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A parametric study of ramp-induced planar shock-wave/turbulent-boundary-layer interactions (SBLIs) is carried out at hypersonic conditions (Mach number 6.0) by means of numerical simulation of the Reynolds-averaged Navier–Stokes (RANS) equations, with the eventual goal of establishing wall temperature and Reynolds number effects. Comparison with available experimental data shows that RANS is capable of predicting the main features of hypersonic oblique SBLI, namely, typical size and distribution of the wall-surface pressure, and heat transfer. A large number of flow cases, at low ([Formula: see text]) and high Reynolds number ([Formula: see text]), were computed to examine the scaling of the heat transfer over a wide range of wall temperatures. As expected, the interaction zone of hypersonic ramp-induced SBLI is reduced as the wall is cooled. A simple power law for heat transfer originally introduced by Back and Cuffel (AIAA Journal, Vol. 8, No. 10, 1970, pp. 1871–1873) is here considered to account for hypersonic ramp-induced SBLI, which is found to successfully collapse the data to the distributions obtained for supersonic, cold/hot interactions.

A dynamic and reconfigurable satellite-to-ground communication system research based on LoRa technology is proposed in this paper. Starting from the application background of the internet of things (IoT) on the low earth orbit (LEO) satellite, this paper proposes a modulation parameter dynamic and reconfigurable technology for the Long Range (LoRa) signal, so as to improve the reliability of the IoT used in the satellite-to-ground communication system. Then, a parameter reconstitution method is designed in detail according to the satellite-to-ground communication link budget, and this method has the advantages of low complexity and less resources. Meanwhile, in this communication system, a desired bit rate can be selected based on the parameter reconstitution method to support different application. Simulation results show that the dynamic and reconfigurable satellite-to-ground communication system can work as expected, and this research possesses a great guidance in making the space-based IoT.

A direct numerical simulation database (Zuo et al., J. Fluid Mech. vol. 877, 2019, 167–195) is analyzed to investigate the size of the separation bubble and the relation with the pressure rise in the conical shock wave/turbulent boundary layer interaction. The characteristics of the separation bubbles in the supersonic turbulent boundary layer induced by conical shock waves are discussed. The inviscid reflected conical shock theory is analyzed, to conclude that regular reflection transition to Mach reflection will occur somewhere along the spanwise direction in the interaction zone. Then, the typical shape of a turbulent separation bubble in the conical shock wave/turbulent boundary layer interaction is described. The scaling between the size of the turbulent separation bubble and moderate conical shock intensities is established, giving a potential alternative method to rapidly predict the size of turbulent separation bubbles, especially in the supersonic viscous corrections under condition of boundary layer separation.

Context Several small studies have suggested that the gut microbiome might influence osteoporosis, but there is little evidence from human metabolomics studies to explain this association. Objective This study examined the association of gut microbiome dysbiosis with osteoporosis and explored the potential pathways through which this association occurs using fecal and serum metabolomics. Methods We analyzed the composition of the gut microbiota by 16S rRNA profiling and bone mineral density using dual-energy X-ray absorptiometry in 1776 community-based adults. Targeted metabolomics in feces (15 categories) and serum (12 categories) were further analyzed in 971 participants using ultra-high-performance liquid chromatography coupled to tandem mass spectrometry. Results This study showed that osteoporosis was related to the beta diversity, taxonomy, and functional composition of the gut microbiota. The relative abundance of Actinobacillus, Blautia, Oscillospira, Bacteroides, and Phascolarctobacterium was positively associated with osteoporosis. However, Veillonellaceae other, Collinsella, and Ruminococcaceae other were inversely associated with the presence of osteoporosis. The association between microbiota biomarkers and osteoporosis was related to levels of peptidases and transcription machinery in microbial function. Fecal and serum metabolomics analyses suggested that tyrosine and tryptophan metabolism and valine, leucine, and isoleucine degradation were significantly linked to the identified microbiota biomarkers and to osteoporosis, respectively. Conclusion This large population-based study provided robust evidence connecting gut dysbiosis, fecal metabolomics, and serum metabolomics with osteoporosis. Our results suggest that gut dysbiosis and amino acid metabolism could be targets for intervention in osteoporosis.

