a bit spotty (Casino Royale came before Assignment Disaster), but gives you an idea of how the series developed. There really is SO much less written about Aarons than Fleming--out of all proportion to the disparity in book sales, and Durell obviously sold a whole lot of books. Casino Royale came out shortly before the first Durell, but basically vanished without a ripple in the American market. The next Bond, Live and Let Die, didn't do much better stateside, but got a lot of very favorable notices from prominent critics. Fleming was originally considered to be a worthy successor to Raymond Chandler (Chandler himself liked the early 007 novels). That's after the first Durell came out, but hard to think Gold Medal wasn't paying attention as the Bond buzz grew louder--Durell got tweaked quite a lot in later novels, probably not just in reaction to Bond, but in part. He was not originally a superspy--just a capable one. In terms of book sales, Bond didn't become a force to reckon with in the U.S. until 1961, when President Kennedy said From Russia With Love was one of his ten favorite books. That created a run on 007, which led to Dr. No being made into a movie (not the first adaptation, but the first that clicked), and once the Connery films started coming out, everybody else in the spy-biz was an also-ran. Curious, though--with the plethora of spy films in the 1960's, featuring Bond, Helm, Flint, Palmer, Quiller, etc--why were there no Durell films? If they'd started in the late 50's/early 60's they could have gotten Robert Mitchum to play him (Cape Fear was 1962, and Mitchum was at the absolute top of his game then--not even Connery was quite in that league). If that had happened, the whole story could have gone very differently (though I don't see Mitchum doing more than one or two). When exactly did the Durell books start selling big? Though there were quite a few of them by 1961, that doesn't mean the series had really taken hold, since Gold Medal wasn't really about mainstream best-sellers most of the time--Durell was probably doing tidy book sales from the first, but I'd guess it was the Bond-inspired spy craze of the 60's that sent his sales skyrocketing, and made it possible for Aarons to finally make his mark, after a long career of writing little-noticed mysteries. So yes, I think James Bond was an influence on Sam Durell, but only well after both had made their respective debuts.

Chris, point taken. After all my yak-yak about not judging a book by it's cover, I shouldn't neccessarily clump this writer in with the faux Bonds. Hope you're doing well, Chris. I hadn't seen any of your posts here or on IMDB for a while and was wondering if you'd been effected by the Hurricane or something--I'm seeing your posts again so I'm glad you're OK. We may not agree on much but I've always gotten a kick out of your snarky sense of humor. Peace be with you!;-)

Title: Assignment to Disaster

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Publications

Human activity the common link between disasters around the world

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Disasters such as cyclones, floods, and droughts are more connected than we might think, and human activity is the common thread, a UN report released on Wednesday reveals.

The study from the UN University, the academic and research arm of the UN, looks at 10 different disasters that occurred in 2020 and 2021, and finds that, even though they occurred in very different locations and do not initially appear to have much in common, they are, in fact, interconnected.

A consequence of human influence

The study builds on the ground-breaking Intergovernmental Panel on Climate Change ( IPCC ) assessment released on 9 August, and based on improved data on historic heating, which showed that human influence has warmed the climate at a rate that is unprecedented in at least the last 2,000 years. António Guterres, the UN Secretary-General described the IPCC assessment as a “code red for humanity”.

Over the 2020-2021 period covered by the UN University, several record-breaking disasters took place, including the COVID-19 pandemic, a cold wave which crippled the US state of Texas, wildfires which destroyed almost 5 million acres of Amazon rainforest, and 9 heavy storms in Viet Nam - in the span of only 7 weeks.

Arctic-Texas link

Whilst these disasters occurred thousands of miles apart, the study shows how they are related to one another, and can have consequences for people living in distant places.

An example of this is the recent heatwave in the Arctic and cold wave in Texas. In 2020, the Arctic experienced unusually high air temperatures, and the second-lowest amount of sea ice cover on record.

This warm air destabilized the polar vortex, a spinning mass of cold air above the North Pole, allowing colder air to move southward into North America, contributing to the sub-zero temperatures in Texas, during which the power grid froze up, and 210 people died.

COVID and the Cyclone

Another example of the connections between disasters included in the study and the pandemic, is Cyclone Amphan, which struck the border region of India and Bangladesh.

In an area where almost 50 per cent of the population is living under the poverty line, the COVID-19 pandemic and subsequent lockdowns left many people without any way to make a living, including migrant workers who were forced to return to their home areas and were housed in cyclone shelters while under quarantine.

When the region was hit by Cyclone Amphan, many people, concerned over social distancing, hygiene and privacy, avoided the shelters and decided to weather the storm in unsecure locations. In the aftermath, there was a spike in COVID-19 cases, compounding the 100 fatalities directly caused by Amphan, which also caused damage in excess of 13 billion USD and displaced 4.9 million people.

Root causes

The new report identifies three root causes that affected most of the events in the analysis: human-induced greenhouse gas emissions, insufficient disaster risk management, and undervaluing environmental costs and benefits in decision-making.

The first of these, human induced greenhouse gas emissions, is identified as one of the reasons why Texas experienced freezing temperatures, but these emissions also contribute to the formation of super cyclones such as Cyclone Amphan, on the other side of the world.

Insufficient disaster risk management, notes the study, was one of the reasons why Texas experienced such high losses of life and excessive infrastructure damage during the cold snap, and also contributed to the high losses caused by the Central Viet Nam floods.

The report also shows how the record rate of deforestation in the Amazon is linked to the high global demand for meat: this demand has led to an increase in the need for soy, which is used as animal feed for poultry. As a result, tracts of forest are being cut down.

“What we can learn from this report is that disasters we see happening around the world are much more interconnected than we may realize, and they are also connected to individual behaviour”, says one of the report’s authors, UNU scientist Jack O’Connor. “Our actions have consequences, for all of us,”

Solutions also linked

However, Mr. O’Connor is adamant that, just as the problems are interlinked, so are the solutions.

The report shows that cutting harmful greenhouse gas emissions can positively affect the outcome of many different types of disasters, prevent a further increase in the frequency and severity of hazards, and protect biodiversity and ecosystems.

Interconnected disasters

Interconnected Disaster Risks 2020/2021, is released by the United Nations University’s Institute for Environment and Human Security ( UNU-EHS ), which conducts research on risks and adaptation related to environmental hazards and global change.

The institute’s research promotes policies and programmes to reduce these risks, taking into account the interplay between environmental and societal factors.

Research areas include climate change adaptation by incorporating insurance-related approaches, environmentally-induced migration and social vulnerability, ecosystem-based solutions to adaptation and disaster risk reduction, and models and tools to analyse vulnerability and risks linked to natural hazards.

• disaster risk management

What’s Disaster Management Plan? Preparation and Implementation Process

In today’s increasingly unpredictable world, the significance of having a robust Disaster Management Plan (DMP) cannot be overstated. This blog delves into the essence of a DMP, outlining its critical role in safeguarding communities and organizations against the devastating impacts of natural and man-made disasters.

We’ll guide you through the meticulous process of preparing and implementing a DMP, from conducting thorough risk assessments to engaging in recovery efforts post-disaster. Our aim is to equip you with the knowledge and tools necessary to develop a comprehensive plan that not only mitigates risks but also enhances resilience, ensuring a swift and effective response in times of crisis.

What’s Disaster Management Plan?

A Disaster Management Plan (DMP) is a strategic document that outlines the procedures, strategies, and actions to manage and mitigate the impacts of disasters, whether natural or man-made. The primary goal of a DMP is to minimize the damage and disruption caused by disasters and to ensure a quick, effective, and coordinated response to emergency situations. The plan typically covers various phases of disaster management, including:

• Prevention : Measures taken to prevent the occurrence of a disaster or to reduce its impact.
• Preparedness : Activities and preparations made in advance to ensure an effective response to the impact of disasters, including emergency response plans, stockpiling supplies, and training responders.
• Response : Actions taken in the immediate aftermath of a disaster to save lives, protect property, and meet basic human needs.
• Recovery : Steps to return the community to normal or near-normal conditions, including the restoration of basic services and the repair of physical, social, and economic damages.

Effective disaster management plans are comprehensive and considerate of the specific vulnerabilities, capacities, and needs of the community or organization they are designed to protect. They involve the collaboration of various stakeholders, including government agencies, non-governmental organizations (NGOs), community groups, and the private sector, to ensure a coordinated and efficient approach to disaster management.

Preparation and Implementation Process Of Disaster Management Plan

Preparing and implementing a Disaster Management Plan (DMP) involves a series of strategic steps to ensure that an organization or community is ready to respond effectively to disasters. Here’s a general guide on how to prepare and implement a DMP:

1. Risk Assessment

• Identify Hazards : Determine what types of natural or man-made disasters could potentially impact the area or organization. This could include floods, earthquakes, fires, terrorist attacks, pandemics, etc.
• Analyze Vulnerabilities : Assess the susceptibility of the community or organization to these hazards. Consider factors like location, population density, infrastructure, and resources.
• Estimate Risks : Evaluate the potential impact of identified hazards, taking into account the likelihood of occurrence and the severity of their consequences.

2. Planning

• Set Objectives : Define clear, achievable goals for the disaster management plan, such as minimizing loss of life, protecting property, and ensuring rapid recovery.
• Develop Strategies : Outline specific strategies for disaster prevention, preparedness, response, and recovery. This should include roles and responsibilities, communication plans, and resource allocation.
• Create Plans : Develop detailed action plans for each phase of disaster management. This includes evacuation plans , emergency response procedures, and recovery plans.

3. Resources and Logistics

• Identify Resources : List all available resources, including personnel, equipment, facilities, and financial resources, that can be mobilized in the event of a disaster.
• Establish Partnerships : Collaborate with local authorities, NGOs, community organizations, and the private sector to ensure a coordinated response.
• Logistics Planning : Plan for the logistics of delivering supplies, equipment, and services before, during, and after a disaster.

4. Training and Exercises

• Training Programs : Develop and conduct training programs for emergency responders, staff, and volunteers on their roles and responsibilities within the DMP.
• Conduct Drills and Simulations : Regularly organize drills and simulation exercises to practice emergency response procedures and identify any weaknesses in the plan.

5. Implementation and Monitoring

• Implement the Plan : Put the disaster management plan into action, ensuring that all stakeholders are aware of their roles and responsibilities.
• Continuous Monitoring : Regularly monitor hazards and review the effectiveness of the disaster management plan, making adjustments as necessary.

6. Review and Update

• Evaluate Performance : After drills, simulations, or actual disaster events, evaluate the performance of the disaster response and identify areas for improvement.
• Revise the Plan : Regularly update the disaster management plan to reflect new risks, lessons learned, and changes in resources or capabilities.

7. Communication

• Internal Communication : Ensure effective communication channels within the organization or community for the dissemination of information and coordination of response efforts.
• Public Information : Develop a plan for communicating with the public before, during, and after a disaster, including emergency alerts, updates, and recovery information.

Implementing a Disaster Management Plan is an ongoing process that requires commitment, coordination, and cooperation among all stakeholders involved. It’s crucial to foster a culture of preparedness and resilience to enhance the effectiveness of disaster response and recovery efforts.

In conclusion, the development and execution of a Disaster Management Plan is a critical endeavor that demands meticulous planning, steadfast commitment, and continuous improvement. By understanding the risks, preparing comprehensively, and implementing effectively, communities and organizations can significantly mitigate the impacts of disasters. This process not only safeguards lives and assets but also enhances resilience, ensuring a quicker recovery and a return to normalcy.

Remember, the strength of a Disaster Management Plan lies in its ability to adapt and evolve, incorporating lessons learned from past incidents to better prepare for future challenges. Through collaboration, education, and proactive measures, we can build a safer and more prepared society.

Saad Iftikhar

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Responding to a simulated disaster in the virtual or live classroom: Is there a difference in BSN student learning?

Lisa kirk wiese.

a C.E. Lynn College of Nursing, Florida Atlantic University, BC84, #333, 777 Glades Road, Boca Raton, FL 33431, USA

Tamara Love

b C.E. Lynn College of Nursing, Florida Atlantic University, BC84, 777 Glades Road, Boca Raton, FL 33431, USA

Rhonda Goodman

c C.E. Lynn College of Nursing, Florida Atlantic University, BC84, #325, 777 Glades Road, Boca Raton, FL 33431, USA

This study aim was to investigate if prelicensure baccalaureate nursing students gained more knowledge from a live or virtual disaster simulation. The study goal was to inform the use of e-learning or traditional textbooks in undergraduate nursing population health courses.

Background:

Weather-related disasters have increased in frequency and severity in the past ten years, with 2020 being the most active storm season ever seen ( National Oceanographic and Atmospheric Administration, 2021 .) Even with advances in early warning systems and mitigation efforts, educating student nurses in disaster response remains a priority. Due to the impact of Covid-19 quarantine policies, many in-person student learning labs and clinical experiences were cancelled. However, virtual simulation offers an alternative to developing nursing student skills and clinical reasoning ability ( Aebersold, 2018 ; Fogg et al., 2020 ).

A randomized quasi-experimental, repeated measures 2 × 2 crossover design ( Kim, 2018 ) was applied, which allowed students to participate in both the live and virtual simulations.

Analysis was conducted using paired samples t -test to evaluate knowledge gains. To measure students’ self-assessment of knowledge, Unver et al. (2018) 12-item survey was administered. To explore students’ own perceptions about the disaster simulations, semi-structured interview questions were offered through private Wiki postings. The responses were analyzed using Saldanã’s in vivo coding (2015) and thematic analysis.

Students retained more empirical knowledge following the virtual assignment as compared to the disaster simulation, except in two items addressing triage. Neither age, years of education, or GPA impacted test results. However, students’ own assessment of learning did not differ between live and virtual simulations. In all but three items, students perceived a significant increase ( p < .05) in their learning following the simulation, regardless whether it was live or virtual.

In narrative responses, students overwhelmingly cited the benefit of an in-person simulation. However, they did not believe that they were prepared adequately for the live simulation. They also expressed that they would be more prepared if the simulation was repeated. Students expressed discomfort, even distress, regarding not being able to care adequately for everyone, even though it was a simulation (See Table 5 ). This highlighted that live simulations can affect students emotionally, and follow-up debriefing is essential to help in both acknowledging and processing student feelings.

Examples of survey responses to item asking about future simulation recommendations.

Conclusion:

These findings, which support the use of virtual disaster training in nursing education, are especially important in the light of Covid-19 and increasing threat of storm disasters.

1. Introduction

Weather-related disasters have increased in frequency and severity in the past ten years, with 2020 being the most active storm season ever seen ( National Oceanographic and Atmospheric Administration, 2021 ). The winter months leading into 2021 culminated in the deadliest and costliest winter storms on record, with 125 deaths and 10 billion dollars in costs ( National Oceanographic and Atmospheric Administration, 2021 ). Even with advances in early warning systems and mitigation efforts, educating student nurses in disaster response remains a priority. However, as the number of nursing schools increase, the demand for student clinical experiences has grown beyond what is often available. Narrowed preceptor-student ratios mandated by hospital partners, and lack of willing and experienced preceptors who were not already fatigued from teaching, have further limited the availability of clinical sites ( Billings, 2015 ).

Prior to the pandemic, simulation was becoming more prevalent in prelicensure nursing education, as the benefits of learning critical skills using simulation are well documented ( Curl et al., 2016 ). National standards for nursing education now support up to 50% of clinical education occurring in simulation ( Curl et al., 2016 ). The Covid-19 pandemic of 2020 placed further demands for alternatives to face-to-face experiential learning.

