Prioritization Strategies Research Articles

Optimizing androgen receptor prioritization using high-throughput assay-based activity models.

Introduction: Computational models using data from high-throughput screening assays have promise for prioritizing and screening chemicals for testing under the U.S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP). The purpose of this work was to demonstrate a data processing method for the determination of optimal minimal assay batteries from a larger comprehensive model, to provide a uniform method of evaluating the performance of future minimal assay batteries compared with the androgen receptor (AR) pathway model, and to incorporate chemical cluster analysis into this evaluation. Although several of the assays in the AR pathway model are no longer available through the original vendor, this approach could be used for future evaluations of minimal assay models for prioritization and screening. Methods: We compared two previously published models and found that an expanded 14-assay model had higher sensitivity for antagonists, whereas the original 11-assay model had slightly higher sensitivity for agonists. We then investigated subsets of assays in the original AR pathway model to optimize overall testing strategies that minimize cost while maintaining sensitivity across a broad chemical space. Results and Discussion: Evaluation of the critical assays across subset models derived from the 14-assay model identified three critical assays for predicting antagonism and two critical assays for predicting agonism. A minimum of nine assays is required for predicting agonism and antagonism with high sensitivity (95%). However, testing workflows guided by chemical structure-based clusters can reduce the average number of assays needed per chemical by basing the assays selected for testing on the likelihood of a chemical being an AR agonist, according to its structure. Our results show that a multi-stage testing workflow can provide 95% sensitivity while requiring only 48% of the resources required for running all assays from the original full models. The resources can be reduced further by incorporating in silico activity predictions. Conclusion: This work illustrates a data-driven approach that incorporates chemical clustering and simultaneous consideration of antagonism and agonism mechanisms to more efficiently screen chemicals. This case study provides a proof of concept for prioritization and screening strategies that can be utilized in future analyses to minimize the overall number of assays needed for predicting AR activity, which will maximize the number of chemicals that can be tested and allow data-driven prioritization of chemicals for further screening under the EDSP.

Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy

For understanding a chemical compound’s mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top 20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$20\\%$$\\end{document} of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at: https://bioquimio.udla.edu.ec/tidentification01/. Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling.Graphical

Neural Oscillations and Functional Significances for Prioritizing Dual-Task Walking in Parkinson's Disease.

Task prioritization involves allocating brain resources in a dual-task scenario, but the mechanistic details of how prioritization strategies affect dual-task walking performance for Parkinson's disease (PD) are little understood. We investigated the performance benefits and corresponding neural signatures for people with PD during dual-task walking, using gait-prioritization (GP) and manual-prioritization (MP) strategies. Participants (N = 34) were asked to hold two inter-locking rings while walking and to prioritize either taking big steps (GP strategy) or separating the two rings (MP strategy). Gait parameters and ring-touch time were measured, and scalp electroencephalograph was performed. Compared with the MP strategy, the GP strategy yielded faster walking speed and longer step length, whereas ring-touch time did not significantly differ between the two strategies. The MP strategy led to higher alpha (8-12 Hz) power in the posterior cortex and beta (13-35 Hz) power in the left frontal-temporal area, but the GP strategy was associated with stronger network connectivity in the beta band. Changes in walking speed and step length because of prioritization negatively correlated with changes in alpha power. Prioritization-related changes in ring-touch time correlated negatively with changes in beta power but positively with changes in beta network connectivity. A GP strategy in dual-task walking for PD can enhance walking speed and step length without compromising performance in a secondary manual task. This strategy augments attentional focus and facilitates compensatory reinforcement of inter-regional information exchange.

Multi-feature SEIR model for epidemic analysis and vaccine prioritization.

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.

Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry.

