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Abstract This study aimed to retrospectively analyze the perioperative and postoperative follow-up data of patients with super obesity who had undergone RYGB, SG, BPD/DS, and SADI-S. A retrospective observational study was conducted to analyze the perioperative and postoperative follow-up data of 60 patients with super obesity who had undergone bariatric surgery. A total of 34 men and 26 women were included in this study. The participants had an average preoperative BMI of 53.81 ± 3.25 kg/m2. The body weight and BMI of all four patient groups decreased significantly at 3, 6, and 12 months postoperatively compared with the preoperative values. Additionally, the TWL (%) and EWL (%) of all four groups increased gradually over the same period. Compared with the preoperative values, the systolic and diastolic blood pressure, glycosylated hemoglobin, uric acid, triglycerides, and total cholesterol decreased to varying degrees in the four groups 1 year postoperatively. RYGB, SG, BPD/DS, and SADI-S are all safe and effective in treating super obese patients and improving their metabolic diseases to a certain extent.

Inflammatory bowel disease (IBD) is an idiopathic and persistent inflammatory illness of the bowels, leading to a substantial burden on both society and patients due to its high incidence and recurrence. The pathogenesis of IBD is multifaceted, partly attributed to the imbalance of immune responses toward the gut microbiota. There is a correlation between the severity of the disease and the imbalance in the oral microbiota, which has been discovered in recent research highlighting the role of oral microbes in the development of IBD. In addition, various oral conditions, such as angular cheilitis and periodontitis, are common extraintestinal manifestations (EIMs) of IBD and are associated with the severity of colonic inflammation. However, it is still unclear exactly how the oral microbiota contributes to the pathogenesis of IBD. This review sheds light on the probable causal involvement of oral microbiota in intestinal inflammation by providing an overview of the evidence, developments, and future directions regarding the relationship between oral microbiota and IBD. Changes in the oral microbiota can serve as markers for IBD, aiding in early diagnosis and predicting disease progression. Promising advances in probiotic-mediated oral microbiome modification and antibiotic-targeted eradication of specific oral pathogens hold potential to prevent IBD recurrence.

Determining a rational mix ratio for lime-fly ash-stabilized loess (LFSL) can achieve multiple benefits of economy, environmental protection, and engineering quality improvement. This research was designed to optimize the mix ratios of LFSL applied in the pavement structure, subgrade, slopes, and foundations by conducting the unconfined compressive strength (UCS) test, California bearing ratio (CBR) test, resilient moduli (MR) test, triaxial test and uniaxial consolidation test on LFSL and lime-stabilized loess (LSL). Combined with the initial consumption of lime (ICL) test result and relevant specifications, the optimized mix ratios are as follows: 2% lime + 11.82% fly ash and 2% lime + 3.95% fly ash can reach the strength requirement of road base and subbase, respectively; 2% lime + 3.84% fly ash in building foundations and subgrade and 2% lime + 3.55% fly ash in slopes can reach the equal improvement effect of the LSL with 8% lime content. Three-factor comprehensive models were established and fitted in well with the experimental results of the LFSL with 2% lime content. Moreover, the development of correlations between UCS and other mechanical indices offers a shortcut for engineering property estimation. Finally, based on the abundant literature on LSL, another approach to estimating engineering quality was proposed for the LFSL with 2% lime content, which enhances the universality and practicability of the estimation models further.

Abstract To evaluate the efficacy and nutrition of single-anastomosis duodenal-ileal bypass with sleeve gastrectomy (SADI-S) in Chinese obese patients in the first postoperative year. Clinical data of 66 obese patients who underwent SADI-S surgery at China-Japan Union Hospital of Jilin University from November 2018 to May 2022 were retrospectively collected. The weight, body mass index (BMI), percentage of excess weight loss (%EWL), and percentage of total weight loss (%TWL) were recorded at 3, 6, and 12 months after surgery. Moreover, metabolic disease remission and nutrient deficiencies were assessed at 1 year postoperatively. Overall, 66 patients (38 males and 28 females) were recruited, with a mean age of 35 (18–61) years and an average preoperative BMI of 42.94 kg/m2. Before surgery, 38 patients had type 2 diabetes mellitus (T2DM), 46 patients had hyperuricemia (HUA), 45 patients had hypertension (HTN), 35 patients had hyperlipidemia, 12 patients had hypercholesterolemia, 12 patients had hyper-low-density lipoproteinemia, and 14 patients had gastroesophageal reflux disease symptoms (GERD). All patients had undergone a DaVinci robotic or laparoscopic SADI-S surgery, and none converted to laparotomy or died. Four patients developed postoperative complications and were cured and discharged after conservative treatment or surgical treatment. At 3, 6 and 12 months, the average %EWL was 62.07 ± 26.56, 85.93 ± 27.92, and 106.65 ± 29.65%, %TWL was 22.67 ± 4.94, 32.10 ± 5.18, and 40.56 ± 7.89%, respectively. Fasting blood glucose (FBG), glycated hemoglobin (HbA1c), uric acid (UA), triglycerides (TG), blood pressure (BP), and other indexes were significantly lower after one year post-surgery compared with the preoperative period (P

As we all know, semantic segmentation of remote sensing (RS) images is to classify the images pixel by pixel to realize the semantic decoupling of the images. Most traditional semantic decoupling methods only decouple and do not perform scale-separation operations, which leads to serious problems. In the semantic decoupling process, if the feature extractor is too large, it will ignore the small-scale targets; if the feature extractor is too small, it will lead to the separation of large-scale target objects and reduce the segmentation accuracy. To address this concern, we propose a scale-separated semantic decoupled transformer (SSDT), which first performs scale-separation in the semantic decoupling process and uses the obtained scale information-rich semantic features to guide the Transformer to extract features. The network consists of five modules, scale-separated patch extraction (SPE), semantic decoupled transformer (SDT), scale-separated feature extraction (SFE), semantic decoupling (SD), and multiview feature fusion decoder (MFFD). In particular, SPE turns the original image into a linear embedding sequence of three scales; SD divides pixels into different semantic clusters by K-means, and further obtains scale information-rich semantic features; SDT improves the intraclass compactness and interclass looseness by calculating the similarity between semantic features and image features, the core of which is decoupled attention. Finally, MFFD is proposed to fuse salient features from different perspectives to further enhance the feature representation. Our experiments on two large-scale fine-resolution RS image datasets (Vaihingen and Potsdam) demonstrate the effectiveness of the proposed SSDT strategy in RS image semantic segmentation tasks.

As the Internet of Things (IoT) industry grows, the risk of network protocol security threats has also increased. One protocol that has come under scrutiny for its security vulnerabilities is MQTT (Message Queuing Telemetry Transport), which is widely used. To address this issue, an automated execution program called fuzz has been developed to verify the security of MQTT brokers. This program is provided with various random and unexpected input data and monitored for different responses, such as acknowledgments, crashes, failures, or memory leaks. To generate a significant number of realistic MQTT protocols, we have proposed a Generative Adversarial Networks (GAN)-based protocol fuzzer called SGANFuzz. Our experimental results show that SGANFuzz has successfully detected 6 vulnerabilities among 7 MQTT implementations, including 3 CVE bugs. Compared to the state-of-the-art fuzzing tools, SGANFuzz has proven to be the most efficient fuzzing tool in terms of vulnerability detection and has expanded the feedback coverage by receiving more unique network responses from MQTT brokers.

