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Revealing competitive interfacial reactions in high-energy Li–S batteries

Daily briefing: Mutation lets octopuses make proteins with precision

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‘This time, it’s the other way around’: how Indonesia is reclaiming the science of human history

LARES-2 satellite measures frame-dragging effect around the Earth

Predicting temporal stability and resilience from resistance and recovery

Protocol for a Delphi consensus study to identify priority characteristics of integrated care for individuals with severe mental illness and comorbid physical disorders in Europe

Introduction Individuals with severe mental illness (SMI) experience persistent and complex physical health needs that remain insufficiently addressed. While integrated care represents a promising solution, there is no consensus among stakeholders regarding what constitutes best-practice organizational models for this population. As part of the European Mental and Physical Health Initiative for People with Severe Mental Disorders (EU-MIND), this study aims to identify expert consensus on the key characteristics of integrated care models for individuals with SMI to support their sustainable implementation across Europe. Methods This study will use an online Delphi process, with up to three rounds, to engage stakeholders from six European countries (Denmark, Finland, France, Italy, Poland, and Sweden). Participants will include people living with SMI, their relatives, health and care professionals, public decision-makers and institutional actors with relevant experience related to the research topic. A minimum sample size of 33 participants per country will be targeted, with the aim of ensuring balanced representation across the different categories of participants. They will be asked to rate the importance of potential key characteristics of integrated care models using Likert scales. A characteristic will be considered to have reached consensus if more than 70% of the respondents agree on its degree of importance. This study complies with the Delphistar reporting guidelines for Delphi studies and has received ethical approval from the Aix-Marseille University Ethics Committee and the Swedish Ethical Review Authority. Discussion This study will provide expert-based guidance on the core characteristics of integrated care for individuals living with SMI. By capturing diverse stakeholder perspectives across countries and healthcare systems, it will help define shared priorities and inform future service design, implementation and policy, supporting sustainable and context-sensitive care development in Europe.

Specific expansion of motor cortical projections in a singing mouse

Theoretical assessment design at a South African school of nursing: A multimethod qualitative exploration

Introduction Theoretical assessment design is crucial in nursing education, ensuring students develop cognitive and problem-solving skills for clinical practice. However, misalignment with learning outcomes and inconsistent cognitive level distribution remain complex issues. Methods and findings This multimethod qualitative study explored theoretical assessment design in a South African nursing school through in-depth interviews with nurse educators and a document review of moderators’ reports. Stratified purposive sampling ensured diverse representation across National Qualifications Framework Levels 5–8. Data saturation was reached after nine interviews, analysed using Creswell and Creswell’s six-step thematic framework. The document review analysed 70 moderation reports (22 internal and 48 external) from 2015 to 2019, focusing on feedback related to final theoretical assessments. Content analysis, following Krippendorff’s framework, was used to identify themes and patterns. Findings revealed an overemphasis on lower-order cognitive skills (Bloom’s taxonomy), inconsistent question distribution, and misalignment with national qualification standards. Educators acknowledged these issues but cited time constraints, inadequate training, and institutional pressures as contributing factors. Moderation reports confirmed assessment inconsistencies, emphasising the need for better alignment with constructive alignment principles. Triangulation of data highlighted a gap between perceived best practices and actual assessment quality, suggesting assessments do not fully support higher-order cognitive skill development. Conclusion To improve the validity and reliability of theoretical assessments, nursing programmes should prioritise training in assessment design, strengthen alignment with learning outcomes, and implement moderation strategies to address inconsistencies. These findings contribute to the broader discourse on improving assessment practices in nursing education globally.

Reconstructing IDF curves from daily rainfall records in data-scarce regions: A statistical method based on temporal disaggregation and gumbel modeling

