Browse Articles

A multilevel network approach to revealing patterns of online political selective exposure

Selective exposure, individuals’ inclination to seek out information that supports their beliefs while avoiding information that contradicts them, plays an important role in the emergence of polarization and echo chambers. In the political domain, selective exposure is usually measured on a left-right ideology scale, ignoring finer details. To bridge the gap, this work introduces a multilevel analysis framework based on a multi-scale community detection approach. To test this approach, we combine survey and Twitter/X data collected during the 2022 Brazilian Presidential Election and investigate selective exposure patterns among survey respondents in their choices of whom to follow. We construct a bipartite network connecting survey respondents with political influencers and project it onto the influencer nodes. Applying multi-scale community detection to this projection uncovers a hierarchical clustering of political influencers, where each cluster is more frequently co-followed by a specific subgroup of survey respondents compared to others. Different indices of selective exposure, such as Community Overlap, Identity Diversity, Information Diversity, Structural Integration, and Connectivity Inequality, suggest that the characteristics of the influencer communities engaged by survey respondents vary with the level of community resolution. This finding indicates that online political selective exposure exhibits a more complex structure than a mere left-right dichotomy. Moreover, depending on the resolution level we consider, we find different associations between network indices of exposure patterns and 189 individual attributes of the survey respondents. For example, at finer levels, survey respondents’ Community Overlap is associated with several factors, such as ideological position, demographics, news consumption frequency, and incivility perception. In comparison, only their ideological position is a significant factor at coarser levels. Our work demonstrates that measuring selective exposure at a single level, such as left and right, misses important information necessary to capture this phenomenon correctly.

“If we lose it, we are worried”: Individual and provider level perceptions towards weight change among people living with HIV who undergo TB screening in routine health care settings in Gauteng Province, South Africa

Background HIV weakens the immune system, increasing the risk of tuberculosis (TB) in people with living HIV (PLHIV). People living with HIV and on antiretroviral treatment (ART) often experience physical body size changes. Studies have found a significant discrepancy between PLHIV’s self-reported weight loss and their measured weight loss when being screened for TB using the WHO tool. To understand this inconsistency, a qualitative sub-study was conducted to explore perceptions and attitudes towards weight change among adults attending HIV care, as well as health care workers in public clinics in Gauteng, South Africa. Methods Our qualitative study was nested within the XPHACTOR study. A total of seven focus group discussions were conducted, five with adult participants attending for HIV care and two with health care workers and research staff in clinics around Gauteng. Inductive thematic analysis was used to analyse the data. Findings The majority of PLHIV preferred to gain weight due to fear of stigma associated with weight loss. Weight loss is associated with HIV/AIDS, suggesting that people attending HIV care may underreport weight loss in the context of a TB symptoms screening tool because they fear stigma. Participants reported that weight changes impacted their daily lives and had psychological effects on them. Some PLHIV described lipodystrophy as disproportional weight gain. Culture and media have an influence on the perception of ideal body size and shape for both men and women. Conclusions Underreporting weight loss might result in poor sensitivity of the WHO TB screening tool and suggests that we need either alternative ways to determine weight loss or screening tools for TB that are less dependent on reported symptoms.

Trends and geographic patterns of overweight and obesity among Tanzanian adults: Evidence from the 2010–2022 Demographic and Health Surveys

Overweight and obesity are risk factors for several non-communicable diseases. In Tanzania, despite the increasing public health concern, detailed spatial information on the distribution of overweight and obesity is limited. This study aimed to determine the prevalence, show spatial and temporal variations and identify factors that impact overweight and obesity in Tanzania. We used cross-sectional survey secondary data from the Tanzanian Demographic and Health Surveys (DHS) which collected anthropometric measurements in women aged 15−49 years in 2010, and 2015−16, and both women and men in the 2022 survey. Spatial interpolation was performed to estimate prevalence at unsampled locations, while generalized additive models were used to identify factors and assess their effect on the spatial distribution of overweight and obesity risk. The study included 33,787 participants (9,029 in 2010, 11,940 in 2015−16, and 12,818 in 2022). The overall mean age was 29 (SD = 10) years. The prevalence of overweight and obesity among women increased by 45.45%, rising from 22% in 2010 to 32% in 2022, with higher rates observed in urban areas and among wealthier and more educated women. In 2022, women were disproportionately affected, with 32% being overweight or obese compared to 15% of men, and 45% were urban women and 23% urban men. Age and wealth index were consistent significant factors across all surveys while place of residence was a significant factor in 2010 and 2015. Geographic disparities were evident, with the eastern, southern highlands, and northern regions showing higher prevalence compared to the lake zone. Overweight and obesity are increasing in Tanzania, driven by wealth and age. Urban residence was a significant factor in early years and its influence declined in 2022. The observed regional disparities highlight the urgent need for targeted and multi-sectoral interventions.

