Browse Articles

Correction: Defect induced improved capacitive performance of MnS incorporated MoO3 nanocomposite for supercapacitor electrodes in aqueous electrolytes

Correction: MICU2, a Paralog of MICU1, Resides within the Mitochondrial Uniporter Complex to Regulate Calcium Handling

Genus- and host-associated codon usage bias patterns in coronavirus spike genes

Codon usage bias (CUB) reflects the combined effects of mutational pressure and natural selection and provides insight into viral evolution and host adaptation. Although previous studies have examined CUB in individual coronaviruses or at the whole-genome level, systematic comparative analyses focusing on the spike (S) gene—an important determinant of viral evolution and host adaptation—across all four coronavirus genera including Alphacoronavirus, Betacoronavirus, Gammacoronavirus, and Deltacoronavirus , remain limited. In this study, we analyzed CUB in coronavirus spike genes across multiple genera and host groups. Codon usage indices, including codon adaptation index (CAI), effective number of codons (ENC), and GC content at the third synonymous codon position (GC3s), were evaluated alongside multivariate and clustering approaches, including correspondence analysis, hierarchical clustering, heatmap visualization, and ENC–GC3s analysis. Significant differences in CAI and ENC were observed among coronavirus genera, whereas GC3s showed no significant variation, indicating that codon usage patterns are structured primarily by phylogenetic relationships rather than nucleotide composition alone. Multivariate and clustering analyses further supported genus-level organization of codon usage profiles. In contrast, host-based comparisons showed that CAI varied significantly across host groups, while ENC and GC3s remained relatively stable, suggesting that host-associated translational selection influences codon preference without substantially altering overall codon bias strength. Heatmap analysis revealed enrichment of A/U-ending codons and underrepresentation of C/G-ending codons across coronavirus genomes, with consistent suppression of (cytosine-guanine dinucleotides) CpG-containing codons. ENC–GC3s analysis indicated that most genomes deviate from the expected neutral curve, suggesting that factors beyond mutational bias contribute to codon usage patterns. These findings indicate that codon usage bias in coronavirus spike genes is shaped by a combination of virus-intrinsic constraints and host-associated selective pressures, providing a gene-centric, cross-genera framework for understanding coronavirus evolution and host adaptation.

Fine-tuning and structured prompting strategies for question answering over full-text biomedical research articles

Objectives The ability of large language models (LLMs) to answer targeted scientific questions by synthesizing information from research articles remains an open research challenge. Methods We evaluated the effects of fine-tuning and a question-specific prompting strategy to answer 16 pre-defined questions about HIV drug resistance studies, including whether viral genetic sequences were reported and the demographics and antiviral treatments of the individuals from whom sequences were obtained. For fine-tuning, we constructed an instruction set comprising 250 HIV drug resistance studies, with 16 questions per study and corresponding answers and explanations. For question-specific prompting, we developed a set of if-then rules tailored to each question. We compared the performance of three base models – GPT-4o-mini-2024-07-18 (GPT-4o), Meta Llama-3.1-70B-Instruct (Llama-3.1-70B), and Meta Llama-3.1-8B-Instruct (Llama-3.1-8B) – with their performance using fine-tuning, question specific prompting, and fine-tuning followed by question-specific prompting. Performance was assessed using accuracy, precision, recall, and F1 score, averaged over 150 held-out studies not used for fine-tuning. Comparisons were performed using Wilcoxon signed-rank tests. Results Fine-tuning increased precision by 5% for GPT-4o, 16% for Llama-3.1-70B, and 8% for Llama-3.1-8B, although this increase reached statistical significance only for Llama-3.1-70B. Fine-tuning also significantly increased recall for GPT-4o by 11%. Question specific prompting increased recall for all three models (6% for GPT-4o, 7% for Llama-3.1-70B, and 18% for Llama-3.1-8B), with statistically significant improvements observed only for Llama-3.1-8B. Applying question specific prompting to each of the fine-tuned models did not yield additional improvements beyond fine-tuning alone. When pooled across the three models, fine-tuning was associated with a greater effect on precision than recall (OR = 4.35; p = 0.001; Fisher’s exact test), whereas question-specific prompting led to a greater effect on recall than on precision (OR= 7.09; p = 0.0001; Fisher’s exact test). Conclusions In this domain-focused proof-of-concept study, fine-tuning and question-specific prompting each led to improvement in one or more metrics for each of the three models. Pooled analyses indicated that fine-tuning improved precision, whereas question specific prompting preferentially improved recall.

