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Abstract Background Previous deep learning methods for predicting protein binding pockets mainly employed 3D convolution, yet an abundance of convolution operations may lead the model to excessively prioritize local information, thus overlooking global information. Moreover, it is essential for us to account for the influence of diverse protein folding structural classes. Because proteins classified differently structurally exhibit varying biological functions, whereas those within the same structural class share similar functional attributes. Results We proposed LVPocket, a novel method that synergistically captures both local and global information of protein structure through the integration of Transformer encoders, which help the model achieve better performance in binding pockets prediction. And then we tailored prediction models for data of four distinct structural classes of proteins using the transfer learning. The four fine-tuned models were trained on the baseline LVPocket model which was trained on the sc-PDB dataset. LVPocket exhibits superior performance on three independent datasets compared to current state-of-the-art methods. Additionally, the fine-tuned model outperforms the baseline model in terms of performance. Scientific contribution We present a novel model structure for predicting protein binding pockets that provides a solution for relying on extensive convolutional computation while neglecting global information about protein structures. Furthermore, we tackle the impact of different protein folding structures on binding pocket prediction tasks through the application of transfer learning methods. Graphical Abstract

Abstract Background To evaluate the incidence of metabolic syndrome (MetS) in patients with unilateral and bilateral staghorn calculi (SC) and evaluate the impact on the outcome of percutaneous nephrolithotomy (PCNL). Methods The clinical data of patients who underwent PCNL for the treatment of SC between 2019 and 2022 were retrospectively reviewed. SC was divided into unilateral and bilateral. The incidence of MetS was compared between the patients with unilateral SC and the patients with bilateral SC, and the impact on the outcome of PCNL was assessed. Results A total of 1778 patients underwent PCNL between 2019 and 2022. After screening computed tomography, 379 patients were confirmed to have SC, finally, leaving 310 patients with follow-up and complete data to be included in the study. Eighty-four had bilateral SC and 226 had unilateral SC. The patients with bilateral SC had a significantly higher body mass index and higher rates of complete staghorn stones and metabolic syndrome. Higher body mass index, hypertension, diabetes mellitus, hyperlipidaemia, and MetS were present in 62.58%, 44.84%, 21.94%, 60.65% and 27.42% of all patients, respectively. The number of MetS components remained significantly associated with bilateral SC. Specifically, when the number of MetS components increases from 0 to 3–4, the likelihood of developing bilateral staghorn calculi increases by 21.967 times. Eighty-five patients with MetS( +) had a higher rate of overall complications (number (N)(%), 29 (34.12) vs.33 (14.46), P

Microscale energetic materials have garnered considerable attention because they can be integrated in microelectromechanical systems for several potential applications. Hexanitrostilbene (HNS) has been widely utilized in ignition and initiation devices owing to its excellent thermal and shock stability and high sensitivity to short pulse shock waves. To meet the energy release requirements of micro-detonation devices, HNS sticks were prepared using direct ink writing and their reaction and flame propagation properties were studied. HNS particles sized 17.5 µm to 5 µm and ∼500 nm was prepared. Then, the viscosity of HNS-based inks prepared via acoustic resonance technology decreased when the particle size decreased or when the HNS content reduced from 97 wt.%, 95 wt.%, and 92 wt.%, to 90 wt.%. Additionally, the HNS-based inks with different binders exhibited different viscosity. The effect of the inner diameter of needle (0.4 mm, 0.6 mm, and 0.9 mm), printing rate (13 mm/s, 15 mm/s, 17 mm/s, 19 mm/s), and pressure (0.05 MPa, 0.1 MPa, 0.15 MPa, 0.2 MPa) on the width of HNS sticks was also elucidated. HNS-based sticks with a diameter of 0.9 mm underwent self-sustaining combustion reactions, and the burning rate increased from 5.1 mm/s to 6.8 mm/s as the particle size decreased from 17.5 µm to 500 nm. Overall, this work provides an effective approach to prepare microscale HNS for integration into micro-energetic devices.

Objective: To assess the efficacy and safety of combining traditional Chinese medicine (TCM), specifically Chinese herbal medicine (CHM), with Western medicine (WM), compared to WM alone to treat breast cancer endocrine therapy-related osteoporosis (BCET-OP) by meta-analysis. Methods: Thirty-eight randomized controlled trials involving 2170 participants were analyzed. Eight databases were searched for articles published between inception and December 2023. Quality assessment was performed using the Risk of Bias 2 tool. Results: Significant increases were observed in the TCM-WM group in lumbar vertebrae bone mineral density (BMD) (P

Abstract Translational control is crucial for protein production in various biological contexts. Here, we use Ribo-seq and RNA-seq to show that genes related to oxidative phosphorylation are translationally downregulated during heart regeneration. We find that Nat10 regulates the expression of Uqcr11 and Uqcrb mRNAs in mouse and human cardiomyocytes. In mice, overexpression of Nat10 in cardiomyocytes promotes cardiac regeneration and improves cardiac function after injury. Conversely, treating neonatal mice with Remodelin—a Nat10 pharmacological inhibitor—or genetically removing Nat10 from their cardiomyocytes both inhibit heart regeneration. Mechanistically, Nat10 suppresses the expression of Uqcr11 and Uqcrb independently of its ac4C enzyme activity. This suppression weakens mitochondrial respiration and enhances the glycolytic capacity of the cardiomyocytes, leading to metabolic reprogramming. We also observe that the expression of Nat10 is downregulated in the cardiomyocytes of P7 male pig hearts compared to P1 controls. The levels of Nat10 are also lower in female human failing hearts than non-failing hearts. We further identify the specific binding regions of Nat10, and validate the pro-proliferative effects of Nat10 in cardiomyocytes derived from human embryonic stem cells. Our findings indicate that Nat10 is an epigenetic regulator during heart regeneration and could potentially become a clinical target.

