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Abstract Glioblastoma (GBM) is associated with a median survival rate of less than 15 months, necessitating innovative treatment approaches. This study investigates the safety and efficacy of the low-frequency magnetic field (LFMF) OM-100 instrument in GBM therapy. In vitro experiments utilized normal astrocyte and GBM cell lines, determining that OM-100 at 100 kHz for 72 h selectively targeted GBM cells without harming normal cells. Subsequent analyses revealed OM-100’s impact on cell viability, apoptosis, migration, invasion, reactive oxide species levels, and PD-L1 expression. In vivo studies on mice with U87-induced GBM demonstrated OM-100's synergy with anti-PD-1 therapy, leading to significant tumor volume reduction and increased apoptosis. Notably, OM-100 exhibited safety in healthy mice. Overall, OM-100 could enhance anti-PD-1 immunotherapy effectiveness probably by directly inhibiting tumor proliferation and migration as well as promoting PD-L1 expression, offering a promising therapeutic strategy for GBM treatment.

Abstract The early diagnosis of esophageal cancer (EC) is extremely challenging due to a lack of effective diagnostic methods. The study presented herein aims to assess whether serum volatile organic compounds (VOCs) could be utilised as emerging diagnostic biomarkers for EC. Gas chromatography-ion mobility spectrometry (GC-IMS) was used to detect VOCs in the serum samples of 55 patients with EC, with samples from 84 healthy controls (HCs) patients analysed as a comparison. All machine learning analyses were based on data from serum VOCs obtained by GC-IMS. A total of 33 substance peak heights were detected in all patient serum samples. The ROC analysis revealed that four machine learning models were effective in facilitating the diagnosis of EC. In addition, the random forests model for 5 VOCs had an AUC of 0.951, with sensitivities and specificities of 94.1 and 96.0%, respectively.

Abstract The evolution and mechanism of ground collapse caused by underground water pipeline leakage have become increasingly significant as more urban areas experience collapses. Based on the principle of similarity, and considering the engineering context of road collapses in Anqing City, Anhui Province, this study designed a 3 m × 2 m × 2 m rupture-collapse model test device. Digital Image Correlation (DIC) technology was employed to investigate the erosion process and collapse mechanisms caused by underground pipeline leakage. The results indicate that groundwater seepage provides the driving force for collapses, combined with the migration space provided by defects, collectively triggering the collapses. When groundwater seepage is minimal, the cohesive forces between soil particles maintain soil stability. As groundwater seepage increases, the soil particle framework is eroded, leading to soil structure destabilization and collapse initiation. The depth of collapse significantly influences stress evolution: stress evolution intensity beneath and above the collapse pit is positively correlated with the distance from the collapse pit bottom, but negatively correlated with the distance from the defect. The research provides insights for the early warning and management of ground collapse.

Abstract The utilization of drag-reducing polymers has long been hindered by their irritancy, corrosiveness, and toxicity across various domains. In this investigation, we explored sodium alginate, a natural drag reducer, for its efficacy in reducing drag and its resilience to shear in millimeter-scale pipelines. Initially, an experimental setup was devised to assess the drag reduction capabilities of sodium alginate at varying concentrations and flow rates using Response Surface Methodology (RSM). The relationship between drag reduction (DR), concentration (C), and flow rate (Q) was established by analyzing the experimental data. Subsequently, variance analysis was employed to validate the data accuracy, with a comparison between predicted and experimental DR values revealing an error margin within ± 20%. Analysis of cyclic shear testing of sodium alginate solution in tubes demonstrated its effectiveness as a shear flow drag reducer. Furthermore, results from laser particle size analysis indicated minimal molecular breakage of sodium alginate during cyclic shear.

Abstract To investigate the damage mechanisms in granite’s physical and mechanical properties after high-temperature water quenching, this study employed MTS815.04 for uniaxial compression tests on thermally treated specimens, with concurrent acoustic emission monitoring, and utilized nanoindentation for micromechanical analysis. The results show that with increasing temperature, granite's peak strength and elastic modulus decrease, with a sharp decline after 400–500 °C, corresponding to a significant increase in the internal damage, which can be detected by acoustic emission monitoring. Below 500 °C, macroscopic mechanical degradation is due to mineral thermophysical property differences, while above 500 °C, microcrack development is the main deterioration factor. The failure mode shifts from tensile to tensile-shear complex to shear failure, with transition points at 400 °C and 800 °C. The results of this study are of certain reference value for improving the efficiency of extracting thermal energy from dry-hot rocks and providing security guidance for the tunnel restoration process following fire damage.

Abstract Retinoblastoma is one of the most common ocular malignancies in children. Bmi-1, a member of the Polycomb group family of transcriptional repressors, is expressed in a variety of tumors. The purpose of our study was to explore the role of Bmi-1 in retinoblastoma. RT-qPCR and western blot were used for calculating the mRNA and protein levels of Bmi-1 and RKIP. MTT, Wound healing and Transwell assays were performed to measure the proliferation, migration and invasion in retinoblastoma cells. Cell apoptosis was detected by flow cytometry. The volume and mass of transplanted tumors were detected in nude mice. Bmi-1 was over expressed, and RKIP was low expressed in retinoblastoma cells. Bmi-1 promoted cell proliferation, migration and invasion and suppressed cell apoptosis of Y79 and SO-RB50 cells. Downregulation of Bmi-1 and overexpression of RKIP inhibited cell proliferation, migration and invasion, and increased cell apoptosis. The functions of Bmi-1 knockdown on retinoblastoma cells were blocked by RKIP knockdown, but promoted by RKIP. Down-regulated Bmi-1 inhibited xenograft tumor growth, and RKIP exacerbated this inhibitory effect. Bmi-1 served as a potential therapeutic target for improving the efficacy of clinical treatment in retinoblastoma. All the findings revealed the functions of Bmi-1/RKIP axis in retinoblastoma tumorigenesis.

