Browse Articles

Li-In-S composite foil with built-in electric fields to stabilize Li/Li6PS5Cl interface for long-life all-solid-state batteries

Pathogenicity evaluation of fungal isolates associated with dry rot on cold-stored potato tubers for the identification of predominant pathogens

Quantification of molar sub-regions suggests increasing herbivory during primate origins

HS-RankFormer for efficient and robust RGB-to-Hyperspectral image reconstruction across domains

Microglial CD31 suppresses Aβ clearance and promotes Alzheimer pathology in 5×FAD mice

Abstract Microglia play crucial roles in Alzheimer’s disease (AD), yet the molecular mechanisms are unclear. Here, we show that CD31, a recognized endothelial marker, is predominantly expressed in microglia but not in neurons or astrocytes, and it is significantly elevated in the brains of AD patients and mouse models. Microglia-specific CD31 knockdown in 5xFAD mice substantially attenuated the dysregulated transcription networks, suppressed microglia hyperactivation and the disease-associated microglia (DAM), mitigated Aβ deposition and inflammation, and eventually improved cognitive functions in mice. Mechanistically, CD31 knockdown damaged the simultaneous recruitment of Src homology phosphatase 2 (SHP2) and STAT3, leading to a reduced dephosphorylation and enhanced activation of STAT3, a transcription factor. STAT3 activation increased transcription of membrane metalloendopeptidase (MME) and promoted Aβ clearance. Collectively, this study identifies microglial CD31, by regulating SHP2–STAT3–MME axis, plays a role in AD pathogenesis and targeting CD31 is promising in AD drug development.

Foliar spraying of Bradyrhizobium and Anabaena improve stress tolerance under deficit irrigation of Cowpea

Abstract Water deficit is considered one of the most significant factors limiting cowpea productivity, necessitating sustainable strategies to improve crop tolerance to drought stress. This study investigated the effects of foliar application of Bradyrhizobium and Anabaena on the growth, physiological performance, and productivity of cowpea under different irrigation intervals. Under the 15-day irrigation interval (water-stressed condition), the combined Bradyrhizobium and Anabaena treatment increased total seed yield by more than 30% compared with the stressed untreated control. This confirms the strong role of the consortium in improving drought tolerance and productivity under limited irrigation. Anabaena was identified as Anabaena cylindrica using rpoC1 gene sequence analysis. Phytohormone content of A . cylindrica was quantified by HPLC and bioactive compounds from Bradyrhizobium sp. and A . cylindrica were detected by GC- MS. The study evaluated nodule number, leaf N, K, Na contents, K/Na ratio, chlorophyll, carotenoids, total soluble sugars (TSS), relative water content (RWC), proline concentration, antioxidant enzymes (APX and CAT), and soil enzyme activities (dehydrogenase and urease) along with yield components and total seed yield. Results showed that the consortium treatment significantly improved chlorophyll, carotenoids, TSS, and RWC, while reducing proline accumulation compared with stressed control. It also increased root nodulation, enhanced N and K uptake, reduced Na accumulation, and improved K/Na ratio. It stimulated CAT and APX activities in leaves and enhanced soil enzyme activities, leading to improved yield components and overall productivity under drought stress. These findings highlight the potential of combining Bradyrhizobium and Anabaena cylindrica as an eco-friendly and sustainable biostimulant strategy to enhance cowpea productivity and drought resilience under water-limited conditions.

Precision timekeeping with atomic clocks: evolution and future directions

Design load analysis for electrification of a 55-kW agricultural tractor based on workload

Abstract This study aimed to determine and analyze the design loads required for the electrification of a 55‑kW agricultural tractor through field experiments. A measurement system was installed to record data from the engine, driving axles, power take‑off (PTO), and hydraulic pump during plow tillage, rotary tillage, and driving operation in a silt loam paddy field. The study specifically focused on power requirement analysis, load duration distribution (LDD), and rainflow counting (RFC)–based load spectrum generation for durability assessment of the electric tractor powertrain. Plow tillage imposed the highest loads, with total power peaking at 52.6 kW (95% of rated power) dominated by axle torque, while rotary tillage was PTO‑driven and driving operation showed low average loads with intermittent traction peaks. Compared with a previously studied 78‑kW tractor, the 55‑kW tractor exhibited lower overall power requirement and a more traction‑balanced distribution, while hydraulic requirements remained minimal. LDD and RFC analyses revealed that a few load cases and low‑amplitude cycles dominate the operating profile, and critical high‑load cycles occur primarily during tillage. These findings provide essential design data for electric powertrain components and establish a systematic measurement‑to‑spectrum methodology for deriving design loads for agricultural machinery, supporting the future development of utility electric tractors and durability‑driven powertrain design.