Recent advances in high-resolution mass spectrometry (HRMS) have enabled the detection of thousands of chemicals from a single sample, while computational methods have improved the identification and quantification of these chemicals in the absence of reference standards typically required in targeted analysis. However, to determine the presence of chemicals of interest that may pose an overall impact on ecological and human health, prioritization strategies must be used to effectively and efficiently highlight chemicals for further investigation. Prioritization can be based on a chemical's physicochemical properties, structure, exposure, and toxicity, in addition to its regulatory status. This Perspective aims to provide a framework for the strategies used for chemical prioritization that can be implemented to facilitate high-quality research and communication of results. These strategies are categorized as either "online" or "offline" prioritization techniques. Online prioritization techniques trigger the isolation and fragmentation of ions from the low-energy mass spectra in real time, with user-defined parameters. Offline prioritization techniques, in contrast, highlight chemicals of interest after the data has been acquired; detected features can be filtered and ranked based on the relative abundance or the predicted structure, toxicity, and concentration imputed from the tandem mass spectrum (MS2). Here we provide an overview of these prioritization techniques and how they have been successfully implemented and reported in the literature to find chemicals of elevated risk to human and ecological environments. A complete list of software and tools is available from https://nontargetedanalysis.org/.

Impact of the COVID-19 pandemic on breast cancer pathological stage at diagnosis in Tunisian patients.

Breast cancer (BC) patients' diagnosis and management was affected by a global reorganization after the Coronavirus disease 2019 (COVID-19). Our study aimed to assess the impact of the pandemic on the pathological stage of newly diagnosed patients with BC compared to pre-pandemic and to identify predictive factors of tumor advanced stage. Pathological records of all consecutive newly operated BC patients between March 2020 and December 2021 were reviewed retrospectively. Clinical and pathological prognostic factors of BC were collected and compared between pre-pandemic and pandemic periods. Then, predictive factors of tumor advanced stage were identified. Of the 225 cases included in the analysis, 98.7% were females and 1.3% were males. The median time from first histological diagnosis to first surgical treatment was enlarged by 42 days with a significant difference between the two periods (p = 0.002). Newly diagnosed BC patients during the COVID-19 pandemic were operated at a more advanced stage (54.1% vs 36.2%, p = 0.007), had a greater lymphovascular invasion (p = 0.002), lymph node metastasis (p = 0.015) and are more commonly of IBC NST histological type (p = 0.005). Moreover, multivariate analyses showed that the pandemic period (AOR = 2.28; p = 0.016) and the lympho-vascular invasion (p < 0.001) were independently associated with advanced stage of tumors. Our findings proved an increase in alarming rates of advanced stage BC associated with the COVID-19 crisis. These findings support recommendations for a quick restoration of BC screening at full capacity, with adequate prioritization strategies to mitigate harm.

Identifying and developing strategies for implementation of a guided internet- and mobile-based infant sleep intervention in well-baby and community mental health clinics using group concept mapping

BackgroundThis study aimed to identify strategies for the implementation of a guided internet- and mobile-based intervention (IMI) for infant sleep problems (“Sleep Well, Little Sweetheart”) in well-baby and community mental health clinics.Study designWe used group concept mapping, a two-phased mixed methods approach, conducted as a two-day workshop in each clinic. We recruited 20 participants from four clinics and collected sorting and rating data for implementation strategies based on the Expert Recommendations for Implementing Change taxonomy and brainstorming sessions. Data were analyzed using descriptive statistics, multidimensional scaling, and hierarchical cluster analysis to create cluster maps, laddergrams, and Go-Zone graphs. Participants were presented with the results and discussed and interpreted the findings at each of the clinics in spring 2022.ResultsParticipants identified 10 clusters of strategies, of which Training, Embedding and Coherence, User Involvement and Participation, and Clinician Support and Implementation Counseling were rated as most important and feasible. Economy and Funding and Interactive and Interdisciplinary Collaboration were rated significantly lower on importance and feasibility compared to many of the clusters (all ps < 0.05). There was a correlation between the importance and feasibility ratings (r =.62, p =.004).ConclusionsThe use of group concept mapping made it possible to efficiently examine well-baby and community clinics’ perspectives on complex issues, and to acquire specific knowledge to allow for the planning and prioritization of strategies for implementation. These results suggest areas of priority for the implementation of IMIs related to infant sleep problems.Trial registrationThe study was pre-registered at Open Science Framework (www.osf.io/emct8).