Abstract Motivation The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy. Results Accurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepAEG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing simplified molecular input line entry specification data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepAEG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepAEG in guiding the design of specific cancer treatment regimens.

Abstract Introduction Forest restoration is an effective way to promote ecosystem functions and mitigate climate change. However, how forest restoration affect soil C:N:P stoichiometry and microbial biodiversity, as well as their linkage across contrasting forest types globally remains largely illusive. Materials and Methods Here we conducted a global meta‐analysis by synthesizing 121 published papers with 1649 observations to explore how forest restoration affect soil C:N:P stoichiometry and microbial biodiversity globally. Results Forest restoration significantly increased soil total carbon (C), nitrogen (N) and phosphorus (P) content, whereas having no significant impact on most microbial diversity indicator, except for an enhancement in bacterial operational taxonomic unit and fungal Simpson. Meanwhile, forest restoration effects on soil C:N:P stoichiometry varied with different forest types, with promoting more soil C and P in ectomycorrhizal than those in arbuscular mycorrhizal forests. Meanwhile, forest restoration induced changes in soil N and P were positively correlated with microbial Shannon index. More importantly, forest restoration effects on soil C:N:P stoichiometry and microbial biodiversity were regulated by climate factors such as mean annual temperature and mean annual precipitation. Conclusion Our results highlight the crucial role of forest restoration in decoupling the biogeochemical cycles of C, N and P through changes in microbial biodiversity. Therefore, incorporating the decouple effects of forest restoration on soil C:N:P stoichiometry into Earth system models may improve predictions of climate–forest feedbacks in the Anthropocene.

Abstract Accurately predicting the binding affinity between proteins and ligands is crucial for drug discovery. Recent advances in graph neural networks (GNNs) have made significant progress in learning representations of protein-ligand complexes to estimate binding affinities. To improve the performance of GNNs, there frequently needs to look into protein-ligand complexes from geometric perspectives. While the “off-the-shelf” GNNs could incorporate some basic geometric structures of molecules, such as distances and angles, through modeling the complexes as homophilic graphs, these solutions seldom take into account the higher-level geometric attributes like curvatures and homology, and also heterophilic interactions.To address these limitations, we introduce the Curvature-based Adaptive Graph Neural Network (CurvAGN). This GNN comprises two components: a curvature block and an adaptive attention guided neural block (AGN). The curvature block encodes multiscale curvature informaton, then the AGN, based on an adaptive graph attention mechanism, incorporates geometry structure including angle, distance, and multiscale curvature, long-range molecular interactions, and heterophily of the graph into the protein-ligand complex representation. We demonstrate the superiority of our proposed model through experiments conducted on the PDBbind-V2016 core dataset.

Abstract Electrospun nanofibers (NFs) have shown excellent properties including high porosity, abundant active sites, controllable diameter, uniform and designable structure, high mechanical strength, and superior resistance to external destruction, which are ideal nanoreactors for in situ characterizations. Among various techniques, in situ transmission electron microscopy (TEM) has enabled operando observation at the atomic level due to its high temporal and spatial resolution combined with excellent sensitivity, which is of great importance for rational materials design and performance improvement. In this review, the basic knowledge of in situ TEM techniques and the advantages of electrospun nanoreactors for in situ TEM characterization are first introduced. The recent development in electrospun nanoreactors for studying the physical properties, structural evolution, phase transition, and formation mechanisms of materials using in situ TEM is then summarized. The electrochemical behaviors of carbon nanofibers (CNFs), metal/metal oxide NFs, and solid‐electrolyte interphase for different rechargeable batteries are highlighted. Finally, challenges faced by electrospun nanoreactors for in situ TEM characterization are discussed and potential solutions are proposed to advance this field.