Abstract Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

Abstract Dilated cardiomyopathy (DCM) is a common cause of heart failure, thromboembolism, arrhythmias, and sudden cardiac death. The quality of life and long-term survival rates of patients with dilated DCM have greatly improved in recent decades. Nevertheless, the clinical prognosis for DCM patients remains unfavorable. The primary driving factors underlying the pathogenesis of DCM remain incompletely understood. The present study aimed to identify driving factors underlying the pathogenesis of DCM from the perspective of gene regulatory networks. Single-cell RNA sequencing data and bulk RNA data were obtained from the Gene Expression Omnibus (GEO) database. Differential gene analysis, single-cell genomics analysis, and functional enrichment analysis were conducted using R software. The construction of Gene Regulatory Networks was performed using Python. We used the pySCENIC method to analyze the single-cell data and identified 401 regulons. Through variance decomposition, we selected 19 regulons that showed significant responsiveness to DCM. Next, we employed the ssGSEA method to assess regulons in two bulk RNA datasets. Significant statistical differences were observed in 9 and 13 regulons in each dataset. By intersecting these differentiated regulons and identifying shared targets that appeared at least twice, we successfully pinpointed three differentially expressed targets across both datasets. In this study, we assessed and identified 19 gene regulatory networks that were responsive to the disease. Furthermore, we validated these networks using two bulk RNA datasets of DCM. The elucidation of dysregulated regulons and targets (CDKN1A, SAT1, ZFP36) enhances the molecular understanding of DCM, aiding in the development of tailored therapies for patients.

Abstract Circulating tumor cells (CTCs) as a liquid biopsy have great potential in clinical applications and basic cancer research, but their clinical use in gastric cancer remains unclear. This study investigated whether CTCs could be used as a potential prognosis predictor in patients with gastric cancer. A total of 120 patients with pathologically confirmed gastric cancer were enrolled from January 1, 2015, to December 1, 2019. All patients were initially diagnosed without previous treatment, and then the number of CTCs was detected using the NEimFISH method before radical surgical resection. Regular follow-up was performed in all patients, and the correlations between the number of CTCs and clinical endpoints, such as disease-free survival (DFS) and overall survival (OS), were evaluated. The univariate and multivariate hazard ratios were calculated using the Cox proportional hazard model. Based on the number of CTCs, we defined CTCs ≥ 2 per 7.5 mL of whole blood as the positive group and CTCs

Abstract The preoperative distinguishment of lymph nodes (LN) with metastasis plays a pivotal role in guiding the surgical extension for gastric cancer (GC). We aim to identify the preparative risk factors for LN metastasis in GC patients. We retrospectively reviewed 424 patients who underwent radical GC resection in our medical center between Jan 2011 and Dec 2018. Multivariate logistic regression was employed to identify risk factors for LN metastasis, while multivariate COX regression was utilized to evaluate prognostic factors. The median overall survival of patients with or without LN metastases was 31 and 58 months, respectively. In multivariate analysis, lower albumin (OR = 0.512; P = 0.004) and prealbumin (OR = 0.367, P = 0.001) and higher CEA (OR = 3.178, P

Abstract This study aimed to investigate the effects of exercise on excessive mitochondrial fission, insulin resistance, and inflammation in the muscles of diabetic rats. The role of the irisin/AMPK pathway in regulating exercise effects was also determined. Thirty-two 8-week-old male Wistar rats were randomly divided into four groups (n = 8 per group): one control group (Con) and three experimental groups. Type 2 diabetes mellitus (T2DM) was induced in the experimental groups via a high-fat diet followed by a single intraperitoneal injection of streptozotocin (STZ) at a dosage of 30 mg/kg body weight. After T2DM induction, groups were assigned as sedentary (DM), subjected to 8 weeks of treadmill exercise training (Ex), or exercise training combined with 8-week cycloRGDyk treatment (ExRg). Upon completion of the last training session, all rats were euthanized and samples of fasting blood and soleus muscle were collected for analysis using ELISA, immunofluorescence, RT-qPCR, and Western blotting. Statistical differences between groups were analyzed using one-way ANOVA, and differences between two groups were assessed using t-tests. Our findings demonstrate that exercise training markedly ameliorated hyperglycaemia, hyperlipidaemia, and insulin resistance in diabetic rats (p

Abstract The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.

Abstract This mediation analysis aimed to investigate the associations among areal bone mineral density, mobility-related brain atrophy, and specific gait patterns. A total of 595 participants from the Taizhou Imaging Study, who underwent both gait and bone mineral density measurements, were included in this cross-sectional analysis. We used a wearable gait tracking device to collect quantitative gait parameters and then summarized them into independent gait domains with factor analysis. Bone mineral density was measured in the lumbar spine, femoral neck, and total hip using dual-energy X-ray absorptiometry. Magnetic resonance images were obtained on a 3.0-Tesla scanner, and the volumes of brain regions related to mobility were computed using FreeSurfer. Lower bone mineral density was found to be associated with higher gait variability, especially at the site of the lumbar spine (β = 0.174, FDR = 0.001). Besides, higher gait variability was correlated with mobility-related brain atrophy, like the primary motor cortex (β = 0.147, FDR = 0.006), sensorimotor cortex (β = 0.153, FDR = 0.006), and entorhinal cortex (β = 0.106, FDR = 0.043). Bidirectional mediation analysis revealed that regional brain atrophy contributed to higher gait variability through the low lumbar spine bone mineral density (for the primary motor cortex, P = 0.018; for the sensorimotor cortex, P = 0.010) and the low lumbar spine bone mineral density contributed to higher gait variability through the primary motor and sensorimotor cortices (P = 0.026 and 0.010, respectively).

Abstract The micro-scale gas adsorption–desorption characteristics determine the macro-scale gas transport and production behavior. To reveal the three-dimensional stress state-induced gas adsorption–desorption characteristics in coal-bearing shale reservoirs from a micro-scale perspective, the coal-bearing shale samples from the Dongbaowei Coal Mine in the Shuangyashan Basin were chosen as the research subject. Isothermal adsorption–desorption experiments under three-dimensional stress state were conducted using the low field nuclear magnetic resonance (L-NMR) T 2 spectrum method to simulate the in-situ coal-bearing shale gas adsorption–desorption process. The average effective stress was used as the equivalent stress indicator for coal-bearing shale, and the integral of nuclear magnetic resonance T 2 spectrum amplitude was employed as the gas characterization indicator for coal-bearing shale. A quantitative analysis was performed to examine the relationship between gas adsorption in coal-bearing shale and the average effective stress. And a quantitative analysis was performed to examine the relationship between the macroscopic and microscopic gas quantities of coal-bearing shale. Experimental findings: (1) The adsorption–desorption process of coal-bearing shale gas follows the L-F function model and the D-A-d function model respectively with respect to the amount of gas and the average effective stress. (2) There is a logarithmic relationship between the macroscopic and microscopic gas quantities of coal-bearing shale during the adsorption–desorption process. This quantitatively characterizes the differences in the curves, which may be related to the elastic–plastic deformation, damage and fracture of the micropores in coal-bearing shale, as well as the hysteresis of gas desorption and the stress field of the gas occurrence state.

