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Abstract Tumour-host immune interactions lead to complex changes in the tumour microenvironment (TME), impacting progression, metastasis and response to therapy. While it is clear that cancer cells can have the capacity to alter immune landscapes, our understanding of this process is incomplete. Herein we show that endocytic trafficking at the plasma membrane, mediated by the small GTPase ARF6, enables melanoma cells to impose an immunosuppressive TME that accelerates tumour development. This ARF6-dependent TME is vulnerable to immune checkpoint blockade therapy (ICB) but in murine melanoma, loss of Arf6 causes resistance to ICB. Likewise, downregulation of ARF6 in patient tumours correlates with inferior overall survival after ICB. Mechanistically, these phenotypes are at least partially explained by ARF6-dependent recycling, which controls plasma membrane density of the interferon-gamma receptor. Collectively, our findings reveal the importance of endomembrane trafficking in outfitting tumour cells with the ability to shape their immune microenvironment and respond to immunotherapy.

Sex reversal and sex ratio bias are common in mollusks, and temperature is one of the most important environmental factors influencing gonadal development and differentiation. Here, we obtained the full-lengh cDNAs of Hsdmrt1 and Hsfoxl2 and found that they were predominantly expressed in the gonads. Furthermore, Hsdmrt1 and Hsfoxl2 showed a sexually dimorphic expression pattern, with Hsdmrt1 biased toward males and Hsfolx2 favoring females. The expression of Hsfoxl2 was inhibited by Hsdmrt1. The sex ratios of 1–3-year-old mussels H. schlegelii were assessed, and the sex ratio bias was from male to female, accompanied by a significant decrease in log10 (Hsdmrt1/Hsfoxl2). And log10(Hsdmrt1/Hsfoxl2) value outside the interval [-2,1] was considered a stable sexual state. The sex reversal in H. schlegelii was reproduced by simulating temperatures, in which male mussels at 27 °C underwent sex reversal to become females; females at 19°C underwent sex reversal to males, and the log10(Hsdmrt1/Hsfoxl2) value was altered to be in the [-2,1] interval. These results support the hypothesis that an imbalance in the expression of either gene leads to a sex reversal and that this may be regulated by temperature. This study deepens our understanding of the mechanisms of sex reversal and gonadal development in bivalves.

Abstract Objective Previous studies proved the effect of long-term exposure to air pollution or physical activity (PA) on the risk of systemic inflammation-induced multimorbidity (SIIM), while the evidence regarding their joint effects was rare, especially in low- and middle-income countries. Therefore, we aimed to examine the extent of interaction or joint relations of PA and air pollution with SIIM. Methods This study included 72,172 participants from China Multi-Ethnic Cohort.The average concentrations of ambient particulate matter pollutants (PM1, PM2.5, and PM10) were estimated using satellite-based random forest models. Self-reported information on a range of physical activities related to occupation, housework, commuting, and leisure activities was collected by an interviewer-administered questionnaire. A total of 11 chronic inflammatory systemic diseases were assessed based on self-reported lifetime diagnosis or medical examinations. SIIM was defined as having ≥ 2 chronic diseases related to systemic inflammation. Logistic regression models were used to assess the complex associations of air pollution particulate matter and PA with SIIM. Results We found positive associations between long-term air pollution particulates exposure and SIIM, with odds ratios (95%CI) of 1.07 (1.03 to 1.11), 1.18 (1.13 to 1.24), and 1.08 (1.05 to 1.12) per 10 µg/m3 increase in PM1, PM2.5, and PM10. No significant multiplicative interaction was found between ambient air pollutant exposure and PA on SIIM, whereas negative additive interaction was observed between long-term exposure to PM2.5 and PA on SIIM. The positive associations between low volume PA and SIIM were stronger among those exposed to high-level air pollution particulates. Compared with individuals engaged in high volume PA and exposed to low-level ambient air pollutants, those engaged in low volume PA and exposed to high-level ambient air pollutants had a higher risk of SIIM (OR = 1.49 in PM1 exposure, OR = 1.84 in PM2.5 exposure, OR = 1.19 in PM10 exposure). Conclusions Long-term (3 years average) exposure to PM1, PM2.5, and PM10 was associated with an increased risk of SIIM. The associations were modified by PA, highlighting PA’s importance in reducing SIIM for all people, especially those living in high-level air pollution regions.