Retraction: Evaluation of chemopreventive effects of Acanthus ilicifolius against azoxymethane-induced aberrant crypt foci in the rat colon

Surface protein glycosylation conserved in the human pathogen Mycoplasma genitalium and retained in the synthetic organism JCVI-Syn3A

Protein glycosylation has been reported in all forms of life. The genus Mycoplasma is composed of highly genome-streamlined bacterial symbionts, making them model organisms for investigating minimal genome concepts. Previous work from our group showed mycoplasmas scavenge hexoses from exogenous oligosaccharides to glycosylate surface proteins at serine, threonine, asparagine, and glutamine residues without utilizing a consensus sequence as seen in canonical glycosylation systems. We report here that this surface protein hexosylation system is conserved in Mycoplasma genitalium, a human urogenital pathogen with a 580-kbp genome that can be cultured axenically. We also report this modification is found in the ruminant pathogen Mycoplasma mycoides subsp. capri and is conserved in JCVI-Syn3A, a nonpathogenic mycoplasma with a synthetic minimal M. mycoides genome containing genes that are essential for survival and robust growth under axenic culture conditions. In contrast to known glycoproteins, we have detected evidence of glycosylation of aspartic acid and glutamic acid residues, which expands the pool of potential glycosyl acceptors in bacteria to include the acidic amino acids.

Risk factors for abdominal aortic aneurysm in general populations: A systematic review and meta-analysis

Background Abdominal aortic aneurysm (AAA) has a high mortality rate after rupture. This study systematically explored the risk factors associated with AAA in the general population using a meta-analytic approach. Materials and methods We conducted a systematic search of PubMed, Embase, and Cochrane Library databases to identify relevant literature. The search was conducted through May 2025. Factors considered in more than three studies were included in the analysis. Odds ratios (ORs) with 95% confidence intervals (CIs) were used as effect estimates and all pooled analyses were performed using a random-effects model. Results Thirty-four studies reporting 34,551 AAA cases were selected for meta-analysis. Increased risk of AAA was associated with male (OR: 3.78; 95% CI: 2.80–5.10; P < 0.001), current or ever smoking (OR: 3.39; 95% CI: 2.57–4.48; P < 0.001), hypertension (OR: 1.31; 95% CI: 1.21–1.42; P < 0.001), dyslipidemia (OR: 1.33; 95% CI: 1.24–1.43; P < 0.001), coronary artery disease (OR: 1.81; 95% CI: 1.66–1.98; P < 0.001), cerebrovascular disease (OR: 1.32; 95% CI: 1.18–1.48; P < 0.001), peripheral vascular disease (OR: 1.67; 95% CI: 1.47–1.91; P < 0.001), chronic obstructive pulmonary disease (OR: 1.58; 95% CI: 1.31–1.90; P < 0.001), renal disease (OR: 1.91; 95% CI: 1.28–2.83; P = 0.001) and family history of AAA (OR: 2.26; 95% CI: 1.58–3.25; P < 0.001). However, diabetes mellitus was associated with a reduced risk of AAA (OR: 0.84; 95% CI: 0.74–0.95; P = 0.007). Furthermore, the risk of AAA was not affected by advanced age, alcohol intake, cancer, being overweight, or physical activity. The association between AAA risk and sex, smoking, hypertension, diabetes mellitus, renal disease, and a family history of AAA differs between Eastern and Western countries. Conclusions We systematically explored the risk factors for AAA. AAA represent a significant public health concern. Thus, early intervention and health education targeting these risk factors are necessary to prevent their occurrence. Registration: INPLASY2023120024

Human papillomavirus, sexually transmitted infections, and antimicrobial resistance in West Africa: Estimating population burden and understanding exposures to accelerate vaccine impact and drive new interventions: The PHASE survey protocol

