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Sparse general matrix–matrix multiplication (SpGEMM) is a crucial and complex computational task in many practical applications. Improving the performance of SpGEMM on SIMT processors like modern GPUs is challenging due to the unpredictable sparsity of sparse matrices. Although existing GPU solutions have made progress in improving performance through advanced algorithm design, they ignore some optimizations related to specific processor architectures. This can result in a partially inefficient implementation of their algorithms. This paper focuses on optimizing four inefficient parts of the NSparse algorithm on DCU (a GPU-like accelerator). The optimizations include: 1) setting parameters to improve the load balance of the second matrix by extracting maximum row information at runtime; 2) reducing overhead of binning operations by making full use of registers and shared memory effectively; 3) improving numerical SpGEMM performance by adjusting its calculation mode; and 4) enhancing global load balance through finer-grained grouping and kernel configurations. Experiment results demonstrate that when compared to five state-of-the-art SpGEMM algorithms (bhSparse, KokkosKernels, NSparse, rocSparse, and spECK), our optimized method achieves an average of 7.99x (up to 18.2x), 8.01x (up to 20.83x), 2.37x (up to 6.16x), 1.82x (up to 4.20x), and 1.63x (up to 5.01x) speedups on 29 sparse matrices with different sparse structures, respectively. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is a complex syndrome with poorly understood mechanisms driving its early progression (GOLD stages 1–2). Elucidating the genetic factors that influence early-stage COPD, particularly those related to airway inflammation and remodeling, is crucial. This study analyzed lung tissue sequencing data from patients with early-stage COPD (GSE47460) and smoke-exposed mice. We employed Weighted Gene Co-Expression Network Analysis (WGCNA) and machine learning to identify potentially pathogenic genes. Further analyses included single-cell sequencing from both mice and COPD patients to pinpoint gene expression in specific cell types. Cell–cell communication and pseudotemporal analyses were conducted, with findings validated in smoke-exposed mice. Additionally, Mendelian randomization (MR) was used to confirm the association between candidate genes and lung function/COPD. Finally, functional validation was performed in vitro using cell cultures. Machine learning analysis of 30 differentially expressed genes identified 8 key genes, with CLEC5A emerging as a potential pathogenic factor in early-stage COPD. Bioinformatics analyses suggested a role for CLEC5A in macrophage-mediated inflammation during COPD. Two-sample Mendelian randomization linked CLEC5A single nucleotide polymorphisms (SNPs) with Forced Expiratory Volume in One Second (FEV1), FEV1/Forced Vital Capacity (FVC) and early/later on COPD. In vitro, the knockdown of CLEC5A led to a reduction in inflammatory markers within macrophages. Our study identifies CLEC5A as a critical gene in early-stage COPD, contributing to its pathogenesis through pro-inflammatory mechanisms. This discovery offers valuable insights for developing early diagnosis and treatment strategies for COPD and highlights CLEC5A as a promising target for further investigation. [ABSTRACT FROM AUTHOR]

A series of Co-Mo sulfide catalysts with tube-like hollow structure were prepared by a low temperature pre-sulfurization method using various CoMoO4 as precursors synthesized by coprecipitation process at different temperature. The crystallite structure of CoMoO4 precursors determined the properties of Co-Mo sulfide catalysts, including pore structure, concentration of CoMoS active phase, microstructure of MoS2 slabs, and desulfurization activity. The higher temperature led to better crystallinity of CoMoO4 precursor, resulting in less CoMoS active phase and fewer Mo atoms at the corner sites of the pre-sulfurized catalyst. In addition, the MoS2 slabs with shorter length and more stacking layers (especially Co-promoted MoS2 slabs) in the catalysts are also formed at appropriate precursor preparation temperature, which are favorable for forming more unsaturated coordination sites (especially corner sites). According to the results of hydrodesulfurization of dibenzothiophene on the Co-Mo sulfide catalysts, the reaction rate and the production yield are highly dependent on the number of surface-active centers, while the activity is mainly attributed to the Type II CoMoS species. [ABSTRACT FROM AUTHOR]

Introduction: Anemia and malnutrition are recognized indicators of suboptimal physical condition in chronic inflammatory diseases. This study aimed to examine the association between anemia, low body mass index (BMI), and clinical outcomes in axial spondyloarthritis (axSpA). Method: This cross-sectional analysis utilized data from the multicenter ChinaSpA cohort. A total of 4146 participants with axSpA were categorized into four groups based on BMI and hemoglobin levels: those with both anemia and low BMI, those with anemia only, those with low BMI only, and those with neither condition. Logistic regression analyses were performed to analyze the association between anemia, low BMI, inflammation status, functional impairment, and disease activity. Results: Anemia was present in 13.94%, low BMI in 11.99%, and both conditions in 2.15% of axSpA participants. Those with both anemia and low BMI showed significantly higher levels of inflammation (hypersensitive C-reactive protein [hsCRP] 30.60 mg/L vs. 8.44 mg/L), functional impairment (Bath Ankylosing Spondylitis Functional Index [BASFI] 3.80 vs. 2.10), and disease activity (Bath Ankylosing Spondylitis Disease Activity Index [BASDAI] 4.52 ± 2.04 vs. 3.67 ± 2.21; Ankylosing Spondylitis Disease Activity Score calculated with C-reactive protein [ASDAS_CRP] 3.51 ± 1.10 vs. 2.62 ± 1.21) compared to those without these conditions. After adjusting for sex and age, significant associations were observed between elevated hsCRP levels and the presence of low BMI (odds ratio [OR] 1.44, 95% CI 1.17–1.78), anemia (OR 1.91, 95% CI 1.56–2.32), and their concurrent presence (OR 3.59, 95% CI 2.22–5.80). Similarly, increased BASFI was significantly associated with low BMI (OR 1.57, 95% CI 1.25–1.97), anemia (OR 1.47, 95% CI 1.19–1.80), and their combination (OR 3.11, 95% CI 2.02–4.78). Conclusion: All-cause anemia and low BMI are prevalent complications in patients with axSpA, exhibiting a significant correlation with elevated inflammation status and functional impairment. The simultaneous occurrence of anemia and low BMI particularly exacerbates clinical outcomes, emphasizing the critical role of comprehensive nutritional assessment and management in the therapeutic strategy for axSpA. [ABSTRACT FROM AUTHOR]