PurposeCentral obesity may contribute to breast cancer (BC); however, there is no dose–response relationship. This meta-analysis examined the effects of central obesity on BC and their potential dose–response relationship.MethodsIn the present study, PubMed, Medline, Embase, and Web of Science were searched on 1 August 2022 for published articles. We included the prospective cohort and case–control studies that reported the relationship between central obesity and BC. Summary effect size estimates were expressed as risk ratios (RRs) or odds ratios (ORs) with 95% confidence intervals (95% CI) and were evaluated using random-effect models. The inconsistency index (I2) was used to quantify the heterogeneity magnitude derived from the random-effects Mantel–Haenszel model.ResultsThis meta-analysis included 57 studies (26 case–control and 31 prospective cohort) as of August 2022. Case–control studies indicated that waist circumference (WC) (adjusted OR = 1.18; 95% CI: 1.00–1.38; P = 0.051) and waist-to-hip ratio (WHR) (adjusted OR = 1.28; 95% CI: 1.07–1.53; P = 0.008) were significantly positively related to BC. Subgroup analysis showed that central obesity measured by WC increased the premenopausal (adjusted OR = 1.15; 95% CI: 0.99–1.34; P = 0.063) and postmenopausal (adjusted OR = 1.18; 95% CI: 1.03–1.36; P = 0.018) BC risk and the same relationship appeared in WHR between premenopausal (adjusted OR = 1.38; 95% CI: 1.19–1.59; P < 0.001) and postmenopausal (adjusted OR = 1.41; 95% CI: 1.22–1.64; P < 0.001). The same relationship was observed in hormone receptor-positive (HR+) (adjusted ORWC = 1.26; 95% CI: 1.02–1.57; P = 0.035, adjusted ORWHR = 1.41; 95% CI: 1.00–1.98; P = 0.051) and hormone receptor-negative (HR–) (adjusted ORWC = 1.44; 95% CI: 1.13–1.83; P = 0.003, adjusted ORWHR = 1.42; 95% CI: 0.95–2.13; P = 0.087) BCs. Prospective cohort studies indicated that high WC (adjusted RR = 1.12; 95% CI: 1.08–1.16; P < 0.001) and WHR (adjusted RR = 1.05; 95% CI: 1.018–1.09; P = 0.017) may increase BC risk. Subgroup analysis demonstrated a significant correlation during premenopausal (adjusted RR = 1.08; 95% CI: 1.02–1.14; P = 0.007) and postmenopausal (adjusted RR = 1.14; 95% CI: 1.10–1.19; P < 0.001) between BC and central obesity measured by WC, and WHR was significantly positively related to BC both premenopausal (adjusted RRpre = 1.04; 95% CI: 0.98–1.11; P = 0.169) and postmenopausal (adjusted RRpost = 1.04; 95% CI: 1.02–1.07; P = 0.002). Regarding molecular subtype, central obesity was significantly associated with HR+ (adjusted ORWC = 1.13; 95% CI: 1.07–1.19; P < 0.001, adjusted ORWHR = 1.03; 95% CI: 0.98–1.07; P = 0.244) and HR– BCs (adjusted ORWC =1.11; 95% CI: 0.99–1.24; P = 0.086, adjusted ORWHR =1.01; 95% CI: 0.91–1.13; P = 0.808). Our dose–response analysis revealed a J-shaped trend in the relationship between central obesity and BC (measured by WC and WHR) in case–control studies and an inverted J-shaped trend between BMI (during premenopausal) and BC in the prospective cohort.ConclusionCentral obesity is a risk factor for premenopausal and postmenopausal BC, and WC and WHR may predict it. Regarding the BC subtype, central obesity is proven to be a risk of ER+ and ER- BCs. The dose–response analysis revealed that when BMI (during premenopausal) exceeded 23.40 kg/m2, the risk of BC began to decrease, and WC higher than 83.80 cm or WHR exceeded 0.78 could efficiently increase the BC risk.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022365788.

Abstract Graph level anomaly detection (GLAD) aims to spot anomalous graphs that structure pattern and feature information are different from most normal graphs in a graph set, which is rarely studied by other researchers but has significant application value. For instance, GLAD can be used to distinguish some different characteristic molecules in drug discovery and chemical analysis. However, GLAD mainly faces the following three challenges: (1) learning more comprehensive graph level representations to differ normal graphs and abnormal graphs, (2) designing an effective graph anomaly evaluation paradigm to capture graph anomalies from the local and global graph perspectives, (3) overcoming the number imbalance problem of normal and abnormal graphs. In this paper, we combine graph neural networks and contrastive learning to build an end-to-end GLAD framework for solving the three challenges above. We aim to design a new graph level anomaly evaluation way, which first utilizes the contrastive learning strategy to enhance different level representations of normal graphs from node and graph levels by a graph convolution autoencoder with perturbed graph encoder. Then, we evaluate the error of them with corresponding representations of the generated reconstruction graph to detect anomalous graphs. Extensive experiments on ten real-world datasets from three areas, such as molecular, protein and social network anomaly graphs, show that our model can effectively detect graph level anomaly from the majority and outperform existing advanced methods.

To study the acoustic characteristics of sound scattered from laminated structures such as elastic plates and shells, it is usually required to solve the Lamb waves’ dispersion equations. Many traditional root-finding methods such as bisection, the Newton–Raphson method, and the Muller method are not able to tackle the problem completely. A simple but powerful method named local peaks search (LPS) is proposed to overcome their drawbacks. Firstly, the non-zero part of the dispersion equation is defined as the dispersion function, and its reciprocal is used to transform the zeros (i.e., roots) into local peaks. Secondly, the chosen complex domain is discretized, and the coarse local domains where the local peaks exist are determined by the direct search method globally. Thirdly, the Muller method is applied to obtain the refined locations of local peaks. Lastly, in order to refine the results, a hierarchical scheme is designed and the iteration of the above procedures is implemented; the error is set to be 10−16 as the stop criteria. The accuracy of the LPS method is validated by comparing it with the bisection method for the problem of elastic plates in the vacuum. The acoustic echo structures are analyzed experimentally. By computation of Lamb waves’ phase velocity, the critical angles are derived numerically and compared with the results acquired by an experiment using monostatic sound transducers. In this way, it is validated that the elastic scattered wave components are the highlights shown in the time-angle figure. Furthermore, the work can be applied for non-destructive testing, especially underwater structural health monitoring.

