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Abstract Aims/Introduction The prevalence and pathophysiological background of type 2 diabetes mellitus vary across ethnicities, and can affect treatment responses. Adding lixisenatide to basal insulin (BI) in type 2 diabetes mellitus patients has shown improvements in glycated hemoglobin (HbA1c) and postprandial glycemic (PPG) excursions, without increasing hypoglycemic events. We aim to compare the efficacy of lixisenatide in Asian and white patients inadequately controlled with basal insulin. Materials and Methods An individual‐level pooled analysis of two multi‐national phase III studies, GetGoal‐L and GetGoal‐L‐C, was carried out to assess the efficacy of lixisenatide versus placebo as an add‐on treatment to BI ± metformin in Asian and white patients with type 2 diabetes mellitus. Change in HbA1c, 2‐h PPG and PPG excursion were analyzed, along with possible predictors of glycemic control. Results Pooled data showed that baseline characteristics were similar between Asian and white patients with the exception of bodyweight, body mass index and BI dose being higher in white patients. After 24 weeks, lixisenatide reduced HbA1c in both ethnic groups, with no statistically significant difference between the two groups (Asian patients least squares mean difference −0.49, 95% confidence interval −0.68 to − 0.30 and white patients least squares mean difference −0.45, 95% confidence interval −0.63 to − 0.26; P = 0.6287). Similarly, no significant difference was found in 2‐h PPG reduction between both groups (least squares mean difference for Asian vs white patients: −3.37 vs −3.93; P = 0.3203). Treatment with lixisenatide contributed to HbA1c reduction of −0.56% after adjustment of baseline HbA1c level in Asian patients, and −0.41% in white patients. Conclusions Adding lixisenatide to BI significantly reduced HbA1c and 2‐h PPG levels in both Asian and white participants with type 2 diabetes mellitus. No differences in treatment effect were observed between the two populations.

Abstract Background Mesenchymal stem cells (MSCs) show promising therapeutic potential in treating type 2 diabetes mellitus (T2DM) in clinical studies. Accumulating evidence has suggested that the therapeutic effects of MSCs are not due to their direct differentiation into functional β-cells but are instead mediated by their paracrine functions. Among them, exosomes, nano-sized extracellular vesicles, are important substances that exert paracrine functions. However, the underlying mechanisms of exosomes in ameliorating T2DM remain largely unknown. Methods Bone marrow mesenchymal stem cell (bmMSC)-derived exosomes (bmMDEs) were administrated to T2DM rats and high-glucose-treated primary islets in order to detect their effects on β-cell dedifferentiation. Differential miRNAs were then screened via miRNA sequencing, and miR-146a was isolated after functional verification. TargetScan, reporter gene detection, insulin secretion assays, and qPCR validation were used to predict downstream target genes and involved signaling pathways of miR-146a. Results Our results showed that bmMDEs reversed diabetic β-cell dedifferentiation and improved β-cell insulin secretion both in vitro and in vivo. Results of miRNA sequencing in bmMDEs and subsequent functional screening demonstrated that miR-146a, a highly conserved miRNA, improved β-cell function. We further found that miR-146a directly targeted Numb, a membrane-bound protein involved in cell fate determination, leading to activation of β-catenin signaling in β-cells. Exosomes derived from miR-146a-knockdown bmMSCs lost the ability to improve β-cell function. Conclusions These findings demonstrate that bmMSC-derived exosomal miR-146a protects against diabetic β-cell dysfunction by acting on the NUMB/β-catenin signaling pathway, which may represent a novel therapeutic strategy for T2DM.

In recent years, there has been an increased interest in hydrogen energy due to a desire to reduce greenhouse gas emissions by utilizing hydrogen for numerous applications. Some countries (e.g., Japan, Iceland, and parts of Europe) have made great strides in the advancement of hydrogen generation and utilization. However, in the United States, there remains significant reservation and public uncertainty on the use and integration of hydrogen into the energy ecosystem. Massachusetts, similar to many other states and small countries, faces technical, infrastructure, policy, safety, and acceptance challenges with regards to hydrogen production and utilization. A hydrogen economy has the potential to provide economic benefits, a reduction in greenhouse gas emissions, and sector coupling to provide a resilient energy grid. In this paper, the issues associated with integrating hydrogen into Massachusetts and other similar states or regions are studied to determine which hydrogen applications have the most potential, understand the technical and integration challenges, and identify how a hydrogen energy economy may be beneficial. Additionally, hydrogen’s safety concerns and possible contribution to greenhouse gas emissions are also reviewed. Ultimately, a set of eight recommendations is made to guide the Commonwealth’s consideration of hydrogen as a key component of its policies on carbon emissions and energy.