In this study, the crystal structure analysis by particle swarm optimization (CALYPSO) was used to search the structure of negative LiBn− (n = 2–12) clusters, and the corresponding ground-state structures and low energy isomers were determined. Based on the predicted ground state structures, the doped Li atom has little modification on Bn− skeleton, which can be seen from the charge analysis that it is caused by the transfer of electron from dopant to Bn− part. The photoelectron spectroscopy (PES) simulations were conducted using time-dependent density functional theory (TD-DFT). By analyzing the stability, we find LiB8− cluster with high symmetry C7V with ideal stability, and it has a classic umbrella structure. Deep analysis of the bonding characteristics of the LiB8− cluster shows that the B–B covalent bonds in the B8− part and the electrostatic interaction between the B8− wheel and the Li atom stabilize the LiB8− cluster. [ABSTRACT FROM AUTHOR]

Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not. This paradigm, although effective, is inefficient because the detectors have to go through all patches, severely hindering the inference speed. This paper presents an objectness activation network (OAN) to help detectors focus on fewer patches but achieve more efficient inference and more accurate results, enabling a simple and effective solution to object detection in large images. In brief, OAN is a light fully-convolutional network for judging whether each patch contains objects or not, which can be easily integrated into many object detectors and jointly trained with them end-to-end. We extensively evaluate our OAN with five advanced detectors. Using OAN, all five detectors acquire more than 30.0% speed-up on three large-scale aerial image datasets, meanwhile with consistent accuracy improvements. On extremely large Gaofen-2 images (29200 × 27620 pixels), our OAN improves the detection speed by 70.5%. Moreover, we extend our OAN to driving-scene object detection and 4K video object detection, boosting the detection speed by 112.1% and 75.0%, respectively, without sacrificing the accuracy. [ABSTRACT FROM AUTHOR]

Herein, we present a deep-learning technique for reconstructing the dark-matter density field from the redshift-space distribution of dark-matter halos. We built a UNet-architecture neural network and trained it using the COmoving Lagrangian Acceleration fast simulation, which is an approximation of the N-body simulation with 5123 particles in a box size of 500 h−1Mpc. Further, we tested the resulting UNet model not only with training-like test samples but also with standard N-body simulations, such as the Jiutian simulation with 61443 particles in a box size of 1000 h−1Mpc and the ELUCID simulation, which has a different cosmology. The real-space dark-matter density fields in the three simulations can be reconstructed reliably with only a small reduction of the cross-correlation power spectrum at 1% and 10% levels at k = 0.1 and 0.3 h Mpc−1, respectively. The reconstruction clearly helps to correct for redshift-space distortions and is unaffected by the different cosmologies between the training (Planck2018) and test samples (WMAP5). Furthermore, we tested the application of the UNet-reconstructed density field to obtain the velocity & tidal field and found that this approach provides better results compared with the traditional approach based on the linear bias model, showing a 12.2% improvement in the correlation slope and a 21.1% reduction in the scatter between the predicted and true velocities. Thus, our method is highly efficient and has excellent extrapolation reliability beyond the training set. This provides an ideal solution for determining the three-dimensional underlying density field from the plentiful galaxy survey data. [ABSTRACT FROM AUTHOR]

A neuron's regenerative capacity is governed by its intrinsic and extrinsic environment. Both peripheral and central neurons exhibit cell-type-dependent axon regeneration, but the underlying mechanism is unclear. Glia provide a milieu essential for regeneration. However, the routes of glia-neuron signaling remain underexplored. Here, we show that regeneration specificity is determined by the axotomy-induced Ca2+ transients only in the fly regenerative neurons, which is mediated by L-type calcium channels, constituting the core intrinsic machinery. Peripheral glia regulate axon regeneration via a three-layered and balanced modulation. Glia-derived tumor necrosis factor acts through its neuronal receptor to maintain calcium channel expression after injury. Glia sustain calcium channel opening by enhancing membrane hyperpolarization via the inwardly-rectifying potassium channel (Irk1). Glia also release adenosine which signals through neuronal adenosine receptor (AdoR) to activate HCN channels (Ih) and dampen Ca2+ transients. Together, we identify a multifaceted glia-neuron coupling which can be hijacked to promote neural repair. Limited neuron regeneration is the key barrier to recovery after central nervous system damage. Here, the authors show that axon regeneration is regulated by glia in a multi-layered manner controlling regeneration-dependent neuronal calcium channels. [ABSTRACT FROM AUTHOR]