In early work, Kaplan (2012) demonstrated the effectiveness of disaster simulations based on both quantitative and qualitative indications of positive (95%) student learning and colleagues. Other researchers demonstrated that virtual reality simulation consistently provides a realistic, immersive, and genuinely convincing environment for disaster training ( Farra and Miller, 2013 ). Virtual reality simulation opportunities have continued to expand rapidly to meet the learning styles of today’s nursing students. This includes the iGeneration or Gen Z (born between 1995 and 2015) and Millennials or Gen Y, (born between 1980 and 1994), both of whom are well-known for their technological sophistication ( Bell, 2013 ). Metaanalyses of published studies within the past five years revealed that disaster simulation experiences contribute to and increase student learning in several ways ( Cant and Cooper, 2017 ), but more experimental research is needed to measure effects on learning outcomes ( Staykova et al., 2017 )). Furthermore, virtual simulation is receiving more attention as a teaching/learning modality due to the impact of Covid-19 quarantine policies: Many in-person student learning labs and clinical experiences were cancelled due to pandemic-related school closures and hospital restrictions. However, virtual simulation offers an alternative to developing nursing student skills and clinical reasoning ability ( Aebersold, 2018 ; Fogg et al., 2020 ). Virtual simulation is also referred to as learning online, distance learning, web-based learning, or e-learning. E-learning indicates the use of electronic (online) textbooks, which often include interactive components, in which the user must respond to questions using the computer keyboard to move forward through the content or activities. Virtual simulations can be conducted any time, without face-to-face interaction. In contrast, live simulations are usually conducted during class time after traditional textbook reading assignments and in-person didactic teaching by faculty that are known to the students.

The motive for this study emerged during the piloting of a new e-learning textbook in an undergraduate nursing population health class. The students were offered the choice to participate in an online “e-learning” virtual disaster simulation format over reporting to campus for a live simulation. The majority of the students (75%) voted for the live disaster simulation. However, the textbook publisher was offering incentives to pilot their new online “e-learning” textbook which contained virtual disaster simulations. This consequently raised the question “Is a virtual disaster simulation as effective as a live disaster simulation in achieving similar learning among today’s Gen Y and Z students?” The study purpose was to determine if the type of disaster simulation (live or virtual) made a difference in a) student scores on a post-simulation formal assessment by examination and b) student perceptions of learning. The hypotheses, based on the students’ initial preferences and performances related to face-to-face disaster training, was that (A) live simulations would produce higher examination scores than the virtual simulations, and that (B) students would perceive that greater learning occurred from the live simulations. The specific aims were (1) to identify differences or similarities in students’ examination scores, and (2) to explore student perceptions of learning, after participating in both the live and virtual simulations. The primary expected outcome was that both student test scores and perceptions of learning would be higher in the live simulation group than the virtual simulation group. This predicted outcome was based on past experiences of high assessment scores averaging 88%, using traditional examination methods after face-to-face disaster simulations. The secondary outcome was that there would be a clear indication of which type of simulation students preferred. The expected goal was to inform the choice of assigning an e-learning textbook with virtual simulations, or a less-costly traditional textbook with a live simulation, for future undergraduate population health courses.

This education-focused research proposal was motivated by the Association of Community Health Nurse Educators (ACHNE) priority of “improving the public health nursing workforce” by increasing competency during disasters. This proposal also addressed a current goal of the funding agency, Wolters Kluwer, offered through ACHNE, to explore best educational practices in the classroom related to a public health topic of disaster preparedness and management.

The theories of Situated Cognition ( Brown et al., 1989 ) and Nursing as Caring ( Boykin and Schoenhofer, 2001 ) served as the conceptual frameworks for this study. The three key components comprising situated cognition are Embeddedness, Extension, and Embodiment. Farra et al. (2015) demonstrated the usefulness of these concepts in virtual simulations (see the definitions below in Fig. 1 ). For our study, “Situated Cognition” ( Brown et al., 1989 ) was also adaptable for the live simulation, as represented in Fig. 1 . The word “virtual” could be represented also as “live” as in the example, The Learner enters the Live Environment rather than the Virtual environment (by engagement in a live simulation.) Additional similarities between the live and virtual simulations included cognition linkage to sensorimotor brain and body, visual representation, and psychomotor and affective learning outcomes. Differences lay in the ability for repetition (first rectangle) and repeated practice (second rectangle) that are available in the virtual simulation but were not in the live simulation ( Fig. 1 ).

Situated Cognition. ( Farra et al., 2015 ). Virtual reality disaster training: Translation to practice. Nurse Education in Practice, 15(1), 53–57).

The holistic concepts grounding the Nursing as Caring theory, such as patient-centered care and the importance of seeing beyond what may be immediately obvious was applied when building the qualitative inquiry. The qualitative questions posed to the students in the Wiki discussion are available in Fig. 2 . Boykin & Schoenhofer’s specific concepts were discussed between the researchers during the in vivo analysis, including that all persons are unique, that it is the nurse’s responsibility to determine “what matters most” to the person being nursed, and that authentic presence is essential in answering the call to nurse (2001). Focusing on these concepts during the reading and rereading of the students’ statements facilitated the identification of general themes and selection of in vivo codes that were more likely to reflect the participants’ meanings.

Qualitative inquiry debriefing questions.

2. Literature review

Using a combination of all of the search terms, online disaster simulations in nursing education , across all data bases available to the university (including PubMed, CINAHL, & Science Direct) and criteria of “peer-reviewed, nursing and allied health discipline, within the past five years in full-text” a total of 390 articles were available. Narrowing the search to include the Boolean phrase “disaster simulation nursing” or “related text” such as emergency preparedness within the title or full text, 60 relevant articles published from 2013 to the present emerged. The search was updated this year in preparation for this article using the equivalent search terms.

Of those 60 studies, fifteen were eliminated for duplicity including metaanalyses, or reported as conference proceedings. Articles were eliminated if the primary focus was on a related topic rather than the simulation itself, such as discussion of models to use in triage, targeting of post-graduate audiences, or general discussion on simulation. Those focusing on specific populations such as veterans (2) and pediatrics (3) or for a different primary purpose other than an localized incident such as interprofessional team-building (5), use of case studies or debriefing (5), widespread mass casualty involving multiple agencies (5), safety (3), ethics (2), transport issues (2), and evaluating a new scale (1) were excluded as a means of narrowing the scope of this review. Only two additional articles meeting the research criteria were found when searching the above references.

In total, eleven studies were located that met the research criteria. Four studies focused on virtual simulations, three on live, and four on both live and virtual simulations. The number of overall articles found were congruent with an integrative review conducted during the previous ten years by Jose (2014) searching in three similar databases (Ovid rather than Science Direct) using the key words of disaster, preparedness, nursing, education. From Jose’s review, 109 articles emerged, but only ten articles met the research criteria. The summarized findings from these articles are reported as follows.

2.1. Live simulations

Following a didactic lecture and in-class live simulation, researchers found a significant improvement in t -test results ( p < .01) between pre-post test scores ( Alim et al., 2015 ). They also concluded from observer ratings and student interviews that the classroom training ( n = 309) and drill ( n = 25) improved student learning. The simulation included students and no-student actors with moulage and multiple hospital departments. This study targeted associate and diploma degree students.

A study conducted by Davis et al. (2020) in a southeastern United States nursing college included 391 BSN students. The researchers conducted a quasi-experimental, pre-post test design using high fidelity simulation to assess the effectiveness on student knowledge and preparedness. Results indicated an increase in scores after participation in the simulation at every level. In their scale of Disaster Knowledge Competency Scores, there was a statistically significant ( p < 0.01) difference in post test scores for Junior I ( p = 0.0022) and ( p < 0.0001) for Senior II. Although increases in knowledge were reported for the Senior Level I, it did not reach statistical significance.

At a university which had a Disaster Simulation Lab, a full-scale mass casualty incident (MCI) was implemented (Kim and Lee, 2020). The simulation environment included pre-hospital and hospital sections, with videos displayed on a large screen and sound effects played on loudspeakers. The results showed that participants were initially likely to triage insufficiently prior to the intervention. There was a statistically significant difference ( p < .001) in effective triage, and positive attitudes after the intervention. Self-reported teamwork was high, with leadership and team coordination scoring the highest. In addition, awareness of roles, communication, and satisfaction with the simulation were rated highly.

Unver et al. (2018) also conducted a quasi-experimental, pre-post test design using a high-fidelity simulator among associate degree nursing students to examine the effectiveness of a live simulation upon student learning. Results included that prior to the intervention, less than half (43%) of students felt confident in their disaster preparedness. A statistically significant ( p < 0.05) difference was found in their scores on the Scale of Perception of Disaster Preparedness among Nurses in the post-disaster evaluation. Confidence in disaster training is of paramount importance.

In a large teaching hospital with a state-of-the-art simulation lab, a full-scale mass casualty incident (MCI) was conducted ( Fletcher et al., 2015 ) Fifteen students received moulage and played disaster victims following a bus accident. Eight other students were divided into groups and assigned the task of conducting 30-second triage. All participants felt it was a valuable learning experience. The extensive moulage (such as dried chicken bones for compound fractures, and glass shards embedded in wigs), fog machines, strobe lights and emergency siren sounds were utilized. However, adequate attention to other tasks, such as establishing a safe zone, delegating others to create staging areas by severity of injury, recording persons and injuries, finding family members, and arranging for transport were not included. During the debriefing, the students talked about their feelings of abandoning victims for whom death was imminent, feelings of helplessness, and trying to cope with bystanders was discussed.

2.2. Virtual simulations

Mixed methods research conducted among 82 nursing students demonstrated that computer simulations were extremely well received ( Donovan et al., 2018 ). Themes that emerged from the analysis of narrative data included improved prioritization, benefits of role-modeled nursing care, engaged critical thinking, and decreased anxiety levels. Quantitative results also supported positive student performance. However, the computer simulations were used as preparatory work prior to the static and high-fidelity simulations, and not as stand-alone learning.

Researchers examined 14 databases between 2010 and 2015 for proof of effectiveness of virtual simulation for any health provider learners, and found overwhelming evidence that “Online virtual simulation was comparable or superior to traditional simulation methods where increased engagement with learning occurred in a safe environment with convenient access” ( Duff et al., 2016 , p. 383). Students consistently reported that virtual patients were more realistic than standard actor patients or students, and that they preferred the increased length of scenarios and engagement with learning. This information is imperative to the advantages of virtual simulations.

Another group of researchers investigated online learning by comparing web-based module instruction (the control group) with web-based modules and virtual reality simulation ( Farra et al., 2015 ). Knowledge was evaluated using a 20-item measure that underwent content validity appraisal by both education and disaster experts, and reliability testing using test-retest ( r = .72). Three measurement points (pre, post, and two-months post) showed that in the web-based module, knowledge actually decreased more over time, and in virtual reality simulation, knowledge was retained, with the most significant difference ( p < .0001) between the two groups at the most distant third measurement. The researchers also cited numerous studies that have “explored the use of virtual reality simulation for disaster training with ”great success” (p. 55). They did not compare virtual simulation with live training.

Two studies were found that compared live with virtual simulations. In the first, researchers used mannequins and virtual scenarios ( Wilson et al., 2014 ). Faculty provided classroom didactic training first, then the online virtual simulation to 54 BSN students. They also used a quasi-experimental crossover design with random assignment to a computer-based case simulation and a human patient simulation using mannequins. Although overall scores were higher in the human patient simulation group, the researchers found that different phases in each elicited better performance, suggesting that a combination of both online virtual simulation and live virtual simulation may be ideal for learning.

In the second article comparing live with virtual simulations, researchers compared the effectiveness of virtual reality to clinical simulations among a variety of health profession students in executing the START (Simple Triage and Rapid Treatment) triage model. They also examined the levels of salivary cortisol (a-amylase) in measuring the stress produced in each. The found that the virtual reality method was as efficient as clinical simulation for training on the START model execution, and caused less stress. Specifically, the percentage of victims that were triaged correctly averaged 88.1% ( SD = 9.) for the Clinical Simulation with Actors groups, and 87% ( SD = 7.2) for the Virtual Reality Simulation group, with no significant differences ( p = 0.6) between both groups. However, the increase in salivary cortisol was significantly greater ( p < 0.001) for the Clinical Simulation with Actors group (182.2, SD = 148.7 U/L) than the Virtual Reality Simulation group (80.7, SD = 109.7 U/mL).

In summary, multiple researchers highlighted in their studies that 1) disaster preparedness education through simulations has been found to be highly effective for learning and, 2) more rigorous studies are needed in this area due to the wide array of methodologies, data collection, and analysis. Students transitioning to the registered nurse role who may be called upon to lead during times of disaster events must receive adequate training to appropriately and confidently apply disaster management skills. This study added to the current literature by comparing two methods that would be feasible in the general classroom, particularly when access to large simulation laboratories or external emergency care and/or interdisciplinary venues are limited.

A randomized quasi-experimental repeated measures 2 × 2 cross-over design ( Kim, 2018 ) was applied, which allowed students to participate in both the live and virtual simulations. Analysis was conducted using paired samples t -test to compare pre-post knowledge scores. Pearson’s correlational analysis was applied to determine if any of the sociodemographic variables (age, years of education, and nursing courses GPA) were associated with scores. To measure students’ self-assessment of knowledge, Unver and colleague’s (2018) 12-item survey regarding disaster simulation was administered. To explore student perceptions about learning from disaster simulations, and not just their knowledge, semi-structured interview questions were offered through private Wiki postings. The interview responses were analyzed using Saldaña’s in vivo coding (2015) and thematic analysis. This qualitative inquiry contributed to a greater understanding regarding potential barriers or successes in learning between the two platforms (live and virtual).

3.1. Setting

Students attending a Florida public university in their junior year, first semester of a traditional undergraduate BSN track, and accelerated (prior health science degree holding) students in their second of five semesters participated in this study. The XXX county setting included a diverse community. Over 23% of the general population are > 65. African Americans comprise 19.4%, while Hispanics 21.5%. Over 30% are speakers of a non-English language, as compared to the national average of 21.5%, including 4.8% Creole speakers, which is unusually high. This diversity was represented in the university’s nursing student body of 43% African American, 13% Hispanic, and 4% Asian. The average GPA for admission in this class was 3.75.

3.2. Sample

The target population was 90 junior baccalaureate science (BSN) nursing students from varied ethnicities and race, but with similar education backgrounds. A minimum GPA of 3.5 was required for admittance to the BSN program at this university. Applying GPower 3.1 paired samples a-priori parameters of.5 medium effect size and 95% power, differences were calculated between the two-dependent means/matched pairs criteria ( Faul et al., 2007 ). This resulted in a recommended sample size of 45 for each group, which was similar to the design of a 2 × 2 crossover design in a nursing simulation involving 82 students in comparing roleplay versus lecture ( Kim, 2018 ), and other online disaster simulation studies ( Donovan et al., 2018 ; Duff et al., 2016 ). Students were randomized into either the live simulation or the virtual simulation as their first experience by drawing folded slips of paper with a choice from a basket. Neither the student nor the faculty was aware to which group the student was being assigned. Inclusion criteria were any juniors taking population health during the Fall 2019 semester. Exclusion criteria were any students who would be unable to participate in both forms (virtual and live simulations), or who were members of previous population health classes.

The importance of adhering to ethical standards of nursing conduct by faculty included supporting the student’s autonomy to engage in class activities, avoiding maleficience (harm) that could be caused by faculty disregard or disapproval of students’ live simulation performances, and upholding beneficence by promoting safe, student-centered learning. Students were informed that likewise, their conduct was expected to be congruent with the American Nursing Association Code of Conduct ( Haddad and Geiger, 2021 ) ethical principles, such as veracity (truth--telling), integrity (thoroughly completing all activities in each section of the virtual disaster simulation), and fidelity to their future communities through a full commitment to learning. Participation was voluntary, and informed consent was obtained from students prior to their participation in the simulation. Student protections and confidentiality parameters were followed per the university’s Insititutional Review Board (#719852.) There was no penalty for not participating in the randomized trial. Data Collection occurred following completion of voluntarily signed consents. Following the intervention, all students (both groups) were offered the opportunity to participate in the mode of simulation to which they did not participate earlier. Gift cards were offered to help offset the cost of the textbook with the added virtual simulation.

The overall Goals of Student Learning are available in Fig. 3 .

Goals of learning for disaster simulations.

3.3. Measures

To meet the specific aims, measures included a sociodemographic survey, pre-and post-test based on an Elsevier test bank, and a self-assessment of knowledge gained. Each of these measures were reviewed by three other faculty members with at least five years experience in teaching public health nursing, and one faculty member whom has worked with supervising disaster shelters for over 40 years, and two who were CERT trained. Content validity of both the formal assessment and the live simulation were also examined by the public health nurse educators, discriminant validity was examined to some degree by the pre-test (measuring the knowledge base of students without disaster training). The principal investigator was familiar with conducting the live disaster simulation, which she designed and taught to the population health class during the past three years.