Objectives This study aims to explore the spatial and spatiotemporal distribution of pertussis in Hunan Province, and provide a scientific basis for targeting preventive measures in areas with a high incidence of pertussis.Design In this retrospective spatial and spatiotemporal (ecological) study, the surveillance and population data of Hunan Province from 2009 to 2019 were analysed. The ArcGIS V.10.3 software was used for spatial autocorrelation analysis and visual display, and SaTScan V.9.6 software was used for statistical analysis of spatiotemporal scan data.Settings Confirmed and suspected pertussis cases with current addresses in Hunan Province and onset dates between 1 January 2009 and 31 December 2019 were included in the study.Participants The study used aggregated data, including 6796 confirmed and suspected pertussis cases.Results The seasonal peak occurred between March and September, and scattered children were at high risk. The global Moran’s I was between 0.107 and 0.341 (p

Abstract Aims/Introduction Excessive dietary salt or low potassium intakes are strongly correlated with insulin resistance (IR) and type 2 diabetes mellitus. In epidemiological and experimental studies, increased serum retinol‐binding protein 4 (RBP4) contributes to the pathogenesis of type 2 diabetes mellitus. Herein, we hypothesized that RBP4 might be an adipocyte‐derived “signal” that plays the crucial role in salt‐related insulin resistance or type 2 diabetes mellitus. This study aimed to assess whether salt consumption and potassium supplementation influence serum RBP4 levels in healthy individuals. Materials and Methods A total of 42 participants (aged 25–50 years) in a rural area of Northern China were successively provided normal (3 days at baseline), low‐salt (7 days; 3 g/day NaCl) and high‐salt (7 days; 18 g/day) diets, and a high‐salt diet with potassium additive (7 days; 18 g/day NaCl and 4.5 g/day KCl). Urinary sodium and potassium were measured to ensure compliance to dietary intervention. Then, RBP4 levels were evaluated by enzyme‐linked immunosorbent assay. Results High salt intake significantly raised serum RBP4 levels in healthy participants (17.5 ± 0.68 vs 28.6 ± 1.02 µg/mL). This phenomenon was abrogated by potassium supplementation (28.6 ± 1.02 vs 17.6 ± 0.88 µg/mL). In addition, RBP4 levels presented positive (r = 0.528, P

In practice, the cross-domain transfer of data distribution and the sample imbalance of fault status are inevitable, but one or both are often ignored, which restricts the adaptability and classification accuracy of the generated fault diagnosis (FD) model. Accordingly, an entropy-optimized method is proposed in this paper based on an unsupervised domain-adaptive technique to enhance FD model training. For the training, pseudosamples and labels corresponding to the target samples are generated through data augmentation and self-training strategies to diminish the distribution discrepancy between the source and target domains. Meanwhile, an adaptive conditional entropy loss function is developed to improve the data quality of the semisupervised learning, with which reliable samples are generated for the training. According to the experiment results, compared with other state-of-the-art algorithms, our method can achieve significant accuracy improvement in rolling bearing FD. Typically, the accuracy improvement compared with the baseline Convolutional Neural Network (CNN) is achieved by over 13.23%.

Resumo Fundamento Existem informações conflitantes sobre se a ultrassonografia pulmonar avaliada por linhas B tem valor prognóstico em pacientes com insuficiência cardíaca (ICa). Objetivos Avaliar o valor prognóstico da ultrassonografia pulmonar avaliada por linhas B em pacientes com ICa. Métodos Quatro bases de dados (PubMed, EMBASE, Cochrane Library e Scopus) foram sistematicamente pesquisadas para identificar artigos relevantes. Reunimos a razão de risco (RR) e o intervalo de confiança de 95% (IC) de estudos elegíveis e realizamos análises de heterogeneidade, avaliação de qualidade e viés de publicação. Os dados foram agrupados usando um modelo de efeitos fixos ou de efeito aleatório. Um valor de p 15 e > 30 na alta hospitalar foram significativamente associadas ao aumento do risco de desfecho combinado de mortalidade por todas as causas ou hospitalização por ICa (RR, 3,37, IC de 95%, 1,52-7,47; p = 0,003; RR, 4,01, IC de 95%, 2,29-7,01; p 30 na alta foi significativamente associado ao aumento do risco de hospitalização por ICa (RR, 9,01, IC de 95%, 2,80-28,93; p 3 aumentou significativamente o risco de desfecho combinado de mortalidade por todas as causas ou hospitalização por ICa em pacientes ambulatoriais com ICa (RR, 3,21, IC de 95%, 2,09-4,93; I2 = 10%; p