Urban regions in the tropics often face challenges in hydrological design due to the lack of high-resolution rainfall data. This study presents a method for generating intensity, duration, frequency (IDF) curves using only daily rainfall records, applied to the case of Habana del Este, Cuba. Daily maximum rainfall data from eight pluviometric stations (2010–2024) were combined with subdaily observations from a reference pluviograph. Two strategies were used: direct integration for compatible stations and temporal disaggregation for incompatible ones. Rainfall intensities for return periods between 2 and 1000 years were estimated using Gumbel frequency analysis and fitted to the Sherman model through nonlinear regression. The resulting IDF curves were unified into a single regional model using a weighted average of rainfall intensities from the compatible and disaggregated station groups, followed by final Sherman model fitting. The main contribution of this study is an integrated reconstruction framework that links station compatibility screening, selective use of observed subdaily rainfall structure, disaggregation of non-compatible daily records, and weighted regional unification within a single reproducible workflow. The final curves showed excellent agreement across durations and return levels (R 2  > 0.998), with strong internal consistency between estimation methods. Validation using linear and log log plots confirmed the robustness of the approach. This method provides a practical and statistically sound solution for IDF curve development in data scarce tropical regions, offering direct support for infrastructure planning, hydraulic design, and climate resilience where subdaily rainfall observations are limited.

Genetic yield of next-generation sequencing for detecting monogenic familial hypercholesterolemia in uzbek patients with coronary artery disease

Background Familial hypercholesterolaemia (FH) is an inherited disorder with markedly elevated LDL-C and increased risk of premature atherosclerotic cardiovascular disease, most often caused by pathogenic variants in LDLR and less frequently APOB / PCSK9 (or recessive LDLRAP1 ). FH is commonly assessed using the Dutch Lipid Clinic Network (DLCN) score (definite >8, probable 6–8, possible 3–5). In Uzbekistan, genetic evidence for FH remains limited and largely based on candidate-variant studies, and the diagnostic yield of NGS for monogenic FH in CAD patients is not well defined. Aim For the first time in Uzbekistan and Central Asia, to investigate FH-associated monogenic variants using next-generation sequencing (NGS) and to assess the validity of the DLCN criteria against genetic testing as the diagnostic reference standard in Uzbek patients with CAD and suspected FH. Methods This study included 95 patients with coronary artery disease (CAD) who underwent targeted NGS of LDLR , APOB , PCSK9 , and LDLRAP1 . The suspected/phenotypic FH group comprised 56 patients: 53 with DLCN-predicted heterozygous FH (HeFH)—possible (3–5 points, n = 22), probable (6–8 points, n = 16), and definite (>8 points, n = 15)—and 3 siblings from one family with a homozygous FH (HoFH) phenotype. The control group included 39 CAD patients with hypercholesterolemia without an FH diagnosis (DLCN 1–2 points). Only pathogenic/likely pathogenic (P/LP) variants were used for genetic confirmation of FH. Results Pathogenic/likely pathogenic variants were detected in 10/53 (18.9%) DLCN-predicted HeFH patients and in all three HoFH siblings. Genetic confirmation rates (PPV) were 46.7% (7/15) in definite HeFH, 12.5% (2/16) in probable HeFH, and 4.5% (1/22) in possible HeFH; no P/LP variants were detected in controls (0/39). Using a DLCN >8 threshold, sensitivity was 70.0% (7/10) and specificity was 90.2% (74/82) in the CAD cohort excluding the HoFH family. Conclusion NGS confirmed the highest diagnostic yield in patients with DLCN >8, supporting its use as a practical threshold to prioritise genetic testing; however, monogenic FH may still be present in patients with probable or possible DLCN scores.

A multi-objective portfolio optimization model incorporating sentiment analysis of quarterly reports and LSTM-based price prediction

Sentiment analysis (SA) of natural language text has become as a powerful instrument for enhancing financial market predictions. Quarterly reports from companies, in particular, offer a rich source of data for sentiment analysis, providing key insights into a company’s performance, strategic actions, and future prospects. These reports can significantly influence investor decisions regarding asset investments. Notwithstanding the potential, prior research has not investigated sentiment analysis concerning these resources in portfolio optimization. To fill this void, we propose an innovative three-stage approach to constructing stock portfolios. In the first stage, we perform sentiment analysis on companies’ quarterly reports using the FinBERT model to assess the sentiment surrounding each company. In the second stage, we utilize a Long-Short-Term Memory (LSTM) model for forecasting future prices, which enables the calculation of expected returns and the covariance matrix. In final stage, we present a three-objective portfolio optimization model that incorporates risk, return, and sentiment-derived trend features. We solve this model using the Weighted Goal Programming (WGP) method. Our results indicate that the proposed model effectively supports portfolio optimization. Moreover, the model is implemented using data from companies that are part of the Dow Jones Industrial Average (DJIA), and findings demonstrate high accuracy, confirming the practical potential of the proposed approach.

