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Multitarget therapeutic potential of sulforaphane in ethidium bromide-induced neurotoxicity in multiple sclerosis-like pathology: comparison with omaveloxolone and dimethyl fumarate on neuroprotection and systemic recovery

Unravelling the key factors governing O2 evolution upon charging a reversible LiOH-based nonaqueous Li | |O2 battery

Leveraging microcredentials for sustainability literacy in higher education: A case study of reflective thinking and learning impact in science

Microcredentials are becoming increasingly popular in higher education. Despite their growing popularity, there is limited exploration of microcredentials’ potential for lifelong learning and their role in sustainability education within curricula. Furthermore, the use of and evaluation of reflective thinking linked to science and sustainability within these online offerings is unexplored. Undertaken in 2024, a large undergraduate science subject, Complex Case Studies in Science (n = 435), embedded a short microcredential titled ‘Sustain ability : Think, Care, Do’ to foster sustainability learning outcomes. The microcredential introduces learners to key concepts of sustainability, understanding diverse worldviews, unpacking their values and how to think systemically, as well as the relevance of these literacies to their science discipline. Our paper explores the learning of a participating cohort of undergraduate students (n = 33) in developing sustainability literacies through this online intervention to culminate in a final reflective assessment of the subject. Using reflexive thematic analysis, we analysed the students’ reflective assessment task. Our findings demonstrate how an online microcredential enhanced science students’ understanding of sustainability, re-evaluation of their daily practices and professional identities, and the development of literacies that grapple with complexity and interdisciplinary solutions across sustainability domains. We also quantified their engagement through behavioural analytics, which varied greatly as expected with an independent self-paced online course. We share these insights as a blueprint for higher education practitioners to integrate online sustainability microcredentials into their curricula at scale.

A GIS-based fuzzy-AHP framework for delineating hydrogeologically and socially sensitive recharge zones in Southern Odisha, India

Continuous electricity from charged total dissolved solids in wastewater using a wood-based ion-selective power generator

Abstract Exploring the potential for secondary utilization of wastewater is a prudent strategy to achieve “take-make-use-reuse” circular economy. Taking advantages of wood’s hierarchical structure and large surface area, in this project, we fabricate surface-encapsulated anion-selective and cation-selective wood membranes (comprising up to 98% eco-friendly materials) through a two-step process: dip-coating with either positively charged 2(dimethylamino)ethyl methacrylate or negatively charged acrylic acid, followed by energy-efficient sunlight-induced polymerization. The output voltage and current of a single modified wood cell (20 × 20 × 3 mm 3 ) in modulated wastewater from flue gas desulfurization are 55 mV and 0.6 µA, respectively, tenfold higher than that of untreated wood cells. When five cells are connected in series, the output voltage reaches 0.27 V, sufficient to power simple electronic devices. This underscores its potential for scaling up and its viability for future applications in industrial power plants.

Lightweight real-time detectors of apple-leaf diseases operating on embedded devices

Agricultural leaf disease detection is crucial for early intervention and yield protection in precision agriculture. Among representative economic crops, such as apples, leaf lesions are typically small and appear in complex backgrounds, making accurate detection performed on resource-constrained embedded devices challenging. To address this, we propose a lightweight small-object detection models, namely the dynamic Differential Compensation Lightweight-YOLO (DCL-YOLO) model and its pruned version (DCL-YOLO-P), based on YOLO11n. A novel Dual-Aspect Feature Complementary Mapping (DAFCM) module type is embedded in their backbone to recover lost semantic and spatial information, while the original YOLO11n’s neck is replaced by an Efficient Enhanced Cross-Scale Feature Fusion (EE-CSFF) module, which incorporates Gated Differential Convolutional Fusion (GDCF) modules to strengthen cross-scale information flow and small-object representation. Experimental results obtained on the ALDSOD dataset show that, compared with the YOLO11n baseline, DCL-YOLO improves recall from 81.9% to 84.6%, mAP 50 from 86.8% to 88.4%, and mAP 50:95 from 47.0% to 47.8%, while also reducing the parameter count from 2.58 M to 1.91 M and Giga Floating-Point Operations (GFLOPs) from 6.3 to 5.5. After applying Layer-Adaptive Magnitude-based Pruning (LAMP), the parameter count and GFLOPs are further reduced to 0.75 M and 2.7, respectively, with mAP 50 and mAP 50:95 still exceeding the baseline by 1.2 and 0.5 percentage points, respectively. When deployed on an embedded device, the pruned model achieved 15.2 FPS and 139 msec per image, confirming its applicability in real-time scenarios. Furthermore, cross-domain validation, performed on the Global Wheat Head Detection (GWHD) dataset, indicates the stable generalization capabilities of the proposed models across environmental domain shifts. The DCL-YOLO’s source code is publicly available at: https://github.com/q123-code/dcl-yolo .