Human papillomavirus (HPV) infection is a primary cause of preventable deaths from cervical cancer, a condition of profound inequality with approximately 90% of deaths occurring in low- and middle-income countries, particularly in sub-Saharan Africa. In May 2018, the WHO Director-General declared a Joint Global Commitment to Cervical Cancer Elimination, highlighting the critical role of HPV vaccines in achieving this goal. However, there is a lack of systemically collected data on HPV prevalence in The Gambia, and impact data from high-income countries may not be reliably extrapolated to West African settings due to geographical variation in HPV types and distinct behavioural, biological, and sociodemographic exposures. The Gambia introduced a two-dose HPV vaccination schedule in 2019, but coverage has been very low, interrupted mainly by the COVID-19 pandemic. This presents a key opportunity to generate vital baseline data on HPV prevalence in the population before potential scale-up of vaccination efforts. The PHASE survey, a multi-stage cluster survey, aims to establish the baseline, population prevalence estimates of high-risk and low-risk, vaccine-type and non-vaccine-type HPV infection in 15- to 49-year-old females in The Gambia by measuring urinary HPV-DNA. The survey will also quantify the effects of various exposures on HPV prevalence, including sexual behaviour, the presence of other sexually-transmitted infections (STIs) - Neisseria gonorrhoea (NG), Chlamydia trachomatis (CT), Trichomonas vaginalis (TV), Mycoplasma genitalium (MG), syphilis, as well as blood borne viruses, human immunodeficiency virus (HIV), hepatitis B and hepatitis C; obstetric history, socio-demographic characteristics, and cervical cancer screening and/or treatment. Additionally, the study will provide important antimicrobial resistance (AMR) data for NG and MG in sub-Saharan Africa, a region poorly represented in global surveillance programs. This data is needed to guide regional treatment guidelines and advocate for new solutions, including gonococcal vaccines. The AMR data are expected to immediately influence recommendations regarding the appropriate choice of antibiotics for syndromic STI management in West Africa and hence to address an important driver of AMR in the sub-region. Leveraging on the Medical Research Council Unit The Gambia funded Health Demographic Surveillance system (HDSS) as its sampling frame, the survey will utilize validated diagnostic assays and culturally sensitive data collection methods, to ensure both scientific rigor and local relevance. Tools such as Audio Computer-Assisted Self-Interviewing (ACASI) technology, developed in consultation with local community advisory boards, are included to reduce social desirability bias in reporting sexual behaviour. This approach aims to maximize both the reliability and cultural appropriateness of the findings. This study directly addresses the critical need for baseline epidemiological data on HPV in a West African setting to accelerate vaccine impact and drive new interventions towards cervical cancer elimination. By understanding other factors that influence HPV (like other STIs, sexual behaviour, etc.), the study aims to ensure that, when the vaccine’s impact is measured later, changes in other confounding factors that may impact on HPV prevalence can be accounted for. The study will also establish the population prevalence of the measured STIs and their relationship to common symptoms and other adverse health outcomes related to STIs.

Household resilience and its role in sustaining food security in rural Bangladesh

Food insecurity and agriculture in South Asia, including Bangladesh, pose significant threats to the well-being and livelihoods of its people. Building adaptive capacities and resilient food systems is crucial for sustainable livelihoods. This study employs the Resilience Index Measurement and Analysis II framework to construct a Resilience Capacity Index (RCI) and analyze its relationship with food security using data from the Bangladesh Integrated Household Survey 2018. The study applies Exploratory Factor Analysis and Structural Equation Modeling to examine the impact of key resilience components such as Access to Basic Services, Adaptive Capacity, and Assets on household resilience. The findings reveal that access to basic services, land assets, and farm equipment positively influences households’ resilience capacity. However, the presence of livestock assets has a negative impact, potentially due to market volatility, climate vulnerability, and disease outbreaks. Additionally, adaptive capacity has a positive but insignificant influence on RCI, suggesting that without enhancing economic opportunities, institutional support, and inclusive development strategies, adaptive capacity could not be enough to foster resilience. However, resilient capacity enhances food security metrics such as the Food Consumption Score and Expenditure. These findings underscore the importance of policies that focus on increasing and maintaining access to basic services, promoting sustainable land management practices, and strengthening social safety nets. This study emphasizes the importance of focusing on livestock assets to ensure their sustainability by stabilizing the livestock market, improving veterinary services, and providing subsidies to reduce maintenance costs.

