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Two new C-glucofuranosyl flavones apigenin 6-C-β-glucofuranoside (1) and apigenin 6-C-α-glucofuranoside (2) together with four known compounds (3–6) were isolated from the flowers of Gypsophila oldhamiana. Their structures were elucidated on the basis of extensive spectroscopic analyses. These compounds were evaluated for the cytotoxic activities against four human cancer cell lines and did not exhibit any significant bioactivities (IC50 values > 10 μM). [ABSTRACT FROM AUTHOR]

Abstract Purpose To evaluate the impact of Implantable Collamer Lens (ICL) implantation on anterior chamber angle parameters and posterior segment structures in highly myopic eyes and explore potential correlations between these changes. The study aimed to assess alterations in superficial and deep vessel density (SVD, DVD), foveal avascular zone (FAZ) area, and retinal nerve fiber layer (RNFL) thickness to clarify the safety profile of ICL implantation. Methods Prospective observational study, included 36 highly myopic eyes undergoing ICL implantation in surgery group and 23 non-surgical control eyes in non-surgery group. Anterior chamber parameters were assessed using AS-OCT, and posterior segment changes, including SVD, DVD, FAZ, and RNFL, were evaluated using OCT and OCTA preoperatively and at intervals up to 3 months postoperatively. Statistical analyses included paired t-tests, Wilcoxon tests, and Spearman correlation. Results ICL implantation significantly improved uncorrected distance visual acuity (UDVA) (p

Abstract Sepsis is a life-threatening severe organ dysfunction, and the pathogenesis remains uncertain. Increasing evidence suggests that circRNAs, mRNAs, and microRNAs can interact to jointly regulate the development of sepsis. Identifying the interaction between ceRNA regulatory networks and sepsis may contribute to our deeper understanding of the pathogenesis of sepsis, bring new insights into early recognition and treatment of sepsis. Blood samples from sepsis patients in the Affiliated Hospital of Southwest Medical University were collected. RNA sequencing (mRNA/circRNA) was performed on Survivor group (n = 26) and Non-survivor group (n = 6), then quality control and differential expression analysis were performed. Subsequently, GO analysis was performed on the differential expression genes; Meta-analysis was used to screen for prognostic related genes; 10 × Single-cell RNA sequencing was used to annotate the cell distribution of core genes. Finally, combined with base complementary pairing and intergroup correlation analysis, a sepsis-associated circRNA-miRNA-mRNA regulatory network was constructed. Differential expression analysis screened 28 mRNAs and 16 circRNAs. GO results showed that differential expression genes were mainly involved in membrane raft, actin cytoskeleton, regulation of immune response, negative regulation of cAMP-dependent protein kinase activity, etc. Meta-analysis screened 2 core genes, GSPT1 and NPRL3, which are associated with sepsis prognosis. 10 × Single-cell RNA sequencing showed that GSPT1 and NPRL3 were widely localized in immune cells, mainly macrophages and T cells. A ceRNA network consisting of 4 circRNA, 26 miRNA, and 2 mRNA was constructed. GSPT1 and NPRL3 were lowly expressed in the sepsis Survivor group, compared with Non-survivor group, which may become novel prognostic biomarkers for sepsis. A sepsis-related ceRNA networks, which consists of 4 circRNA, 26 miRNA, and 2 core gene, may guide mechanistic studies.

Abstract Pancreatic cancer is a lethal disease with an insidious onset, and little is known about its early molecular events. Here, we found that the sterol regulatory element-binding protein 1 (SREBP1) expression is gradually upregulated during the initiation of pancreatic cancer. Through in vitro 3D culture of pancreatic acinar cells and experiments in LSL-Kras G12D/+ ;Pdx1-Cre (KC) mice, we found that pharmacological inhibition of SREBP1 suppressed pancreatic tumorigenesis. In vitro, either knockdown or pharmacological inhibition of SREBP1 suppressed tumor proliferation but SREBP1 overexpression promoted tumor proliferation. In LSL-Kras G12D/+ ;Trp53 fl/+ ;Pdx1-Cre (KPC) mice, we confirmed the tumor-promoting role of SREBP1 in pancreatic cancer progression. Mechanistically, we revealed SOX9 as a downstream target of SREPB1. SREBP1 inhibition decreased SOX9 expression in both acinar cells and pancreatic cancer cells. Indeed, we identified SREBP1 binding sites in the SOX9 promoter region and reported that SOX9 is transcriptionally regulated by SREBP1. Taken together, our findings demonstrate that SREBP1/SOX9 inhibition suppresses pancreatic cancer initiation and growth, suggesting that SREBP1 could serve as a potential target for cancer screening and treatment.

Abstract Retinal blood vessels are the only blood vessels in the human body that can be observed non-invasively. Changes in vessel morphology are closely associated with hypertension, diabetes, cardiovascular disease and other systemic diseases, and computers can help doctors identify these changes by automatically segmenting blood vessels in fundus images. If we train a highly accurate segmentation model on one dataset (source domain) and apply it to another dataset (target domain) with a different data distribution, the segmentation accuracy will drop sharply, which is called the domain shift problem. This paper proposes a novel unsupervised domain adaptation method to address this problem. It uses a teacher–student framework to generate pseudo labels for the target domain image, and trains the student network with a combination of source domain loss and domain adaptation loss; finally, the weights of the teacher network are updated from the exponential moving average of the student network and used for the target domain segmentation. We reconstructed the encoder and decoder of the network into a full-resolution refined model by computing the training loss at multiple semantic levels and multiple label resolutions. We validated our method on two publicly available datasets DRIVE and STARE. From STARE to DRIVE, the accuracy, sensitivity, and specificity are 0.9633, 0.8616,and 0.9733, respectively. From DRIVE to STARE, the accuracy, sensitivity, and specificity are 0.9687, 0.8470, and 0.9785, respectively. Our method outperforms most state-of-the-art unsupervised methods. Compared with domain adaptation methods, our method also has the best F1 score (0.8053) from STARE to DRIVE and a competitive F1 score (0.8001) from DRIVE to STARE.