Abstract The problem of repeated epidemic fluctuations in the normalized prevention and control stage is revealed by data from January 20, 2020, to January 30, 2023. In order to improve the collaborative response of the public and government departments to public health emergencies and avoid repeated fluctuations of the epidemic, a tripartite evolutionary game model of the public, local government, and central government departments is constructed, focusing on the evolutionary paths and evolutionary stabilization strategies of the three subjects, and the influence of each element on the evolutionary results is simulated by numerical simulation in Matlab, and based on the inadequacy of the static reward and punishment mechanism, a dynamic Based on the shortcomings of static reward and punishment mechanism, dynamic reward and punishment mechanism is introduced to control the stability of the evolving system. The study shows that (1) with the increase of the initial willingness of the three parties, the rate of the public choosing the discretionary flow strategy slows down, and the collaborative prevention and control process can be accelerated. (2) The reward and punishment mechanism of central government departments has a positive incentive effect on the local government's strict prevention and control and the public's conscious isolation. Appropriately increasing rewards, formulating reasonable subsidy strategies, and increasing penalties for violations are conducive to the overall optimization of the system, and the punishment mechanism is most sensitive to the regulation of the public's discretionary mobility behavior. (3) Government departments' prevention and control costs can influence their enthusiasm for strict prevention and control and real-time supervision. Reducing the human resources cost, time cost, and financial cost of prevention and control is conducive to government departments performing their duties more responsibly. (4) The static punishment mechanism fails to make timely adjustments according to the strategy choice of each actor. It cannot control the stability of the evolving system. In contrast, the dynamic punishment mechanism considers the punishment parameters to link the casual isolation rate with the lenient prevention and control rate, which can effectively control the system's fluctuating instability and is the system's stability control strategy. Finally, combining theoretical and simulation analysis, management suggestions are made for controlling repeated fluctuations of the epidemic in practice, and the research limitations of this paper are explained.

Abstract In this article, an engineering problem of three co-rotating exciters with the circular distribution in a vibrating system is investigated. The dynamical model constructed by the motion differential equations is established. By introducing the small parameter averaged method in the dynamic equation, the synchronization and stability conditions of the electromechanical coupling dynamical model is derived. To illustrate the necessity of the controlling method, the self-synchronization of the vibrating system is firstly analyzed with the theory, numerical simulations and experiments. With the self-synchronization results, it is indicated that the ellipse trajectory which is needed in the industry can’t be realizefd by the self-synchronization motion of the vibrating system. And then, a fuzzy PID controlling method based on the master–slave controlling strategy is introduced in the vibrating system to realize the controlled synchronization. The Lyapunov stability criterion is given to certify the stability of the controlling system. Through some simulations and experiments, the effectiveness of controlled synchronization is illustrated in the discussion. Finally, the present work illuminates the feasibility and practicality for designing some new types of vibrating screens in the industry.

Abstract High altitude exposure increases the risk of myocardial ischemia (MI) and subsequent cardiovascular death. Machine learning techniques have been used to develop cardiovascular disease prediction models, but no reports exist for high altitude induced myocardial ischemia. Our objective was to establish a machine learning-based MI prediction model and identify key risk factors. Using a prospective cohort study, a predictive model was developed and validated for high-altitude MI. We consolidated the health examination and self-reported electronic questionnaire data (collected between January and June 2022 in 920th Joint Logistic Support Force Hospital of china) of soldiers undergoing high-altitude training, along with the health examination and second self-reported electronic questionnaire data (collected between December 2022 and January 2023) subsequent to their completion on the plateau, into a unified dataset. Participants were subsequently allocated to either the training or test dataset in a 3:1 ratio using random assignment. A predictive model based on clinical features, physical examination, and laboratory results was designed using the training dataset, and the model's performance was evaluated using the area under the receiver operating characteristic curve score (AUC) in the test dataset. Using the training dataset (n = 2141), we developed a myocardial ischemia prediction model with high accuracy (AUC = 0.86) when validated on the test dataset (n = 714). The model was based on five laboratory results: Eosinophils percentage (Eos.Per), Globulin (G), Ca, Glucose (GLU), and Aspartate aminotransferase (AST). Our concise and accurate high-altitude myocardial ischemia incidence prediction model, based on five laboratory results, may be used to identify risks in advance and help individuals and groups prepare before entering high-altitude areas. Further external validation, including female and different age groups, is necessary.

Abstract The study of walkability is of great significance to the construction of healthy cities. In this paper, taking 1283 articles of walkability, which were included in Web of Science, as the research object. This paper adopts to analysis the research progress by using the method of scientometrics and knowledge networks analysis. Objectively and systematically analyze the research progress of walkability abroad from the aspects of publication overview, knowledge foundation, research direction and hot spots, etc. It is found that foreign researches on walkability mainly include three core directions: walkability and physical activity, walkability evaluation, walkability and urban design. Among them, walkability and physical activity orientation have been studied from various perspectives, such as various groups, various environmental types, different behavioral patterns and various chronic diseases. With the increasingly prominent urbanization problems and the rapid development of new technologies, multiple data, new methods and interdisciplinary cooperation will actively promote the vigorous development of walking suitability research.

Abstract This study aimed to reveal the soil reinforcement by shrub root systems after repeated stress from external forces, such as high winds and runoff, for extended periods in the wind-hydraulic compound erosion zone. Using the widely distributed Shandong mine area soil and water-conserving plant species, Caragana microphylla, Hippophae rhamnoides, and Artemisia ordosica, cyclic loading tests were conducted on taproots of the three plant species (1–5 mm diameter) via a TY8000 servo-type machine to investigate the taproots’ tensile properties response to repeated loading–unloading using simulated high wind pulling and runoff scouring. Our study revealed that the tensile force was positively correlated with the root diameter but the tensile strength was negatively correlated under monotonic and cyclic loading of the three plants’ taproots. However, after cyclic loading, the three plant species' taproots significantly enhanced the tensile force and strength more than monotonic loading (P

Abstract In this study, we investigated the drag reduction property of a premixed slurry drag reducer in a millimeter-scale pipe. The aim of this study is to establish the correlation between Darcy friction factor and drag reducer concentration (C) and volume flow (Q). First, the experimental plan was designed by using the response surface method (RSM), and then the experimental data were processed to establish the quadratic correlation between the response variable and the description variable. After that, ANOVA analysis of variance was used to verify the accuracy of the experimental data and the correlation. Finally, the prediction model is extended to a larger concentration and volume flow range, and it is found that the accuracy between the predicted value of friction coefficient and the experimental value is ± 30%, thus verifying that the correlation is suitable for the small-scale fully turbulent region. Compared with traditional experimental design and correlation methods, the implementation of Response Surface Methodology (RSM) in this study not only reduces the experimental time but also yields a more robust correlation for predicting the friction coefficient.