BACKGROUND Many studies have examined associations between dietary isoflavones and atherosclerosis, but few used objective biomarkers. OBJECTIVES We examined the associations of isoflavone biomarkers (primary analyses) and equol production (secondary analyses) with the progression of carotid intima-media thickness (cIMT), and whether inflammation, systolic blood pressure (SBP), blood lipids, and sex hormone-binding globulin (SHBG) mediated these associations, in Chinese adults. METHODS This 8.8-y prospective study included 2572 subjects (40-75 y old) from the GNHS (Guangzhou Nutrition and Health Study; 2008-2019). The concentrations of daidzein, genistein, and equol were assayed by an HPLC-tandem MS in serum (n = 2572) at baseline and in urine (n = 2220) at 3-y intervals. The cIMT of the common carotid artery (CCA) and bifurcation segment were measured by B-mode ultrasound every 3 y, and the progressions of cIMT ( ∆cIMT) were estimated using the regression method. RESULTS Multivariable linear mixed-effects models (LMEMs) and ANCOVA revealed that subjects with higher serum isoflavones tended to have lower increases of CCA-cIMT. The mean ± SEM differences in 8.8-y ∆CCA-cIMT between extreme tertiles of serum isoflavones were -17.1 ± 8.4, -20.6 ± 8.3, and -23.3 ± 10.4 μm for daidzein, total isoflavone, and equol (P-trends

The recent advances in unmanned aerial vehicles (UAVs) enormously improve their utility and expand their application scope. The UAV and swarm implementation further prevail in Smart City practices with the aid of edge computing and urban Internet of Things. The lead–follow formation in UAV swarm is an important organization means and has been adopted in diverse exercises, for its efficiency and ease of control. However, the reliability of centralization makes the entire swarm system in risk of collapse and instability, if a fatal fault incident happens in the leader. The motivation is to build a mechanism helping the distributed swarm recover from possible failures. Existing ways include assigning definite backups, temporary clustering and traversing to select a new leader are traditional ways that lack flexibility and adaptability. In this article, we propose a voting-based leader election scheme inspired by the Raft method in distributed computation consensus to solve the problem. We further discuss the impact of communication conditions imposed on the decentralized voting process by implementing a network resource pool. To dynamically evaluate UAV individuals, we outline measurement design principles and provide a realizable calculation example. Lastly but not least, empirical simulation results manifest better performance than the Raft-based method. Our voting-based approach exhibits advantages and is a promising way for quick regrouping and fault recovery in lead–follow swarms.

Recent years have witnessed the emergence of worldwide megalopolises and the accompanying public safety events, making urban safety a top priority in modern urban management. Among various threats, dangerous goods such as gas and hazardous chemicals transported through cities have bred repeated tragedies and become the deadly “bomb” we sleep with every day. While tremendous research efforts have been devoted to dealing with dangerous goods transportation (DGT) issues, further study is still in great need to quantify this problem and explore its intrinsic dynamics from a big data perspective. In this article, we present a novel system called DGeye , to feature a fusion between DGT trajectory data and residential population data for dangers perception and prediction. Specifically, DGeye first develops a probabilistic graphical model-based approach to mine spatio-temporally adjacent risk patterns from population-aware risk trajectories. Then, DGeye builds the novel causality network among risk patterns for risk pain-point identification, risk source attribution, and online risky state prediction. Experiments on both Beijing and Tianjin cities demonstrate the effectiveness of DGeye in real-life DGT risk management. As a case in point, our report powered by DGeye successfully drove the government to lay down gas pipelines for the famous Guijie food street in Beijing.

Hypersonic non-equilibrium air flows over a laminar double-cone configuration are numerically investigated. In this paper, the effects of thermochemical non-equilibrium gas, chemical non-equilibrium gas, and thermally perfect gas models on the prediction of the wall-surface heat transfer and pressure are investigated. Comparing to the high-enthalpy experimental data, the results of simulations indicate that all models have a similar tendency, and underpredict the length of the separation region, while the heat transfer peaks are overpredicted. The experimental data for surface pressure and heat transfer is insensitive to the choice of the thermally perfect gas model and non-equilibrium thermochemistry models. Quantitatively, the thermochemical non-equilibrium gas and thermally perfect gas models predict the same separation and reattachment locations within the uncertainty in the simulation, but the chemical non-equilibrium gas model predicts a smaller separation region.

