Rapid Deployment Research Articles

Generating the Evidence Base for Convalescent Plasma Use for a New Infectious Disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) swept across the world in the waning months of 2019 and emerged as the cause of the coronavirus disease 19 (COVID-19) pandemic in early 2020. The use of convalescent plasma (CP) for prior respiratory pandemics provided a strong biological rationale for the rapid deployment of COVID-19 convalescent plasma (CCP) in early 2020 when no validated treatments or prior immunity existed. CCP is an antiviral agent, with its activity against SARS-CoV-2 stemming from specific antibodies elicited by the virus. Early efforts to investigate the efficacy of CCP in randomized clinical trials (RCTs) that targeted hospitalized patients with COVID-19 did not demonstrate the overall efficacy of CCP despite signals of benefit in certain subgroups, such as those treated earlier in disease. In contrast, studies adhering to the principles of antibody therapy in their study design, choice of patient population, and product qualification, i.e., those that administered high levels of specific antibody during the viral phase of disease in immunocompromised or very early in immunocompetent individuals, demonstrated benefits. In this chapter, we leverage the knowledge gained from clinical studies of CCP for COVID-19 to propose a framework for future studies of CP for a new infectious disease. This framework includes obtaining high-quality CP and designing clinical studies that adhere to the principles of antibody therapy to generate a robust evidence base for using CP.

Single Additional Stitch During Rapid Deployment Aortic Valve Replacement to Reduce Postimplant Pacemaker Needs.

Single Additional Stitch During Rapid Deployment Aortic Valve Replacement to Reduce Postimplant Pacemaker Needs.

Lessons from the COVID-19 Pandemic: Promoting Vaccination and Public Health Resilience, a Narrative Review.

The COVID-19 pandemic has underscored the critical importance of adaptable and resilient public health systems capable of rapid response to emerging health crises. This paper synthesizes the lessons learned from the COVID-19 vaccination campaign and explores strategies to enhance vaccine uptake in the post-pandemic era. Key challenges identified include logistical, economic, sociocultural, and policy dimensions that impact vaccination efforts, particularly in low-resource settings. The analysis highlights the need for resilient supply chains, effective communication, community engagement, and equitable access to healthcare resources. The rapid development and deployment of mRNA vaccines exemplify the potential of innovative vaccine technologies, though public trust and acceptance remain crucial. Strategies such as partnerships with local leaders, tailored messaging, and integration of digital tools are essential for combating vaccine hesitancy. By applying these insights, future vaccination campaigns can be more efficient, equitable, and resilient, ultimately improving public health outcomes globally. This paper aims to inform policy and practice, ensuring that public health strategies are evidence based and context specific, thus better preparing for future health challenges.

Galaxy Helm chart: a standardized method for deploying production Galaxy servers.

The Galaxy application is a popular open-source framework for data intensive sciences, counting thousands of monthly users across more than 100 public servers. To support a growing number of users and a greater variety of use cases, the complexity of a production-grade Galaxy installation has also grown, requiring more administration effort. There is a need for a rapid and reproducible Galaxy deployment method that can be maintained at high-availability with minimal maintenance. We describe the Galaxy Helm chart that codifies all elements of a production-grade Galaxy installation into a single package. Deployable on Kubernetes clusters, the chart encapsulates supporting software services and implements the best-practices model for running Galaxy. It is also the most rapid method available for deploying a scalable, production-grade Galaxy instance on one's own infrastructure. The chart is highly configurable, allowing systems administrators to swap dependent services if desired. Notable uses of the chart include on-demand, fully-automated deployments on AnVIL, providing training infrastructure for the Bioconductor project, and as the AWS-recommended solution for running Galaxy on the Amazon cloud. The source code for Galaxy Helm is available at https://github.com/galaxyproject/galaxy-helm, the corresponding Helm package at https://github.com/CloudVE/helm-charts, and the required Galaxy container image https://github.com/galaxyproject/galaxy-docker-k8s.