The sociodemographic survey included questions regarding age, gender, race, ethnicity, current nursing courses GPA, and three items regarding the type and length of previous disaster emergency management in these categories: 1) education/training, 2) professional experience during disasters, and 3) personal experience with disaster. The pre- and post- tests were each twenty questions taken from the Disaster Management chapters testbank in the e-textbook, which were derived from empirically validated and widely tested questions. Two sets of tests were created to accommodate for students participating in both types of simulations. Psychometric analysis was conducted on the student score results from the tests. Unver et al. (2018) 12-item survey regarding disaster simulation was administered as a means of measuring students’ self-assessment of learning.

3.4. Simulation procedures

Procedures for both the live and virtual simulations consisted of three phases; preparation, simulation, and debriefing. To prepare for the live simulation, the students drew cards naming their roles. During the first hour, they applied moulage to victims, and reviewed supplies that were available with the First Aid kit, including triage tape. In preparation for the virtual simulation, students were directed to complete the textbook readings and modules ( Disaster Assessment and Response , Module 3, and Public Health Nursing in Post Disaster Recovery , Module 4). For the live simulation phase, students reported to their assigned rooms. The scenario presented to students was that they were in a bus returning home from a university academic competition when they encountered a tornado. You-tube footage from a local news station was shown of an approaching tornado from a bus window that actually occurred in our state. Each index card that students drew for their assigned role also included instructions for acting out the role on one side, and brief tips for the triage nurse to consider if needed. For the virtual simulation phase, students completed the disaster simulations that were available in Modules 3 and 4 of the of the text.

For the live simulation debriefing phase, faculty met with students and conducted open-ended discussions regarding “what went well”, “what could have been improved”, and how they felt when acting out their assigned roles. For the virtual simulations, students completed the seven open-ended questions presented in private Wiki online discussions with faculty that explored the students’ lived experience of both the simulation (See Fig. 2 ). For both simulations, students were asked “what mattered most” ( Boykin and Schoenhofer, 2001 ) to them when engaging in the simulations.

3.5. Data analysis

Test scores were analyzed in terms of mean, median, mode, frequencies, and percentage of both individual and total of item scores. Descriptive and Pearson’s r correlational analyses were used to examine the relationship between sociodemographic variables (age, gender, race, ethnicity, self-reported GPA, prior personal or professional disaster experience, years of post-high school education) with student test scores. Multiple linear regression analysis was applied for evaluating if any of the independent variables significantly predicted student scores. Paired samples t -test were used to quantify differences in learning based on pre-and post-intervention summative assessments. In addition, narrative student responses from the qualitative semi-structured interview questions as a function of debriefing were explored using in Saldaña’s vivo coding and thematic analysis to add to the understanding of student perceptions of barriers and successes to learning.

The majority of students participated in both phases of the study; N = 80 of 90 (89%). Six students who chose not to engage in the study cited the cost of purchasing the virtual textbook, even with the gift card support. Four others did not offer a reason for refraining from enrolling in the study. All ten students received the disaster didactic content via the traditional lecture format in the classroom with their colleagues. The sociodemographics of the sample ( Table 1 ) revealed that the participants, as expected, were a highly racially and ethnically diverse group, as the university is the most racially and ethnically diverse public university in the state.

Participant Characteristics (N = 80).

4.1. Correlations between sociodemographic variables and test scores

Using Pearson’s r, there were no significant correlations found between the continuous dependent variables of age or GPA and the independent variable of test score following either the live or disaster simulation. Neither was there any significant correlation using Pearson chi-square analysis between the categorical variables of gender, disaster training, or professional experience with students’ knowledge scores. As expected, there were significant correlations between students with higher knowledge scores and professional work experience. ( r = .56, p . =.01). However, only four students reported personal experience with disasters, although 21 had prior Red Cross table-top training. Of the 50 students indicating no prior training or education in disaster management, 35 (70%) of them indicated that the virtual simulation greatly improved learning ( Table 2 ). Results were very similar among students who reported no personal experience; 61% reported that virtual simulation greatly improved learning ( Table 2 ).

Effect of prior professional training or personal experience on students’ perceptions of improved knowledge after participating in disaster simulations.

4.2. Perception of learning between live and virtual simulations

As seen in Table 3 , and using Unver et al. (2018) measure, significant correlations were found overall between the pre-test and post test scores after the live simulation using a paired samples t -test ( t = 3.57, 78, P = .001). Significant increases in perceptions of learning were demonstrated across all but three items;.

Comparison of significance in learning between virtual and live simulations.

#1: “Combined theoretical and practical knowledge” demonstrated student perception that the virtual simulation learning was significant ( p = .02), but the live simulation learning was not ( p = .68).

#6: “Improved self esteem before a clinical assignment” did not achieve a significant difference in perceived learning in either the live or virtual simulation. However, the students did not have any clinical assignment associated with this learning activity.

#7: “I felt like a nurse” showed students rating the virtual activity as near significant learning ( p. =.06) compared to the live simulation, which did not show any trend toward significance ( p = .166).

4.3. Knowledge scores

Comparing the total test scores on a 25-item test derived from an Elsevier test bank revealed that students retained more knowledge following the virtual assignment as compared to the disaster simulation, except for two items addressing triage (establishing priority of care and use of triage tape). There was a five-point difference in average test scores between the live ( M = 15.93, SD = 6.44) and virtual ( M = 20.55, SD = 4.75). Reliability testing of the formal assessment was favorable. Following the pre-test for both the live and virtual simulation, the Cronbach’s alpha was a = .84 and.83 respectfully. The post-test results were similar, with a Cronbach’s alpha of.82 and.81 ( Cronbach, 1951 ).

4.4. Qualitative inquiry

The participants answered a series of open-ended questions (See Fig. 2 ) in the format of an online Wiki-post assignment in the course learning management system. In vivo coding (Saldana, 2015) was the style of content analysis used to study student responses qualitatively to gain more understanding of their perceptions about disaster simulation training. This approach of content analysis relies on the participants’ own words to serve as themes rather than the investigator’s interpretation, thus preserving the participant’s language and perspective.

Three of the researchers independently read the transcripts, reread them, and began classifying the content into categories of main ideas. They then selected a phrase by the participant that reflected the category, prior to meeting to identifying similarities and reconciling differences. Three in vivo codes were agreed upon each type of simulation from the participants’ own verbiage as illustrated in Table 4 .

In vivo coding to student responses in qualitative inquiry.

5. Discussion

The purpose of this study was to answer the research question “Is a virtual disaster simulation as effective as a live disaster simulation in achieving similar learning among today’s Gen Y and Z students?” Several key findings were elicited from this study. First, both hypotheses were disproved, as students retained more knowledge following the virtual assignment as compared to the disaster simulation, except for two items addressing triage. This outcome was based on results from both the formal assessment (examination scores) conducted after the student’s first live or virtual simulation. In addition, students’ own perceptions of learning did not differ between live and virtual simulations, as shown by the results from the Unver et al. (2018) survey. In all but three items, students perceived a significant increase ( p < .05) in their learning following the simulation - regardless whether it was live or virtual. Furthermore, neither age, years of education, or GPA impacted formal assessment results, as indicated by exam scores.

Another unexpected finding was found during the qualitative exploration of the live simulation. Although the students overwhelmingly cited the benefit of an in-person simulation, they did not believe that they were prepared adequately. They also expressed that they would be more prepared if the simulation was repeated. Students expressed discomfort, even distress, regarding not being able to care adequately for everyone, even though it was a simulation (See Table 5 ). This highlighted that simulations can affect students emotionally, and follow-up debriefing is essential to help in both acknowledging and processing student feelings.

In contrast, students wrote in the Wiki posts that the virtual approach was a safe, fun, and effective means of learning how to act as a nurse. Students also noted the high quality and comprehensive content available in the virtual simulations. This suggests that perhaps more effort needs to be made by faculty to improve the didactic content and delivery associated with the live simulation. Of note was that 35% of students offered comments recommending that both the live and virtual simulation would be ideal, with the virtual offered first. Examples of these comments are provided in Table 5 . However, the cost of the e-learning textbook may be a barrier to future use. To access the e-learning textbook, students must enter a code that is provided after purchase. Traditional textbooks are often less costly initially, and previously owned books are frequently available for purchase online at an even greater reduced rate.

5.1. Limitations

Limitations of this study included the cross-over effect; students participated in either the live or virtual simulation prior to engaging in the other. Thus, experience with the first type of simulation likely had some effect on the second. This was minimized somewhat by administering the formal assessment (examination) after each student’s first disaster simulation. The Unver survey and qualitative reflection were completed after both types of simulation were completed, and provided important insights.

Other limitations included testing in only one school of nursing, and inability to control for extraneous influences, such as technical difficulties, logistics in conducting ten groups of eight students each through a live simulation, and availability of persons experienced in applying moulage. Other potential sources of bias may have been present in the strength and consistency among test bank questions used to evaluate didactic learning, and lack of student honesty or adequate reflection in answering the narrative inquiries following the intervention. Furthermore, this was a fairly homogeneous group in terms of high GPAs and maturity; all students had completed a prior baccalaureate degree in a health-related field, so these results are not generalizable to nursing student bodies with different levels of academic prowess.

An important restriction to adopting the textbook with the virtual simulation was the cost of the e-textbook. Numerous students commented on this in the qualitative survey. Many students verbalized in class that if it had not been for the grant support, that they would not have been able to afford the additional $75 for the text, which is not resellable with the e-book option. Discussion is underway with the publisher to ascertain if the e-book subscription could be limited to six months rather than a year, and the cost decreased by half.

Additional limitations that potentially may have occurred based on reports in the current literature include the following: Alfred et al. (2015) reported on multiple strategies used in preparing nursing students in disaster management. Their work, and five other recent studies were not reviewed in this proposal, due to their emphasis on interdisciplinary/interagency disaster drills, which is outside the scope of this study. Rafferty-Semon et al. (2017) created and tested a novel and effective simulation for training students to serve at the point of collaboration (POC) i.e. disaster shelters, for which tabletop trainings have occurred, but not simulations. Chilton and Alfred (2017) investigated the use of both virtual simulations and live training for personal emergency preparedness among undergraduate students and RNS, but did not use online simulations with pre-licensure students. Researchers also tested a new topic in disaster simulation; offering a simulation that addresses personal readiness, including a “to go” bag. None of these additional three types of disasters a) interdisciplinary. b) points of collaboration, and c) personal disaster preparedness, were incorporated into this proposal. Any of these would no doubt be beneficial, but this study was limited in scope. The ten students who did not participate in the study were given a separate assignment to identify five learning points after completing the traditional textbook assignments, which could be another means of evaluating learning after a virtual simulation.

5.2. Conclusion

These findings, which support the use of virtual disaster training in nursing education, are especially important in the light of Covid-19 and increasing threat of storm disasters. Our results were congruent with those reported in a new systematic review of 69 studies by Foronda et al. (2020) . The majority (86%) of authors concluded that virtual simulation was an effective pedagogy for meeting learning outcomes. Through both the quantitative and qualitative student responses, we learned that significant learning was achieved with the virtual simulation.

It would be helpful to incorporate student suggestions in live simulations, such as strengthening didactic content, particularly in regards to triage and priority-setting. An interesting suggestion was made managing color-coded systems when tape is not available: The triage nurse could write the letter of the corresponding triage tape letter on the bottom of the shoe, e.g., Y for yellow (meaning needing eventual but not critical medical attention), when highlighters or tape are not available. An important note is that the most important learning mechanism following any simulation is the debriefing period. The effectiveness of achieving positive knowledge and self-efficacy outcomes through simulation debriefing regardless of mode; in person, on paper, or by self-reflection, has shown to be very effective ( Verkuyl et al., 2018 ). It is essential to include a debrief with instructor involvement and feedback regardless of the mechanisms used for the simulation. In addition to reinforcing learning, debriefing can facilitate student expression of feelings and concerns emerging from the simulation, and provide an opportunity for faculty to address “what matters most” ( Boykin and Schoenhofer, 2001 ) to the student following a disaster learning experience.

Acknowledgements

This grant was funded by the Association of Community Health Nurse Educators, in cooperation with Wolters Kluwer. Grant name: Responding to a Simulated Disaster in the Virtual or Live Classroom: Is there a Difference in BSN Student Learning? Funding source: Wolters Kluwer Education Research Award, administered by Association of Community Health Nursing Educators.

Funding received from the Association of Community Health Nurse Educators/Wolters Kluwer Educational Grant.

Funding for this work was awarded by the Assocation of Community Health Nurse Eduators, with support provided by Wolters Kluwer.

CRediT authorship contribution statement

This material has not been published in part or whole elsewhere*, it is not currently being considered for publication elsewhere, and all authors have been personally and actively involved in substantive work leading to the report and will hold themselves jointly and individually responsible for its content. We added the IRB approval number. Expected ethical standards, including those regarding truth and rigor in reporting were maintained. No conflicts of interest emerged: We were careful to avoid any potential bias in analyzing results, despite the work being funded by the Association of Community Health Nurse Educators in association with a Wolters Kluwer Educational Grant.

All three authors (Wiese, Love, and Goodman) participated in all five activities: 1, 2, 3, 4, and 5. Specifically regarding #2, Dr. Love participated/assisted Dr. Wiese with the virtual simulation and Dr. Goodman with the live simulation.

*We have previously written to your journal, per the advice of Dr. Farra, requesting permission to reprint her figure regarding Situated Cognition in Simulations.

Conflict of interest

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INFORMS International Conference on Service Science

INFORMS-CSS 2022: City, Society, and Digital Transformation pp 345–355 Cite as

Complex Task Assignment of Aviation Emergency Rescue Based on Multiagent Reinforcement Learning

• Che Shen 23 &
• Xianbing Wang 24  
• Conference paper
• First Online: 11 December 2022

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Part of the book series: Lecture Notes in Operations Research ((LNOR))

Emergency rescue is a powerful countermeasure to disasters, among which Aviation Emergency Rescue (AER) is irreplaceable thanks to its unique aviation attribute. However, traditional optimization methods are not capable of the dynamic task allocation of AER. This study performs a Multiagent Reinforcement Learning (MARL) model to handle the complex task assignment problem faced in AER, carries out a detailed analysis of the problem, and do comparative experiments with the Nearby policy and Best-fit policy. The result shows that the MARL model outperforms other simple models in AER.

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Acknowledgements

This research is supported by the National Key Research and Development Program of China (Grant No.2016YFC0802603).

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Department of Data Science and Technology, The Hong Kong University of Science and Technology, Hong Kong SAR, 999077, China

College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, Jiangsu, China

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Shen, C., Wang, X. (2022). Complex Task Assignment of Aviation Emergency Rescue Based on Multiagent Reinforcement Learning. In: Qiu, R., Chan, W.K.V., Chen, W., Badr, Y., Zhang, C. (eds) City, Society, and Digital Transformation. INFORMS-CSS 2022. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-15644-1_26

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• IAS Preparation
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• Disaster Management India

Disaster Management in India

Disruption on a massive scale, either natural or man-made, occurring in short or long periods is termed a Disaster. Disaster management in India has been an important point of discussion owing to frequent natural disasters ranging from earthquakes, floods, drought, etc. This makes the issue of disaster management worthwhile to consider as part of the preparation for the IAS Exam .

Explore the Ultimate Guide to IAS Exam Preparation Download The E-Book Now!

In this post, you will read all about disaster and disaster management in the Indian context. IAS aspirants can also download the Disaster Management notes PDF.

Loss of life and property due to these disasters has been steadily mounting throughout the world due to inadequate technology to combat disasters, rise in population, climate change, and continuing ecological degradation. The global efforts to manage disasters have proven to be insufficient to match the frequency and magnitude of natural disasters.

Table of Contents:

What is a Disaster?

A disaster is defined as a disruption on a massive scale, either natural or man-made, occurring in short or long periods. Disasters can lead to human, material, economic or environmental hardships, which can be beyond the bearable capacity of the affected society. As per statistics, India as a whole is vulnerable to 30 different types of disasters that will affect the economic, social, and human development potential to such an extent that it will have long-term effects on productivity and macro-economic performance.

