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Interface-engineered nickel oxyhydroxide on carbon nanofibers for efficient urea oxidation and wastewater-to-energy conversion

Urea is a common nitrogenous pollutant in agricultural and industrial wastewaters, and its electrochemical oxidation in alkaline media enables simultaneous detoxification and energy recovery. Herein, we report an interface-engineering strategy based on P,N-modified nickel–carbon nanofibers prepared via a scalable electrospinning–carbonization process using ammonium phosphate as a multifunctional precursor. During thermal treatment, phosphorus and nitrogen heteroatoms are incorporated into the carbon framework while regulating the surface chemistry of embedded nickel nanoparticles, promoting the formation of a NiOOH-ready interfacial layer. Structural analyses (XRD, SEM/TEM) confirm the formation of metallic Ni domains uniformly dispersed within turbostratic carbon nanofibers. XPS reveals the coexistence of Ni 0 /Ni +2 /Ni +3 species along with phosphate- and nitrogen-derived functionalities that enhance electronic modulation and redox reversibility. In 1.0 M KOH, the optimized composition exhibits a Ni +2 /Ni +3 redox charge of ~12.5 mC.cm −2 . For urea electrooxidation (0.5 M urea), the catalyst delivers a maximum current density of ~115 mA.cm −2 with a low onset potential of 0.37 V (vs Ag/AgCl). The apparent activation energy is 9.82 kJ.mol −1 , indicating a kinetically favorable NiOOH-mediated pathway. Chronoamperometry shows stable operation with ~70–80% current retention over 1000 s. When implemented as an anode in a membrane-less direct urea fuel cell, the material achieves a peak power density of ~1.1 W.m −2 at 35 °C, demonstrating practical wastewater-to-energy feasibility. This work highlights how dual heteroatom modification of carbon nanofibers can regulate nickel interfacial chemistry, enabling efficient urea remediation coupled with renewable power generation.

Correction: Comparative physicochemical properties and anti-inflammatory activity of natural and artificial musk for quality evaluation

Adapting, piloting, and evaluating a pediatric lead screening and risk-reduction intervention in Nairobi: A hybrid implementation-effectiveness trial protocol

Background Childhood lead exposure is prevalent worldwide including low- and middle-income countries (LMICs). Structured screening and prevention programs to address pediatric lead exposure are largely absent in these settings. Adapted interventions are needed to close this implementation gap in an urban African context. This paper describes the protocol for the Lead Exposure Intervention Program (LEIP), which aims to adapt, pilot, and evaluate a pediatric lead exposure screening and risk-reduction protocol in Nairobi, Kenya. Methods LEIP is a multi-phase, hybrid type 3 implementation-effectiveness study. Phase 1 is a formative one-arm study leveraging an existing mother–child cohort and stakeholder-led tools adaptation to pilot a program comprising blood lead level (BLL) screening with a lead risk survey and tailored caregiver risk reduction messaging. Phase 2 is a randomized trial in public sector clinics. In this phase, approximately 1,500 children will be screened to identify 100 with elevated BLL (≥5 µg/dL) for enrollment, who will then be randomized 1:1 to receive either clinic-only risk-reduction messaging or the same clinic-based messaging plus a home visit for environmental assessment and additional tailored messaging. Follow-up at 3 and 9 months will assess caregiver recall of key messages and adoption of recommended exposure-reduction behaviors, as well as changes in child BLL. Phase 3 involves qualitative interviews with caregivers and key stakeholders to identify multi-level barriers and facilitators to intervention uptake. Quantitative and qualitative findings will be integrated to inform refinements for scale-up. Discussion This study represents a critical opportunity to develop and evaluate an adaptive, screening-based lead exposure intervention tailored to the urban LMIC context. By incorporating implementation science principles and stakeholder-driven design, LEIP is well-positioned to inform scalable national and regional approaches. The inclusion of both quantitative and qualitative components enhances the protocol’s ability to capture multilevel dynamics of uptake, fidelity, and sustainability, and generate actionable insights for future large-scale implementations. Trial registration ClinicalTrials.gov NCT07401251

A structural equation modeling framework for estimating symptom burden based on symptom clusters in cancer survivors

Markov reads Puškin, again: A statistical journey into the poetic world of Evgenij Onegin