Abstract Background Hepatocellular carcinoma (HCC), a malignant tumor that seriously endangering health, has aroused widespread concern in the field of public health. Previous researches have noted the relationships between immune cells and HCC, but the causal relationship was uncertain. Methods In this study, a bidirectional two sample Mendelian randomization (MR) analysis was utilized to access the causal relationship between immune cell characteristics and HCC. According to the open-access data, we investigated the causal relationship between 731 immune cell characteristics and HCC risk. Results After screening by IVW approach, increased levels of 8 immune traits and reduced levels of 7 immune traits could lead to changes in HCC risk. These 15 immune cells were distributed in the Monocyte (4 cells), Treg panel (4 cells), TBNK (3 cells), Maturation stages of T cell panel (3 cells), and cDC panel (1 cells). Furthermore, HCC was identified to have causal effects on 21 immunophenotypes. Among these immune cells, hepatocarcinogenesis had the greatest impact on CD4 on EM CD4 + and CD33 on Mo MDSC. Conclusions This study enhances our comprehension of the interaction between immune cells and HCC risk, furnishing novel avenues to explore the mechanisms of HCC.

Abstract To investigate the potential association between body mass index (BMI) and the clinicopathological features of patients with clear cell renal cell carcinoma (ccRCC). We retrospectively analyzed data from 2541 patients who underwent partial or radical nephrectomy for renal masses between 2013 and 2023 in a single institution. Patients were divided into normal-weight, overweight, and obese groups based on the Chinese BMI classification. Clinicopathological features, including pathologic tumor size, pathologic T (pT) stage, Fuhrman grade or WHO/ISUP grade, renal capsular invasion, perirenal fat or renal sinus fat invasion, and vein cancerous embolus were compared among the groups using Student’s t-test or one-way ANOVA for normally distributed continuous variables, and the chi-square or Fisher’s test for categorical variables. A total of 2541 ccRCC patients having a median BMI of 24.9 (interquartile range 22.7–27.0) were evaluated. No significant association was found between the pathological tumor diameter and BMI among the normal-weight, overweight, and obese groups (normal-weight vs. overweight, p = 0.31; normal-weight vs. obese, p = 0.21). There was no statistical difference in pT stage (normal-weight vs. overweight, p = 0.28; normal-weight vs. obese, p = 0.23). No statistically significant difference was observed in the distribution of Fuhrman/ISUP grade (p = 0.12), proportion of patients with renal capsular invasion (p = 0.49), perirenal fat or renal sinus fat invasion (p = 1.00), and vein cancerous embolus (p = 0.11) between the normal-weight and overweight groups. However, patients in the obese group tended to have low Fuhrman or WHO/ISUP grades (p

Abstract Ultraviolet (UV) detection is extensively used in a variety of applications. However, the storage and processing of information after detection require multiple components, resulting in increased energy consumption and data transmission latency. In this paper, a reconfigurable UV photodetector based on CeO2/SrTiO3 heterostructures is demonstrated with in-sensor computing capabilities achieved through interface engineering. We show that the non-volatile storage capability of the device could be significantly improved by the introduction of an oxygen reservoir. A photodetector array operated as a single-layer neural network was constructed, in which edge detection and pattern recognition were realized without the need for external memory and computing units. The location and classification of corona discharges in real-world environments were also simulated and achieved an accuracy of 100%. The approach proposed here offers promising avenues and material options for creating non-volatile smart photodetectors.

Abstract We explore the decay of bound neutrons in the JUNO liquid scintillator detector into invisible particles (e.g., $$n\rightarrow 3 \nu $$ n → 3 ν or $$nn \rightarrow 2 \nu $$ n n → 2 ν ), which do not produce an observable signal. The invisible decay includes two decay modes: $$ n \rightarrow { inv} $$ n → inv and $$ nn \rightarrow { inv} $$ n n → inv . The invisible decays of s-shell neutrons in $$^{12}\textrm{C}$$ 12 C will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $${\bar{\nu }}_e$$ ν ¯ e , natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $$\tau /B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, \textrm{years}$$ τ / B ( n → inv ) > 5.0 × 10 31 years and $$\tau /B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, \textrm{years}$$ τ / B ( n n → inv ) > 1.4 × 10 32 years .

Abstract Background Quantitative methods for estimating the workload of primary healthcare (PHC) workers are essential for improving the performance of PHC institutions. However, measuring the workload of PHC workers is challenging due to the diverse and complex range of services covered by PHC. This study aims to use an equivalent value (EV)-based approach to assess the workload of PHC workers and inform policymakers about the current workload burden in Beijing, China. Methods The EV-based workload assessment system was designed by three main steps: identifying the list of essential PHC service items provided by PHC workers, quantifying the EV of each service item, and calculating the corresponding workload for PHC workers and community health centers (CHCs). The study included 18 CHCs, which were divided into three groups based on population density and topography: Group I (eight urban CHCs), Group II (six CHCs in semi-mountainous areas), and Group III (four CHCs in mountainous areas). Data were collected from local health information system, which automatically collected real-time service volume data for 500 PHC service items at 18 CHCs in the sample district in Beijing from 2017 to 2021. Results This study identified 503 essential PHC service items and defined their EVs. The theoretical full-capacity workload per PHC worker was 6024 EVs, serving as the base workload. The actual annual workload per PHC worker was 7240.0 EVs during 2017–2021. The base workload per budgeted position for the three types of CHCs was 6468.6 EVs for Group I, 5268.5 EVs for Group II, and 5038.7 EVs for Group III. Compared with the actual workload of 7702.3 EVs, 6568.3 EVs, and 5979.0 EVs in each group, respectively, all PHC workers in the sample district were overburdened during the study period. Conclusions The EV-based method provides a feasible solution for comprehensively assessing the workload of publicly funded PHC institutions in other regions. This study offers valuable insights to help local policymakers understand the workload burden of PHC workers, objectively evaluate their performance, and guide future health workforce planning.

