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Abstract Predicting the dynamic mechanical characteristics of rocks during freeze–thaw cycles (FTC) is crucial for comprehending the damage process of FTC and averting disasters in rock engineering in cold climates. Nevertheless, the conventional mathematical regression approach has constraints in accurately forecasting the dynamic compressive strength (DCS) of rocks under these circumstances. Hence, this study presents an optimized approach by merging the Coati Optimization Algorithm (COA) with Random Forest (RF) to offer a reliable solution for nondestructive prediction of DCS of rocks in cold locations. Initially, a database of the DCS of rocks after a series of FTC was constructed, and these data were obtained by performing the Split Hopkinson Pressure Bar Test on rocks after FTC. The main influencing factors of the test can be summarized into 10, and PCA was employed to decrease the number of dimensions in the dataset, and the microtests were used to explain the mechanism of the main influencing factors. Additionally, the Backpropagation Neural Network and RF are used to construct the prediction model of DCS of rock, and six optimization techniques were employed for optimizing the hyperparameters of the model. Ultimately, the 12 hybrid prediction models underwent a thorough and unbiased evaluation utilizing a range of evaluation indicators. The outcomes of the research concluded that the COA-RF model is most recommended for application in engineering practice, and it achieved the highest score of 10 in the combined score of the training and testing phases, with the lowest RMSE (4.570,8.769), the lowest MAE (3.155,5.653), the lowest MAPE (0.028,0.050), the highest R 2 (0.983,0.94).

From a practical point of view, grain structure heterogeneities are key parameters that control the rock response and still remains a challenge to incorporate in a quantitative manner. One of the less discussed topics in the context of the grain-based model (GBM) in the particle flow code (PFC) is the contact heterogeneities and the appropriate contact model to mimic the grain boundary behavior. Generally, the smooth joint (SJ) model and linear parallel bond (LPB) model are used to simulate the grain boundary behavior. However, the literature does not document the suitability of different models for specific problems. Another challenge in implementing GBM in PFC is that only a single bonding parameter is used at the grain boundaries. The aim of this study is to investigate the responses of a laboratory-scale specimen with SJ and LPB models, considering grain boundary heterogeneous and homogeneous contact parameters. Uniaxial and biaxial compression tests are performed to calibrate the response of Creighton granite. The stress–strain curves, volumetric dilation, inter-crack (crack in the grain boundary), and intra-crack (crack within the grain) development, and failure patterns associated with different contact models are examined. It was found that both the SJ and LPB models can reproduce the pre-peak behavior observed for a granitic rock type. However, the LPB model is unable to reproduce the post-peak behavior. Due to the large interlocking effect originating from the balls in contact and the ball size in the LPB model, local dilation is induced at the grain boundaries. This overestimates the volumetric dilation and residual shear strength. The LPB model tends to result in discontinuous inter-cracks and stress localization in the rock specimen, resulting in fine fragments at the rock surface during failure.

Abstract Bladder urothelial carcinoma (BLCA) presents a persistent challenge in clinical management. Despite recent advancements demonstrating the BLCA efficacy of immune checkpoint inhibitors (ICI) in BLCA patients, there remains a critical need to identify and expand the subset of individuals who benefit from this treatment. Mitochondria, as pivotal regulators of various cell death pathways in eukaryotic cells, exert significant influence over tumor cell fate and survival. In this study, our objective was to investigate biomarkers centered around mitochondrial function and cell death mechanisms to facilitate prognostic prediction and guide therapeutic decision-making in BLCA. Utilizing ssGSEA and LASSO regression, we developed a prognostic signature termed mitochondrial function and cell death (mtPCD). Subsequently, we evaluated the associations between mtPCD score and diverse clinical outcomes, including prognosis, functional pathway enrichment, immune cell infiltration, immunotherapy response analysis and drug sensitivity, within high- and low-risk subgroups. Additionally, we employed single-cell level functional assays, RT-qPCR, and immunohistochemistry to validate the differential expression of genes comprising the mtPCD signature. The mtPCD signature comprises a panel of 10 highly influential genes, strongly correlated with survival outcomes in BLCA patients and exhibiting robust predictive capabilities. Importantly, individuals classified as high-risk according to mtPCD score displayed a subdued overall immune response, characterized by diminished immunotherapeutic efficacy. In summary, our findings highlight the development of a novel prognostic signature, which not only holds promise as a biomarker for BLCA prognosis but also offers insights into the immune landscape of BLCA. This paradigm may pave the way for personalized treatment strategies in BLCA management.

With the accelerated evolution of digital technologies such as big data, cloud computing, and artificial intelligence, the trends of intelligence, informatization, and digitization have become imminent across various domains. Deep earth engineering, as a major strategic scientific and technological issue for the nation, is inevitably poised for intelligent upgrades. However, the complex characteristics of “three highs and one disturbance” in deep rock masses pose some severe challenges to the intelligent transformation of deep earth engineering. In order to achieve an efficient integration of deep earth engineering and digital technology, this study focuses on the intelligent practice path based on “perception, transmission, interpretation, analysis, decision”. The representative research progress in intelligent perception, real-time transmission, information interpretation, data analysis, intelligent decision-making and other fields in underground engineering is systematically reviewed, and the targeted development directions for deep earth engineering intelligent development are proposed, including multi perception, fast response, big data, optimal methods, precise models, strong platforms and easy promotion. Research findings indicate that ① The cutting-edge deep earth engineering perception technologies include fiber optic sensors, MEMS sensors, computer vision, automated robots, etc. After data collection is completed, the real-time response of data is achieved through the wireless communication protocols that combine the advantages of simple configuration, strong fault tolerance, and good mobility, in order to achieve an accurate collection and real-time transmission of deep earth engineering monitoring data. ② Deep earth engineering survey technology yields data types such as images, waves, and point clouds. Numerous models for interpreting and analyzing raw data exist. The application of next-generation artificial intelligence technologies, such as artificial neural networks and deep learning, significantly improves the efficiency of interpretation and analysis. ③ The intelligent decision-making system possesses efficient learning capabilities, adapting to the uncertainty in complex environments through cyclic autonomous learning to provide intelligent solutions for decision-making problems. Currently, China’s policies and industrial systems for the intelligent construction in the deep earth engineering have been essentially established, with a plethora of intelligent construction systems already applied in practical settings. Building upon this foundation, the study envisions the development direction of digital deep earth engineering in four aspects: intelligent collection and information interpretation, surrounding rock evaluation and safety assessment, surrounding rock control and dynamic repair, and platform development and application promotion. Furthermore, a comprehensive evaluation and analysis system for surrounding rock stability of deep earth engineering based on multi-source information is constructed.