Neuropathic pain is a debilitating pathological condition that presents significant therapeutic challenges in clinical practice. Unfortunately, current pharmacological treatments for neuropathic pain lack clinical efficacy and often lead to harmful adverse reactions. As G protein-coupled receptors (GPCRs) are widely distributed throughout the body, including the pain transmission pathway and descending inhibition pathway, the development of novel neuropathic pain treatments based on GPCRs allosteric modulation theory is gaining momentum. Extensive research has shown that allosteric modulators targeting GPCRs on the pain pathway can effectively alleviate symptoms of neuropathic pain while reducing or eliminating adverse effects. This review aims to provide a comprehensive summary of the progress made in GPCRs allosteric modulators in the treatment of neuropathic pain, and discuss the potential benefits and adverse factors of this treatment. We will also concentrate on the development of biased agonists of GPCRs, and based on important examples of biased agonist development in recent years, we will describe universal strategies for designing structure-based biased agonists. It is foreseeable that, with the continuous improvement of GPCRs allosteric modulation and biased agonist theory, effective GPCRs allosteric drugs will eventually be available for the treatment of neuropathic pain with acceptable safety.

This study proposes an innovative deep learning-aided approach based on generative adversarial networks named AttGAN, which is specialized for solving the spatio-temporal super-resolution problem of ocean datasets with complex waveforms and turbulent features. The proposed method can efficiently restore the desired fine high-frequency details while maintaining high efficiency. The key idea of the proposed approach is to incorporate an attention gate in the generator, which allows for emphasizing salient features for super-resolution tasks. In addition, residual convolutional blocks are used in the generator and discriminator to extract features. By establishing regression loss, physical constraints, and adversarial loss into a comprehensive loss function, a generator that exhibits high fidelity and strong generalization capabilities is trained. The proposed AttGAN is evaluated by experiments conducted on two datasets, and the experimental results validate its superiority over state-of-the-art physical constraint models in both qualitative and quantitative metrics. Moreover, the AttGAN has faster processing times than the corresponding ocean numerical models and better quality of results.Impact statement

To support the research and development of asphalt binder with low embrittlement point in cold areas, the microscopic mechanism and effect of polymer modifier on embrittlement and viscosity of asphalt were analysed by molecular dynamics method. The mixed molecular models of three polymer modified asphalt commonly used in cold areas and the interactive molecular models of each component of asphalt and modifier were established. The micromechanical properties of high-content polymer modified asphalt were studied by three important indices, namely brittleness point, shear viscosity and elastic modulus, and the interaction behaviour of each component of asphalt and modifier molecules was analysed by the layer model. The following conclusions were obtained: The brittleness point of modified asphalt can be decreased by increasing the polymer modifier content, with a notable reduction of 25.54 % observed when the content reaches 7.5 %. However, this is accompanied by a subsequent increase in shear viscosity at mixing temperatures, with the highest increase reaching 16.9 %. At low temperature environment, the diffusion rate of the modifier is closely related to its own type, temperature and the type of asphalt components, and in different modified asphalt, the law of the diffusion rate of each asphalt component is not completely consistent, so it is necessary to fully consider the influence of the proportion of asphalt components, and choose the modifier sensitive to the corresponding components. The above research results have an important guiding role in the scientific formulation of the design and test scheme of low-brittleness-point asphalt binder and the completion of the research and development of low-brittleness-point asphalt binder for cold areas.

Background: This meta-analysis explores alterations in the gut microbiota of patients with Multiple Sclerosis (MS) using 16S ribosomal RNA (rRNA) gene sequencing. Methods: Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our comprehensive review spanned major databases, including PubMed, Web of Science, Embase, Cochrane, and Ovid, targeting observational studies that implemented 16S rRNA gene sequencing on fecal specimens. The quality of these studies was meticulously evaluated using the Newcastle-Ottawa scale. Results: Our search yielded 26 relevant studies conducted between 2015-2022, encompassing 2885 participants. No significant differences were observed in alpha diversity indices (Shannon, Chao1, Operational Taxonomic Units (OTU), and Simpson) between MS patients and controls in general. Nonetheless, subgroup analyses according to disease activity using the Shannon index highlighted a significant decrease in microbial diversity during MS’s active phase. Similarly, an evaluation focusing on MS phenotype revealed diminished diversity in individuals with relapsing-remitting MS (RRMS). Microbial composition analysis revealed no consistent increase in pro-inflammatory Bacteroidetes or decrease in anti-inflammatory Firmicutes within the MS cohort. Conclusion: The gut microbiome’s role in MS presents a complex panorama, where alterations in microbial composition might hold greater significance to disease mechanisms than diversity changes. The impact of clinical factors such as disease activity and phenotype are moderately significant, underscoring the need for further research to elucidate these relationships. Prospective research should employ longitudinal methodologies to elucidate the chronological interplay among gut microbiota, disease evolution, and therapeutic strategies.

Modified Benney-Luke equation (mBL equation) is a three-dimensional temporal-spatial equation with complex structures, that is a high-dimensional partial differential equation (PDE), it is also a new equation of the physical ocean field, and its solution is important for studying the internal wave-wave interaction of inclined seafloor. For conventional PDE solvers such as the pseudo-spectral method, it is difficult to solve mBL equation with both accuracy and speed. Physics-informed neural network (PINN) incorporates physical prior knowledge in deep neural networks, which can solve PDE with relative accuracy and speed. However, PINN is only suitable for solving low-dimensional PDE with simple structures, and not suitable for solving high-dimensional PDE with complex structures. This is mainly because high-dimensional PDEs usually have complex structures and high-order derivatives and are likely to be high-dimensional non-convex functions, and the high-dimensional non-convex optimization problem is an NP-hard problem, resulting in the PINN easily falling into inaccurate local optimal solutions when solving high-dimensional PDEs. Therefore, we improve the PINN for the characteristics of mBL equation and propose “DF-ParPINN: parallel PINN based on velocity potential field division and single time slice focus” to solve mBL equation with large amounts of data. DF-ParPINN consists of three modules: temporal-spatial division module of overall velocity potential field, data rational selection module of multiple time slices, and parallel computation module of high-velocity fields and low-velocity fields. The experimental results show that the solution time of DF-ParPINN is no more than 0.5s, and its accuracy is much higher than that of PINN, PIRNN, cPINN, and DeepONet. Moreover, the relative error of DF-ParPINN after deep training 1000000 epochs can be reduced to less than 0.1. The validity of DF-ParPINN proves that the improved PINN also can solve high dimensional PDE with complex structures and large amounts of data quickly and accurately, which is of great significance to the deep learning of the physical ocean field.

El Niño-Southern Oscillation (ENSO), a cyclic climate phenomenon spanning interannual and decadal timescales, exerts substantial impacts on the global weather patterns and ecosystems. Recently, deep learning has brought considerable advances in the accurate prediction of ENSO occurrence. However, the current models are insufficient to characterize the evolutionary behavior of the ENSO, particularly lacking comprehensive modeling of local-range and longrange spatiotemporal interdependencies, and the incorporation of calendar monthly and seasonal properties. To make up this gap, we propose a Two-Stage SpatioTemporal (TSST) autoregressive model that couples the meteorological factor prediction with ENSO indicator prediction. The first stage predicts the meteorological time series by leveraging self-attention ConvLSTM network which captures both the local and the global spatial-temporal dependencies. The temporal embeddings of calendar months and seasonal information are further incorporated to preserves repeatedly-occurring-yet-hidden patterns in meteorological series. The second stage uses multiple layers to extract higher level of features from predicted meteorological factors progressively to generate ENSO indicators. The results demonstrate that our model outperforms the state-of-the-art ENSO prediction models, effectively predicting ENSO up to 24 months and mitigating the spring predictability barrier.