Capturing and interpreting wildfire spread dynamics: attention-based spatiotemporal models using ConvLSTM networks

Predicting the trajectory of geographical events, such as wildfire spread, presents a formidable task due to the dynamic associations among influential biophysical factors. Geo-events like wildfires frequently display short and long-range spatial and temporal correlations. Short-range effects are the direct contact and near-contact spread of the fire front. Long-range effects are represented by processes such as spotting, where firebrands carried by the wind ignite fires distant from the flaming front, altering the shape and speed of an advancing fire front. This study addresses these modeling challenges by clearly defining and analyzing the scale-dependent spatiotemporal dynamics that influence wildfire spread, focusing on the interplay between biophysical factors and fire behavior. We propose two unique attention-based spatiotemporal models using Convolutional Long Short-Term Memory (ConvLSTM) networks. These models are designed to learn and capture a range of local to global and short and long-range spatiotemporal correlations. The proposed models were tested on two datasets: a high-resolution wildfire spread dataset produced with a semi-empirical percolation model and a satellite observed wildfire spread data in California 2012–2021. Results indicate that attention-based models accurately predict fire front movements that are consistent with known wildfire spread-biophysical dynamics. Our research suggests there is considerable potential for attention mechanisms to capture the spatiotemporal behavior of wildfire spread, with model transferability, that can guide rapid deployment of wildfire management operations. We also highlight directions for future studies that focus on how the self-attention mechanism could enhance model performance for a range of geospatial applications.

Landscape analysis of the Kenyan policy on the treatment and prevention of diarrheal disease among under-5 children

ObjectiveDiarrhoea remains a leading cause of morbidity and death among under-5 children in Kenya, despite multipronged policy and programme initiatives to increase access to treatment. This study interrogates the comprehensiveness...

Germinal Center Response to mRNA Vaccination and Impact of Immunological Imprinting on Subsequent Vaccination.

Vaccines are the most effective intervention currently available, offering protective immunity against targeted pathogens. The emergence of the coronavirus disease 2019 pandemic has prompted rapid development and deployment of lipid nanoparticle encapsulated, mRNA-based vaccines. While these vaccines have demonstrated remarkable immunogenicity, concerns persist regarding their ability to confer durable protective immunity to continuously evolving severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. This review focuses on human B cell responses induced by SARS-CoV-2 mRNA vaccination, with particular emphasis on the crucial role of germinal center reactions in shaping enduring protective immunity. Additionally, we explored observations of immunological imprinting and dynamics of recalled pre-existing immunity following variants of concern-based booster vaccination. Insights from this review contribute to comprehensive understanding B cell responses to mRNA vaccination in humans, thereby refining vaccination strategies for optimal and sustained protection against evolving coronavirus variants.

Drone Warfare and Threshold of the Use of Force

The proliferation of drones among both state and non-state actors, as evidenced by their widespread use, signals a shift towards more accessible forms of military engagement. In this context, the paper discusses how drones, as unmanned, remotely operated aircraft, have revolutionized warfare by enabling remote engagement and minimizing personnel risk. Concepts such as risk compensation, the 'body bag syndrome', and the potential for drones to lower the threshold for use of force has also been explored. The precision and long endurance of drones, coupled with their cost-effectiveness, emerge as double-edged swords, enhancing military capabilities while raising questions about the ease of resorting to force. The paper highlights the operational advantages of drones over manned aircraft, including rapid deployment, reduced training requirements, and operational flexibility. This paper also explores the transformative role of drone technology in contemporary military operations and examines the impact of drones on the threshold of use of force in contemporary times. Following the thematic analysis of qualitative sources backed by quantitative data, this paper explores how drones have lowered the threshold for the use of force.