All-water supercapacitor enabled by 1-nm clay channels

Abstract Water confined to channels one nanometer thick exhibits electrochemical behavior distinct from bulk water, including enhanced protonic conductivity and large dielectric anisotropy. Here, we exploit these characteristics to design a scalable electrochemical energy storage system-a “blue capacitor”-constructed entirely from naturally abundant materials. By assembling layered clays and conductive graphene, we produce 1-nm-thick channels in which confined water acts as the sole electrolyte. We systematically study different clay types, the electrode composition, and separator thickness using complementary physicochemical and electrochemical techniques. The device operates stably up to 1.6  ± 0.1 V, achieves specific capacitances of 40 F g −1 , 97  ± 2% coulombic efficiency, and stable performance over more than 60,000 charge-discharge cycles at a voltage window of 1 V and a scan rate of 10 mA. Structural and dynamic analyses validate the device architecture, water purity, and proton transport in the nanopores. These results demonstrate that nanoconfined water can function as an electrolyte in a macroscopic electrochemical device, providing a platform for exploring sustainable aqueous energy storage systems.

Data driven water quality assessment using machine learning and synthetic data generation

Abstract Providing fresh and clean water to all is the objective of Sustainable Development Goal 6. Consuming clean water improves health for all living beings worldwide. Therefore, accurately estimating the water quality and classifying the type of water (i.e. whether it is used for potable purposes or not) based on physicochemical parameters is considered a challenging issue due to the dynamic nature of water quality data, the selection of physical or data-driven models used to estimate the water quality and the lack of a large water dataset for training the data-driven models. The primary objective of this research is to generate synthetic dataset(s) and propose state-of-the-art machine learning models for predicting water quality. In this study, synthetic data generation via oversampling is used to balance the water quality dataset and train models on a balanced dataset for water quality classification and prediction. The experimental results indicate that the quality of the synthetic Drinking Water Final dataset produced using SMOTE is satisfactory, as evidenced by a Maximum Mean Discrepancy (MMD) score of 0.0067. Furthermore, the test accuracy of the GB and XGB machine learning models is notably high at 99.47% on the synthetic Drinking Water Final dataset. The performance of the ML model on the synthetic Drinking Water Final dataset generated by a GAN is outstanding; however, the MMD score indicates that the quality of the synthetic data is subpar. Likewise, the MMD score quality of the synthetic Water Quality Analysis dataset produced using SMOTE (MMD = 0.0006) and GAN (MMD = 0.0016) is noteworthy. The comprehensive evaluation findings demonstrate that the synthetic dataset produced through oversampling techniques will enhance the models’ predictive accuracy.

HMGCS1 drives cholesterol-dependent membrane repair and shields tumor cells from lymphocyte attack

Abstract Cytotoxic lymphocytes use perforin to form plasma membrane (PM) pores in tumor cells, thereby enabling granzyme-mediated cell death. However, whether and how tumor metabolism enables PM repair to evade immunity is unclear. In this study, using a functional screen targeting 111 metabolic enzymes, we identified hydroxymethylglutaryl-CoA synthase 1 (HMGCS1) as critical for repairing perforin-induced PM damage. HMGCS1 promotes PM repair by initiating de novo cholesterol synthesis, enhancing tumor cell resistance to lymphocyte-mediated killing and impairing the efficacy of NK, CAR-T, and anti-PD-1-based immunotherapies. Beyond its structural role, cholesterol directly binds charged multivesicular body protein 4b (CHMP4B) to enhance its PM localization, facilitating PM repair. Furthermore, oncogenic activation, cytokine, and hypoxia induce c-Jun activation, up-regulating HMGCS1 expression. In lung cancer patients, elevated c-Jun activation, HMGCS1 expression, cholesterol content and PM CHMP4B correlate with reduced anti-PD-1 immunotherapy efficacy. Our findings reveal a tumor immune evasion mechanism wherein HMGCS1 drives cholesterol-dependent PM repair by activating the cholesterol synthesis. Targeting HMGCS1 enhances the effectiveness of immunotherapies.