An evaluation of modifications to NHS Health Checks: A study protocol

Cardiovascular disease (CVD) is the leading cause of death globally; in the UK it contributes to a quarter of all deaths. In 2009, the UK National Health Service launched the NHS Health Check (NHSHC) programme to address CVD by assessing all adults aged between 40 and 74 years for CVD risk factors. Encouraging uptake of NHSHCs has proved challenging and areas for development have been identified nationally, prompting modifications to NHSHC practice in some localities. This protocol article describes a programme of research that will evaluate the impact of NHSHC modifications on attendance and outcomes in a large English local authority area. Modifications to NHSHC delivery (delivery of a modified NHSHC by a healthy lifestyle service rather than via general practice led delivery) and NHSHC invitation processes (implementation of text message prompts and reminders and an additional online booking option) will be evaluated. The research consists of six workstreams within a mixed methods framework: 1) Quantitative analysis of client NHSHCs records; 2) Focus groups with healthy lifestyle staff involved in coordinating and delivering the modified NHSHCs sessions; 3) Interviews with staff from pilot GP practices whose roles involve the coordination or delivery of NHSHCs; 4) Interviews with clients who have attended a healthy lifestyle service NHSHC; 5) Health economic resource and cost evaluation; 6) Data analysis, synthesis, and dissemination. The research programme’s breadth and its novel nature, mean that it will provide valuable findings for those commissioning and delivering NHSHCs nationally, and for the wider public health community.

Persistence of a declining anuran species across its distribution

Information on a species’ population dynamics, such as changes in abundance and distribution, can be used to identify declining populations and initiate conservation efforts and protections. For the Ornate Chorus Frog (Pseudacris ornata), anecdotal observations of local extirpation and population declines have been noted, but trends in its range-wide population status are generally unknown. We used 2227 verified records of Ornate Chorus Frog presence from across the species’ distribution, grouped into 407 populations, and a modified Cormack-Jolly-Seber survival analysis to estimate the probability that historical Ornate Chorus Frog populations persist in the year 2024. Our results suggested that > 36% of historical Ornate Chorus Frog populations are possibly extirpated (probability of persistence < 0.5) and that 33% of populations had a probability of persistence > 0.9. Many of these extant populations occurred in northwestern Florida, southeastern Alabama, and southern Georgia, USA. The probability of persistence was positively influenced by habitat suitability and mean winter precipitation and negatively influenced by urban imperviousness. Ornate Chorus Frogs in protected areas had a higher average probability of persistence compared to populations that were not in protected areas. Our study fills a knowledge gap by identifying regions where Ornate Chorus Frog populations are likely thriving and regions where they may be extinct.

Genetic epidemiology of moyamoya disease and CADASIL in over 120,000 healthy Korean individuals: Insights into cerebrovascular disorders

Background East Asia has one of the highest global stroke burdens. Genetically, moyamoya disease (MMD) and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) contribute to this burden. This study investigated the allele frequencies and estimated the genetic prevalence of RNF213 and NOTCH3 variants in a large Korean population. Methods Between July 2021 and August 2024, 129,933 individuals who underwent health checkups were included. RNF213 p.Arg4810Lys and three NOTCH3 variants, p.Arg544Cys, p.Arg640Cys, and p.Arg75Pro, were analyzed using next-generation sequencing. Allele frequencies were calculated, and genetic prevalence was estimated using the Hardy-Weinberg equilibrium. Results The allele frequency of RNF213 p.Arg4810Lys was 1.08%, with 13 homozygotes. The estimated genetic prevalence of MMD was 1 in 47 individuals. For NOTCH3, the allele frequencies were 0.07% for p.Arg544Cys, 0.07% for p.Arg640Cys, and 0.04% for p.Arg75Pro, corresponding to an estimated genetic prevalence of CADASIL of 1 in 277 individuals. Eight individuals carried both the RNF213 and NOTCH3 variants. Conclusion This large-scale study highlights the substantial genetic burden of RNF213 and NOTCH3 variants in Koreans, which partially contributes to the high stroke burden in East Asia. These findings emphasize the importance of population-specific genetic studies to optimize cerebrovascular disorder management in East Asia.