Scaling up solar and wind electricity: empirical modelling and a disruptive scenario for their deployments in Aotearoa New Zealand

ABSTRACT Deployment of wind and solar electricity technologies is crucial for the energy transition, yet anticipated deployment rates differ widely often underestimating actual deployments. This issue is significant for countries like Aotearoa New Zealand, leading the way in major energy transitions. This study addresses two main issues: the historical underestimation of deployment rates and the need to synthesise various future scenarios to assess their potential impacts on the energy system. To address these problems, an empirical modelling approach based on logistic growth is adopted to create and analyse scenarios for the deployment of solar and wind technologies in New Zealand. We propose two scenarios: a reference growth scenario, which represents prevailing estimations for the deployment of solar and wind from previous forecasts; and a disruptive scenario, which anticipates a more rapid deployment pace. Both scenarios envision phasing out fossil fuels by 2030 but differ in the remaining electricity supply. In the disruptive scenario, the installed electricity generation broadens, in which solar and wind generation could represent 72% of the electricity generation capacity by 2050. Our findings emphasise the transformative potential of rapid wind and solar technologies deployment and highlight the importance of preparing for a disruptive future that challenges conventional expectations.

Adaptive task migration strategy with delay risk control and reinforcement learning for emergency monitoring

The timely and reliable handling of post-disaster emergency monitoring tasks is crucial for effective rescue operations. UAV-assisted edge computing plays a pivotal role in the rapid deployment of such systems. However, challenges persist due to communication and computation resource bottlenecks when dealing with delay-sensitive monitoring tasks. In dynamic post-disaster environments, effective task scheduling and resource allocation decisions directly impact the system’s ability to process tasks. Therefore, this paper proposes an adaptive task migration decision-making system for emergency monitoring tasks in UAV-assisted edge computing. Firstly, We decomposed the optimization objectives based on the task processing workflow, then devised a stepwise delay risk control and resource recovery mechanism based on early discarding. Secondly, by integrating multi-agent reinforcement learning (MARL), optimal strategies for task offloading, UAV queue scheduling, and communication resource allocation are learned to enhance the decision system’s environmental awareness and maximize the successful completion of emergency monitoring tasks. Simulation experiments demonstrate that the algorithm significantly improves the success rate of migration tasks and data processing capacity, thereby validating its convergence and effectiveness.

Quantifying spatial peat depth with seismic micronodes and the implications for carbon stock estimates

Peatlands are a major store of soil carbon, due to their high concentration of carbon-rich decayed plant material. Consequently, accurate assessment of peat volumes is important for determining land-use carbon budgets, especially in the Northern hemisphere. Determination of carbon stocks at the scale of individual peat sites has principally relied on either mechanical probing or electromagnetic geophysical methods. In this study, we investigated the use of seismic nodal instrumentation for quantifying peat depth. We used Stryde™ nodes for a deployment at the Whixall moss in Shropshire, England. We measured seismic arrival times from peat-bottom reflections, as well as dispersive surface waves to invert for a model of variable peat depth along a linear cross-section. The use of very small seismic nodes (micronodes) allows for particularly rapid deployment on challenging terrain.

Design optimization of mobile vehicle-borne microgrids

Interest in isolated microgrids continues to increase as more applications are being explored, such as military bases located in isolated areas, supplying critical infrastructures during emergencies, etc. This paper considers the integration of packaging into the optimal design of a vehicle-borne microgrid suitable for such an application with space limitations. The advantages of this type of microgrid are mobility and the ability to operate while mobile or stationary. Various types of micro-sources are considered to meet multiple loads. To this end, a mixed-integer nonlinear problem is formulated to integrate multiple micro-source sizing and packaging options. The relationship between loads and capital investment is investigated through four different case studies. The case study results show that the proposed optimization framework is highly flexible and effective in finding a design solution for varying operational scenarios. Unlike existing approaches, the proposed method considers packaging constraints in the selection of micro sources ensuring microgrid mobility and rapid deployment, and also identifies a packaging solution for the selected micro sources. A comparative case study showed that the existing approach results in a 20 kW PV favoring renewable sources while the proposed method results in a 10 kW PV prioritizing the packaging and mobility.

Virtual Patient/Family Communication in the Acute Care Setting.