Abstract Aspirin, as a widely used anti-inflammatory drug, has been shown to exert anti-cancer effects in a variety of cancers. PD-L1 is widely expressed in tumor cells and inhibits anti-tumor immunity. This study aims to clarify whether aspirin exerts its anti-hepatocellular carcinoma (HCC) effect by inhibiting PD-L1 expression. The rat model of HCC was established by drinking 0.01% diethylnitrosamine (DEN), and aspirin was given by gavage. The gross and blood biochemical indexes of rats were analyzed. CD4 and CD8 expression in liver tissues were investigated by immunohistochemistry. CCK8 assay was used to detect the inhibitory effect of aspirin on the proliferation of HCC cells. The regulatory effect of aspirin on PD-L1 expression was analyzed by western blot. As a result, the tumor number and liver weight ratio in the DEN + ASA group were lower than those in the DEN group (P = 0.006, P = 0.046). Compared with the DEN group, the expression of CD4 in the DEN + ASA group was significantly increased, while CD8 was decreased (all P

Abstract This paper introduces an advanced inventory replenishment optimization approach tailored for the Physical Internet (PI), addressing the dynamic and complex nature of this environment. We propose a hybrid Simulated Annealing–Genetic Algorithm (SA–GA), engineered to optimize the balance between exploration and exploitation, ensuring adaptability and efficiency in a variety of PI contexts. The study also presents an enriched mathematical model integrating dynamic demand, and multi-objective optimization. The SA–GA algorithm emerges as a novel contribution, characterized by its computational efficiency and adaptability, marking an advancement in PI inventory management. The incorporation of real-time data analytics in our dynamic inventory replenishment strategy enhances adaptability and responsiveness, while the robust mathematical model offers a versatile tool for both theoretical analysis and practical application. Collectively, these innovations help bridge existing gaps in PI inventory management and serve as a reference for other similar studies.

Abstract Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer, and more effective prognostic markers are needed. Lactic acid has been proved to be an important metabolite involved in cancer development, metastasis, and the tumor microenvironment, affecting the prognosis of patients. The role of lactic acid metabolism regulators (LAMRs) in HCC is still unclear. In this study, we analyzed the status of LAMRs, a gene list containing lactate from Molecular Signatures database, in HCC and consensus clustering was performed based on these LAMRs. Cluster B showed higher infiltrations of immune cells, higher TME scores, and a poorer prognosis. We further constructed a risk score based on DEGs using LASSO and COX regression analysis between two clusters, which could effectively predict the prognosis of TCGA-LIHC patients. The GSE14520 cohort confirmed the result. We also examined the correlation of risk scores with clinical characteristics, genetic mutations, drug sensitivity, immune checkpoint inhibitors(ICIs), and immunotherapy. In conclusion, our findings will facilitate a further understanding of the role of partial lactate metabolism related genes in HCC and suggest a new risk score to predict prognosis.

Abstract Early diagnosis of esophageal cancer (EC) is extremely challenging. The study presented herein aimed to assess whether urinary volatile organic compounds (VOCs) may be emerging diagnostic biomarkers for EC. Urine samples were collected from EC patients and healthy controls (HCs). Gas chromatography-ion mobility spectrometry (GC-IMS) was next utilised for volatile organic compound detection and predictive models were constructed using machine learning algorithms. ROC curve analysis indicated that an 8-VOCs based machine learning model could aid the diagnosis of EC, with the Random Forests having a maximum AUC of 0.874 and sensitivities and specificities of 84.2% and 90.6%, respectively. Urine VOC analysis aids in the diagnosis of EC.

Abstract With the increase of mining depth and intensity, coal and gas outburst dynamic disasters occur frequently. In order to deeply study the macroscopic fracture mechanism of coal body and evolution characteristics analysis of impact force, taking the outburst coal seam of Pingmei No. 11 Coal Mine and Sunjiawan coal seam of Hengda Coal Mine as the research objects, the simulation roadway test system of self-developed true triaxial coal and gas outburst is applied to carry out the simulation test of deep coal and gas outburst with buried depths of 1000 m, 1200 m, 1400 m and 1600 m. During the test, the overlying strata stress is simulated by axial compression, the surrounding rock stress is simulated by confining pressure, the gas pressure is simulated by pore pressure, the impact force and acoustic emission monitoring technology are introduced, and the coal seam gas pressure is simulated by mixture pressure of 45% CO2 and 55% N2. From the viewpoint of fracture mechanics, the crack propagation mechanism of coal in the outburst launching area is discussed, the evolution characteristics of impact force and gas pressure are analyzed, and the influence law between acoustic emission signal and impact force is revealed. From the viewpoint of energy conversion, the transformation character of gas internal energy to impact kinetic energy (gas pressure to impact force) are analyzed. The results show that the generation of I-type crack is a prerequisite for outburst catastrophe. With the crack propagation, I-type and II-type cracks intersect and penetrate, resulting in internal structural damage and skeleton instability of coal. Gas wrapped fragmentized coal body thrown, outburst occurs. There is obvious negative pressure in the roadway after outburst. The occurrence of negative pressure is greatly affected by the physical and mechanical properties of coal, ground stress and gas pressure. Impact kinetic energy is mainly provided by gas internal energy. Part of the gas pressure is converted into impact force. The strength and duration of the impact force are determined by the gas pressure. Under the condition of deep working conditions (high ground stress and low gas pressure), the propagation of impact force in the roadway is more hindered. Both impact force and acoustic emission signals can monitor the occurrence of outburst. The peak point of acoustic emission ringing count is earlier than the impact force. The acoustic emission signal can monitor the outburst hazard earlier. The impact force can more specifically reflect the coal fracture.

