PLoS ONE最新文献

Social media platforms have become central channels for emotional communication, posing new challenges for fine-grained sentiment analysis due to their high contextual variability, multimodal content, and pervasive ambiguity. Traditional end-to-end sentiment models often struggle to capture compositional or conflicting emotional cues in user-generated texts. This study presents a modular multi-agent architecture for sentiment analysis, implemented with the LLaMA-3.3-70B-Instruct model and guided by system-level design principles. The framework decomposes emotion inference into three coordinated stages, perception, reasoning, and resolution, each managed by a specialized agent trained with parameter-efficient tuning strategies. A meta-agent mediates conflicting predictions through a coordination protocol based on confidence estimation and discourse consistency, enabling adaptive consensus formation. Evaluations on the GoEmotions v2, SemEval-2024, and Twitter benchmarks demonstrate that the proposed system achieves higher accuracy, robustness, and interpretability compared with existing baselines. These findings indicate that architectural decomposition combined with collaborative reasoning enhances reliability and transparency in sentiment analysis, offering a scalable pathway toward intelligent and emotionally aware computational systems.

The necropsy of an elephant represents a rare event for most veterinary pathology facilities outside of Africa and Asia. Here, we report a comprehensible, abundantly illustrated, step-by-step protocol adapted to the special technical and anatomical peculiarities of elephant necropsies with regard to the needed equipment, aspects of transmissible disease prevention and workplace safety, personnel, time efforts, as well as important elephant diseases and their zoonotic potential. Detailed instructions for dissection, macroscopic examination and sampling of all relevant organs and tissues are provided, along with checklists for preparation and smooth execution of elephant necropsies. Using the featured protocols, a complete elephant necropsy with generation of extensive samples for histological, microbiological, and molecular analyses can be performed by 10-12 persons (5-6 pathologists and 5-6 assistants) within 4-6 hours in case of an adult animal.

After rainfall-induced landslides enter the creep deformation stage, timely mitigation is often challenging, making reasonable and effective early warning critical for reducing disaster losses. This study focuses on the Tanjiawan landslide, introducing the concept of "a single rainfall process" to characterize the rainfall process affecting landslide deformation. Based on a detailed analysis of deformation characteristics such as displacement and displacement rate under rainfall, the least squares method is used to identify the "failure inflection point" and "stable inflection point" on the "step-like" deformation curve to determine the accelerated deformation interval. This approach further establishes the antecedent rainfall threshold (Pe), current rainfall (P), and displacement rate threshold (V). Subsequently, a refined dynamic early warning model is developed by integrating the function F(V, P, Pe) with a Logistic regression model. The findings indicate: (1) The deformation of the Tanjiawan landslide is closely correlated with rainfall processes, with cumulative displacement curves exhibiting distinct "step-like" characteristics and displacement rates showing a "lagged attenuation" phenomenon. (2) Finer monitoring cycles enable more precise capture of dynamic landslide deformation, resulting in more reliable displacement rate thresholds. (3) The landslide early warning model can dynamically adjust monitoring cycles based on the evolutionary characteristics of deformation stages, achieving adaptive monitoring optimization.

Cardiovascular diseases (CVDs) are leading causes of morbidity and mortality globally, with a growing burden in low- and middle-income countries such as Ethiopia. Early detection is limited by resource constraints, low screening uptake, and a lack of predictive tools tailored to local healthcare systems. This study presents an interpretable ensemble machine learning framework for predicting CVD risk via structured electronic medical record (EMR) data from public hospitals in Addis Ababa. We trained an XGBoost classifier on 20,960 anonymized records containing demographic, clinical, and physiological attributes. Preprocessing involves handling missing values, outlier capping, one-hot encoding, rare-category grouping, and dimensionality reduction. SHapley additive explanations (SHAPs) were used for feature attribution, and a large language model (Gemini) was used to translate SHAP outputs into plain-language narratives to enhance interpretability. The model achieved an accuracy of 0.99, with strong precision (0.99), recall (0.98), and F1-scores across both classes. SHAP analysis identified general_plan, history of present illness (HPI), musculoskeletal system (MSS) and diagnosis as key predictors. The integration of SHAP and LLMs provided transparent, clinician-friendly insights into model outputs, supporting adoption in resource-limited settings. This study demonstrates that combining ensemble learning with explainability techniques can yield highly accurate and interpretable CVD prediction models, offering potential for integration into clinical decision-support systems in Ethiopia.