Abstract Background Breast cancer (BC) is the most prevalent malignancy in women. Potential therapeutic targets for BC are of great significance. In our previous study, we found that prenylated rab acceptor 1 domain family member 2 (PRAF2) is an oncogene in BC. However, the exact mechanism of PRAF2 in BC cancer promotion is still not fully understood. Methods Pan-cancer analysis of PRAF2 was performed in the TIMER, Kaplan‒Meier, UALCAN and GEPIA databases.The prognostic value of PRAF2 in BC was investigated in the GEPIA database. The influence of PRAF2 on immune infiltration in BC was analyzed in the TISIDE and TIMER databases. Finally, we validated the expression of PRAF2 in our institutional samples. After downregulating PRAF2 in two BC cell lines, we tested cell proliferation by CCK-8 and Wound healing assays. Results PRAF2 was highly expressed in various cancers, including BC, and in most BC cell lines. Higher expression of PRAF2 indicated poorer overall survival (OS) but not disease-free survival (DFS). Higher expression of PRAF2 is an independent prognostic factor in BC.PRAF2 is more highly expressed in BC than in the corresponding normal tissues. Downregulation of PRAF2 in BC can significantly inhibit viability and migration. Conclusions PRAF2 is highly expressed in various cancers, including BC. The expression of PRAF2 is related to Liquid–Liquid Phase Separation in BC. Finally, PRAF2 is upregulated in BC based on our institutional data. Downregulation of PRAF2 significantly inhibits cellular viability、migration in BC. PRAF2 may be a potential biomarker and therapeutic target for BC.

Abstract The Caco-2 cell model has been widely used to assess the intestinal permeability of drug candidates in vitro, owing to its morphological and functional similarity to human enterocytes. While Caco-2 cell assay is considered safe and cost-effective, it is also characterized by being time-consuming. Therefore, computational models that achieve high accuracies in predicting Caco-2 permeability are crucial for enhancing the efficiency of oral drug development. In this study, we conducted an in-depth analysis of the characteristics of an augmented Caco-2 permeability dataset, and evaluated a diverse range of machine learning algorithms in combination with different molecular representations. The results indicated that XGBoost generally provided better predictions than comparable models for the test sets. In addition, we investigated the transferability of machine learning models trained on publicly available data to internal pharmaceutical industry datasets. Our findings, based on the Shanghai Qilu’s in-house dataset, showed that the boosting models retained a degree of predictive efficacy when applied to industry data. Furthermore, Y-randomization test and applicability domain analysis were employed to assess the robustness and generalizability of these models. Matched Molecular Pair Analysis (MMPA) was utilized to extract chemical transformation rules. We believe that the model developed in this study could represent a reliable tool for assessing Caco-2 permeability during early-stage drug discovery and the chemical transformation rules derived here could provide insights for optimizing Caco-2 permeability. Scientific contribution A comprehensive validation of various machine learning algorithms combined with diverse molecular representations on a large dataset for predicting Caco-2 permeability was reported. The transferability of machine learning models trained on publicly available data to internal pharmaceutical industry datasets was also investigated. Matched molecular pair analysis was carried out to provide reasonable suggestions for researchers to improve the Caco-2 permeability of compounds. Graphical Abstract

IntroductionClimate change isone of the major challenges facing the world today, causing frequent extreme weather events that significantly impact human production, life, and the ecological environment. Traditional climate prediction models largely rely on the simulation of physical processes. While they have achieved some success, these models still face issues such as complexity, high computational cost, and insufficient handling of multivariable nonlinear relationships.MethodsIn light of this, this paper proposes a hybrid deep learning model based on Transformer-Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) to improve the accuracy of climate predictions. Firstly, the Transformer model is introduced to capture the complex patterns in cimate data time series through its powerful sequence modeling capabilities. Secondly, CNN is utilized to extract local features and capture short-term changes. Lastly, LSTM is adept at handling long-term dependencies, ensuring the model can remember and utilize information over extended time spans.Results and DiscussionExperiments conducted on temperature data from Guangdong Province in China validate the performance of the proposed model. Compared to four different climate prediction decomposition methods, the proposed hybrid model with the Transformer method performs the best. The resuts also show that the Transformer-CNN-LSTM hybrid model outperforms other hybrid models on five evaluation metrics, indicating that the proposed model provides more accurate predictions and more stable fitting results.

