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P/TGMS (Photo/thermo-sensitive genic male sterile) lines are crucial resources for two-line hybrid rice breeding. Previous studies revealed that slow development is a general mechanism for sterility-fertility conversion of P/TGMS in Arabidopsis. However, the difference in P/TGMS genes between rice and Arabidopsis suggests the presence of a distinct P/TGMS mechanism in rice. In this study, we isolated a novel P/TGMS line, ostms19, which shows sterility under high-temperature conditions and fertility under low-temperature conditions. OsTMS19 encodes a novel pentatricopeptide repeat (PPR) protein essential for pollen formation, in which a point mutation GTA(Val) to GCA(Ala) leads to ostms19 P/TGMS phenotype. It is highly expressed in the tapetum and localized to mitochondria. Under high temperature or long-day photoperiod conditions, excessive ROS accumulation in ostms19 anthers during pollen mitosis disrupts gene expression and intine formation, causing male sterility. Conversely, under low temperature or short-day photoperiod conditions, ROS can be effectively scavenged in anthers, resulting in fertility restoration. This indicates that ROS homeostasis is critical for fertility conversion. This relationship between ROS homeostasis and fertility conversion has also been observed in other tested rice P/TGMS lines. Therefore, we propose that ROS homeostasis is a general mechanism for the sterility-fertility conversion of rice P/TGMS lines.

Energy-energy correlators (EECs) are promising observables to study the dynamics of jet evolution in the quark-gluon plasma (QGP) through its imprint on angular scales in the energy flux of final-state particles. We carry out the first complete calculation of EECs using realistic simulations of high-energy heavy-ion collisions and dissect the different dynamics underlying the final distribution through analyses of jet propagation in a uniform medium. The EECs of γ-jets in heavy-ion collisions are found to be enhanced by the medium response from elastic scatterings instead of induced gluon radiation at large angles. In the meantime, EECs are suppressed at small angles due to energy loss and transverse momentum broadening of jet shower partons. These modifications are further shown to be sensitive to the angular scale of the in-medium interaction, as characterized by the Debye screening mass. Experimental verification and measurement of such modifications will shed light on this scale and the short-distance structure of the QGP in heavy-ion collisions.

Abstract Background Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. Results We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. Conclusions During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.

Pseudopoda Jäger, 2000 is currently the largest genus in the family Sparassidae Bertkau, 1872, comprising 255 species. Of these, 154 species have been recorded in China, representing 60.4% of the global species.In October 2023, a spider survey was conducted in Shennongjia National Nature Reserve, Hubei Province, China. After examination and morphological comparison, one new species of the genus Pseudopoda was identified and is described here.In this paper, a new species of Pseudopoda collected in Hubei Province, China, is proposed and named Pseudopoda guanmenshan sp. nov. A detailed description, diagnosis, photographs and distribution map of the new species are provided.

Abstract Urban open spaces (UOS) are crucial for urban life, offering benefits across individual and societal levels. However, the understanding of the systematic dynamic of UOS scaling with city size and its potential non-linear performance remains a limited clarity area. This study bridges this gap by integrating urban scaling laws with remote sensing data from 1990 to 2020, creating a framework to analyze UOS trends in China. Our findings reveal that UOS growth is sub-linear scaling with city size, exhibiting economies of scale with scaling exponents between 0.55 and 0.65 and suggesting potential shortages. The distribution structure of UOS across cities is becoming increasingly balanced, as indicated by the rising Zipf’s slope from 0.66 to 0.88. Southeastern coastal cities outperform, highlighting spatial variations and path dependency in UOS development. Additionally, using metrics of Scale-adjusted metropolitan indicator (SAMI) and the ratio of open space consumption to population growth rates (OCRPGR), we observe a trend towards more coordinated development between UOS and population, with a declining proportion of uncoordinated cities. Our long-term, large sample coverage study of UOS in China may offer positive significance for urban ecological planning and management in similar rapidly urbanizing countries, contributing to critical insights for quantifying and monitoring urban sustainable development.

Introduction Osteoporotic fractures are a leading cause of disability and contribute significantly to medical care costs worldwide. Variations in bone mineral density and the risk of osteoporosis are notably influenced by altitude. This study aims to longitudinally examine individuals with osteoporosis and low bone mass at three different altitudes (low, high and very high) to understand the effects of high-altitude environments on bone density.Methods and analysis This multicentre, prospective cohort study will involve 893 participants divided into three groups based on altitude: low (500–1500 m), high (2500–4500 m) and very high (4500–5500 m). Participants will undergo comprehensive diagnostic assessments, including demographic data collection, structured questionnaires, medical examinations and clinical laboratory tests. Follow-up visits will occur annually for a minimum of 5 years. The primary outcome will be changes in bone mineral density values. Secondary outcomes will include the incidence of osteoporosis and osteoporotic fractures. Cox proportional hazard models will be used to calculate the risk associated with osteoporotic events and related fractures.Ethics and dissemination The study has been approved by the Institutional Review Board of the Hospital of Chengdu Office of People’s Government of Tibetan Autonomous Region (No: 2024-70). The acquired insights will be disseminated via academic forums, scholarly articles and stakeholder engagement sessions.Trial registrationnumber ChiCTR2300078872.