Causal mapping of psychological and occupational risk factors for suicidal ideation in psychiatric nurses using Bayesian networks: A multicenter cross-sectional study

Psychiatric nurses represent a high-stress occupational group that experiences elevated levels of suicidal ideation (SI), emphasizing the need for focused mental health interventions. The main purpose of this study was to examine the prevalence of SI among psychiatric nurses and to identify the psychological and occupational factors associated with SI. A total of 1,835 psychiatric nurses completed questionnaires on depressive symptoms (PHQ-9), SI, quality of work-related life (QWL), and burnout. Multivariate logistic regression and phenotypic network analyses were conducted to identify factors associated with SI and the potential pathways linking depressive symptoms, burnout, and QWL to SI. The results indicated that 11.33% of the participants had SI in the past two weeks. Multivariate logistic regression revealed that emotional exhaustion, depersonalization, personal accomplishment, stress at work, general well-being, and the home-work interface were significant predictors of SI. Network analysis further revealed that psychomotor changes, guilt, sad mood, low energy, and appetite changes were the symptoms most directly associated with SI. In addition, sad mood, general well-being, and work-home interface were linked to job and career satisfaction, whereas sad mood and low energy were associated with emotional exhaustion and SI. These findings contribute valuable large-scale evidence on the mental health challenges faced by psychiatric nurses and highlight the importance of addressing mood disturbances, energy loss, and work-related stress in SI prevention efforts for this vulnerable group.

Personality traits, panel tenure, survey topic, and context as predictors of survey nonresponse patterns in high-frequency online longitudinal surveys

An extensive literature studies the relation between demographic and socio-economic characteristics and attrition in longitudinal studies. In this study, we analyze the independent effects of non-demographic variables—respondent personality traits, panel tenure, and survey topics, using unique datasets from two recently completed high-frequency online longitudinal studies conducted in the U.S. We used latent class analysis to group respondents into various classes based on similarities in their nonresponse patterns across all survey waves, which revealed substantial variations in patterns of nonresponse. Our results indicate that respondent personality traits were strong predictors of nonresponse patterns. Specifically, conscientiousness is positively associated with a lower likelihood of wave nonresponses. In contrast, more open, extroverted, neurotic, and agreeable respondents are more likely to exhibit higher wave nonresponses, but with effect sizes smaller than that of conscientiousness. We found no significant demographic effects on wave nonresponse in one of the studies focused on aging and well-being. However, in the study primarily focused on COVID-19-related topics conducted during the pandemic, we found a few significant demographic effects. Collectively, our findings suggest that personality traits may play a more significant role than conventional demographic and household variables in predicting nonresponse patterns in high frequency (at least one survey per month) online surveys.

CQ-CNN: A lightweight hybrid classical–quantum convolutional neural network for Alzheimer’s disease detection using 3D structural brain MRI

The automatic detection of Alzheimer’s disease (AD) using 3D volumetric MRI data is a complex, multi-domain challenge that has traditionally been addressed by training classical convolutional neural networks (CNNs). With the rise of quantum computing and its potential to replace classical systems in the future, there is a growing need to: (i) develop automated systems for AD detection that run on quantum computers, (ii) explore the capabilities of current-generation classical-quantum architectures, and (iii) identify their potential limitations and advantages. To reduce the complexity of multi-domain expertise while addressing the emerging demands of quantum-based automated systems, our contribution in this paper is twofold. First, we introduce a simple preprocessing framework that converts 3D MRI volumetric data into 2D slices. Second, we propose CQ-CNN, a parameterized quantum circuit (PQC)-based lightweight hybrid classical-quantum convolutional neural network that leverages the computational capabilities of both classical and quantum systems. Our experiments on the OASIS-2 dataset reveal a significant limitation in current hybrid classical-quantum architectures, as they face difficulties converging when class images are highly similar, such as between moderate dementia and non-dementia classes of AD, which leads to gradient failure and optimization stagnation. However, when convergence is achieved, the quantum model demonstrates a promising quantum advantage by attaining state-of-the-art accuracy with far fewer parameters than classical models. For instance, our β8-3-qubit model achieves 97.5% accuracy using only 13.7K parameters (0.05 MB), which is 5.67% higher than a classical model with the same parameter count. Nevertheless, our results highlight the need for improved quantum optimization methods to support the practical deployment of hybrid classical-quantum models in AD detection and related medical imaging tasks.