BackgroundPatients with distant metastases from neuroblastoma (NB) usually have a poorer prognosis, and early diagnosis is essential to prevent distant metastases. The aim was to develop a machine-learning model for predicting the risk of distant metastasis in patients with neuroblastoma to aid clinical diagnosis and treatment decisions.MethodsWe built a predictive model using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018 on 1,542 patients with neuroblastoma. Seven machine-learning methods were employed to forecast the likelihood of neuroblastoma distant metastases. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for building machine learning models. Secondly, the subject operating characteristic area under the curve (AUC), Precision-Recall (PR) curves, decision curve analysis (DCA), and calibration curves were used to assess model performance. To further explain the optimal model, the Shapley summation interpretation method (SHAP) was applied. Ultimately, the best model was used to create an online calculator that estimates the likelihood of neuroblastoma distant metastases.ResultsThe study included 1,542 patients with neuroblastoma, multifactorial logistic regression analysis showed that age, histology, tumor size, tumor grade, primary site, surgery, chemotherapy, and radiotherapy were independent risk factors for distant metastasis of neuroblastoma (P

INADEQUATE (Incredible Natural Abundance DoublE QUAntum Transfer Experiment) is one of the most important techniques in revealing the carbon skeleton of organic solids in solid-state NMR spectroscopy. Nevertheless, its use for structural analysis is quite limited due to the low natural abundance of 13C–13C connectivity (∼0.01%) and thus low sensitivity. Particularly, in semi-solids like rubbers, the sensitivity will be further significantly reduced by the inefficient cross polarization from 1H to 13C due to molecular motions induced averaging of 1H–13C dipolar couplings. Herein, in this study, we demonstrate that transient nuclear Overhauser effect (NOE) can be used to efficiently enhance 13C signals, and thus enable rapid acquisition of two-dimensional (2D) 13C INADEQUATE spectra of rubbers. Using chlorobutyl rubber as the model system, it is found that an overall signal-to-noise ratio (SNR) enhancement about 22% can be achieved, leading to significant time-saving by about 33% as compared to the direct polarization-based INADEQUATE experiment. Further experimental results on natural rubber and ethylene propylene diene monomer (EPDM) rubber are also shown to demonstrate the robust performance of transient NOE enhanced INADEQUATE experiment.

Compound 1 was previously identified by our team as a glycogen phosphorylase (GP) inhibitor with glucose-lowering activity and demonstrated to have protective effects against myocardial and cerebral ischemia. However, its impact on muscle function has not been clarified. This study is the first to evaluate the long-term effects of GP inhibitors on muscle function and metabolism. After a 28-day administration of Compound 1, we performed assays to assess muscle function and biochemical parameters in rats. We observed reductions in peak holding force, duration, tetanic contraction force, single-contraction force, and electromyographic signals under 20 s and 10 min contraction stimuli. The metabolic analysis showed no significant effects on muscle glycogen, ATP, lactic acid, and uric acid levels at low doses. In contrast, medium to high doses resulted in increased glycogen, decreased ATP, and reduced lactic acid (only at high doses), without affecting uric acid. These findings suggest that Compound 1 may adversely affect muscle function in rats, potentially due to the glycogen inhibition effects of GP inhibitors. This study provides crucial safety data and insights into the long-term effects of GP inhibitors on rat muscles, which will guide future developments and applications.

The porcine reproductive and respiratory syndrome virus (PRRSV) belongs to the Arteriviridae family and is a single-stranded, positively stranded RNA virus. The currently available PRRSV vaccines are mainly inactivated and attenuated vaccines, yet none of the commercial vaccines can provide comprehensive, long-lasting, and effective protection against PRRSV. SR717 is a pyridazine-3-carboxamide compound, which is commonly used as a non-nucleoside STING agonist with antitumor and antiviral activities. Nevertheless, there is no evidence that SR717 has any antiviral effects against PRRSV. In this study, a dose-dependent inhibitory effect of SR717 was observed against numerous strains of PRRSV using qRT-PCR, IFA, and WB methods. Furthermore, SR717 was found to stimulate the production of anti-viral molecules and trigger the activation of the signaling cascade known as the stimulator of interferon genes (STING) pathway, which contributed to hindering the reproduction of viruses by a certain margin. Collectively, these results indicate that SR717 is capable of inhibiting PRRSV infection in vitro and may have potential as an antiviral drug against PRRSV.

BackgroundChatGPT, an artificial intelligence (AI) based on large-scale language models, has sparked interest in the field of health care. Nonetheless, the capabilities of AI in text comprehension and generation are constrained by the quality and volume of available training data for a specific language, and the performance of AI across different languages requires further investigation. While AI harbors substantial potential in medicine, it is imperative to tackle challenges such as the formulation of clinical care standards; facilitating cultural transitions in medical education and practice; and managing ethical issues including data privacy, consent, and bias. ObjectiveThe study aimed to evaluate ChatGPT’s performance in processing Chinese Postgraduate Examination for Clinical Medicine questions, assess its clinical reasoning ability, investigate potential limitations with the Chinese language, and explore its potential as a valuable tool for medical professionals in the Chinese context. MethodsA data set of Chinese Postgraduate Examination for Clinical Medicine questions was used to assess the effectiveness of ChatGPT’s (version 3.5) medical knowledge in the Chinese language, which has a data set of 165 medical questions that were divided into three categories: (1) common questions (n=90) assessing basic medical knowledge, (2) case analysis questions (n=45) focusing on clinical decision-making through patient case evaluations, and (3) multichoice questions (n=30) requiring the selection of multiple correct answers. First of all, we assessed whether ChatGPT could meet the stringent cutoff score defined by the government agency, which requires a performance within the top 20% of candidates. Additionally, in our evaluation of ChatGPT’s performance on both original and encoded medical questions, 3 primary indicators were used: accuracy, concordance (which validates the answer), and the frequency of insights. ResultsOur evaluation revealed that ChatGPT scored 153.5 out of 300 for original questions in Chinese, which signifies the minimum score set to ensure that at least 20% more candidates pass than the enrollment quota. However, ChatGPT had low accuracy in answering open-ended medical questions, with only 31.5% total accuracy. The accuracy for common questions, multichoice questions, and case analysis questions was 42%, 37%, and 17%, respectively. ChatGPT achieved a 90% concordance across all questions. Among correct responses, the concordance was 100%, significantly exceeding that of incorrect responses (n=57, 50%; P