Disasters can be classified into the following categories:

• Water and Climate Disaster: Flood, hail storms, cloudburst, cyclones, heat waves, cold waves, droughts, hurricanes. (Read about Cyclone Disaster Management separately at the linked article.)
• Geological Disaster: Landslides, earthquakes, volcanic eruptions, tornadoes
• Biological Disaster: Viral epidemics, pest attacks, cattle epidemic, and locust plagues
• Industrial Disaster: Chemical and industrial accidents, mine shaft fires, oil spills,
• Nuclear Disasters: Nuclear core meltdowns, radiation poisoning
• Man-made disasters: Urban and forest fires, oil spill, the collapse of huge building structures

What is Disaster Management?

In this section, we define what is disaster management as per the Disaster Management Act of 2005.

The Disaster Management Act of 2005 defines Disaster Management as an integrated process of planning, organizing, coordinating and implementing measures which are necessary for-

• Prevention of threat of any disaster
• Reduction of risk of any disaster or its consequences
• Readiness to deal with any disaster
• Promptness in dealing with a disaster
• Assessing the severity of the effects of any disaster
• Rescue and relief
• Rehabilitation and Reconstruction

Agencies involved in Disaster Management

• National Disaster Management Authority (NDMA):- The National Disaster Management Authority , or the NDMA, is an apex body for disaster management, headed by the Prime Minister of India. It is responsible for the supervision, direction, and control of the National Disaster Response Force (NDRF).
• National Executive Committee (NEC):- The NEC is composed of high profile ministerial members from the government of India that include the Union Home Secretary as Chairperson, and the Secretaries to the Government of India (GoI)like Ministries/Departments of Agriculture, Atomic Energy, Defence, Drinking Water Supply, Environment and Forests, etc. The NEC prepares the National Plan for Disaster Management as per the National Policy on Disaster Management.
• State Disaster Management Authority (SDMA):- The Chief Minister of the respective state is the head of the SDMA.The State Government has a State Executive Committee (SEC) which assists the State Disaster Management Authority (SDMA) on Disaster Management.
• District Disaster Management Authority (DDMA):- The DDMA is headed by the District Collector, Deputy Commissioner or District Magistrate depending on the situation, with the elected representatives of the local authority as the Co-Chairperson. The DDMA ensures that the guidelines framed by the NDMA and the SDMA are followed by all the departments of the State Government at the District level and the local authorities in the District.
• Local Authorities:- Local authorities would include Panchayati Raj Institutions (PRI), Municipalities, District and Cantonment 11 Institutional and Legal Arrangements Boards, and Town Planning Authorities which control and manage civic services.

Now let’s have a look at some of the types of disasters and the means to combat them.

Biological Disasters

Definition: The devastating effects caused by an enormous spread of a certain kind of living organism that may spread disease, viruses, or an infestation of plant, animal, or insect life on an epidemic or pandemic level.

• Epidemic Level – Indicates a disaster that affects many people in a given area or community.
• Pandemic Level – Indicates a disaster that affects a much larger region, sometimes an entire continent or even the whole planet. For example, the recent H1N1 or Swine Flu pandemic.

To know more about Bio-Terrorism threat to India and India’s Preparedness visit the linked article.

Biological Disasters – Important points to remember for UPSC

1. The nodal Ministry for handling epidemics – Ministry of Health and Family Welfare

• Decision-making
• Advisory body
• Emergency medical relief providing

2. The primary responsibility of dealing with biological disasters is with the State Governments. (Reason – Health is a State Subject).

3. The nodal agency for investigating outbreaks – National Institute of Communicable Diseases (NICD)

4. Nodal ministry for Biological Warfare – Ministry of Home Affairs ( Biological warfare is the use of biological agents as an act of war)

Biological Disasters – Classifications

Charles Baldwin developed the symbol for biohazard in 1966.

The US Centres for Disease Control classifies biohazards into four biosafety levels as follows:

• BSL-1: Bacteria and Viruses including Bacillus subtilis, some cell cultures, canine hepatitis, and non-infectious bacteria. Protection is only facial protection and gloves.
• BSL-2: Bacteria and viruses that cause only mild disease to humans, or are difficult to contract via aerosol in a lab setting such as hepatitis A, B, C, mumps, measles, HIV, etc. Protection – use of autoclaves for sterilizing and biological safety cabinets.
• BSL-3: Bacteria and viruses causing severe to fatal disease in humans. Example: West Nile virus, anthrax, MERS coronavirus. Protection – Stringent safety protocols such as the use of respirators to prevent airborne infection.
• BSL-4: Potentially fatal (to human beings) viruses like Ebola virus, Marburg virus, Lassa fever virus, etc. Protection – use of a positive pressure personnel suit, with a segregated air supply.

Legislations for prevention of Biohazards in India

The following legislations have been enacted in India for the prevention of biohazards and implementation of protective, eradicative and containing measures when there is an outbreak:

• The Water (Prevention and Control of Pollution) Act, 1974
• The Air (Prevention and Control of Pollution) Act, 1981
• The Environmental (Protection) Act, 1986 and the Rules (1986)
• Disaster Management Act 2005, provides for the institutional and operational framework for disaster prevention, mitigation, response, preparedness, and recovery at all levels.
• Air Prevention and Control of Pollution Act 1981
• Disaster Management Act of 2005

Prevention of Biological Hazards

The basic measure to prevent and control biohazards is the elimination of the source of contamination. Some of the prevention methods are as follows:

Preventive Measures for workers in the field (Medical)

• Engineering controls – to help prevent the spread of such disasters including proper ventilation, installing negative pressure, and usage of UV lamps.
• Personal hygiene – washing hands with liquid soap, proper care for clothes that have been exposed to a probably contaminated environment.
• Personal protection equipment – masks, protective clothing, gloves, face shield, eye shield, shoe covers.
• Sterilization – Using ultra heat or high pressure to eliminate bacteria or using biocide to kill microbes.
• Respiratory protection – surgical masks, respirators, powered air-purifying respirators (PAPR), air-supplying respirators.

Prevention of Biological Hazards (Environmental Management)

Safe water supply, proper maintenance of sewage pipelines – to prevent waterborne diseases such as cholera, typhoid, hepatitis, dysentery, etc.

Awareness of personal hygiene and provision for washing, cleaning, bathing, avoiding overcrowding, etc.

Vector control:

Environmental engineering work and generic integrated vector control measures.

Water management, not permitting water to stagnate and collect and other methods to eliminate breeding places for vectors.

Regular spraying of insecticides, outdoor fogging, etc. for controlling vectors.

Controlling the population of rodents.

Post-disaster Epidemics Prevention

The risk of epidemics is increased after any biological disaster.

Integrated Disease Surveillance Systems (IDSS) monitors the sources, modes of diseases spreading, and investigates the epidemics.

Detection and Containment of Outbreaks

This consists of four steps as given under:

• Recognizing and diagnosing by primary healthcare practitioners.
• Communicating surveillance information to public health authorities.
• Epidemiological analysis of surveillance data
• Public health measures and delivering proper medical treatment.

Legal Framework for Biological Disasters

• The Epidemic Diseases Act was enacted in the year 1897. (Read about RSTV’s In-Depth Analysis on Epidemic Diseases Act 1897 in the linked article.)
• This Act does not provide any power to the centre to intervene in biological emergencies.
• It has to be substituted by an Act that takes care of the prevailing and foreseeable public health needs including emergencies such as BT attacks and the use of biological weapons by an adversary, cross-border issues, and international spread of diseases.
• It should give enough powers to the central and state governments and local authorities to act with impunity, notify affected areas, restrict movement or quarantine the affected area, enter any premises to take samples of suspected materials, and seal them.
• The Act should also establish controls over biological sample transfer, biosecurity and biosafety of materials/laboratories.

Institutional Framework

In the Ministry of Health & Family Welfare (MoH&FW), public health needs to be accorded high priority with a separate Additional Directorate General of Health and Sanitation (DGHS) for public health. In some states, there is a separate department of public health. States that do not have such arrangements will also have to take initiatives to establish such a department.

Operational Framework

At the national level, there is no policy on biological disasters. The existing contingency plan of MoH&FW is about 10 years old and needs extensive revision. All components related to public health, namely apex institutions, field epidemiology, surveillance, teaching, training, research, etc., need to be strengthened.

At the operational level, Command and Control (C&C) are identifiable clearly at the district level, where the district collector is vested with certain powers to requisition resources, notify a disease, inspect any premises, seek help from the Army, state or centre, enforce quarantine, etc. However, there is no concept of an incident command system wherein the entire action is brought under the ambit of an incident commander with support from the disciplines of logistics, finance, and technical teams, etc. There is an urgent need for establishing an incident command system in every district.

There is a shortage of medical and paramedical staff at the district and sub-district levels. There is also an acute shortage of public health specialists, epidemiologists, clinical microbiologists, and virologists.

Biosafety laboratories are required for the prompt diagnosis of the agents for the effective management of biological disasters. There is no BSL-4 laboratory in the human health sector. BSL- 3 laboratories are also limited. Major issues remain regarding biosecurity, the indigenous capability of preparing diagnostic reagents, and quality assurance.

Lack of an Integrated Ambulance Network (IAN). There is no ambulance system with advanced life-support facilities that are capable of working in biological disasters.

State-run hospitals have limited medical supplies. Even in normal situations, a patient has to buy medicines. There is a lack of stockpile of drugs, important vaccines like anthrax vaccine, PPE, or diagnostics for surge capacity. In a crisis, there is further incapacitation due to tedious procurement procedures.

National Disaster Response Force (NDRF) :- The command and supervision of the NDRF would be under the Director-General of Civil Defence and National Disaster Response Force selected by the Central Government. Currently, the NDRF comprises of eight battalions who will be positioned at different locations as per the requirements.

Read about Crowd Disaster Management in the linked article.

Disaster Prevention and Mitigation

Proper planning and mitigation measures can play a leading role in risk-prone areas to minimize the worst effects of hazards such as earthquakes, floods, and cyclones. These are the key areas which should be addressed to achieve this objective:

• Risk Assessment and Vulnerability Mapping: Mapping and vulnerability analysis in a multi-risk structure will be conducted utilizing Geographic Information System (GIS) based databases like the National Database for Emergency Management (NDEM) and National Spatial Data Infrastructure (NSDI).
• Increasing Trend of Disasters in Urban Areas:- Steps to prevent unplanned urbanization must be undertaken, with the plan of action formulated being given the highest priority. State Governments/UTs concerned on the other hand focus on urban drainage systems with special attention on non-obstruction of natural drainage systems.
• Critical Infrastructure:- Critical infrastructure like roads, dams, bridges, irrigation canals, bridges, power stations, railway lines, delta water distribution networks, ports and rivers, and coastal embankments should be continuously checked for safety standards concerning worldwide safety benchmarks and fortified if the current measures prove to be inadequate.
• Environmentally Sustainable Development: – Environmental considerations and developmental efforts, should be handled simultaneously for ensuring sustainability.
• Climate Change Adaptation:-. The challenges of the increase in the frequency and intensity of natural disasters like cyclones, floods, and droughts should be tackled in a sustained and effective manner with the promotion of strategies for climate change adaptation and disaster risk reduction.

The topics of internal security and disaster management are diverse and also important for both the prelims and the mains exams. These topics are also highly linked with current affairs. Almost every question asked from them is related to current events. So, apart from standard textbooks, you should rely on newspapers and news analyses as well for these sections. To read on how to prepare for internal security and disaster management , check the linked article.

Multiple Choice Question

• The National Disaster Management Authority, or the NDMA, is an apex body for disaster management, headed by the Prime Minister of India. It is responsible for the supervision, direction, and control of the National Disaster Response Force (NDRF).
• The DDMA is headed by the District Collector, Deputy Commissioner or District Magistrate depending on the situation, with the elected representatives of the local authority as the Co-Chairperson.
• The Governor of the respective state is the head of the State Disaster Management Authority
• The Epidemic Diseases Act was enacted in the year 1897.

Choose the correct answer from the below-given options

A) All of the above statements are false.

B) All of the above statements are true.

C) Only statements 2, 3, and 4 are true

D) Only statements 1, 2, and 4 are true

Candidates can find the general pattern of the Civil Service Exam by visiting the UPSC Syllabus page.

Frequently Asked Questions on Disaster Management in India

Q 1. what is the aim of disaster management in india, q 2. what is disaster risk management, q 3. in how many categories can disasters be classified.

Ans. Disaster can be classified into the following categories:

• Water and Climate Disaster
• Geological Disaster
• Biological Disaster
• Industrial Disaster
• Nuclear Disasters
• Man-made disasters

Q 4. Which body is responsible for Disaster Management in India?

Q 5. what is the disaster management act 2005.

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President Joseph R. Biden, Jr. Approves Major Disaster Declaration for California

WASHINGTON   ­– FEMA announced that federal disaster assistance has been made available to the state of California to supplement recovery efforts in the areas affected by the severe winter storms, tornadoes, flooding, landslides and mudslides from January 31 to February 9, 2024.

Public Assistance federal funding is available to the state, tribal and eligible local governments and certain private nonprofit organizations on a cost-sharing basis for emergency work and the repair or replacement of facilities damaged by the storms in Butte, Glenn, Los Angeles, Monterey, San Luis Obispo, Santa Barbara, Santa Cruz, Sutter, and Ventura counties.

Federal funding is also available on a cost-sharing basis for hazard mitigation measures statewide.

Andrew F. Grant has been named as the Federal Coordinating Officer for federal recovery operations in the affected area. Additional designations may be made later if requested by the state and warranted by the results of further assessments.

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President Joseph R. Biden, Jr. Approves Alaska Disaster   Declaration

Today, President Joseph R. Biden, Jr. declared that a major disaster exists in the State of Alaska and ordered Federal assistance to supplement state, tribal, and local recovery efforts in the areas affected by a severe storm, flooding, and landslides on November 20, 2023. Federal funding is available to state, tribal, and eligible local governments and certain private nonprofit organizations on a cost-sharing basis for emergency work and the repair or replacement of facilities damaged by the severe storm, flooding, and landslides in the Prince of Wales-Hyder Census Area, Southeast Island Regional Educational Attendance Area, and the City and Borough of Wrangell.

Federal funding is also available on a cost-sharing basis for hazard mitigation measures statewide.

Mr. Brian F. Schiller of the Federal Emergency Management Agency (FEMA) has been appointed to coordinate Federal recovery operations in the affected areas. 

Additional designations may be made at a later date if requested by the state and warranted by the results of further damage assessments.

FOR FURTHER INFORMATION MEDIA SHOULD CONTACT THE FEMA NEWS DESK AT (202) 646-3272 OR [email protected] .

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Milwaukee Brewers Boot Former Highly-Regarded Prospect Off 40-Man Roster

In a series of roster moves on Wednesday, the Milwaukee Brewers designated former highly-regarded prospect Vladimir Gutierrez for assignment.

• Author: brady farkas

In this story:

They'll now have a week to trade him, release him or outright him to the minors. He never even pitched a game for the Brewers after being claimed off waivers from the Toronto Blue Jays earlier this year.

Per the team on social media:

RHP Tobias Myers selected from Triple-A Nashville. LHP Jared Koenig optioned to Nashville. RHP Vladimir Gutierrez designated for assignment.

RHP Tobias Myers selected from Triple-A Nashville. LHP Jared Koenig optioned to Nashville. RHP Vladimir Gutierrez designated for assignment. pic.twitter.com/EMyDhKD2mG — Milwaukee Brewers (@Brewers) April 17, 2024

The 28-year-old native of Cuba has spent parts of three years in the big leagues with the Reds and Marlins. He made his debut in 2021 and actually started 22 games for Cincy, pitching to a 4.74 ERA. Unfortuantely, that success didn't translate. He had a 7.61 ERA in 10 appearances in 2022 and missed all of 2023 with injury.

This offseason, he joined up with the Marlins, appearing in one game for Miami. He then landed with Toronto and Milwaukee but didn't appear in a game for either of them.

Given his age and former high prospect status, he's likely to get more opportunities, it's just a question of where. Perhaps he could stay in the Brewers organization as a depth piece.