Exercise Training in High-Risk Populations: A Scientific Statement From the American Heart Association

There is broad consensus on the benefits of aerobic exercise training in patients with cardiovascular disease to improve cardiorespiratory fitness and lower the risk of adverse cardiovascular events. However, certain high-risk populations such as those with frailty, stroke, spinal cord injury, rheumatological conditions, or genetic cardiomyopathies and recipients of advanced heart failure therapies or cardiac implantable electronic devices warrant special considerations with regard to exercise training. This scientific statement summarizes the present state and future directions of exercise training for these high-risk populations, including functional deficits, responses to exercise training, modifications in training programs required to maximize safety and efficacy, and knowledge gaps in this field. Key findings common across most of these high-risk populations include (1) increased barriers to participation in exercise training at multiple levels; (2) low baseline cardiorespiratory fitness, creating heightened need for exercise interventions; (3) modifications to exercise prescription, frequently emphasizing strength, balance, and flexibility in addition to aerobic training, as well as accommodations with enhanced supervision and specialized equipment as needed; and (4) functional and quality-of-life gains in response to appropriately designed exercise programs that match or exceed those in more traditional populations. Future research is needed to further develop patient-centered training regimens designed to address the unique and heterogeneous needs of these populations, evaluate their impact on clinical and patient-centered outcomes, and advance scalable and equitable delivery of exercise therapies proven safe and effective.

Spatiotemporal network traffic forecasting using FFT-enhanced inputs and a ConvNeXt3D-mamba framework

As transistors get smaller, electrodes must keep shrinking too

Caloric restriction improves glycemic control via the adiponectin–ceramide axis in non-obese men and women: the CALERIE™ 2 randomized controlled trial

Frequency and causes of upper and lower extremity injury in maxillofacial trauma department of lady reading hospital peshawar

Kekulé superconductivity in twisted magic angle bilayer graphene

Associations between physical activity, physical fitness, and body composition in adults living in Germany: a cross-sectional replication study

Abstract Previous work showed that physical fitness (PF) is more strongly related to body composition (BC) than self-reported physical activity (PA) in adults. In this study, we provide post-COVID BC statistics and evaluate the reproducibility of previously observed associations between PA, PF, and BC. We analyzed cross-sectional data from 320 adults aged 34–82 years collected in 2025 and compared them with data from 2021. PA was assessed using a validated questionnaire. PF was measured through a standardized performance test battery and BC was obtained via bioelectrical impedance analysis. Associations between PA, PF, and BC were analyzed using sex-specific linear regression models. No significant differences in BC were found between 2021 and 2025. Participants had higher PA and muscular strength but lower coordination in 2025 compared to 2021. PF showed stronger associations with BC than PA. Muscular strength remained the most important predictor of BC and showed the strongest association with phase angle (males: β = 0.40, p < .001; females: β = 0.31, p = .002). The consistency of these associations across two independent samples from 2021 to 2025 indicates a robust pattern under different societal conditions and highlights the importance of PF for supporting healthy aging with regard to BC.

Publisher Correction: Spatial transcriptomics uncovers vasculature-centered cellular interactions driving Japanese encephalitis progression in a mouse model

FMR analysis by machine learning leads to remarkable insights into the magnetic anisotropy of $$\text {Co}_{{25}}\text {Fe}_{{75}}$$ thin films

Abstract The traditional approach to analyzing ferromagnetic resonance spectroscopy (FMR) data can produce inconsistent material parameters when measurements are analyzed at broadband and fixed-frequency conditions separately [Nat. Comm.  8 , 234 (2017), Figs. 4 and 5 ]. Machine learning-based global optimization addresses this issue by simultaneously analyzing all FMR data, independent of frequency. Through a comprehensive reanalysis of published data and analysis of independent measurements on epitaxial $$\text {Co}_{{25}}\text {Fe}_{{75}}$$ thin films, we demonstrate that this method yields identical magnetic anisotropy parameters at both broadband and fixed-frequency conditions. In contrast, traditional fitting methods produce differences up to 7% when applied to broadband and fixed-frequency measurements separately. This methodology also enables direct extraction of fundamental parameters, such as the g -factor and magnetization, from FMR data alone, with results consistent with independent measurements. By leveraging measurements for all frequencies, the machine learning approach facilitates self-consistent and frequency-independent material evaluation and effectively distinguishes intrinsic properties from measurement artifacts.