The lack of a sufficient and efficient way to simultaneously perceive general underwater mechanical stimuli, physical contact, and fluidic flow has been a bottleneck for many aquatic applications. To address this challenge, dynamics‐oriented underwater mechanoreceptor interface (DOUMI), a bioinspired mechanoreception system that realizes simultaneous contact and flow perception using a single receptor, is introduced. This receptor, response‐elevated‐and‐expanded hair‐like tactile mechanoreceptor (REEM), is inspired by the mechanoreceptive mechanism of aquatic arthropods. REEM combines structural features from different mechanoreceptive sensilla, enabling it to capture a wide range of stimulus dynamics. Under different stimuli, REEM encodes stimuli dynamics as its oscillations with distinct spectral attributes. Those oscillations are efficiently transferred through mechanical processes and imaging, enabling vision‐based extraction and further analysis. Therefore, by evaluating the oscillation dynamics with tailored wavelet‐based indices, DOUMI can distinguish between contact‐ and flow‐induced oscillations at each receptor unit with 90.5% accuracy. Furthermore, DOUMI provides comprehensive 2D mechanoreception with a scalable array of REEMs, delivering capabilities like stimuli spatiotemporal visualization, flow trend detection, and scenario classification with an accuracy of 99.5%. With its robustness and operational efficiency in underwater environments, DOUMI can be easily adapted to existing applications using common materials and hardware, establishing a new, streamlined paradigm for underwater general mechanoreception.

Abstract The human muscle bundle generates versatile movements with synchronous neurosensory, enabling human to undertake complex tasks, which inspires researches into functional integration of motions and sensing in actuators for robots. Although soft actuators have developed diverse motion capabilities utilizing the inherent compliance, the simultaneous‐sensing approaches typically involve adding sensing components or embedding certain‐signal‐field substrates, resulting in structural complexity and discrepant deformations between the actuation parts with high‐dimensional motions and the sensing parts with heterogeneous stiffnesses. Inspired by the muscle‐bundle multifiber mechanism, a multicavity functional integration (McFI) approach is proposed for soft pneumatic actuators to simultaneously realize multidimensional motions and sensing by separating and coordinating active and passive cavities. A bio‐inspired interweaving foldable endomysium (BIFE) is introduced to construct and reinforce the multicavity chamber with optimized purposive foldability, enabling 3D printing single‐material fabrication. Performing elongation, contraction, and bidirectional bending, the McFI actuator senses its spatial position, orientation, and axial force, based on the kinematic and sensing models built on multi‐cavity pressures. Two McFI‐actuator‐driven robots are built: a soft crawling robot with path reconstruction and a narrow‐maneuverable soft gripper with object exteroception, validating the practicality in stand‐alone use of the actuator and the potential for intelligent soft robotic innovation of the McFI approach.

Background Numerous studies have demonstrated limited survival benefits of transarterial chemoembolization (TACE) alone in the treatment of intermediate-stage hepatocellular carcinoma (HCC) beyond up-to-seven criteria. The advent of immunotherapy, particularly immune checkpoint inhibitors (ICIs), has opened new avenues for HCC treatment. However, TACE combined with ICIs has not been investigated for patients with intermediate-stage HCC beyond the up-to-seven criteria. The study aims to evaluate the efficacy and safety of this treatment strategy for such patients.Methods In this single-arm, prospective, phase II study, we enrolled eligible patients with HCC who were treated with TACE plus programmed cell death protein 1 (PD-1) inhibitors (sintilimab) from April 2021 to February 2023. The study’s primary objectives were to assess progression-free survival (PFS) and safety. Secondary objectives included measuring the objective response rate (ORR) and disease control rate (DCR) as per both Response Evaluation Criteria in Solid Tumors (RECIST) V.1.1 and modified RECIST (mRECIST) criteria, as well as overall survival (OS). Additionally, we conducted correlation analyses to identify predictors influencing the efficacy of tumor treatment.Result 20 patients participated in this study, with a median follow-up duration of 22.0 months. Median PFS was 8.4 months (95% CI: 4.7 to 19.7) according to both RECIST V.1.1 and mRECIST. The ORR was 30.0% (95% CI: 14.6% to 51.9%) per RECIST 1.1% and 60% (95% CI: 38.7% to 78.1%) per mRECIST. DCR was 95.0% (95% CI: 76.4% to 99.1%) according to both RECIST V.1.1 and mRECIST. Median OS was not yet reached. Notably, 20% (4/20) of patients underwent successful conversion to curative surgical resection. Treatment-related adverse events (TRAEs) mainly included elevated aspartate aminotransferase levels (19/20, 95.0%), elevated alanine aminotransferase levels (18/20, 90.0%), hypothyroidism (18/20, 90.0%), and reduced appetite (10/20, 50.0%). Among all participants, only one experienced grade 3 TRAE (myocarditis). We employed the Elastic Net regression model to analyze radiomic features from tumor and peritumoral areas to predict the efficacy of this treatment strategy.Conclusion TACE plus PD-1 inhibitors demonstrated promising efficacy and an acceptable safety profile, suggesting it as a potential treatment option for patients with intermediate-stage HCC beyond up-to-seven criteria. Furthermore, our study indicates that specific image-based features may serve as predictors for patients likely to benefit from this treatment approach.Trial registration number NCT04842565.

The large particle size asphalt mixture with nominal maximum aggregate size 53 mm(LSAM-50) has good technical and economic performance and will become an effective technical way to build a full-thick long-life asphalt pavement with Chinese characteristics. In order to reveal the mechanical properties and influencing factors of LSAM-50 in depth, a numerical test method for the mechanical properties of the large particle size LSAM-50 asphalt mixture was developed, and a reasonable specimen size for LSAM-50 performance test was proposed by combining the numerical test and the indoor test. The results show that: LSAM-50 numerical test conditions are the calculation time step 10-3 s/step, the loading rate is 2 mm/min (uniaxial compression numerical test) and 50 mm/min (splitting numerical test) when LSAM-50 numerical experiment calculation rate and numerical experiment accuracy are better; after the size of the specimen reaches 200×160mm, the influence of the size effect is eliminated. The reasonable specimen size of LSAM-50 is Φ200mm×h160mm; the LSAM-50 numerical test method and the indoor test have low error Less than 10%.