Fraud detection from massive user behaviors is often regarded as trying to find a needle in a haystack. In this paper, we suggest abnormal behavioral patterns can be better revealed if both sequential and interaction behaviors of users can be modeled simultaneously, which however has rarely been addressed in prior work. Along this line, we propose a COllective Sequence and INteraction (COSIN) model, in which the behavioral sequences and interactions between source and target users in a dynamic interaction network are modeled uniformly in a probabilistic graphical model. More specifically, the sequential schema is modeled with a hierarchical Hidden Markov Model, and meanwhile it is shifted to the interaction schema to generate the interaction counts through Poisson factorization. A hybrid Gibbs-Variational algorithm is then proposed for efficient parameter estimation of the COSIN model. We conduct extensive experiments on both synthetic and real-world telecom datasets in different scales, and the results show that the proposed model outperforms some competitive baseline methods and is scalable. A case is further presented to show the precious explainability of the model.

In recent years, short text topic modeling has drawn considerable attentions from interdisciplinary researchers. Various customized topic models have been proposed to tackle the semantic sparseness nature of short texts. Most (if not all) of them follow the bag-of-words assumption, which, however, is not adequate since word order and phrases are often critical to capturing the meaning of texts. On the other hand, while some existing topic models are sensitive to word order, they do not perform well on short texts due to the severe data sparseness. To address these issues, we propose the Pseudo-document-based Topical N-Grams model (PTNG), which alleviates the data sparsity problem of short texts while is sensitive to word order. Extensive experiments on three real-world data sets with state-of-the-art baselines demonstrate the high quality of topics learned by PTNG according to UCI coherence scores and more discriminative semantic representation of short texts according to classification results.

Employer Brand Evaluation (EBE) is to understand an employer’s unique characteristics to identify competitive edges. Traditional approaches rely heavily on employers’ financial information, including financial reports and filings submitted to the Securities and Exchange Commission (SEC), which may not be readily available for private companies. Fortunately, online recruitment services provide a variety of employers’ information from their employees’ online ratings and comments, which enables EBE from an employee’s perspective. To this end, in this article, we propose a method named Company Profiling–based Collaborative Topic Regression (CPCTR) to collaboratively model both textual (i.e., reviews) and numerical information (i.e., salaries and ratings) for learning latent structural patterns of employer brands. With identified patterns, we can effectively conduct both qualitative opinion analysis and quantitative salary benchmarking. Moreover, a Gaussian processes--based extension, GPCTR, is proposed to capture the complex correlation among heterogeneous information. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and generalizability of our methods in real-life applications. The results clearly show that our methods outperform state-of-the-art baselines and enable a comprehensive understanding of EBE.

Background This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19). Methods The training cohort included consecutive COVID-19 patients at the First People’s Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020. Results A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80–.95); threshold, −2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92–.99); threshold, −2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68–.93) and 0.88 (.75–.96) for the clinical model and laboratory model, respectively. Conclusions We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.

The relationship of polyunsaturated fatty acids (PUFAs) with cardiovascular risk is still controversial. We aimed to determine whether erythrocyte n-3 and n-6 PUFAs are related to the risk of carotid atherosclerosis.From 2008 to 2019, baseline erythrocyte n-3 and n-6 PUFAs were determined in a cohort of 4040 Chinese adults (40-75 ys). The intima-media thickness (IMT) at the common carotid artery (CCA) and bifurcation of the carotid artery (BIF) and carotid plaque were assessed using ultrasonography at baseline and every 3 years.During a median follow-up of 8.8 years, we identified the following newly diagnosed cases: 535 cases of CCAIMT thickening, 654 cases of BIFIMT thickening, and 850 cases of carotid plaque. Higher erythrocyte docosahexaenoic acid (DHA) and arachidonic acid (ARA) and lower gamma-linolenic acid (GLA) were associated with decreased risks of BIFIMT thickening. N-3 eicosatrienoic acid (ETrA), docosapentaenoic acid (DPA), and n-6 dodecylthioacetic acid (DTA) presented a significant beneficial association with carotid IMT thickening in the short-term (2.8 y) follow-up (all p trend0.02), although the association was attenuated in the relatively long-term (8.8 y) follow-up. In addition, carotid plaque risk was found to be inversely associated with ETrA and DHA but positively associated with alpha-linolenic acid (ALA). N-6 linolenic acid (LA) and eicosadienoic acid (EDA) were not significantly associated with carotid atherosclerosis risk.Higher erythrocyte very-long-chain n-3 and n-6 PUFAs (especially DHA and ARA) and lower erythrocyte GLA are associated with lower carotid atherosclerosis risk, suggesting potential cardioprotective roles of very-long-chain PUFAs.