Abstract Machine learning applied to digital pathology has been increasingly used to assess kidney function and diagnose the underlying cause of chronic kidney disease (CKD). We developed a novel computational framework, clustering-based spatial analysis (CluSA), that leverages unsupervised learning to learn spatial relationships between local visual patterns in kidney tissue. This framework minimizes the need for time-consuming and impractical expert annotations. 107,471 histopathology images obtained from 172 biopsy cores were used in the clustering and in the deep learning model. To incorporate spatial information over the clustered image patterns on the biopsy sample, we spatially encoded clustered patterns with colors and performed spatial analysis through graph neural network. A random forest classifier with various groups of features were used to predict CKD. For predicting eGFR at the biopsy, we achieved a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. AUC was 0.96. For predicting eGFR changes in one-year, we achieved a sensitivity of 0.83, specificity of 0.85, and accuracy of 0.84. AUC was 0.85. This study presents the first spatial analysis based on unsupervised machine learning algorithms. Without expert annotation, CluSA framework can not only accurately classify and predict the degree of kidney function at the biopsy and in one year, but also identify novel predictors of kidney function and renal prognosis.

Abstract A central goal of modern magnetic resonance imaging (MRI) is to reduce the time required to produce high-quality images. Efforts have included hardware and software innovations such as parallel imaging, compressed sensing, and deep learning-based reconstruction. Here, we propose and demonstrate a Bayesian method to build statistical libraries of magnetic resonance (MR) images in k-space and use these libraries to identify optimal subsampling paths and reconstruction processes. Specifically, we compute a multivariate normal distribution based upon Gaussian processes using a publicly available library of T1-weighted images of healthy brains. We combine this library with physics-informed envelope functions to only retain meaningful correlations in k-space. This covariance function is then used to select a series of ring-shaped subsampling paths using Bayesian optimization such that they optimally explore space while remaining practically realizable in commercial MRI systems. Combining optimized subsampling paths found for a range of images, we compute a generalized sampling path that, when used for novel images, produces superlative structural similarity and error in comparison to previously reported reconstruction processes (i.e. 96.3% structural similarity and

Abstract Ectoine has gained considerable attention as a high-value chemical with significant application potential and market demand. This study aimed to increase ectoine yields by blocking the metabolic shunt pathway of l-aspartate-4-semialdehyde, the precursor substrate in ectoine synthesis. The homoserine dehydrogenase encoded by hom in H. campaniensis strain XH26 is responsible for the metabolic shunt of l-aspartate-4-semialdehyde to glycine. CRISPR/Cas9 technology was used to seamlessly knockout hom, blocking the metabolic shunt pathway to increase ectoine yields. The ectoine yield of XH26/Δhom was 351.13 mg (g CDW)−1 after 48 h of incubation in 500 mL shake flasks using optimal medium with 1.5 mol L−1 NaCl, which was significantly higher than the 239.18 mg (g CDW)−1 of the wild-type strain. Additionally, the absence of the ectoine metabolic shunt pathway affects betaine synthesis, and thus the betaine yields of XH26/Δhom was 19.98 mg (g CDW)−1, considerably lower than the 69.58 mg (g CDW)−1 of the wild-type strain. Batch fermentation parameters were optimized, and the wild-type strain and XH26/Δhom were fermented in 3 L fermenters, resulting in a high ectoine yield of 587.09 mg (g CDW)−1 for the defective strain, which was significantly greater than the ectoine yield of 385.03 mg (g CDW)−1 of the wild-type strain. This study showed that blocking the metabolic shunt of synthetic substrates effectively increases ectoine production, and a reduction in the competitively compatible solute betaine appears to promote increased ectoine synthesis.

Abstract The improvement of heat transfer inside the solar heat exchangers is important for the development of solar energy in an urban area. In this study, the usage of a non-uniform magnetic field on the thermal efficiency of the nanofluid (Fe3O4) streaming inside the U-turn pipe of solar heat exchangers is examined. Computational fluid dynamic is applied to visualize the nanofluid flow inside the solar heat exchanger. The role of magnetic intensity and Reynolds number on thermal efficiency are fully investigated. The effect of single and triple sources of the magnetic field is also studied in our research. Obtained results indicate that the usage of the magnetic field results in the production of vortex in the base fluid and heat transfer improves inside the domain. Our finding indicates that the usage of the magnetic field with Mn = 25 K would improve the average heat transfer by about 21% along the U-turn pipe of solar heat exchangers.

Abstract RNA interference is a highly conserved mechanism wherein several types of non-coding small RNAs regulate gene expression at the transcriptional or post-transcriptional level, modulating plant growth, development, antiviral defence, and stress responses. Argonaute (AGO), DCL (Dicer-like), and RNA-dependent RNA polymerase (RDR) are key proteins in this process. Here, these three protein families were identified in Chenopodium quinoa. Further, their phylogenetic relationships with Arabidopsis, their domains, three-dimensional structure modelling, subcellular localization, and functional annotation and expression were analysed. Whole-genome sequence analysis predicted 21 CqAGO, eight CqDCL, and 11 CqRDR genes in quinoa. All three protein families clustered into phylogenetic clades corresponding to those of Arabidopsis, including three AGO clades, four DCL clades, and four RDR clades, suggesting evolutionary conservation. Domain and protein structure analyses of the three gene families showed almost complete homogeneity among members of the same group. Gene ontology annotation revealed that the predicted gene families might be directly involved in RNAi and other important pathways. Largely, these gene families showed significant tissue-specific expression patterns, RNA-sequencing (RNA-seq) data revealed that 20 CqAGO, seven CqDCL, and ten CqRDR genes tended to have preferential expression in inflorescences. Most of them being downregulated in response to drought, cold, salt and low phosphate stress. To our knowledge, this is the first study to elucidate these key protein families involved in the RNAi pathway in quinoa, which are significant for understanding the mechanisms underlying stress responses in this plant.

