Priority List Research Articles

Priorities, opportunities, and challenges for integrating microorganisms into Earth system models for climate change prediction.

Climate change jeopardizes human health, global biodiversity, and sustainability of the biosphere. To make reliable predictions about climate change, scientists use Earth system models (ESMs) that integrate physical, chemical, and biological processes occurring on land, the oceans, and the atmosphere. Although critical for catalyzing coupled biogeochemical processes, microorganisms have traditionally been left out of ESMs. Here, we generate a "top 10" list of priorities, opportunities, and challenges for the explicit integration of microorganisms into ESMs. We discuss the need for coarse-graining microbial information into functionally relevant categories, as well as the capacity for microorganisms to rapidly evolve in response to climate-change drivers. Microbiologists are uniquely positioned to collect novel and valuable information necessary for next-generation ESMs, but this requires data harmonization and transdisciplinary collaboration to effectively guide adaptation strategies and mitigation policy.

Multi-factor based Regression Test Case Prioritization using Fuzzy Logic

The maintenance level activity generally done after the modification in the software to check whether it is functioning right or not is termed as regression testing. Test case prioritization, a key practice, involves strategically ordering test cases based on specific criteria to enhance the efficiency of fault detection within a condensed time frame. The fuzzy rule base serves as an alternative to the conventional crisp value set, offering a nuanced approach beyond binary outcomes (Yes or No). The primary objective of this research is to address critical factors often overlooked in existing literature on prioritization. Notably, prevalent approaches focus on singular factors during test case prioritization, highlighting the need for a comprehensive technique. To enhance the prioritization of test cases, there is a demand for a method that considers multi-factors or combinations thereof, ultimately increasing effectiveness. This paper introduces an innovative approach a multi-factors regression test-case prioritization technique utilizing fuzzy rules. The methodology aims to optimize the prioritization of test cases, striking a balance between effectiveness and time efficiency. Fuzzy rules are formulated to assess the effectiveness of a prioritized set of test cases in developing the proposed approach. A user-friendly tool has been developed to facilitate the application of this technique, allowing users to input relevant factors and subsequently prioritize test cases accordingly. Through extensive experiments using the developed tool, the effectiveness of the proposed approach has been validated. The results demonstrate that the priority lists of test cases generated for different projects, considering multi-factors, show greater promise compared to techniques relying solely on a single factor for prioritization.

Abstract 853: Assessment of racial and ethnic disparities in environmental exposure-related cancer burden

Abstract The United States Environmental Protection Agency designates superfund sites as regions contaminated with hazardous waste. These sites have previously been associated with adverse health outcomes, including an increased incidence of cancer. Historically, communities with racial and ethnic minorities and people of low socioeconomic status have served as prime locations for these sites, as these communities have lacked the ability to advocate against this land use. Here, we ask whether racial and ethnic minorities are disproportionately exposed to these sites or other regions of hazardous waste in our catchment area at the Case Comprehensive Cancer Center, and in the state of Ohio. Further, we are examining whether areas of high exposure are associated with adverse health outcomes including higher cancer incidence and mortality. We utilized the Environmental Justice Index (EJI) database from the Centers for Disease Control and Prevention to assess, on a census tract level, the percent of racial and ethnic minorities in a region, defined as all persons except white, non-Hispanic individuals. We also queried hazardous and toxic waste sites including regions with a national priority list site, a toxic release inventory site, treatment, storage, and disposal sites, risk management plan sites, coal mines, and lead mines. We intersected these data and found that census tracts with a higher percentage of minority persons were more likely to be exposed to hazardous and toxic sites. Our current studies are assessing whether this high environmental burden is contributing to disproportionately worse cancer burden for these individuals. We will test this by comparing EJI data with data from the Ohio Cancer Incidence Surveillance System database to assess cancer incidence and mortality in Ohio. This will also provide an avenue for identifying specific exposures that may contribute to different cancer types. Further, we plan to assess if persons with low socioeconomic status or those living with disabilities are disproportionately affected by environmental burden. Overall, understanding environmental burden and health outcomes will facilitate the prioritization of communities at greatest need, contributing to greater health equity. Citation Format: Jessica R. Bobbitt, Fangzhou Liu, Jennifer Cullen, Ruth A. Keri. Assessment of racial and ethnic disparities in environmental exposure-related cancer burden [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 853.