Patient and family-centered care strategies see patients and families as valuable healthcare team members and are therefore treated as essential clinical partners to achieve safe, high-quality care. Traditionally, hospitals have relied on physical presence as the primary strategy for including families in shared decision-making. During COVID-19, widespread visitor restrictions removed this primary strategy. Hospitals rapidly deployed virtual communication tools to address the challenges visitor restrictions created. The rapid deployment produced benefits and unintended consequences, especially for the healthcare providers. Through a series of qualitative interviews with family members and healthcare providers, consistent themes were identified and mapped to the sociotechnical model. This project identified barriers to adoption and proposed corresponding success criteria. Although the project focused on implementing virtual communication in the context of the COVID-19 pandemic, virtual communication strategies can likely support family-centered care goals outside of the pandemic context.

Effectiveness of a suction device for containment of pathogenic aerosols and droplets.

As the global community begins recovering from the COVID-19 pandemic, the challenges due to its aftermath remain. This health crisis has highlighted challenges associated with airborne pathogens and their capacity for rapid transmission. While many solutions have emerged to tackle this challenge, very few devices exist that are inexpensive, easy to manufacture, and versatile enough for various settings. This paper presents a novel suction device designed to counteract the spread of aerosols and droplets and be cost-effective and adaptable to diverse environments. We also conducted an experimental study to evaluate the device's effectiveness using an artificial cough generator, a particle counter, and a mannequin in an isolated system. We measured droplet removal rates with simulated single and repeated cough incidents. Also, measurements were taken at four distinct areas to compare its effectiveness on direct plume versus indirect particle removal. The device reduced airborne disease transmission risk, as evidenced by its capacity to decrease the half-life of aerosol volume from 23.6 minutes to 15.6 minutes, effectively capturing aerosol-sized droplets known for their extended airborne persistence. The suction device lessened the peak total droplet volume from peak counts. At 22 minutes post peak droplet count, the count had dropped 24% without the suction device and 43% with the suction device. The experiment's findings confirm the suction device's capability to effectively remove droplets from the environment, making it a vital tool in enhancing indoor air quality. Given the sustained performance of the suction device irrespective of single or multiple cough events, this demonstrates its potential utility in reducing the risk of airborne disease transmission. 3D printing for fabrication opens the possibility of a rapid iterative design process, flexibility for different configurations, and rapid global deployment for future pandemics.

Adapting LoRa Ground Stations for Low-latency Imaging and Inference from LoRa-enabled CubeSats

Recent years have seen the rapid deployment of low-cost CubeSats in low-Earth orbit, many of which experience significant latency (several hours) from the time information is gathered to the time it is communicated to the ground. This is primarily due to the limited availability of ground infrastructure that is bulky to deploy and expensive to rent. This article explores the opportunity in leveraging the extensive terrestrial LoRa infrastructure as a solution. However, the limited bandwidth and large amount of Doppler on CubeSats precludes these LoRa links to communicate rich satellite Earth images—instead, the CubeSats can at best send short messages. This article details our experience in designing LoRa-based satellite ground infrastructure that requires software-only modifications to receive packets from LoRa-enabled CubeSats recently launched by our team. We present Vista, a communication system that adapts encoding onboard the CubeSat and decoding configuration on commercial LoRa ground stations to allow images to be communicated. We perform a detailed evaluation of Vista by leveraging wireless channel measurements from a recent CubeSat (2021), and show that Vista can achieve 55.55% lower latency in retrieving data with 12.02 dB improvement in packet retrieval in the presence of terrestrial interference. We then evaluate Vista on a case study on land-use classification over images transmitted over the CubeSat link to further demonstrate a 4.56 dB improvement in image PSNR and 1.38× increase in classification accuracy over baseline approaches.