An integrated multi-omics study of key mediators and therapeutic targets for doxorubicin-induced atrial fibrillation

Background Doxorubicin (DOX), a widely used chemotherapeutic agent for cancer patients, is associated with a significant risk of inducing atrial fibrillation (AF), a serious cardiac complication that impairs patient prognosis. However, the specific molecular and cellular mechanisms linking DOX cardiotoxicity to AF pathogenesis remain poorly understood. Methods Following processing pharmacovigilance analysis of DOX-related AF events, we employed an integrative multi-omics strategy. Differentially expressed genes (DEGs) were first identified from the atrial transcriptomic dataset. Network toxicology was used to predict DOX targets, which were intersected with AF-related genes and DEGs to identify candidate targets. Functional analyses and protein-protein interaction network analysis was applied to pinpoint hub genes. Their predictive performance was validated in independent datasets. Gene set enrichment analysis (GSEA) and immune infiltration profiling (CIBERSORT) were conducted to elucidate biological functions and immune context. Molecular docking simulations validated direct interactions between DOX and selected proteins. Finally, single-cell RNA sequencing (scRNA-seq) analysis resolved the cell-type-specific expression patterns of key targets. Results Functional analyses implicated the candidate genes in critical pathways. 5 hub genes were further selected from candidate genes using the MCC algorithm. Among 5 hub genes, we identified and validated the combination of CCR2 , PDE5A , and CXCR2 showed high predictive accuracy for AF (mean AUC = 0.87), with identifying and validating CCR2 and PDE5A significantly and differentially expressed. GSEA linked CCR2 and PDE5A showed different pathways. Immune infiltration analysis revealed significant alterations in macrophages, monocytes, and T cell subsets in AF tissues. Molecular docking confirmed stable, high-affinity binding between DOX and both CCR2 and PDE5A (binding energy < −7 kcal/mol). Crucially, scRNA-seq analysis demonstrated that CCR2 and PDE5A were differentially expressed in atrial macrophages and fibroblasts respectively. Conclusion This study suggests that CCR2 and PDE5A may serve as central mediators and potential therapeutic targets for DOX-induced AF, though these findings require experimental validation.

Effect of sex hormones, garlic and fennel extracts in layers’ breeders diet on inherited offspring sex

This study aimed to examine the effect of dietary supplementation with sex hormones, garlic, and fennel extracts in layer breeders on the molecular sex ratio of their progeny. One hundred layer breeders, aged sixty-five weeks, were assigned to five treatments with five replicates of four hens each in a completely randomized design (CRD) for five weeks. The experimental treatments consisted of: (1) a control diet (corn and soybean meal-based), (2) control diet + testosterone (1 mg/kg), (3) control diet + progesterone (1 mg/kg), (4) control diet + fennel extract (400 mg/kg), and (5) control diet + garlic extract (400 mg/kg). In the third and fifth weeks of the experiment, blood samples were collected from the wing vein of layer breeders to measure sex hormone levels. At the end of the fifth week, eggs were gathered over two consecutive days and incubated at 37.5°C. The results suggest that fennel extract increased serum testosterone levels compared to the control throughout the study period ( P  = 0.057). Garlic and fennel extracts and progesterone increased the female sex ratio, while testosterone treatment increased the male sex ratio compared to the control ( P  = 0.056), although these differences were not statistically significant ( P  > 0.05). The experimental treatments significantly influenced the percentage of embryos produced ( P  < 0.05). No significant effect was observed on blood glucose levels ( P  = 0.076), though numerical differences were noted. Treatment 2 (testosterone) resulted in the lowest female sex ratio and blood glucose levels, whereas treatments 3 (progesterone), 4 (fennel extract), and 5 (garlic extract) yielded the highest female sex ratios and blood glucose levels. These findings suggest that the treatments’ effects may be linked to their impact on blood glucose levels. The results indicate that dietary interventions affect the offspring sex ratio.