Milwaukee enters play on Wednesday at 10-6 overall. They'll take on the San Diego Padres on Wednesday at 1:10 p.m. ET.

Michael King (2-0, 4.19 ERA) gets the ball for San Diego (11-9) while Bryse Wilson pitches for Milwaukee. The 26-year-old is 1-0 with a 5.19 ERA thus far.

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Fernando Tatis Jr. Does Something He's Never Done in His Career on Thursday

Los Angeles Dodgers' Star Does Something Nearly Never Done in Last 100 Years of Team History

New York Yankees' Legend Jorge Posada Dishes on Career, Baseball and Acting in a Miller Lite Commercial

New York Mets Provide Important Information Regarding Ace Pitcher's Health

Chicago White Sox to Put Former Atlanta Braves Standout Near Top of Rotation

Rays outfielder Josh Lowe set to start rehab assignment Thursday

• Marc Topkin Times staff

ST. PETERSBURG — Outfielder Josh Lowe’s return to the lineup is starting to come into view for the Rays.

Manager Kevin Cash said Lowe, the team’s most productive left-handed hitter last season, is slated to begin a rehab assignment Thursday with Triple-A Durham.

And he might not need too much time there.

“It’s kind of all just how he feels,” Cash said. “(Sunday), he said he feels really good. It’s going to take him a minute to get his timing going. But if he feels good, that’s most important.”

Lowe initially was sidelined in late February, after playing in two spring games, due to left hip inflammation. He was working toward a March 17 return to the lineup with he strained his right oblique.

He started playing in extended spring games last week. Assuming he gets through a Tuesday game with no issues, he will join Durham Wednesday in Worcester, Massachusetts, and play on Thursday. A return to the Rays before the end of April seems possible and would add much-needed left-handed power, plus speed, to the lineup.

Another lefty hitter expected to play a key role, Jonathan Aranda, also is progressing toward a return.

Aranda had two pins in his fractured right ring finger removed Monday and is expected to start taking swings in the next few days.

“He felt like he was going to be pretty much full-go to start ramping up,” Cash said. “He’s been throwing. He felt like he would be able to swing pretty pain-free. So if that’s the case, we’ll start working to get him built up.”

Cash said Aranda, who was injured March 19, will go through the standard progression of batting cage drills, starting with hitting balls off a tee, and work toward taking batting practice on the field before starting a rehab assignment.

“So, still a ways away,” Cash said, “but (moving) in the right direction.”

Hey, it’s you guys again

Starters have different ways of dealing with facing the same team in consecutive outings. Expect Rays right-hander Aaron Civale to take a cerebral approach in making adjustments Tuesday against the Angels.

“He’s a unique mind in terms of how he approaches his starts,” pitching coach Kyle Snyder said Monday. “This is a unique situation, (playing) out of division and you’re facing a team twice in a row.

“But he is very good at kind of reverse-engineering things that have happened. And being able to act on those, whether it’s the second time through (the order in a game) or the second time he faces a team in a week. And it’s pretty impressive the lengths that he goes through in terms of preparing himself and his ability to realize how to keep guys in between (and off-balance). He’s going to lean on some of what he was successful in doing that last time, and then probably just try to apply that.”

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Civale worked five innings April 9 at Angel Stadium, allowing a two-run homer to Mike Trout, as well as an unearned run. He walked one and struck out four.

Jackie Robinson Day reflections

The Angels’ Ron Washington, one of two current Black managers in the majors, said celebrating Jackie Robinson Day, as was done throughout Major League Baseball on Monday, is very important: “It means everything, because I don’t think I’ll be sitting here managing and talking to you (reporters) if it wasn’t for Jackie Robinson breaking that barrier.” ... All players and coaches wore No. 42 in Robinson’s honor.

NFL free-agent receiver Marquez Valdes-Scantling, a St. Petersburg native and product of Lakewood High and USF, threw out the first pitch, which was more than a little bit outside. … The Rays Baseball Foundation and Rowdies Soccer Fund on Monday announced $25,000 Racial Equity Grants to the Helen Gordon Davis Centre for Women, James B. Sanderlin Neighborhood Family Center, Sing Out and Read, and Where Love Grows Inc. as part of their annual contribution “to support organizations committed to ending systemic racism.”

Sign up for the Sports Today newsletter to get daily updates on the Bucs, Rays, Lightning and college football across Florida.

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Marc Topkin is a sports reporter covering the Tampa Bay Rays. Reach him at [email protected].

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Brewers Select Tobias Myers, Designate Vladimir Gutierrez For Assignment

By Darragh McDonald | April 17, 2024 at 12:55pm CDT

The Brewers announced that they have selected the contract of right-hander Tobias Myers . In corresponding moves, they optioned left-hander Jared Koenig to Triple-A Nashville and designated right-hander Vladimir Gutierrez for assignment.

Myers, 25, will be making his major league debut as soon as he gets into a game, though he took quite a circuitous route to get here. Drafted by the Orioles way back in 2016, he was traded to the Rays the following year as the O’s acquired Tim Beckham . Ahead of the 2021 Rule 5 deadline, he was flipped to Cleveland in exchange for Junior Caminero and then added to Cleveland’s 40-man roster. Myers was designated for assignment in July of 2022, getting traded to the Giants for cash. He was later claimed off waivers by the White Sox, though that club outrighted him off their roster towards the end of the 2022 season. He reached minor league free agency and signed a minor league deal with the Brewers prior to 2023.

Along that winding road, he saw his prospect stock rise and fall. Baseball America considered him the Rays’ #15 prospect going into 2018, which was on the heels of a strong 2017 performance wherein Myers tossed 56 minors league innings with a 3.54 earned run average, 31.9% strikeout rate and 4.4% walk rate. Were in not for a very unlucky 52.1% strand rate, his performance would have been even better, which is why his FIP was a tiny 1.81.

But his strikeout-to-walk ratios were less impressive in the two following two seasons. He had a combed 3.05 ERA over 2018 and 2019 but with a subpar 18.7% strikeout rate and average-ish 8.1% walk rate. The minors were canceled by the pandemic in 2020 but Myers bounced back somewhat in 2021. He tossed 117 2/3 innings over 25 outings, 22 starts, with a 3.90 ERA, 30.5% strikeout rate and 5.8% walk rate. It was then that he was traded to the Guards for Caminero and BA ranked him the #23 prospect in Cleveland’s system.

But in 2022, as he bounced to the Guardians, Giants and White Sox, he tossed 76 innings on the farm with a ghastly 7.82 ERA, striking out just 14.2% of opponents while giving them free passes at a 13.5% clip.

With the Brewers last year, he improved in terms of strikeouts and walks but the long ball was an issue. He threw 140 2/3 frames with a 29.3% strikeout rate and 7.7% walk rate, but the 30 home runs allowed led to a 4.93 ERA. So far this year, he’s made three Triple-A starts with a 1.84 ERA in that small sample.

Given the inconsistency, it’s hard to know what to expect from Myers at this point, but the Brewers have largely been getting decent results out of him in the past year-plus. Since he’s stretched out, he can give the club a bit of length. He has a couple of options and can provide the club with some roster flexibility well into the future if he continues to hang onto his 40-man spot.

Gutierrez, 28, was claimed off waivers by the Brewers less than two weeks ago. He was optioned to Triple-A and made two appearances on the farm before getting bumped off the 40-man roster today. Milwaukee will have one week to trade him or pass him through waivers.

He was once a highly-touted prospect himself but has a 5.47 ERA through 154 2/3 major league innings thus far, mostly with the Reds. He missed most of 2023 while recovering from Tommy John surgery and was outrighted by the Reds at season’s end. He signed a minor league deal with the Marlins this winter and was selected to the roster but was designated for assignment after one appearance, which led him to the Brewers via the aforementioned waiver claim.

He could perhaps garner interest from other clubs, either due to his previous prospect pedigree or the various injuries piling up around the league or both. The fact that Gutierrez still has a couple of options means he won’t even need an active roster spot.

11 hours ago

As a Guardians fan, I would prefer to never see the name Tobias Myers ever again

10 hours ago

The Guardians traded Junior Caminero for him.

8 hours ago

Thank you for the concise summary.

Wow… He must have hurt you badly. Remember that time heals the pain.

6 hours ago

In all seriousness I actually wish the best for him but with the benefit of hindsight that was a terrible trade lol

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Mozilla Foundation Security Advisory 2024-18

Security vulnerabilities fixed in firefox 125.

• Firefox 125

# CVE-2024-3852: GetBoundName in the JIT returned the wrong object

Description.

GetBoundName could return the wrong version of an object when JIT optimizations were applied.

• Bug 1883542

# CVE-2024-3853: Use-after-free if garbage collection runs during realm initialization

A use-after-free could result if a JavaScript realm was in the process of being initialized when a garbage collection started.

• Bug 1884427

# CVE-2024-3854: Out-of-bounds-read after mis-optimized switch statement

In some code patterns the JIT incorrectly optimized switch statements and generated code with out-of-bounds-reads.

• Bug 1884552

# CVE-2024-3855: Incorrect JIT optimization of MSubstr leads to out-of-bounds reads

In certain cases the JIT incorrectly optimized MSubstr operations, which led to out-of-bounds reads.

• Bug 1885828

# CVE-2024-3856: Use-after-free in WASM garbage collection

A use-after-free could occur during WASM execution if garbage collection ran during the creation of an array.

• Bug 1885829

# CVE-2024-3857: Incorrect JITting of arguments led to use-after-free during garbage collection

The JIT created incorrect code for arguments in certain cases. This led to potential use-after-free crashes during garbage collection.

• Bug 1886683

# CVE-2024-3858: Corrupt pointer dereference in js::CheckTracedThing<js::Shape>

It was possible to mutate a JavaScript object so that the JIT could crash while tracing it.

• Bug 1888892

# CVE-2024-3859: Integer-overflow led to out-of-bounds-read in the OpenType sanitizer

On 32-bit versions there were integer-overflows that led to an out-of-bounds-read that potentially could be triggered by a malformed OpenType font.

• Bug 1874489

# CVE-2024-3860: Crash when tracing empty shape lists

An out-of-memory condition during object initialization could result in an empty shape list. If the JIT subsequently traced the object it would crash.

• Bug 1881417

# CVE-2024-3861: Potential use-after-free due to AlignedBuffer self-move

If an AlignedBuffer were assigned to itself, the subsequent self-move could result in an incorrect reference count and later use-after-free.

• Bug 1883158

# CVE-2024-3862: Potential use of uninitialized memory in MarkStack assignment operator on self-assignment

The MarkStack assignment operator, part of the JavaScript engine, could access uninitialized memory if it were used in a self-assignment.

• Bug 1884457

# CVE-2024-3863: Download Protections were bypassed by .xrm-ms files on Windows

The executable file warning was not presented when downloading .xrm-ms files. Note: This issue only affected Windows operating systems. Other operating systems are unaffected.

• Bug 1885855

# CVE-2024-3302: Denial of Service using HTTP/2 CONTINUATION frames

There was no limit to the number of HTTP/2 CONTINUATION frames that would be processed. A server could abuse this to create an Out of Memory condition in the browser.

• Bug 1881183
• VU#421644 - HTTP/2 CONTINUATION frames can be utilized for DoS attacks

# CVE-2024-3864: Memory safety bug fixed in Firefox 125, Firefox ESR 115.10, and Thunderbird 115.10

Memory safety bug present in Firefox 124, Firefox ESR 115.9, and Thunderbird 115.9. This bug showed evidence of memory corruption and we presume that with enough effort this could have been exploited to run arbitrary code.

• Memory safety bug fixed in Firefox 125, Firefox ESR 115.10, and Thunderbird 115.10

# CVE-2024-3865: Memory safety bugs fixed in Firefox 125

Memory safety bugs present in Firefox 124. Some of these bugs showed evidence of memory corruption and we presume that with enough effort some of these could have been exploited to run arbitrary code.

• Memory safety bugs fixed in Firefox 125

• Open access
• Published: 15 April 2024

Demuxafy : improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods

• Drew Neavin 1 ,
• Anne Senabouth 1 ,
• Himanshi Arora 1 , 2 ,
• Jimmy Tsz Hang Lee 3 ,
• Aida Ripoll-Cladellas 4 ,
• sc-eQTLGen Consortium ,
• Lude Franke 5 ,
• Shyam Prabhakar 6 , 7 , 8 ,
• Chun Jimmie Ye 9 , 10 , 11 , 12 ,
• Davis J. McCarthy 13 , 14 ,
• Marta Melé 4 ,
• Martin Hemberg 15 &
• Joseph E. Powell   ORCID: orcid.org/0000-0002-5070-4124 1 , 16  

Genome Biology volume  25 , Article number:  94 ( 2024 ) Cite this article

42 Accesses

Metrics details

Recent innovations in single-cell RNA-sequencing (scRNA-seq) provide the technology to investigate biological questions at cellular resolution. Pooling cells from multiple individuals has become a common strategy, and droplets can subsequently be assigned to a specific individual by leveraging their inherent genetic differences. An implicit challenge with scRNA-seq is the occurrence of doublets—droplets containing two or more cells. We develop Demuxafy, a framework to enhance donor assignment and doublet removal through the consensus intersection of multiple demultiplexing and doublet detecting methods. Demuxafy significantly improves droplet assignment by separating singlets from doublets and classifying the correct individual.

Droplet-based single-cell RNA sequencing (scRNA-seq) technologies have provided the tools to profile tens of thousands of single-cell transcriptomes simultaneously [ 1 ]. With these technological advances, combining cells from multiple samples in a single capture is common, increasing the sample size while simultaneously reducing batch effects, cost, and time. In addition, following cell capture and sequencing, the droplets can be demultiplexed—each droplet accurately assigned to each individual in the pool [ 2 , 3 , 4 , 5 , 6 , 7 ].

Many scRNA-seq experiments now capture upwards of 20,000 droplets, resulting in ~16% (3,200) doublets [ 8 ]. Current demultiplexing methods can also identify doublets—droplets containing two or more cells—from different individuals (heterogenic doublets). These doublets can significantly alter scientific conclusions if they are not effectively removed. Therefore, it is essential to remove doublets from droplet-based single-cell captures.

However, demultiplexing methods cannot identify droplets containing multiple cells from the same individual (homogenic doublets) and, therefore, cannot identify all doublets in a single capture. If left in the dataset, those doublets could appear as transitional cells between two distinct cell types or a completely new cell type. Accordingly, additional methods have been developed to identify heterotypic doublets (droplets that contain two cells from different cell types) by comparing the transcriptional profile of each droplet to doublets simulated from the dataset [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. It is important to recognise that demultiplexing methods achieve two functions—segregation of cells from different donors and separation of singlets from doublets—while doublet detecting methods solely classify singlets versus doublets.

Therefore, demultiplexing and transcription-based doublet detecting methods provide complementary information to improve doublet detection, providing a cleaner dataset and more robust scientific results. There are currently five genetic-based demultiplexing [ 2 , 3 , 4 , 5 , 6 , 7 , 16 ] and seven transcription-based doublet-detecting methods implemented in various languages [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Under different scenarios, each method is subject to varying performance and, in some instances, biases in their ability to accurately assign cells or detect doublets from certain conditions. The best combination of methods is currently unclear but will undoubtedly depend on the dataset and research question.

Therefore, we set out to identify the best combination of genetic-based demultiplexing and transcription-based doublet-detecting methods to remove doublets and partition singlets from different donors correctly. In addition, we have developed a software platform ( Demuxafy ) that performs these intersectional methods and provides additional commands to simplify the execution and interpretation of results for each method (Fig. 1 a).