Abstract Background Glutathione-S transferases (GSTs) comprise a series of critical enzymes involved in detoxification of endogenous or xenobiotic compounds. Among several GSTs, Glutathione S-transferases mu (GSTM) has been implicated in a number of cancer types. However, the prognostic value and potential functions of the GSTM family genes have not been investigated in lung adenocarcinoma (LUAD). Methods We examined the expression of GSTM5 in LUAD and identified associations among GSTM5 expression, clinicopathological features, survival data from the Cancer Genome Atlas (TCGA). The correlation between GSTM5 DNA methylation and its expression was analyzed using the MEXPRESS tool and UCSC Xena browser. The methylation status of GSTM5 in the promoter region in lung cancer cells was measured by methylation-specific PCR (MSP). After 5-aza-2'-deoxycytidine treatment of lung cancer cells, expression of GSTM5, cell proliferation and migration were assessed by RT-PCR, CCK-8 and transwell assays, respectively. Results The results showed that GSTM5 was abnormally down-regulated in LUAD patients’ tissues, and patients with low GSTM5 expression level had significantly shorter OS. Cox regression analyses revealed that GSTM5 was associated with overall survival (OS) of LUAD patients, which expression was an independent prognostic indicator in terms of OS (hazard ratio: 0.848; 95% CI: 0.762–0.945; P = 0.003). In addition, we found the promoter region of GSTM5 was hypermethylated in the tumor tissue compared with adjacent normal tissues, and the average methylation level of GSTM5 were moderately correlated with its expression. Moreover, methylation-specific PCR also showed that the GSTM5 gene promoter was hypermethylated in lung cancer cells, and treatment with 5-Aza-CdR can restore the gene expression and inhibit cell proliferation and migration. Finally, Gene Set Enrichment Analysis (GSEA) revealed that low GSTM5 expression was significantly related to DNA repair pathways. Conclusion Our data demonstrate that low GSTM5 expression and its high DNA methylation status may act as a novel putative molecular target gene for LUAD.

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence, and internet of things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human–computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: Intelligent computing is not only intelligence oriented but also intelligence driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy, and an abundance of innovations in the theories, systems, and applications of intelligent computing is expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners.

In recent decades, great changes have taken place in the anesthesiology of China in anesthesia equipment, anesthesiology theory of basic, clinical research, and clinical anesthesia technology. Despite this, there is still a shortage of doctors with higher medical education in anesthesiology, which is also a major problem facing for anesthesia discipline of China, especially in Tibet, Xinjiang, and Inner Mongolia and other regions. Due to the high cold and low oxygen environment in the Tibet Autonomous Region, the development of medical education is backward, especially anesthesia medical education. With the improvement of anesthesia medical technology, we should continue to optimize and reform the local anesthesia medical teaching methods, so as to make substantial reform of anesthesia medical education in Tibet. This paper discusses the main problems in anesthesia medical education and clinical anesthesia in Tibet, as well as its reform ideas and methods. It aims to improve the level of anesthesiology education in Tibet, so as to improve the clinical professional ability of doctor anesthesia, and then better benefit to the local people.

Abstract Background Non-Invasive Continuous Arterial Pressure system (NICAP) allows continuous monitoring, timely detection of hypotension, and avoiding risks from invasive procedures. A previous study showed good comparability of NICAP with arterial line in people with no evidence of cardiovascular disease. Therefore, the goal of this study was to investigate whether NICAP could be accurately applied to elderly patients. Methods In this single-centered observational study, forty-one patients above 65 undergoing elective surgeries requiring artery catheterizations were enrolled from July 17, 2020, to June 25, 2021. Radial artery cannulation and NICAP monitoring were started before anesthesia. Blood pressure during the anesthesia induction and the whole surgery, trend of blood pressure changes, time needed for establishing continuous monitoring, and complications were recorded. Results A total of 6751 valid pairs of blood pressure measurements were analyzed. In the Bland-Altman analysis, the arithmetic means for systolic, diastolic, and mean arterial pressure were 2.2, 3.3, and 2.8 mmHg, respectively. NICAP and arterial line correlation coefficients for systolic, diastolic, and mean arterial pressure were 0.49, 0.33, and 0.45, respectively. In the trending analysis, the polar concordance rates at 30 degrees were 70.9% for systolic, 67.7% for diastolic, and 69.3% for mean arterial blood pressure. During the anesthesia induction, the arithmetic means for systolic, diastolic, and mean arterial pressure in the Bland-Altman analysis were 1.7, -0.2, and 0.5 mmHg, respectively. NICAP and arterial line correlation coefficients for systolic, diastolic, and mean arterial pressure were 0.78, 0.61 and 0.75, respectively. No severe complications occurred. Conclusions NICAP has a poor correlation with the arterial line in elderly patients for the whole surgery or during anesthesia induction. Moreover, it showed poor comparability in the detection of blood pressure change trends with arterial lines. Our findings suggest that NICAP might not be sufficiently accurate to be applied clinically in elderly patients with comorbidities. More accurate calibration and iteration are needed.

To develop soil holding capacity of NH4+and reduce the energy consumption of industrial fertilizer production, different dosages of clinoptilolite mixed with soil were used to absorb NH4+, which was also simulated using Freundlich and Langmuir equation under different conditions. The results showed that NH4+adsorption of clinoptilolite-amended soil changed from multilayer adsorption to single-layer adsorption with the increase in clinoptilolite application rates. The optimal ratio of clinoptilolite applied to the soil for high adsorption efficiency was 0.01g 10g−1. The co-existence of Na+and K+increased the NH4+adsorption capacity of clinoptilolite-amended soil relative to the existence of single Na+. This showed that K+was less competitive towards NH4+than Na+. Decreasing water quantity, which increased the total soil adsorption capacity by a factor of 2 to 3, had the most significant effect and helped regulate the release of nitrogen in clinoptilolite-amended soil. In sum, clinoptilolite can enhance soil nitrogen holding capacity, which shows great potentials to reduce the energy consumption of industrial urea production.