BackgroundCopper (Cu) homeostasis are important processes in the cause of metabolic diseases, but the association between Cu and obesity remains unclear.MethodsParticipants were drawn from the 2011-2016 National Health and Nutrition Examination Survey (NHANES). Weighted logistic regression assessed the associations of serum Cu concentrations (tertiles) with obesity and central obesity in individuals without comorbidities. Obesity was defined as a BMI ≥30.0 kg/m2, and central obesity was defined as a waist circumference ≥80 cm for women and ≥95 cm for men.ResultsThis cross-sectional study included 1,665 adults without comorbidities, representing 24,744,034 people (mean age 35.1 years, 48.5% female). High serum Cu levels (tertile 3: ≥19.19 μmol/L) were associated with higher odds of obesity (adjusted odds ratio [OR]: 4.48, 95% CI[confidence interval]: 2.44-8.32) and central obesity (OR: 2.36, 95% CI: 1.19-4.66) compared to low serum Cu levels (tertile 1: ≤15.64 μmol/L). The dose-response curve showed a nonlinear association between Cu levels and obesity (P-nonlinear = 0.02) and a linear association with central obesity (P-nonlinear = 0.21).ConclusionThis study suggests that higher serum Cu levels are associated with increased odds of obesity in healthy American adults.

BackgroundThe benefit of first-line use of sodium-dependent glucose transport 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RAs) in type 2 diabetes mellitus (T2DM) with low risk of cardiovascular diseases are not clear.MethodsPubMed, EMBASE and Cochrane Library databases were searched to identify eligible randomized controlled trials. We used the odds ratio (OR) and mean difference (MD) and the corresponding 95% confidence interval (CI) to assess the dichotomous and continuous variable, respectively.ResultsThirteen studies involving 2,885 T2DM at low risk of cardiovascular diseases were included. Compared to placebo, first line use of SGLT2i significantly reduced glycosylated hemoglobin type A1C (HbA1c) (MD: -0.72), weight (MD: -1.32) and fasting plasma glucose (FPG) (MD: -27.05) levels. Compared with metformin, SGLT2i reduced body weight (MD: -1.50) and FPG (MD: -10.13) more effectively, with similar reduction for HbA1c (MD: -0.05). No significant increased safety adverse was found for SGLT2i, including nasopharyngitis (OR: 1.07), urinary tract infection (OR: 2.31), diarrhea (OR: 1.18) and hypoglycemia (OR: 1.06). GLP-1RAs significantly reduced HbA1c (MD: -1.13), weight (MD: -2.12) and FPG (MD: -31.44) levels as first-line therapy compared to placebo. GLP-1RAs significantly increased occurrence of diarrhea (OR: 2.18), hypoglycemia (OR: 3.10), vomiting (OR: 8.22), and nausea (OR: 4.41).ConclusionFirst line use of SGLT2i and GLP-1RAs is effective in reducing HbA1c, weight, and FPG levels in T2DM patients at low risk for cardiovascular disease. SGLT2i may be superior to metformin in controlling body weight and FPG. GLP-1RAs may increase the occurrence of diarrhea, hypoglycemia, vomiting, and nausea.Systematic review registrationPROSPERO (International Prospective Register of Systematic Reviews. https://www.york.ac.uk/inst/crd, CRD42022347233).

Abstract The high-altitude Duku Highway is characterized by complex terrain changes and frequent geological hazards, which severely impact the lives of local residents and the sustainable development of the regional economy. The lack of understanding of terrain deformation, coupled with scarce foundational observation data, makes it challenging to apply mainstream susceptibility assessment methods such as slope modeling and causality analysis. Consequently, this study utilizes Sentinel-1 A data and employs the SBAS-InSAR technique to extract and analyze the deformation characteristics of 184 hazard areas along the Duku Highway over nearly three years. Furthermore, the Correlation Clustering Evaluation Model is proposed, attributing hazard properties to unsupervised spatial clustering results, thus enabling the study of hazard susceptibility in data-scarce regions without prior knowledge. The results indicate that the SBAS-InSAR coherence is 0.64, with a validation accuracy of 85%. The high, relatively-high, and moderate susceptibility areas account for 24.7%, 17.1%, and 32.6% of the total area, respectively. The rapid uplift of terrain due to plate compression is a major factor leading to frequent hazards in high and relatively-high susceptibility areas. These regions may spontaneously experience cyclic hazards (minimum of 2 months) without extreme external factors. The research findings offer new insights into regional hazards and provide a basis for the sustainable management of highways.

In order to improve the yield of jackfruit peel pectin (JFPP) and obtain high-purity pectin with good functional properties, this study optimized the extraction process of pectin by ultrasound-assisted cellulase enzyme method through response surface experiments. The extracted pectin was preliminarily purified, including degreasing, deproteinization, decolorization, and dialysis concentration process. The purified pectin was then enzymatically esterified and modified by de-esterification to obtain de-esterified jackfruit peel pectin (DE-JFPP), and a comparative analysis was conducted on the basic composition, molecular weight, monosaccharide composition, functional group characteristics, thermal stability, and microstructure of the pectin before and after modification. The results showed that the optimal conditions for the extraction process were enzyme dosage of 0.7%, the enzymatic hydrolysis temperature of 30.4 ℃, and the pH of 4.6. It was observed that the yield of JFPP was 15.26%±0.07% under the specified conditions. The galacturonic acid content in the modified pectin increased from 79.10% to 83.24%, with a decrease in total sugar content from 43.42% to 41.90%, a decrease in esterification degree from 84.25% to 32.36%, and a decrease in weight-average molecular weight from 29.24 ku to 28.49 ku. Both JFPP and DE-JFPP contained eleven monosaccharides and belonged to the RG-I pectin with a decrease in the degree of branching in DE-JFPP. Fourier transform infrared (FTIR) spectroscopy demonstrated that both the pectin before and after modification had characteristic absorption peaks of pectin, and the peak area corresponding to the esterified carboxyl acid group decreased after modification. The JFPP and DE-JFPP demonstrated high thermal stability, with solubilization temperatures of 160.22 ℃ and 123.28 ℃, and degradation temperatures of 255.11 ℃ and 230.56 ℃, respectively. Scanning electron micrographs (SEM) showed that JFPP had a smooth surface and a compact structure, and DE-JFPP had a rough surface and a loose structure. This study optimized the extraction process of jackfruit peel pectin, improving its yield. Results demonstrated that the esterified pectin had favorable physicochemical properties and microstructure, meeting requirements for gel carrier systems in various fields and expanding the utilization space of jackfruit peel pectin.