The invisibility of health effects associated with water pollution within disease burden estimates: Analysis from a Colombian Andean watershed

Modernization is marked by an increase in pollution-related health risks due to the rapid economic and technological growth. In the Global South, there is concern regarding the disease burden attributable to chemical contaminants released into the environment, especially in river basins, where populations are exposed through multiple pathways. While significant research exists linking chemical exposure to disease, mostly in occupational settings, less data is available for the open environment. Emerging pollutants have shown ecotoxicological and genotoxic effects at low concentrations in both terrestrial and aquatic ecosystems, with humans at the apex. Despite such evidence, water quality standards focus primarily on communicable diseases risks, and the burden of disease method mainly includes gastrointestinal and respiratory infections. Therefore, the use of DALYs as a prioritization strategy needs to be evaluated to suggest alternatives for integrated analysis. Our aim was to enhance the comprehension of watersheds as socioecological entities that necessitate inclusive indicators to display the intricate and diverse links between water, health, and the resultant inequities. Based on previous research and information collected in a tropical Andean watershed at southwestern Colombia, we characterized drivers of pollution and health hazards. Using standardized methods for calculating DALYs, we estimated the water-related environmental burden of communicable and noncommunicable diseases. Estimates of disease burden were analyzed to find out disparities driven by ethnic, gender and socioeconomic status. Results indicate that significant inequalities persist affecting the most vulnerable populations for preventable communicable diseases. Furthermore, the impact of noncommunicable diseases, which are linked to chemical pollution from individual substances and their cocktail effect, remains largely invisible due to insufficient data and research. The econometric approach of the DALY addition effect needs to be expanded with historical and critical perspectives to expose the profound social and health inequities present in the socioecological systems of the Global South.

Organizational Expectations Regarding Documentation Practices in Athletic Training.

Although guidance is available, no nationally recognized standard exists for medical documentation in athletic training, leaving individual organizations responsible for setting expectations and enforcing policies. Previous research has examined clinician documentation behaviors; however, the supervisor's role in creating policy and procedures, communicating expectations, and ensuring accountability has not been investigated. To investigate supervisor practices regarding support, hindrance, and enforcement of medical documentation standards at an individual organization level. Mixed-methods study. Online surveys and follow-up interviews. We criterion sampled supervising athletic trainers (n = 1107) in National Collegiate Athletic Association member schools. The survey collected responses from 64 participants (age = 43 ± 11 years; years of experience as a supervisor = 12 ± 10; access rate = 9.6%; completion rate = 66.7%), and 12 (age = 35 ± 6 years; years of experience as a supervisor = 8 ± 5) participated in a follow-up interview. We used measures of central tendency to summarize survey data and the consensual qualitative research approach with a 3-person data analysis team and multiphase process to create a consensus codebook. We established trustworthiness using multiple-analyst triangulation, member checking, and internal and external auditing. Fewer than half of supervisors reported having formal written organization-level documentation policies (n = 45/93, 48%) and procedures (n = 32/93, 34%) and an expected timeline for completing documentation (n = 24/84, 29%). Participants described a framework relative to orienting new and existing employees, communicating policies and procedures, strategies for holding employees accountable, and identifying purpose. Limitations included lack of time, prioritization of other roles and responsibilities, and assumptions of prior training and record quality. Despite a lack of clear policies, procedures, expectations, prioritization, and accountability strategies, supervisors still felt confident in their employees' abilities to create complete and accurate records. This highlights a gap between supervisor and employee perceptions, as practicing athletic trainers have reported uncertainty regarding documentation practices in previous studies. Although supervisors perceive high confidence in their employees, clear organization standards, employer prioritization, and mechanisms for accountability surrounding documentation will result in improved patient care delivery, system outcomes, and legal compliance.

NeoHunter: Flexible software for systematically detecting neoantigens from sequencing data

AbstractComplicated molecular alterations in tumors generate various mutant peptides. Some of these mutant peptides can be presented to the cell surface and then elicit immune responses, and such mutant peptides are called neoantigens. Accurate detection of neoantigens could help to design personalized cancer vaccines. Although some computational frameworks for neoantigen detection have been proposed, most of them can only detect SNV‐ and indel‐derived neoantigens. In addition, current frameworks adopt oversimplified neoantigen prioritization strategies. These factors hinder the comprehensive and effective detection of neoantigens. We developed NeoHunter, flexible software to systematically detect and prioritize neoantigens from sequencing data in different formats. NeoHunter can detect not only SNV‐ and indel‐derived neoantigens but also gene fusion‐ and aberrant splicing‐derived neoantigens. NeoHunter supports both direct and indirect immunogenicity evaluation strategies to prioritize candidate neoantigens. These strategies utilize binding characteristics, existing biological big data, and T‐cell receptor specificity to ensure accurate detection and prioritization. We applied NeoHunter to the TESLA dataset, cohorts of melanoma and non‐small cell lung cancer patients. NeoHunter achieved high performance across the TESLA cancer patients and detected 79% (27 out of 34) of validated neoantigens in total. SNV‐ and indel‐derived neoantigens accounted for 90% of the top 100 candidate neoantigens while neoantigens from aberrant splicing accounted for 9%. Gene fusion‐derived neoantigens were detected in one patient. NeoHunter is a powerful tool to ‘catch all’ neoantigens and is available for free academic use on Github (XuegongLab/NeoHunter).