Objective Herein, we investigated whether mesenchymal stem cells (MSCs) transplantation combined with electroacupuncture (EA) treatment could decrease the proportion of proinflammatory microglia/macrophages and neurotoxic A1 reactive astrocytes and inhibit glial scar formation to enhance axonal regeneration after spinal cord injury (SCI). Methods Adult rats were divided into 5 groups after complete transection of the spinal cord at the T10 level: a control group, a nonacupoint EA (NA-EA) group, an EA group, an MSC group, and an MSCs+EA group. Immunofluorescence labeling, quantitative real-time polymerase chain reaction, enzyme-linked immunosorbent assay, and Western blots were performed. Results The results showed that MSCs+EA treatment reduced the proportion of proinflammatory M1 subtype microglia/macrophages, but increased the differentiation of anti-inflammatory M2 phenotype cells, thereby suppressing the mRNA and protein expression of proinflammatory cytokines (tumor necrosis factor-α and IL-1β) and increasing the expression of an anti-inflammatory cytokine (interleukin [IL]-10) on days 7 and 14 after SCI. The changes in expression correlated with the attenuated neurotoxic A1 reactive astrocytes and glial scar, which in turn facilitated the axonal regeneration of the injured spinal cord. In vitro, the proinflammatory cytokines increased the level of proliferation of astrocytes and increased the expression levels of C3, glial fibrillary acidic protein, and chondroitin sulfate proteoglycan. These effects were blocked by administering inhibitors of ErbB1 and signal transducer and activator of transcription 3 (STAT3) (AG1478 and AG490) and IL-10. Conclusion These findings showed that MSCs+EA treatment synergistically regulated the microglia/macrophage subpopulation to reduce inflammation, the formation of neurotoxic A1 astrocytes, and glial scars. This was achieved by downregulating the ErbB1-STAT3 signal pathway, thereby providing a favorable microenvironment conducive to axonal regeneration after SCI.

Background The health effects of particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) might differ depending on compositional variations. Little is known about the joint effect of PM2.5 constituents on metabolic syndrome and cardiovascular disease (CVD). This study aims to evaluate the combined associations of PM2.5 components with CVD, identify the most detrimental constituent, and further quantify the mediation effect of metabolic syndrome. Methods and Results A total of 14 427 adults were included in a cohort study in Sichuan, China, and were followed to obtain the diagnosis of CVD until 2021. Metabolic syndrome was defined by the simultaneous occurrence of multiple metabolic disorders measured at baseline. The concentrations of PM2.5 chemical constituents within a 1‐km2 grid were derived based on satellite‐ and ground‐based detection methods. Cox proportional hazard models showed that black carbon, organic matter (OM), nitrate, ammonium, chloride, and sulfate were positively associated with CVD risks, with hazard ratios (HRs) ranging from 1.24 to 2.11 (all P

Traditional industrial development models have already caused an imbalance in ecosystem services supply and demand (ESSD). As a response strategy, industrial transformation and upgrading (ITU) are increasingly playing a vital role in regulating the relationship among ESSD. However, research on the interplay and impact of these two aspects remains insufficient. Therefore, a comprehensive analysis of the complex effects of ITU on ESSD is essential for industrial planning and environmental management. We developed a theoretical framework to interpret how ITU impacts ESSD, from the perspective of the natural-social-economic composite ecosystem. Using the Yangtze River Economic Belt (YREB) as an example, we employed the Dynamic Spatial Durbin model (DSDM) to reveal the spatiotemporal effects of ITU on ESSD for four distinct types. The results indicated that from 2010 to 2021, the total supply of ecosystem services in the YREB exceeded the demand. The kernel density curves of ESSD ratios related to food production, carbon sequestration, water conservation, and soil conservation all show a trend of peak shifting to the bottom right over time. Additionally, pronounced spatial heterogeneity was observed in the ESSD ratios, with a notable clustering trend over time. Furthermore, the analysis of spatiotemporal effects decomposition revealed that ITU had a detrimental influence on the equilibrium between the supply and demand of food production, with this adverse effect intensifying and manifesting a stronger spatial spillover as time progressed. Positive effects were observed in the supply and demand for carbon sequestration and water conservation, both demonstrating significant spatial spillover effects. Steady negative spatiotemporal effects were noted for soil conservation supply and demand. Finally, we clarified the principal pathways through which ITU impacts ESSD. It offers policy recommendations for industrial development and ecosystem stewardship to mitigate the ESSD imbalance.