ABSTRACTLegionella pneumophila (L. pneumophila) is a prevalent pathogenic bacterium responsible for significant global health concerns. Nonetheless, the precise pathogenic mechanisms of L. pneumophila have still remained elusive. Autophagy, a direct cellular response to L. pneumophila infection and other pathogens, involves the recognition and degradation of these invaders in lysosomes. Histone deacetylase 6 (HDAC6), a distinctive member of the histone deacetylase family, plays a multifaceted role in autophagy regulation. This study aimed to investigate the role of HDAC6 in macrophage autophagy via the autophagolysosomal pathway, leading to alleviate L. pneumophila-induced pneumonia. The results revealed a substantial upregulation of HDAC6 expression level in murine lung tissues infected by L. pneumophila. Notably, mice lacking HDAC6 exhibited a protective response against L. pneumophila-induced pulmonary tissue inflammation, which was characterized by the reduced bacterial load and diminished release of pro-inflammatory cytokines. Transcriptomic analysis has shed light on the regulatory role of HDAC6 in L. pneumophila infection in mice, particularly through the autophagy pathway of macrophages. Validation using L. pneumophila-induced macrophages from mice with HDAC6 gene knockout demonstrated a decrease in cellular bacterial load, activation of the autophagolysosomal pathway, and enhancement of cellular autophagic flux. In summary, the findings indicated that HDAC6 knockout could lead to the upregulation of p-ULK1 expression level, promoting the autophagy-lysosomal pathway, increasing autophagic flux, and ultimately strengthening the bactericidal capacity of macrophages. This contributes to the alleviation of L. pneumophila-induced pneumonia.

Abstract Aims We intended to characterize the superiority of triglyceride glucose‐body mass index (TyG‐BMI) in predicting type 2 diabetes mellitus (T2DM) compared with triglyceride glucose (TyG) and homeostatic model assessment for insulin resistance (HOMA‐IR). Methods A total of 699 nondiabetic participants in the Da Qing IGT and Diabetes Study were involved in the present analysis and classified according to the median of baseline TyG‐BMI, namely the G1 (low TyG‐BMI) and G2 (high TyG‐BMI) groups. Information on developing diabetes was assessed from 1986 to 2020. Results During the 34‐year follow‐up, after adjustment for confounders, the G2 group had a higher risk of developing type 2 diabetes than the G1 group (hazard ratio [HR]: 1.92, 95% confidence interval [CI]: 1.51–2.45, p 0.05). Time‐dependent receiver operator characteristics curves suggested that TyG‐BMI exhibited higher predictive ability than TyG (6‐year: area under the curve [AUC]TyG‐BMI vs. AUCTyG, 0.78 vs. 0.70, p = 0.03; 34‐year: AUCTyG‐BMI vs. AUCTyG, 0.79 vs. 0.73, p = 0.04) and HOMA‐IR (6‐year: AUCTyG‐BMI vs. AUCHOMA‐IR, 0.78 vs. 0.70, p = 0.07; 34‐year: AUCTyG‐BMI vs. AUCHOMA‐IR, 0.79 vs. 0.71, p = 0.04) in both short and long terms, and the thresholds of TyG‐BMI to predict type 2 diabetes were relatively stable (195.24–208.41) over the 34‐year follow‐up. Conclusions In this post hoc study, higher TyG‐BMI was associated with an increased risk of type 2 diabetes and demonstrated better predictability than TyG and HOMA‐IR, favoring the application of TyG‐BMI as a potential tool for evaluating the risk of type 2 diabetes in clinical practice.

Small molecule drugs play a pivotal role in the arsenal of anticancer pharmacological agents. Nonetheless, their small size poses a challenge when directly visualizing their localization, distribution, mechanism of action (MOA), and target engagement at the subcellular level in real time. We propose a strategy for developing triple-functioning drug beacons that seamlessly integrate therapeutically relevant bioactivity, precise subcellular localization, and direct visualization capabilities within a single molecular entity. As a proof of concept, we have meticulously designed and constructed a boronic acid fluorescence drug beacon using coumarin–hemicyanine (CHB). Our CHB design includes three pivotal features: a boronic acid moiety that binds both adenosine triphosphate (ATP) and adenosine diphosphate (ADP), thus depleting their levels and disrupting the energy supply within mitochondria; a positively charged component that targets the drug beacon to mitochondria; and a sizeable conjugated luminophore that emits fluorescence, facilitating the application of structured illumination microscopy (SIM). Our study indicates the exceptional responsiveness of our proof-of-concept drug beacon to ADP and ATP, its efficacy in inhibiting tumor growth, and its ability to facilitate the tracking of ADP and ATP distribution around the mitochondrial cristae. Furthermore, our investigation reveals that the micro-dynamics of CHB induce mitochondrial dysfunction by causing damage to the mitochondrial cristae and mitochondrial DNA. Altogether, our findings highlight the potential of SIM in conjunction with visual drug design as a potent tool for monitoring the in situ MOA of small molecule anticancer compounds. This approach represents a crucial advancement in addressing a current challenge within the field of small molecule drug discovery and validation.