BackgroundPhysical activity (PA) is considered a favorable preventive intervention for postpartum depression (PPD), but evidence defining a corresponding dose-response relationship is lacking. This meta-analysis was conducted to assess the protective effects of PA on PPD and define a potential dose-response relationship between them.MethodsPubMed, Medline, Embase, and Web of Science were searched from 1968 to May 2022. Only randomized control trials (RCTs) and prospective studies were considered, and the PICOS tool was used to identify eligible articles based on the inclusion and exclusion criteria. Effect-size estimates were unified as odds ratio (OR) and 95% confidence interval (CI). We calculated the ORs and their 95% CI for studies that did not report them using the Practical Meta-Analysis Effect Size Calculator.ResultsA total of 23 studies were eligible, including 14 RCTs and 9 prospective cohort studies. The overall analysis showed a statistically significant positive association between PA and PPD prevention (adjusted OR = 0.73; 95% CI: 0.61–0.87; P < 0.001). Subgroup analyses indicated that studies conducted in Europe demonstrated a significant correlation between PA and reduced PPD risk (adjusted OR = 0.85, 95% CI: 0.76–0.95, P = 0.004). Concerning PA type, sports activity was associated with relieving PPD symptoms (adjusted OR = 0.89, 95% CI: 0.78 to 1.00, P < 0.001), while work (adjusted OR = 1.05, 95% CI: 0.37–2.97, P = 0.065) and household activities (adjusted OR = 1.16, 95% CI: 0.89–1.52, P = 0.986) contributed to a greater risk of PPD. Our dose-response analysis revealed a reverse J-shaped trend between ascending PA duration and PPD incidence.ConclusionThis meta-analysis identified PA as a potential intervention to reduce the risk of PPD. The dose-response analysis revealed that at least 90 min of PA per week could efficiently decrease the risk of PPD.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022335731.

Abstract The enhancement of light‐defect images such as extremely low‐light, low‐light and dim‐light has always been a research hotspot. Most of the existing methods are excellent in specific illuminations, and there is much room for improvement in processing light‐defect images with different illuminations. Therefore, this study proposes an efficient framework based on deep learning to enhance various light‐defect images. The proposed framework estimates the reflectance component and illumination component. Next, we propose a generator guided by an attention mechanism in the reflectance part to repair the light‐defect in the dark. In addition, we design a colour loss function for the problem of colour distortion in the enhanced images. Finally, the illumination map of the light‐defect images is adjusted adaptively. Extensive experiments are conducted to demonstrate that our method can not only deal with the images with different illuminations but also enhance the images with clearer details and richer colours. At the same time, we prove its superiority by comparing it with state‐of‐the‐art methods under both visual quality comparison and quantitative comparison of various datasets and real‐world images.

Soil microbes produce more methane as temperatures warm, but it is unclear if they acclimate to heat, or keep producing more of the greenhouse gas. Here the authors use artificial wetland warming experiments to show that after initial spikes in methane emissions after warming, emissions level out over time.

Studies of key metabolite variations and their biological mechanisms in cerebral infarction (CI) have increased our understanding of the pathophysiology of the disease. However, how metabolite variations in different periods of CI influence these biological processes and whether key metabolites from different periods may better predict disease progression are still unknown. We performed a systematic investigation using the metabonomics method. Various metabolites in different pathways were investigated by serum metabolic profiling of 143 patients diagnosed with CI and 59 healthy controls. Phe-Phe, carnitine C18:1, palmitic acid, cis-8,11,14-eicosatrienoic acid, palmitoleic acid, 1-linoleoyl-rac-glycerol, MAG 18:1, MAG 20:3, phosphoric acid, 5α-dihydrotestosterone, Ca, K, and GGT were the major components in the early period of CI. GCDCA, glycocholate, PC 36:5, LPC 18:2, and PA showed obvious changes in the intermediate time. In contrast, trans-vaccenic acid, linolenic acid, linoleic acid, all-cis-4,7,10,13,16-docosapentaenoic acid, arachidonic acid, DHA, FFA 18:1, FFA 18:2, FFA 18:3, FFA 20:4, FFA 22:6, PC 34:1, PC 36:3, PC 38:4, ALP, and Crea displayed changes in the later time. More importantly, we found that phenylalanine metabolism, medium-chain acylcarnitines, long-chain acylcarnitines, choline, DHEA, LPC 18:0, LPC 18:1, FFA 18:0, FFA 22:4, TG, ALB, IDBIL, and DBIL played vital roles in the development of different periods of CI. Increased phenylacetyl-L-glutamine was detected and may be a biomarker for CI. It was of great significance that we identified key metabolic pathways and risk metabolites in different periods of CI different from those previously reported. Specific data are detailed in the Conclusion section. In addition, we also explored metabolite differences of CI patients complicated with high blood glucose compared with healthy controls. Further work in this area may inform personalized treatment approaches in clinical practice for CI by experimentally elucidating the pathophysiological mechanisms.

The hydrodynamic damping characteristics of elastic rotating propeller blades are investigated theoretically and experimentally. Firstly, the modal hydrodynamic damping ratio of propeller blade rotating in water is theoretically predicted based on the cantilever plate theory. Then, a series of tests are designed to determine the mechanical, acoustic radiation and hydrodynamic damping of the first-order bending mode of the rotating blade. During the hydrodynamic damping test, the bending vibration of the blade is excited by the rotation-induced turbulence and measured by two strain gauges attached to the blade root. The results show that the theoretical prediction is in good agreement with the experimental results. The hydrodynamic damping increases linearly with the rotational speed of the propeller. Theoretical analysis also shows that the modal hydrodynamic damping ratio of the first-order bending mode is much larger than that of higher-order bending modes. The hydrodynamic damping decays exponentially with the increase of frequency ratio while increases slightly with the increase of propeller advance ratio which represents the ratio of free stream fluid speed to the propeller tip speed.

