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Environmental modelling of climatic sensitivity and performance stability of semi-transparent photovoltaic systems in a tropical coastal region
Abstract This study investigates the climatic sensitivity and long-term performance stability of a semi-transparent photovoltaic (STPV) system operating in a tropical coastal region of Indonesia. Using a decade of daily meteorological data (2012–2022), we developed a multivariate regression-based environmental modelling approach to evaluate the influence of key climatic variables on performance ratio (PR) and energy yield. Three modelling structures were considered, including a full-variable model, a simplified model based on global tilted irradiance (GTI) and ambient temperature, and a constant PR benchmark. The results indicate that GTI and temperature are the dominant climatic drivers, accounting for most of the meaningful variability in PR. The simplified GTI-temperature model achieved predictive performance comparable to the full model, suggesting that a parsimonious formulation can retain most of the explanatory power while reducing data requirements. The estimated PR values ranged between 0.78 and 0.80, consistent with reported values for tropical photovoltaic systems. Despite observable seasonal and interannual climatic variability, the system exhibited relatively stable performance over the study period, with no clear monotonic decline in energy yield. These findings highlight the applicability of simplified environmental models for performance assessment and planning in data-scarce tropical coastal regions.
Impact of boundary conditions and geometric tolerances dynamics on MEMS double touch mode capacitance pressure sensor applying M-shaped silicon design
Assessment of construction waste aggregates as a road raw material using response surface methods
A partially quantum‑informed crystal graph network for direct adsorption energy prediction in catalysis
AOC1 regulates labor initiation through spermidine-induced autophagy of placental trophoblast cells via EIF5A hypusination
Bioremediation of heavy metal contamination: recent developments and future directions
Abstract Heavy metal pollution has silently insinuated itself into the fabric of modern life – from the vegetables on our dinner plates and the tap water we drink to the cosmetics we use daily. This reality underscores global environmental and public health crises intensified by industrialization and urban expansion. Conventional physical and chemical remediation methods for heavy metals, while effective to a degree, often involve prohibitive costs and risk disrupting the delicate balance of the original ecosystem. Consequently, the search for green, sustainable, and economically viable remediation alternatives has become imperative. This special issue brings together nine cutting-edge research papers that explore recent advances in heavy metal pollution control and resource recovery from diverse angles – including microbial remediation, plant–microbe combined approaches, bioleaching for resource utilization, soil amendment applications, and the ecological toxicity of nanoparticles. Collectively, these studies offer theoretical insights and novel practical strategies to support the development of efficient and sustainable technologies for managing heavy metal contamination. These research results can pave the way for deeper investigation into the efficacy of the proposed remediation with the eventual aim of taking the science from the lab to the field.
HRS-7535, an oral small-molecule GLP-1 receptor agonist, in Chinese adults with obesity without diabetes: a randomized, double-blind, placebo-controlled phase 2 trial
Correlation of ultrasonic welding parameters with microstructural evolution and mechanical-electrical reliability of Al-CuNi joints for energy storage systems
Fluorouridine labeling enables spatiotemporal mapping of transcriptomes and enhancers in vivo
A study of calibrating seismic design response spectrum based on high-intensity ground motion records
Abstract The seismic design response spectrum is a vital parameter for determining the potential seismic load of the engineering structure. Differential evolution algorithm (DE) with a novel hybrid mutation operator is utilized to calibrate the spectral parameters in order to enhance iteration efficiency. This study calibrates the seismic design response spectrum for China based on the Chinese seismic intensity scale and compares the results with the Code for Seismic Design of Buildings (CSDB2010).The calibration spectra are based on strong ground motion records with destructive power exceeding the Chinese seismic intensity 7 and above. The characteristics of the calibration spectral parameters are analyzed and subsequently compared with the design spectra of the Code for Seismic Design of Buildings (CSDB2010). It is found that: Increasing the number of iterations can enhance the fitting goodness of DE between the calibration spectrum and the record response spectrum. The average site characteristic period ( T g ) for rock and hard soil site conditions (Class I and II) is greater than the T g specified in CSDB2010. The average T g for intensity 10 + is greater than the average T g for intensity 7, 8, and 9. T g increases with the seismic intensity. The average spectra platform value ( β max ) from this study gradually approaches the β max in CSDB2010 as the intensity increases. The average attenuation index ( γ ) at different intensities of this study is greater than γ in CSDB2010.