Analyzing the diurnal pattern of soil respiration is essential for understanding the potential mechanisms of carbon cycle. Additionally, long-term and high temporal frequency soil respiration observations are crucial and valuable at different time scales, particularly in alpine regions. Therefore, it is critical to conduct research on the diurnal pattern of soil respiration in areas sensitive to global warming. Here, four group (including soil respiration, heterotrophic respiration, net ecosystem carbon exchange, and net ecosystem carbon exchange under warming condition) of experiments were designed with a total 12 chambers, and data collected during growing season in 2023 (April to September) obtained by automatic observation system at hour-scale and four-parameter Gaussian equation were used to investigate the diurnal pattern of soil respiration and net ecosystem carbon exchange in alpine meadow ecosystem on the northeastern Qinghai-Tibet Plateau. The results indicated that: 1) the soil temperature was crucial factor for the variations in diurnal pattern of soil respiration and net ecosystem carbon exchange in four experiments. The response of the heterotrophic components to the diurnal variation in soil temperature induced diurnal lag between soil respiration and soil temperature, with mean lag time of 1.7 h. 2) The diurnal peak time of net ecosystem carbon exchange was 1–3 h earlier than respiration, and the diurnal variation of soil tempiration was one of the main driving forces. 3) Additionally, warming advanced the diurnal peak time of net ecosystem carbon exchange by 1.2–2 h through explaining the parameters of the Gaussian function. It also promoted microbial resistance, thereby limiting the increase in soil respiration during nighttime. The study suggested that temperature varied soil microenvironment and biochemical processes in alpine meadow ecosystem, thus affecting the diurnal pattern. Indeed, this study focused solely on traditional factors, and further investigations were needed, particularly from a biotic perspective, to validate these conclusions.

Abstract The observation errors in precipitation gauges contribute to diminished precision in precipitation data sets. To reduce the impact of these errors, the World Meteorological Organization Solid Precipitation Intercomparison Experiments recommended the Double Fence Intercomparison Reference as a reference standard for precipitation measurements. This study proposed a new rain, snow, and mixed precipitation adjustment method for national standard precipitation gauges, using DFIR‐measured precipitation as the standard values. This method was used to adjust for systematic errors, including wind‐induced errors, wetting loss, and trace precipitation, in precipitation data collected by 785 stations in China from 1961 to 2020. After bias adjustment, the annual precipitation increased by 6.1–177.9 mm (with an average of 52.7 mm), accounting for 3.3%–52.1% (8.9%) of the total precipitation. The average annual error‐adjustment amounts for wind‐induced error, wetting loss, and trace precipitation were 21.9 (3.6% of total precipitation), 26.6 (4.7%), and 4.2 mm (1.3%), respectively. The adjustment percentage in winter was higher than that in summer, with the high‐adjusted‐percentage regions predominantly located in areas with drought, high proportion of snowfall, and strong wind speeds. Additionally, the annual average error‐adjustment amounts for rain, snow, and mixed precipitation respectively accounted for 5.2%, 38.2%, and 17.1% of their corresponding total amounts, indicating the significance of bias adjustment, particularly for snow and mixed precipitation, in the northern and Qinghai‐Tibet Plateau regions. Therefore, bias adjustment is necessary to enhance the accuracy of the precipitation data set in China.

Abstract The structural diversity of biological macromolecules in different environments contributes complexity to enzymological processes vital for cellular functions. Fluorescence resonance energy transfer and electron microscopy are used to investigate the enzymatic reaction of T4 DNA ligase catalyzing the ligation of nicked DNA. The data show that both the ligase–AMP complex and the ligase–AMP–DNA complex can have four conformations. This finding suggests the parallel occurrence of four ligation reaction pathways, each characterized by specific conformations of the ligase–AMP complex that persist in the ligase–AMP–DNA complex. Notably, these complexes have DNA bending angles of ≈0°, 20°, 60°, or 100°. The mechanism of parallel reactions challenges the conventional notion of simple sequential reaction steps occurring among multiple conformations. The results provide insights into the dynamic conformational changes and the versatile attributes of T4 DNA ligase and suggest that the parallel multiple reaction pathways may correspond to diverse T4 DNA ligase functions. This mechanism may potentially have evolved as an adaptive strategy across evolutionary history to navigate complex environments.

Abstract As a hot area of population inflow in the process of urbanization, the urban expansion area faces rapid growth of surface urban heat island (SUHI). However, the multi-dimensional evolutionary characteristics of SUHI in urban expansion areas are still unclear. Through analyzing the evolution of SUHI range ratio, mean intensity and maximum intensity, in this study we identified the comprehensive evolutionary pattern of summer SUHI in urban expansion areas of 31 major cities in China during 2000–2018, and further investigated the corresponding dominant influencing factors. The results showed that the SUHI range and intensity in urban expansion areas exhibited a significant increasing trend in 81% and 71% of the cities respectively during the summer daytime. The cities with declining SUHI range and intensity were distributed in northwestern high-altitude areas with low economic level, while the cities with dominant increases in the SUHI range were distributed in eastern low-altitude areas with high economic level. Climate conditions and population distribution were the dominant influencing factors of intensity-dominated increasing and balanced increasing types, respectively. The intensity and range two-dimensional increasing type had experienced the most severe SUHI growth, with large proportion of secondary industry as the main influencing factor. This study highlighted the importance of multi-dimensional characteristics of SUHI evolution, which provided a new insight to understand SUHI change in urban expansion areas and associated mitigation measures.

The Kezhayi to Gongnaisi section of the Duku expressway is predominantly characterized by alpine landforms, with steep terrain cutting that provides conducive conditions for avalanche development. The study on the evaluation of snow avalanche susceptibility in this area is a crucial prerequisite for the safety construction and operation of the Duku expressway. The 149 snow avalanche points were collected by employing remote sensing interpretation and field investigations. Through correlation analysis of these factors, 10 evaluation factors were selected, forming the avalanche evaluation factor system. Subsequently, the non-avalanche points and original avalanche points were extracted using the K-means clustering method and random method to create a sample set. Machine learning techniques, including multilayer perceptron (MLP) and support vector machine (SVM) algorithms, were utilized to assess avalanche susceptibility in the study area. The results show that the sample datasets extracted by the random and K-means clustering methods were used for training, the Kappa coefficient of the R-SVM, R-MLP, K-SVM, and K-MLP models were greater than 0.6. These four sets of models exhibited a high degree of consistency between the predicted results and actual values of the validation dataset. The AUC (area under curve) value trained by MLP increased from 0.762 to 0.983, while the AUC value trained by SVM increased from 0.724 to 0.951. Based on the K-MLP model partition with the highest evaluation accuracy, the snow avalanche development in the research area has a relatively minor impact on the proposed route but may pose a threat to tunnel entrances. This study provides theoretical support and methodological references for the construction, operation and mitigation of sonw avalanche disasters for the Duku expressway.