Pesticides are widely used in agriculture and disease control, and dichlorodiphenyltrichloroethane (DDT) is one of the most used pesticides in human history. Besides its significant contributions in pest control in agriculture, DDT was credited as having saved millions of human lives for controlling malaria and other deadly insect-transmitted diseases. Even today, the use of DDT in some countries for malaria control cannot be replaced without endangering people who live there. The recent COVID-19 pandemic has changed our lives and reminded us of the challenges in dealing with infectious diseases, especially deadly ones including malaria. However, DDT and its metabolites are stable, persist long, are found in almost every corner of the world, and their persistent effects on humans, animals, and the environment must be seriously considered. This review will focus on the history of DDT use for agriculture and malaria control, the pathways for the spread of DDT, benefits and risks of DDT use, DDT exposure to animals, humans, and the environment, and the associated human health risks. These knowledge and findings of DDT will benefit the selection and management of pesticides worldwide. [ABSTRACT FROM AUTHOR]

Anomaly detectors are used to distinguish differences between normal and abnormal data, which are usually implemented by evaluating and ranking the anomaly scores of each instance. A static unsupervised streaming anomaly detector is difficult to dynamically adjust anomaly score calculation. In real scenarios, anomaly detection often needs to be regulated by human feedback, which benefits adjusting anomaly detectors. In this paper, we propose a human-machine interactive streaming anomaly detection method, named ISPForest, which can be adaptively updated online under the guidance of human feedback. In particular, the feedback will be used to adjust the anomaly score calculation and structure of the detector, ideally attaining more accurate anomaly scores in the future. Our main contribution is to improve the tree-based streaming anomaly detection model that can be updated online from perspectives of anomaly score calculation and model structure. Our approach is instantiated for the powerful class of tree-based streaming anomaly detectors, and we conduct experiments on a range of benchmark datasets. The results demonstrate that the utility of incorporating feedback can improve the performance of anomaly detectors with a few human efforts. [ABSTRACT FROM AUTHOR]

It is unknown how hydrophobic organic contaminants (HOCs) are distributed and how they affect the environment in high-fat nuts and their planted soil. The profile of HOCs in walnut/soil system was investigated in this study. Polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and organochlorine pesticides (OCPs) were found in walnuts at concentrations of 0.67, 127, and 116 μg/kg, respectively. The target hazard quotients (THQ) of 17 PCBs, 16 PAHs, and 21 OCPs from walnut consumption by human were 0.06, 0.01, and 0.11, respectively. The highest concentrations of HOC in the soil were found in Nap and toxaphene, with concentrations of 2580 and 902 μg/kg, respectively. Bioaccumulation factors (BAF) and biota-sediment accumulation factors (BSAF) in walnuts were ranged from <0.01 to 7.04 and <0.01 to 3.83, respectively. Concentrations of most individual HOCs in soil samples were significantly correlated with soil organic matter (SOM) (p < 0.01) and minerals (p < 0.01), with maximum correlation coefficients of 0.70 (OM-PCB81) and −0.84 (P-BaP). According to this study, high-fat walnuts do not have a high bioaccumulation of HOCs from soil, and the risk of consumption is within the safe range. [ABSTRACT FROM AUTHOR]

Elastic full waveform inversion (EFWI) has increasingly been applied in seismic exploration as computer performance improves. EFWI significantly improves calculation efficiency, but requires very large computer storage space and suffers inter-parameter trade-off and local minima problems. Preconditioning the gradients based on elastic wave mode decomposition can effectively mitigate inter-parameter trade-offs, but the decomposition-based scheme may further increase the memory usage, which limits EFWI application. The equivalent staggered grid (ESG) scheme in acoustic medium requires less memory usage and generates results numerically equivalent to those using the standard staggered grid (SSG) scheme. In this paper, we extend the ESG scheme to second-order elastic wave equations in terms of velocity, producing results numerically equivalent to the SSG ones based on first-order velocity–stress wave equations while reducing memory usage by 45% compared with the SSG scheme. We then apply the ESG scheme to EFWI and derive the formula of the preconditioned gradient of the S-wave velocity. Finally, three numerical examples demonstrate that applying the ESG scheme to decomposition-based EFWI can significantly reduce computer memory usage and mitigate the trade-offs between the P- and S-wave velocities. [ABSTRACT FROM AUTHOR]