Protected areas are the most prevalent strategy to mitigate biodiversity loss and deforestation, especially in biodiversity hotspots like Madagascar. This pre-analysis plan outlines the data, methods, and identification strategy that will be used to assess the impact on deforestation of terrestrial protected areas created in Madagascar between 2002 and 2022. We will employ coarsened exact matching and difference-in-differences methods to evaluate forest cover loss, leveraging 24 years of high-resolution satellite data. We will incorporate buffer zones to assess spillover and leakage effects. This study addresses limitations of previous ones by combining accurate protected area delimitation and characteristics, a longer temporal coverage, improved characterization of the diverse forest ecosystems, and state-of-the art econometric methods. We will then assess heterogeneity of conservation effects, to better understand the determinants of protected area effectiveness.

Purpose/objective: Despite legal mandates to provide appropriate accommodations for students with disabilities in the U.S., many report gaps between what they need and receive. This study examined the role of disability level, visibility, and demographic factors in predicting unmet academic accommodation needs and attitudes toward requesting accommodations.

Research method/design: A sample of 409 adults who had had disabilities during their school years and who still had them completed an online survey assessing their current disability level, current disability visibility (invisible, semi-visible, or visible), unmet academic accommodation needs across all levels of schooling in aggregate, and current attitudes toward requesting academic accommodations.

Results: Individuals with invisible disabilities in comparison to those with semi-visible or visible disabilities reported unmet needs for having a quiet or sensory room, extended time to take tests and exams, sensory objects (e.g., fidget toys), and an Individualized Education Plan. However, those with visible and semi-visible disabilities reported unmet need for having an educational assistant or tutor, recording equipment or a portable notetaking device, a modified or adapted course curriculum, and a computer, laptop or tablet with specialized software or apps. After controlling for disability severity and demographic variables, individuals with more visible disabilities had lower unmet academic accommodation needs compared to those with an invisible disability, as well as more positive attitudes toward requesting accommodations.

Conclusion/implications: Assisting students with disabilities-especially those with invisible disabilities-may enhance disabled students' experience of academic accommodations and empower them to advocate when those needs are unmet.

In eukaryotic species, RNA silencing is a conserved mechanism for controlling gene expression. The RNA-dependent RNA polymerase (RDR), argonaute (AGO), and dicer-like (DCL) proteins are essential for RNA silencing. The RNA interference (RNAi) system regulates eukaryotic gene expression throughout growth, development, and stress response. It is also closely linked the post-transcriptional gene silencing (PTGS) process. The potato is one of the four major food crops and a staple meal in the world that has a great potential to combat global malnutrition. However, no genome-wide analysis of the silencing gene family has yet to be conducted in the economically significant plant potato. In this study, we identified 29 (6 StDCL, 14 StAGO and 9 StRDR) candidate genes in potato. These genes correspond to the Arabidopsis thaliana RNAi silencing genes. The analysis of the conserved domain, motif, and gene structure for StDCL, StAGO, and StRDR genes showed higher homogeneity within the same gene family. The Gene Ontology (GO) enrichment analysis exhibited that the identified RNAi genes could be involved in RNA silencing and associated metabolic pathways. A number of important transcription factors (TFs), BBR-BPC, bHLH, bZIP, C2H2, Dof, ERF, MIKC MADS, WRKY families, were identified by network and sub-network analyses between TFs and candidate RNAi gene families. Furthermore, the cis-acting regulatory elements (CREs) related to light, stress and hormone responsive functions and tissue-specific expression were identified in candidate genes. These genome wide analyses of these RNAi gene families provide valuable information related to RNA silencing which might be helpful for potato improvement in the breeding program.

Introduction: Mortality from advanced HIV disease (AHD) remains high and current strategies to promptly test people eligible for AHD screening, are insufficient. Task sharing for point of care (POC) testing utilizing lay health workers (LHW) is recommended, however it is marginally practised in many countries. This study sought to describe the feasibility and testing outcomes of task-shared implementation of the AHD POC diagnostic tests utilizing LHW and professional health care workers (HCW).

Methods: This was a cross-sectional mixed-methods implementation study in seven primary and three secondary health facilities, in Mozambique and Democratic Republic of Congo (DRC). From March to November 2022, consenting HIV positive adults eligible for AHD screening, were offered Visitect CD4 lateral flow assay (LFA), and or subsequently urinary Mycobacterium tuberculosis lipoarabinomannan antigen (TB LAM) and cryptococcal antigen (CrAg) tests. The primary study outcome was the proportion of testers (LHW and HCW) who found it 'easy' to integrate the 3 POC tests within their routine work together with their opinions on the value of task shared AHD POC testing.