This study applies symbolic time series analysis and Markov modeling to explore the phonological structure of Evgenij Onegin —as captured through a graphemic vowel/consonant (V/C) encoding—and one contemporary Italian translation. Using a binary encoding inspired by Markov’s original scheme, we construct minimalist probabilistic models that capture both local V/C dependencies and large–scale sequential patterns. A compact four-state Markov chain is shown to be descriptively accurate and generative, reproducing key features of the original sequences such as autocorrelation and memory depth. All findings are exploratory in nature and aim to highlight structural regularities while suggesting hypotheses about underlying narrative dynamics. The analysis reveals a marked asymmetry between the Russian and Italian texts: the original exhibits a gradual decline in memory depth, whereas the translation maintains a more uniform profile. To further investigate this divergence, we introduce phonological probes — short symbolic patterns that link surface structure to narrative-relevant cues. Tracked across the unfolding text, these probes reveal subtle connections between graphemic form and thematic development, particularly in the Russian original. By revisiting Markov’s original proposal of applying symbolic analysis to a literary text and pairing it with contemporary tools from computational statistics and data science, this study shows that even minimalist Markov models can support exploratory analysis of complex poetic material. When complemented by a coarse layer of linguistic annotation, such models provide a general framework for comparative poetics and demonstrate that stylized structural patterns remain accessible through simple representations grounded in linguistic form.

Integration of gene expression analysis and molecular docking in revealing stress-responsive functions of potato chitinases

Mapping metabolic reprogramming in lung and breast cancer through integrative bioinformatics

Metabolic reprogramming is central to cancer biology, enabling tumor cells to sustain rapid proliferation, resist stress, and adapt to therapy. However, these alterations are highly heterogeneous across cancer types, and current treatments rarely exploit subtype-specific metabolic vulnerabilities. To address this gap, we developed a unified bioinformatics framework that integrates transcriptomic profiling (UALCAN), drug–gene interactions (DGIdb), gene–disease associations (Open Targets), pathway enrichment (Enrichr), and protein–protein interaction networks (STRING/Cytoscape). This pipeline was applied to lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LSCC), breast cancer (BRCA), and metastatic breast tumors (MET500) to uncover cancer type–specific metabolic programs and prioritize translational targets. Our analysis revealed distinct signatures: LUAD showed glycolytic activation, LSCC coupled glycolysis with oxidative phosphorylation, BRCA favored anabolic and lipogenic pathways, and MET500 tumors adopted stress-adaptive states with elevated antioxidant and autophagy programs. Integration of pharmacological evidence highlighted clinically actionable interactions between metabolic genes and FDA-approved drugs, including ASNS–asparaginase, DHODH–teriflunomide, and G6PD–rasburicase. Gene–disease associations further prioritized G6PD, SLC2A1, and TK1 as robust targets strongly linked to lung and breast cancers. Pathway enrichment pinpointed the pentose phosphate pathway, pyrimidine metabolism, and glutathione metabolism as conserved axes sustaining tumor survival, while network analysis positioned the G6PD–PGD hub as a central metabolic node connecting glucose uptake, redox balance, and nucleotide biosynthesis. To place these bioinformatics-derived findings within a functional and clinical context, we complemented the computational analyses with patient survival assessment, clinical trial screening, and targeted literature appraisal. Survival analysis demonstrated cancer type–specific prognostic relevance for selected metabolic genes, while clinical and literature-based screening revealed both ongoing translational efforts and substantial gaps between computational target prioritization and experimental or clinical validation. This integrative analysis shows that cancer metabolism is altered in subtype-specific ways that can be systematically mapped to reveal potential therapeutic targets. By linking transcriptomic evidence with drug–gene interactions and clinical context, this framework provides a scalable approach for cancer metabolism research and supports the prioritization of pathways with potential translational relevance.