In power grid construction projects, ensuring the safety of construction workers by eliminating potential risks has always been important yet difficult. Thanks to the breakthrough in deep learning, it becomes possible to adopt deep learning based object detection technologies to enable intelligent safety monitoring on power grid construction sites. However, due to the complex terrain of the power grid construction site, there is a lack of a dataset of power grid construction scenes. In this paper, a high-quality power grid construction dataset is constructed. In order to reduce the expensive annotation work, we propose to adopt contrastive self-supervised learning to pretrain a feature extraction encoder and integrate it with YOLOv5 by modifying its backbone network for safety-related object detection. The proposed network is referred to as Contrastive Res-YOLOv5 Network (CORY-Net). Experimental results show that contrastive self-supervised pretraining can efficiently improve the object detection performance on the power grid construction dataset and speed up the convergence of the model. With only 1400 pretraining epochs, the proposed CORY-Net outperforms the original YOLOv5 by contributing 1.32% to the mAP@.5 and 3.54% to the mAP@.5:.95. Compared to the supervised object detection benchmarks, the proposed CORY-Net achieves the highest mAP@.5 in 9 out of 22 categories of targets and the highest overall mAP@.5.

Background New evidence from studies on risk factors for mortality in hemodialysis (HD) patients with COVID-19 became available. We aimed to review the clinical risk factors for fatal outcomes in these patients. Methods We performed meta-analysis using the PubMed, EMBASE, and Cochrane databases. A fixed- or random-effects model was used for calculating heterogeneity. We used contour-enhanced funnel plot and Egger’s tests to assess potential publication bias. Results Twenty-one studies were included. The proportion of males was lower in the survivor group than in the non-survivor group (OR = 0.75, 95% CI [0.61, 0.94]). The proportion of respiratory diseases was significantly lower in the survivor group than in the non-survivor group (OR = 0.42, 95% CI [0.29, 0.60]). The proportion of patients with fever, cough, and dyspnea was significantly lower in the survivor group (fever: OR = 0.53, 95% CI [0.31, 0.92]; cough: OR = 0.50, 95% CI [0.38, 0.65]; dyspnea: OR = 0.25, 95% CI [0.14, 0.47]) than in the non-survivor group. Compared with the non-survivor group, the survivor group had higher albumin and platelet levels and lower leucocyte counts. Conclusions Male patients might have a higher risk of developing severe COVID-19. Comorbidities, such as respiratory diseases could also greatly influence the clinical prognosis of COVID-19. Clinical features, such as fever, dyspnea, cough, and abnormal platelet, leucocyte, and albumin levels, could imply eventual death. Our findings will help clinicians identify markers for the detection of high mortality risk in HD patients at an early stage of COVID-19.

BackgroundThe previous studies have shown that individuals with hypertension and anxiety have a higher mean left ventricular mass index (LVMI) and QTc dispersion. We explored the associations between anxiety and left ventricular hypertrophy (LVH) and between anxiety and transmural dispersion of repolarization (TDR) (as detected by T peak-T end interval/QT interval, Tp–Te/QT ratio) in patients with hypertension.MethodsA total of 353 patients with uncomplicated hypertension from the Shaanxi Provincial People’s Hospital were enrolled between 2017 and 2021. Anxiety was defined as a Hamilton Anxiety Scale (HAM-A) score ≥ 14. Logistic regression models were used to estimate the association between HAM-A and LVH. The association between HAM-A score and Tp–Te/QT was estimated using linear regression.ResultsParticipants were divided into two groups based on the presence of anxiety. LVMI was significantly higher in patients with hypertension and anxiety than in those with hypertension without anxiety (no anxiety: 84.36 ± 23.82, anxiety: 105.75 ± 25.45 g/m2, p < 0.001). HAM-A score was positively correlated with LVMI (r = 0.578, p < 0.001) and with Tp–Te/QT (r = 0.252, p < 0.001). Logistic regression models showed that patients with hypertension and anxiety were at higher risk of LVH than were patients with hypertension without anxiety (adjusted OR, 2.44, 95% CI, 1.35–4.43, p = 0.003). The linear regression analysis showed that the HAM-A score was associated with Tp–Te/QT ratio (adjusted β, 0.001, 95% CI, 0.001–0.002, p = 0.013). There was an interaction between sex and anxiety for LVH risk (p for interaction = 0.035) and for increased Tp–Te/QT (p for interaction = 0.014). After stratification by sex, anxiety was associated with increased risk for LVH in men with hypertension (adjusted OR, 5.56, 95% CI, 2.07–14.98, p = 0.001), but not in women (adjusted: OR, 1.44, 95% CI, 0.64–3.26, p = 0.377) with hypertension. The HAM-A score was also positively associated with Tp–Te/QT ratio in male (adjusted β, 0.002, 95% CI, 0.001–0.003, p < 0.001), but not in women (adjusted β, 0.001, 95% CI, –0.0002–0.002, p = 0.165).ConclusionOur results indicated that anxiety was associated with LVH and with increased TDR in men with hypertension, but not in women with hypertension.