Study design and qualitative method classifications. a  Demuxafy is a platform to perform demultiplexing and doublet detecting with consistent documentation. Demuxafy also provides wrapper scripts to quickly summarize the results from each method and assign clusters to each individual with reference genotypes when a reference-free demultiplexing method is used. Finally, Demuxafy provides a script to easily combine the results from multiple different methods into a single data frame and it provides a final assignment for each droplet based on the combination of multiple methods. In addition, Demuxafy provides summaries of the number of droplets classified as singlets or doublets by each method and a summary of the number of droplets assigned to each individual by each of the demultiplexing methods. b  Two datasets are included in this analysis - a PBMC dataset and a fibroblast dataset. The PBMC dataset contains 74 pools that captured approximately 20,000 droplets each with 12-16 donor cells multiplexed per pool. The fibroblast dataset contains 11 pools of roughly 7,000 droplets per pool with sizes ranging from six to eight donors per pool. All pools were processed by all demultiplexing and doublet detecting methods and the droplet and donor classifications were compared between the methods and between the PBMCs and fibroblasts. Then the PBMC droplets that were classified as singlets by all methods were taken as ‘true singlets’ and used to generate new pools in silico. Those pools were then processed by each of the demultiplexing and doublet detecting methods and intersectional combinations of demultiplexing and doublet detecting methods were tested for different experimental designs

To compare the demultiplexing and doublet detecting methods, we utilised two large, multiplexed datasets—one that contained ~1.4 million peripheral blood mononuclear cells (PBMCs) from 1,034 donors [ 17 ] and one with ~94,000 fibroblasts from 81 donors [ 18 ]. We used the true singlets from the PBMC dataset to generate new in silico pools to assess the performance of each method and the multi-method intersectional combinations (Fig. 1 b).

Here, we compare 14 demultiplexing and doublet detecting methods with different methodological approaches, capabilities, and intersectional combinations. Seven of those are demultiplexing methods ( Demuxalot [ 6 ], Demuxlet [ 3 ], Dropulation [ 5 ], Freemuxlet [ 16 ], ScSplit [ 7 ], Souporcell [ 4 ], and Vireo [ 2 ]) which leverage the common genetic variation between individuals to identify cells that came from each individual and to identify heterogenic doublets. The seven remaining methods ( DoubletDecon [ 9 ], DoubletDetection [ 14 ], DoubletFinder [ 10 ], ScDblFinder [ 11 ], Scds [ 12 ], Scrublet [ 13 ], and Solo [ 15 ]) identify doublets based on their similarity to simulated doublets generated by adding the transcriptional profiles of two randomly selected droplets in the dataset. These methods assume that the proportion of real doublets in the dataset is low, so combining any two droplets will likely represent the combination of two singlets.

We identify critical differences in the performance of demultiplexing and doublet detecting methods to classify droplets correctly. In the case of the demultiplexing techniques, their performance depends on their ability to identify singlets from doublets and assign a singlet to the correct individual. For doublet detecting methods, the performance is based solely on their ability to differentiate a singlet from a doublet. We identify limitations in identifying specific doublet types and cell types by some methods. In addition, we compare the intersectional combinations of these methods for multiple experimental designs and demonstrate that intersectional approaches significantly outperform all individual techniques. Thus, the intersectional methods provide enhanced singlet classification and doublet removal—a critical but often under-valued step of droplet-based scRNA-seq processing. Our results demonstrate that intersectional combinations of demultiplexing and doublet detecting software provide significant advantages in droplet-based scRNA-seq preprocessing that can alter results and conclusions drawn from the data. Finally, to provide easy implementation of our intersectional approach, we provide Demuxafy ( https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/index.html ) a complete platform to perform demultiplexing and doublet detecting intersectional methods (Fig. 1 a).

Study design

To evaluate demultiplexing and doublet detecting methods, we developed an experimental design that applies the different techniques to empirical pools and pools generated in silico from the combination of true singlets—droplets identified as singlets by every method (Fig. 1 a). For the first phase of this study, we used two empirical multiplexed datasets—the peripheral blood mononuclear cell (PBMC) dataset containing ~1.4 million cells from 1034 donors and a fibroblast dataset of ~94,000 cells from 81 individuals (Additional file 1 : Table S1). We chose these two cell systems to assess the methods in heterogeneous (PBMC) and homogeneous (fibroblast) cell types.

Demultiplexing and doublet detecting methods perform similarly for heterogeneous and homogeneous cell types

We applied the demultiplexing methods ( Demuxalot , Demuxlet , Dropulation , Freemuxlet , ScSplit , Souporcell , and Vireo ) and doublet detecting methods ( DoubletDecon , DoubletDetection , DoubletFinder , ScDblFinder , Scds , Scrublet , and Solo ) to the two datasets and assessed the results from each method. We first compared the droplet assignments by identifying the number of singlets and doublets identified by a given method that were consistently annotated by all methods (Fig. 2 a–d). We also identified the percentage of droplets that were annotated consistently between pairs of methods (Additional file 2 : Fig S1). In the cases where two demultiplexing methods were compared to one another, both the droplet type (singlet or doublet) and the assignment of the droplet to an individual had to match to be considered in agreement. In all other comparisons (i.e. demultiplexing versus doublet detecting and doublet detecting versus doublet detecting), only the droplet type (singlet or doublet) was considered for agreement since doublet detecting methods cannot annotate donor assignment. We found that the two method types were more similar to other methods of the same type (i.e., demultiplexing versus demultiplexing and doublet detecting versus doublet detecting) than they were to methods from a different type (demultiplexing methods versus doublet detecting methods; Supplementary Fig 1). We found that the similarity of the demultiplexing and doublet detecting methods was consistent in the PBMC and fibroblast datasets (Pearson correlation R = 0.78, P -value < 2×10 −16 ; Fig S1a-c). In addition, demultiplexing methods were more similar than doublet detecting methods for both the PBMC and fibroblast datasets (Wilcoxon rank-sum test: P < 0.01; Fig. 2 a–b and Additional file 2 : Fig S1).

Demultiplexing and Doublet Detecting Method Performance Comparison. a  The proportion of droplets classified as singlets and doublets by each method in the PBMCs. b  The number of other methods that classified the singlets and doublets identified by each method in the PBMCs. c  The proportion of droplets classified as singlets and doublets by each method in the fibroblasts. d The number of other methods that classified the singlets and doublets identified by each method in the fibroblasts. e - f The performance of each method when the majority classification of each droplet is considered the correct annotation in the PBMCs ( e ) and fibroblasts ( f ). g - h  The number of droplets classified as singlets (box plots) and doublets (bar plots) by all methods in the PBMC ( g ) and fibroblast ( h ) pools. i - j  The number of donors that were not identified by each method in each pool for PBMCs ( i ) and fibroblasts ( j ). PBMC: peripheral blood mononuclear cell. MCC: Matthew’s correlationcoefficient

The number of unique molecular identifiers (UMIs) and genes decreased in droplets that were classified as singlets by a larger number of methods while the mitochondrial percentage increased in both PBMCs and fibroblasts (Additional file 2 : Fig S2).

We next interrogated the performance of each method using the Matthew’s correlation coefficient (MCC) to calculate the consistency between Demuxify and true droplet classification. We identified consistent trends in the MCC scores for each method between the PBMCs (Fig. 2 e) and fibroblasts (Fig. 2 f). These data indicate that the methods behave similarly, relative to one another, for heterogeneous and homogeneous datasets.

Next, we sought to identify the droplets concordantly classified by all demultiplexing and doublet detecting methods in the PBMC and fibroblast datasets. On average, 732 singlets were identified for each individual by all the methods in the PBMC dataset. Likewise, 494 droplets were identified as singlets for each individual by all the methods in the fibroblast pools. However, the concordance of doublets identified by all methods was very low for both datasets (Fig. 2 e–f). Notably, the consistency of classifying a droplet as a doublet by all methods was relatively low (Fig. 2 b,d,g, and h). This suggests that doublet identification is not consistent between all the methods. Therefore, further investigation is required to identify the reasons for these inconsistencies between methods. It also suggests that combining multiple methods for doublet classification may be necessary for more complete doublet removal. Further, some methods could not identify all the individuals in each pool (Fig. 2 i–j). The non-concordance between different methods demonstrates the need to effectively test each method on a dataset where the droplet types are known.

Computational resources vary for demultiplexing and doublet detecting methods

We recorded each method’s computational resources for the PBMC pools, with ~20,000 cells captured per pool (Additional file 1 : Table S1). Of the demultiplexing methods, ScSplit took the most time (multiple days) and required the most steps, but Demuxalot , Demuxlet , and Freemuxlet used the most memory. Solo took the longest time (median 13 h) and most memory to run for the doublet detecting methods but is the only method built to be run directly from the command line, making it easy to implement (Additional file 2 : Fig S3).

Generate pools with known singlets and doublets

However, there is no gold standard to identify which droplets are singlets or doublets. Therefore, in the second phase of our experimental design (Fig. 1 b), we used the PBMC droplets classified as singlets by all methods to generate new pools in silico. We chose to use the PBMC dataset since our first analyses indicated that method performance is similar for homogeneous (fibroblast) and heterogeneous (PBMC) cell types (Fig. 2 and Additional file 2 : Fig S1) and because we had many more individuals available to generate in silico pools from the PBMC dataset (Additional file 1 : Table S1).

We generated 70 pools—10 each of pools that included 2, 4, 8, 16, 32, 64, or 128 individuals (Additional file 1 : Table S2). We assume a maximum 20% doublet rate as it is unlikely researchers would use a technology that has a higher doublet rate (Fig. 3 a).

In silico Pool Doublet Annotation and Method Performance. a  The percent of singlets and doublets in the in -silico pools - separated by the number of multiplexed individuals per pool. b  The percentage and number of doublets that are heterogenic (detectable by demultiplexing methods), heterotypic (detectable by doublet detecting methods), both (detectable by either method category) and neither (not detectable with current methods) for each multiplexed pool size. c  Percent of droplets that each of the demultiplexing and doublet detecting methods classified correctly for singlets and doublet subtypes for different multiplexed pool sizes. d  Matthew’s Correlation Coefficient (MCC) for each of the methods for each of the multiplexed pool sizes. e  Balanced accuracy for each of the methods for each of the multiplexed pool sizes

We used azimuth to classify the PBMC cell types for each droplet used to generate the in silico pools [ 19 ] (Additional file 2 : Fig S4). As these pools have been generated in silico using empirical singlets that have been well annotated, we next identified the proportion of doublets in each pool that were heterogenic, heterotypic, both, and neither. This approach demonstrates that a significant percentage of doublets are only detectable by doublet detecting methods (homogenic and heterotypic) for pools with 16 or fewer donors multiplexed (Fig. 3 b).

While the total number of doublets that would be missed if only using demultiplexing methods appears small for fewer multiplexed individuals (Fig. 3 b), it is important to recognise that this is partly a function of the ~732 singlet cells per individual used to generate these pools. Hence, the in silico pools with fewer individuals also have fewer cells. Therefore, to obtain numbers of doublets that are directly comparable to one another, we calculated the number of each doublet type that would be expected to be captured with 20,000 cells when 2, 4, 8, 16, or 32 individuals were multiplexed (Additional file 2 : Fig S5). These results demonstrate that many doublets would be falsely classified as singlets since they are homogenic when just using demultiplexing methods for a pool of 20,000 cells captured with a 16% doublet rate (Additional file 2 : Fig S5). However, as more individuals are multiplexed, the number of droplets that would not be detectable by demultiplexing methods (homogenic) decreases. This suggests that typical workflows that use only one demultiplexing method to remove doublets from pools that capture 20,000 droplets with 16 or fewer multiplexed individuals fail to adequately remove between 173 (16 multiplexed individuals) and 1,325 (2 multiplexed individuals) doublets that are homogenic and heterotypic which could be detected by doublet detecting methods (Additional file 2 : Fig S5). Therefore, a technique that uses both demultiplexing and doublet detecting methods in parallel will complement more complete doublet removal methods. Consequently, we next set out to identify the demultiplexing and doublet detecting methods that perform the best on their own and in concert with other methods.

Doublet and singlet droplet classification effectiveness varies for demultiplexing and doublet detecting methods

Demultiplexing methods fail to classify homogenic doublets.

We next investigated the percentage of the droplets that were correctly classified by each demultiplexing and doublet detecting method. In addition to the seven demultiplexing methods, we also included Demuxalot with the additional steps to refine the genotypes that can then be used for demultiplexing— Demuxalot (refined). Demultiplexing methods correctly classify a large portion of the singlets and heterogenic doublets (Fig. 3 c). This pattern is highly consistent across different cell types, with the notable exceptions being decreased correct classifications for erythrocytes and platelets when greater than 16 individuals are multiplexed (Additional file 2 : Fig S6).

However, Demuxalot consistently demonstrates the highest correct heterogenic doublet classification. Further, the percentage of the heterogenic doublets classified correctly by Souporcell decreases when large numbers of donors are multiplexed. ScSplit is not as effective as the other demultiplexing methods at classifying heterogenic doublets, partly due to the unique doublet classification method, which assumes that the doublets will generate a single cluster separate from the donors (Table 1 ). Importantly, the demultiplexing methods identify almost none of the homogenic doublets for any multiplexed pool size—demonstrating the need to include doublet detecting methods to supplement the demultiplexing method doublet detection.

Doublet detecting method classification performances vary greatly

In addition to assessing each of the methods with default settings, we also evaluated ScDblFinder with ‘known doublets’ provided. This method can take already known doublets and use them when detecting doublets. For these cases, we used the droplets that were classified as doublets by all the demultiplexing methods as ‘known doublets’.

Most of the methods classified a similarly high percentage of singlets correctly, with the exceptions of DoubletDecon and DoubletFinder for all pool sizes (Fig. 3 c). However, unlike the demultiplexing methods, there are explicit cell-type-specific biases for many of the doublet detecting methods (Additional file 2 : Fig S7). These differences are most notable for cell types with fewer cells (i.e. ASDC and cDC2) and proliferating cells (i.e. CD4 Proliferating, CD8 Proliferating, and NK Proliferating). Further, all of the softwares demonstrate high correct percentages for some cell types including CD4 Naïve and CD8 Naïve (Additional file 2 : Fig S7).

As expected, all doublet detecting methods identified heterotypic doublets more effectively than homotypic doublets (Fig. 3 c). However, ScDblFinder and Scrublet classified the most doublets correctly across all doublet types for pools containing 16 individuals or fewer. Solo was more effective at identifying doublets than Scds for pools containing more than 16 individuals. It is also important to note that it was not feasible to run DoubletDecon for the largest pools containing 128 multiplexed individuals and an average of 115,802 droplets (range: 113,594–119,126 droplets). ScDblFinder performed similarly when executed with and without known doublets (Pearson correlation P = 2.5 × 10 -40 ). This suggests that providing known doublets to ScDblFinder does not offer an added benefit.

Performances vary between demultiplexing and doublet detecting method and across the number of multiplexed individuals

We assessed the overall performance of each method with two metrics: the balanced accuracy and the MCC. We chose to use balanced accuracy since, with unbalanced group sizes, it is a better measure of performance than accuracy itself. Further, the MCC has been demonstrated as a more reliable statistical measure of performance since it considers all possible categories—true singlets (true positives), false singlets (false positives), true doublets (true negatives), and false doublets (false negatives). Therefore, a high score on the MCC scale indicates high performance in each metric. However, we provide additional performance metrics for each method (Additional file 1 : Table S3). For demultiplexing methods, both the droplet type (singlet or doublet) and the individual assignment were required to be considered a ‘true singlet’. In contrast, only the droplet type (singlet or doublet) was needed for doublet detection methods.

The MCC and balanced accuracy metrics are similar (Spearman’s ⍴ = 0.87; P < 2.2 × 10 -308 ). Further, the performance of Souporcell decreases for pools with more than 32 individuals multiplexed for both metrics (Student’s t -test for MCC: P < 1.1 × 10 -9 and balanced accuracy: P < 8.1 × 10 -11 ). Scds , ScDblFinder , and Scrublet are among the top-performing doublet detecting methods Fig. 3 d–e).

Overall, between 0.4 and 78.8% of droplets were incorrectly classified by the demultiplexing or doublet detecting methods depending on the technique and the multiplexed pool size (Additional file 2 : Fig S8). Demuxalot (refined) and DoubletDetection demonstrated the lowest percentage of incorrect droplets with about 1% wrong in the smaller pools (two multiplexed individuals) and about 3% incorrect in pools with at least 16 multiplexed individuals. Since some transitional states and cell types are present in low percentages in total cell populations (i.e. ASDCs at 0.02%), incorrect classification of droplets could alter scientific interpretations of the data, and it is, therefore, ideal for decreasing the number of erroneous assignments as much as possible.