Comparative gene expression analysis of nestin-expressing hair follicle-derived cells

Tool-stone selection in the African Middle stone age at Sibhudu cave

The raw materials people used for making stone tools may contain information about their territory, exchange routs or the selection criteria they employed during provisioning. In this study, we measure the mechanical properties of different tool-stones used by foragers living during the Middle Stone Age at Sibhudu Cave in South Africa. The site yielded a long and continuous sequence that saw transitions between different raw materials and tool forms. We evaluate the quality of these different stones for tool making and use, attempting to find correlations between selected raw materials and the tools made from them. We find that the raw materials used at Sibhudu have substantially different qualities, some being easy to flake but weak upon use, some being tough during stone knapping and resistant during use. Comparing these data with the appearance and disappearance of tool types throughout the Sibhudu sequence, we note that tool-stones requiring lower flaking forces were more often retouched than those requiring great forces. Elongated products, blades, were mostly made from materials with better fracture predictability, suggesting an understanding of the basic requirements for standardising the tool knapping process. Use-related qualities, such as resistance to dulling, appear to have been of lesser importance at Sibhudu. Our results suggest that the site’s occupants had a good understanding of the qualities of rocks for specific knapping processes.

Molecular networking-based GC/MS profiling of Citrus japonica Thunb. peel and pulp lipophilic fractions and their antimicrobial potential against diabetic foot ulcer pathogens

Abstract Diabetic ulcers are a serious complication of diabetes, often exacerbated by microbial infections that hinder wound healing. This study investigates the antimicrobial potential of non-polar constituents derived from the peel and pulp of Citrus japonica (kumquat) as natural antimicrobial agents for managing microbial infections associated with diabetic ulcers. Gas Chromatography-Mass Spectrometry (GC-MS)-based molecular networking enriches the annotation of 113 and 92 compounds in the peel and pulp lipophilic fractions, respectively. Moreover, preliminary in vitro antimicrobial assays demonstrated that the pulp non-polar extract possesses weak to moderate efficacy against common diabetic ulcer pathogens, including Pseudomonas aeruginosa , Staphylococcus aureus , and Candida albicans , with inhibition zone diameters (mm) of 13.67 ± 0.89, 19.33 ± 0.22 and 20.5 ± 0.17 compared to those of the standard 12.33 ± 0.22, 19.83 ± 0.39 and 10.71 ± 0.05 as well as MIC values (mg/mL) of 3.75 ± 0.04, 0.938 ± 0.002 and 1.88 ± 0.001, respectively. Furthermore, Principal Component Analysis (PCA)-based GC-MS revealed notable variations in the chemical profiles of the peel and pulp lipophilic fractions, with the pulp showing higher concentrations of trimethyl citrate and azelaic acid ester, compounds related to antibacterial and antifungal properties. This finding suggests that non-polar matter, especially from the pulp, could be a promising natural antimicrobial agent for treating diabetic ulcers. Further research is required to assess its efficacy in clinical applications and to explore its mechanisms of action in promoting wound healing and preventing infections.

Effect of ciprofol–etomidate mixtures for deep sedation during gastrointestinal endoscopy: Protocol for a three-arm, double-blind randomized controlled trial

Introduction Deep sedation for gastrointestinal endoscopy can be achieved using either ciprofol or etomidate, both associated with distinct adverse events such as cardiopulmonary depression, myoclonus, and postoperative nausea and vomiting. This study aims to evaluate the safety and efficacy of two ciprofol–etomidate mixtures at varying volume ratios compared with ciprofol alone in patients undergoing gastrointestinal endoscopy. Methods This three-arm prospective study will include 135 participants aged 18–65 years who are scheduled for gastrointestinal endoscopy under deep sedation. Patients will be randomly assigned in a 1:1:1 ratio to receive either ciprofol alone or a ciprofol–etomidate mixture at volume-to-volume ratios of 1:1 or 2:1. The primary outcome is the composite incidence of various adverse events, including hypotension, hypoxemia, bradycardia, tachycardia, injection site pain, myoclonus, and nausea and vomiting. The secondary outcomes include the success rate of sedation, induction time, awakening time, recovery time, vital signs, and patient satisfaction. Analyses will be conducted using intention-to-treat and per-protocol approaches. Discussion This three-arm randomized controlled trial will determine the potential benefits of combining ciprofol with etomidate for deep sedation during gastrointestinal endoscopy, with a focus on enhanced cardiopulmonary stability, reduced injection site pain, decreased incidence of myoclonus, and lower rates of nausea and vomiting. Trial registration This trial has been registered on ChiCTR.gov.cn (ChiCTR2400093109), on Nov 28, 2024.