Abstract Background Mesenchymal stem cell (MSC)-based therapy is currently considered to be an effective treatment strategy for diabetes and hepatic disorders, such as liver cirrhosis and non-alcoholic fatty liver disease. Exosomes are important mediators of cellular connections, and increasing evidence has suggested that exosomes derived from MSCs may be used as direct therapeutic agents; their mechanisms of action, however, remain largely unclear. Here, we evaluated the efficacy and molecular mechanisms of human umbilical cord MSC-derived exosomes (HucMDEs) on hepatic glucose and lipid metabolism in type 2 diabetes mellitus (T2DM). Methods HucMDEs were used to treat T2DM rats, as well as palmitic acid (PA)-treated L-O2 cells, in order to determine the effects of HucMDEs on hepatic glucose and lipid metabolism. To evaluate the changes in autophagy and potential signaling pathways, autophagy-related proteins (BECN1, microtubule-associated protein 1 light chain 3 beta [MAP 1LC3B]), autophagy-related genes (ATGs, ATG5, and ATG7), AMP-activated protein kinase (AMPK), and phosphorylated AMPK (p-AMPK) were assessed by Western blotting. Results HucMDEs promoted hepatic glycolysis, glycogen storage, and lipolysis, and reduced gluconeogenesis. Additionally, autophagy potentially contributed to the effects of HucMDE treatment. Transmission electron microscopy revealed an increased formation of autophagosomes in HucMDE-treated groups, and the autophagy marker proteins, BECN1 and MAP 1LC3B, were also increased. Moreover, autophagy inhibitor 3-methyladenine significantly reduced the effects of HucMDEs on glucose and lipid metabolism in T2DM rats. Based on its phosphorylation status, we found that the AMPK signaling pathway was activated and induced autophagy in T2DM rats and PA-treated L-O2 cells. Meanwhile, the transfection of AMPK siRNA or application of the AMPK inhibitor, Comp C, weakened the therapeutic effects of HucMDEs on glucose and lipid metabolism. Conclusions These findings demonstrate that HucMDEs improved hepatic glucose and lipid metabolism in T2DM rats by activating autophagy via the AMPK pathway, which provides novel evidence suggesting the potential for HucMDEs in clinically treating T2DM patients.

Abstract Background Air pollution and pulmonary tuberculosis (PTB) are still serious worldwide problems, especially in areas of developing countries. Whether there is an association between high ambient air pollutant concentrations and PTB has not been fully explored. Methods Bayesian spatial-temporal models were constructed to analyse the association between ambient air pollutants (particulate matter with aerodynamic diameters of ≤10 μm (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2)) and PTB incidence, adjusting for socioeconomic covariates. We collected data on pulmonary TB, ambient air pollution (PM10, SO2 and NO2) concentrations and socioeconomic covariates from 17 prefectures in the central Chinese province of Hubei between Jan 1, 2006, and Dec 31, 2015. Results For every annual 10 μg/m3 increase in SO2, the relative risk (RR) of PTB incidence was 1.046 (95% credible interval [CI], 1.038–1.054) in the study area. Moreover, we found positive associations with each annual 10 μg/m3 increase in ambient air pollutants (PM10, SO2 and NO2) in females but only with SO2 in males. A significant association for each 10 μg/m3 increase in SO2 was observed in all the age groups, with a significant association for PM10 only in children under 14 years of age. A significant response relationship was also observed at a 0–1 month moving average lag for each 10 μg/m3 increase in SO2. Conclusions High ambient air pollution concentrations in areas of developing countries might increase the risk of regional PTB incidence, especially for women and young people. Precautions and protective measures and efforts to reduce ambient air pollutant concentrations should be strengthened in developing countries.

Driving behavior analysis has diverse applications in intelligent transportation systems (ITSs). The naturalistic driving data potentially contain rich information regarding human drivers' habits and skills in practical and natural driving conditions. But mining knowledge from them is challenging. In this paper, we propose a novel approach for analyzing driving stability using naturalistic driving data. Our method can extract features, based on the random matrix theory, to reflect the statistical difference between actual driving data and the data that would be generated by a theoretically ideal driver, and thus imply the skillful level of a driver in terms of vehicle control in both longitudinal and lateral directions. The execution of our method on a practical ITS dataset is conducted. Using the extracted features, a driving behavior analysis application that partitions drivers into clusters to identify common driving stability characteristics is demonstrated and discussed.