Background: Different types of sedentary behavior are associated with several health outcomes, but the causality of these associations remains unclear.To conduct a systematic review and meta-analysis of Mendelian randomization (MR) studies investigating the associations between sedentary behaviors and health outcomes.A systematic search on PubMed, Embase, Web of Science, Scopus, and PsycINFO up to August 2023 was conducted to identify eligible MR studies. We selected studies that assessed associations of genetically determined sedentary behaviors and health outcomes. A meta-analysis was conducted to examine the causal associations when two or more MR studies were available. We graded the evidence level of each MR association based on the results of the main method and sensitivity analyses in MR studies.A total of 31 studies with 168 MR associations between six types of sedentary behavior and 47 health outcomes were included. Results from meta-analyses suggested a total of 47 significant causal associations between sedentary behaviors and health outcomes. Notably, more leisure TV watching is robustly correlated with increased risks of myocardial infarction, coronary artery disease, all-cause ischemic stroke, and type 2 diabetes. Conversely, robust inverse associations were observed between leisure computer use and risks of rheumatoid arthritis, Alzheimer’s disease, and gastroesophageal reflux disease.These findings suggest that different types of sedentary behavior have distinct causal effects on health outcomes. Therefore, interventions should focus not only on reducing sedentary time but also on promoting healthier types of sedentary behavior.CRD42023453828.Objectives: Different types of sedentary behavior are associated with several health outcomes, but the causality of these associations remains unclear.To conduct a systematic review and meta-analysis of Mendelian randomization (MR) studies investigating the associations between sedentary behaviors and health outcomes.A systematic search on PubMed, Embase, Web of Science, Scopus, and PsycINFO up to August 2023 was conducted to identify eligible MR studies. We selected studies that assessed associations of genetically determined sedentary behaviors and health outcomes. A meta-analysis was conducted to examine the causal associations when two or more MR studies were available. We graded the evidence level of each MR association based on the results of the main method and sensitivity analyses in MR studies.A total of 31 studies with 168 MR associations between six types of sedentary behavior and 47 health outcomes were included. Results from meta-analyses suggested a total of 47 significant causal associations between sedentary behaviors and health outcomes. Notably, more leisure TV watching is robustly correlated with increased risks of myocardial infarction, coronary artery disease, all-cause ischemic stroke, and type 2 diabetes. Conversely, robust inverse associations were observed between leisure computer use and risks of rheumatoid arthritis, Alzheimer’s disease, and gastroesophageal reflux disease.These findings suggest that different types of sedentary behavior have distinct causal effects on health outcomes. Therefore, interventions should focus not only on reducing sedentary time but also on promoting healthier types of sedentary behavior.CRD42023453828.Methods: Different types of sedentary behavior are associated with several health outcomes, but the causality of these associations remains unclear.To conduct a systematic review and meta-analysis of Mendelian randomization (MR) studies investigating the associations between sedentary behaviors and health outcomes.A systematic search on PubMed, Embase, Web of Science, Scopus, and PsycINFO up to August 2023 was conducted to identify eligible MR studies. We selected studies that assessed associations of genetically determined sedentary behaviors and health outcomes. A meta-analysis was conducted to examine the causal associations when two or more MR studies were available. We graded the evidence level of each MR association based on the results of the main method and sensitivity analyses in MR studies.A total of 31 studies with 168 MR associations between six types of sedentary behavior and 47 health outcomes were included. Results from meta-analyses suggested a total of 47 significant causal associations between sedentary behaviors and health outcomes. Notably, more leisure TV watching is robustly correlated with increased risks of myocardial infarction, coronary artery disease, all-cause ischemic stroke, and type 2 diabetes. Conversely, robust inverse associations were observed between leisure computer use and risks of rheumatoid arthritis, Alzheimer’s disease, and gastroesophageal reflux disease.These findings suggest that different types of sedentary behavior have distinct causal effects on health outcomes. Therefore, interventions should focus not only on reducing sedentary time but also on promoting healthier types of sedentary behavior.CRD42023453828.Results: Different types of sedentary behavior are associated with several health outcomes, but the causality of these associations remains unclear.To conduct a systematic review and meta-analysis of Mendelian randomization (MR) studies investigating the associations between sedentary behaviors and health outcomes.A systematic search on PubMed, Embase, Web of Science, Scopus, and PsycINFO up to August 2023 was conducted to identify eligible MR studies. We selected studies that assessed associations of genetically determined sedentary behaviors and health outcomes. A meta-analysis was conducted to examine the causal associations when two or more MR studies were available. We graded the evidence level of each MR association based on the results of the main method and sensitivity analyses in MR studies.A total of 31 studies with 168 MR associations between six types of sedentary behavior and 47 health outcomes were included. Results from meta-analyses suggested a total of 47 significant causal associations between sedentary behaviors and health outcomes. Notably, more leisure TV watching is robustly correlated with increased risks of myocardial infarction, coronary artery disease, all-cause ischemic stroke, and type 2 diabetes. Conversely, robust inverse associations were observed between leisure computer use and risks of rheumatoid arthritis, Alzheimer’s disease, and gastroesophageal reflux disease.These findings suggest that different types of sedentary behavior have distinct causal effects on health outcomes. Therefore, interventions should focus not only on reducing sedentary time but also on promoting healthier types of sedentary behavior.CRD42023453828.Conclusion: Different types of sedentary behavior are associated with several health outcomes, but the causality of these associations remains unclear.To conduct a systematic review and meta-analysis of Mendelian randomization (MR) studies investigating the associations between sedentary behaviors and health outcomes.A systematic search on PubMed, Embase, Web of Science, Scopus, and PsycINFO up to August 2023 was conducted to identify eligible MR studies. We selected studies that assessed associations of genetically determined sedentary behaviors and health outcomes. A meta-analysis was conducted to examine the causal associations when two or more MR studies were available. We graded the evidence level of each MR association based on the results of the main method and sensitivity analyses in MR studies.A total of 31 studies with 168 MR associations between six types of sedentary behavior and 47 health outcomes were included. Results from meta-analyses suggested a total of 47 significant causal associations between sedentary behaviors and health outcomes. Notably, more leisure TV watching is robustly correlated with increased risks of myocardial infarction, coronary artery disease, all-cause ischemic stroke, and type 2 diabetes. Conversely, robust inverse associations were observed between leisure computer use and risks of rheumatoid arthritis, Alzheimer’s disease, and gastroesophageal reflux disease.These findings suggest that different types of sedentary behavior have distinct causal effects on health outcomes. Therefore, interventions should focus not only on reducing sedentary time but also on promoting healthier types of sedentary behavior.CRD42023453828.PROSPERO Registration: Different types of sedentary behavior are associated with several health outcomes, but the causality of these associations remains unclear.To conduct a systematic review and meta-analysis of Mendelian randomization (MR) studies investigating the associations between sedentary behaviors and health outcomes.A systematic search on PubMed, Embase, Web of Science, Scopus, and PsycINFO up to August 2023 was conducted to identify eligible MR studies. We selected studies that assessed associations of genetically determined sedentary behaviors and health outcomes. A meta-analysis was conducted to examine the causal associations when two or more MR studies were available. We graded the evidence level of each MR association based on the results of the main method and sensitivity analyses in MR studies.A total of 31 studies with 168 MR associations between six types of sedentary behavior and 47 health outcomes were included. Results from meta-analyses suggested a total of 47 significant causal associations between sedentary behaviors and health outcomes. Notably, more leisure TV watching is robustly correlated with increased risks of myocardial infarction, coronary artery disease, all-cause ischemic stroke, and type 2 diabetes. Conversely, robust inverse associations were observed between leisure computer use and risks of rheumatoid arthritis, Alzheimer’s disease, and gastroesophageal reflux disease.These findings suggest that different types of sedentary behavior have distinct causal effects on health outcomes. Therefore, interventions should focus not only on reducing sedentary time but also on promoting healthier types of sedentary behavior.CRD42023453828. [ABSTRACT FROM AUTHOR]