In opportunistic networks, opportunistic routing protocols are used for nodes to forward messages, which are the core technology to realize various opportunistic network applications. However, the existing opportunistic routing protocols generally have three problems: Key nodes forwarded too many messages, resulting in unbalanced energy consumption; Delivered message still occupies the cache space of nodes, resulting in the loss of undelivered message; Nodes constantly sense their surroundings and forward messages, resulting in rapid energy depletion. To better solve these problems, this paper proposes HP-ECD: heuristic Prophet routing protocol based on energy balance, cache optimization, and asynchronous dormancy. Based on Prophet, First, the HP-ECD defines the message forwarding benefits based on multiple node attributes and message importance, to realize the energy balance mechanism of nodes. Then, the HP-ECD designs the message delivery list based on the node history information, to realize the cache optimization mechanism of nodes. Finally, the HP-ECD determines whether a node is in dormancy based on the node running status, to realize the asynchronous dormancy mechanism of nodes. Simulation results show that, compared with Epidemic, Prophet, and EC-CW, HP-ECD greatly improves the message delivery rate and average remaining energy, and reduces the routing overhead rate and average storage time.

IntroductionWe tried to apply a new surgical method to treat obesity combined with pancreatic pseudocyst and achieved satisfactory results.Case and presentationWe report a case of a severely obese patient with pancreatic pseudocyst who underwent robotic-assisted sleeve gastrectomy, while the pseudocyst was incised and cyst-jejunostomy was performed. The operation was successful, and the patient was discharged on the 8th day after the procedure. There were no complications during the perioperative period. After 12 months of follow-up examinations, the patient's pancreatic pseudocyst disappeared. Additionally, there was a significant decrease in body weight, body mass index, and other indicators. As a result, obesity and related metabolic diseases were completely relieved.ConclusionsThis case summarizes and presents the experience of using robotic bariatric surgery for the treatment of pancreatic pseudocyst. This case report indicates that this surgical procedure is both safe and effective for patients with pancreatic pseudocyst who also have obesity and related metabolic diseases.

Abstract Background Stroke is a common and frequently-occurring disease in older people. It has the characteristics of high morbidity, high mortality, high recurrence rate and high disability rate. Most stroke risk studies are based on pathophysiology, however psychosocial factors such as diet quality are often understudied. The aim of this study was to assess stroke risk in urban community residents in Tianjin and investigate the factors that affect the dietary quality of older stroke high-risk populations. Methods Using a cross-sectional, multicenter study, recruit people aged 60 to 80 in Tianjin. Dietary intake data were obtained through a validated food frequency questionnaire, which were used to calculate Alternate Healthy Eating Index-2010 (AHEI-2010) and to analyze its association with sociodemographic characteristics, stroke risk factors and health marker variables. Results A total of 1068 participants from 4 community health service centers in Tianjin were recruited, including 300 low-risk individuals and 768 high-risk individuals. Compared with the low-risk group (62.75 ± 3.59), the AHEI-2010 mean score of the high-risk group (56.83 ± 6.54) was significantly lower. The top three most common risk factors among participants were dyslipidemia (80.3%), hypertension (60.6%), and physical inactivity (58.2%). Multiple logistic regression showed that diet quality was independently and significantly associated with stroke risk (OR = 0.765; 95%CI: 0.690–0.848, p

Fluid dynamic calculations play a crucial role in understanding marine biochemical dynamic processes, impacting the behavior, interactions, and distribution of biochemical components in aquatic environments. The numerical simulation of fluid dynamics is a challenging task, particularly in real-world scenarios where fluid motion is highly complex. Traditional numerical simulation methods enhance accuracy by increasing the resolution of the computational grid. However, this approach comes with a higher computational demand. Recent advancements have introduced an alternative by leveraging deep learning techniques for fluid dynamic simulations. These methods utilize discretized learned coefficients to achieve high-precision solutions on low-resolution grids, effectively reducing the computational burden while maintaining accuracy. Yet, existing fluid numerical simulation methods based on deep learning are limited by their single-scale analysis of spatially correlated physical fields, which fails to capture the diverse scale characteristics inherent in flow fields governed by complex laws in different physical space. Additionally, these models lack an effective approach to enhance correlation interactions among dynamic fields within the same system. To tackle these challenges, we propose the Spatial Multi-Scale Feature Extract Neural Network based on Physical Heterogeneous Interaction (PHI-SMFE). The PHI module is designed to extract heterogeneity and interaction information from diverse dynamic fields, while the SMFE module focuses on capturing multi-scale features in fluid dynamic fields. We utilize channel-biased convolution to implement a separation strategy, reducing the processing of redundant feature information. Furthermore, the traditional solution module based on the finite volume method is integrated into the network to facilitate the numerical solution of the discretized dynamic field in subsequent time steps. Comparative analysis with the current state-of-the-art model reveals that our proposed method offers a 41% increase in simulation accuracy and a 12.7% decrease in inference time during the iterative evolution of unsteady flow. These results underscore the superior performance of our model in terms of both simulation accuracy and computational speedup, establishing it as a state-of-the-art solution.

The present study aims to reveal the deterioration law and service life of concrete durability in the saline soil area of western China. For this purpose, the indoor accelerated tests with multiple damage factors including compound salt erosion, dry-wet cycle, freeze-thaw damage, and solar radiation were designed. The deterioration mechanisms of concrete durability were analysed from both macroscopic and microscopic aspects. The Birnbaum-Saunders (B-S) model was selected for modeling, and parameters were estimated by various methods such as moment estimation method (MEM), inverse estimation method (IMEM), quantile estimation Method (QEM), regression estimation method (REM), and maximum likelihood method (MIM). The results showed that under the action of multiple damage factors, the surface of the concrete gradually becomes rough and pitted from the initial smooth and flat surface. As the concrete’s interior gel is not dense, rod-shaped, and needle-shaped corrosion products were formed. For the two-parameter model, the reliability curve, density curve, and failure rate curve obtained by the QEM differ the most from the other 5 types of parameter estimation methods. When the MEM is used for parameter estimation, the difference between the two-parameter model and the three-parameter model under the same reliability level is relatively small.

Abstract Short QT syndrome (SQTS) is a rare but dangerous genetic disease. In this research, we conducted a comprehensive in silico investigation into the arrhythmogenesis in KCNH2 T618I-associated SQTS using a multi-scale human ventricle model. A Markov chain model of I Kr was developed firstly to reproduce the experimental observations. It was then incorporated into cell, tissue, and organ models to explore how the mutation provided substrates for ventricular arrhythmias. Using this T618I Markov model, we explicitly revealed the subcellular level functional alterations by T618I mutation, particularly the changes of ion channel states that are difficult to demonstrate in wet experiments. The following tissue and organ models also successfully reproduced the changed dynamics of reentrant spiral waves and impaired rate adaptions in hearts of T618I mutation. In terms of pharmacotherapy, we replicated the different effects of a drug under various conditions using identical mathematical descriptions for drugs. This study not only simulated the actions of an effective drug (quinidine) at various physiological levels, but also elucidated why the I Kr inhibitor sotalol failed in SQT1 patients through profoundly analyzing its mutation-dependent actions.

Virtual marine scene authoring plays an important role in generating large-scale 3D scenes and it has a wide range of applications in computer animation and simulation. Existing marine scene authoring methods either produce periodic patterns or generate unnatural group distributions when tiling marine entities such as schools of fish and groups of reefs. To this end, we propose a new large-scale marine scene authoring method based on real examples in order to create more natural and realistic results. Our method first extracts the distribution of multiple marine entities from real images to create Octahedral Blocks, and then we use a modified Wang Cubes algorithm to quickly tile the 3D marine scene. As a result, our method is able to generate aperiodic tiling results with diverse distributions of density and orientation of entities. We validate the effectiveness of our method through intensive comparative experiments. User study results show that our method can generate satisfactory results which are in accord with human preferences.