Freeze-thaw cycles caused by climate change can change the structure and strength of the soil. In seasonally frozen soil areas, the use of improved loess as a filling material must consider the effects of freeze-thaw cycles. With the increasingly severe global environmental problems, the search for suitable new environmental protection improvement materials has become one of the hotspots in soil performance improvement research. The purpose of this paper is to use lignin fiber to improve the engineering performance and freeze-thaw resistance of loess and to reduce the negative impact of engineering construction on the environment of the loess area. Based on a series of triaxial shear tests, the effects of freeze-thaw cycles on the stress-strain relationship, shear strength, and Mohr-Coulomb’s strength parameters of loess reinforced with lignin fiber were analyzed. Combined with the volume change and microstructure characteristics of fiber-reinforced loess before and after the freeze-thaw cycles, the reasons for the effects of the freeze-thaw cycles on the shear strength characteristics of fiber-reinforced loess are discussed. The research results showed that after 15 freeze-thaw cycles, the shear strength of loess reinforced with 1% fiber increased by 0.15%, 2.05%, and 1.35% at 80, 140, and 200 kPa, respectively. The shear strength of the reinforced loess with other fiber contents decreases to different degrees, and the maximum reduction ratio can reach 9.54%. Freeze-thaw cycles changed the variation of shear strength and strength parameters with fiber content. When the fiber content is less than 1%, the shear strength, cohesion, and friction angle of fiber-reinforced loess increase the fastest after freeze-thaw cycles. When the fiber content is 1%, the overall destruction effect of freeze-thaw cycles on fiber-reinforced loess is inhibited, and the soil has the best freeze-thaw resistance.

When analyzing agriculture’s total factor productivity, traditional measurement approaches do not take into account the inefficiency value. The production functions are assumed to be analyzed on basis of the random boundaries, which makes the analysis results inaccurate and unreliable. As a result, this paper proposes an analytical approach for agricultural total factor productivity based on the stochastic block model (SBM), which combines the benefits of statistics and machine learning. It uses the SBM direction distance function and the Luenberger productivity index to measure the agricultural efficiency, total factor productivity, and their components. The research study considers the data from 31 provinces from 2006 to 2018 years. First, one output indicator and six input indicators are established. The time-varying variations of the national agricultural inefficiency value and its source decomposition under variable scale returns are then determined using the SBM-based algorithm of agricultural total factor productivity and the obtained sample data. The changes of the agricultural total factor productivity and its components are comprehensively analyzed. Following an examination of the elements impacting agricultural efficiency and productivity, the socioeconomic development of the agricultural total factor productivity is examined in order to achieve efficient growth.

Tetracycline (TC) is widely-used antibiotic pollutant with high toxicity, refractory, persistence and bacteriostasis, and its removal from water needs to be enhanced. In this work, a novel Graphite-UiO-66(Zr)/Ti electrode was successfully prepared and evaluated for electrochemical oxidation degradation of TC. The electrochemical performance tests indicate the Graphite-UiO-66(Zr)/Ti electrode had higher electrochemical oxidation activity, which achieved higher TC removal efficiency (98.1% ± 1.5%) than Ti plate (65.2% ± 3.5%), Graphite-MIL-53(Al)/Ti electrode (79.5% ± 2.9%) and Graphite-MIL-100(Fe)/Ti electrode (89.0% ± 2.6%). The influence of operating condition was also systematically studied, and the optimized condition was pH 5.0, 20 mA/cm2 current density and 0.1 M electrolyte (Na2SO4). Through the liquid chromatography mass spectrometry (LC-MS), the TC degradation pathway by Graphite-UiO-66(Zr)/Ti electrode oxidation was proposed. Under the •OH free radical oxidative decomposition effect, the double bond, phenolic group and amine group of TC were attacked. TC was transformed into intermediate product ① (m/z = 447), then was further degraded to intermediates ② (m/z = 401) and ③ (m/z = 417). The latter was fragmented into small fractions ④ (m/z = 194), ⑤but-2-enedioic acid (m/z = 116) and ⑥oxalic acid (m/z = 90, the proposed intermediate). In addition, TC removal remained at 89.6% ± 2.7% in the sixth cycle of operation, which confirmed the efficient reusability and stability for antibiotics removal from water.