Fraud detection is usually compared to finding a needle in a haystack and remains a challenging task because fraudulent acts are buried in massive amounts of normal behavior and true intentions may be disguised in a single snapshot. Indeed, fraudulent incidents usually take place in consecutive time steps to gain illegal benefits, which provides unique clues for probing fraudulent behavior by considering a complete behavioral sequence rather than detecting fraud from a snapshot of behavior. Additionally, fraudulent behavior may involve different parties, such that the interaction patterns between sources and targets can help distinguish fraudulent acts from normal behavior. Therefore, in this paper, we model the attributed behavioral sequences generated from consecutive behaviors in order to capture the sequential patterns, while those that deviate from the pattern can be detected as fraudulence. Considering the characteristics of the behavioral sequence, we propose a novel model, NHA-LSTM, by augmenting the traditional LSTM with a modified forget gate, where the interval time between consecutive time steps is considered. Furthermore, we design a self-historical attention mechanism to allow for long time dependencies, which can help identify repeated or cyclical appearances. In addition, we propose an enhanced network embedding method, FraudWalk, to construct embeddings for the nodes in the interaction network with regard to higher-order interactions and particular time constraints for revealing potential group fraudulence. The node embeddings, along with the feature vectors, are fed into the model to capture the interactions between sources and targets. To validate the effectiveness of sequential behavior embeddings, we experiment on a real-world telecommunication dataset with prediction and classification tasks based on the learned embeddings. The experimental results show that the learned embeddings can better identify fraudulent behavior. Finally, we visualize the weights of the attention mechanism to provide a rational interpretation of human behavioral patterns.

Presently, in the context of the novel coronavirus pneumonia epidemic, several antibiotics are overused in hospitals, causing heavy pressure on the hospital’s wastewater treatment process. Therefore, developing stable, safe, and efficient hospital wastewater treatment equipment is crucial. Herein, a bench-scale electrooxidation equipment for hospital wastewater was used to evaluate the removal effect of the main antibiotic levofloxacin (LVX) in hospital wastewater using response surface methodology (RSM). During the degradation process, the influence of the following five factors on total organic carbon (TOC) removal was discussed and the best reaction condition was obtained: current density, initial pH, flow rate, chloride ion concentration, and reaction time of 39.6 A/m2, 6.5, 50 mL/min, 4‰, and 120 min, respectively. The TOC removal could reach 41% after a reaction time of 120 min, which was consistent with the result predicted by the response surface (40.48%). Moreover, the morphology and properties of the electrode were analyzed. The degradation pathway of LVX was analyzed using high-performance liquid chromatography–mass spectrometry (LC–MS). Subsequently, the bench-scale electrooxidation equipment was changed into onboard-scale electrooxidation equipment, and the onboard-scale equipment was promoted to several hospitals in Dalian.

Micro-service is a prevalent framework for Internet services. Its loosing coupling design has gained popularity recently. Loosing coupling services offer convenience, scalability and flexibility, however bring unexpected maintenance problems. This paper focuses on the problem where service administrations attempt to monitor the runtime services performance and status with mined service logs. To achieve it, we propose to extract unstructured logs as log patterns and visualise the services temporal relations using temporal graphs. Our analysis and data mining method can help to give new insights in system management area and non-trivial analysis process. The early experiments and analysis can clearly reveal inner structures of system and help to gain deep understanding.

Aiming at the scarcity of Low Earth Orbit (LEO) satellite spectrum resources, this paper proposes an algorithm of interference signal feature extraction and pattern classification based on deep learning to further improve the stability of satellite–ground communication links. The algorithm can successfully predict the interference signal pattern, start–stop time, frequency change range and other parameters, and has the advantages of excellent interference detection performance, high detection accuracy and small parameter prediction error, etc. It can be applied in the field of channel monitoring of communication satellite-to-ground communication links, and realize the repeated and efficient utilization of spectrum resources. Experiments show that the precision and recall of the algorithm for detecting five kinds of interference signals are all close to 100%, the prediction error of starting and ending time is less than 4 ms, and the prediction error of starting and ending frequency is less than 6 KHz.