IntroductionAs global temperatures continue to rise, extreme weather phenomena such as El Niño and the Southern Oscillation (ENSO) near the equatorial Pacific Ocean are occurring more frequently and leading to tropical cyclones, droughts, and a series of extreme weather disasters. Accurately predicting ENSO in advance can greatly reduce the serious damage to human society, economy, and ecological environment. However, existing methods often neglect the data relation between geographical regions and meteorological factors, hindering the accuracy of ENSO prediction.MethodsTo overcome this problem, we propose a residual network with geographical and meteorological attention to capture important geographical information and explore the spatio-temporal correlation of different meteorological factors. Specifically, we propose two main attention modules: (1) the Geographical Semantic Information Enhancement Module (GSIEM), which selectively attends to important geographical regions and filters out irrelevant noise through a spatial-axis attention map, and (2) the Meteorological Factors Discriminating Enhancement Module (MFDEM), which aims to learn the spatio-temporal dependency of different meteorological factors using a learnable channel-axis weight map. We then integrate our proposed two attention modules into the backbone using residual connection, enhancing the model's prediction ability.ResultsWe conducted extensive experimental comparisons and ablation studies to evaluate the performance of our proposed method. The results show that our method outperforms existing state-of-the-art methods in ENSO prediction, with a significant improvement in prediction accuracy.DiscussionOur proposed method effectively captures geographical and meteorological information, facilitating accurate ENSO prediction. The attention modules we proposed can effectively filter out irrelevant noise and learn the spatio-temporal dependency of different meteorological factors, contributing to the superior performance of our model. Overall, our study provides a novel approach for ENSO prediction and has great potential for practical applications.

By virtue of the rapid development of ocean observation technologies, tens of petabytes of data archives have been recorded, among which, the largest portion are those derived from the orbital satellites, embodying the character of ocean surface. Nevertheless, the insufficiency of information below the subsurface restricts the utilization of these data and the understanding of ocean dynamics. To circumvent these difficulties, we present the spatially three-dimensional reconstruction of ocean hydrographic profiles at depth based on the satellites and in-situ measurement data. In this manuscript, long short-term memory network (LSTM) and Gaussian process regression (GPR) methods are invoked to predict the temperature and salinity profiles in the northwest Pacific Ocean, and to improve computational and storage efficiency, the proper orthogonal decomposition (POD) method is subtly incorporated into these two models. LSTM and GPR show satisfactory results, with the root mean square error (RMSE) of temperature is less than 1.45, and the RMSE of salinity is less than 0.19. The incorporation of the POD method substantially accelerates efficiency, particularly in the LSTM model, which improves 7.5-fold without significant accuracy loss. The sensitivity of different sea surface parameters on the reconstructed profiles reveals that sea surface height anomaly and latitude significantly influence the reconstruction of temperature anomaly (TA) and salinity anomaly (SA) profiles. Besides, sea surface salinity and sea surface temperature anomalies can improve the model's estimation ability for the upper TAs and SAs, respectively. The contribution of monthly climatology to temperature and salinity profile estimation is also explored in this paper. It is shown that adding monthly mean climatology to the input of the model can achieve more accurate estimates.

BackgroundPatients with multiple sclerosis (MS) suffer from repetitive neurological deterioration, while anxiety may play a significant role in the disease’s progression.ObjectiveTo explore the prevalence of anxiety in MS and to investigate the risk factors related to anxiety in MS patients.MethodsAn analysis of four databases, PubMed, Web of Science, EMBASE, and Cochrane Library, has been conducted to determine the prevalence or risk factors for anxiety in MS published before May 2021.ResultsIn total, 32 studies were found to be eligible. Anxiety prevalence was estimated to be 36% based on the pooled estimates [the 95% confidence interval (CI) = [0.30–0.42], I2 = 98.4%]. Significant risk factors for developing of anxiety were as follows: age at survey [the weighted mean difference (WMD) = 0.96, 95% CI = [0.86–1.06], I2 = 43.8%], female [the odd ratio (OR) = 1.78, 95% CI = [1.38–2.30], I2 = 0%], living together (OR 2.83, 95% CI = [1.74–4.59], I2 = 0%), past psychiatric history (OR 2.42, 95% CI = [1.56–3.75], I2 = 0%), depression (OR 7.89, 95% CI = [3.71–16.81], I2 = 0%), not taking MS medication (OR 2.33, 95% CI = [1.29–4.21], I2 = 77.8%), relapsing-remitting MS (RRMS) (OR 1.50, 95% CI = [0.94–2.37], I2 = 53.5%), and baseline Expanded Disability Status Scale (EDSS) (OR 0.84, 95% CI = [0.48–1.21], I2 = 62.2%).ConclusionAn estimated 36% of people with MS suffer from anxiety. And anxiety rates in MS patients are significantly associated with age, gender, living together, prior psychiatric history, depression, drug compliance, RRMS, and baseline EDSS.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=287069, identifier CRD42021287069.

In actual operation, there are security risks to the data of the network control system, mainly in the form of possible eavesdropping of signals in the transmission channel and parameters in the controller leading to data leakage. In this paper, we propose a scheme for encrypting linear controllers using fully homomorphic encryption, which effectively removes these security risks and substantially improves the security of networked control systems. Meanwhile, this paper uses precomputation to handle data encryption, which eliminates the encryption time and solves the drawback of fully homomorphic encryption that it is difficult to apply due to the efficiency problem. Compared to previous schemes with precomputation, for the first time, we propose two methods to mitigate the problem of the slight security degradation caused by precomputation, which makes our scheme more secure. Finally, we provide numerical simulation results to support our scheme, and the data show that the encrypted controller achieves normal control and improves safety and efficiency.

Condition-based molecular generation can generate a large number of molecules with particular properties, expanding the virtual drug screening library, and accelerating the process of drug discovery. In this study, we combined a molecular graph structure and sequential representations using a generative pretrained transformer (GPT) architecture for generating molecules conditionally. The incorporation of graph structure information facilitated a better comprehension of molecular topological features, and the augmentation of a sequential contextual understanding of GPT architecture facilitated molecular generation. The experiments indicate that our model efficiently produces molecules with the desired properties, with valid and unique metrics that are close to 100%. Faced with the typical task of generating molecules based on a scaffold in drug discovery, our model is able to preserve scaffold information and generate molecules with low similarity and specified properties.

Abstract Background Hepatitis B virus can induce hepatocellular carcinoma (HCC) by inducing a host immune response against infected hepatocytes. C-terminally truncated middle surface protein (MHBSt) has been reported to contribute to HCC through transcriptional activation in epidemiology studies, while the underlying mechanism of MHBSt-induced HCC is unknown. Methods In this study, a premature stop at codon 167 in MHBS (MHBSt167) was investigated into eukaryotic expression plasmid pcDNA3.1(-). MHBSt167 expressed plasmid was transfected into the L02 cell line, cell proliferation was analyzed by CCK-8 and high-content screening assays, the cell cycle was analyzed by flow cytometry, and epithelial-to-mesenchymal transition and autophagy were analyzed by immunoblotting and immunofluorescence. NF-κB activation and the MHBSt167-induced immune response were analyzed by immunoblotting and immunofluorescence. IFN-α, IFN-β and IL-1α expression were analyzed by qPCR. Autophagy inhibitors were used to analyze the relationship between the immune response and autophagy. Results The results showed that MHBSt167 promoted L02 cell proliferation, accelerated cell cycle progression from the S to G2 phase and promoted epithelial-to-mesenchymal transition through ER-stress, leading to autophagy and NF-κB activation and increased immune-related factor expression. The MHBSt167-induced acceleration of cell proliferation and the cell cycle was abolished by autophagy or NF-κB inhibitors. Conclusion In summary, MHBSt167 could promote cell proliferation, accelerate cell cycle progression, induce EMT and activate autophagy through ER-stress to induce the host immune response, supporting a potential role of MHBSt167 in contributing to carcinogenesis.