Recently, microservice has been a popular architecture to construct cloud-native systems. This novel architecture brings agility and accelerates the software development process significantly. However, it is not easy to manage and operate microservice systems due to their scale and complexity. Many approaches are proposed to automatically operate microservice systems such as anomaly detection. Nevertheless, those methods cannot be sufficiently validated and compared due to a lack of real microservice systems, which leads to the slow process of intelligent operation. These challenges inspire us to build a system named “VWR”, a framework of Virtual War Room for operating microservice applications which allows users to simulate their microservice architectures with low overhead and inject multiple types of faults into the microservice system with chaos engineering. VWR can mimic user requests and record the end-to-end tracing data (i.e., service call chains) for each request in a way consistent with OpenTracing. With easily designed tests and the produced streaming tracing data, the users can validate the performance of their intelligent operation algorithms and improve the algorithms as needed. Besides, based on the streaming tracing data generated by VWR, we introduce a novel unsupervised anomaly detection algorithm based on Matrix Sketch and set it as a default intelligent operation algorithm in VWR. This algorithm can detect anomalies by analyzing high-dimensional performance data collected from a microservice system in a streaming manner. The experimental result in VWR shows that the matrix sketch based method can precisely detect anomalies in microservice systems and outperform some widely used anomaly detection methods such as isolation forest in some scenario. We believe more approaches on the intelligent operation of microservice systems can be constructed based on VWR.

Recently, the quantification of VANET security has drawn significant attention due to the lack of standard computational metrics. The salient features of VANET, such as highly dynamic connections, sensitive information sharing, and unreliable fading channels, make the security quantification challenging. Accurate measurement for VANET security depends on the sufficient understanding of “context”, or making sense of the states, environment, or situation. This article proposes a context-aware security quantification scheme for VANET based on the Markov chain. Firstly, a Homogeneous Continuous-Time Markov Chain (HCTMC)-based security state model is designed for VANET. The value of each state of the HCTMC is determined with a value function that incorporates the security strength of transmitted data, dynamic and randomness of the vehicular channel, and transmission delay of the current situated environment of VANETs. Finally, the state transition matrix is derived based on the Homogeneous Discrete-Time Markov Chain (HDTMC) and Homogeneous Poisson Process (HPP). Simulation results show that the security quantification method enables the VANET's system to adopt context-aware defense strategies according to the situated environment.

In this study, three-dimensional (3D) hydrogels were used for human hepatocellular carcinoma (HepG2) cells culture systems in vitro and establishment of an in vivo xenografted tumor model. Based on our previous work on the biotin-conjugated pullulan acetate nanoparticles (Bio-PA NPs) as anticancer drug carriers, we further studied the anti-tumor effect of the NPs in two-dimensional (2D) and 3D cell culture system. When embedded in 3D hydrogels, HepG2 cells formed tumor spheroids and the cytoplasmic actin microfilamentrates were rearranged over a period of 7 days. In vitro cytotoxicity results indicated that HepG2 cells in 3D hydrogels were more resistant to Bio-PA NPs treatments compared to the 2D system. The tumor formation rate of in vivo xenografted tumor model using 3D culture systems method was 98.2%, which was significantly higher than that using of 2D cultured cells (76.4%). Then we injected the 3D HepG2 cells systems in the right anterior axillary of female Balb/c nude mice, and evaluate the in vivo anti-tumor efficacy of Bio-PA NPs. In summary, these results suggested that HepG2 cells in 3D hydrogel system has shown the potential to provide an in vitro and in vivo model and for the evaluation of Bio-PA NPs.

Low temperatures stress is one of the important factors limiting rice yield, especially during rice anther development, and can cause pollen sterility and decrease grain yield. In our study, low-temperature stress decreased pollen viability and spikelet fertility by affecting the sugar, nitrogen and amino acid contents of anthers. We performed RNA-seq and ISO-seq experiments to study the genome-wide transcript expression profiles in low-temperature anthers. A total of 4,859 differentially expressed transcripts were detected between the low-temperature and control groups. Gene ontology enrichment analysis revealed significant terms related to cold tolerance. Hexokinase and glutamate decarboxylase participating in starch and sucrose metabolism may play important roles in the response to cold stress. Using weighted gene co-expression network analysis, nine hub transcripts were found that could improve cold tolerance throughout the meiosis period of rice: Os02t0219000-01 (interferon-related developmental regulator protein), Os01t0218350-00 (tetratricopeptide repeat-containing thioredoxin), Os08t0197700-00 (luminal-binding protein 5), Os11t0200000-01 (histone deacetylase 19), Os03t0758700-01 (WD40 repeat domain-containing protein), Os06t0220500-01 (7-deoxyloganetin glucosyltransferase), Pacbio.T01382 (sucrose synthase 1), Os01t0172400-01 (phospholipase D alpha 1), and Os01t0261200-01 (NAC domain-containing protein 74). In the PPI network, the protein minichromosome maintenance 4 (MCM4) may play an important role in DNA replication induced by cold stress.