Hard-to-detect mutations explain how common autoimmune diseases arise

Byzantine robust federated learning for heterogeneous brain MRI using multisignal gradient fingerprinting and adaptive trust aggregation

Abstract Federated learning enables collaborative training across institutions without centralizing patient data, but remains vulnerable to malicious clients and severe non-IID data heterogeneity. We propose a trust-aware federated learning framework for brain MRI that combines multi-signal gradient fingerprinting with adaptive aggregation to achieve Byzantine robustness. Each client update is characterized by a six-dimensional fingerprint (variational-autoencoder reconstruction error, cosine similarity to a server reference, peer similarity, gradient norm, sign consistency, and Monte Carlo Shapley contribution). A dual-attention module and a reinforcement-learning controller map these signals into trust weights and integrate with FedBN-P (Federated Batch Normalization with Proximal regularization), an optimizer co-designed for stability under heterogeneous and adversarial conditions. We evaluate on MNIST, CIFAR-10, Alzheimer’s MRI, and the OASIS brain-MRI cohort (approximately 87 test samples, used strictly as proof-of-concept) under both standard and strengthened threat models (up to 40% malicious clients). Attack-specific ablation confirms a defense-in-depth design: VAE fingerprinting is the primary noise-attack defense (3.60 pp accuracy drop upon removal), Shapley values safeguard accuracy under scaling (10.16 pp drop), and reinforcement learning improves detection consistency under dynamic attack schedules. Three-seed paired-test validation further shows detection F1 outperforms FLTrust by up to 44 pp on Non-IID Gaussian noise; a white-box adaptive attacker degrades the detector but not model accuracy, confirming the layered design. End-to-end wall-clock overhead is + 8.8% over FedAvg with identical communication volume. The framework achieves F1 above 0.98 for gradient-scaling attacks while preserving accuracy under magnitude-preserving attacks where explicit detection remains limited. Multi-site validation on larger federated cohorts (e.g., ADNI, UK Biobank, FeTS) is required before any clinical-deployment claim can be made.

Supporting entrepreneurial resilience: An experimental study protocol

Entrepreneurial activity is shaped by the structure and functioning of the entrepreneurial ecosystem, yet there is limited consensus on how entrepreneurial resilience is defined or measured. This study protocol outlines the development of a novel, empirically grounded measure of entrepreneurial resilience, conceptualised as a multidimensional and dynamic capability that enables adaptation under contextual hardship and resource constraints. Guided by a capability-based framework, the study draws on prior literature and employs qualitative methods, including a multi-round Delphi study and stakeholder interviews and focus groups. The study components will identify context-specific mechanisms and translate expert consensus into measurable and policy-relevant indicators. By setting out a transparent and reproducible methodology, this protocol contributes to the entrepreneurship and behavioural economics literature and provides a foundation for future empirical work and targeted policy interventions to support business continuity in volatile environments.

Robots run this laboratory in Japan — and are changing how scientists work

Larvicidal activity of essential oils and nanoemulsions against Culex pipiens larvae (Diptera: Culicidae)

Abstract Mosquitoes and mosquito-borne diseases are a growing global challenge. Vector control strategies have recently transitioned from chemical insecticides to botanical products. This study evaluated the larvicidal efficacy of dill, lime, and wormwood essential oils (EOs), along with their combinations and nanoemulsions (NEs), against Culex pipiens third-instar larvae over 48 h. Gas chromatography mass spectrometry (GC–MS) analysis revealed major components for dill EO (Apiol 25.46%, Carvone 24.13%, and D-Limonene 23.47%), lime EO (D-Limonene 24.16%, α-Terpineol 13.12%, and β-Pinene 11.98%), and wormwood EO (davanone 33.8%, and camphor 25.67%). The TEM image showed spherical NE droplets (40–160 nm). The droplet size and polydispersity index (PDI) were (164 nm and 0.2), (211.6 nm and 0.2), and (160.8 nm and 0.6) for dill, lime, and wormwood NEs, respectively. Based on LC 50 values after 30 min, the dill EO showed the highest larvicidal activity (LC 50  = 286.9 ppm; confidence interval (CI) 226.85-341.52 ppm), followed by dill NE (LC 50  = 518.9 ppm; CI 290.4 -765.1 ppm) and then (dill + lime) binary combination (LC 50  = 1502 ppm; CI 1403.9-1605.4 ppm). The EO showed faster larvicidal activity than NEs, particularly within 30 min. NEs didn’t significantly enhance efficacy over crude EOs. Enzyme assays showed variable effects: dill, lime, and wormwood EOs reduced GST activity, whereas their corresponding NEs generally increased enzyme activity at most exposure times. These findings indicate that botanical formulations, particularly those derived from dill, may serve as promising biodegradable alternatives for vector control. However, optimizing essential oil ratios is needed to maximize synergistic effects, and comprehensive assessments of toxicity and environmental impact are required before large-scale application.