Purpose of Review: This review aims to emphasize interesting and important new findings with a focus on the spectrum of spondyloarthritis (SpA) in China. Recent Findings: Over the past decade, significant advances have been made in the investigation of SpA epidemiology, the exploration of genetic and environmental risk factors, the identification of clinical features, and the updating of treatment protocols in the Chinese population. The prevalence of ankylosing spondylitis (AS) in China is 0.20–0.42%, and the prevalence of HLA-B27 in AS patients is 88.8–89.4%. HLA-B*2704 is the most common subtype in Chinese AS patients, followed by HLA-B*2705. HLA-A*01, more precisely HLA-A*01:01, may be associated with psoriatic arthritis (PsA). Tumor necrosis factor inhibitors and IL-17A inhibitors have been shown to be effective and safe for AS patients in China. Juvenile-onset AS is relatively rare, accounting for only 9.1% of the AS population. The prevalence of arthritis related to inflammatory bowel disease is 6.9 to 7.2%. A Chinese study showed that the most frequently prescribed medication was methotrexate (66.4%). Biological agents were prescribed in only16.4% of patients with PsA. Summary: This review summarizes the latest research in the epidemiology, pathogenesis, clinical manifestations, and management of SpA among Chinese populations. Multiple HLA associations with SpA have also been described, and it is hoped that discoveries of such ethnic-specific risk factor(s) and understanding of their pathological mechanisms may potentially lead to newer targeted therapies for the Chinese populations worldwide. [ABSTRACT FROM AUTHOR]

Segmental overgrowth of the skeletal muscles with bone involvement in body extremities, predominantly affecting the upper limb, is an extremely rare condition with only 40–50 affected children described clinically. The molecular pathogenesis of this disorder remains largely unclear except for the identification of a somatic PIK3CA mutation in each of the six patients genetically tested, all restricted to upper limbs in the literature. This study aimed to further characterize the molecular defects for patients affected with segmental overgrowth of the skeletal muscles by analyzing a 9-gene panel selected from the PI3K/AKT/mTOR pathway and genes associated with other related conditions. Nineteen unrelated patients were chosen for this study, comprising ten upper limb (nine unilateral and one bilateral) and nine lower limb (eight unilateral and one bilateral) cases with variable bone involvement. In each case, an activating PIK3CA mutation (p.E110del, p.N345K, p.E542K, p.E545K, p.H1047R, or p.H1047L) was identified in the affected muscle tissue with variant allele frequencies ranging from 13.88 to 30.43%, while no mutation was detected in the paired peripheral blood sample, indicating somatic mosaicism. All detected mutations were limited to PIK3CA and were previously reported in other overgrowth syndromes currently categorized under the PIK3CA-Related Overgrowth Spectrum (PROS). Our study provides strong molecular evidence that isolated segmental overgrowth of the skeletal muscle with bone involvement is a subtype of PROS. Our findings expand the PROS clinical presentations with a newly molecularly classified condition and can provide guidance in clinical and molecular diagnosis and treatment for patients with this condition. [ABSTRACT FROM AUTHOR]

The tree-based model is widely applied in classification and regression problems because of its interpretability. Self-adaptive forest models are proposed for adapting to dynamic environments by using active learning and online learning techniques. However, most existing self-adaptive forest models are designed under a single-agent situation. With the development of the IoT, data is distributed across multiple edge devices without geographic restrictions. A global model is trained by distributed data across multiple devices. Therefore, extending a single-agent self-adaptive forest model to a multi-agent one is useful to make the original tree-based models glow with new vitality. In a multi-agent system, the privacy-preserving problem should be addressed when sharing knowledge between agents. In this paper, we propose PMSF, a privacy-preserving multi-agent self-adaptive forest framework via federated learning. We utilize differential privacy to prevent attackers from getting the data statistics. No private data is uploaded into the server in our framework and only updated parameters are uploaded. Finally, We design local adaptation and global update procedures to ensure the ability of self-adaptation of the forest model and the ability of privacy protection in each agent, which can further improve the performance of self-adaptive forest models. To demonstrate the superiority and effectiveness of our framework, we conduct extensive experiments in an identity authentication case with two datasets. [ABSTRACT FROM AUTHOR]

To improve accuracy and efficiency of forward modeling in the frequency domain, a 125-point finite-difference scheme is proposed. At present, the optimized difference format based on the rotating coordinate system is widely used, but it only suitable for equally sampling interval, and the optimized difference format based on the average-derivative method can be applied to different spaced sampling while improving the sampling accuracy. In this paper, we firstly introduce a 125-point optimized scheme for the three-dimensional scalar wave equation. Then, according to the optimized difference scheme, the 125-point optimized difference coefficient is calculated for different spatial sampling spacing ratios. Compared with the optimal 27-point scheme, grid points number reduces from 4 points to 2.5 per wavelength, higher efficiency and suitable for unequal directional sampling intervals. In addition, the higher accuracy of 125-point scheme means it requires more storage and computation cost. Numerical results show that the optimized 125-point difference format has higher accuracy than the classical 27-point difference format. [ABSTRACT FROM AUTHOR]