FEDI-CODE: A federated and causally-informed framework for dementia risk prediction using multi-site patient data

Early detection of dementia is critical for timely intervention and disease management, yet it remains a challenging task due to the fragmented nature of healthcare data and the need for privacy-preserving solutions. This paper proposes FEDI-CODE, a Federated and Causally Informed Dementia Estimation framework that integrates deep learning, federated learning, and counterfactual inference to predict dementia risk across distributed patient data sources. FEDI-CODE is designed to operate without centralizing sensitive medical data, enabling collaborative training across institutions while preserving privacy. It combines temporal modeling of longitudinal imaging and clinical data with individualized treatment effect estimation for modifiable risk factors such as alcohol consumption, weight, and cardiovascular indicators. A fusion module aggregates representations from each site to form a global prediction head. Extensive experiments on simulated multi-site dementia datasets demonstrate that FEDI-CODE achieves an accuracy of 83.7%, a precision of 83%, a recall of 81%, an F1-score of 82%, and an AUC-ROC of 0.86, outperforming standard federated models and deep learning baselines by notable margins. The model also generalizes well to external datasets, achieving 79.2% accuracy and 0.80 AUC-ROC, confirming its robustness. Furthermore, FEDI-CODE produces interpretable causal insights by estimating individual treatment effects, offering actionable clinical value. These results highlight FEDI-CODE as a scalable, interpretable, and privacy-aware solution for early dementia screening and personalized risk assessment.

User perceptions of a point-of-use water filtration device: Qualitative, focus group study

Universal access to clean water is sixth of the United Nations sustainable development goals. It has not yet been achieved. Boiling water, for water purification, is cost prohibitive and polluting. Diarrheal diseases linked to unsafe water cause morbidity and mortality in young children in Uganda, where most if the rural population lacks access to treated water. Bacterial water contaminants include Escherichia coli and Salmonella Typhi . Community perceptions of water quality and mitigation efforts in rural Uganda is unknown. In the absence of safe drinking water, a low-cost method of water purification is needed. In vitro studies of plant xylem filtration found this technique successful in removing coliform bacteria and other pollutants. The purpose of this study was to explore perceptions of water quality impact on the lives and health of residents in a rural community in Southwestern Uganda and obtain reactions to a prototype point-of-use water filtration device to determine the possibility of implementing its use in this community. Qualitative descriptive analysis of translated transcripts of purposefully designed focus groups was conducted. Participants included 36 adult residents of a rural community in Uganda. Groups led by experienced Ugandan facilitators in the local language, no researchers from the USA were present. No participants had access to treated water, past efforts to improve water failed due to lack of follow-up. Community members were aware of the poor quality of their water and described water associated illnesses in family members. Obtaining and boiling water required time, money, and resources with households using an average of 142 liters of water/day. Response to the device was mixed, with some excited at the possibility of using it and others expressing concerns regarding durability and cost. Future efforts must address concerns about costs and durability of any point-of-use device and will require extensive planning for sustainability.