Abstract Global warming accelerates water cycle, causing more droughts globally that challenge monitoring and forecasting. The Standardized Precipitation Evapotranspiration Index (SPEI) is used to assess drought characteristics and response time of natural and economic systems at various timescales. However, existing SPEI datasets have coarse spatial or temporal resolution or limited spatial extent, restricting their ability to accurately identify the start or end dates or the extent of drought at the global scale. To narrow these gaps, we developed a global daily SPEI dataset (SPEI-GD), with a 0.25° spatial resolution from 1982 to 2021 at multiple timescales (5, 30, 90, 180 and 360 days), based on the precipitation from European Center for Medium Weather Forecasting Reanalysis V5 (ERA5) dataset and the potential evapotranspiration from Singer’s dataset. Compared to widely used SPEIbase dataset, the SPEI-GD can improve the spatial-temporal resolution and the accuracy of SPEI in areas where meteorological sites are lacking. The SPEI-GD significantly correlates with site-based SPEI and soil moisture. Our dataset solidly supports sub-seasonal and daily-scale global and regional drought research.

Abstract Sonic hedgehog (SHH) and heat shock protein 90β (HSP90β) have been implicated in nonalcoholic steatohepatitis (NASH) but their molecular mechanisms of action remain elusive. We find that HSP90β is a key SHH downstream molecule for promoting NASH process. In hepatocytes, SHH reduces HSP90β ubiquitylation through deubiquitylase USP31, thus preventing HSP90β degradation and promoting hepatic lipid synthesis. HSP90β significantly increases in NASH mouse model, leading to secretion of exosomes enriched with miR-28-5p. miR-28-5p directly targetes and decreases Rap1b levels, which in turn promotes NF-κB transcriptional activity in macrophages and stimulates the expression of inflammatory factors. Genetic deletion, pharmacological inhibition of the SHH-HSP90β axis, or delivery of miR-28-5p to macrophages in the male mice liver, impairs NASH symptomatic development. Importantly, there is a markedly higher abundance of miR-28-5p in NASH patient sera. Taken together, the SHH-HSP90β-miR-28-5p axis offers promising therapeutic targets against NASH, and serum miR-28-5p may serve as a NASH diagnostic biomarker.

Abstract To verify the reliability and accuracy of wall thickness ratio analysis to determine the degree of bone healing, fracture models were established with 6 beagles. X-ray, micro-CT, and CT scans were performed at 24 weeks. The healthy side and the affected side were used to simulate the three-dimensional geometric model after internal fixation, and the mesh was divided. The mean and median CT wall thickness values were obtained through the wall thickness analysis. X-ray, CT, micro-CT, and gross appearance were used to determine the degree of bone healing, which was compared with wall thickness analysis. There was a positive correlation between the average CT value and the median wall thickness. The correlation coefficient analysis of the median wall thickness ratio (R2) and healing index ratio (R3) showed a positive correlation. The results of the wall thickness ratio (R2) and the healing index ratio (R3) were used to determine bone healing, and the results were consistent with the results of the actual mechanical test and image analysis. The results of wall thickness ratio analysis were significantly correlated with the degree of bone healing. This method is simple, rapid, and practical to analyze and judge the degree of bone healing.

Breast cancer stands as the most prevalent malignancy among women, with radiotherapy serving as a primary treatment modality. Despite radiotherapy, a subset of breast cancer patients experiences local recurrence, attributed to the intrinsic resistance of tumors to radiation. Therefore, there is a compelling need to explore novel approaches that can enhance cytotoxic effects through alternative mechanisms. Traditional Chinese Medicine (TCM) and its active constituents exhibit diverse pharmacological actions, including anti-tumor effects, offering extensive possibilities to identify effective components capable of overcoming radiotherapy resistance. This review delineates the mechanisms underlying radiotherapy resistance in breast cancer, along with potential candidate Chinese herbal medicines that may sensitize breast cancer cells to radiotherapy. The exploration of such herbal interventions holds promise for improving therapeutic outcomes in the context of breast cancer radiotherapy resistance.

This study assessed the presence of potentially toxic elements (PTEs) in China’s northeastern black soil belt, an area with limited prior research. We collected 304 soil samples (0–20 cm) from Gonghe Town, Hailun City, and analyzed the PTE contamination degree using the single-factor pollution index and Nemerow pollution index. The results demonstrated that the mean concentrations of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn) were 11.16, 0.11, 65.29, 22.56, 0.03, 27.07, 26.09, and 66.01 mg/kg, respectively. Source apportionment was conducted via correlation analysis, principal component analysis, and positive matrix factorization, identifying four main sources: natural (33.2%), irrigation (29.5%), fuel (23.4%), and fertilizer (13.2%). The ecological risk index indicated a slight ecological risk, while the human health risk showed that non-carcinogenic risks were negligible and carcinogenic risks were acceptable. Our findings emphasize the need to prioritize controlling PTEs from fertilizer, particularly cadmium, and to a lesser extent, irrigation and fuel sources, focusing on As, Pband Hg. This research provides critical insights for policymakers aiming to manage PTE contamination in black soils.

Avalanche susceptibility assessment is a core aspect of regional avalanche early warning and risk analysis and is of great significance for disaster prevention and mitigation on proposed highways. Using sky–ground integration investigation, 83 avalanche points within the G219 Wen Quan to Horgos transportation corridor were identified, and the avalanche hazard susceptibility of the transportation corridor was partitioned using the certainty factor (CF) model and the coupled coefficient of the certainty factor–Geodetector (CF-GD) model. The CF model analysis presented nine elements of natural conditions which influence avalanche development; then, by applying the Geodetector for each of the factors, a weighting coefficient was given depending on its importance for avalanche occurrence. The results demonstrate the following: (1) According to the receiver operating characteristic (ROC) curve used to verify the accuracy, the area under the ROC curve (AUC) value for the CF-GD coupled model is 0.889, which is better than the value of 0.836 of the CF model’s evaluation accuracy, and the coupled model improves the accuracy by about 6.34% compared with the single model, indicating that the coupled model is more accurate. The results provide avalanche prevention and control recommendations for the G219 Wen Quan to Horgos transportation corridor. (2) The slope orientation, slope gradient, and mean winter temperature gradient are the main factors for avalanche development in the study area. (3) The results were validated based on the AUC values. The AUCs of the CF-GD coupled model and the CF model were 0.889 and 0.836, respectively. The accuracy of the coupled model was improved by about 6.34% compared to the single model, and the coupled CF-GD model was more accurate. The results provide avalanche control recommendations for the G219 Wen Quan to Horgos transportation corridor.