Influence of dust deposition on optimal sizing of PV-battery system in arid region: a case study for Saudi Arabia

Abstract Dust accumulation is a major challenge for PV systems in arid regions, reducing efficiency and increasing system costs. This study analyzes the influence of dust on the optimal sizing of a stand-alone PV-battery system for a typical household in Saudi Arabia. Dust samples were examined using SEM and EDS, showing irregular particles (1–100 µm, average ~ 50 µm) composed mainly of oxygen (58.6%) and silicon (11.8%). These particles reduced glass transmittance from 1 (clean) to 0.704, 0.496, and 0.349 at dust densities of 0.7, 1.4, and 2.1 mg/cm 2 , respectively. A MATLAB-based techno-economic model was developed to optimize system design under each condition while ensuring 0% loss of power supply probability. Under clean conditions, to meet a typical household load of approximately 2 kW, the optimal system required 10.5 kWp PV (21 modules) and 5 batteries at an LCOE of 0.22 $/kWh. At 0.7 mg/cm 2 , the requirement increased to 15 kWp PV (30 modules) with 5 batteries at 0.27 $/kWh. For 1.4 mg/cm 2 , 19.5 kWp PV (39 modules) and 6 batteries were needed, raising LCOE to 0.33 $/kWh. At 2.1 mg/cm 2 , the system required 21 kWp PV (42 modules) and 9 batteries, resulting in 0.40 $/kWh, an 82% cost increase. Results highlight the need for dust-mitigation strategies to ensure reliable and cost-effective PV system performance overall.

Mycobacterium tuberculosis transmission from tuberculosis patients with and without recognized symptoms: a case-contact study in eastern China

Structured spontaneous activity through delta oscillation-based discrete states in the medaka telencephalon

Scalable Boltzmann generators for equilibrium sampling of large-scale materials

Abstract Generating equilibrium ensembles of structures is essential for modeling molecules and materials, yet traditional simulators like molecular dynamics suffer from limited sampling efficiency. Boltzmann Generators introduced the concept of one-shot deep learning for equilibrium sampling, but scalability to large systems has remained a major challenge. Here, we overcome this scaling limitation with a Boltzmann Generator architecture that can model large materials systems. Our approach combines augmented coupling flows with graph neural networks to exploit local environments, enabling energy-based training and rapid inference. Compared to previous designs, it trains faster, uses fewer resources, and achieves superior sampling efficiency. Crucially, it transfers to much larger system sizes, allowing efficient sampling of materials with simulation cells exceeding a thousand atoms. We demonstrate its capabilities on Lennard-Jones crystals, mW water ice phases, and the silicon phase diagram, producing accurate equilibrium ensembles and free energies across scales where finite-size effects vanish.

Absence of co-occurrence between HER2 amplification and dMMR/MSI in a large multicentric colorectal cancer cohort

Economic specialization and heterogeneous temperature-economy relationships suggest net costs of climate change in Europe

Abstract Econometric studies of temperature and GDP imply that warming harms hot countries, benefits cool ones, and that a single globally optimal temperature exists. We show that such aggregate relationships mask substantial spatial and sectoral heterogeneity and can mislead mitigation and adaptation policy. Using administrative district-level data for Europe on Gross Value Added (GVA) and GDP growth, we estimate the contemporaneous effects of temperature at national, district, and industry scales. In contrast to earlier global studies, warmer-than-average years reduce growth in relatively cold districts (0–14°C) and raise it in warmer regions (>14°C), with the pattern reversing at the extremes (<0°C and >20°C). This U-shaped relationship implies an average effect across Europe of -0.19 percentage points on annual growth, rather than the +0.18 benefit reported previously. Under RCP4.5, annual growth falls by 0.20 to 1.24 percentage points by 2070–2099, highlighting local temperature optima and heterogeneous vulnerabilities both within countries and across regions and sectors.

Hypericin and berberine promote elongation of hair peg-like sprouting in hair follicle organoids via oxytocin signaling activation

SARS-CoV-2 Omicron BA.2.86 and JN.1 expand tropism in human proximal intestinal epithelium

Abstract Omicron SARS-CoV-2 has diversified into multiple sub-lineages, complicating assessment of their intrinsic phenotypes due to background population immunity. We compare replication and biological characteristics of variants from BA.1 to JN.1 using human bronchial and lung explants, airway organoids, colon cells, and proximal intestinal enteroids. XBB.1.5 and EG.5.1 achieve higher replication titres in respiratory tissues than BA.2.86 and JN.1, indicating enhanced respiratory fitness. EG.5.1 displays dual cell-entry pathways and greater replication in alveolar epithelial cells, supporting increased lung tropism and pathogenicity. In contrast, BA.2.86 and JN.1 rely on TMPRSS2-mediated entry in airways. Notably, BA.2.86 and JN.1 replicate more efficiently than EG.5.1 in proximal intestinal enteroids in an ACE2- and TMPRSS2-dependent manner, but not in colon cells. JN.1 exhibits elevated intestinal tropism with limited proinflammatory cytokine induction, suggesting potential for faecal transmission. Here we show XBB.1.5 and EG.5.1 greater transmissibility and severity potential whereas BA.2.86 and JN.1 exhibit enhanced intestinal adaptation.

DHX33 inhibitors induced transcriptional changes for a subset of genes in cancer cells