Unmanned Aerial Vehicles (UAVs) can be deployed as aerial wireless base stations which dynamically cover the wireless communication networks for Ground Users (GUs). The most challenging problem is how to control multi-UAVs to achieve on-demand coverage of wireless communication networks while maintaining connectivity among them. In this paper, the cooperative trajectory optimization of UAVs is studied to maximize the communication efficiency in the dynamic deployment of UAVs for emergency communication scenarios. We transform the problem into a Markov game problem and propose a distributed trajectory optimization algorithm, Double-Stream Attention multi-agent Actor-Critic (DSAAC), based on Multi-Agent Deep Reinforcement Learning (MADRL). The throughput, safety distance, and power consumption of UAVs are comprehensively taken into account for designing a practical reward function. For complex emergency communication scenarios, we design a double data stream network structure that provides a capacity for the Actor network to process state changes. Thus, UAVs can sense the movement trends of the GUs as well as other UAVs. To establish effective cooperation strategies for UAVs, we develop a hierarchical multi-head attention encoder in the Critic network. This encoder can reduce the redundant information through the attention mechanism, which resolves the problem of the curse of dimensionality as the number of both UAVs and GUs increases. We construct a simulation environment for emergency networks with multi-UAVs and compare the effects of the different numbers of GUs and UAVs on algorithms. The DSAAC algorithm improves communication efficiency by 56.7%, throughput by 71.2%, energy saving by 19.8%, and reduces the number of crashes by 57.7%.

Prefilming air-blast atomizers are widely used in modern gas turbine combustors. Due to insufficient awareness of the coupling mechanism of multi-stage swirling flow in gas turbines, there is a lack of effective methods for flow field optimization in combustor. In this study, the effect of some critical parameters on the flow field of a prefilming air-blast atomizer was analyzed with CFD. The parameters include the angle and number of the first swirler blades, the angle of the second swirler blades and the angle of sleeve. Furthermore, the coupling mechanism of two-stage swirling airflows of prefilming air-blast atomizer was discussed. Moreover, the influence of the interaction between two-stage counter swirling airflows on the characteristics of flow field was explained. The results show that with the increase in SNi, the axial length of the primary recirculation zone decreased, while the radial width increased. The starting position of primary recirculation zone (PRZ) moves forward with the increase in SNo. Reducing the sleeve angle β helps to form the primary recirculation zone. The results indicate that it is the transition of tangential velocity of airflow to radial velocity that promotes the formation of the PRZ. These results provide theoretical support for optimization of the flow field in swirl combustor.

Background and Aims: Diagonal earlobe crease (ELC) has been considered a potential cutaneous marker of atherosclerosis. However, the potential mechanism by which ELC and atherosclerosis are linked has not been adequately defined. Roles of adropin and irisin, novel biomarkers of endothelial function, in ELC have not been well-studied. This study aimed to test whether individuals with ELC are deficient in adropin and irisin, a characteristic that would likely promote endothelial dysfunction and provide a plausible common pathological basis for atherosclerosis and ELC.Methods: Patients diagnosed with coronary artery disease (CAD) with (n = 45) and without (n = 45) ELC were consecutively enrolled in the study. The ages of the patients enrolled ranged from 40–70 years. Other patients (n = 45) without ELC or CAD were recruited as the control group. All patients underwent coronary angiography. Serum adropin and irisin concentrations were assessed via enzyme-linked immunosorbent assay.Results: Circulating levels of irisin in the ELC group were significantly lower than those in the non-ELC group, and were highest in the control group. Serum adropin levels of the ELC group were significantly lower than those of the non-ELC group (P < 0.001). Interestingly, although the serum adropin level of the control group was greater than that of the non-ELC group, the difference failed to achieve statistical significance. In subgroup analysis of CAD and ELC, both serum adropin and irisin levels of the CAD and ELC groups were lower than those of the control group (P < 0.001). Receiver-operating characteristic curve analysis revealed that adropin and irisin have similar prognostic power for CAD and ELC.Conclusions: Low adropin and irisin were significantly associated with CAD and ELC. The deficiencies in adropin and irisin may be a common cause of both atherosclerosis and ELC, which explains why patients with ELC are prone to CAD.

Unmanned Aerial Vehicles (UAVs) can be employed as low-altitude aerial base stations (UAV-BSs) to provide communication services for ground users (GUs). However, most existing works mainly focus on optimizing coverage and maximizing throughput, without considering the fairness of the GUs in communication services. This may result in certain GUs being underserviced by UAV-BSs in pursuit of maximum throughput. In this paper, we study the problem of UAV-assisted communication with the consideration of user fairness. We first design a Ratio Fair (RF) metric by weighting fairness and throughput to evaluate the tradeoff between fairness and communication efficiency when UAV-BSs serve GUs. The problem is formulated as a mixed-integer non-convex optimization problem based on the RF metric and we propose a UAV-Assisted Fair Communication (UAFC) algorithm based on multi-agent deep reinforcement learning to maximize the fair throughput of the system. The UAFC algorithm comprehensively considers fair throughput, UAV-BSs coverage, and flight status to design a reasonable reward function. In addition, the UAFC algorithm establishes an information sharing mechanism based on gated functions by sharing neural networks, which effectively reduces the distributed decision-making uncertainty of UAV-BSs. To reduce the impact of state dimension imbalance on the convergence of the algorithm, we design a new state decomposing and coupling actor network architecture. Simulation results show that the proposed UAFC algorithm increases fair throughput by 5.62%, 26.57% and fair index by 1.99%, 13.82% compared to the MATD3 and MADDPG algorithms, respectively. Meanwhile, UAFC can also meet energy consumption limitation and network connectivity requirement.