Machine learning for predicting emergency department visits in patients with type 2 diabetes: A real-world, multi-institutional study

Background Patients with type 2 diabetes mellitus (T2DM) prone to acute diabetic complications are at high risk for emergency department (ED) visits, which often precede hospitalization and mortality. Identifying these high-risk phenotypes before deterioration is critical for preventative care. We developed machine learning (ML) models using large-scale, real-world electronic medical records, including prescription data, to predict the possibility of ED visits in patients with T2DM and support proactive interventions in primary care settings. Methods We analyzed the electronic health record data of five independent institutions, creating a comprehensive dataset of 220,720 patients. The data included dynamic clinical parameters such as vital signs, laboratory results, and prescription histories. The cohort was randomly split into a training set ( n  = 176,576) and a test set ( n  = 44,144). The primary outcome was the first ED visit. We developed multiple ML models using an automated ML framework and optimized them using hyperparameter tuning of the training set. Model performances were evaluated using the area under the receiver operating characteristic (AUROC) curve, and feature importance was analyzed using SHAP values to ensure interpretability. Results Among the screened population, 49,770 (22.6%) experienced at least one ED visit, distributed proportionally across the training and test datasets. The CatBoost model demonstrated superior predictive performance, achieving an AUROC of 0.87 (95% CI, 0.862–0.871) on the test dataset. The model identified modifiable risk factors as key predictors; Diastolic blood pressure was the most significant variable, followed by serum creatinine and systolic blood pressure. Conclusions This ML-based predictive model can accurately identify high-risk patients with T2DM who are likely to visit the ED based on readily available clinical variables. By enabling healthcare providers to shift from reactive treatment to proactive risk management, it has the potential to reduce the burden of ED visits due to acute complications in T2DM.

Dysregulated glucocorticoid-responsive immune genes in peripheral blood mononuclear cells as a shared molecular signature of autism spectrum disorder and irritable bowel syndrome

Background Autism spectrum disorder (ASD) is frequently accompanied by gastrointestinal (GI) disturbances resembling irritable bowel syndrome (IBS). While dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis and impaired glucocorticoid-responsive immune (GRI) signaling are proposed links between these disorders, the precise molecular mechanisms remain poorly understood. Methods We performed an integrative transcriptomic analysis of peripheral blood mononuclear cells (PBMCs) from ASD and IBS cohorts. Our approach combined single-sample Gene Set Enrichment Analysis (ssGSEA), differential expression profiling, weighted gene co-expression network analysis (WGCNA), and machine-learning-based feature selection. We utilized single-cell RNA sequencing to resolve cellular sources, while transcription factor, miRNA, and Connectivity Map (CMap) analyses identified regulatory mechanisms and potential drug candidates for reversing GRI-associated signatures. Results GRI-associated transcriptional activity was markedly elevated in the ASD group and moderately upregulated in the IBS group. Network and enrichment analyses revealed a convergence of immune recognition and cytokine signaling pathways. We identified four core genes—LRFN1 , NUAK2 , TMEM154 , and GAPT—that consistently discriminated disease status. These genes were primarily expressed in monocytes, natural killer (NK) cells, and B cells. Regulatory analysis implicated stress-responsive transcriptional control and extensive miRNA modulation in these processes. CMap analysis identified RN-486, saracatinib, and batimastat as compounds predicted to restore GRI homeostasis. Conclusions These findings define a shared GRI-associated molecular signature linking systemic stress adaptation to immune dysregulation along the brain–gut axis. This study provides novel mechanistic insights and identifies potential transcriptomic biomarkers and therapeutic targets addressing the shared molecular architecture between ASD and IBS.

Computational approaches and the future of urban crime research

Cloacal microbiome variation in wild and captive Eastern Indigo Snakes (Drymarchon couperi) with and without Cryptosporidium serpentis infection