Bistatic radar exhibits spatial isomerism and diverse configurations, leading to unique clutter characteristics distinct from those of monostatic radar. The clutter rank serves as a pivotal indicator of clutter characteristics, enabling the quantification of clutter severity. Space-time adaptive processing (STAP) is a critical technique to detect moving targets, and clutter rank determines the number of independent and identically distributed (IID) training samples and the degree of freedom (DOF) for effective suppression of clutter that STAP requires. Therefore, the accurate estimation of clutter rank for bistatic radar can provide a crucial indicator for designing and constructing STAP processors, thereby facilitating fast and efficient clutter suppression in bistatic radar systems. This study is based on the idea that clutter rank is the number of prolate spheroidal wave function (PSWF) orthogonal bases utilized for approximating the clutter signal. Firstly, the challenge of utilizing PSWF orthogonal bases for approximating the clutter signal in bistatic radar is elucidated. This pertains to the fact that, unlike monostatic radar clutter, bistatic radar clutter is not capable of being expressed as a single-frequency signal. The clutter rank estimation for bistatic radar is thus derived as the frequency bandwidth estimation. Secondly, to achieve this estimation, the frequency distribution of each individual scattering unit is investigated, thereby determining their extending frequency broadening (EFB) as compared to that of single-frequency. Subsequently, the integral average of EFB across the entire range bin is computed, ultimately enabling the acquisition of bistatic radar’s frequency bandwidth. Finally, the estimation method is extended to non-side-looking mode and limited observation areas with pattern modulation. Simulation experiments confirm that our proposed method provides accurate clutter rank estimations, surpassing 99% proportions of large eigenvalues across various bistatic configurations, observation modes, and areas.

Existing methods of hyperspectral anomaly detection still face several challenges: (1) Due to the limitations of self-supervision, avoiding the identity mapping of anomalies remains difficult; (2) the ineffective interaction between spatial and spectral features leads to the insufficient utilization of spatial information; and (3) current methods are not adaptable to the detection of multi-scale anomaly targets. To address the aforementioned challenges, we proposed a blind-spot network based on multi-scale blind-spot convolution for HAD. The multi-scale mask convolution module is employed to adapt to diverse scales of anomaly targets, while the dynamic fusion module is introduced to integrate the advantages of mask convolutions at different scales. The proposed approach includes a spatial–spectral joint module and a background feature attention mechanism to enhance the interaction between spatial–spectral features, with a specific emphasis on highlighting the significance of background features within the network. Furthermore, we propose a preprocessing technique that combines pixel shuffle down-sampling (PD) with spatial spectral joint screening. This approach addresses anomalous identity mapping and enables finite-scale mask convolution for better detection of targets at various scales. The proposed approach was assessed on four real hyperspectral datasets comprising anomaly targets of different scales. The experimental results demonstrate the effectiveness and superior performance of the proposed methodology compared with nine state-of-the-art methods.

The design and optimization of sensor array configurations is a significant challenge for distributed SAR-GMTI radar systems because the system performance of distributed array radar is a comprehensive result of several conflicting evaluation indicators. This paper developed a multi-objective intelligent optimization method to solve the global optimal problem of array configurations in terms of achieving optimal GMTI performance. Firstly, to formulate the relationship between array configuration and GMTI performance, we established three objective functions derived from evaluating indicators of SAR-GMTI performance. Specifically, in the objective functions, we proposed a novel clutter covariance matrix model that added several typical non-ideal factors of the real-world detection environment. This provides a way to build a bridge between the array configuration, environment clutter, and GMTI performance. Then, we proposed an improved multi-objective snake optimization algorithm (IMOSOA) that combined the Pareto optimization mechanism with snake optimization to solve the multi-objective optimization problem while reconciling the conflicts between different objective functions. Meanwhile, some significant improvements were made to speed up convergence. That is, tent chaotic mapping-based initialization, multi-group coevolution, and individual mutation strategies were applied to solve the non-convergence problem of global searching. Finally, in the case of an airborne SAR-GMTI system, numerical experiments demonstrated that the proposed IMOSOA has superior performance than other contrast methods, especially in terms of GMTI applications.

Diabetes serves as a severe chronic disease that severely affects the normal life of human beings. Diabetes causes the complication of diabetic wound dysfunction, which is characterized by sustained inflammation, altered angiogenesis, delayed epithelialization and abnormal secretion of protease. Dang-Gui-Bu-Xue decoction (DBD) is a Chinese traditional medicine that comprises Radix Astragali and Radix Angelicae sinensis and is widely applied in treatment of multiple diseases owing to its functions against inflammation, lipid peroxidation and oxidative stress. Nevertheless, the impact of DBD on diabetic wound healing remains elusive. In this study, we aimed to explore the function of DBD in the regulation of wound healing. We observed that the gavage administration of DBD reduced the wound area, inflammatory infiltration, inflammatory factor levesl, and enhanced granulation tissue formation, wound extracellular matrix (ECM) production, and CD31 accumulation in the diabetic rat wound model, and the co-treatment of gavage administration and the external administration of gauze containing DBD further improved the wound healing effect, while the combination of Notch signaling inhibitor DAPT ((N- [N- (3, 5-difluorophenacetyl)-l-alanyl]-s-phenylglycinet-butyl ester)) could attenuate the improvement. Regarding to the mechanism, the expression levels of Notch1, Delta-like canonical Notch ligand 4 (Dll4), Jagged1, and Hairy Enhancer of Split-1 (Hes1) were increased by DBD, while the treatment of DAPT impaired the effect in the rats. Furthermore, we found that the high glucose (HG)-inhibited viability and tube formation were induced by DBD in human umbilical vein endothelial cells (HUVECs), in which DAPT could reverse this effect. Therefore, we concluded that DBD contributed to wound healing by the activation of Notch signaling. Our finding provides new insight into the potential role of DBD in promoting diabetic wound healing.