Mitochondrial dysfunction and autophagy activation underlie NK cell impairment induced by Cannabis

Cannabis use continues to rise in Canada, prompting concerns due to its potential impact on immune function. This study investigated the effect of a cannabis joint extract (CJE) on natural killer (NK) cells and explored the mechanisms underlying its potential anti-inflammatory properties. Peripheral blood mononuclear cells (PBMCs) were exposed to varying concentrations of CJE to assess cytotoxicity. Flow cytometry was employed to evaluate oxidative stress, autophagy, mitochondrial membrane potential, caspase-3 activation, and DNA damage. Additionally, NK cell cytotoxicity, migration, and adhesion were analyzed. Data indicated that CJE exposure led to dose-dependent cytotoxicity in NK cells, primarily through apoptosis. Specifically, at a concentration of 3 μg/mL, CJE significantly increased reactive oxygen species (ROS), autophagy markers, caspase activation, and DNA damage, while reducing mitochondrial membrane potential. Moreover, CJE impaired NK cell-mediated killing of HeLa cells, though their migratory and adhesive abilities were unaffected. These findings evidence that cannabis can detrimentally affect NK cell viability and function via mechanisms involving autophagy and caspase-dependent apoptosis.

Mental illness stigma and its impact on help-seeking behavior among residents of the Eastern Region, Saudi Arabia: A cross-sectional study

Background Mental illnesses are documented as a public health concern; they are a leading cause of disability and poor quality of life among individuals. Most people with mental illness do not receive treatment due to many factors, including stigma, which significantly impacts the individual’s readiness to seek help. Methods This is a cross-sectional study design conducted among individuals 18 years of age or older who lived in Al Ahsa. The data were collected through a structured questionnaire. A total of 1085 individuals participated. Descriptive statistics, chi-square test and binary logistic regression analysis were used to investigate the association and the predictors of help-seeking behavior. Results Overall, the prevalence of mental illness-related stigma was 23.1%. There was a significant association between gender and some factors associated with mental illness stigma (p < 0.05). The proportion of individuals who might not seek help with mental illnesses due to stigma was 61.0%. Multivariate analysis indicates that the individuals who had mental illness-related stigma were more likely to avoid seeking help compared to the individuals who did not have stigma (B = 0.53, OR = 1.71, 95% CI: 1.25–2.34). The young adult group aged 30–41 years was found to be more likely to seek help (B = 0.47, OR = 1.80, 95% CI: 1.01–3.20) than the other age groups. However, gender, education, employment status, and marital status were not associated with help-seeking behavior (p > 0.05). Conclusion The study indicates that the occurrences of stigma associated with mental illness was 23.1%. Approximately two-thirds of the participants in the study avoided seeking assistance for mental issues. Our multivariate analysis confirmed that the stigma related to mental illness is significantly linked to help-seeking behavior.Specifically, it was found that young adults are more likely to seek help compared to other age groups. Participants living in rural areas were less likely to seek help. These findings have significant implications for the formulation of interventions aimed at addressing mental illness stigma and promoting help-seeking behaviors.

Serum IgA and IgM levels in hemochromatosis probands with HFE p.C282Y homozygosity

Background HFE p.C282Y (rs1800562) homozygotes, including those with normal iron phenotypes, have increased risks of infection and death from infections, although serum IgA and IgM in cohorts of adults with p.C282Y homozygosity are not reported. Methods We compiled serum IgA and IgM levels at diagnosis of hemochromatosis in probands with p.C282Y homozygosity, investigated associations of IgA and IgM with clinical characteristics, blood count measures, and iron phenotypes, and compared mean IgA and IgM of probands with combined/weighted means of published adult European cohorts not selected for hemochromatosis. Results There were 73 probands (36 men, 37 women; mean age 51 ± 13 y). Fifty probands (68.5%) had human leukocyte antigen (HLA)-A*03. Mean IgA ± standard deviation [95% confidence interval] was 2.11 ± 1.06 g/L [1.87, 2.35]. Mean IgM was 1.11 ± 0.75 g/L [0.94, 1.28]. IgM was inversely associated with age (Pearson’s r 73  = –0.2733; p = 0.019). A multiple regression on IgA revealed no significant association with other characteristics. A regression on IgM revealed one positive association (daily alcohol intake; p = 0.036) and one negative association (age; p = 0.016). Mean IgA of male and female probands and corresponding mean IgA of Europeans in two cohorts (918 men, 458 women) did not differ significantly. Mean IgM of probands was lower than the mean IgM of Europeans in four cohorts (men 1.03 ± 0.84 g/L vs. 1.35 ± 0.55 g/L (n = 1084), respectively (p < 0.001); women 1.18 ± 0.67 g/L vs. 1.57 ± 0.68 g/L (n = 622), respectively (p < 0.001)). Conclusions There is no significant association of serum IgA in HFE p.C282Y homozygotes with the clinical and laboratory characteristics we studied. Serum IgM levels are positively associated with daily alcohol intake, are inversely associated with age, and are lower than those of Europeans not selected for hemochromatosis.