Due to its corrosion resistance, modifiable mechanical properties, and electrical conductivity, etc., CuNi alloy has found a wide variety of industrial applications, especially in the marine environment. It is highly desired if the mechanical strength and wear resistance of CuNi alloy can reach a level comparable to that of steel while keeps its corrosion resistance. In this study, effects of Mo, B, and their combinations on microstructure and performance of CuNi alloy were investigated, including formation of second phases, hardness, and resistances to wear, corrosion and corrosive wear. It was demonstrated that the Mo and B additives were effective in strengthening the CuNi alloy while maintaining reasonable corrosion resistance. In particular, the combination of Mo and B additives was more effective than a single additive to strengthen the alloy. Mo and B additives have demonstrated their great promise as new alloying elements to modify CuNi alloys. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) aims at searching the best policy model for decision making, and has been shown powerful for sequential recommendations. The training of the policy by RL, however, is placed in an environment. In many real-world applications, the policy training in the real environment can cause an unbearable cost due to the exploration. Environment estimation from the past data is thus an appealing way to release the power of RL in these applications. The estimation of the environment is, basically, to extract the causal effect model from the data. However, real-world applications are often too complex to offer fully observable environment information. Therefore, quite possibly there are unobserved variables lying behind the data, which can obstruct an effective estimation of the environment. In this paper, by treating the hidden variables as a hidden policy, we propose a partially-observed multi-agent environment estimation (POMEE) approach to learn the partially-observed environment. To make a better extraction of the causal relationship between actions and rewards, we design a deep uplift inference network (DUIN) model to learn the causal effects of different actions. By implementing the environment model in the DUIN structure, we propose a POMEE with uplift inference (POMEE-UI) approach to generate a partially-observed environment with a causal reward mechanism. We analyze the effect of our method in both artificial and real-world environments. We first use an artificial recommender environment, abstracted from a real-world application, to verify the effectiveness of POMEE-UI. We then test POMEE-UI in the real application of Didi Chuxing. Experiment results show that POMEE-UI can effectively estimate the hidden variables, leading to a more reliable virtual environment. The online A/B testing results show that POMEE can derive a well-performing recommender policy in the real-world application. [ABSTRACT FROM AUTHOR]

Attention mechanism plays an important role in the perception and cognition of human beings. Among others, many machine learning models have been developed to memorize the sequential data, such as the Long Short-Term Memory (LSTM) network and its extensions. However, due to lack of the attention mechanism, they cannot pay special attention to the important parts of the sequences. In this paper, we present a novel machine learning method called attention-augmented machine memory (AAMM). It seamlessly integrates the attention mechanism into the memory cell of LSTM. As a result, it facilitates the network to focus on valuable information in the sequences and ignore irrelevant information during its learning. We have conducted experiments on two sequence classification tasks for pattern classification and sentiment analysis, respectively. The experimental results demonstrate the advantages of AAMM over LSTM and some other related approaches. Hence, AAMM can be considered as a substitute of LSTM in the sequence learning applications. [ABSTRACT FROM AUTHOR]

As a crucial material for fabrication of lithium-ion battery current collector, the properties of electrodeposited copper foil are closely related to the battery performances. How to improve its properties is thus of great importance for battery design and manufacturing. In this paper, we reported a novel composite additive, consisting of collagen, glycerol, hydroxyethyl cellulose, and sodium polydisulfide dipropane sulfonate, for electrodeposition of high-quality copper foil from conventional acid copper sulfate bath. Meanwhile, a pulse superimposed on direct current was used during the preparation process of copper foil. It was demonstrated that the additive could improve the foil's performances, through changing the kinetic parameters of copper electrodeposition in sulfate bath. The optimized bath has a higher allowable current density during superimposed current electrodeposition, in contrast to the situation in direct current electrodeposition. This may then lead to an increase in deposition rate of copper foil, and a further improvement in surface roughness, grain size, residual stress, as well as elongation. Specially, the superimposed current offers a number of adjustable parameters for modulating copper foil's properties in a wide range. [ABSTRACT FROM AUTHOR]

Objective: Glycerol-based biosynthesis of 3-hydroxypropionic acid (3-HP) in Klebsiella pneumoniae involves two reactions: glycerol conversion to 3-hydroxypropionaldehyde (3-HPA) by glycerol dehydratase, and 3-HPA conversion to 3-HP by aldehyde dehydrogenase (ALDH). The ALDH catalysis consumes a lot of cofactor nicotinamide adenine dinucleotide (NAD+), which constrains 3-HP production. Results: Here we report that intensifying niacin-based biosynthesis of NAD+ can substantially enhance 3-HP production. We constructed tac promoter-driven NAD+ synthesis pathway in K. pneumoniae. The strain only overexpressing nicotinate phosphoribosyltransferase (PncB) showed 14.24% increase in the production of NAD+ relative to the stain harboring an empty vector. When PncB was coexpressed with PuuC (one of native ALDHs), the recombinant strain exhibited increased ALDH activity but slightly reduced 3-HP production due to plasmid burden. When 30 mg niacin l−1 (a substrate for biosynthesis of NAD+) was added into shake flask, the strain produced 0.55 g 3-HP l−1, which was 2.75 times that of the control. In a 5-L bioreactor, replenishment of niacin led to 36.43% increase of 3-HP production. Conclusions: These results indicated that intensifying niacin-based biosynthesis of NAD+ boosts 3-HP production. [ABSTRACT FROM AUTHOR]