A communication-based identification of critical drones in malicious drone swarm networks

Accurate identification of critical malicious drones is crucial for optimizing directed energy attacks and maximizing their effectiveness. However, current studies on critical drone identification are still in the preliminary stage and almost rely on the traditional centrality methods that do not address the distributed features of drone swarms. This leads to inaccurate identification of critical drones, resulting in the low efficiency of directed energy attacks. Therefore, this paper proposes a new critical drone identification method based on the distributed features, communication intensity, and communication scale of drones. Specifically, this paper first constructs a dynamic communication prediction network (DCPN) of drone swarms based on the 3D position and interaction range, which predicts the dynamic communication between drones. Then, this paper proposes a new method called dynamic giant connected component (GCC)-based scale-intensity centrality (DGSIC) that combines the local, global, and community structure of DCPN to identify critical nodes with stronger communication capabilities. The dynamic strategy involves the iterative identification of one critical node at each step, considering the evolving network configuration and ensuring the identified node remains the most critical in the present network. Additionally, the prioritization strategy is employed to identify the nodes within the GCC, which can significantly impact the network connectivity and communication. DGSIC optimizes the attack sequence for directed energy attacks, facilitating the rapid dissolution of malicious drone swarms. Extensive experiments in four simulated networks and eight real-world networks demonstrate the superior robustness and cascading failure performance of DGSIC.

The hidden impact of alcohol on young victims: an analysis of alcohol-related police offences resulting in hospitalisation

BackgroundAlcohol-related harm (ARH) is a significant public health concern affecting young individuals, particularly those involved in alcohol-related police incidents resulting in hospitalisation. However, the impact of alcohol on young victims remains under researched. This study aimed to identify the characteristics of offenders and victims involved in these incidents, analyse the types of offences, and understand the under-ascertainment of ARH in hospital records.MethodsA retrospective longitudinal study of 12–24-year-olds born between 1980 and 2005 was conducted using linked data from hospital admissions, emergency department presentations, and police incident records. Alcohol-related incidents were identified based on the attending officers’ opinions in the Western Australia Police’s Incident Management System (IMS). Logistic and log-binomial regression were utilised to analyse the factors associated with victimisation and under-ascertainment of ARH.ResultsOur study included 22,747 individuals (11,433 victims and 11,314 offenders) involved in alcohol-related police incidents, with a small majority of victims being female (53%, n = 6,074) and a large majority of offenders being male (84.3%, n = 9,532). Most victims did not receive a diagnosis of ARH (71%, n = 760). Women were 10 times more likely to have been a victim in ARH police incidents and 2 times more likely to have an undiagnosed alcohol-related hospital admission than men. Victims and offenders predominantly came from disadvantaged areas and major cities. Aboriginal individuals were overrepresented as both offenders and victims. A significant proportion of individuals experienced emergency department presentations or hospital admissions, with head injuries being the most common. Assault causing bodily harm was the most prevalent offence resulting in hospitalisation (66%, n = 2,018).ConclusionsThere is a noteworthy disparity between the quantity of hospital admissions attributed to alcohol-related incidents and the number of cases that are formally classified as ARH in the hospital system. This disparity highlights a more profound issue of substantial under-ascertainment or inadequate identification of ARH than previously acknowledged. Our findings justify the prioritisation of prevention strategies, beyond improvement in the documentation of alcohol-related hospitalisation. Considering the scale of the problem, and the underestimation of the burden of alcohol-related hospitalisation, a proportional increase in investment is necessary to achieve population-level reductions in ARH.