Potential of extracellular vesicle-derived microRNAs as a platform for biomarker discovery in acute lymphoblastic leukemia

Background Extracellular vesicle (EV)-derived microRNAs (miRNAs) represent a promising platform for biomarker discovery in acute lymphoblastic leukemia (ALL). This study evaluated the biomarker potential of EV-derived miRNAs isolated from five ALL cell lines. Methods Human ALL cell lines were cultured in EV-depleted fetal bovine serum. These included two parental cell lines, CCL-119 and CRL-3273, and three related cell lines previously characterized as exhibiting chemoresistance-associated phenotypes: CRL-2264 and CRL-2265, derived from CCL-119, and CRL-3274, derived from CRL-3273. EVs were isolated using a commercial size-exclusion chromatography-based method and characterized by nanoparticle tracking analysis, transmission electron microscopy, and immunoblotting for CD9, CD63, and CD81. Small RNA sequencing was subsequently performed. All data processing and visualization were conducted using R statistical software. Results Across all samples, 2,656 EV-derived miRNAs were identified. Among these, three EV-derived miRNAs were prioritized based on consistent directional differences in this exploratory analysis: miR-1226-5p and miR-760 were downregulated, whereas miR-29b-3p was upregulated. To further assess their potential clinical relevance, we evaluated associations between survival and the expression of predicted target genes using the GSE5314 dataset. Higher expression of AHI1 , a gene implicated in leukemogenesis and drug resistance and linked to downregulated miR-760 in our models, was associated with poor survival in patients with ALL. Conclusions This exploratory study identified three EV-derived miRNAs, miR-1226-5p, miR-760, and miR-29b-3p, together with the related gene AHI1 , as candidate biomarker leads in ALL cell line models. Further validation using patient-derived EVs, plasma samples, and subtype-aware clinical cohorts is required before clinical interpretation.

Simulating arsenic migration in arid farmland soils under high-arsenic groundwater irrigation in Xinjiang

Arsenic (As) is a toxic element widely distributed in the environment. A simulation irrigation experiment was conducted to investigate the migration characteristics of As(V) in farmland soils of the Kuitun River Basin, Xinjiang, under different irrigation scenarios. Irrigation water quality was simulated by altering evaporation intensities, pH values and TDS. Changes in As content in the soil solution were then analyzed. Ion exchange membranes were inserted directly into the soil to visualize As distribution in the soil profile, and the As adsorbed on the membranes was analyzed via SEM-EDS, providing spatial distribution information that corroborated the quantitative measurements of As in soil solution, thereby achieving visualization of As distribution within the soil profile. The results indicated that all three factors significantly influenced the distribution of As(V) in the top 0–10 cm of the soil layer. Under weak evaporation conditions, As migrated further both vertically and horizontally in the soil profile. Conversely, increasing the pH and salt content of the irrigation water promoted the downward penetration of As from the topsoil. These findings suggest that in the arid agricultural region, irrigation with less saline and alkaline groundwater may immobilize As effectively in the top soil layers and mitigate the potential risks associated with As soil contamination.