Study region: China. Study focus: The snow water equivalent (SWE) characterizes the hydrological significance of the snow and is essential for the study of water resources in snow-covered areas. However, SWE measurements are time-consuming and labour-intensive, and the amount of observed SWE data globally is small compared to snow depth (SD), particularly in China. This has limited the study of snow hydrology in China. To this end, we parameterize the SWE and briefly analyse the spatiotemporal distribution and variations of the SWE in China. New hydrological insights for the region: The mean absolute error, root mean square error and Nash-Sutcliffe efficiency coefficient of the estimation model are 0.49 g cm−2, 0.81 g cm−2, and 0.80, respectively, which indicate that the model performs well overall. The estimation model is able to accurately estimate SWE for different seasons and altitudes in China. However, due to the influence of SD, the estimation model is more applicable in snow-rich areas such as the Northern Xinjiang, Northeast China and middle and lower Yangtze River Plain, and less applicable in the Qinghai-Tibet Plateau. The trend of SWE in China increases at high latitudes and low altitudes and decreases at low latitudes and high altitudes. The seasonality trend of SWE varies widely in different regions. The results of the study provide a new approach to the study of snow hydrology in China.

This study aims to improve the triggering current and dynamic characteristics of thyristor chips by designing new gate structures and multilayer semiconductor structures. Thyristor chips are essential components in power electronic devices, widely used in fields such as power transmission, motor control, and energy conversion systems. Trigger current is a key factor in the performance of thyristor chips, which is responsible for initiating the conduction state of the thyristor and carrying high current under high voltage conditions. This study improved the triggering current and dynamic characteristics of thyristor chips by optimizing the chip structure, especially by introducing multilayer P/N semiconductor design and improving the gate structure. The research results provide important guidance for the future development of thyristor chips and also contribute to the advancement of semiconductor technology.

Credit risk assessment is an important process in bank financial risk management. Traditional machine-learning methods cannot solve the problem of data islands and the high error rate of two-way decisions, which is not conducive to banks’ accurate credit risk assessment of users. To this end, this paper establishes a federated three-way decision incremental naive Bayes bank user credit risk assessment model (FTwNB) that supports asymmetric encryption, uses federated learning to break down data barriers between banks, and uses asymmetric encryption to protect data security for federated processes. At the same time, the model combines the three-way decision methods to realize the three-way classification of user credit (good, bad and delayed judgment), so as to avoid the loss of bank interests caused by the forced division of uncertain users. In addition, the model also incorporates incremental learning steps to eliminate training samples with poor data quality to further improve the model performance. This paper takes German Credit data and Default of Credit Card Clients data as examples to conduct simulation experiments. The result shows that the performance of the FTwNB model has been greatly improved, which verifies that it has good credit risk assessment capabilities.

With the development of artificial intelligence, the ability to capture the background characteristics of hyperspectral imagery (HSI) has improved, showing promising performance in hyperspectral anomaly detection (HAD) tasks. However, existing methods proposed in recent years still suffer from certain limitations: (1) Constraints are lacking in the deep feature learning process in terms of the issue of the absence of prior background and anomaly information. (2) Hyperspectral anomaly detectors with traditional self-supervised deep learning methods fail to ensure prioritized reconstruction of the background. (3) The architecture of fully connected deep networks in hyperspectral anomaly detectors leads to low utilization of spatial information and the destruction of the original spatial relationship in hyperspectral imagery and disregards the spectral correlation between adjacent pixels. (4) Hypotheses or assumptions for background and anomaly distributions restrict the performance of many hyperspectral anomaly detectors because the distributions of background land covers are usually complex and not assumable in real-world hyperspectral imagery. In consideration of the above problems, in this paper, we propose a novel fully convolutional auto-encoder based on dual clustering and latent feature adversarial consistency (FCAE-DCAC) for HAD, which is carried out with self-supervised learning-based processing. Firstly, density-based spatial clustering of applications with a noise algorithm and connected component analysis are utilized for successive spectral and spatial clustering to obtain more precise prior background and anomaly information, which facilitates the separation between background and anomaly samples during the training of our method. Subsequently, a novel fully convolutional auto-encoder (FCAE) integrated with a spatial–spectral joint attention (SSJA) mechanism is proposed to enhance the utilization of spatial information and augment feature expression. In addition, a latent feature adversarial consistency network with the ability to learn actual background distribution in hyperspectral imagery is proposed to achieve pure background reconstruction. Finally, a triplet loss is introduced to enhance the separability between background and anomaly, and the reconstruction residual serves as the anomaly detection result. We evaluate the proposed method based on seven groups of real-world hyperspectral datasets, and the experimental results confirm the effectiveness and superior performance of the proposed method versus nine state-of-the-art methods.

Phishing has been the primary means utilized by attackers to conduct cyber fraud. As national anti-cyber fraud efforts continue to increase, the technical confrontation of various phishing activities has also escalated, bringing significant pressure to phishing detection work. For instance, current phishing attacks often employ images in place of text and apply small-scale shifts or rotations to high-weight website logo images to evade traditional detection algorithms that rely on text or image features. To address the problem of escalating adversarial phishing technologies, a phishing detection algorithm based on the attention mechanism and feature fusion was proposed, and a hierarchical classification model was established. This model included two stages of fusion involving domain names, web structure, web text, and web icons, capable of effectively countering various technical adversarial strategies employed by attackers. In the first stage, the algorithm leveraged the lightweight characteristics of the machine learning model to pre-recall a subset of suspicious domain names from a multitude of domain names. This was achieved by fusing the structural features of domain names, text, and web pages. In the second stage, based on the candidate subset, the attention mechanism was introduced to enhance the extraction of global text association features between the samples and the counterfeited objects. Additionally, the contrast features between the samples and the icons of the counterfeited objects were intensified, and a deep classification model fusing text and image features was established. The effectiveness of the algorithm was ultimately verified. This hierarchical detection method effectively avoids the extraction of image data from a large number of domain names to be detected, significantly improving detection efficiency while ensuring the accuracy of detection.