The Eastern Indigo Snake (EIS; Drymarchon couperi ), a federally threatened species native to the southeastern United States, serves as a valuable model for examining the effects of captivity and infection on gastrointestinal microbial composition in reptiles. As an alternative to direct gut sampling, we examined the cloacal microbiomes of EISs to evaluate changes in microbial community structure across our study groups. This study assessed the cloacal microbiome of wild and captive EISs using shotgun metagenomic sequencing. Samples were divided into three groups for comparative microbiome analysis: captive snakes positive for Cryptosporidium serpentis ( C. serpentis ), captive snakes negative for C. serpentis , and wild snakes. Alpha (Shannon index, paired Wilcoxon test) and beta diversity (Bray-Curtis dissimilarity, PERMANOVA, CAP) metrics were used to assess microbial diversity and community composition across groups. Furthermore, a linear discriminant analysis effect size (LEfSe) was used to identify microbial taxa significantly enriched in C. serpentis -positive versus C. serpentis -negative captive snakes. Bacterial, fungal, bacteriophage, nematode, and protozoan taxa were significantly enriched in C. serpentis -positive snakes compared with C. serpentis -negative captive snakes, based on a linear discriminant analysis (LDA) score ≥ 2.5 and p  ≤ 0.05. Total taxa species Shannon diversity was consistent between C. serpentis -positive and negative captive snakes (p = 0.55) while wild snake samples were significantly more diverse (p = 0.026). Wild snakes also exhibited a significantly increased Shannon diversity of fungi (p = 0.044), protozoa (p = 0.012), and nematodes (p = 0.008) compared to their captive counterparts. This study offers the first in-depth characterization of the cloacal microbiome in reptiles, specifically in EISs, using shotgun metagenomic sequencing. The findings establish a foundation for exploring microbiota–host interactions with implications for reptile health, disease ecology, and conservation management.

Evaluation of tri-plate rapid on-farm culture system to make therapeutic decisions for mastitis cases in dairy cattle

The empirical use of antibiotics for clinical mastitis is a principal driver of antimicrobial resistance in dairy farming. While on-farm culture systems represent a promising strategy for targeted therapy, robust evidence of their efficacy in heterogeneous commercial settings is still needed. We conducted a randomized controlled trial across 16 commercial dairy farms. Cows with clinical mastitis (CM) were allocated to a Positive Control Treatment (PCT) group (n = 57), receiving immediate empirical intramammary (IMM) antibiotics, or a Culture Based Group (CBG) (n = 46), where treatment was directed by a tri-plate on-farm culture system after 24-hour incubation. The cows were considered experimental unit with mixed-effect models within cluster correlation. Data was statistically analyzed using chi square tests, paired t-tests and Kaplan Meier survival analysis via SPSS (version 20.0). The culture-guided protocol enabled a reduction in antibiotic use, eliminating treatment for the 45.6% of CBG cases (no bacterial growth or Gram-negative infections). The clinical cure rates between the groups (CBG 82.6% vs. PCT 75.4%) were not statistically significant (p = 0.28). Similarly, bacteriological cure rates were comparable between (PCT 71.9% vs. CBG 71.7%, p = 0.987). However, the CBG approach revealed significantly lower treatment failure rate (17.3% vs. 24.5%, p < 0.001) with a shorter median time to clinical cure (3 days vs. 7 days, p < 0.001). At the herd level, the strategy was associated with a significant increase in milk yield (+6.94 L/day, p < 0.001) and reduction in somatic cell count (−56.8%, p < 0.001). The tri-plate on-farm culture system is an effective antimicrobial stewardship tool, facilitating a substantial reduction in antibiotic use while accelerating clinical recovery and improving udder health in commercial dairy operations.

Predicting the finished fabric width and areal density (Grams per Square Meter) of commercially produced plain Single Jersey (100% Cotton) Knitted Fabric using Fuzzy Inference System (FIS)

The purpose of this research is to predict Finished Fabric Width (FW) & Areal Density (GSM) of 100% cotton plain single jersey knitted fabric by building a fuzzy inference model incorporating key input parameters such as Stitch Length (SL), Yarn Fineness or Count (YC) and Machine Diameter (D). More than 30,000 mass production-grade data points have been used to generate the model with remarkable precision. Once the model was prepared, it was verified using new experimental data. The Coefficient of Determination (R 2 ), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) between the actual and the predicted FW were found to be 0.979, 1.214%, 1.103, respectively. For GSM the corresponding metrics were 0.940, 1.661%, 3.892, respectively. Both prediction outcomes showed excellent precision, justifying the model's applicability in the textile industry for predicting two important knit fabric parameters namely FW and GSM. The system's reliability was ensured by using a large set of industry standard data. This, combined with the adaptation of carefully designed fuzzy logic rules based on proven scientific method, significantly contributed to producing more accurate results. Together, all these aspects make the system stand out from similar studies, offering a practical and trustworthy approach for real world textile application with enhanced process optimization.