In order to ensure the quality of the results of ocean investigation, observation and monitoring as well as to ensure their compliance of relative laws, branches of Ministry of Natural Resources regularly assessed the running status of the quality management systems of the ocean inspection bodies and laboratories, which was to confirm their ability of obtaining reliable data and results from the marine tasks. However, these assessments were usually based on qualitative methods; a systematic and quantitative method required further research. The paper focused on the particular requirements of marine tasks, recognized 3 relative aspects: deployment of resources, processes of implementation and effectiveness of results, and divided them into 6 indicators: employees, instruments, methods and their application, quality controlling, carriers of outputs and improvement. Meanwhile, 3 "one-vote veto" indicators were distinguished: legitimacy of the organization’s qualification, authenticity of the data and result of the quality acceptance test. On the basis of these, an evaluation model depending on weighted accumulation method was established, the weights were acquired through Analytic Hierarchy Process method, the standards and the rule of grading were set, and an index system to evaluate running effectiveness of quality management systems of ocean inspection bodies and laboratories was constructed. At the end of the paper, an advised procedure to conduct the evaluation method was illustrated, and a demonstration on assessing the marine ecology monitoring organizations was given, which showed the practicability of the evaluation method.

Numerical simulation of fluid is a great challenge as it contains extremely complicated variations with a high Reynolds number. Usually, very high-resolution grids are required to capture the very fine changes during the physical process of the fluid to achieve accurate simulation, which will result in a vast number of computations. This issue will continue to be a bottleneck problem until a deep-learning solution is proposed to utilize large-scale grids with adaptively adjusted coefficients during the spatial discretization procedure—instead of traditional methods that adopt small grids with fixed coefficients—so that the computation cost is dramatically reduced and accuracy is preserved. This breakthrough will represent a significant improvement in the numerical simulation of fluid. However, previously proposed deep-learning-based methods always predict the coefficients considering only the spatial correlation among grids, which provides relatively limited context and thus cannot sufficiently describe patterns along the temporal dimension, implying that the spatiotemporal correlation of coefficients is not well learned. We propose the time-sequence-involved space discretization neural network (TSI-SD) to extract grid correlations from spatial and temporal views together to address this problem. This novel deep neural network is transformed from a classic CONV-LSTM backbone with careful modification by adding temporal information into two-dimensional spatial grids along the x-axis and y-axis separately at the first step and then fusing them through a post-fusion neural network. After that, we combine the TSI-SD with the finite volume format as an advection solver for passive scalar advection in a two-dimensional unsteady flow. Compared with previous methods that only consider spatial context, our method can achieve higher simulation accuracy, while computation is also decreased as we find that after adding temporal data, one of the input features, the concentration field, is redundant and should no longer be adopted during the spatial discretization procedure, which results in a sharp decrease of parameter scale and achieves high efficiency. Comprehensive experiments, including a comparison with SOTA methods and sufficient ablation studies, were carried out to verify the accurate and efficient performance and highlight the advantages of the proposed method.

Marine natural product (MNP) entity property information is the basis of marine drug development, and this entity property information can be obtained from the original literature. However, the traditional methods require several manual annotations, the accuracy of the model is low and slow, and the problem of inconsistent lexical contexts cannot be solved well. In order to solve the aforementioned problems, this study proposes a named entity recognition method based on the attention mechanism, inflated convolutional neural network (IDCNN), and conditional random field (CRF), combining the attention mechanism that can use the lexicality of words to make attention-weighted mentions of the extracted features, the ability of the inflated convolutional neural network to parallelize operations and long- and short-term memory, and the excellent learning ability. A named entity recognition algorithm model is developed for the automatic recognition of entity information in the MNP domain literature. Experiments demonstrate that the proposed model can properly identify entity information from the unstructured chapter-level literature and outperform the control model in several metrics. In addition, we construct an unstructured text dataset related to MNPs from an open-source dataset, which can be used for the research and development of resource scarcity scenarios.

Phosphorus-containing polybutylene succinate (P-PBS) was prepared from the third monomer phenylphosphonic acid to improve the performance of biodegradable materials and endow plants with nutritional functions. The P-PBS/polylactic acid (PLA) composite was produced by blending P-PBS and PLA; its crystallization, thermodynamic, water vapor permeability, degradation, and plant nutrition properties were studied. Introducing phenylphosphoric acid destroyed the regularity of the PBS chain, decreased the crystallinity, and increased the polarity. With PLA, the composite crystallinity was further reduced. A certain intermolecular force combines P-PBS/PLA. When the P-PBS amount was 60%, the two-phase melting point was close, the two-phase compatibility was high, and the crystallinity was the lowest. Additionally, the molecular chain flexibility of the composite was the highest with the optimal thermal and mechanical properties. Furthermore, with 60% P-PBS, the composite material had the highest water vapor transmission rate for possessing the largest two-phase contact surface, which can be used as a mulching film. The composite was degraded in soil supernatant for 6 months, with a 60%–70% degradation rate. LC-MS identification revealed that the degradation products were succinic acid, butanediol, and phenylphosphonic acid oligomers, among which phosphorus-containing small molecules enhanced lettuce growth.

Background: Carbon monoxide (CO) is gaining increased attention in air pollution-induced arrhythmias. The severe cardiotoxic consequences of CO urgently require effective pharmacotherapy to treat it. However, existing evidence demonstrates that CO can induce arrhythmias by directly affecting multiple ion channels, which is a pathway distinct from heart ischemia and has received less concern in clinical treatment.Objective: To evaluate the efficacy of some common clinical antiarrhythmic drugs for CO-induced arrhythmias, and to propose a potential pharmacotherapy for CO-induced arrhythmias through the virtual pathological cell and tissue models.Methods: Two pathological models describing CO effects on healthy and failing hearts were constructed as control baseline models. After this, we first assessed the efficacy of some common antiarrhythmic drugs like ranolazine, amiodarone, nifedipine, etc., by incorporating their ion channel-level effects into the cell model. Cellular biomarkers like action potential duration and tissue-level biomarkers such as the QT interval from pseudo-ECGs were obtained to assess the drug efficacy. In addition, we also evaluated multiple specific IKr activators in a similar way to multi-channel blocking drugs, as the IKr activator showed great potency in dealing with CO-induced pathological changes.Results: Simulation results showed that the tested seven antiarrhythmic drugs failed to rescue the heart from CO-induced arrhythmias in terms of the action potential and the ECG manifestation. Some of them even worsened the condition of arrhythmogenesis. In contrast, IKr activators like HW-0168 effectively alleviated the proarrhythmic effects of CO.Conclusion: Current antiarrhythmic drugs including the ranolazine suggested in previous studies did not achieve therapeutic effects for the cardiotoxicity of CO, and we showed that the specific IKr activator is a promising pharmacotherapy for the treatment of CO-induced arrhythmias.