The impact of coal miners’ emotions on unsafe behavior: A study on the mediated effects with a moderating role

This study aims to reveal how emotions influence Unsafe Behavior among coal miners, addressing the increasingly severe safety issues in the coal mining industry. As the complexity of mining environments grows, the impact of workers’ emotional states on safety behaviors has garnered widespread attention. Based on emotion regulation theory and psychological mediation models, we analyzed survey data from 250 workers across multiple subsidiary mining units of a single coal mining enterprise in China, employing regression analysis and Bootstrap methods to examine the relationships among emotions, unsafe psychological states, and Unsafe Behavior. Additionally, we introduced safety climate as a moderating variable to enhance the explanatory power of the model. The results indicate that positive emotions significantly reduce the occurrence of Unsafe Behavior by lowering unsafe psychological states, whereas negative emotions significantly increase Unsafe Behavior by enhancing unsafe psychological states. Furthermore, unsafe psychological states play a mediating role between emotions and Unsafe Behavior, highlighting the crucial role of psychological factors in the emotional influence on behavior. Further analysis shows that safety climate moderates the relationship between negative emotions and unsafe psychological states. Specifically, under a high safety climate, the impact of negative emotions on unsafe psychological states is weakened. This study provides theoretical support and practical reference for safety management in the coal mining industry, offers empirical evidence for the development of emotional regulation and psychological intervention strategies, and emphasizes the importance of fostering a favorable safety climate.

Comparative maxicircle analysis in Trypanosoma species from the LSRM clade highlights patterns in an underexplored lineage

Trypanosoma lainsoni, Trypanosoma platydactyli, and Trypanosoma scelopori are kinetoplastid flagellates belonging to the LSRM clade of the genus Trypanosoma. These parasites have a unique mitochondrial DNA structure, the kinetoplast, comprising catenated maxicircles and minicircles. However, genetic information on the kinetoplasts of these species remains unknown. In this study, we assembled and analyzed maxicircles from different isolates of T. lainsoni, T. platydactyli, and T. scelopori to address the current gap in genetic information about the LSRM clade and explore their phylogenetic relationships within the Trypanosoma genus. The maxicircle of T. lainsoni isolate Le29, including the coding and divergent regions, was de novo assembled combining Illumina and Oxford Nanopore Technologies, revealing a length of 49,306 bp. Additional isolates of T. lainsoni (Ca37 and Ca47), as well as T. platydactyli and T. scelopori, were sequenced with Illumina, yielding complete coding regions and partial divergent regions for all. As in other trypanosomes, coding regions exhibited conserved synteny in gene order and RNA editing patterns. We found that mRNA editing by U-insertion was higher in T. lainsoni than in T. cruzi, suggesting a partial loss of editing in the latter. Phylogenetic analyses based on the coding region positioned the LSRM clade alongside the Aquatic clade as one of the most ancestral groups within non-salivarian trypanosomes and supported the ancestral position of the Trypanosoma brucei clade, contrasting with previous reports. Finally, analysis of the maxicircle divergent region suggests a gradual transition from long to short repeat structures in non-salivarian trypanosomes. This study provides the first characterization of the T. lainsoni maxicircle and related LSRM clade species. These findings provide new insights into the ancestral relationships within the group, the evolution of the divergent region of the maxicircles and propose RNA editing has been evolving within the genus.

Analysis and optimization of student learning paths based on CRNN and sequential data

The analysis and optimization of student learning paths have become increasingly critical in modern education, as they enable personalized learning experiences and improved academic outcomes. However, existing approaches often struggle to effectively model the temporal dynamics and knowledge point relationships inherent in learning behaviors. To address this challenge, this study proposes a novel framework that integrates Convolutional Recurrent Neural Networks (CRNN) for sequential feature extraction, Transformer models for knowledge point association modeling, and Reinforcement Learning (RL) for dynamic path optimization. Experimental results demonstrate significant improvements in learning completion rates (15% increase) and test scores (12% improvement) compared to baseline methods. The findings highlight that the integration of CRNN, Transformer, and RL provides a robust and scalable solution for personalized learning path analysis, offering actionable insights and adaptive recommendations to enhance student learning experiences and outcomes. This framework not only advances the field of learning analytics but also paves the way for more effective and inclusive educational technologies.