Are retention strategies used in National Institute for Health and Care Research, Health Technology Assessment trials supported by evidence for their effectiveness? A systematic mapping review

Background and Aims Poor retention of trial participants is common and can result in significant methodological, statistical, ethical, and financial challenges. To improve trial efficiency, we aimed to assess the extent to which commonly used strategies to retain participants within trials are supported by evidence for their effectiveness. Method A systematic methodological review was carried out to identify commonly used retention strategies in National Institute for Health and Care Research (NIHR) Health Technology Assessment (HTA) trials (January 2020–June 2022). Strategies were then mapped to evidence for their effectiveness from the most recent Cochrane retention review (published 2021), and a future Study Within A Trial (SWAT) priority list was created. Results Amongst 80 trials, the most frequently reported retention strategies were: flexibility with data collection method/location (53%); participant diaries (38%); use of routine data (29%); PPI input (26%); telephone reminders for participants (26%); postal reminders for participants (25%); monitoring approaches (21%); offering flexibility with timing of data collection (20%); pre-paid return postage (18%); prioritising collection of key outcomes (15%); and participant newsletters (15%). Out of the 56 identified strategies, mostly no, very low or low evidence for their effectiveness was identified (64%; 14%; 13% respectively). Discussion and Conclusions Commonly used retention strategies are lacking good quality evidence for their effectiveness. The findings support the need for more SWATs and help identify priority areas for future SWAT research. These priorities could be used with other priority lists to inform future SWAT conduct.

Identifying High-Risk ZIP Codes for Childhood Lead Exposure: A Statewide ZCTA-Level Priority List for North Carolina.

Research has consistently shown that there is no safe blood lead level (BLL) for children. Despite progress in lead poisoning prevention, lead exposure remains a persistent threat to the health and neurological development of children. To identify high-risk ZIP codes for use by families and health care providers for the entire state of North Carolina, we developed a risk model using ZIP Code Tabula-tion Area (ZCTA)-level census data. We obtained all available BLL testing data from the North Carolina Department of Health and Human Services for the years 2010-2015 via data use agreement. We fit a multivariable regression model with the ZCTA-level mean of log normalized BLLs as the de-pendent variable and ZCTA-level census data for known risk factors of childhood lead exposure as predictors. We used this model to create a priority risk categorization. We organized ZCTAs into 20 quantiles, or priority risk categories, that can be used in local and statewide screening programs. The first six (of 20) quantiles were identified as particularly high-risk areas for childhood lead exposure. Because BLL testing is not universal, the BLL testing data used in this study are likely biased toward those most at risk for lead exposure. This study demonstrates the utility of ZCTA-level census data in identifying high-risk ZIP codes for childhood lead exposure, which can be used to ensure that the highest-risk children are tested in a timely manner. This approach can be replicated to address lead exposure nationally.

The utility of Drosophila melanogaster as a fungal infection model.

Invasive fungal diseases have profound effects upon human health and are on increase globally. The World Health Organization (WHO) in 2022 published the fungal priority list calling for improved public health interventions and advance research. Drosophila melanogaster presents an excellent model system to dissect host-pathogen interactions and has been proved valuable to study immunopathogenesis of fungal diseases. In this review we highlight the recent advances in fungal-Drosophila interplay with an emphasis on the recently published WHO's fungal priority list and we focus on available tools and technologies.

Research priorities for water, sanitation and hygiene (WASH) in humanitarian crises: A global prioritisation exercise