ObjectiveThe aim of the study was to evaluate non-invasive brain stimulation (NIBS) [including transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES)] on neurological symptoms in patients with multiple sclerosis (PwMS).MethodWe searched PubMed, Embase, Cochrane Library, Web of Science and Ovid MEDLINE until February 2022. And we evaluated the included studies for methodological quality by the Cochrane bias risk assessment tool and assessed the studies' certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. We performed network meta analysis (NMA) by using Stata 15 and ranked the results of the NMA by using the surface under the cumulative ranking curve (SUCRA) ranking chart.ResultTwenty seven clinical trials were finally included (N = 596, 66.4% women). For the immediate effects, rTMS over M1 yielded the most optimal scheme for fatigue reduction among all the interventions compared to the sham stimulation groups [MD = −0.85, 95% CI (−1.57, −0.14)] (SUCRA = 82.6%). iTBS over M1 yielded the most signifcant reduced pain level than the sham groups did [MD = −1.26, 95% CI (−2.40, −0.11)] (SUCRA = 98.4%). tDCS over F3 was the best protocol of NIBS to improve quality of life (QOL) [MD = 1.41, 95% CI = (0.45,2.36)] (SUCRA = 76.7%), and iTBS over M1 may significantly reduce spasticity compared to sham stimulation [MD = −1.20, 95% CI = (−1.99, −0.41)] (SUCRA = 90.3%). Furthermore, rTMS, tRNS, and tDCS on certain areas may improve PwMS accuracy, response time, manual dexterity, pain relief and QOL, but does not show statistically significant differences. The evidence assessed using GRADE is very low.ConclusionBased on the NMA and SUCRA ranking, we can conclude that symptoms including fatigue, pain, spasticity, and QOL can be improved by following NIBS protocol after treatment. Nonetheless, most of the included studies lack a good methodology, and more high-quality randomized clinical trials are needed.

Creating and maintaining a domain-specific database of research institutions, academic experts and scholarly literature is essential to expanding national marine science and technology. Knowledge graphs (KGs) have now been widely used in both industry and academia to address real-world problems. Despite the abundance of generic KGs, there is a vital need to build domain-specific knowledge graphs in the marine sciences domain. In addition, there is still not an effective method for named entity recognition when constructing a knowledge graph, especially when including data from both scientific and social media sources. This article presents a novel marine science domain-based knowledge graph framework. This framework involves capturing marine domain data into KG representations. The proposed approach utilizes various entity information based on marine domain experts to enrich the semantic content of the knowledge graph. To enhance named entity recognition accuracy, we propose a novel TrellisNet-CRF model. Our experiment results demonstrate that the TrellisNet-CRF model reached a 96.99% accuracy rate for marine domain named entity recognition, which outperforms the current state-of-the-art baseline. The effectiveness of the TrellisNet-CRF module was then further demonstrated and confirmed on entity recognition and visualization tasks.

Cardiovascular diseases are the primary cause of death of humans, and among these, ventricular arrhythmias are the most common cause of death. There is plausible evidence implicating inflammation in the etiology of ventricular fibrillation (VF). In the case of systemic inflammation caused by an overactive immune response, the induced inflammatory cytokines directly affect the function of ion channels in cardiomyocytes, leading to a prolonged action potential duration (APD). However, the mechanistic links between inflammatory cytokine-induced molecular and cellular influences and inflammation-associated ventricular arrhythmias need to be elucidated. The present study aimed to determine the potential impact of systemic inflammation on ventricular electrophysiology by means of multiscale virtual heart models. The experimental data on the ionic current of three major cytokines [i.e., tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1β), and interleukin-6 (IL-6)] were incorporated into the cell model, and the effects of each cytokine and their combined effect on the cell action potential (AP) were evaluated. Moreover, the integral effect of these cytokines on the conduction of excitation waves was also investigated in a tissue model. The simulation results suggested that inflammatory cytokines significantly prolonged APD, enhanced the transmural and regional repolarization heterogeneities that predispose to arrhythmias, and reduced the adaptability of ventricular tissue to fast heart rates. In addition, simulated pseudo-ECGs showed a prolonged QT interval—a manifestation consistent with clinical observations. In summary, the present study provides new insights into ventricular arrhythmias associated with inflammation.

Abstract Background Hepatitis B virus (HBV) infection is difficult to cure. HBV-specific immune tolerance plays a key role in HBV persistence, and enhancing cellular and humoral immunity will improve the control of HBV infection. The purpose of the study was to explore the anti-HBV and immunostimulatory effects of msiRNAs that introduce unpaired uridine bulges in the passenger strand. Methods msiRNAs targeting the HBV S and X genes were designed and named msiHBs and msiHBx, respectively. HepG2 cells were cotransfected with siRNA or msiRNA and the HBV replication-competent plasmid pHY106-wta or pHY106-X15. HepG2.215 cells were transfected with siRNA or msiRNA. The levels of HBsAg, HBeAg, and the cytokines TNF-α, IFN-α, IFN-β, IL-1α, and IL-6 in the culture supernatant was detected by ELISA. The levels of intracellular HBV RNA, nuclear HBV replication intermediates, and HBV DNA in the supernatant were measured by quantitative RT-PCR and PCR. The levels of HBV replication intermediates were detected by Southern blotting. Peripheral blood mononuclear cells were transfected with siRNA or msiRNA, and the levels of secreted cytokines IFN-α and IFN-β were detected by ELISA. The bioactivity of type I interferons in the supernatants was detected by the virus protection assay. Results msiHBx treatment led to a significant decrease in HBsAg (to a negative level) and HBV DNA (95.5%) in the supernatant and intrahepatocellular HBV replication intermediates (89.8%) in HepG2 cells with transient HBV replication and in HepG2.2.15 cells. There was no significant difference between msiHBx and siHBx in terms of the reduction in HBV proteins and HBV replication (P > 0.05). Compared with siHBx, msiHBx treatment of HepG2 cells transfected with the HBV replication-competent plasmid led to a significant increase in the levels of the antiviral cytokines TNF-α (3.3-fold), IFN-α (1.4-fold), and IFN-β (2.5-fold) (P

In this study, a new approach using a novel ensemble classification algorithm based on ECG morphological features is proposed for accurate detection of heart ventricular and atrial abnormalities. First, the raw ECG signal is preprocessed and the main character waves are detected. Second, a combination of ECG morphological features is proposed and extracted from the selected ECG segments. The proposed feature set contains morphological parameters, morphological visual pattern of QRS complex, and principle components of the third level and fourth level of a four-level Sym8 wavelet-decomposed ECG waveform. Next, a novel ensemble classification algorithm, with the key idea of integrating the knowledge acquired by several popular classification algorithms for this task into an ensemble system, is proposed so that the accuracy and robustness over various arrhythmia types could be improved. Finally, the features are applied to the proposed ensemble classification algorithm for abnormality detection. The proposed approach achieved an overall accuracy of 98.68% when it was validated on fifteen heartbeat types from the MIT-BIH arrhythmia database (MITDB), according to the Association for Advancement of Medical Instrumentation (AAMI) standard. The classification accuracies of the six main types – normal beat (N), right bundled branch blocks beat (R), left bundled branch blocks beat (L), atrial premature beat (A), premature ventricular contractions beat (V), and paced beat (P) are 98.75%, 99.77%, 99.70%, 94.81%, 98.57%, and 99.94%, respectively. The proposed approach proves a solid result in comparison with component classification algorithms as well as recent peer works.