Abstract Remdesivir (GS-5734; VEKLURY) is a single diastereomer monophosphoramidate prodrug of an adenosine analog (GS-441524). Remdesivir is taken up by target cells and metabolized in multiple steps to form the active nucleoside triphosphate (GS-443902), which acts as a potent inhibitor of viral RNA-dependent RNA polymerases. Remdesivir and GS-441524 have antiviral activity against multiple RNA viruses. Here, we expand the evaluation of remdesivir’s antiviral activity to members of the families Flaviviridae, Picornaviridae, Filoviridae, Orthomyxoviridae, and Hepadnaviridae. Using cell-based assays, we show that remdesivir can inhibit infection of flaviviruses (such as dengue 1–4, West Nile, yellow fever, Zika viruses), picornaviruses (such as enterovirus and rhinovirus), and filoviruses (such as various Ebola, Marburg, and Sudan virus isolates, including novel geographic isolates), but is ineffective or is significantly less effective against orthomyxoviruses (influenza A and B viruses), or hepadnaviruses B, D, and E. In addition, remdesivir shows no antagonistic effect when combined with favipiravir, another broadly acting antiviral nucleoside analog, and has minimal interaction with a panel of concomitant medications. Our data further support remdesivir as a broad-spectrum antiviral agent that has the potential to address multiple unmet medical needs, including those related to antiviral pandemic preparedness.

Abstract The current staging method is inadequate to identify high-risk recurrence patients with stage II colon cancer (CC). Using a systematic and comprehensive-biomarker discovery and validation method, we aimed to construct a lncRNA-based signature to improve the prognostic prediction of stage II CC. We identified 1,377 differently expressed lncRNAs by analyzing 16 paired stage II CC tumor tissue and adjacent normal mucosal tissue from the TCGA dataset. Subsequently, using a univariable and step multivariable Cox regression model, we trained an 11-lncRNA signature in the training cohort (n = 141), which could divide patients into high-risk and low-risk groups (AUC at 3 years = 0.801, 95% CI: 0.724–0.877; AUC at 5 years = 0.801, 95% CI: 0.718–0.885). Significantly, patients in the high-risk group had poorer recurrence-free survival (RFS) compared with the low-risk group (log-rank test, P

Abstract Treatment of COVID-19 with a soluble version of ACE2 that binds to SARS-CoV-2 virions before they enter host cells is a promising approach, however it needs to be optimized and adapted to emerging viral variants. The computational workflow presented here consists of molecular dynamics simulations for spike RBD-hACE2 binding affinity assessments of multiple spike RBD/hACE2 variants and a novel convolutional neural network architecture working on pairs of voxelized force-fields for efficient search-space reduction. We identified hACE2-Fc K31W and multi-mutation variants as high-affinity candidates, which we validated in vitro with virus neutralization assays. We evaluated binding affinities of these ACE2 variants with the RBDs of Omicron BA.3, Omicron BA.4/BA.5, and Omicron BA.2.75 in silico. In addition, candidates produced in Nicotiana benthamiana, an expression organism for potential large-scale production, showed a 4.6-fold reduction in half-maximal inhibitory concentration (IC50) compared with the same variant produced in CHO cells and an almost six-fold IC50 reduction compared with wild-type hACE2-Fc.

Abstract Coal and gas outburst is one of the geological disasters that seriously threaten the safety of coal mines production. In recent years, with the increase of mining depth, outbursts become frequent. To further explore the occurrence mechanism of deep coal and gas outburst, a self-developed true triaxial coal and gas outburst simulation device was used to simulate the coal and gas outburst at different depths. The results show that with the increase of simulation depth, the critical gas pressure of outburst gradually decreases, and the unit outburst intensity increases sharply. The gas threshold of deep coal and gas outburst is lower. During the incubation and excitation, gas pressure has three special variation rules, namely self-increasing characteristic, stage and instantaneous. In the early incubation, acoustic emission (AE) energy is at a low level, low energy frequency is dominant; in the later incubation, AE energy increases greatly, high energy frequency is dominant. From the perspective of AE energy, a quantitative index that reflects the danger of coal and gas outburst in the incubation is defined, which provides a scientific reference for the prediction and prevention of disasters of coal and gas outburst in deep mining.

Abstract The outbreak of the COVID-19 and the Russia Ukraine war has had a great impact on the rice supply chain. Compared with other grain supply chains, rice supply chain has more complex structure and data. Using digital means to realize the dynamic supervision of rice supply chain is helpful to ensure the quality and safety of rice. This study aimed to build a dynamic supervision model suited to the circulation characteristics of the rice supply chain and implement contractualization, analysis, and verification. First, based on an analysis of key information in the supervision of the rice supply chain, we built a dynamic supervision model framework based on blockchain and smart contracts. Second, under the logical framework of a regulatory model, we custom designed three types of smart contracts: initialization smart contract, model-verification smart contract, and credit-evaluation smart contract. To implement the model, we combined an asymmetric encryption algorithm, virtual regret minimization algorithm, and multisource heterogeneous fusion algorithm. We then analyzed the feasibility of the algorithm and the model operation process. Finally, based on the dynamic supervision model and smart contract, a prototype system is designed for example verification. The results showed that the dynamic supervision model and prototype system could achieve the real-time management of the rice supply chain in terms of business information, hazard information, and personnel information. It could also achieve dynamic and credible supervision of the rice supply chain’s entire life cycle at the information level. This new research is to apply information technology to the digital management of grain supply chain. It can strengthen the digital supervision of the agricultural product industry.

Abstract By establishing a three-dimensional finite element model of ankle injury arising from supination and external rotation, the stress characteristics of the posterior malleolar surface can be obtained, and analysis of the corresponding stress on the lateral ankle can be conducted. Thin-layer computed tomography images of normal ankle joint in the supination and external rotation nonweight-bearing states was selected, to construct a three-dimensional data model of each ankle joint. A load was applied to examine different ankle joint stress values and pressure distributions on the surface of the posterior ankle joint. A 600 N vertical compressive and 10 Nm internal rotation load was applied in Stage III (removing the anterior tibiofibular ligament and the posterior tibiofibular ligament) of SER (supination-external rotation). When the lateral malleolar was intact, the maximum stress (132.7 MPa) was located at the point of attachment of the posterior tibiofibular ligament to the fibula, and the maximum pressure of the posterior malleolar surface was lower than 4.505 MPa. When a lateral malleolar fracture was present, the maximum stress (82.72 MPa) was located on the fibular fracture surface, and the maximum pressure of the posterior malleolar surface was 8.022 MPa. This study shows that reconstruction of the lateral malleolus in supination-external rotation ankle injuries significantly affects the stress distribution at the posterior malleolar joint surface. Through this reconstruction, the pressure distribution of the posterior malleolar joint surface can be significantly reduced.