Water, sanitation and hygiene (WASH) interventions should provide access to safe water and sanitation, and promote good hygiene practices with dignity, comfort and security. Multiple systematic reviews have noted evidence gaps and is a paucity of good quality evidence related to the effectiveness and implementation of WASH programmes and interventions in humanitarian crises. The aim of this study was to generate consensus-based actionable research priorities for the humanitarian WASH sector. A research prioritisation exercise was conducted by adapting the Child Health and Nutrition Research Initiative (CHNRI) method. Research questions were compiled from a rapid scoping review, key informant interviews (KIIs) and focus group discussions (FGDs). Questions were reviewed by a technical expert group. An online survey was circulated to score research questions according to five criteria. An overall prioritisation score was calculated and weighted to prioritise questions. A diverse range of 286 global WASH and health experts engaged in the CHNRI process. A list of 128 questions were generated and scored by experts. Prioritised research questions focussed on evaluating existing interventions or programmes, and making iterative changes to current programmes. Other important questions centred on describing WASH conditions and associated health risks in crises contexts. Priorities were also stratified by gender, organisation and region to understand differences globally. The WASH in Crises Research Agenda has identified key research questions of most importance to those implementing WASH in humanitarian crises and has established a list of research priorities. The identified priorities reinforce how more evidence is needed, and underlines the need for research to evaluate current practices in order to improve the quality of humanitarian response. Stakeholders, including donors, international and national organisations, governments and academic institutions, are invited to use this research agenda to encourage, inspire and enable relevant and high-quality research that will be used to inform humanitarian responses.

Why the c-suite should care about anti-money laundering

Complying with anti-money laundering (AML) regulations isn't easy, and too often it slips down the compliance priority list. But it's important that organisations develop a strong AML culture, and this needs to start with guidance from the top.

Nucleic-Acid-Based Molecular Fungal Diagnostics: A Way to a Better Future.

The world has seen a tremendous increase in the number of fungal infections during the past two decades. Recently, the World Health Organisation released the pathogen priority list for fungal infections, signifying the importance of these infections in the fields of research and public health. Microbiology laboratories demand an upgrade in the diagnostic system to keep up with the increased burden of these infections. Diagnosis of fungal infections using conventional techniques has always faced limitations in terms of specificity, sensitivity, and turnaround time. Although these methods are the core pillars of the diagnosis, there is an increased need for molecular approaches. Molecular techniques have revolutionised the field of fungal diagnostics. The diverse array of molecular techniques, including techniques like Polymerase Chain Reaction (PCR), have emerged as a cornerstone in fungal diagnostics. Molecular techniques have transformed fungal diagnostics, providing powerful tools for the rapid and accurate identification of pathogens. As these technologies continue to evolve, their integration into routine clinical practice holds the promise of improving patient outcomes through timely and targeted antifungal interventions. This review will cover the molecular approaches involved in fungal diagnostics, moving from the basic techniques to the advanced-level nucleic-acid-based molecular approaches providing a high throughput and decreased turnaround time for the diagnosis of serious fungal infections.

Military logistics planning models for enemy targets attack by a swarm of combat drones

This article describes and investigates the planning aspect of military actions aimed at destroying enemy targets with the help of an attack drone swarm. This study attempts to solve the task of operational-tactical planning of a massive attack on enemy targets with the help of combat drones, which have different combat potential characteristics. It analyzes the problems of unmanned aerial vehicles (UAVs) swarms’ usage, which ensures maximum efficiency during combat operations. The article shows that in order to plan effective military operations, it is necessary to form the following logistical sequence: identification of relevant targets set, formation of drones into a swarm to attack targets, distribution of drones by targets, and planning flight routes of a drone swarm in conditions of military threats. It concludes that for the effective use of a combat drone swarm, it is necessary to plan logistical actions in advance to inflict maximum damage on the enemy and successfully fulfill the operational and tactical goals of the military leadership. The purpose of this study is to create information technology models that will allow planning logistical military actions for the effective use of combat drone swarms to defeat enemy targets. This article describes a systematic representation of logistical military operations for combat drone swarms. It also analyzes enemy targets, which are represented in the form of a priority list with the characteristics of relevance, the necessary combat potential to hit the targets, the risks of approaching the targets, and the flight time of the drones. From the list of targets, a sublist is formed, considering the combat potential of the drone swarm and the necessary potential to defeat the selected enemy targets. The optimization model helps to distribute the swarm of drones into groups to achieve the enemy targets and destroy them. The movement of drones is planned considering flight zones, possible anti-drone actions of the enemy, and the risks of military threats. Any Logic agent simulation platform can be used to create a simulated flight model of a drone swarm to selected enemy targets. Modeling makes it possible to form rational flight routes of a drone swarm under conditions of military threats from the enemy. An example is given to illustrate the formation of logistical actions for planning a massive attack on enemy targets with the help of a drone swarm. The scientific novelty of this study is related to the solution of the urgent problem of planning logistical military operations for the effective use of a combat drone swarm to destroy enemy targets. The results of this study should be used for the operational and tactical planning of logistical military operations to defeat enemy targets with the help of a combat drone swarm.