The diffusion wake accompanying the jet-induced Mach cone provides a unique probe of the properties of quark-gluon plasma in high-energy heavy-ion collisions. It can be characterized by a depletion of soft hadrons in the opposite direction of the propagating jet. We explore the 3D structure of the diffusion wake induced by γ-triggered jets in Pb+Pb collisions at the LHC energy within the coupled linear Boltzmann transport and hydro model. We identify a valley structure caused by the diffusion wake on top of a ridge from the initial multiple parton interaction (MPI) in jet-hadron correlation as a function of rapidity and azimuthal angle. This leads to a double-peak structure in the rapidity distribution of soft hadrons in the opposite direction of the jets as an unambiguous signal of the diffusion wake. Using a two-Gaussian fit, we extract the diffusion wake and MPI contributions to the double peak. The diffusion wake valley is found to deepen with the jet energy loss as characterized by the γ-jet asymmetry. Its sensitivity to the equation of state and shear viscosity is also studied.

To analyze the speciation distribution of arsenic in agaric, shiitake mushroom, matsutake and agrocybe, the ion chromatography-inductive coupled plasma mass spectrometer (IC-ICP-MS) was used to determine arsenobetaine, dimethyl arsenic, arsenous acid, arsenic choline, monomethyl arsenic and arsenic acid, and the methodological investigation and content determination were carried out. The results showed that the method could completely separate all six arsenic forms within 5 minutes, and the peak patterns were good. The linear relationship of the method was good (mass concentration of 0.5~20 μg/L, r>0.999). The detection limit and quantification limit of six arsenic species were not more than 0.005 and 0.017 mg/kg respectively. The recovery rate of six arsenic forms in agaric, agrocybe and shiitake mushroom could reach 80%~120% with standard addition. For matsutake, the standard addition recovery rate could also reach 80%~120% when adding right standard amounts (0.05 mg/kg dimethyl arsenic, arsenic choline and arsenic acid; 0.2 mg/kg arsenite and monomethyl arsenic; 5 mg/kg arsenic betaine). Combined with the dehydration rate of dry products, the inorganic arsenic content of the tested samples met the requirements of GB 2762-2022. The content of total arsenic in matsutake was the highest, but the proportion of inorganic arsenic（arsenic choline+arsenic acid） in total arsenic was the lowest 3.6%~6.8%, and the highest proportion was arsenobetaine (75.8%~87.3%). The main form of arsenic in agaric, agrocybe, and shiitake mushroom were inorganic arsenic. The proportion of inorganic arsenic to total arsenic could reach 58.4%~66.1%、60.0%~66.7%、81.2%~91.7%, respectively. There was a risk of food safety when the total arsenic content was high.

Abstract Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive types of squamous cell carcinoma and represents a significant proportion of esophageal cancer. Metabolic reprogramming plays a key role in the occurrence and development of ESCC. Unsupervised clustering analysis was employed to stratify ESCC samples into three clusters: MPC1-lipid type, MPC2-amino acid type, and MPC3-energy type, based on the enrichment scores of metabolic pathways extracted from the Reactome database. The MPC3 cluster exhibited characteristics of energy metabolism, with heightened glycolysis, cofactors, and nucleotide metabolism, showing a trend toward increased aggressiveness and poorer survival rates. On the other hand, MPC1 and MPC2 primarily involved lipid and amino acid metabolism, respectively. In addition, liquid chromatography‒mass spectrometry-based metabolite profiles and potential therapeutic agents were explored and compared among ESCC cell lines with different MPCs. MPC3 amplified energy metabolism markers, especially carnitines. In contrast, MPC1 and MPC2 predominantly had elevated levels of lipids (primarily triacylglycerol) and amino acids, respectively. Furthermore, MPC3 demonstrated a suboptimal clinical response to PD-L1 immunotherapy but showed increased sensitivity to the doramapimod chemotherapy regimen, as evident from drug sensitivity evaluations. These insights pave the way for a more personalized therapeutic approach, potentially enhancing treatment precision for ESCC patients.

Abstract Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institute’s L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.

As an exogenous pollutant, the microplastics accumulated in soil could alter microbial activity. The use of plastic mulch in greenhouse vegetable planting not only enhances vegetable yield and quality, but also leads to the formation and accumulation of microplastics in the soil over time. It is essential to determine the characteristics of microplastics and microorganisms in soils with varying years of planting is crucial for ensuring vegetable quality and quantity. Therefore, this study investigated the abundance and particle size of microplastics, the concentration of phthalates (PAEs), and the dynamics of soil microbial activity in greenhouse soils with different planting years (5, 10, 15, 20, 25, and 30 years). Results showed that microplastics increased in abundance, particle size, and PAEs concentration as planting years progressed. Specifically, the abundance of microplastics rose from 70.0 ± 8.7 to 224.0 ± 10.4 items/kg, with the proportion of microplastics sized 0–2 mm increasing from 14.02 to 69.11 %, and the total PAEs concentration in the soil escalating from 0.31 to 1.89 mg/kg. Additionally, Bacteroidetes and Actinobacteria levels increased, correlating with organic matter degradation. Metabolic pathway linked to degradation were enriched according to KEGG analysis. Correlation analysis revealed that microplastics notably decreased soil pH, creating an acidic environment that boosted urease activity and the relative abundance of Nitrospirae. This study sheds light on the accumulation characteristics of microplastics and their impact on soil microbial activity following prolonged planting.