Phytic acid was investigated as an organophosphorus pollutant. Titanium dioxide (TiO2) doped with Ce and N was prepared using the sol-gel method and loaded onto a modified shell power to produce modified shell powder/Ce-N-TiO2 (Msp/CeNT). The combined effects of adsorption and photocatalysis on phytic acid were explored. The actual phytic acid degradation rate with the composite photocatalyst was higher than the sum of the adsorption removal rate of phytic acid using the modified shell powder and the photocatalytic degradation removal rate of phytic acid using the Ce-N-TiO2 photocatalyst. Msp/CeNT synergistically affected adsorption and photocatalytic degradation. The effects of different factors, such as reaction temperature, catalyst dosage, initial pH, stirring speed, and light intensity, on the combined effect were investigated. The results showed that the synergistic effect increases with the increase of light intensity. Increasing the reaction temperature, catalyst dosage, initial pH, and stirring speed first increased and then decreased the synergistic effect of the composite photocatalysts. Phytic acid (69.54%) was degraded within 4 h when the temperature, pH, catalyst dosage, stirring speed, and light intensity were 25 °C, 5, 1 g/L, 300 rpm, and 500 W, respectively. We investigated and prepared a composite photocatalytic material, developing a new theoretical method for degrading organophosphorus dissolved in water and providing a basis for treating lake eutrophication as a practical application.

Summary: Background: Increasing evidence suggests a complex interplay between psychiatric disorders and metabolic dysregulations. However, most research has been limited to specific disorder pairs, leaving a significant gap in our understanding of the broader psycho-metabolic nexus. Methods: This study leveraged large-scale cohort data and genome-wide association study (GWAS) summary statistics, covering 8 common psychiatric disorders and 43 metabolic traits. We introduced a comprehensive analytical strategy to identify shared genetic bases sequentially, from key genetic correlation regions to local pleiotropy and pleiotropic genes. Finally, we developed polygenic risk score (PRS) models to translate these findings into clinical applications. Findings: We identified significant bidirectional clinical risks between psychiatric disorders and metabolic dysregulations among 310,848 participants from the UK Biobank. Genetic correlation analysis confirmed 104 robust trait pairs, revealing 1088 key genomic regions, including critical hotspots such as chr3: 47588462-50387742. Cross-trait meta-analysis uncovered 388 pleiotropic single nucleotide variants (SNVs) and 126 shared causal variants. Among variants, 45 novel SNVs were associated with psychiatric disorders and 75 novel SNVs were associated with metabolic traits, shedding light on new targets to unravel the mechanism of comorbidity. Notably, RBM6, a gene involved in alternative splicing and cellular stress response regulation, emerged as a key pleiotropic gene. When psychiatric and metabolic genetic information were integrated, PRS models demonstrated enhanced predictive power. Interpretation: The study highlights the intertwined genetic and clinical relationships between psychiatric disorders and metabolic dysregulations, emphasising the need for integrated approaches in diagnosis and treatment. Funding: The National Key Research and Development Program of China (2023YFC2506200, SHH). The National Natural Science Foundation of China (82273741, SY).

BackgroundSterofundin (SF) is one of the most widely used electrolyte solutions in almost all areas of medicine, with particular importance in intensive care. It provides powerful correction of acid-base imbalances, ion fluctuations, and impaired energy metabolism, which are the three most important characteristics after myocardial infarction (MI). However, whether and how SF protects the heart from post-MI damage are largely unknown.Methods and ResultsPretreating mice with SF before MI surgery reduced the number of reactive oxygen species (ROS)-positive and TUNEL-positive cells. As a result, the infarcted area cardiac fibrosis in the MI mice was reduced and cardiac performance in the MI mice improved. Moreover, RNA-seq analysis demonstrated that SF caused the gene expression profile of MI mice to shift toward that of sham mice, with a significant decrease in apoptosis-, ROS-, and inflammation-associated gene enrichment. RNA-seq analysis also demonstrated that SF induced the upregulation of autophagy-associated gene enrichment. Western blotting confirmed the RNA-seq analysis results, showing that SF induced the upregulation of an autophagic flux. When the autophagic flux was blocked with the autophagy inhibitor 3-methyladenine, the protective effect of SF was reduced.ConclusionSF protects the heart from post-MI damage, and one of the underlying mechanisms could be its autophagy modifications. This study is the first to reveal a previously unrecognized role of electrolyte solutions in post-MI intensive care.

IntroductionThe aim of this study is to compare the injury patterns of female water polo players before and after the implementation of the Male-Assisted Female Training (MAFT) program. The study seeks to identify key factors influencing these changes and propose corresponding injury prevention measures.MethodsWe utilized pattern analysis and classification techniques to explore the injury data. A Hypergraph Neural Network (HGNN) was employed for pattern extraction, where each athlete was represented as a node in a hypergraph, with node dimensions capturing high-order relational embedding information. We applied the graph Laplacian operator to aggregate neighborhood features and visualize structural and feature differences in hypergraphs based on different influencing factors. Additionally, we introduced graph structure regularization to improve classification accuracy and prevent overfitting in the relatively small dataset, enhancing our ability to identify critical factors affecting injury types.ResultsThe analysis revealed significant differences in injury patterns before and after the MAFT program, with specific influencing factors being identified through both pattern recognition and classification techniques. The classification models, supported by graph structure regularization, achieved improved accuracy in distinguishing key features that contributed to changes in injury types.DiscussionThese findings provide insights into the critical factors influencing injury patterns in female water polo players and highlight the effectiveness of the MAFT program in mitigating certain injury risks. Based on the identified features, we propose targeted preventive measures to reduce injury incidence, particularly in relation to changes brought about by the MAFT training mode. Further research is needed to refine these measures and explore their long-term effectiveness.