Pumps and valves are primary power and control devices in water supply piping systems. A collaborative operational scheme is very important for a series pump-valve system to decrease the transient pressure during the startup process. In order to analyze the influence of the operational time differences between the pump and the valve on the transient process, a complicated pump system was numerically simulated using the method of characteristics (MOC). The boundary conditions of the pump and the valve were separately established by equating an auxiliary element in the discrete mesh. The transient pressure, pump speed, and flow were studied for various time differences and the valve opening process for the series pump-valve startup process. Furthermore, an optimal collaborative scheme was presented to prevent inverse rotation and overpressure during the startup process. The results show that a reasonable time-lapse and fast opening can prevent the backward flow and reverse rotation, as well as control the transient maximal pressure during the system startup process. [ABSTRACT FROM AUTHOR]

Background: The studies of miRNAs are vibrant and remain at the forefront in the cardiovascular system. Emerging studies indicate that the genetic polymorphisms of the miRNA gene may affect lipid metabolism; this study aims to clarify the specific correlations between the rs2910164 and rs3746444 polymorphisms and lipid levels.Methods and Results: A comprehensive search of literature was performed from December 31, 2020, to May 31, 2021, by searching of the PubMed and the Cochrane databases. The standardized mean difference (SMD) and 95% confidence interval (CI) were used to evaluate the differences in lipid levels between the genotypes. rs2910164, a functional polymorphism in the miRNA-146a gene, was associated with increased triglycerides (TG) (SMD = 0.35, 95% CI = 0.15–0.54, p < 0.001), total cholesterol (TC) (SMD = 0.43, 95% CI = 0.16–0.70, p < 0.001), and low-density lipoprotein cholesterol (LDL-C) (SMD = 0.37, 95% CI = 0.11–0.63, p = 0.01) as well as decreased high-density lipoprotein cholesterol (HDL-C) (SMD = −0.27, 95% CI = −0.47−0.07, p = 0.01) levels. rs3746444, a functional polymorphism in the miRNA-499a gene, was only correlated with decreased TG (SMD = −0.09, 95% CI = −0.17−0.01, P = 0.03) levels.Conclusions: The miRNA-146a rs2910164 polymorphism is significantly associated with atherogenic dyslipidemia.

In recent years, there have been many deep learning research projects based on two-dimensional object detection and three-dimensional point cloud recognition. However, relatively few of these combine the two, and the number of projects based on a three-dimensional workpiece recognition method for the industrial field is even fewer. In this paper, to perform the recognition task for polishing workpieces in manufacturing, we use RGB-D images as input, and propose our designed PolishNet-2d for two-dimensional workpiece detection and our designed PolishNet-3d for three-dimensional workpiece recognition. The two deep neural networks are employed together in series to first detect and then recognize polishing workpieces in an industrial environment. For this paper, a large number of experiments have been carried out on deep learning datasets of polishing workpieces. These datasets were created by us to contain diverse combinations of real and simulated workpieces in real and simulated industrial environments. The contributions of this paper are as follows: (1) A rotation parameter learning network is proposed and a two-dimensional workpiece detection neural network, named PolishNet-2d, which was constructed by integrating our designed algorithms with the backbone networks ResNet101 and region proposal network (RPN), is introduced; (2) A hierarchical feature extraction network is proposed and a three-dimensional workpiece recognition neural network, named PolishNet-3d, which was constructed by integrating our designed algorithms with the backbone network PointNet, is introduced; (3) PolishNet-2d and PolishNet-3d are employed in series, with the detection output of PolishNet-2d being used as the input for PolishNet-3d for its recognition tasks: the workpiece regions are detected in the RGB image; the workpiece point cloud is segmented in the corresponding regions in the depth image, and lastly the segmented point cloud is placed into PolishNet-3d to identify the workpiece; (4) For the experiments in this paper, datasets containing rich and diverse data types of polishing workpieces for industrial fields have been constructed; (5) Numerous experimental results on polishing workpiece datasets show that the conjunction of PolishNet-2d and PolishNet-3d can achieve exemplary recognition results on polishing workpiece datasets.