Short QT syndrome (SQTS) is a genetic disease characterized by constantly short QT intervals and high risks of sudden death. SQTS6 is one of the identified SQTS genotype variants associated with the CACNA2D1 S755T mutation. However, the pathogenesis of SQTS induced arrhythmias remains unclear. To identify the underlying mechanisms of SQTS6 induced arrhythmias, a multi-scale human ventricle model comprising cell to organ levels was built. Cellular data was fitted at the cell level to reproduce the electrophysiological alterations reported in experiments. The influences were further explored at tissue and organ levels using idealized strand or tissue sheet models, and realistic ventricular slice and three-dimensional organ models. Simulation results suggested that, at the cellular level, the action potential duration (APD) and the effective refractory period (ERP) of myocytes were significantly abbreviated in the mutation condition. The unevenly changed APD and ERP led to transmural heterogeneity remodeling, and resulted in decreased temporal vulnerability. In addition, the S755T mutation shortened the critical length for initiating reentrant spiral waves, which enhanced the spatial vulnerability and provided substrates for reentry arrhythmias. Regarding the sustainability of arrhythmias, the evoked spiral waves or scroll waves persisted in the mutation condition but did not persist in the wild-type condition. The present study clearly suggested that the CACNA1DC S755T mutation can facilitate the initiation and maintenance of ventricular arrhythmias, and therefore contributes to higher risks of ventricular arrhythmias in SQTS6 patients.

Abstract Classical simulation of quantum computation is vital for verifying quantum devices and assessing quantum algorithms. We present a new quantum circuit simulator developed on the Sunway TaihuLight supercomputer. Compared with other simulators, the present one is distinguished in two aspects. First, our simulator is more versatile. The simulator consists of three mutually independent parts to compute the full, partial and single amplitudes of a quantum state with different methods. It has the function of emulating the effect of noise and support more kinds of quantum operations. Second, our simulator is of high efficiency. The simulator is designed in a two-level parallel structure to be implemented efficiently on the distributed many-core Sunway TaihuLight supercomputer. Random quantum circuits can be simulated with 40, 75 and 200 qubits on the full, partial and single amplitude, respectively. As illustrative applications of the simulator, we present a quantum fast Poisson solver and an algorithm for quantum arithmetic of evaluating transcendental functions. Our simulator is expected to have broader applications in developing quantum algorithms in various fields.

Abstract Background Osteoarthritis (OA) is a metabolic disorder and able to be relieved by traditional Chinese medicines. However, the effect of Ligusticum wallichii on OA is unknown. Methods Cytokine IL-1β and L. wallichii extracts were used to stimulate the primary mouse chondrocytes. MTT assay was used to measure the cell viability. The mRNA and protein level of each gene were test by qRT-PCR and western blotting, respectively. The rate of apoptotic cell was measured by flow cytometry. GC/MS-based metabolomics was utilized to characterize the variation of metabolome. Results Here, we found that L. wallichii attenuated the IL-1β-induced apoptosis, inflammatory response, and extracellular matrix (ECM) degradation in mouse chondrocytes. Then we used GC/MS-based metabolomics to characterize the variation of metabolomes. The established metabolic profile of mouse chondrocytes showed that the abundance of most metabolites (n = 40) altered by IL-1β stimulation could be repressed by L. wallichii treatment. Multivariate data analysis identified that cholesterol, linoleic acid, hexadecandioic acid, proline, l-valine, l-leucine, pyruvate, palmitic acid, and proline are the most key biomarkers for understanding the metabolic role of L. wallichii in IL-1β-treated chondrocytes. Further pathway analysis using these metabolites enriched fourteen metabolic pathways, which were dramatically changed in IL-1β-treated chondrocytes and capable of being reprogrammed by L. wallichii incubation. These enriched pathways were involved in carbon metabolisms, fatty acid biosynthesis, and amino acid metabolisms. Conclusions These findings provide potential clues that metabolic strategies are linked to protective mechanisms of L. wallichii treatment in IL-1β-stimulated chondrocytes and emphasize the importance of metabolic strategies against inflammatory responses in OA development.

ECG is a non-invasive tool used to detect cardiac arrhythmias. Many arrhythmias classification solutions with various ECG features have been reported in literature. In this work, a new method combined with a novel morphological feature is proposed for accurate recognition and classification of arrhythmias. First, the events of the ECG signals are detected. Then, parametric features of ECG morphology, i.e., amplitude, interval and duration, are extracted from selected ECG regions. Next, a novel feature for analyzing QRS complex morphology changes as visual patterns as well as a new clustering-based feature extraction algorithm is proposed. Finally, the feature vectors are applied to three well-known classifiers (neural network, SVM, and KNN) for automatic diagnosis. The proposed method was assessed with all fifteen types of heartbeats as recommended by the Association for Advancement of Medical Instrumentation from the MIT-BIH arrhythmia database and achieved the best overall accuracy of 97.70% based on KNN, using the combined parametric and visual pattern features of ECG Morphology. The accuracies for the six main types - normal (N), left bundled branch blocks (L), right bundled branch blocks (R), premature ventricular contractions (V), atrial premature beats (A) and paced beats (P) are 97.79%, 99.50%, 99.59%, 97.69%, 89.70%, and 99.92%, respectively. Comparisons with peer works prove a marginal progress in automatic heart arrhythmia classification performance.

Molecular property prediction is important to drug design. With the development of artificial intelligence, deep learning methods are effective for extracting molecular features. In this paper, we propose a multichannel substructure-graph gated recurrent unit (GRU) architecture, which is a novel GRU-based neural network with attention mechanisms applied to molecular substructures to learn and predict properties. In the architecture, molecular features are extracted at the node level and molecule level for capturing fine-grained and coarse-grained information. In addition, three bidirectional GRUs are adopted to extract the features on three channels to generate the molecular representations. Different attention weights are assigned to the entities in the molecule to evaluate their contributions. Experiments are implemented to compare our model with benchmark models in molecular property prediction for both regression and classification tasks, and the results show that our model has strong robustness and generalizability.

Anomaly detection over HTTP traffic has attracted much attention in recent years, which plays a vital role in many domains. This article proposes an efficient machine learning approach to detect anomalous HTTP traffic that addresses the problems of existing methods, such as data redundancy and high training complexity. This algorithm draws on natural language processing (NLP) technology, uses the Word2vec algorithm to deal with the semantic gap, and implements Term Frequency-Inverse Document Frequency (TF-IDF) weighted mapping of HTTP traffic to construct a low-dimensional paragraph vector representation to reduce training complexity. Then we employs boosting algorithm Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) to build an efficient and accurate anomaly detection model. The proposed method is tested on some artificial data sets, such as HTTP DATASET CSIC 2010, UNSW-NB15, and Malicious-URLs. Experimental results reveal that both the boosting algorithms have high detection accuracy, high true positive rate, and low false positive rate. Compared with other anomaly detection methods, the proposed algorithms require relatively short running time and low CPU memory consumption.