Results: A total of 1542 patients were screened for AHD by 35 LHW (34 counsellors, and 1 lay educator) and 45 professional HCW (28 nurses and 9 clinical officers and 8 doctors). In the study period, LHW conducted a median number of 27 [IQR: 16-34] Visitect CD4 LFA tests, whereas nurses, clinical officers together with doctors conducted 19 and 11 respectively. Visitect CD4 LFA increased CD4 testing by 10.7% in Munhava (Beira) and 22.9% in CHK (DRC), complementing existing CD4 testing instruments. Among testers who completed the feasibility survey, nearly sixty percent of testers (25/42; LHW in particular) found it easy to integrate AHD POC testing within their routine workflow. The prevalence of AHD was 39.2% (604/1542). A total of 34% (146/430) and 5.4% (22/407) of patients tested positive for urine TB LAM and plasma CrAg respectively. Of these, 82.2% (120/146) and 36.4% (8/22) had a documented therapeutic intervention. The median time for completing the Visitect CD4 LFA and conveying the results to the clinician was 59 minutes [IQR: 48-71].

Conclusion: Task-shared integrated testing for AHD at POC among LHW and professional HCW, is feasible and can improve access to AHD testing. However, as POC testing responsibilities become shared, documentation of testing activities could increase in complexity and can be easily fragmented, especially when there is limited supervision. Nevertheless, LHW are well suited for POC testing due to limited availability and higher clinical workload of other HCW.

Health-related quality of life (HRQoL) reflects physical and mental well-being and is increasingly important in aging populations, yet traditional approaches often fail to capture the complex causal pathways among its determinants. We analyzed 2,566 adults aged 50-81 years from the Korean Genome and Epidemiology Study using the Short Form-12 (Physical Component Summary [PCS] and Mental Component Summary [MCS]). A causal Bayesian network was learned using the PC algorithm of Spirtes and Glymour with hierarchical constraints to ensure causal interpretability. We then estimated the causal effects of each variable on tail-defined outcomes-poor (bottom quartile) and good (top quartile) PCS and MCS-and quantified pairwise interaction effects. The network revealed how upstream factors propagate through direct and indirect pathways to shape HRQoL. Notably, PCS and MCS shared common upstream causes but showed no direct causal connection. Quantifying these causal pathways through relative risk (RR) estimates revealed the magnitude of individual factor effects. For poor PCS, severe insomnia (RR = 1.98), high stress (RR = 1.45), low physical activity (RR = 1.39), and multimorbidity (RR = 1.36) were the principal risk factors. For poor MCS, high stress (RR = 3.28) and severe insomnia (RR = 2.72) dominated. Notably, low BMI increased poor MCS risk (RR = 1.20), consistent with frailty pathways. The patterns for good outcomes largely mirrored these findings, with favorable levels showing protective effects. Interaction analyses revealed substantial synergistic effects: severe insomnia with high stress increased poor MCS probability by 6.44 percentage points (pp) beyond additivity, while high stress with physical inactivity added 4.77 pp. For good MCS, low insomnia with low stress (+4.72 pp) and low BMI with exercise (+4.21 pp) showed synergy, whereas stress with inactivity exhibited antagonism (-4.00 pp). These results support integrated interventions that combine sleep improvement, stress reduction, physical activity promotion, and multimorbidity management to improve HRQoL in aging populations.

This study examines the online information-seeking behavior of international students in the United States. Following the onset of COVID-19, their need for timely and relevant information becomes critical. Despite greater challenges than domestic students, limited research explores how international students use online platforms to meet their unique information needs. With online communities being essential sources of information and bridges for online social capital, our study analyzes the r/f1visa subreddit to examine international students' information-seeking patterns before and during the COVID-19 pandemic. Additionally, we identify unmet information needs through members' interactions and recurring questions. Our analysis reveals a shift in topics, with pandemic discussions focusing on travel, financial difficulties, and entry concerns, while pre-pandemic conversations primarily about employment. The increased similarity among recurring questions during the pandemic suggests a convergence of shared struggles that fosters solidarity and emotional support, even as many informational needs remain inadequately addressed. By examining international students' information needs through the theoretical lens of online social capital, this study contributes to understanding how crisis conditions reshape the dynamics of online communities, blurring traditional distinctions between bonding and bridging capital. The findings can inform universities, policymakers, and online community designers in developing more responsive and inclusive information environments that recognize both the instrumental and emotional support functions of digital platforms for international students.