False singlets and doublets demonstrate different metrics than correctly classified droplets

We next asked whether specific cell metrics might contribute to false singlet and doublet classifications for different methods. Therefore, we compared the number of genes, number of UMIs, mitochondrial percentage and ribosomal percentage of the false singlets and doublets to equal numbers of correctly classified cells for each demultiplexing and doublet detecting method.

The number of UMIs (Additional file 2 : Fig S9 and Additional file 1 : Table S4) and genes (Additional file 2 : Fig S10 and Additional file 1 : Table S5) demonstrated very similar distributions for all comparisons and all methods (Spearman ⍴ = 0.99, P < 2.2 × 10 -308 ). The number of UMIs and genes were consistently higher in false singlets and lower in false doublets for most demultiplexing methods except some smaller pool sizes (Additional file 2 : Fig S9a and Additional file 2 : Fig S10a; Additional file 1 : Table S4 and Additional file 1 : Table S5). The number of UMIs and genes was consistently higher in droplets falsely classified as singlets by the doublet detecting methods than the correctly identified droplets (Additional file 2 : Fig S9b and Additional file 2 : Fig S10b; Additional file 1 : Table S4 and Additional file 1 : Table S5). However, there was less consistency in the number of UMIs and genes detected in false singlets than correctly classified droplets between the different doublet detecting methods (Additional file 2 : Fig S9b and Additional file 2 : Fig S10b; Additional file 1 : Table S4 and Additional file 1 : Table S5).

The ribosomal percentage of the droplets falsely classified as singlets or doublets is similar to the correctly classified droplets for most methods—although they are statistically different for larger pool sizes (Additional file 2 : Fig S11a and Additional file 1 : Table S6). However, the false doublets classified by some demultiplexing methods ( Demuxalot , Demuxalot (refined), Demuxlet , ScSplit , Souporcell , and Vireo ) demonstrated higher ribosomal percentages. Some doublet detecting methods ( ScDblFinder , ScDblFinder with known doublets and Solo) demonstrated higher ribosomal percentages for the false doublets while other demonstrated lower ribosomal percentages ( DoubletDecon , DoubletDetection , and DoubletFinder ; Additional file 2 : Fig S11b and Additional file 1 : Table S6).

Like the ribosomal percentage, the mitochondrial percentage in false singlets is also relatively similar to correctly classified droplets for both demultiplexing (Additional file 2 : Fig S12a and Additional file 1 : Table S7) and doublet detecting methods (Additional file 2 : Fig S12b). The mitochondrial percentage for false doublets is statistically lower than the correctly classified droplets for a few larger pools for Freemuxlet , ScSplit , and Souporcell . The doublet detecting method Solo also demonstrates a small but significant decrease in mitochondrial percentage in the false doublets compared to the correctly annotated droplets. However, other doublet detecting methods including DoubletFinder and the larger pools of most other methods demonstrated a significant increase in mitochondrial percent in the false doublets compared to the correctly annotated droplets (Additional file 2 : Fig S12b).

Overall, these results demonstrate a strong relationship between the number of genes and UMIs and limited influence of ribosomal or mitochondrial percentage in a droplet and false classification, suggesting that the number of genes and UMIs can significantly bias singlet and doublet classification by demultiplexing and doublet detecting methods.

Ambient RNA, number of reads per cell, and uneven pooling impact method performance

To further quantify the variables that impact the performance of each method, we simulated four conditions that could occur with single-cell RNA-seq experiments: (1) decreased number of reads (reduced 50%), (2) increased ambient RNA (10%, 20% and 50%), (3) increased mitochondrial RNA (5%, 10% and 25%) and 4) uneven donor pooling from single donor spiking (0.5 or 0.75 proportion of pool from one donor). We chose these scenarios because they are common technical effects that can occur.

We observed a consistent decrease in the demultiplexing method performance when the number of reads were decreased by 50% but the degree of the effect varied for each method and was larger in pools containing more multiplexed donors (Additional file 2 : Fig S13a and Additional file 1 : Table S8). Decreasing the number of reads did not have a detectable impact on the performance of the doublet detecting methods.

Simulating additional ambient RNA (10%, 20%, or 50%) decreased the performance of all the demultiplexing methods (Additional file 2 : Fig S13b and Additional file 1 : Table S9) but some were unimpacted in pools that had 16 or fewer individuals multiplexed ( Souporcell and Vireo ). The performance of some of the doublet detecting methods were impacted by the ambient RNA but the performance of most methods did not decrease. Scrublet and ScDblFinder were the doublet detecting methods most impacted by ambient RNA but only in pools with at least 32 multiplexed donors (Additional file 2 : Fig S13b and Additional file 1 : Table S9).

Increased mitochondrial percent did not impact the performance of demultiplexing or doublet detecting methods (Additional file 2 : Fig S13c and Additional file 1 : Table S10).

We also tested whether experimental designs that pooling uneven proportions of donors would alter performance. We tested scenarios where either half the pool was composed of a single donor (0.5 spiked donor proportion) or where three quarters of the pool was composed of a single donor. This experimental design significantly reduced the demultiplexing method performance (Additional file 2 : Fig S13d and Additional file 1 : Table S11) with the smallest influence on Freemuxlet . The performance of most of the doublet detecting methods were unimpacted except for DoubletDetection that demonstrated significant decreases in performance in pools where at least 16 donors were multiplexed. Intriguingly, the performance of Solo increased with the spiked donor pools when the pools consisted of 16 donors or less.

Our results demonstrate significant differences in overall performance between different demultiplexing and doublet detecting methods. We further noticed some differences in the use of the methods. Therefore, we have accumulated these results and each method’s unique characteristics and benefits in a heatmap for visual interpretation (Fig. 4 ).

Assessment of each of the demultiplexing and doublet detecting methods. Assessments of a variety of metrics for each of the demultiplexing (top) and doublet detecting (bottom) methods

Framework for improving singlet classifications via method combinations

After identifying the demultiplexing and doublet detecting methods that performed well individually, we next sought to test whether using intersectional combinations of multiple methods would enhance droplet classifications and provide a software platform— Demuxafy —capable of supporting the execution of these intersectional combinations.

We recognise that different experimental designs will be required for each project. As such, we considered this when testing combinations of methods. We considered multiple experiment designs and two different intersectional methods: (1) more than half had to classify a droplet as a singlet to be called a singlet and (2) at least half of the methods had to classify a droplet as a singlet to be called a singlet. Significantly, these two intersectional methods only differ when an even number of methods are being considered. For combinations that include demultiplexing methods, the individual called by the majority of the methods is the individual used for that droplet. When ties occur, the individual is considered ‘unassigned’.

Combining multiple doublet detecting methods improve doublet removal for non-multiplexed experimental designs

For the non-multiplexed experimental design, we considered all possible method combinations (Additional file 1 : Table S12). We identified important differences depending on the number of droplets captured and have provided recommendations accordingly. We identified that DoubletFinder , Scrublet , ScDblFinder and Scds is the ideal combination for balanced droplet calling when less than 2,000 droplets are captured. Scds and ScDblFinder or Scrublet , Scds and ScDblFinder is the best combination when 2,000–10,000 droplets are captured. Scds , Scrublet, ScDblFinder and DoubletDetection is the best combination when 10,000–20,000 droplets are captured and Scrublet , Scds , DoubletDetection and ScDblFinder . It is important to note that even a slight increase in the MCC significantly impacts the number of true singlets and true doublets classified with the degree of benefit highly dependent on the original method performance. The combined method increases the MCC compared to individual doublet detecting methods on average by 0.11 and up to 0.33—a significant improvement in the MCC ( t -test FDR < 0.05 for 95% of comparisons). For all combinations, the intersectional droplet method requires more than half of the methods to consider the droplet a singlet to classify it as a singlet (Fig. 5 ).

Recommended Method Combinations Dependent on Experimental Design. Method combinations are provided for different experimental designs, including those that are not multiplexed (left) and multiplexed (right), including experiments that have reference SNP genotypes available vs those that do not and finally, multiplexed experiments with different numbers of individuals multiplexed. The each bar represents either a single method (shown with the coloured icon above the bar) or a combination of methods (shown with the addition of the methods and an arrow indicating the bar). The proportion of true singlets, true doublets, false singlets and false doublets for each method or combination of methods is shown with the filled barplot and the MCC is shown with the black points overlaid on the barplot. MCC: Matthew’s Correlation Coefficient

Demuxafy performs better than Chord

Chord is an ensemble machine learning doublet detecting method that uses Scds and DoubletFinder to identify doublets. We compared Demuxafy using Scds and DoubletFinder to Chord and identified that Demuxafy outperformed Chord in pools that contained at least eight donors and was equivalent in pools that contained less than eight donors (Additional file 2 : Fig S14). This is because Chord classifies more droplets as false singlets and false doublets than Demuxafy . In addition, Chord failed to complete for two of the pools that contained 128 multiplexed donors.

Combining multiple demultiplexing and doublet detecting methods improve doublet removal for multiplexed experimental designs

For experiments where 16 or fewer individuals are multiplexed with reference SNP genotypes available, we considered all possible combinations between the demultiplexing and doublet detecting methods except ScDblFinder with known doublets due to its highly similar performance to ScDblFinder (Fig 3 ; Additional file 1 : Table S13). The best combinations are DoubletFinder , Scds , ScDblFinder , Vireo and Demuxalot (refined) (<~5 donors) and Scrublet , ScDblFinder , DoubletDetection , Dropulation and Demuxalot (refined) (Fig. 5 ). These intersectional methods increase the MCC compared to the individual methods ( t -test FDR < 0.05), generally resulting in increased true singlets and doublets compared to the individual methods. The improvement in MCC depends on every single method’s performance but, on average, increases by 0.22 and up to 0.71. For experiments where the reference SNP genotypes are unknown, the individuals multiplexed in the pool with 16 or fewer individuals multiplexed, DoubletFinder , ScDblFinder, Souporcell and Vireo (<~5 donors) and Scds , ScDblFinder , DoubletDetection , Souporcell and Vireo are the ideal methods (Fig. 5 ). These intersectional methods again significantly increase the MCC up to 0.87 compared to any of the individual techniques that could be used for this experimental design ( t -test FDR < 0.05 for 94.2% of comparisons). In both cases, singlets should only be called if more than half of the methods in the combination classify the droplet as a singlet.

Combining multiple demultiplexing methods improves doublet removal for large multiplexed experimental designs

For experiments that multiplex more than 16 individuals, we considered the combinations between all demultiplexing methods (Additional file 1 : Table S14) since only a small proportion of the doublets would be undetectable by demultiplexing methods (droplets that are homogenic; Fig 3 b). To balance doublet removal and maintain true singlets, we recommend the combination of Demuxalot (refined) and Dropulation . These method combinations significantly increase the MCC by, on average, 0.09 compared to all the individual methods ( t -test FDR < 0.05). This substantially increases true singlets and true doublets relative to the individual methods. If reference SNP genotypes are not available for the individuals multiplexed in the pools, Vireo performs the best (≥ 16 multiplexed individuals; Fig. 5 ). This is the only scenario in which executing a single method is advantageous to a combination of methods. This is likely due to the fact that most of the methods perform poorly for larger pool sizes (Fig. 3 c).

These results collectively demonstrate that, regardless of the experimental design, demultiplexing and doublet detecting approaches that intersect multiple methods significantly enhance droplet classification. This is consistent across different pool sizes and will improve singlet annotation.

Demuxafy improves doublet removal and improves usability

To make our intersectional approaches accessible to other researchers, we have developed Demuxafy ( https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/index.html ) - an easy-to-use software platform powered by Singularity. This platform provides the requirements and instructions to execute each demultiplexing and doublet detecting methods. In addition, Demuxafy provides wrapper scripts that simplify method execution and effectively summarise results. We also offer tools that help estimate expected numbers of doublets and provide method combination recommendations based on scRNA-seq pool characteristics. Demuxafy also combines the results from multiple different methods, provides classification combination summaries, and provides final integrated combination classifications based on the intersectional techniques selected by the user. The significant advantages of Demuxafy include a centralised location to execute each of these methods, simplified ways to combine methods with an intersectional approach, and summary tables and figures that enable practical interpretation of multiplexed datasets (Fig. 1 a).

Demultiplexing and doublet detecting methods have made large-scale scRNA-seq experiments achievable. However, many demultiplexing and doublet detecting methods have been developed in the recent past, and it is unclear how their performances compare. Further, the demultiplexing techniques best detect heterogenic doublets while doublet detecting methods identify heterotypic doublets. Therefore, we hypothesised that demultiplexing and doublet detecting methods would be complementary and be more effective at removing doublets than demultiplexing methods alone.

Indeed, we demonstrated the benefit of utilising a combination of demultiplexing and doublet detecting methods. The optimal intersectional combination of methods depends on the experimental design and capture characteristics. Our results suggest super loaded captures—where a high percentage of doublets is expected—will benefit from multiplexing. Further, when many donors are multiplexed (>16), doublet detecting is not required as there are few doublets that are homogenic and heterotypic.

We have provided different method combination recommendations based on the experimental design. This decision is highly dependent on the research question.

Conclusions

Overall, our results provide researchers with important demultiplexing and doublet detecting performance assessments and combinatorial recommendations. Our software platform, Demuxafy ( https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/index.html ), provides a simple implementation of our methods in any research lab around the world, providing cleaner scRNA-seq datasets and enhancing interpretation of results.

PBMC scRNA-seq data

Blood samples were collected and processed as described previously [ 17 ]. Briefly, mononuclear cells were isolated from whole blood samples and stored in liquid nitrogen until thawed for scRNA-seq capture. Equal numbers of cells from 12 to 16 samples were multiplexed per pool and single-cell suspensions were super loaded on a Chromium Single Cell Chip A (10x Genomics) to capture 20,000 droplets per pool. Single-cell libraries were processed per manufacturer instructions and the 10× Genomics Cell Ranger Single Cell Software Suite (v 2.2.0) was used to process the data and map it to GRCh38. Cellbender v0.1.0 was used to identify empty droplets. Almost all droplets reported by Cell Ranger were identified to contain cells by Cellbender (mean: 99.97%). The quality control metrics of each pool are demonstrated in Additional file 2 : Fig S15.

PBMC DNA SNP genotyping

SNP genotype data were prepared as described previously [ 17 ]. Briefly, DNA was extracted from blood with the QIAamp Blood Mini kit and genotyped on the Illumina Infinium Global Screening Array. SNP genotypes were processed with Plink and GCTA before imputing on the Michigan Imputation Server using Eagle v2.3 for phasing and Minimac3 for imputation based on the Haplotype Reference Consortium panel (HRCr1.1). SNP genotypes were then lifted to hg38 and filtered for > 1% minor allele frequency (MAF) and an R 2 > 0.3.

Fibroblast scRNA-seq data

The fibroblast scRNA-seq data has been described previously [ 18 ]. Briefly, human skin punch biopsies from donors over the age of 18 were cultured in DMEM high glucose supplemented with 10% fetal bovine serum (FBS), L-glutamine, 100 U/mL penicillin and 100 μg/mL (Thermo Fisher Scientific, USA).

For scRNA-seq, viable cells were flow sorted and single cell suspensions were loaded onto a 10× Genomics Single Cell 3’ Chip and were processed per 10× instructions and the Cell Ranger Single Cell Software Suite from 10× Genomics was used to process the sequencing data into transcript count tables as previously described [ 18 ]. Cellbender v0.1.0 was used to identify empty droplets. Almost all droplets reported by Cell Ranger were identified to contain cells by Cellbender (mean: 99.65%). The quality control metrics of each pool are demonstrated in Additional file 2 : Fig S16.

Fibroblast DNA SNP genotyping

The DNA SNP genotyping for fibroblast samples has been described previously [ 18 ]. Briefly, DNA from each donor was genotyped on an Infinium HumanCore-24 v1.1 BeadChip (Illumina). GenomeStudioTM V2.0 (Illumina), Plink and GenomeStudio were used to process the SNP genotypes. Eagle V2.3.5 was used to phase the SNPs and it was imputed with the Michigan Imputation server using minimac3 and the 1000 genome phase 3 reference panel as described previously [ 18 ].