Conserving the Pilbara leaf-nosed bat: directions for future research and management

The Pilbara leaf-nosed bat (Rhinonicteris aurantia ‘Pilbara form’) is a threatened insectivorous microbat endemic to the Pilbara region of Western Australia. It is at risk of significant population decline, particularly due to habitat loss from mining. To address its key threats, stakeholders from a range of backgrounds collectively listed and prioritised research actions in 2013. This exercise was repeated in 2022 to evaluate and update progress of the research priorities. We conducted a review of scientific and grey literature (2013–2023) to: (1) identify the work undertaken to date that addressed the research priorities identified in the 2013 stakeholder workshop; (2) identify the current knowledge gaps in Pilbara leaf-nosed bat ecology, conservation, and threatening processes; and (3) highlight how these gaps relate to the newly ranked 2022 priority list for both research and management outcomes. We found that the 2013 research priorities were largely unaddressed, with most publications and unpublished industry reports in the review period primarily reporting presence or monitoring data around areas of interest to the resources sector, with minimal focus on the species’ conservation or management directly. Therefore, the knowledge gaps identified in 2013 were still largely relevant in 2022. Further targeted research is required to meet the 2022 priorities and better understand the habitat characteristics, movement ecology, disturbance buffer requirements, and cumulative impacts of mining on Pilbara leaf-nosed bat colonies. Overall, targeted research beyond traditional mining outputs, changes to regulatory procedure, and collaborative, adaptive management, are key to supporting the long-term persistence of Pilbara leaf-nosed bats.

An Evaluation of Research Priorities in Occupational Health Physiotherapy

Background: A list of research priorities in occupational health (OH) physiotherapy was identified at a national OH Physiotherapy Conference in the United Kingdom (UK) in 2018. Objective: To update the current research priorities in OH physiotherapy by expert consensus at an OH physiotherapy virtual meeting in the UK. Materials and Methods: A nominal group technique was used as a consensus technique. Fifteen participants with relevant qualifications and experience in OH physiotherapy agreed to participate and were handed the original list of research priorities and asked to rank it in order of their current importance. The resultant scores gave an indication of the level of consensus within the expert group. Participants were also asked to suggest other research topics of important that were not previously identified. These new topics were not used in the ranking process. Results: Following the expert ranking process eight topics were identified as key priorities providing an indication of where research funding ought to be directed. The benefits of OH physiotherapy based on health outcomes and return of investment was identified as the most important research to be undertaken. Conclusion: This project confirms both current and new research priorities to highlight areas for investment and to grow the overall evidence base.

Species and ecological communities as management surrogates for threatened biodiversity

In the face of the ongoing biodiversity crisis and limited conservation funding, surrogate approaches have become a valuable tool to represent biodiversity. Management surrogates are those that indirectly benefit an ecological system or species by representing the management requirements of co-occurring biodiversity. Recent findings highlight the cost-effective potential of surrogate species in managing threatened species, however, evaluating higher levels of biodiversity as management surrogates remains unexplored. Here, we sought to maximize conservation outcomes for threatened species and threatened ecological communities (TECs) by prioritizing management based on overlapping distributions, threats, and costs. We describe a prioritization framework for identifying TECs that could serve as cost-effective surrogates, and compare it with prioritizing threatened species only or both species and TECs. We show that when the objective is to maximize benefits for threatened species, a community approach performs poorly due to limited geographic overlap and high costs, while prioritizing species returned 7.5 times more benefits delivered to species under the same budget. Yet, if the objective is to maximize benefits across species and TECs simultaneously, a combined approach including both as surrogates delivers the greatest benefit for the same costs as a species-only approach. Range sizes and taxonomic groups significantly influenced the priority list, with threatened invertebrates and TECs of smaller ranges more likely to be selected as surrogates. Overall, this study emphasizes the importance of incorporating accurate data on factors such as threats and costs for identifying effective management surrogates, and highlights the potential benefits of prioritizing across multiple biodiversity features.