Onshore seismic exploration analyzes seismic wave propagation in elastic media, which includes the conversion between P- and S-waves. The development of multi-wave and multi-component seismic exploration methods provides data that enable onshore elastic wave full-waveform inversion. However, most data sets of onshore exploration are single component obtained from the particle-motion response from the vertical geophone. When the aiming area has a low-velocity zone, the ray path of reflected wave that propagates to the detector is nearly perpendicular to the ground surface, so that we call it P-wave data. In this paper, we focus on multi-parameter waveform inversion using P-wave reflection seismic data. Although only P-wave data are received, it still contains the converted P-wave information, and the converted P-wave energy gradually increases as the offset increases. As seismic acquisition technology, observation systems and science develop, the folds and acquisition offset increase significantly, and the seismic data contain important converted P-wave information. In this paper, the first-order elastic velocity–stress equation is decomposed to obtain the scalar-P-wave equation from which the S-wave velocity is included firstly. Then we present the theoretical framework for onshore multi-parameter full-waveform inversion using P-wave data. In order to explore the inversion potential of the P-wave data (extracting the S-wave velocity from the converted P-wave information) and accuracy and stability of the P- and S-wave velocity inverted by our method, we carry out numerical tests via different inversion strategies, by using the P-wave data regarded as containing converted P-wave information, and get successful results. [ABSTRACT FROM AUTHOR]

In contrast to the description provided by Archard's equation, material loss under a wearing force is more or less nonlinear. Excluding the wear regime transition and other possible factors such as oxidation, this nonlinear relationship may result from variations in the mechanical behavior of materials such as strain hardening. Although this is a classic topic, relevant issues have still not been fully clarified or quantified. Better understanding of the nonlinearity would help achieve good control of the wear of materials, especially surface wear encountered in nano/microsystems, which are sensitive to surface damage or deterioration. In this study, we investigated the nonlinearity of material loss versus wearing force, without involving the wear regime transition, for three alloys, Al5182 (fcc), AZ31B alloy (hcp), and steel (bcc). An attempt is made to study this phenomenon in depth and extract additional information on the material behavior from the nonlinearity. [ABSTRACT FROM AUTHOR]

The postmenopausal state is associated with an increased risk of metabolic disorder including reduced energy expenditure and weight gain, leading to higher cardiovascular and cancer risks among other diseases. Mitochondrial-derived peptide (MOTS-c) is a 16–amino acid peptide encoded by mitochondrial DNA. Here, we showed that MOTS-c treatment in mice prevented ovariectomy-induced obesity and insulin resistance. After ovariectomy, low levels of estrogens increased fat mass overload and disturbed normal adipose function, forcing the development of insulin resistance. MOTS-c treatment increased brown fat activation and reduced OVX-induced fat accumulation and inflammatory invasion in white adipose tissue, which contributes to the lower level of fatty acid in serum and liver. Moreover, MOTS-c activated AMPK pathway to improve energy dissipation and insulin sensitivity. And a blocker of AMPK pathway was found to attenuate the role of MOTS-c in the regulation of adipocyte lipid metabolism. In conclusion, MOTS-c is a high potential candidate for chronic treatment of menopausal induced metabolic dysfunction. Key messages: • MOTS-c prevents ovariectomy (OVX)-induced body weight gain and insulin resistance. • MOTS-c reduces fat mass and suppresses inflammatory response under OVX condition. • MOTS-c sustains the activity of the brown adipose under OVX condition. • MOTS-c mediates AMPK pathway activation to control adipose metabolic homeostasis. [ABSTRACT FROM AUTHOR]

The preload operation of jack-up in complex multi-layered foundation requires enhanced understanding of its behaviour in punchthrough accident and suitable safety analysis tools for the assessment of their reliability for a particular site. In this study, reliability analysis model of jack-up against punch-through is established considering structural uncertainty. In order to identify the failure state, an improved reliability solution method has been developed based on Sparse Auto-Encoder (SAE) deep learning network model. Sparse self-coding algorithm is used to the training of the deep network, and Softmax regression model is established to solve the identification and classification problem of the output layer. The first application of the technique was the study of HYSY 941 jack-up platform. More specifically, numerical calculations of structure ultimate bearing capacity have been undertaken, and the influence of model parameters on the prediction accuracy of the failure state is discussed. The results show that implicit performance function can be constructed accurately using SAE-MC method by reflecting the relationship between different critical safety state and structural vulnerability. Compared with traditional BP neural network, deep learning network has higher prediction accuracy to failure probability. The dynamic risk grade in the process of preload operation can be determined quantitatively using the reliability analysis method mentioned in this paper. [ABSTRACT FROM AUTHOR]

The combination of chromium-free passivation and nano-electrodeposition was reported to replace chromic acid passivation for electrogalvanized steel. The microstructure, corrosion resistance, scratch resistance and wear resistance of nanocrystalline zinc coating after chromium-free passivation were investigated. It was demonstrated that the nanocrystalline zinc coating after chromium-free passivation mainly consists of ZnO, Zn3(PO4)2, Ti2O3 and/or (Zn(OH)2, ZnHPO4 and TiO2), which possesses better comprehensive properties than conventional coarse-grained zinc coating after commercial trivalent chromium passivation. The reason was also discussed in detail. [ABSTRACT FROM AUTHOR]