Connecting the fields: How ICT improve agricultural productivity in sub‐Saharan Africa

AbstractThis study seeks to conduct a comprehensive analysis of the influence of information and communication technology (ICT) on agricultural productivity in sub‐Saharan Africa (SSA), both through direct and indirect mechanisms. Utilizing data gathered from a sample of 20 sub‐Saharan African nations spanning the period from 1995 to 2014, we employ a rigorous examination through static and dynamic panel models. Our findings robustly indicate a substantial and positive impact stemming from the presence of mobile phones and internet coverage on agricultural productivity in the region. Furthermore, we uncover the indirect effects of ICT, notably through its influence on farmers' education levels and their access to essential agricultural inputs, such as fertilizers. To validate the robustness of our results, we additionally establish a positive and statistically significant association between an ICT adoption index and agricultural productivity in SSA. In light of these findings, we recommend the prioritization of strategies aimed at fortifying the adoption of communication technologies within the agricultural sector. In addition, efforts should be directed toward enhancing farmers' education and improving access to fertilizers, leveraging the potential of ICT tools as key enablers in this process.

Big Data Analytics-Driven Project Management Strategies

The integration of Artificial Intelligence (AI) and Big Data Analytics (BDA) in project management has become a critical enabler of efficiency in managing large-scale, complex projects. This research paper delves into how AI-driven big data analytics can revolutionize traditional project management methodologies by introducing dynamic scheduling, real-time risk prediction, and automated task prioritization strategies. These advanced techniques, which leverage machine learning (ML) models and extensive historical project data, enable a shift from reactive to proactive project management, ensuring that risks and resource constraints are identified and addressed before they impact project delivery. By analyzing massive datasets, including historical performance metrics, resource availability, and project timelines, AI-driven systems can forecast delays, assess risk levels dynamically, and adapt schedules in real-time. This proactive approach facilitates better decision-making, optimized resource allocation, and improved project outcomes. The study is anchored on the premise that the sheer volume of data generated in large-scale projects often overwhelms traditional project management systems. By incorporating AI and BDA, project managers can better utilize this data, turning it into actionable insights that inform intelligent decision-making. Machine learning algorithms, particularly those specializing in predictive analytics, are capable of identifying patterns that elude human analysis, allowing for the accurate forecasting of project risks, schedule slippage, and task dependencies. This ability to predict potential issues, such as resource bottlenecks or unforeseen delays, enables project teams to implement mitigative actions in advance, thus reducing the likelihood of project failure. Furthermore, dynamic scheduling is a key focus of this research, as AI-powered models can continuously adjust project timelines based on real-time data. These models consider variables such as resource utilization rates, task dependencies, and evolving project constraints, offering adaptive scheduling mechanisms that evolve throughout the project lifecycle. The automated task prioritization system, powered by BDA, ensures that the most critical tasks receive the appropriate level of attention at the right time, improving project performance and enhancing resource efficiency. Through natural language processing (NLP) and advanced data mining techniques, AI models can also analyze project documentation and communication channels to detect potential risks and suggest task adjustments. The paper also discusses the application of AI in risk prediction, focusing on how AI models can analyze risk factors from historical data, including resource constraints, financial limitations, and market volatility, to produce risk profiles that project managers can use for strategic planning. Real-time risk assessments, made possible by the integration of AI and BDA, can help project teams stay ahead of potential disruptions. This allows for more accurate contingency planning and reduces the overall risk to project timelines and budgets. Practical applications of these AI-driven strategies are presented through case studies of large-scale projects in various industries, including construction, information technology, and healthcare. These case studies demonstrate how AI-powered analytics have been successfully implemented to enhance project efficiency, optimize resource allocation, and minimize risks in complex projects. The study underscores the importance of integrating these technologies into modern project management frameworks to cope with the increasing complexity of projects in today’s fast-paced business environment. While the potential benefits of AI and BDA in project management are substantial, this paper also addresses the challenges associated with their implementation. One significant challenge is the quality and availability of data required to train AI models effectively. Incomplete or inaccurate data can lead to unreliable forecasts, compromising the project’s success. Additionally, the paper explores the issues of data privacy and security in AI-driven project management systems, highlighting the need for robust data governance frameworks to ensure the ethical use of AI technologies. Another key consideration is the resistance to change within organizations, where traditional project management methods are deeply ingrained. The paper emphasizes the need for a cultural shift towards data-driven decision-making and suggests strategies for fostering an environment conducive to AI adoption. This includes training project management teams to work alongside AI systems and fostering collaboration between AI experts and project managers to ensure smooth implementation and operation. Finally, this research outlines future trends in AI and BDA for project management, suggesting that further advancements in AI technologies, such as reinforcement learning and more sophisticated natural language processing algorithms, will drive the next generation of intelligent project management systems. These future systems are expected to be even more adept at handling the complexities of large-scale projects, offering real-time solutions to unforeseen challenges and adapting dynamically to changing project requirements.