With the rapid development of digital technology, online fitness live streaming platforms have become effective tools for helping users enhance their physical activity and health. However, the attitudes of Generation Z users, who form a significant portion of the platform’s user base, remain unclear. Identifying the factors influencing Generation Z’s use of these platforms and improving user satisfaction can assist in refining platform design and services, promoting sustainable development. A mixed-methods approach was employed to collect 1,788 user reviews, which were consolidated into 40 items. A questionnaire was then distributed to 314 respondents. In the first round, 165 open-ended questionnaires were distributed to identify specific influencing factors through factor analysis, leading to the development of a user experience evaluation scale. In the second round, 314 scale-based questionnaires were distributed, and structural equation modeling (SEM) was employed to explore the relationships between various factors and user satisfaction, resulting in the construction of a user satisfaction model. The factors influencing Generation Z users’ engagement with the platforms include social interaction, usefulness, convenience, functional quality, and technical quality. Among these, social interaction is the most critical factor affecting user satisfaction. Generation Z has unique needs regarding digital platforms. Therefore, platforms should provide services tailored to their psychological and behavioral characteristics, optimize business operation models, and further enhance the user experience.

ABSTRACT Transcription factors (TFs) involved in sexual reproduction in filamentous fungi have been characterized. However, we have little understanding of how these TFs synergize within regulatory networks resulting in sexual development. We investigated 13 TFs in Fusarium graminearum, whose knockouts exhibited abortive or arrested phenotypes during sexual development to elucidate the transcriptional regulatory cascade underlying the development of the sexual fruiting bodies. A Bayesian network of the TFs was inferred based on transcriptomic data from key stages of sexual development. We evaluated in silico knockout impacts to the networks of the developmental phenotypes among the TFs and guided knockout transcriptomics experiments to properly assess regulatory roles of genes with same developmental phenotypes. Additional transcriptome data were collected for the TF knockouts guided by the stage at which their phenotypes appeared and by the cognate in silico prediction. Global TF networks revealed that TFs within the mating-type locus (MAT genes) trigger a transcriptional cascade involving TFs that affected early stages of sexual development. Notably, PNA1, whose knockout mutants produced exceptionally small protoperithecia, was shown to be an upstream activator for MAT genes and several TFs essential for ascospore production. In addition, knockout mutants of SUB1 produced excessive numbers of protoperithecia, wherein MAT genes and pheromone-related genes exhibited dysregulated expression. We conclude that PNA1 and SUB1 play central and suppressive roles in initiating sexual reproduction, respectively. This comprehensive investigation contributes to our understanding of the transcriptional framework governing the multicellular body plan during sexual reproduction in F. graminearum.IMPORTANCEUnderstanding transcriptional regulation of sexual development is crucial to the elucidation of the complex reproductive biology in Fusarium graminearum. We performed gene knockouts on 13 transcription factors (TFs), demonstrating knockout phenotypes affecting distinct stages of sexual development. Using transcriptomic data across stages of sexual development, we inferred a Bayesian network of these TFs that guided experiments to assess the robustness of gene interactions using a systems biology approach. We discovered that the mating-type locus (MAT genes) initiates a transcriptional cascade, with PNA1 identified as an upstream activator essential for early sexual development and ascospore production. Conversely, SUB1 was found to play a suppressive role, with knockout mutants exhibiting excessive protoperithecia due to abnormally high expression of MAT and pheromone-related genes. These findings highlight the central roles of PNA1 and SUB1 in regulating other gene activity related to sexual reproduction, contributing to a deeper understanding of the mechanisms of the multiple TFs that regulate sexual development.

NQO1 is a FAD containing NAD(P)H-dependent oxidoreductase that catalyzes the reduction of quinones and related substrates, which plays an important role in the treatment of non-small cell lung cancer (NSCLC). Based on the indolequinone structure from 5-methoxy-2-methylindole, the indolequinone of NQO1 agonists was first coupled with amino-evodiamine derivatives by esterification reaction, and sixteen new compounds targeting NQO1 were developed. Among them, compounds 11b and 12d (IC50 = 2.72 or 3.66 µM, respectively) were showed better activity by cytotoxicity assay than the reference drug EVO (IC50 = 19.65 µM). Furthermore, the results of flow cytometry analysis showed that compounds 11b and 12d promoted apoptosis in A549 cells, blocked the cell cycle to the G2/M stage and caused a burst of reactive oxygen species. Western blotting experiments revealed that compounds 11b and 12d, after 24 h of treatment in A549 cells, downregulate the expression of Keap1 while upregulating the expression of Nrf2, NQO1, and HO-1. This suggests that compounds 11b and 12d increase cellular antioxidant capacity by regulating the Keap1/Nrf2/NQO1 antioxidant pathway. In vivo anti-tumor experiments showed that the reference drugs EVO (TGI = 15.94 %) and 5-Fu (TGI = 27.54 %) inhibited the proliferation of tumor tissue, while compound 11b could better inhibit the proliferation of tumor tissue (TGI = 39.13 %). In conclusion, our research results suggest that compounds 11b and 12d are potent agonism of the NQO1 signaling pathway and provide a potential opportunity to improve the treatment of NSCLC.