Longitudinal evaluation of an electronic audit and feedback system for patient safety in a large tertiary hospital setting.

Objective: This study sought to assess the impact of a novel electronic audit and feedback (e-A&F) system on patient outcomes. Methods: The e-A&F intervention was implemented in a tertiary hospital and involved near real-time feedback via web-based dashboards. We used a segmented regression analysis of interrupted time series. We modelled the pre-post change in outcomes for the (1) announcement of this priority list, and (2) implementation of the e-A&F intervention to have affected patient outcomes. Results: Across the study period there were 222,792 episodes of inpatient care, of which 13,904 episodes were found to contain one or more HACs, a risk of 6.24%. From the point of the first intervention until the end of the study the overall risk of a HAC reduced from 8.57% to 4.12% - a 51.93% reduction. Of this reduction the proportion attributed to each of these interventions was found to be 29.99% for the announcement of the priority list and 21.93% for the implementation of the e-A&F intervention. Discussion: Our findings lend evidence to a mechanism that the announcement of a measurement framework, at a national level, can lead to local strategies, such as e-A&F, that lead to significant continued improvements over time.

Multicriteria satisfaction analysis to analyse passenger perceptions of air travel service quality in Croatia

As people travel by air more often, they become more demanding in the services that they expect and the price that they are willing to pay, which can affect their loyalty to a given airline or airport. Air transport companies must gain and retain customers in order to remain competitive. Here we assessed passenger perceptions of air travel service quality using multicriteria satisfaction analysis (MUSA) that may provide deeper insights into consumer behaviour than previous studies. During a three-year period from 2016 to 2018, we assessed perceptions of air travellers using a survey that asked about air travel experiences within Croatia in the year prior to the year of the survey in order to avoid the influence of current feelings. Then we analysed survey responses using MUSA method and found that overall passenger satisfaction depended strongly on service at the departure airport, which many of our respondents judged to be inadequate. We have additionally fine-tuned the improvement priority list by incorporating passenger demand levels. Our results may help guide efforts at air transport companies to improve service quality and to better understand how passengers perceive their services.

CPRNC: Channels pruning via reverse neuron crowding for model compression

Channel pruning is an efficient technique for model compression, removing redundant parts of a convolutional neural network with minor degradation in classification accuracy. Previous criteria of channel pruning ignore neurons’ intrinsic relationship and the high correlation with input samples. Inspired by the visual crowding phenomenon in neuroscience, this paper presents a novel channel pruning method via reverse neuron crowding, dubbed CPRNC, to address this issue. First, CPRNC involves a neuron crowding degree measure (NCDM) module, which builds the relationship model among all artificial neurons by observing their crowding behaviors. Subsequently, each channel’s importance is evaluated by the crowding degree of corresponding channels. Considering that the channel importance is affected by the characteristic of input samples, CPRNC designs a neuron crowding degree recalibrate (NCDR) module. NCDR emphasizes discriminative samples to recalibrate the channel priority list generated by NCDM, further enhancing the precision of the pruning criterion. Experimental results show that CPRNC achieves performance that competes with state-of-the-art pruning methods, including dynamic channel pruning and learning-based pruning. For example, we prune ResNet-50 with 56.7% FLOPs on the large-scale dataset ImageNet1K with only a 0.19% decrease in accuracy. At low pruning rates, CPRNC achieves lossless compression, e.g., the pruned ResNet-56 on CIFAR-10 increases accuracy by 0.13% over the baseline model at 56.3% FLOPs reduction.

Minimizing total completion time with machine-dependent priority lists

We consider a natural, yet challenging variant of the parallel machine scheduling problem in which each machine imposes a preferential order over the jobs and schedules the jobs accordingly once assigned to it. We study the problem of minimizing the total completion time, distinguishing between identical and unrelated machines, machine-dependent and identical priority lists, or a constant number of different priority classes. Additionally, we consider the setting in which the priority list on a machine must satisfy longest processing time first. We resolve the computational complexity of the problem and provide a clear distinction between problems that are polynomial time solvable and APX-hard.