Combining a prioritization strategy and functional studies nominates 5’UTR variants underlying inherited retinal disease

Background5’ untranslated regions (5’UTRs) are essential modulators of protein translation. Predicting the impact of 5’UTR variants is challenging and rarely performed in routine diagnostics. Here, we present a combined approach of a comprehensive prioritization strategy and functional assays to evaluate 5’UTR variation in two large cohorts of patients with inherited retinal diseases (IRDs).MethodsWe performed an isoform-level re-analysis of retinal RNA-seq data to identify the protein-coding transcripts of 378 IRD genes with highest expression in retina. We evaluated the coverage of their 5’UTRs by different whole exome sequencing (WES) kits. The selected 5’UTRs were analyzed in whole genome sequencing (WGS) and WES data from IRD sub-cohorts from the 100,000 Genomes Project (n = 2397 WGS) and an in-house database (n = 1682 WES), respectively. Identified variants were annotated for 5’UTR-relevant features and classified into seven categories based on their predicted functional consequence. We developed a variant prioritization strategy by integrating population frequency, specific criteria for each category, and family and phenotypic data. A selection of candidate variants underwent functional validation using diverse approaches.ResultsIsoform-level re-quantification of retinal gene expression revealed 76 IRD genes with a non-canonical retina-enriched isoform, of which 20 display a fully distinct 5’UTR compared to that of their canonical isoform. Depending on the probe design, 3–20% of IRD genes have 5’UTRs fully captured by WES. After analyzing these regions in both cohorts, we prioritized 11 (likely) pathogenic variants in 10 genes (ARL3, MERTK, NDP, NMNAT1, NPHP4, PAX6, PRPF31, PRPF4, RDH12, RD3), of which 7 were novel. Functional analyses further supported the pathogenicity of three variants. Mis-splicing was demonstrated for the PRPF31:c.-9+1G>T variant. The MERTK:c.-125G>A variant, overlapping a transcriptional start site, was shown to significantly reduce both luciferase mRNA levels and activity. The RDH12:c.-123C>T variant was found in cis with the hypomorphic RDH12:c.701G>A (p.Arg234His) variant in 11 patients. This 5’UTR variant, predicted to introduce an upstream open reading frame, was shown to result in reduced RDH12 protein but unaltered mRNA levels.ConclusionsThis study demonstrates the importance of 5’UTR variants implicated in IRDs and provides a systematic approach for 5’UTR annotation and validation that is applicable to other inherited diseases.

Dataset of United States Incident Management Situation Reports from 2007 to 2021

This paper presents a unique 15-year dataset of Incident Management Situation Reports (IMSR), which document daily wildland fire situations across ten geographical regions in the United States. The IMSR dataset includes summaries for each reported day on national and regional wildfire activities, wildfire-specific activities, and committed fire suppression resources (i.e., personnel and equipment). This dataset is distinct from other wildfire data sources as it provides daily information on national fire suppression resource utilization, national and regional preparedness levels, and management priority for each region and fire. We developed an open-source Java program, IMSR-Tool, to process 3,124 IMSR reports available from 2007 to 2021 to generate this structured IMSR dataset, which can be updated when future reports become available. The dataset presented here and its future extension enable researchers and practitioners to study historical wildfire activity and resource use across regions and time, examine fire management perceptions, evaluate strategies for fire prioritization and fire resource allocation, and exploit other broader usage to improve wildfire management and response in the United States.