Pathways to cost competitive and viable lithium production from Salton Sea geothermal brines

Abstract Lithium supply chains remain heavily concentrated in hard rock and brine resources, creating significant supply risks. Geothermal brines represent an underutilized alternative, yet commercial progress is hindered by the absence of facility-scale cost assessments. Here, we present a techno-economic analysis of large-scale lithium extraction from Salton Sea geothermal brines, drawing on primary company disclosures, process patents, and brine resource modeling. Caused by varying lithium and impurity concentrations, brine dilution over time, and process configurations (e.g., production via carbonation and conversion vs. electrolysis), we find that large-scale production costs may reach ~10,000 United States dollars per ton, but increase up to 22,000 United States dollars per ton with higher certainty of brine modeling, raising concerns about economic competitiveness to conventional low-cost sources. Finally, a project feasibility-focused scenario analyses shows that leveraging brine pre-treatment by-product sales could lower long-term lithium break-even prices by ~5,000 United States dollars per ton, whereas capital cost optimization of 20% could further reduce lithium break-even prices by 10%.

Exposomic fingerprints of emerging contaminants in a mediterranean multi-by‑product dietary supplement: a preliminary study

Abstract The increasing use of reclaimed water in Mediterranean agriculture, together with the valorization of agro-industrial by-products, has created new scenarios for potential contaminant transfer into food-derived products. Plant-based dietary supplements constitute a distinctive exposure niche because plant materials may accumulate environmental contaminants during cultivation, while downstream processing can concentrate both bioactive compounds and xenobiotics. In this preliminary case study, a targeted UHPLC–MS/MS approach was applied to BIOMEDER , a Mediterranean multi-by-product dietary supplement formulated from plant materials cultivated under conditions that included reclaimed-water irrigation, to investigate the occurrence of contaminants of emerging concern (CECs). Pharmaceuticals, pesticides, cyanotoxins, bisphenols, and a lifestyle-related marker were simultaneously evaluated. Twenty-four pharmaceuticals (Σ = 167.8 ± 5.1 ng g −1 ), sixteen pesticides (Σ = 358.0 ± 0.8 ng g −1 ), caffeine (49.5 ± 0.2 ng g −1 ), and four cyanotoxins (Σ = 0.8 ± 0.0 ng g −1 ) were detected, whereas bisphenols were not detected above the method detection limits. Screening-level daily intake estimates, calculated assuming a supplement consumption scenario of 1 g day −1 , indicated higher body-weight-normalized exposure for children (25 kg body weight) than adults (70 kg body weight). Pesticides represented the dominant contaminant class, with phosmet, fluazinam, and chlorantraniliprole contributing most to the overall burden, while piroxicam and diclofenac were the principal pharmaceutical residues. Although the detected concentrations were low, the simultaneous occurrence of multiple contaminant classes highlights the relevance of mixture-based exposure assessment for plant-derived supplements. Because the study was conducted on a single supplement formulation, the results should be interpreted as a proof-of-concept dataset rather than as representative of plant-based supplements in general. To our knowledge, this is the first study reporting the concurrent occurrence of pharmaceuticals, pesticides, cyanotoxins, and a lifestyle-related chemical marker in a Mediterranean agro-industrial by-product dietary supplement, providing a baseline for future exposomic investigations and the development of monitoring strategies within a One Health framework.

Author Correction: Targeted apoptosis of macrophages and osteoclasts in arthritic joints is effective against advanced inflammatory arthritis

Excess serum zinc concentration is associated with incident fracture in men: Tehran lipid and glucose study

Resolving human α versus β cell fate allocation for the generation of stem cell-derived islets

Abstract Stem cell-derived glucagon-(α) and insulin-producing (β) cells allow to engineer in vitro biomimetics of islet of Langerhans, the micro-organ controlling glycemia; however, a knowledge gap in the mechanism by which human stem cell-derived α and β cells are specified persists. Mouse studies postulated that Aristaless Related homeobox (Arx) and Paired box 4 (Pax4) transcription factors cross-inhibit each other in endocrine progenitors to promote α/β fate allocation, respectively. To test this model in human, we combine lineage labelling with single-cell multiomic analysis in our newly generated ARX CFP/CFP ; PAX4 mCherry/mCherry knock-in induced pluripotent stem cell reporter line. Lineage tracing, proteomic and gene regulatory network analysis and potency assays reveal a human specific regulation of α/β cell fate allocation. Pharmacological perturbations previously proposed to trigger α-to-β transdifferentiation or identified by our gene regulatory network lead to enhanced endocrine induction and directed α/β cell fate. Studying mechanisms of endocrinogenesis and fate segregation enables the engineering of islets in vitro, and has broader implications for cell-replacement therapy, disease modelling and drug screening.