It was observed that adding TiC nanoparticles (NPs) to zinc coating increased its corrosion resistance. To understand the beneficial role of TiC nanoparticles in suppressing corrosion, we studied the electrochemical behavior of TiC NP-added nanocomposite zinc coating, in comparison with those of coarse-grained and nanocrystalline zinc coatings, in simulated seawater. The surface electrochemical stability and surface electron stability, which are respectively reflected by corrosion potential and electron work function (EWF), were investigated. It is demonstrated that the increased corrosion resistance of nanocomposite zinc coating is ascribed to the fact that the TiC nanoparticles raise surface electron work function of the coating, corresponding to elevated surface electron stability. [ABSTRACT FROM AUTHOR]

An efficient and green reaction of isatins, 3-amino-1-phenyl-2-pyrazolin-5-one (3-amine-1 H-pyrazole), and $$\beta $$ -diketone in aqueous medium in the presence of $$\hbox {PEG}$$ - $$\hbox {SO}_{{3}}\hbox {H}$$ is reported, and a series of novel spirocompounds was obtained with high yields. The advantages of this reaction were simple procedure, moderate reaction conditions, and wide range of substrates. More importantly, the catalyst $$\hbox {PEG}$$ - $$\hbox {SO}_{{3}}\hbox {H}$$ could be recovered for reuse up to five times. Graphical Abstract: [ABSTRACT FROM AUTHOR]

This paper is focused on the study of the rheology performance of water in oil (W/O) emulsions with novel composites of associative polymer/organically modified montmorillonite (AP/OMMT), further the mechanism of AP/OMMT is explored and then applied into deepwater drilling industry. In this study, both the yield point and gel strengths of W/O emulsions showed stability in a wide temperature range. Based on a combined use of FT-IR, particle-size distribution, XRD and TEM analysis, the probable mechanism was determined due to the identification of the structure of AP/OMMT. The results indicate that the formation of interlayered AP/OMMT between dispersed water droplets and the OMMT layers is primarily responsible for the excellent ability of improving rheology. In addition to this, the slippage and separation of OMMT colloidal particles due to the 'weak multi-points adsorption' also contributed considerably to the rheology. [ABSTRACT FROM AUTHOR]

We present a method based on least-squares reverse time migration with plane-wave encoding (P-LSRTM) for rugged topography. Instead of modifying the wave field before migration, we modify the plane-wave encoding function and fill constant velocity to the area above rugged topography in the model so that P-LSRTM can be directly performed from rugged surface in the way same to shot domain reverse time migration. In order to improve efficiency and reduce I/O (input/output) cost, the dynamic encoding strategy and hybrid encoding strategy are implemented. Numerical test on SEG rugged topography model show that P-LSRTM can suppress migration artifacts in the migration image, and compensate amplitude in the middle-deep part efficiently. Without data correction, P-LSRTM can produce a satisfying image of near-surface if we could get an accurate near-surface velocity model. Moreover, the pre-stack PLSRTM is more robust than conventional RTM in the presence of migration velocity errors. [ABSTRACT FROM AUTHOR]

The Qilian (祁连) Mountain is an active orogenic belt located at the northeastern margin of the Tibetan Plateau. During the process of continuous convergence between Indian and Eurasian plates, the Qilian Mountain grow correspondingly by means of reaction of old faults and generation of new ones. Here we present apatite fission-track data along a river profile crossing three minor fault (the Minle (民乐)-Damaying (大马营) fault, the Huangcheng (皇城)-Taerzhuang (塔尔庄) fault and the Kangningqiao (康宁桥) fault) which compose the North Qilian fault (NQF) to test the timing and patterns of the fault activities. Apatite fission-track (AFT) results indicate that these minor faults experienced two active phases in the Cretaceous and the Oligocene-Miocene. Further research indicate that the initiation timing of faulting became younger northward in both active periods and the later phase probably more active than the former phase. These tectonic activities might be highly related to the docking of the Lhasa Block to the south in the Cretaceous and uplift and expansion of the Tibetan Plateau in the Cenozoic. [ABSTRACT FROM AUTHOR]

The rate and distribution of deformation along the Qilian Mountain, on the northeastern Tibetan Plateau, is needed to understand the evolution of high topography associated with the plateau. Recently, a number of empirical studies have provided support for the contention, common to most models of fluvial incision, that rock uplift rate exerts a first-order control on the gradient of longitudinal river profiles. Along the northern Qilian Mountain, this method is used to extract information about the spatial patterns of differential rock uplift. Analysis of the longitudinal profiles of bedrock channels reveals systematic differences in the channel steepness index along the trend of the frontal ranges. Local comparisons of channel steepness reveal that lithology and precipitation have limited influence on channel steepness. Similarly, there is little evidence suggesting that channel steepness is influenced by differences in the sediment loads. We argue that the distribution of channel steepness in the Qilian Mountain is mostly the result of differential rates of rock uplift. Thus, channel steepness indices reveal a lower rock uplift rate in the eastern portion of the Qilian Mountain and a higher rate in the middle and west. The highest rates appear to occur in the middle-west portions of the range, just to the west of the Yumu Shan. [ABSTRACT FROM AUTHOR]

In this paper, the maximum order of linear multistep methods (LMM) for solving semi-explict index-2 differential-algebraic equations (DAEs) is discussed. For a k-step formula, we prove that the orders of differential variables and algebraic variables do not exceed k+1 and k respectively when k is odd and both orders do not exceed k when k is even. In order to achieve the order k+1, the coefficients in the formula should satisfy some strict conditions. Examples which can achieve the maximum order are given for k=1,2,3. Especially, a class of multistep formula for k=3, not appearing in the literature before, are proposed. Further, a class of predictor-corrector methods are constructed to remove the restriction of the infinite stability. They give the same maximum order as that for solving ODEs. Numerical tests confirm the theoretical results. [ABSTRACT FROM AUTHOR]