Decoding visual object recognition from EEG signals

Brain–computer interfaces (BCIs) and clinical EEG require compact and interpretable decoders, yet scalp sensors mix cortical signals and blur frequency-specific activity. Identifying which cortical regions and features carry discriminative visual information enables efficient, anatomically grounded object recognition decoding. This study localizes the cortical sources of informative EEG signals and identifies compact, mechanism-guided features that are most efficient given fixed data or compute budgets. To address this, we construct a source-space decoding pipeline that projects sensor signals onto anatomically defined cortical regions. Trial-wise activity is summarized within regions of interest (ROIs), and four feature families are extracted from each ROI: band-limited power (delta–gamma), line length (LL) for transient activity, temporal morphology, and couplings reflecting coordination between regions. Per-participant Random Forest (RF) classifiers are trained, and generality is quantified as consistency and ROI importance rankings across participants. A low-dimensional representation based on line length yields the strongest overall performance, while temporal morphology and coupling features contribute less under short RSVP (Rapid Serial Visual Presentation) trials. Relative to the EEG-ImageNet sensor-space baseline (310 features), the 24-ROI LL-only stack shows higher reported mean accuracy while using 92% fewer features (24 features), while a finer-grained, extended visual-pathway ROI set shows higher reported mean accuracy while using 84% fewer features (50 features). Adding a small, anatomically constrained high- γ block produces near-tied performance rather than a consistent improvement. These findings indicate that, for single-trial 0.5 s RSVP decoding, most discriminative information is captured by simple time-domain structure in anatomically defined ROIs. High- γ power remains a useful reference feature family, but its incremental value is limited once LL is included. By grounding features in neuro-informed regions, this approach compares favorably, at the level of reported mean accuracy, with the sensor-space baseline while providing clear anatomical attribution at substantially lower dimensionality, supporting lightweight and interpretable EEG decoding.

Will there be new trends in the public’s attention to express services in the post-COVID-19 era?

To explore the evolution trends and sentiment characteristics of public’s attention toward express services following the comprehensive adjustment of COVID-19 prevention policies in China, and to provide empirical evidence for service quality improvement and regulatory policy optimization, this paper uses Weibo data from December 10, 2022, to January 10, 2023 as research samples. This paper employs the Entropy Weight Method (EWM) to quantify post popularity and identify high-attention content, applies the Dynamic Topic Modeling (DTM) to uncover temporal patterns of public attention themes, and constructs a CNN-LSTM model for sentiment analysis to reveal cognitive differences between official certification bloggers and personal certification bloggers. The findings include: (1) Public sentiment toward express services was predominantly negative, and sentiment fluctuations were highly correlated with logistics pressure events such as the “Double 12” shopping festival, pandemic infection peaks, and the New Year’s Day holiday; (2) Six core topics were identified, with “goods safety” and “after-sales service” representing persistent traditional attention, while “courier rights” and “governments’ requirements for enterprises” emerged as two new trends in the post-pandemic era; (3) Official certification bloggers adopted a macro perspective focusing on enterprise service quality and logistics economy, whereas personal certification bloggers emphasized micro-level experiences regarding last-mile delivery conflicts and labor rights, with both groups converging on the issue of “courier rights.” In the post-COVID-19 era, public’s attention to express services has extended beyond efficiency and quality demands to encompass deeper dimensions such as labor rights protection and corporate social responsibility. Express enterprises should strengthen courier team building and optimize service processes, while governments need to improve industry standard-setting and regulatory incentive mechanisms to promote sustainable industry development.

Interventions to improve hearing aid use in adult auditory rehabilitation: A protocol for an updated systematic review and meta-analysis

Acquired hearing loss is common among adults, with hearing aids being the primary clinical management option. While hearing aids can improve communication and quality of life, many individuals do not use them consistently or at all. This article presents a protocol for an updated systematic review and meta-analysis of interventions aimed at improving or promoting long-term hearing aid use among adults with hearing loss who are fitted with at least one hearing aid. The review is guided by the Behaviour Change Wheel (BCW) and at its centre the COM-B model (Capability, Opportunity, Motivation – Behaviour), which suggests that the interaction of these three components influences behaviour. The BCW provides a comprehensive framework to understand how interventions target behaviour change and the mechanisms through which they operate. The review objective is to assess the effectiveness of interventions that aim to improve or promote hearing aid use among adults with acquired hearing loss who have at least one hearing aid. Understanding which intervention strategies are effective, and which behavioural functions they target, can help shape future research.