Abstract To systematically explore and analyze the microbial composition and function of microbial consortium M44 with straw degradation in the process of subculture at low temperature. In this study, straw degradation characteristics of samples in different culture stages were determined. MiSeq high-throughput sequencing technology was used to analyze the evolution of community structure and its relationship with degradation characteristics of microbial consortium in different culture periods, and the PICRUSt function prediction analysis was performed. The results showed that straw degradation rate, endoglucanase activity, and filter paper enzyme activity of M44 generally decreased with increasing culture algebra. The activities of xylanase, laccase, and lignin peroxidase, as well as VFA content, showing a single-peak curve change with first an increase and then decrease. In the process of subculture, Proteobacteria, Bacteroidetes, and Firmicutes were dominant in different culture stages. Pseudomonas, Flavobacterium, Devosia, Brevundimonas, Trichococcus, Acinetobacter, Dysgonomonas, and Rhizobium were functional bacteria in different culture stages. It was found by PICRUSt function prediction that the functions were concentrated in amino acid transport and metabolism, carbohydrate transship and metabolism related genes, which may contain a large number of fibers and lignin degrading enzyme genes. In this study, the microbial community succession and the gene function in different culture periods were clarified and provide a theoretical basis for screening and rational utilization of microbial consortia.

Abstract Exploring an effective sepsis screening tool that can be widely implemented is important for improving the prognosis of sepsis worldwide. This study aimed to develop a new simple screening tool for sepsis (LIP scoring system) that includes the peripheral blood lymphocyte count, international normalized ratio, and procalcitonin level. In a single-center, prospective, observational study, 444 acute sepsis inpatients and 444 nonsepsis inpatients were ultimately included based on the Sepsis-3 and exclusion criteria. The differences in the Lym, INR, PCT level and other clinical biomarkers were compared between the two groups. Univariable and multivariable logistic regression analyses and receiver operating characteristic analysis were used to establish a LIP screening tool for sepsis with a combination of biomarkers. The Kappa and McNemar tests were used to evaluate the differences between the LIP screening results (LIP score ≥ 3) and Sepsis-3 criteria (SOFA score ≥ 2). Logistic regression analysis showed that the lymphocyte count, INR, PCT level, platelets, neutrophil/lymphocyte ratio (NLR) and prothrombin time (PT) were independent risk factors for the development of sepsis. The ROC analysis showed that the lymphocyte count, INR, and PCT level had high area under the ROC curve values (AUROC (95% CI): Lym 0.84 (0.810–0.860), INR 0.921 (0.902–0.938), PCT level 0.928 (0.909–0.944)). The LIP tool had satisfactory screening efficacy for sepsis (sensitivity, 92.8%; specificity, 94.1%), and a LIP score equal to or greater than 3 points had good agreement with Sepsis-3 criteria in the diagnosis of sepsis (Kappa = 0862 in the Kappa test and P = 0.512 in the McNemar test). The LIP tool has satisfactory sensitivity and specificity for sepsis screening, and it can be used for rapid screening of patients with sepsis in outpatient and emergency departments or in economically underdeveloped areas with limited resources.

Abstract Ageing is co-regulated by genetic and environmental factors. Life on earth lives and evolves in a mild geomagnetic field. Yet, the biological effects of a moderate magnetic field on ageing and the underlying genetic mechanisms remain barely unknown. Here, we report that a moderate static magnetic field (SMF) extends the lifespan of Caenorhabditis elegans, a well-established model organism in ageing research. Consistently, the SMF-treated worms show improved motility and mitochondrial function when aged. We identified from the transcriptomic changes upon SMF treatment that the upregulation of three cytochrome P450 genes are required for SMF-induced longevity. Our findings thus reveal that proper SMF treatment could promote longevity through the well-conserved cytochrome P450 enzymes.

Abstract As a marker of hypercoagulability, plasma D-dimer is associated with progression of many cancers but remains controversial in gastric cancer (GC). We aim to investigate the predictive value of D-dimer for postoperative outcomes after radical gastrectomy of GC patients. We enrolled 903 consecutive patients with GC who underwent radical gastrectomy and the clinicopathological characteristics were compared. Risk factors for overall survival (OS) and disease-free survival (DFS) were determined using multivariate cox regression analysis. We also compared the survival difference based on Kaplan–Meier method after a one-to-one propensity score matching (PSM). Patients with elevated D-dimer had older age (p

Abstract As a systemic inflammatory marker, the significance of NLR in predicting tumor prognosis and early lymph node metastasis is well known, including gastric cancer (GC). However, whether NLR can reflect GC metastasis status remains to be explored. We retrospectively enrolled 1667 GC patients treated in our hospital from December 2010 to December 2018. Patients were grouped according to the presence or absence of metastases. Receiver operating characteristics (ROC) curve analysis was used to evaluate the diagnostic efficacy of markers in assessing GC metastasis. Then we conducted a joint ROC curve analysis. The effects of clinicopathological parameters on GC metastasis were assessed using multiple logistic regression analysis. 743 (44.6%) patients were diagnosed with metastatic GC. Patients with GC metastases have younger age, higher CEA, CA19-9, CA72-4 and NLR. Based on the comparison of AUC, NLR has diagnostic efficacy comparable to that of GC markers. The AUC of NLR combined with GC markers had significantly higher predicting efficacy than that without combination for assessing peritoneal metastasis (P = 0.013), osseous metastasis (P = 0.017) and hepatic metastasis (P

Abstract Job attainment is an important component of socioeconomic status (SES). There is currently a paucity of genomic research on an individual’s job attainment, as well as how it is related to other SES variables and overall well-being at the whole genome level. By incorporating O*NET occupational information into the UK Biobank database, we performed GWAS analyses of six major job attainment characteristics—job complexity, autonomy, innovation, information demands, emotional demands, and physical demands—on 219,483 individuals of European ancestry. The job attainment characteristics had moderate to high pairwise genetic correlations, manifested by three latent factors: cognitive, emotional, and physical requirements. The latent factor of overall job requirement underlying the job attainment traits represented a critical genetic path from educational attainment to income (P

Abstract The current study evaluated the effect of SGLT-2 inhibitor, empagliflozin, on blood pressure reduction in Chinese elderly hypertension patients with type 2 diabetes and investigated its possible mechanisms. 124 patients were randomized to receive 25 mg empagliflozin QD, or placebo double blind for 12 weeks. Patients underwent 24-h ABPM. Endothelial function and arterial stiffness were also measured prior to randomization and at week 12. At week 12, adjusted mean difference versus placebo in change from baseline in mean 24-h SBP was − 8.14 mmHg (95% CI − 10.32, − 3.96, P = 0.005). At week 12, adjusted mean difference versus placebo in change from baseline in mean 24-h DBP was − 5.27 mmHg (95% CI − 8.19, − 1.35, P