Structural basis for distinct protective mechanisms of IGHV3-23 antibodies targeting influenza hemagglutinin stem
Abstract Characterization of antibodies targeting the conserved stem domain of influenza hemagglutinin (HA) is critical for developing broadly protective countermeasures against the influenza virus. From a phage display human antibody library, this study discovers three group 1 HA-specific stem antibodies, namely HB31, HB34, and HB315, all of which are encoded by IGHV3-23. While HB31 and HB34 have minimal neutralization activity in vitro, their Fc-mediated effector functions lead to better in vivo protection than the potently neutralizing HB315. Consistently, cryo-EM analysis suggests that HB31 and HB34 have a higher Fc accessibility than HB315, based on their epitopes and approaching angles. HB31 and HB34 engage a pocket in the upper HA stem that is rarely targeted by known HA stem antibodies, whereas the epitope of HB315 involves the lower stem. Overall, our findings provide insights not only into the structure-function relationship of HA stem antibodies but also into the design of next-generation influenza therapeutics.
Pathogenicity of Cadophora luteo-olivacea on Quercus robur and multi-omics characterization of antagonism by Trichoderma atroviride
Abstract Pedunculate oak ( Quercus robur L.) is a foundation tree species in European forests and reforestation programs, but nursery propagated seedlings can harbor cryptic trunk diseases pathogens. Cadophora luteo-olivacea , known from grapevine trunk diseases, has been detected in oak nurseries, yet its pathogenicity on oak and interactions with antagonistic fungi remain unclear. We fulfilled Koch’s postulates for C. luteo-olivacea isolate CZ_395 on Q. robur seedlings under experimental inoculation conditions and quantified growth reduction of C. luteo-olivacea by Trichoderma atroviride isolate CZ_180 in dual culture. Proteomic and metabolomic profiling of the contact zone was performed at two post contact sampling points, 4 and 8 dpi, to identify candidate molecular signatures associated with the interaction. Inoculated seedlings developed extensive stem lesions (mean 11.9 cm), whereas controls showed minimal wound response (mean 0.9 cm; p < 0.001). In dual culture, T. atroviride reduced the visible colony development and radial growth of C. luteo-olivacea under the tested in vitro conditions. Contact zone proteomics revealed 257 differentially abundant proteins at 8 days, including cell wall targeting hydrolases, secreted proteases, oxidoreductases (44 upregulated), and transporters. Metabolomics detected contact specific changes in amino acids, central carbon intermediates, and lipid-associated features, including reduced ergosterol. This study demonstrates that C. luteo-olivacea can induce necrotic lesions in Q. robur under experimental inoculation conditions and identifies proteomic and metabolomic signatures associated with the interaction between T. atroviride and C. luteo-olivacea , providing a basis for nursery risk assessment and future evaluation of biocontrol potential.
Moderate-temperature DNA cleavage activity of TtAgo activated by dCTP/TthSSB for one-step isothermal microRNAs detection
Age, estimated basal metabolic rate, and subfoveal choroidal thickness: a cross-sectional study based on cataract patients
Abstract To investigate the relationship between the basal metabolic rate (BMR) and subfoveal choroidal thickness (SFCT) and the statistical mediating role of the BMR in age-related changes in SFCT. This cross-sectional study included 119 cataract surgery patients. BMR was calculated by the Mifflin–St Jeor equation, and SFCT was measured via swept-source OCT and its integrated software. Covariates included metabolic indices (e.g., triglyceride-glucose index, hemoglobin level, platelet count, systolic blood pressure, and relevant comorbidities), comorbidities (hypertension, diabetes mellitus, and cardiovascular/cerebrovascular events), and ocular parameters (including intraocular pressure and axial length). Associations were evaluated using linear regression (with nested models for multicollinearity) and bootstrap mediation analysis. BMR was positively correlated with SFCT after adjusting for axial length according to both univariate and multivariate linear regression analyses (all P < 0.05), but significance was lost in the nested models including age and sex because of multicollinearity (VIF > 5). Mediation analysis revealed that age had a total effect on SFCT of -4.4641 ( P < 0.01), with BMR mediating 27.71% of this effect (indirect effect: -1.2368, 95% CI: -2.0745 to -0.5340). In this cohort of cataract patients, BMR did not independently biological affect SFCT but served as a statistical mediating variable, partially elucidating the relationship between SFCT thinning and advancing age. A hypothesis was formulated that metabolic pathway regulation as a potential strategy for preserving age-related ocular health.