Risk factors associated with diabetic foot amputation: A retrospective study from a tertiary hospital in Central Malaysia

Diabetes mellitus continues to escalate as a major global health crisis, with diabetic foot infection (DFI) remaining one of the most serious and preventable complications. Despite advances in multidisciplinary care, amputation rates remain high, particularly in low- and middle-income settings. This retrospective study identifies key predictors associated with the level of amputation; major (above the knee) versus minor (at or below the knee) among patients with DFI following surgical decision-making. Electronic medical records of 434 patients admitted with DFI to a tertiary care hospital in Central Malaysia between January 2010 and December 2019 were analysed. Minor amputations accounted for 70.7% of cases, while major amputations comprised the remainder. Most patients (63.8%) presented with advanced disease, with Wagner grade 4 being the most prevalent (40.3%). Binary logistic regression analysis was employed to determine independent predictors of amputation level. Increasing age (OR = 1.06, p = .013) and higher Wagner classification (OR = 15.16, p < .001) emerged as significant independent predictors of major amputation. These results highlight the need for timely intervention and aggressive limb-salvage strategies in high-risk groups.

Workplace changes, perceived difficulties and migration intentions among Romanian construction workers during the covid-19 pandemic

The COVID-19 pandemic disrupted labour mobility across Europe, but its effects varied substantially across sectors. Construction remained relatively active in Romania, creating a specific context in which workers faced health-related constraints while maintaining employment continuity. This article examines how construction workers in Brașov County perceived workplace changes, pandemic-related difficulties and migration intentions during the COVID-19 pandemic. The study is based on a questionnaire survey conducted among 384 construction workers, with data collected online in February–March 2022. Descriptive statistics were used to summarise workers’ perceptions, working conditions and migration intentions, while chi-square tests of independence and Spearman’s rank-order correlation were applied to examine associations between sociodemographic variables, perceived difficulties, financial impact and willingness to work abroad. The findings indicate that most respondents reported limited changes in workload and working conditions, while the main difficulties were related to mask wearing, compliance with sanitary rules and extended project completion times. Although the overall intention to work abroad was low, perceived financial impact was positively associated with willingness to migrate. The study contributes worker-level evidence on labour mobility intentions in an essential sector during a period of restricted international movement.

Research on the location model of emergency rescue facilities based on disaster risk—A Case study of earthquake disaster

Rational site selection for disaster relief supply reserve depots is crucial for mitigating natural disaster risks. This study constructs a site selection model for relief supply reserve depots based on an analysis of natural disaster risks. It identifies four disaster risk elements: hazard, exposure, vulnerability, and capacity by examining the definition of natural disaster risk and applying principles of disaster system theory. The relationships between these four elements and the selection of relief supply reserve depot locations are analyzed to develop the proposed site selection model. The model applies the entropy weight method and the geometric mean model to calculate comprehensive indicators across multiple regions, and the results for pre-disaster comprehensive regional factors are obtained. The model performs a targeted analysis of post-disaster regional losses by considering the relationships among hazard, exposure, vulnerability, and capacity and by distinguishing the risk characteristics of different disaster types. The study applies responsive technical methods to determine functional criteria for relief supply reserve depots using data sources such as regional GIS and satellite remote sensing data. Through multilevel constraint relationships, the model establishes a regional layout of multi-tiered relief supply reserve depots and ultimately integrates urban planning and other factors to determine candidate areas. The study is demonstrated through an extreme disaster scenario, specifically an earthquake of magnitude 7 or higher, in the western region of Yunnan, China. The resulting layout plan is relatively optimal, validating the effectiveness of the proposed model.