We study the performance of a cooperative vehicular communication system in a highway traffic scenario, where the locations of co-channel interfering vehicles are modeled by a one-dimensional Poisson point process (PPP). Wireless channel modeling campaigns have shown that the statistical patterns of vehicle-to-vehicle (V2V) channels can often be modeled by the Weibull distribution. Due to the complex characteristics of random fading and interference, system performance analysis is involved. To address this issue, we establish a framework for performance analysis in vehicular networks under Weibull fading and one-dimensional Poisson field of interference, where the Weibull probability density function (PDF) is approximated by a finite exponential mixture. By this means, the approximation expressions of the successful/unsuccessful message transmission probabilities for both direct V2V communication and the three-node cooperative vehicular communication are derived through stochastic geometry. Monte-Carlo simulations verify the accuracy of our derivation, as well as the advantages of encouraging cooperation among vehicles. Our methods and results can potentially be used to facilitate stochastic geometric analysis in many other complex vehicular networks under Weibull fading.

Molecular communication is a novel inter-disciplinary communication methodology at the nanoscale, which uses chemical or biological molecules as the information carriers. For many prospective molecular communication applications, the clock synchronization is a major issue. However, the existing solutions use the molecule releasing time for the clock synchronization schemes but ignore the molecule synthesizing time, which is not practical. To overcome this issue, in this paper, we propose a reference broadcast synchronization scheme. One nanomachine sends a broadcast beacon and the other two nanomachines records their receiving times. The receiving times are exchanged by that two nanomachines, then, the clocks between these two nanomachines can be synchronized. Owing to the fact that the information molecules propagate slowly with a large propagation delay, which also depends on the transmitter-receiver distance, so a delay estimation method is adopted in the synchronization scheme. The simulation results evaluate proposed synchronization scheme and show that the proposed scheme outperforms other clock synchronization schemes for molecular communication.

Background: Viral infection causes multiple forms of human cancer, and HPV infection is the primary factor in cervical carcinomas. Recent single-cell RNA-seq studies highlight the tumor heterogeneity present in most cancers, but virally induced tumors have not been studied. HeLa is a well characterized HPV+ cervical cancer cell line. Result: We developed a new high throughput platform to prepare single-cell RNA on a nanoliter scale based on a customized microwell chip. Using this method, we successfully amplified full-length transcripts of 669 single HeLa S3 cells and 40 of them were randomly selected to perform single-cell RNA sequencing. Based on these data, we obtained a comprehensive understanding of the heterogeneity of HeLa S3 cells in gene expression, alternative splicing and fusions. Furthermore, we identified a high diversity of HPV-18 expression and splicing at the single-cell level. By co-expression analysis we identified 283 E6, E7 co-regulated genes, including CDC25, PCNA, PLK4, BUB1B and IRF1 known to interact with HPV viral proteins. Conclusion: Our results reveal the heterogeneity of a virus-infected cell line. It not only provides a transcriptome characterization of HeLa S3 cells at the single cell level, but is a demonstration of the power of single cell RNA-seq analysis of virally infected cells and cancers. [ABSTRACT FROM AUTHOR]

Background: Single-cell resequencing (SCRS) provides many biomedical advances in variations detection at the single-cell level, but it currently relies on whole genome amplification (WGA). Three methods are commonly used for WGA: multiple displacement amplification (MDA), degenerate-oligonucleotide-primed PCR (DOP-PCR) and multiple annealing and looping-based amplification cycles (MALBAC). However, a comprehensive comparison of variations detection performance between these WGA methods has not yet been performed. Results: We systematically compared the advantages and disadvantages of different WGA methods, focusing particularly on variations detection. Low-coverage whole-genome sequencing revealed that DOP-PCR had the highest duplication ratio, but an even read distribution and the best reproducibility and accuracy for detection of copy-number variations (CNVs). However, MDA had significantly higher genome recovery sensitivity (~84 %) than DOP-PCR (~6 %) and MALBAC (~52 %) at high sequencing depth. MALBAC and MDA had comparable single-nucleotide variations detection efficiency, false-positive ratio, and allele drop-out ratio. We further demonstrated that SCRS data amplified by either MDA or MALBAC from a gastric cancer cell line could accurately detect gastric cancer CNVs with comparable sensitivity and specificity, including amplifications of 12p11.22 (KRAS) and 9p24.1 (JAK2, CD274, and PDCD1LG2). Conclusions: Our findings provide a comprehensive comparison of variations detection performance using SCRS amplified by different WGA methods. It will guide researchers to determine which WGA method is best suited to individual experimental needs at single-cell level. [ABSTRACT FROM AUTHOR]