Neural architecture search (NAS) is a popular branch of automatic machine learning (AutoML), which aims to search for efficient network structures. Many prior works have explored a wide range of search algorithms for classification tasks, and have achieved better performance than manually designed network architectures. However, few works have explored NAS for object detection tasks due to the difficulty to train convolution neural networks from scratch. In this paper, we propose a framework, named as FastDARTSDet, to directly search on a larger-scale object detection dataset (MS-COCO). Specifically, we propose to apply differentiable architecture search method (DARTS) to jointly search backbone and feature pyramid network (FPN) architectures for object detection task. Extensive experimental results on MS-COCO show the efficient and efficacy of our method. Specifically, our method achieves 40.0% mean average precision (mAP) on the test set, outperforming many recent NAS methods.

Background and Objective: Antiplatelet therapy (APT) is widely used and believed to be associated with increased poor prognosis by promoting bleeding in patients with intracerebral hemorrhage (ICH). We performed a systematic review and meta-analysis to determine whether prior APT is associated with mortality, functional outcome, and hematoma expansion in ICH patients.Methods: The PubMed, Embase, and Web of Science databases were searched for relevant published studies up to December 11, 2020. Univariate and multivariable adjusted odds ratios (ORs) were pooled using a random effects model. Cochran's chi-squared test (Cochran's Q), the I2 statistic, and meta-regression analysis were used to evaluate the heterogeneity. Meta-regression models were developed to explore sources of heterogeneity. Funnel plots were used to detect publication bias. A trim-and-fill method was performed to identify possible asymmetry and assess the robustness of the conclusions.Results: Thirty-one studies fulfilled the inclusion criteria and exhibited a moderate risk of bias. Prior APT users with intracerebral hemorrhage (ICH) had a slightly increased mortality in both univariate analyses [odds ratio (OR) 1.39, 95% CI 1.24–1.56] and multivariable adjusted analyses (OR 1.41, 95% CI 1.21–1.64). The meta-regression indicated that for each additional day of assessment time, the adjusted OR for the mortality of APT patients decreased by 0.0089 (95% CI: −0.0164 to −0.0015; P = 0.0192) compared to that of non-APT patients. However, prior APT had no effects on poor function outcome (pooled univariate OR: 0.99, 95% CI 0.59–1.66; pooled multivariable adjusted OR: 0.93, 95% CI 0.87–1.07) or hematoma growth (pooled univariate OR: 1.23, 95% CI 0.40–3.74, pooled multivariable adjusted OR: 0.94, 95% CI 0.24–3.60).Conclusions: Prior APT was not associated with hematoma expansion or functional outcomes, but there was modestly increased mortality in prior APT patients. Higher mortality of prior APT patients was related to the strong influence of prior APT use on early mortality.Systematic Review Registration:PROSPERO Identifier [CRD42020215243].

Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.

Abstract Background Colorectal cancer (CRC) remains one of the most common types of cancer and a leading cause of death worldwide. Previous studies indicated that statins may have a potential protective effect on CRC. Methods We conducted this meta‐analysis to systematically assess the overall and cancer‐specific survival benefit of statin uses on CRC patients. Related references were identified through PubMed, the Cochrane Library, Web of Science, EMBASE, and SCOPUS from inception to August 2017. Adjusted hazard ratios (HRs) were adopted to calculate summary hazard ratios (HRs) with 95% confidence intervals (95% CIs), using a random‐effects model. Results Total fourteen studies involving 130 994 patients were included in this meta‐analysis. Six studies reported the association between pre‐diagnosis statin uses and CRC mortality, while 11 studies investigated mortality in patients using statins after CRC diagnosis. For pre‐diagnosis statin uses, the pooled HR of all‐cause mortality (ACM) was 0.85 (95% CI, 0.79‐0.92) and the pooled HR of cancer‐specific mortality (CSM) was 0.82 (95% CI, 0.79‐0.86). In terms of post‐diagnosis statin uses, the pooled HR of ACM was 0.86 (95% CI, 0.76‐0.98), and the pooled HR of CSM was 0.79 (95% CI, 0.70‐0.89). For post‐diagnosis statin uses, there is no difference in ACM when stratified by KRAS gene (KRAS) mutation status. Results of ACM and CSM did not markedly alter in other subgroup analyses. Conclusion Our meta‐analysis demonstrates that both pre‐diagnosis and post‐diagnosis statin uses are associated with reduced ACM and CSM for CRC patients.

Abstract In this paper, we present a polarization sensitive frequency diverse array (PSFDA), which benefits both from frequency diverse array (FDA) and polarization sensitive array (PSA), producing angle-range-polarization-dependent beamforming capability. To simultaneously improve the angle, range and polarization resolution, the ℓ 1-norm minimization-based sparse constraint and ℓ ∞ -norm constraint for sidelobe region are further applied. Besides, the performance of the optimal output signal-to-interference-plus-noise ratio (SINR) is analyzed for such a high-dimensional arrays. Numerical results demonstrate that the proposed PSFDA provides both range and polarization resolution as well as target angle estimation. Moreover, the PSFDA virtually increases the array size by exploiting multiple electromagnetic fields at each physical point. In addition, the proposed beamforming method can effectively null interferences, without degrading the array gain for the signals of interest.