Critical shear stress and erosion rate are two key factors for the prediction of the incipient motion of sediment and the transport of sediment. Seabed seepage can significantly alter the pore pressure gradient within the soil and the hydrodynamics around the surface of the seabed, further affecting erosion processes. Previous studies attempted to theoretically clarify the effect of the seepage force on sediment incipient motion. In this study, a newly designed erosion–seepage system (ESS) that considers the effect of seepage under steady or oscillatory flow is used to simulate the erosion process. Through the designed ESS, the erosion height per unit time was measured directly on the Yellow River sand, and the upward seepage force was applied at the bottom of the soil sample in the process. Then, the relationship between the erosion rate and seepage was established.The experimental results show that upward seepage reduces the critical shear stress of the sand bed and increases the erosion rate of the soils under both steady flow and oscillatory flow conditions. The erosion coefficients in the erosion models decrease with increasing seepage gradient. The effect of seepage on erosion is more obvious when the flow velocity of the steady stream is large, while the effect of seepage on erosion is relatively small under the oscillatory state with a shorter period. However, when violent erosion of soil samples occurs, seepage under both flow conditions greatly increases the erosion rate.

Globally, forest decline and tree mortality are rising due to climate change. As one of the important afforestation trees in northeast China, Pinus sylvestris var. mongolica is suffering from forest decline and the accompanying pests. Certain fungi from the ophiostomatalean contribute to forest pest outbreaks and can be pathogenic to pine trees. However, only a limited number of ophiostomatalean fungi associated with beetles infesting Pinus sylvestris var. mongolica have been identified. In this study, 293 ophiostomatalean fungi were isolated from Acanthocinus griseus, Ips chinensis, and Pissodes nitidus infesting Pinus sylvestris var. mongolica in Heilongjiang Province, including Graphilbum griseum sp. nov., Gra. nitidum sp. nov., Graphilbum sp., and Ophiostoma ips. Ophiostoma ips was the dominant species, followed by Graphilbum sp., Graphilbum griseum, and Gra. nitidum, which accounted for 73.38, 17.41, 7.17, and 2.05% of the isolated ophiostomatalean fungi, respectively. Fungi associated with different beetles are diverse, even within the same host. This study deepens our understanding of the pest-associated fungi of P. sylvestris var. mongolica and provides a basis for exploring the causes of forest decline.

Heat stress is one of the major concerns for wheat production worldwide. Morphological parameters such as germination, leaf area, shoot, and root growth are affected by heat stress, with affected physiological parameters including photosynthesis, respiration, and water relation. Heat stress also leads to the generation of reactive oxygen species that disrupt the membrane systems of thylakoids, chloroplasts, and the plasma membrane. The deactivation of the photosystems, reduction in photosynthesis, and inactivation of Rubisco affect the production of photo-assimilates and their allocation, consequently resulting in reduced grain yield and quality. The development of thermo-tolerant wheat varieties is the most efficient and fundamental approach for coping with global warming. This review provides a comprehensive overview of various aspects related to heat stress tolerance in wheat, including damages caused by heat stress, mechanisms of heat stress tolerance, genes or QTLs regulating heat stress tolerance, and the methodologies of breeding wheat cultivars with high heat stress tolerance. Such insights are essential for developing thermo-tolerant wheat cultivars with high yield potential in response to an increasingly warmer environment.

The PLATZ gene family, known for its pivotal roles in regulating plant growth, development, and stress responses, is of great significance in rice biology and crop improvement efforts. In this study, we undertook a comprehensive identification and analysis of the PLATZ gene family across 10 Oryza genus species, including both cultivated and wild rice varieties. A total of 144 PLATZ genes were identified, demonstrating their widespread distribution. Phylogenetic analysis revealed six distinct groups among these genes, with high sequence similarity among members indicating a common evolutionary origin and potential functional conservation. Further analysis of conserved motifs, domains, and promoter regions provided insights into the transcriptional regulation and potential functions of PLATZ genes. Notably, expression profiling showed differential expression patterns of specific PLATZ genes, such as OsPLATZ7, OsPLATZ9, and OsPLATZ11, under various abiotic stress conditions and hormone treatments, highlighting their important roles in stress adaptation and hormone signaling. Additionally, the consistently high expression of OsPLATZ9 across multiple tissues suggests its involvement in multiple developmental processes. Overall, this study provides a detailed characterization of the PLATZ gene family in rice, laying the foundation for future functional studies and potential applications in agricultural biotechnology.

Compared to wheeled vehicles, tracked robotic vehicles have less ground pressure, greater traction adhesion, and stronger climbing and obstacle crossing capabilities, making them suitable for agricultural production in hilly areas. Good steering performance directly relates to the mobility performance and operating efficiency of tracked robotic vehicles. Affected by the ground slope, the ground pressure distribution of the vehicle’s two tracks is uneven, leading to changes in its steering performance. Therefore, analyzing and researching the steering performance of a tracked robotic vehicle under sloped conditions is of great significance. This study establishes a slope steering model for tracked robotic vehicles based on a ground pressure model of the multi-peak varying amplitude cosine distribution and the shearing displacement relationship between the track and the ground, and analyzes the impact of vehicle structural parameters, road surface parameters, and steering parameters on steering performance. To verify the proposed theoretical model, multi-body dynamics software is utilized for simulation modeling and analysis. Turning tests on different slopes are conducted on a “soil–machine–crop” integrated experimental platform. The relative error between the numerical analysis results and the virtual simulation software’s results is less than 12%, and the relative error between the numerical analysis results and the experimental results is less than 10.3%; the good consistency between the theoretical results and the simulation’s results and the experimental results indicates that the model is, indeed, correct and effective. The established steering model can provide a theoretical basis for the design and control of new steering mechanisms for agricultural tracked robotic vehicles.