Probing the limits of genetic recoding using multi-omics-guided evolution
Predicting and Rationalizing Piezoelectricity in Racemic Bioorganic Molecular Crystals
ABSTRACT Racemic crystals, often presumed to favour centrosymmetric packing and lack polar functionality, conceal an unlikely and underexplored capacity for longitudinal piezoelectricity. Here, we report a broad computational exploration demonstrating that certain bioorganic racemic systems exhibit pronounced piezoelectric responses, comparable to leading single‐crystal materials. Despite their inherent mirror symmetry at the molecular level, the modelled crystal structures lack inversion symmetry, giving rise to significant electrical polarisation under mechanical stress, as predicted by our density functional theory (DFT) calculations. Our top‐performing stable racemic crystals exhibit effective longitudinal piezoelectric strain response of up to 28 pC/N, more than twice that of benchmark DL‐alanine (12.5 pC/N), combined with low dielectric constants (<5) and high mechanical flexibility. These results position our modelled racemic molecular systems as a promising, bio‐friendly platform for soft, sustainable and lead‐free piezoelectric devices. Our findings overturn conventional assumptions about the structural and functional limitations of racemic crystals as a material class, revealing their suitability for next‐generation piezoelectric applications. Six of the racemic organic systems studied exhibit piezoelectric strain coefficients exceeding 10 pC/N, highlighting their viability as efficient and environmentally friendly alternatives to conventional inorganic materials.
Therapeutic efficacy, safety and gametocyte clearance after antimalarial treatment of uncomplicated Plasmodium falciparum and Plasmodium vivax malaria in Northeast Ethiopia
Algal δ13C reveals climate changes during the Cambrian Explosion
A multi-label cascade flexible neural forest model for predicting the subcellular location of multi-site bacterial proteins
Abstract Predicting the subcellular localization of multi-site bacterial proteins remains challenging because label correlations, limited sample size, and low sequence similarity reduce the effectiveness of conventional multi-label classifiers. In this study, we propose a multi-label cascade flexible neural forest (MLCFN Forest) that combines label-powerset-style coding–classification–decoding with a cascade ensemble of flexible neural tree (FNT) groups. The framework preserves label dependencies, decomposes the induced multi-class task into coordinated binary FNT outputs, and progressively reallocates model capacity to low-confidence samples through confidence-guided sample propagation and feature enhancement across layers. We explicitly note that the cascade FNT backbone builds on our previous CFNForest studies for cancer subtype classification, whereas the present work adapts that backbone to multi-label bacterial protein localization by introducing label-set encoding/decoding, multiclass FNT grouping, and a multi-site protein subcellular localization evaluation workflow. Using Gram-negative and Gram-positive benchmark datasets together with AECA-PSSM and PSSM-DWT features, MLCFN Forest consistently outperformed ML-RBF, ML-KNN, ML-LOC, INSDIF, and MLASSO under jackknife evaluation. For example, it achieved OLA/OAA values of 78.3%/76.4% on the Gram-negative AECA-PSSM dataset, 80.7%/78.5% on the Gram-negative PSSM-DWT dataset, and 80.2%/77.6% on the Gram-positive AECA-PSSM dataset. PCA-based low-dimensional experiments further suggested that the model retained good predictive ability after substantial dimensionality reduction. The present study is limited to classical benchmark datasets and does not yet include an independent external test set, strict nested model selection, or direct benchmarking against protein language model predictors; these points are therefore discussed as limitations and future directions.