Background Several descriptive studies have reported that higher neutrophil count (NC) may be correlated with poor prognosis in patients with confirmed COVID‐19 infection. However, the findings from these studies are limited by methodology and data analysis. Methods This study is a cohort study. We non‐selectively and consecutively collected a total of 663 participants in a Chinese hospital from January 7 to February 28. Standardized and two‐piecewise Cox regression model were employed to evaluated the association between baseline neutrophil count (bNC), NC change rate (NCR), and death. Results bNC had a U‐shaped association with death. In the range of 0.1 to ≤1.49×109 /L (HR = 0.19, 95% CI = 0.05 to 0.66) and > 3.55×109 /L of bNC (HR = 2.82, 95% CI = 1.19 to 6.67), the trends on bNC with mortality were opposite. By recursive algorithm, the bNC at which the risk of the death was lower was range of >1.49 to ≤3.55×109 /L (HR=13.64, 95%CI=0.25 to 74.71). In addition, we find NCRs (NCR1 and NCR2) is not associated with COVID‐19‐related deaths. Conclusions Compared with NCR, bNC has the potential to be used for early risk stratification in patients with COVID‐19 infection. The relationship between bNC and mortality was U‐shaped. The safe range of bNC was 1.64 to 4.0 ×109 /L. Identifying the correlation may be helpful for early risk stratification and medical decision making. This article is protected by copyright. All rights reserved.

Background: Little is known about the interplay among dairy intake, gut microbiota and cardiometabolic health in human prospective cohort studies. Methods: The present study included 1780 participants from the Guangzhou Nutrition and Health Study. We examined the prospective association between habitual dairy consumption (total dairy, milk, yogurt) and gut microbial composition using linear regression after adjusting for socio-demographic, lifestyle and dietary factors. The cross-sectional association of dairy-associated microbial features with cardiometabolic risk factors was examined with a linear regression model, adjusting for potential confounders. Serum metabolomic profiles were analyzed by partial correlation analysis. Findings: There was a significant overall difference in gut microbial community structure (β-diversity) comparing the highest with the lowest category for each of total dairy, milk and yogurt (P < 0.05). We observed that dairy-associated microbes and α-diversity indices were inversely associated with blood triglycerides, while positively associated with high-density lipoprotein cholesterol. A follow-up metabolomics analysis revealed the association of targeted serum metabolites with dairy-microbial features and cardiometabolic traits. Specifically, 2-hydroxy-3-methylbutyric acid, 2-hydroxybutyric acid and L-alanine were inversely associated with dairy-microbial score, while positively associated with triglycerides (FDR-corrected P < 0.1). Interpretation: Dairy consumption is associated with the gut microbial composition and a higher α-diversity, which provides new insights into the understanding of dairy-gut microbiota interactions and their relationship with cardiometabolic health. Funding: This work was supported by the National Natural Science Foundation of China, Zhejiang Ten-thousand Talents Program, Westlake University and the 5010 Program for Clinical Researches of the Sun Yat-sen University.

In order to evaluate the performance of the slip critical bolted connection of HSS members, 30 specimens fabricated from high strength steel (HSS) (Q550, Q690 and Q890), are tested to investigate the slip factors of shot-blasted surface and rusted surface after blasting. For comparison, a group of specimens fabricated from mild steel Q345 are tested as well. In order to investigate the effect of the steel strength grade on slip factor, the values of hardness, roughness and capacity of plastic deformation of steels with different strength grades are also tested and analyzed. Based on experimental results and friction theory, it is found that with the same blasting treatment, the surface roughness tends to decrease with the increase of steel hardness, which induces the decreasing of the slip factor. Based on the friction mechanism, the slip factor is not only decided by surface roughness, but also can be affected by the capacity of plastic deformation of the contacting materials. With the increasing of plastic deformation capacity of the steel, the slip factor tends to be higher. According to the test results, the suggested slip factor of HSS specimens with blasted surfaces is 0.40. Moreover, for the specimens with rusted surface after blasting, there may be an increment in the slip factor owing to the increased surface roughness by rusting. However, the positive influence of rusting on the slip factor is not recommended to be considered in the connection design due to its instability. Therefore, the recommended slip factor of the specimen with rusted surface after blasting takes the value of 0.40 as well.