Abstract Thermokarst lakes and ponds (hereafter referred to as thaw lakes) play an important role in the permafrost regions by regulating hydrology, ecology, and biogeochemistry. However, detailed quantitative information on thaw lake extent and distribution remains poorly resolved across the entire permafrost regions on the Qinghai‐Tibet Plateau (QTP). Here, we applied the random forest (RF) model and manual visual vectorization methods to extract thaw lake boundaries on the QTP based on Sentinel‐2 images. Accuracy assessment was comprehensively demonstrated regarding the inherent spatial resolution of imagery and RF model performance. The results showed that the accumulated uncertainty of the total thaw lake area was ±5.75 km2, and the mean accuracy (91.9%) from field‐measured boundaries of 132 thaw lakes supported the accuracy of this inventory. A total of ∼161,300 thaw lakes with sizes ranging from 500 m2 to 3 km2 were detected, with a total area of ∼2,825.45 ± 5.75 km2. Most thaw lakes were detected in the continuous permafrost type (94.1%) and within the elevations of 4,500–5,000 m (68.4%). The small thaw lakes (

Cardiovascular disease is the leading cause of death worldwide and kills over 17 million people per year. In the recent decade, growing epidemiological evidence links air pollution and cardiac arrhythmias, suggesting a detrimental influence of air pollution on cardiac electrophysiological functionality. However, the proarrhythmic mechanisms underlying the air pollution-induced cardiac arrhythmias are not fully understood. The purpose of this work is to provide recent advances in air pollution-induced arrhythmias with a comprehensive review of the literature on the common air pollutants and arrhythmias. Six common air pollutants of widespread concern are discussed, namely particulate matter, carbon monoxide, hydrogen sulfide, sulfur dioxide, nitrogen dioxide, and ozone. The epidemiological and clinical reports in recent years are reviewed by pollutant type, and the recently identified mechanisms including both the general pathways and the direct influences of air pollutants on the cellular electrophysiology are summarized. Particularly, this review focuses on the impaired ion channel functionality underlying the air pollution-induced arrhythmias. Alterations of ionic currents directly by the air pollutants, as well as the alterations mediated by intracellular signaling or other more general pathways are reviewed in this work. Finally, areas for future research are suggested to address several remaining scientific questions.

Ultra-large-scale molecular docking can improve the accuracy of lead compounds in drug discovery. In this study, we developed a molecular docking piece of software, Vina@QNLM, which can use more than 4,80,000 parallel processes to search for potential lead compounds from hundreds of millions of compounds. We proposed a task scheduling mechanism for large-scale parallelism based on Vinardo and Sunway supercomputer architecture. Then, we readopted the core docking algorithm to incorporate the full advantage of the heterogeneous multicore processor architecture in intensive computing. We successfully expanded it to 10, 465, 065 cores (1,61,001 management process elements and 0, 465, 065 computing process elements), with a strong scalability of 55.92%. To the best of our knowledge, this is the first time that 10 million cores are used for molecular docking on Sunway. The introduction of the heterogeneous multicore processor architecture achieved the best speedup, which is 11x more than that of the management process element of Sunway. The performance of Vina@QNLM was comprehensively evaluated using the CASF-2013 and CASF-2016 protein–ligand benchmarks, and the screening power was the highest out of the 27 pieces of software tested in the CASF-2013 benchmark. In some existing applications, we used Vina@QNLM to dock more than 10 million molecules to nine rigid proteins related to SARS-CoV-2 within 8.5 h on 10 million cores. We also developed a platform for the general public to use the software.

The El Niño-Southern Oscillation (ENSO) is one of the main drivers of the interannual climate variability of Earth and can cause a wide range of climate anomalies, so multi year ENSO forecasts are a paramount scientific issue. However, most existing works rely on the conventional iterative mechanism and, thus, fail to provide reliable long-term predictions due to error accumulation. Although methods based on deep learning (DL) apply the parallel modeling scheme for different lead times instead of a single iteration model, they leverage the same DL model for prediction, which can not fully mine the variability of different lead times, resulting in a decrease of prediction accuracy. To solve this problem, we propose a novel parallel deep convolutional neural network (CNN) with a heterogeneous architecture. In this study, by adaptively selecting network architectures for different lead times, we realize variability modeling of different tasks (lead times) and thereby improve the reliability of long-term predictions. Furthermore, we propose a relationship between different prediction lead times and neural network architecture from a unique perspective, namely, the receptive field originally proposed in computer vision. According to the spatio-temporal correlated area and sampling scale of lead times, the size of the convolution kernel and the mesh size of sampling are adjusted as the lead time increases. The Coupled Model Intercomparison Project phase 5 (CMIP5) from 1861 to 2004 and the Simple Ocean Data Assimilation (SODA) from 1871 to 1973 were used for model training, and the GODAS from 1982 to 2017 were used for testing the forecast skill of the model. Experimental results demonstrate that the proposed method outperforms the other well-known methods, especially for long-term predictions.

A surface-based duct (SBD) is an abnormal atmospheric structure with a low probability of occurrence buta strong ability to trap electromagnetic waves. However, the existing research is based on the assumption that the range direction of the surface duct is homogeneous, which will lead to low productivity and large errors when applied in a real-marine environment. To alleviate these issues, we propose a framework for the inversion of inhomogeneous SBD M-profile based on a full-coupled convolutional Transformer (FCCT) deep learning network. We first designed a one-dimensional residual dilated causal convolution autoencoder to extract the feature representations from a high-dimension range direction inhomogeneous M-profile. Second, to improve efficiency and precision, we proposed a full-coupled convolutional Transformer (FCCT) that incorporated dilated causal convolutional layers to gain exponentially receptive field growth of the M-profile and help Transformer-like models improve the receptive field of each range direction inhomogeneous SBD M-profile information. We tested our proposed method performance on two sets of simulated sea clutter power data where the inversion of the simulated data reached 96.99% and 97.69%, which outperformed the existing baseline methods.

One of the most significant environmental changes across the Tibetan Plateau (TP) is the rapid lake expansion. The expansion of thermokarst lakes affects the global biogeochemical cycles and local climate regulation by rising levels, expanding area, and increasing water volumes. Meanwhile, microbial activity contributes greatly to the biogeochemical cycle of carbon in the thermokarst lakes, including organic matter decomposition, soil formation, and mineralization. However, the impact of lake expansion on distribution patterns of microbial communities and methane cycling, especially those of water and sediment under ice, remain unknown. This hinders our ability to assess the true impact of lake expansion on ecosystem services and our ability to accurately investigate greenhouse gas emissions and consumption in thermokarst lakes. Here, we explored the patterns of microorganisms and methane cycling by investigating sediment and water samples at an oriented direction of expansion occurred from four points under ice of a mature-developed thermokarst lake on TP. In addition, the methane concentration of each water layer was examined. Microbial diversity and network complexity were different in our shallow points (MS, SH) and deep points (CE, SH). There are differences of microbial community composition among four points, resulting in the decreased relative abundances of dominant phyla, such as Firmicutes in sediment, Proteobacteria in water, Thermoplasmatota in sediment and water, and increased relative abundance of Actinobacteriota with MS and SH points. Microbial community composition involved in methane cycling also shifted, such as increases in USCγ, Methylomonas, and Methylobacter, with higher relative abundance consistent with low dissolved methane concentration in MS and SH points. There was a strong correlation between changes in microbiota characteristics and changes in water and sediment environmental factors. Together, these results show that lake expansion has an important impact on microbial diversity and methane cycling.

Abstract Background Binding sites are the pockets of proteins that can bind drugs; the discovery of these pockets is a critical step in drug design. With the help of computers, protein pockets prediction can save manpower and financial resources. Results In this paper, a novel protein descriptor for the prediction of binding sites is proposed. Information on non-bonded interactions in the three-dimensional structure of a protein is captured by a combination of geometry-based and energy-based methods. Moreover, due to the rapid development of deep learning, all binding features are extracted to generate three-dimensional grids that are fed into a convolution neural network. Two datasets were introduced into the experiment. The sc-PDB dataset was used for descriptor extraction and binding site prediction, and the PDBbind dataset was used only for testing and verification of the generalization of the method. The comparison with previous methods shows that the proposed descriptor is effective in predicting the binding sites. Conclusions A new protein descriptor is proposed for the prediction of the drug binding sites of proteins. This method combines the three-dimensional structure of a protein and non-bonded interactions with small molecules to involve important factors influencing the formation of binding site. Analysis of the experiments indicates that the descriptor is robust for site prediction.