Integrating deep learning, biological hierarchies, and high-resolution imagery to create a new identification tool for cryptic coral reef fishes

Life on Earth has evolved into a staggering diversity of species, most of which still remain undiscovered, unrecognized, or unmonitored. As our ocean’s richest biodiversity hotspot, coral reefs harbor more than one third of marine biodiversity, but many reef species are small and cryptic and, therefore, difficult to identify and study. Among these, tiny bottom-dwelling (‘cryptobenthic’) fishes have been highlighted as a highly diverse (>3,000 species), understudied, and ecologically important group. However, the classification and monitoring of these fishes depend almost exclusively on the knowledge of few expert scientists, which has resulted in limited knowledge concerning the taxonomy, distribution, and population trends of these fishes. Deep learning-driven image classification—known for its ability to learn complex patterns in visual data—is an ideal candidate for automating taxonomic image classification and therefore broaden participation in ecological monitoring and biodiversity science. We developed CryptoVision , a new taxonomy-aware convolutional neural network with three output heads that explicitly considers taxonomic hierarchies (family, genus, species) and their biological constraints. Built on ResNet50v2 and enhanced with Squeeze-and-Excitation modules, CryptoVision employs a custom taxonomy-focal cross-entropy loss and four hierarchical fusion strategies (standard, concatenation, gating, attention) to assess the algorithm’s performance. Trained on a unique dataset of ~7,600 laboratory-standard and ~18,800 web-sourced images covering 113 species of small reef fishes, our tool highlights the power of integrating deep learning with innovative, taxonomically-informed design and high-resolution imagery. Indeed, CryptoVision achieved a ~ 25% improvement across all metrics when lab-standard imagery was incorporated and among the fusion variants, the gating approach delivered the best calibration (expected calibration error ≈ 0.01) and 90.5% average precision. Finally, guided saliency map analyses of species in the dwarfgoby genus Eviota illustrate that model attention can align with expert-defined morphological traits that represent critical features for species delimitation. Our results demonstrate that taxonomy-aware, multi-output deep learning on curated imagery provides a robust, interpretable framework for scalable biodiversity monitoring, ecological research, and streamlined taxonomic workflows that is particularly well-suited for the many taxa that are typically understudied due to their small size, cryptic nature, or ambiguous taxonomy.

A comparative analysis of deep learning models for disease classification in multi-organ histopathological images

AMGST: Adaptive multi-graph convolution and spatiotemporal attention network for traffic forecasting

Traffic forecasting is crucial for optimizing traffic management and control strategies. As a powerful approach for analyzing and mining graph-structured data, graph convolution has shown great potential in traffic prediction. However, it still struggles to fully capture global spatial correlations and long-term dynamic temporal dependencies inherent in spatiotemporal traffic patterns. Moreover, the quality of the graph structure directly affects the extraction of these correlations. To address these challenges, we propose AMGST, an Adaptive Multi-Graph Convolution and Spatiotemporal Multi-Head Self-Attention Network for traffic forecasting. AMGST integrates an Adaptive Spatiotemporal Embedding (ASTE) generator, a multi-graph diffusion convolution module, and a spatiotemporal attention mechanism. First, dynamic spatiotemporal representations are generated using the ASTE module. Then, the multi-graph diffusion convolution leverages both a maximum mutual information coefficient matrix and an adaptive matrix to extract fine-grained spatial features. A global spatial attention mechanism is applied to capture dynamic spatial correlations, while a temporal attention module models nonlinear temporal dependencies. Experimental results on four public traffic datasets, including both speed and flow measurements, demonstrate that AMGST consistently surpasses the baselines, confirming its effectiveness in providing accurate traffic forecasts.

α-Linolenic acid alleviates ovalbumin-induced allergic rhinitis in mice and modulates GPR120–NF-κB signaling