Antibiotics confound breath-based respiratory disease detection in calves

Breath analysis offers a promising, non-invasive approach for early disease detection in livestock, particularly for bovine respiratory disease (BRD). However, the widespread use of antibiotics in veterinary practice raises concerns about their potential to confound breath-based diagnostics. In this study, we analysed breath samples from 65 calves to evaluate the diagnostic performance of volatile organic compounds (VOCs) as biomarkers of BRD. We identified several candidate ions, including formaldehyde and acetone/propanal, that showed significant elevation at the onset of disease and a modest detection capability (AUC = 0.53-0.77). However, the strongest discrimination between diseased and controls occurred after antibiotic administration, suggesting a confounding pharmacological influence. To isolate this effect, a secondary trial was conducted in which healthy calves were treated with oxytetracycline dihydrate (Alamycin® LA 300). This revealed rapid and transient changes in the breath volatilome, with significant increases in dimethyl sulphide and (C₅H₁₀O)H + within 1 hour, and other VOCs, including formaldehyde and acetone/propanal within 24 hours of treatment. These findings demonstrate that antibiotics can substantially alter breath VOC profiles, potentially mimicking or masking disease signals. We conclude that breath-based diagnosis of BRD holds promise but must account for treatment history to avoid misclassification. Moreover, the reproducible and time-resolved nature of the VOC response suggests that breath analysis could also be developed as a tool for monitoring antimicrobial exposure and optimising therapeutic dosing in livestock.

DNA metabarcoding to estimate diet overlap between the introduced Joro spider (Trichonephila clavata) and three native orb-weaving spiders

The introduction of novel generalist predators to new ecosystems can dramatically alter species interactions and established food webs. Invasive predators may contribute to pest control services; however, a net loss of biodiversity can occur if invasives displace natives through intraguild predation or resource competition. Joro spiders ( Trichonephila clavata ) are an introduced, orb-weaving spider that show rapid range expansion in the United States. Trophic patterns of orb-weaving spiders are largely unknown, and as such, the impact of Joro spiders on established food webs is unclear. We explored patterns of diet composition and prey overlap between Joro spiders and three co-occurring, native orb-weaving species with molecular gut content analysis. In addition, we asked whether the composition of focal native spider diets differed at sites co-inhabited by Joro spiders. We collected female spiders from 52 sites within the Joro spider’s introduced range and analyzed gut content via DNA metabarcoding and high-throughput Illumina sequencing. Despite overlap in many prey taxa consumed, overall diet composition was dissimilar between Joro and native spiders. Joro spider diets were distinct, with at least 26 unique prey taxa not detected in native spider diets. Moreover, native spider diets were similar regardless of whether Joro spiders were present at collection sites. Thus, our initial analysis suggests that while Joro spider diets do overlap with native spiders, their use of many unique food resources does not suggest strong competition. Additional research into web placement and spatial overlap as mechanisms underlying Joro spider invasion success should help clarify the potential for exclusion of native predator populations.

From body hulls to musculoskeletal models: Personalized inertial parameter estimation

Every human body is different, however, current movement analysis does not reflect that, as it heavily relies on generic musculoskeletal models. Usually, these models are scaled to match the participants’ body segment lengths and body weight, but not taking individual body shape into account. This can lead to errors in the estimation of joint forces and torques, which are important to accurately estimate musculoskeletal variables. Thus, we developed a method to estimate body segment inertial parameters based on body hulls acquired via smartphone pictures. From the body hull, we infer the skeletal shape and pose, and then estimate the distribution of bone, lean, and fatty tissues. We then segment the body hull and assign each tissue type a density, which is used to calculate the body segment inertial parameters. To allow for the use of our method with existing data, we also introduce two new generic musculoskeletal models, which are based on the average standing body shapes. Validation using MRI-derived ground-truth models shows that our method creates participant-specific musculoskeletal models that are closer to the MRI-derived ground truth than scaled generic models. Additionally, we performed lab-based gait experiments to evaluate the effect of our method on residual forces and joint moments, where we found that our method leads to a reduction of residual forces of up to 14.9% and a reduction of metabolic cost of up to 12.8% when compared to generic musculoskeletal models. Our new generic models show similar joint moment outcomes, but less reduction of residual forces than the personalized models.