Pollen assemblages and distribution characteristics in surface sediments of karst caves on the Guizhou Plateau, southwestern China

Cave sediments commonly contain crucial sedimentary evidence of past environmental change and human activity. Fossil pollen sequences within these deposits hold great potential for reconstructing paleoenvironmental changes and human–environment relationships; however, the extent to which cave pollen faithfully reflects the external environment remains controversial, especially in complex cave systems. This study conducted pollen analysis in surface sediment samples from two karst caves with complex geometry on the Guizhou Plateau. The pollen assemblages in surface sediment within 5–15 m of the entrance of complex caves with multiple entrances and passages or a single entrance and multiple chambers were highly similar to those of external surface soil/fresh moss samples and they exhibited strong correlations, indicating a good representation of external vegetation. Additionally, the high pollen concentration in the area made it an ideal sampling area for pollen analysis. In the middle-to-rear parts of the caves, although improved ventilation enhanced the representation of pollen assemblages for external vegetation, pollen concentrations were significantly lower than those near the entrance. This necessitates careful selection of samples for pollen analysis from cave sediments. Moreover, humid cave environments, animal transportation, and plant growth within the cave may lead to spatial heterogeneity in pollen assemblages, thereby affecting their representativeness of the external environment. The present study provides theoretical references for understanding the relationship between cave pollen assemblages and the external environment and offers important evidence for future archaeological and paleoenvironmental reconstruction studies in this region using cave pollen.

Mapping unconventional Leishmania in human and animal leishmaniasis: A scoping review protocol on pathogen diversity, geographic distribution and knowledge gaps

Introduction Leishmaniases are a vector-borne parasitic diseases with diverse clinical manifestations involving multiple Leishmania species and animal hosts. While most leishmaniasis cases are caused by a few well characterized Leishmania species, reports describe infections by unconventional or emerging Leishmania taxa, atypical clinical presentations from classical species, and occurrences of atypical Leishmania in animal hosts. These underrecognized infections present diagnostic and therapeutic challenges and are rarely reflected in surveillance systems or clinical guidelines. A systematic mapping of this evolving landscape is needed to guide future diagnostics, policy, and research priorities. Methods and analysis Following the Joanna Briggs Institute (JBI) methodology and PRISMA-ScR guidelines, we will search PubMed, Embase, Cochrane Library (CENTRAL), PROSPERO, Web of Science, and Global Index Medicus, as well as relevant grey literature. Eligible studies will include human cases with clinical presentations that diverge from those typically associated with well-characterized Leishmania species, reports involving unconventional or emerging Leishmania species, and animal cases of veterinary relevance caused by non-classical species, regardless of study design. Dual independent screening of records and data extraction using a standardized charting form will be conducted. Discrepancies between reviewers will be resolved by consensus. Data will be summarized descriptively through tables, figures, and thematic synthesis. Research gaps will be identified to inform future studies and public health strategies. Dissemination This review will use data from published sources and findings will be disseminated through publication in a peer-reviewed journal, presentations at scientific conferences, and sharing with relevant stakeholders. The results are intended to inform clinicians, researchers, and policymakers about the evolving landscape of leishmaniasis and to highlight priorities for future research and surveillance.

Analysis and prediction of the axial compression properties of desert sand concrete with steel tube restraint based on an improved BP neural network model

Accurate analysis and prediction of axial compression are important for ensuring the construction quality and safety of desert sand recycled aggregate concrete confined by steel tubes. In this study, the axial compressive strength and elastic modulus of recycled aggregate concrete with different sand contents, water–cement ratios, and steel constraints were tested to evaluate the effects of these factors on the axial compressive performance of the recycled aggregate concrete. It was determined that a steel tube restraint could effectively improve the ductility of desert sand recycled aggregate concrete. However, with increases in the sand content and water–cement ratio, the peak stress slightly decreased. The axial compressive strength and elastic modulus of the recycled sand aggregate concrete confined by steel tubes exhibited little change in the elastic stage under a functional load. During the initial stage of loading, the lateral strain exhibited strong discrete characteristics. In the peak stress stage, the transverse coefficient gradually increased. Overall, our analysis revealed that axial compressive performance exhibits evident engineering uncertainty under the comprehensive influence of factors such as steel constraint, desert sand content, and water–cement ratio. Therefore, an improved backpropagation (BP) neural network model of the axial compressive properties of recycled aggregate concrete with steel-tube-confined sand was established with the presence of steel constraints, desert sand content, and water–cement ratio serving as inputs, and axial compression strength and elastic modulus as outputs. Engineering verification calculations indicated that the BP neural network model can predict concrete performance under actual working conditions with a small error rate. Compared with traditional models, the neural network model has comprehensive advantages in terms of fitting accuracy, reduced overfitting, and enhanced stability.