Abstract Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discover previously unknown features which predict patient outcomes and overcome substantial interobserver variability, we developed an unsupervised bag-of-words model. Our study applied to the C-PROBE cohort of patients with chronic kidney disease (CKD). 107,471 histopathology images were obtained from 161 biopsy cores and identified important morphological features in biopsy tissue that are highly predictive of the presence of CKD both at the time of biopsy and in one year. To evaluate the performance of our model, we estimated the AUC and its 95% confidence interval. We show that this method is reliable and reproducible and can achieve 0.93 AUC at predicting glomerular filtration rate at the time of biopsy as well as predicting a loss of function at one year. Additionally, with this method, we ranked the identified morphological features according to their importance as diagnostic markers for chronic kidney disease. In this study, we have demonstrated the feasibility of using an unsupervised machine learning method without human input in order to predict the level of kidney function in CKD. The results from our study indicate that the visual dictionary, or visual image pattern, obtained from unsupervised machine learning can predict outcomes using machine-derived values that correspond to both known and unknown clinically relevant features.

Abstract China has a great wealth of lake resources over a great spatial extent and these lakes are highly sensitive to climate changes through their heat and water budgets. However, little is known about the changes in lake surface water temperature (LSWT) across China under the climate warming conditions over the past few decades. In this study, MODIS land surface temperature (LST) data were used to examine the spatial and temporal (diurnal, intra-annual, and inter-annual) variations in LSWT of China’s lakes during 2001–2016. Our results indicated that 169 large lakes included in the study exhibited an overall increasing trend in LSWT, with an average rate of 0.26 °C/decade. The increasing rate of nighttime LSWT is 0.31 °C/decade, faster than that of daytime temperature (0.21 °C/decade). Overall, 121 (71.6%) lakes showed an increase in daytime temperature with a mean rate of 0.38 °C/decade, while the rest 48 (28.4%) lakes decreased in temperature with a mean rate of − 0.21 °C/decade. We also quantitatively analyzed the relationship of the lake surface temperature and diurnal temperature differences (DTDs) with geographical location, topography, and lake morphometry by utilizing multivariate regression analysis. Our analysis suggested that the geographical location (latitude and longitude) and topography (altitude) were primary driving factors in explaining the national lake water temperature variation (P

Abstract The Tibet Autonomous Region of China constitutes a unique and fragile ecosystem that is increasingly influenced by development and global climate change. To protect biodiversity and ecosystem services in Tibet, the Chinese government established a system of nature reserves at a significant cost; however, the effectiveness of nature reserves at protecting both—biodiversity and ecosystem service functions in Tibet is not clear. To determine the success of existing nature reserves, we determined importance areas for the conservation of mammal, plant, bird, amphibian, and reptile species, and for the protection of ecosystem service functions. The results indicated that important conservation areas for endangered plants were mainly distributed in the southern part of Nyingchi City, and for endangered animals, in the southern part of Nyingchi and Shannan Cities. Extremely important conservation areas for ecosystem service functions of carbon sequestration, water and soil protection, and flood regulation were mainly distributed in the southern part of Nyingchi and Shannan Cities, northern and southeastern parts of Nagqu City, and southern part of Ngari area. Based on an analysis of spatial overlap in protection areas, we conclude that existing natural reserves need to be expanded, and new ones need to be established to better protect biodiversity in Tibet Autonomous Region.

Abstract The present study evaluated the effects of dapagliflozin, a SGLT2 inhibitor, or dapagliflozin plus metformin versus metformin monotherapy in patients with metabolic syndrome. This study included patients who admitted in Jiangxi Provincial People’s Hospital from January 1, 2017 to December 31, 2019 and were diagnosed with metabolic syndrome. A total of 248 participants were randomly assigned to divide into three groups: dapagliflozin group; metformin group; dapagliflozin in combined with metformin group. Dapagliflozin group and metformin group were associated with similar improvements in components of metabolic syndrome. Relative to dapagliflozin or metformin monotherapy, dapagliflozin combined with metformin provided greater improvements in components of metabolic syndrome. So did HOMA-IR scores, fasting plasma insulin and inflammatory indicators (hsCRP, PMN/HDL-C and Monocytes/HDL-C). Dapagliflozin improved all components of metabolic syndrome in patients with metabolic syndrome. Furthermore, dapagliflozin combined with metformin showed more meaningful improvements in any of components of metabolic syndrome than dapagliflozin or metformin monotherapy.

Abstract The limit of dynamic endurance during repetitive contractions has been referred to as the point of muscle fatigue, which can be measured by mechanical and electrophysiological parameters combined with subjective estimates of load tolerance for revealing the human real-world capacity required to work continuously. In this study, an isotonic muscular endurance (IME) testing protocol under a psychophysiological fatigue criterion was developed for measuring the retentive capacity of the power output of lower limb muscles. Additionally, to guide the development of electrophysiological evaluation methods, linear and non-linear techniques for creating surface electromyography (sEMG) models were compared in terms of their ability to estimate muscle fatigue. Forty healthy college-aged males performed three trials of an isometric peak torque test and one trial of an IME test for the plantar flexors and knee and hip extensors. Meanwhile, sEMG activity was recorded from the medial gastrocnemius, lateral gastrocnemius, vastus medialis, rectus femoris, vastus lateralis, gluteus maximus, and biceps femoris of the right leg muscles. Linear techniques (amplitude-based parameters, spectral parameters, and instantaneous frequency parameters) and non-linear techniques (a multi-layer perception neural network) were used to predict the time-dependent power output during dynamic contractions. Two mechanical manifestations of muscle fatigue were observed in the IME tests, including power output reduction between the beginning and end of the test and time-dependent progressive power loss. Compared with linear mapping (linear regression) alone or a combination of sEMG variables, non-linear mapping of power loss during dynamic contractions showed significantly higher signal-to-noise ratios and correlation coefficients between the actual and estimated power output. Muscular endurance required in real-world activities can be measured by considering the amount of work produced or the activity duration via the recommended IME testing protocol under a psychophysiological termination criterion. Non-linear mapping techniques provide more powerful mapping of power loss compared with linear mapping in the IME testing protocol.