Sustainable Pangasius Aquaculture Management Strategy using Multidimensional Scaling (MDS) and Analytical Hierarchy Process (AHP) in Tulungagung Regency, East Java, Indonesia

Abstract Aquaculture activities in Tulungagung Regency provide livelihoods for 12,050 households. Therefore, ensuring the sustainability of Pangasius aquaculture activities is crucial for the welfare of fish farmers. This study aims to analyze the sustainability of existing Pangasius aquaculture businesses in Tulungagung Regency, East Java, Indonesia, and to create a sustainable Pangasius aquaculture policy strategy. This quantitative study used Multi-Dimensional Scaling (MDS) and Analytical Hierarchy Process (AHP) analyses to determine the sustainability status of Pangasius aquaculture based on five dimensions: ecological, economic, social, institutional, and technological infrastructure. Among these dimensions, two are less sustainable, particularly infrastructure technology and the economy. Highlight Research The author mentioned four highlights from their research: Tulungagung Regency is East Java Province's major pangasius producer. MDS can assess sustainability across multiple dimensions. AHP enables the prioritization of strategies that must be implemented. The Pangasius aquaculture company in Tulungagung Regency is currently in a sustainable state.

Risk-based prioritization of PFAS using phenotypic and transcriptomic data from human induced pluripotent stem cell-derived hepatocytes and cardiomyocytes.

Per- and polyfluoroalkyl substances (PFAS) are chemicals with important applications; they are persistent in the environment and may pose human health hazards. Regulatory agencies are con­sidering restrictions and bans of PFAS; however, little data exists for informed decisions. Several prioritization strategies were proposed for evaluation of potential hazards of PFAS. Structure-based grouping could expedite the selection of PFAS for testing; still, the hypothesis that structure-effect relationships exist for PFAS requires confirmation. We tested 26 structurally diverse PFAS from 8 groups using human induced pluripotent stem cell-derived hepatocytes and cardiomyocytes, and tested concentration-response effects on cell function and gene expression. Few phenotypic effects were observed in hepatocytes, but negative chronotropy was observed in cardiomyocytes for 8 PFAS. Substance- and cell type-dependent transcriptomic changes were more prominent but lacked substantial group-specific effects. In hepatocytes, we found upregulation of stress-related and extracellular matrix organization pathways, and down-regulation of fat metabolism. In car­diomyocytes, contractility-related pathways were most affected. We derived phenotypic and transcriptomic points of departure and compared them to predicted PFAS exposures. Conservative estimates for bioactivity and exposure were used to derive a bioactivity-to-exposure ratio (BER) for each PFAS; 23 of 26 PFAS had BER > 1. Overall, these data suggest that structure-based PFAS grouping may not be sufficient to predict their biological effects. Testing of individual PFAS may be needed for scientifically-supported decision-making. Our proposed strategy of using two human cell types and considering phenotypic and transcriptomic effects, combined with dose-response analysis and calculation of BER, may be used for PFAS prioritization.

Prioritization before dereplication, an effective strategy to target new metabolites in whole extracts: ghosalin from Murraya paniculata root.

Re-discovery of known metabolites is a common challenge in natural product-based drug discovery, and to avoid re-discovery, dereplication has been proposed for identifying known metabolites at the early stage of isolation. A majority of methods use LCMS to profile the extract and ignore the known mass. LC-HRMS profiling may generate a long mass list of metabolites. The identification of a new metabolite is difficult within the mass list. To overcome this, it was hypothesized that identifying a 'new metabolite' in the whole metabolome is more difficult than identifying it within the class of metabolites. A prioritization strategy was proposed to focus on the elimination of unknown and uncommon metabolites first using the designed bias filters and to prioritize the known secondary metabolites. The study employed Murraya paniculata root for the identification of new metabolites. The LC-HRMS-generated mass list of 509 metabolites was subjected to various filters, which resulted in 93 metabolites. Subsequently, it was subjected to regular dereplication, resulting in 10 coumarins, among which 3 were identified as new. Further, chromatographic efforts led to the isolation of a new coumarin, named ghosalin (1). The structure of the new compound was established through 2D NMR and X-ray crystallography. Cytotoxicity studies revealed that ghosalin has significant cytotoxicity against cancer cell lines. The proposed prioritization strategy demonstrates an alternative way for the rapid annotation of a particular set of metabolites to isolate a new metabolite from the whole metabolome of a plant extract.

Hierarchy of Roadmap Items: Prioritization Strategy Development in Aircraft MRO Industry to Enhance Profit and Sustainability

Hierarchy of Roadmap Items: Prioritization Strategy Development in Aircraft MRO Industry to Enhance Profit and Sustainability