In the cooperative searching scenario, most traditional methods are based on the top–down mechanisms. These mechanisms are usually offline and centralized. The characteristics limit the adaptability of unmanned aerial vehicle (UAV) swarm to the complex mission environments, such as those with inaccurate information of the targets and grids. In order to improve the searching ability of UAV swarm, a novel searching method named emergent search of UAV swarm guided by the target probability map (ESUSTPM) is proposed. ESUSTPM is based on local rules to organize and guide UAV agents to achieve the flocking state, search the mission area and detect the hidden targets concurrently. In ESUSTPM, local rules contain the flocking rules and the guiding rules. The flocking rules are the interactions between the agents, which are designed by a novel constructed function based on two exponential functions in this paper. The new constructed function can better maintain the relatively stable distances between the agents and realize the smooth transition of the positions at the given centers. The local guiding rules based on the target probability information of the nearby grids are firstly designed to realize the multi-function of the swarm, including full area coverage, target detection and reduction in environmental uncertainty (EU). Finally, the simulations verify that ESUSTPM can achieve the full coverage of the mission area while taking into account the target search. The statistical results also indicate that the searching efficiency of the proposed ESUSTPM is higher than the traditional searching algorithms based on the division and allocation of the area or the heuristic algorithms.

Poor cell viability after transplantation has restricted the therapeutic capacity of mesenchymal stem cells (MSCs) for cardiac dysfunction after myocardial infarction (MI). Growth arrest-specific gene 6 (Gas6) encodes a secreted γ-carboxyglutamic acid (Gla)-containing protein that functions in cell growth, adhesion, chemotaxis, mitogenesis and cell survival. In this study, we genetically modified MSCs with Gas6 and evaluated cell survival, cardiac function, and infarct size in a rat model of MI via intramyocardial delivery. Functional studies demonstrated that Gas6 transfer significantly reduced MSC apoptosis, increased survival of MSCs in vitro and in vivo, and that Gas6-engineered MSCs (MSCGas6)-treated animals had smaller infarct size and showed remarkably functional recovery as compared with control MSCs (MSCNull)-treated animals. Mechanistically, Gas6 could enhance phosphatidylinositol 3-kinase (PI3K)/Akt signaling and improve hypoxia-inducible factor-1 alpha (HIF-1α)-driven secretion of four major growth factors (VEGF, bFGF, SDF and IGF-1) in MSCs under hypoxia in an Axl-dependent autocrine manner. The paracrine action of MSCGas6 was further validated by coculture neonatal rat cardiomyocytes with conditioned medium from hypoxia-treated MSCGas6, as well as by pretreatment cardiomyocytes with the specific receptor inhibitors of VEGF, bFGF, SDF and IGF-1. Collectively, our data suggest that Gas6 may advance the efficacy of MSC therapy for post-infarcted heart failure via enhanced Gas6/Axl autocrine prosurvival signaling and paracrine cytoprotective action.

Objective Isoflavones and lignans are phytoestrogens present in plant-based foods, which have a potential preventive effect on breast carcinogenesis. The effects of phytoestrogens on breast cancer may differ according to the hormonal environment. This case-control study aimed to investigate the association between serum phytoestrogens and odds of breast cancer among Chinese pre- and postmenopausal women. Methods A total of 792 cases and 813 age-matched controls were included. Serum isoflavone (daidzein, genistein, glycitein, equol, and formononetin) and lignan (enterodiol and enterolactone) concentrations were measured using a liquid chromatography-tandem mass spectrometry method. Results Significant inverse associations were found between serum total soy isoflavone precursors, daidzein, genistein, formononetin, total lignans, enterodiol, enterolactone, and the odds of breast cancer in premenopausal but not postmenopausal women. For premenopausal women, the adjusted odds ratios (95% confidence intervals) for the highest versus the lowest serum concentration groups were 0.60 (0.41-0.87) for total soy isoflavones precursors, 0.64 (0.44-0.93) for daidzein, 0.62 (0.43-0.90) for genistein, 0.49 (0.35-0.68) for formononetin, 0.38 (0.25-0.57) for total lignans, 0.49 (0.33-0.73) for enterodiol, and 0.49 (0.33-0.74) for enterolactone. However, the interaction between serum phytoestrogens and menopausal status on odds of breast cancer was statistically significant only for daidzein. No significant association was found between serum equol or gycitein and the odds of breast cancer among either pre- or postmenopausal women. Conclusions Higher levels of certain serum isoflavones and lignans were associated with reduced odds of breast cancer in premenopausal women, but the interaction was statistically significant only for daidzein.