Cities are of wide concern to scholars due to their major share of global carbon emissions. Energy-related carbon emissions differ significantly among cities, especially megacities, due to regional heterogeneity in socioeconomic conditions. To analyze the differences in influencing factors on carbon emissions within megacities, and further target emission reductions, measures were developed. Beijing was selected to investigate influencing factor differences in the core zones, developing zones and ecological zones using the STIRPAT model on the county level. The results show the following: (1) Regional heterogeneity existed in Beijing in carbon emissions changes from 2010 to 2022. The carbon emissions of the core zones grew steadily and were demonstrated as a major part of Beijing. (2) There were variations in the influencing factors of emissions. Population size was the major driving factor of emissions in the core zones, while emissions in the developing zones and ecological zones were driven primarily by GDP per capita. Notably, urbanization promoted the increase in carbon emissions in the developing zones, but had a negative influence on emissions in the ecological zones. The energy intensity was the primary negative force of carbon emissions in the three zones. (3) The differences in population, economic scale, industrial structure and technological level lead to the heterogeneity of carbon emissions in Beijing. The three zones should formulate targeted emission reduction measures based on the primary factors of carbon emissions and their functional positioning.

Identifying Galactic PeVatrons (PeV particle accelerators) from ultrahigh-energy (UHE, >100 TeV) γ -ray sources plays a crucial role in revealing the origin of Galactic cosmic rays. The UHE source 1LHAASO J1857+0203u is suggested to be associated with HESS J1858+020, which may be attributed to the possible PeVatron candidate supernova remnant (SNR) G35.6−0.4 or H ii region G35.6−0.5. We perform detailed analysis on the very-high-energy and UHE γ -ray emissions toward this region with data from the Large High Altitude Air Shower Observatory (LHAASO). 1LHAASO J1857+0203u is detected with a significance of 11.6 σ above 100 TeV, indicating the presence of a PeVatron. It has an extent of ∼0 $\mathop{.}\limits^{\unicode{x000B0}}$ 18 with a power-law (PL) spectral index of ∼2.5 at 1–25 TeV and pointlike emission with a PL spectral index of ∼3.2 above 25 TeV. Using archival CO and H i data, we identify some molecular and atomic clouds that may be associated with the TeV γ -ray emissions. Our modeling indicates that the TeV γ -ray emissions are unlikely to arise from clouds illuminated by the protons that escaped from SNR G35.6−0.4. In the scenario in which H ii region G35.6−0.5 could accelerate particles to the UHE band, the observed GeV–TeV γ -ray emission could be well explained by a hadronic model with a PL spectral index of ∼2.0 and cutoff energy of ∼450 TeV. However, an origin in an evolved pulsar wind nebula cannot be ruled out.

Image-to-image translation methods have advanced from focusing on image-level info to incorporating pixel-level and instance-level details. However, with feature-level constraint, deviation occurs when the network overemphasizes convolutional features, neglecting traditional image feature extraction. To address this, we proposed the multimodal image translation algorithm MASSE based on a Singular Squeeze-and-Excitation Network, combining GANs and SENet. It utilizes SVD features to assist the SENet in managing the scaling degree. The SENet employs SVD to extract features and enhance the Excitation operation to obtain new channel attention weights and form attention feature maps. Then, image content features are refined by combining convolutional and attention feature maps, and style features are obtained by the style generator. Finally, content and style features are combined to generate new style images. Ablation experiments showed the optimal SVD parameter is 128, producing the best translation results. According to FID, MASSE outperforms current methods in generating diverse images.

Analyzing the distribution characteristics of precipitation pollution intensity in the basin and identifying the main factors affecting the precipitation pollution intensity are the important basis for realizing the accurate management of diffused pollution. Based on the surface water quality data from four typical sections of the main stream of Qinhuai River Basin and rainfall data collected from 2021 to 2022, the distribution characteristics of precipitation pollution intensity in the basin were analyzed, and representative natural and social factors were selected to construct models of the precipitation pollution intensity of ammonia nitrogen （NH 4 + -N）, permanganate index, and total phosphorus （TP） based on random forest algorithm. Additionally, the main driving factors of precipitation pollution intensity were identified, and the influencing mechanism was analyzed. The results showed that rainfall was the main driving factor for the water quality exceeding the standard in the Qinhuai River Basin, and the proportion of rainfall events within 3 days of when the water quality of the sections in the study area exceeded the standard was between 61.4% and 97.4%. The precipitation pollution intensity of NH 4 + -N and TP showed a gradual increase from upstream to downstream. NH 4 + -N was the primary pollutant in the midstream Qiqiaoweng section and the downstream Sanchahekou section, with the precipitation pollution intensity ranging from 0.12 to 2.98 and from 0.31 to 3.84, respectively. The precipitation pollution intensity of permanganate index in the upstream Yangqiao section was the largest, ranging from 0.41 to 1.35, and the precipitation pollution intensity of the Jiangjundadao section, which is located on the channel of diverted water, was at a lower level compared with that of the other three sections. Water diversion was an important influencing factor for the reduction of precipitation pollution intensity of NH 4 + -N and permanganate index （ P <0.01）； the proportion of built-up area was significantly positively correlated with the precipitation pollution intensity of NH 4 + -N and TP； the proportion of cultivated area was significantly positively correlated with the precipitation pollution intensity of permanganate index, and the proportion of forest land area was significantly negatively correlated with the precipitation pollution intensity of these three indicators. Under the conditions of light rain, moderate rain, and heavy rain, the rainfall was significantly positively correlated with the precipitation pollution intensity of each indicator, and the precipitation pollution intensity remained high under the condition of torrential rain. The post-rain pollution of NH 4 + -N and TP mainly originated from the middle and lower reaches of the study area, which was significantly affected by regional characteristics such as the percentage of built-up area and water diversion （ P <0.01）, and the post-rain pollution of permanganate index mainly originated from the upstream area with a wide distribution of cultivated land area and was significantly affected by flow （ P <0.01）.