Demultiplexing methods

All the demultiplexing methods were built and run from a singularity image.

Demuxalot [ 6 ] is a genotype reference-based single cell demultiplexing method. Demualot v0.2.0 was used in python v3.8.5 to annotate droplets. The likelihoods, posterior probabilities and most likely donor for each droplet were estimated using the Demuxalot Demultiplexer.predict_posteriors function. We also used Demuxalot Demultiplexer.learn_genotypes function to refine the genotypes before estimating the likelihoods, posterior probabilities and likely donor of each droplet with the refined genotypes as well.

The Popscle v0.1-beta suite [ 16 ] for population genomics in single cell data was used for Demuxlet and Freemuxlet demultiplexing methods. The popscle dsc-pileup function was used to create a pileup of variant calls at known genomic locations from aligned sequence reads in each droplet with default arguments.

Demuxlet [ 3 ] is a SNP genotype reference-based single cell demultiplexing method. Demuxlet was run with a genotype error coefficient of 1 and genotype error offset rate of 0.05 and the other default parameters using the popscle demuxlet command from Popscle (v0.1-beta).

Freemuxlet [ 16 ] is a SNP genotype reference-free single cell demultiplexing method. Freemuxlet was run with default parameters including the number of samples included in the pool using the popscle freemuxlet command from Popscle (v0.1-beta).

Dropulation

Dropulation [ 5 ] is a SNP genotype reference-based single cell demultiplexing method that is part of the Drop-seq software. Dropulation from Drop-seq v2.5.1 was implemented for this manuscript. In addition, the method for calling singlets and doublets was provided by the Dropulation developer and implemented in a custom R script available on Github and Zenodo (see “Availability of data and materials”).

ScSplit v1.0.7 [ 7 ] was downloaded from the ScSplit github and the recommended steps for data filtering quality control prior to running ScSplit were followed. Briefly, reads that had read quality lower than 10, were unmapped, were secondary alignments, did not pass filters, were optical PCR duplicates or were duplicate reads were removed. The resulting bam file was then sorted and indexed followed by freebayes to identify single nucleotide variants (SNVs) in the dataset. The resulting SNVs were filtered for quality scores greater than 30 and for variants present in the reference SNP genotype vcf. The resulting filtered bam and vcf files were used as input for the s cSplit count command with default settings to count the number of reference and alternative alleles in each droplet. Next the allele matrices were used to demultiplex the pool and assign cells to different clusters using the scSplit run command including the number of individuals ( -n ) option and all other options set to default. Finally, the individual genotypes were predicted for each cluster using the scSplit genotype command with default parameters.

Souporcell [ 4 ] is a SNP genotype reference-free single cell demultiplexing method. The Souporcell v1.0 singularity image was downloaded via instructions from the gihtub page. The Souporcell pipeline was run using the souporcell_pipeline.py script with default options and the option to include known variant locations ( --common_variants ).

Vireo [ 2 ] is a single cell demultiplexing method that can be used with reference SNP genotypes or without them. For this assessment, Vireo was used with reference SNP genotypes. Per Vireo recommendations, we used model 1 of the cellSNP [ 20 ] version 0.3.2 to make a pileup of SNPs for each droplet with the recommended options using the genotyped reference genotype file as the list of common known SNP and filtered with SNP locations that were covered by at least 20 UMIs and had at least 10% minor allele frequency across all droplets. Vireo version 0.4.2 was then used to demultiplex using reference SNP genotypes and indicating the number of individuals in the pools.

Doublet detecting methods

All doublet detecting methods were built and run from a singularity image.

DoubletDecon

DoubletDecon [ 9 ] is a transcription-based deconvolution method for identifying doublets. DoubletDecon version 1.1.6 analysis was run in R version 3.6.3. SCTransform [ 21 ] from Seurat [ 22 ] version 3.2.2 was used to preprocess the scRNA-seq data and then the Improved_Seurat_Pre_Process function was used to process the SCTransformed scRNA-seq data. Clusters were identified using Seurat function FindClusters with resolution 0.2 and 30 principal components (PCs). Then the Main_Doublet_Decon function was used to deconvolute doublets from singlets for six different rhops—0.6, 0.7, 0.8, 0.9, 1.0 and 1.1. We used a range of rhop values since the doublet annotation by DoubletDecon is dependent on the rhop parameter which is selected by the user. The rhop that resulted in the closest number of doublets to the expected number of doublets was selected on a per-pool basis and used for all subsequent analysis. Expected number of doublets were estimated with the following equation:

where N is the number of droplets captured and D is the number of expected doublets.

DoubletDetection

DoubletDetection [ 14 ] is a transcription-based method for identifying doublets. DoubletDetection version 2.5.2 analysis was run in python version 3.6.8. Droplets without any UMIs were removed before analysis with DoubletDetection . Then the doubletdetection.BoostClassifier function was run with 50 iterations with use_phenograph set to False and standard_scaling set to True. The predicted number of doublets per iteration was visualised across all iterations and any pool that did not converge after 50 iterations, it was run again with increasing numbers of iterations until they reached convergence.

DoubletFinder

DoubletFinder [ 10 ] is a transcription-based doublet detecting method. DoubletFinder version 2.0.3 was implemented in R version 3.6.3. First, droplets that were more than 3 median absolute deviations (mad) away from the median for mitochondrial per cent, ribosomal per cent, number of UMIs or number of genes were removed per developer recommendations. Then the data was normalised with SCTransform followed by cluster identification using FindClusters with resolution 0.3 and 30 principal components (PCs). Then, pKs were selected by the pK that resulted in the largest BC MVN as recommended by DoubletFinder. The pK vs BC MVN relationship was visually inspected for each pool to ensure an effective BC MVN was selected for each pool. Finally, the homotypic doublet proportions were calculated and the number of expected doublets with the highest doublet proportion were classified as doublets per the following equation:

ScDblFinder

ScDblFinder [ 11 ] is a transcription-based method for detecting doublets from scRNA-seq data. ScDblFinder 1.3.25 was implemented in R version 4.0.3. ScDblFinder was implemented with two sets of options. The first included implementation with the expected doublet rate as calculated by:

where N is the number of droplets captured and R is the expected doublet rate. The second condition included the same expected number of doublets and included the doublets that had already been identified by all the demultiplexing methods.

Scds [ 12 ] is a transcription-based doublet detecting method. Scds version 1.1.2 analysis was completed in R version 3.6.3. Scds was implemented with the cxds function and bcds functions with default options followed by the cxds_bcds_hybrid with estNdbl set to TRUE so that doublets will be estimated based on the values from the cxds and bcds functions.

Scrublet [ 13 ] is a transcription-based doublet detecting method for single-cell RNA-seq data. Scrublet was implemented in python version 3.6.3. Scrublet was implemented per developer recommendations with at least 3 counts per droplet, 3 cells expressing a given gene, 30 PCs and a doublet rate based on the following equation:

where N is the number of droplets captured and R is the expected doublet rate. Four different minimum number of variable gene percentiles: 80, 85, 90 and 95. Then, the best variable gene percentile was selected based on the distribution of the simulated doublet scores and the location of the doublet threshold selection. In the case that the selected threshold does not fall between a bimodal distribution, those pools were run again with a manual threshold set.

Solo [ 15 ] is a transcription-based method for detecting doublets in scRNA-seq data. Solo was implemented with default parameters and an expected number of doublets based on the following equation:

where N is the number of droplets captured and D is the number of expected doublets. Solo was additionally implemented in a second run for each pool with the doublets that were identified by all the demultiplexing methods as known doublets to initialize the model.

In silico pool generation

Cells that were identified as singlets by all methods were used to simulate pools. Ten pools containing 2, 4, 8, 16, 32, 64 and 128 individuals were simulated assuming a maximum 20% doublet rate as it is unlikely researchers would use a technology that has a higher doublet rate. The donors for each simulated pool were randomly selected using a custom R script which is available on Github and Zenodo (see ‘Availability of data and materials’). A separate bam for the cell barcodes for each donor was generated using the filterbarcodes function from the sinto package (v0.8.4). Then, the GenerateSyntheticDoublets function provided by the Drop-seq [ 5 ] package was used to simulate new pools containing droplets with known singlets and doublets.

Twenty-one total pools—three pools from each of the different simulated pool sizes (2, 4, 8, 16, 32, 64 and 128 individuals) —were used to simulate different experimental scenarios that may be more challenging for demultiplexing and doublet detecting methods. These include simulating higher ambient RNA, higher mitochondrial percent, decreased read coverage and imbalanced donor proportions as described subsequently.

High ambient RNA simulations

Ambient RNA was simulated by changing the barcodes and UMIs on a random selection of reads for 10, 20 or 50% of the total UMIs. This was executed with a custom R script that is available in Github and Zenodo (see ‘Availability of data and materials’).

High mitochondrial percent simulations

High mitochondrial percent simulations were produced by replacing reads in 5, 10 or 25% of the randomly selected cells with mitochondrial reads. The number of reads to replace was derived from a normal distribution with an average of 30 and a standard deviation of 3. This was executed with a custom R script available in Github and Zenodo (see ‘Availability of data and materials’).

Imbalanced donor simulations

We simulated pools that contained uneven proportions of the donors in the pools to identify if some methods are better at demultiplexing pools containing uneven proportions of each donor in the pool. We simulated pools where 50, 75 or 95% of the pool contained cells from a single donor and the remainder of the pool was even proportions of the remaining donors in the pool. This was executed with a custom R script available in Github and Zenodo (see ‘Availability of data and materials’).

Decrease read coverage simulations

Decreased read coverage of pools was simulated by down-sampling the reads by two-thirds of the original coverage.

Classification annotation

Demultiplexing methods classifications were considered correct if the droplet annotation (singlet or doublet) and the individual annotation was correct. If the droplet type was correct but the individual annotation was incorrect (i.e. classified as a singlet but annotated as the wrong individual), then the droplet was incorrectly classified.

Doublet detecting methods were considered to have correct classifications if the droplet annotation matched the known droplet type.

All downstream analyses were completed in R version 4.0.2.

Availability of data and materials

All data used in this manuscript is publicly available. The PBMC data is available on GEO (Accession: GSE196830) [ 23 ] as originally described in [ 17 ]. The fibroblast data is available on ArrayExpress (Accession Number: E-MTAB-10060) [ 24 ] and as originally described in [ 18 ]. The code used for the analyses in this manuscript are provided on Github ( https://github.com/powellgenomicslab/Demuxafy_manuscript/tree/v4 ) and Zenodo ( https://zenodo.org/records/10813452 ) under an MIT Open Source License [ 25 , 26 ]. Demuxafy is provided as a package with source code available on Github ( https://github.com/drneavin/Demultiplexing_Doublet_Detecting_Docs ) and instructions on ReadTheDocs ( https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/ ) under an MIT Open Source License [ 27 ]. Demuxafy is also available on Zenodo with the link https://zenodo.org/records/10870989 [ 28 ].

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Authors’ Twitter handles

Twitter handles: @drneavin (Drew Neavin), @thjimmylee (Jimmy Tsz Hang Lee), @marta_mele_m (Marta Melé)

Peer review information

Wenjing She was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Review history

The review history is available as Additional file 3 .

This work was funded by the National Health and Medical Research Council (NHMRC) Investigator grant (1175781), and funding from the Goodridge foundation. J.E.P is also supported by a fellowship from the Fok Foundation.

Author information

Authors and affiliations.

Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute for Medical Research, Darlinghurst, NSW, Australia

Drew Neavin, Anne Senabouth, Himanshi Arora & Joseph E. Powell

Present address: Statewide Genomics at NSW Health Pathology, Sydney, NSW, Australia

Himanshi Arora

Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

Jimmy Tsz Hang Lee

Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Catalonia, Spain

Aida Ripoll-Cladellas & Marta Melé

Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

Lude Franke

Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore

Shyam Prabhakar

Population and Global Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore

Cancer Science Institute of Singapore, National University of Singapore, Singapore, Republic of Singapore

Bakar Institute for Computational Health Sciences, University of California, San Francisco, CA, USA

Chun Jimmie Ye

Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA

Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

Chan Zuckerberg Biohub, San Francisco, CA, USA

Bioinformatics and Cellular Genomics, St Vincent’s Institute of Medical Research, Fitzroy, Australia

Davis J. McCarthy

Melbourne Integrative Genomics, School of BioSciences–School of Mathematics & Statistics, Faculty of Science, University of Melbourne, Melbourne, Australia

Present address: The Gene Lay Institute of Immunology and Inflammation, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

Martin Hemberg

UNSW Cellular Genomics Futures Institute, University of New South Wales, Kensington, NSW, Australia

Joseph E. Powell

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DRN and JEP conceived the project idea and study design. JTHL, AR, LF, SP, CJY, DJM, MM and MH provided feedback on experimental design. DRN carried out analyses with support on coding from AS. JTHL and AR tested Demuxafy and provided feedback. DRN and JEP wrote the manuscript. All authors reviewed and provided feedback on the manuscript.

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Correspondence to Drew Neavin or Joseph E. Powell .

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Briefly, all work was approved by the Royal Hobart Hospital, the Hobart Eye Surgeons Clinic, Human Research Ethics Committees of the Royal Victorian Eye and Ear Hospital (11/1031), University of Melbourne (1545394) and University of Tasmania (H0014124) in accordance with the requirements of the National Health & Medical Research Council of Australia (NHMRC) and conformed with the Declaration of Helsinki [ 29 ].

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Competing interests

C.J.Y. is founder for and holds equity in DropPrint Genomics (now ImmunAI) and Survey Genomics, a Scientific Advisory Board member for and holds equity in Related Sciences and ImmunAI, a consultant for and holds equity in Maze Therapeutics, and a consultant for TReX Bio, HiBio, ImYoo, and Santa Ana. Additionally, C.J.Y is also newly an Innovation Investigator for the Arc Institute. C.J.Y. has received research support from Chan Zuckerberg Initiative, Chan Zuckerberg Biohub, Genentech, BioLegend, ScaleBio and Illumina.

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Neavin, D., Senabouth, A., Arora, H. et al. Demuxafy : improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods. Genome Biol 25 , 94 (2024). https://doi.org/10.1186/s13059-024-03224-8

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Received : 07 March 2023

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Published : 15 April 2024

DOI : https://doi.org/10.1186/s13059-024-03224-8

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Yankees’ DJ LeMahieu ready to start rehab…

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Subscriber only, yankees’ dj lemahieu ready to start rehab assignment.

TORONTO — DJ LeMahieu is days away from playing in games.

The third baseman said that he plans on starting a rehab assignment this Thursday or Friday. LeMahieu has been recovering from a nondisplaced fracture , an injury he suffered after fouling a ball off his right foot in spring training.

“I wouldn’t think too many,” LeMahieu said Tuesday when asked how many rehab games he’ll need. “I would probably say less than five.”

However, Aaron Boone didn’t want to put a time limit on the assignment, which is expected to start at Double-A Somerset.

“It’s possible,” the manager said of LeMahieu’s prediction. “We’ll see. Let’s get through the first one first. We’ll just see how the buildup goes.”

LeMahieu had had some trouble ramping up baseball activities earlier in his recovery, particularly when it came to side-to-side fielding. However, he said his foot has felt “way better” over the last week.

LeMahieu added that he doesn’t have any boxes left to check before getting into games.

“I’m ready,” the succinct infielder said.

The Yankees planned on LeMahieu being their everyday third baseman when spring training began, and Boone talked the two-time batting champ up as his preferred leadoff man throughout camp. However, Oswaldo Cabrera has exceeded expectations at the plate while filling in at the hot corner. He entered Tuesday’s game against the Blue Jays hitting .292.

Meanwhile, Anthony Volpe has taken over the leadoff spot. The 22-year-old sophomore entered Tuesday’s contest with a .373 average and .464 OBP.

Volpe was hitting .368 with a .500 OBP when batting first, which is why Boone sounds intent on keeping the shortstop there when LeMahieu returns.

“We’ll see,” the skipper said when asked if he still sees LeMahieu as a leadoff guy. “I mean, I’m probably not taking Anthony out of the leadoff spot. The good thing with DJ is I feel like he can fit a lot of different spots in the order. So we’ll see. It’s still a ways off.”

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