This chapter is devoted to mechanical properties under monotonic action, including the 1D and 3D stress–strain constitutive relations and the statistic for resistance partial coefficients of high-strength steel.

We carry out a parametric study of conical shock-wave/turbulent boundary-layer interactions by means of numerical simulation of the Reynolds-averaged Navier–Stokes (RANS) equations, with the eventu...

Background Little is known about the inter-relationship among fruit and vegetable intake, gut microbiota and metabolites, and type 2 diabetes (T2D) in human prospective cohort study. The aim of the present study was to investigate the prospective association of fruit and vegetable intake with human gut microbiota and to examine the relationship between fruit and vegetable-related gut microbiota and their related metabolites with type 2 diabetes (T2D) risk. Methods This study included 1879 middle-age elderly Chinese adults from Guangzhou Nutrition and Health Study (GNHS). Baseline dietary information was collected using a validated food frequency questionnaire (2008–2013). Fecal samples were collected at follow-up (2015–2019) and analyzed for 16S rRNA sequencing and targeted fecal metabolomics. Blood samples were collected and analyzed for glucose, insulin, and glycated hemoglobin. We used multivariable linear regression and logistic regression models to investigate the prospective associations of fruit and vegetable intake with gut microbiota and the association of the identified gut microbiota (fruit/vegetable-microbiota index) and their related fecal metabolites with T2D risk, respectively. Replications were performed in an independent cohort involving 6626 participants. Results In the GNHS, dietary fruit intake, but not vegetable, was prospectively associated with gut microbiota diversity and composition. The fruit-microbiota index (FMI, created from 31 identified microbial features) was positively associated with fruit intake (p p = 0.003) and the FMI-T2D association (OR 0.90, 95%CI 0.84–0.97) were both successfully replicated in the independent cohort. The FMI-positive associated metabolite sebacic acid was inversely associated with T2D risk (OR 0.67, 95%CI 0.51–0.86). The FMI-negative associated metabolites cholic acid (OR 1.35, 95%CI 1.13–1.62), 3-dehydrocholic acid (OR 1.30, 95%CI 1.09–1.54), oleylcarnitine (OR 1.77, 95%CI 1.45–2.20), linoleylcarnitine (OR 1.66, 95%CI 1.37–2.05), palmitoylcarnitine (OR 1.62, 95%CI 1.33–2.02), and 2-hydroglutaric acid (OR 1.47, 95%CI 1.25–1.72) were positively associated with T2D risk. Conclusions Higher fruit intake-associated gut microbiota and metabolic alteration were associated with a lower risk of T2D, supporting the public dietary recommendation of adopting high fruit intake for the T2D prevention.

Although somatic mutations in Histone 3.3 (H3.3) are well-studied drivers of oncogenesis, the role of germline mutations remains unreported. We analyze 46 patients bearing de novo germline mutations in histone 3 family 3A (H3F3A) or H3F3B with progressive neurologic dysfunction and congenital anomalies without malignancies. Molecular modeling of all 37 variants demonstrated clear disruptions in interactions with DNA, other histones, and histone chaperone proteins. Patient histone posttranslational modifications (PTMs) analysis revealed notably aberrant local PTM patterns distinct from the somatic lysine mutations that cause global PTM dysregulation. RNA sequencing on patient cells demonstrated up-regulated gene expression related to mitosis and cell division, and cellular assays confirmed an increased proliferative capacity. A zebrafish model showed craniofacial anomalies and a defect in Foxd3-derived glia. These data suggest that the mechanism of germline mutations are distinct from cancer-associated somatic histone mutations but may converge on control of cell proliferation.