DeltaFlex—An Additively Manufactured Delta Robot With Compliant Joints: Virtual Prototyping and Experimental Evaluation

Abstract The current study presents the development and validation of a compliant Delta robot with a monolithic structure, which has been fabricated using additive manufacturing (AM). The monolithic design and the use of AM accelerate the robot development cycle by enabling rapid prototyping and deployment while also facilitating experimentation with novel or different robot kinematics. The use of flexible joints for robots presents a challenge in achieving sufficient workspaces. However, parallel architectures are well suited for incorporating compliant joints, as they require lower ranges of motion for individual joints compared to serial architectures. Therefore, the Delta configuration has been chosen for this study. Multibody flexible dynamics (MfBD) simulations have been used as a means to guide design choices and simulate the structural behaviour of the robot. A design for additive manufacturing (DfAM) technique has been adopted to minimize the need for support structures and maximize mechanical strength. The quantitative evaluation of the Delta’s overall performance has been conducted in terms of stiffness and precision. The stiffness test aimed to gauge the robot’s ability to withstand applied loads, whereas the repeatability test assessed its precision and accuracy. This approach offers a promising path for robot design with significant potential for future advancements and practical applications while highlighting the trade-offs that designers should consider when adopting this methodology.

Shape-position perceptive fusion electronic skin with autonomous learning for gesture interaction

Wearable devices, such as data gloves and electronic skins, can perceive human instructions, behaviors and even emotions by tracking a hand's motion, with the help of knowledge learning. The shape or position single-mode sensor in such devices often lacks comprehensive information to perceive interactive gestures. Meanwhile, the limited computing power of wearable applications restricts the multimode fusion of different sensing data and the deployment of deep learning networks. We propose a perceptive fusion electronic skin (PFES) with a bioinspired hierarchical structure that utilizes the magnetization state of a magnetostrictive alloy film to be sensitive to external strain or magnetic field. Installed at the joints of a hand, the PFES realizes perception of curvature (joint shape) and magnetism (joint position) information by mapping corresponding signals to the two-directional continuous distribution such that the two edges represent the contributions of curvature radius and magnetic field, respectively. By autonomously selecting knowledge closer to the user's hand movement characteristics, the reinforced knowledge distillation method is developed to learn and compress a teacher model for rapid deployment on wearable devices. The PFES integrating the autonomous learning algorithm can fuse curvature-magnetism dual information, ultimately achieving human machine interaction with gesture recognition and haptic feedback for cross-space perception and manipulation.

The Importance of Networks and Relationships: Leveraging the Biocontainment Unit Leadership Workgroup for Special Pathogen Outbreak Response.

Developing and sustaining relationships and networks before an emergency occurs is crucial. The Biocontainment Unit Leadership Workgroup is a consortium of the 13 Regional Emerging Special Pathogen Treatment Centers in the United States. Established in 2017, the volunteer-based workgroup is composed of operational leaders dedicated to maintaining readiness for special pathogen care. Monthly meetings focus on addressing operational challenges, sharing best practices, and brainstorming solutions to common problems. Task forces are leveraged to tackle more complex issues that are identified as priorities. In 2022, members of the workgroup were harnessed for response efforts related to mpox, Sudan ebolavirus, and Marburg virus disease. The weekly Outbreak Readiness call is a shared effort between the Biocontainment Unit Leadership Workgroup and the Special Pathogens Research Network of the National Emerging Special Pathogens Training and Education Center. Call participants included leaders of the Regional Emerging Special Pathogen Treatment Centers and federal partners who shared weekly updates on operational readiness of units, case counts, laboratory capacity, available medical countermeasures, and other pertinent information. The routine exchange of real-time information enabled learning and collegial sharing of experiences, highlighted the experience of the network to federal partners, and provided situational awareness of special pathogen outbreaks across the country. The consortium enabled this rapid convening of partners to meet an urgent need for special pathogen response. The weekly Outbreak Readiness call is a communication model and scalable framework that serves both domestic preparedness efforts and international efforts should the need for a collaborative global response arise. In this case study, we describe the framework and experience of this partnership, along with the structure of rapid deployment for group convening.