Prioritization of the ecotoxicological hazard of PAHs towards aquatic species spanning three trophic levels using 2D-QSTR, read-across and machine learning-driven modelling approaches

Polycyclic aromatic hydrocarbons (PAHs) represent a common group of environmental pollutants that endanger various aquatic organisms via various pathways. To better prioritize the ecotoxicological hazard of PAHs to aquatic environment, we used 2D descriptors-based quantitative structure-toxicity relationship (QSTR) to assess the toxicity of PAHs toward six aquatic model organisms spanning three trophic levels. According to strict OECD guideline, six easily interpretable, transferable and reproducible 2D-QSTR models were constructed with high robustness and reliability. A mechanistic interpretation unveiled the key structural factors primarily responsible for controlling the aquatic ecotoxicity of PAHs. Furthermore, quantitative read-across and different machine learning approaches were employed to validate and optimize the modelling approach. Importantly, the optimum QSTR models were further applied for predicting the ecotoxicity of hundreds of untested/unknown PAHs gathered from Pesticide Properties Database (PPDB). Especially, we provided a priority list in terms of the toxicity of unknown PAHs to six aquatic species, along with the corresponding mechanistic interpretation. In summary, the models can serve as valuable tools for aquatic risk assessment and prioritization of untested or completely new PAHs chemicals, providing essential guidance for formulating regulatory policies.

Outcomes of listing for lung and heart-lung transplantation in pulmonary hypertension: comparative experience in France and the UK.

Lung or heart-lung transplantation (LT/HLT) for severe pulmonary hypertension (PH) as the primary disease indication carries a high risk of waiting list mortality and post-transplant complications. France and the UK both have coordinated PH patient services but with different referral pathways for accessing LT services. We conducted a comparative analysis of adult PH patients listed for LT/HLT in the UK and France. We included 211 PH patients in France (2006-2018) and 170 in the UK (2010-2019). Cumulative incidence of transplant, delisting and waiting list death within 3 years were 81%, 4% and 11% in France versus 58%, 10% and 15% in the UK (p<0.001 for transplant and delisting; p=0.1 for death). Median non-priority waiting time was 45 days in France versus 165 days in the UK (p<0.001). High-priority listing occurred in 54% and 51% of transplanted patients respectively in France and the UK (p=0.8). Factors associated with achieving transplantation related to recipients' height, male sex, clinical severity and priority listing status. 1-year post-transplant survival was 78% in France and 72% in the UK (p= 0.04). Access to transplantation for PH patients is better in France than in the UK where more patients were delisted due to clinical deterioration because of longer waiting time. High rates of priority listing occurred in both countries. Survival for those achieving transplantation was slightly better in France. Ensuring optimal outcomes after transplant listing for PH patients is challenging and may involve early listing of higher risk patients, increasing donor lung utilisation and improving allocation rules for these specific patients.

Aspect-Based Sentiment Analysis of Borobudur Temple Reviews Use Support Vector Machine Algorithm

As one of the most popular tourist attractions in Indonesia, Borobudur Temple is currently included in the top ten list of tourism priorities by the Ministry of Tourism. To increase the number of tourists, it is very important to maintain the quality of tourist attractions. Tourist growth is directly related to the number of online reviews of tourist attractions. Tourism managers need more than just reviewing good and negative sentiments to maintain and improve the quality of tourist attractions. Many aspects serve as benchmarks for visitors to come to a tourist spot, such as aspects of ticket prices, location, attractiveness, facilities, accessibility, visual image, and human resources. Therefore, sentiment analysis is needed for each of these aspects to find out aspects that need to be improved in order to increase the number of visitors. Support Vector Machine (SVM) is an algorithm used to categorize aspect-based sentiments. analyzed using SVM, the dataset must first be cleaned and normalized through pre-processing. The results of the analysis show that the aspects of accessibility and visual image need to be improved to maintain and increase the number of visitors. This is because these two aspects have the most negative reviews compared to other aspects. The results of model testing only get an average accuracy value of 0.8148 because the distribution of data for all aspects and reviews is not balanced.