Efficient data dissemination is a big challenge for both IEEE 802.11p (MAC contentions problem) and cellular vehicle-to-everything communications (limited bandwidth in the high dense network). In this paper, a two-level clustering scheme is proposed for efficient data dissemination in 5G V2X communications. In the proposed protocol, level-1 cluster heads (L1CHs) are selected by a fuzzy logic algorithm using three factors, i.e., relative velocity factor, ${k}$ -connectivity factor, and link reliability factor. The slide link vehicle-to-vehicle (V2V) or the Third Generation Partnership Project V2V overcomes the MAC contention problem in L1CHs selections. Next, the level-2 cluster heads are selected by an improved Q-learning aiming to reduce the number of iterations in the gateway selection to LTE base station. The proposed scheme is evaluated under different network conditions showing that our protocol achieves good results compared to the existing schemes.

Nucleosomes composed of histone octamer and DNA are the basic structural unit in the eukaryote chromosome. Under the stimulation of various factors, histones will undergo posttranslational modifications such as methylation, phosphorylation, acetylation, and ubiquitination, which change the three-dimensional structure of chromosomes and affect gene expression. Therefore, the combination of different states of histone modifications modulates gene expression is called histone code. The formation of learning and memory is one of the most important mechanisms for animals to adapt to environmental changes. A large number of studies have shown that histone codes are involved in the formation and consolidation of learning and memory. Here, we review the most recent literature of histone modification in regulating neurogenesis, dendritic spine dynamic, synapse formation, and synaptic plasticity.

In the field of structural health monitoring (SHM), vibration-based structural damage detection is an important technology to ensure the safety of civil structures. By taking advantage of deep learning, this study introduces a data-driven structural damage detection method that combines deep convolutional neural networks (DCNN) and fast Fourier transform (FFT). In this method, the structural vibration data are fed into FFT method to acquire frequency information reflecting structural conditions. Then, DCNN is utilized to automatically extract damage features from frequency information to identify structural damage conditions. To verify the effectiveness of the proposed method, FFT-DCNN is carried out on a three-story building structure and ASCE benchmark. The experimental result shows that the proposed method achieves high accuracy, compared with classic machine-learning algorithms such as support vector machine (SVM), random forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient boosting (xgboost).

To investigate the effect of 14,15-EET on the parthanatos in neurons induced by cerebral ischemia and reperfusion, middle cerebral artery occlusion and reperfusion (MCAO/R) and oxygen glucose deprivation/reoxygenation (OGD/R) were used to simulate cerebral ischemia reperfusion in vivo and in vitro, respectively. TTC staining and the Tunel method were used to detect cerebral infarct volume and neuronal apoptosis. Western blot and immunofluorescence were used to detect poly (ADP-ribose) polymerase-1 (PARP-1) activation and AIF nuclear translocation. The production of reactive oxygen species (ROS) and the expression of antioxidant genes were detected by Mito SOX, DCFH-DA and qPCR methods. MCAO/R increased cerebral infarct volume and neuronal apoptosis in mice, while 14,15-EET pretreatment increased cerebral infarct volume and neuronal apoptosis. OGD/R induced reactive oxygen species generation, PARP-1 cleavage, and AIF nuclear translocation in cortical neurons. 14,15-EET pretreatment could enhance the antioxidant gene expression of glutathione peroxidase (GSH-Px), heme oxygenase-1 (HO-1) and superoxide dismutase (SOD) in cortical neurons after ischemia and reperfusion. 14,15-EET inhibits the neuronal parthanatos induced by MCAO/R through upregulation of the expression of antioxidant genes and by reducing the generation of reactive oxygen species. This study advances the EET neuroprotection theory and provides a scientific basis for targeted clinical drugs that reduce neuronal parthanatos following cerebral ischemia and reperfusion.

Background/Aims: Low ambient temperature is an important risk factor for cardiovascular diseases, and has been shown to lead to cardiac hypertrophy. In this study, we aim to investigate if Resveratrol may inhibit cold exposure-induced cardiac hypertrophy in mice, and if so to clarify its molecular mechanism. Methods: Adult male mice were randomly assigned to Control group (kept at room temperature), Cold group (kept at low air temperature range from 3°C to 5°C) and Resveratrol treatment group (100mg/kg/day) for eight weeks. HE staining, Masson staining and Transmission electron microscopy were employed to detect cardiac structure, fibrosis and myocardial ultrastructure, respectively. Echocardiogram was used to measure myocardial functions. Western blot was used to detect the expression of MAPK pathway and apoptotic proteins. TUENL assay was performed to evaluate cardiomyocyte apoptosis. qRT-PCR was employed to measure the mRNA level. Results: Cold-treated mice showed a higher heart/body weight ratio and heart weight/tibia length ratio compared with control mice, and Resveratrol treatment may suppress these changes in cold-treated mice. Myocardial cross-section area and cardiac collagen volume were larger in cold group than control group, which also can be attenuated by Resveratrol treatment. Also, Resveratrol improved the ultrastructure damage of myocardium such as myofibril disarray in cold group. Echocardiogram measurement showed that EF and FS values in cold group declined apparently as compared to control group, and Resveratrol may improve the reduction of heart functions. The expression of p-JNK, p-p38 and p-ERK relative to total JNK, p38 and ERK in cold group was not altered in cold group and Resveratrol group as compared to control group. Cold-treated mouse hearts also showed the upregulation of hypertrophy-related miRNA-miR-328 but not miR-23a, and Resveratrol treatment can inhibit the increase of miR-328. Finally, Resveratrol treatment also may suppress apoptosis of myocardium in cold-treated mouse hearts via inhibiting Bax and caspase-3 activation. Conclusion: In summary, low ambient temperature can cause enlarged heart, ultrastructure damage of myocardium and weakened functions, and Resveratrol treatment effectively suppressed these changes at least partially via inhibiting cardiomyocyte apoptosis.