Rapid deployment aortic valve implantation in complex patients with infective endocarditis or aortic valve insufficiency

BackgroundNew prosthetic valves and surgical approaches that shorten operation time and improve the outcome of patients with aortic valve (AV) infective endocarditis (IE) and AV insufficiency (AVI) are crucial. The aim of this study was to evaluate the outcome of patients with AV IE or AVI treated with the EDWARDS INTUITY Rapid-Deployment AV prosthesis for this off-label indication.MethodsThis single-centre retrospective study analyzed data from patients who underwent AV replacement with the EDWARDS INTUITY Rapid-Deployment AV prosthesis for AV IE or regurgitation. (n = 8 for IE and n = 6 for AVI).ResultsHeart-lung machine times were significantly shorter in the AVI group (111.3 ± 20.7 min) compared to the IE group (171.9 ± 52.4 min) (p = 0.02). Aortic cross-clamp followed a similar trend (73.7 ± 9.9 min for AVI vs. 113.4 ± 35.6 min for IE) (p = 0.02). The length of ICU stay was also shorter in the AVI group (3.8 ± 2.6 days) compared to the IE group (16.9 ± 8.9 days) (p = 0.005). Postoperative echocardiography revealed no paravalvular leakage or significant valvular dysfunction in any patient. One patient died postoperatively from aspiration pneumonia.ConclusionThe INTUITY valve demonstrates as a safe option for complex AV IE and AVI surgery. Further prospective studies with larger patient cohorts are necessary to confirm these findings and explore the long-term benefits of this approach.

Adverse Events of COVID-19 Vaccines in the United States: Temporal and Spatial Analysis.

The COVID-19 pandemic, caused by SARS-CoV-2, has had a profound impact worldwide, leading to widespread morbidity and mortality. Vaccination against COVID-19 is a critical tool in controlling the spread of the virus and reducing the severity of the disease. However, the rapid development and deployment of COVID-19 vaccines have raised concerns about potential adverse events following immunization (AEFIs). Understanding the temporal and spatial patterns of these AEFIs is crucial for an effective public health response and vaccine safety monitoring. This study aimed to analyze the temporal and spatial characteristics of AEFIs associated with COVID-19 vaccines in the United States reported to the Vaccine Adverse Event Reporting System (VAERS), thereby providing insights into the patterns and distributions of the AEFIs, the safety profile of COVID-19 vaccines, and potential risk factors associated with the AEFIs. We conducted a retrospective analysis of administration data from the Centers for Disease Control and Prevention (n=663,822,575) and reports from the surveillance system VAERS (n=900,522) between 2020 and 2022. To gain a broader understanding of postvaccination AEFIs reported, we categorized them into system organ classes (SOCs) according to the Medical Dictionary for Regulatory Activities. Additionally, we performed temporal analysis to examine the trends of AEFIs in all VAERS reports, those related to Pfizer-BioNTech and Moderna, and the top 10 AEFI trends in serious reports. We also compared the similarity of symptoms across various regions within the United States. Our findings revealed that the most frequently reported symptoms following COVID-19 vaccination were headache (n=141,186, 15.68%), pyrexia (n=122,120, 13.56%), and fatigue (n=121,910, 13.54%). The most common symptom combination was chills and pyrexia (n=56,954, 6.32%). Initially, general disorders and administration site conditions (SOC 22) were the most prevalent class reported. Moderna exhibited a higher reporting rate of AEFIs compared to Pfizer-BioNTech. Over time, we observed a decreasing reporting rate of AEFIs associated with COVID-19 vaccines. In addition, the overall rates of AEFIs between the Pfizer-BioNTech and Moderna vaccines were comparable. In terms of spatial analysis, the middle and north regions of the United States displayed a higher reporting rate of AEFIs associated with COVID-19 vaccines, while the southeast and south-central regions showed notable similarity in symptoms reported. This study provides valuable insights into the temporal and spatial patterns of AEFIs associated with COVID-19 vaccines in the United States. The findings underscore the critical need for increasing vaccination coverage, as well as ongoing surveillance and monitoring of AEFIs. Implementing targeted monitoring programs can facilitate the effective and efficient management of AEFIs, enhancing public confidence in future COVID-19 vaccine campaigns.