The cut-ins (one kind of lane-changing behaviors) have result in severe safety issues, especially at the entrances and exits of urban expressways. Risk prediction and characteristics analysis of cut-ins are part of the essential research for advanced in-vehicle technologies which can reduce crash occurrences. This paper makes some efforts on these purposes. In this paper, twenty-four participants were recruited to conduct the experiments of multi-driver simulation for risky driving data collection. The surrogate measures, Time Exposure Time-to-Collision (TET) and Time Integrated Time-to-collision (TIT) were employed to quantify the risk of cut-ins, then k-means clustering was applied for risk classification of 3 levels. Multiple candidate variables of two kinds were extracted including 10 behavioral variables and 7 driver trait variables. Based on these variables, three prediction models including decision tree (DT), gradient boosting decision tree (GBDT) and long short-term memory (LSTM) are used for predicting the risks of cut-ins. Results from data validity verification show that the data collected from multi-driver simulation experiments is valid compared with real-world data. From results of risk prediction models, the LSTM, with an overall accuracy of 87%, outperforms the GBDT (80.67%) and DT (76.9%). Despite this, this paper also concludes the merits of the DT over the GBDT and LSTM in variable explanation and the results of DT suggest that controlling the proper lane-changing gap and short duration of cut-ins can help reduce risks of cut-ins.

The morphology size of laser cladding is a crucial parameter that significantly impacts the quality and performance of the cladding layer. This study proposes a predictive model for the cladding morphology size based on the Least Squares Support Vector Regression (LSSVR) and the Crowned Porcupine Optimization (CPO) algorithm. Specifically, the proposed model takes three key parameters as inputs: laser power, scanning speed, and powder feeding rate, with the width and height of the cladding layer as outputs. To further enhance the predictive accuracy of the LSSVR model, a CPO-based optimization strategy is applied to adjust the penalty factor and kernel parameters. Consequently, the CPO-LSSVR model is established and evaluated against the LSSVR model and the Genetic Algorithm-optimized Backpropagation Neural Network (GA-BP) model in terms of relative error metrics. The experimental results demonstrate that the CPO-LSSVR model can achieve a significantly improved relative error of no more than 2.5%, indicating a substantial enhancement in predictive accuracy compared to other methods and showcasing its superior predictive performance. The high accuracy of the CPO-LSSVR model can effectively guide the selection of laser cladding process parameters and thereby enhance the quality and efficiency of the cladding process.

The semantic segmentation of the 3D operating environment represents the key to intelligent mining shovels’ autonomous digging and loading operation. However, the complexity of the operating environment of intelligent mining shovels presents challenges, including the variety of scene targets and the uneven number of samples. This results in low accuracy of 3D semantic segmentation and reduces the autonomous operation accuracy of the intelligent mine shovels. To solve these issues, this paper proposes a 3D point cloud semantic segmentation network based on memory enhancement and lightweight attention mechanisms. This model addresses the challenges of an uneven number of sampled scene targets, insufficient extraction of key features to reduce the semantic segmentation accuracy, and an insufficient lightweight level of the model to reduce deployment capability. Firstly, we investigate the memory enhancement learning mechanism, establishing a memory module for key semantic features of the targets. Furthermore, we address the issue of forgetting non-dominant target point cloud features caused by the unbalanced number of samples and enhance the semantic segmentation accuracy. Subsequently, the channel attention mechanism is studied. An attention module based on the statistical characteristics of the channel is established. The adequacy of the expression of the key features is improved by adjusting the weights of the features. This is done in order to improve the accuracy of semantic segmentation further. Finally, the lightweight mechanism is studied by adopting the deep separable convolution instead of conventional convolution to reduce the number of model parameters. Experiments demonstrate that the proposed method can improve the accuracy of semantic segmentation in the 3D scene and reduce the model’s complexity. Semantic segmentation accuracy is improved by 7.15% on average compared with the experimental control methods, which contributes to the improvement of autonomous operation accuracy and safety of intelligent mining shovels.

Laser-arc hybrid additive manufacturing (LAHAM) holds substantial potential in industrial applications, yet ensuring dimensional accuracy remains a major challenge. Accurate prediction and effective control of the geometrical dimensions of the deposited layers are crucial for achieving this accuracy. The width and height of the deposited layers, key indicators of geometric dimensions, directly affect the forming precision. This study conducted experiments and in-depth analysis to investigate the influence of various process parameters on these dimensions and proposed a predictive model for accurate forecasting. It was found that the width of the deposited layers was positively correlated with laser power and arc current and negatively correlated with scanning speed, while the height was negatively correlated with laser power and scanning speed and positively with arc current. Quantitative analysis using the Taguchi method revealed that the arc current had the most significant impact on the dimensions of the deposited layers, followed by scanning speed, with laser power having the least effect. A predictive model based on extreme gradient boosting (XGBoost) was developed and optimized using particle swarm optimization (PSO) for tuning the number of leaf nodes, learning rate, and regularization coefficients, resulting in the PSO-XGBoost model. Compared to models enhanced with PSO-optimized support vector regression (SVR) and XGBoost, the PSO-XGBoost model exhibited higher accuracy, the smallest relative error, and performed better in terms of Mean Relative Error (MRE), Mean Square Error (MSE), and Coefficient of Determination R2 metrics. The high predictive accuracy and minimal error variability of the PSO-XGBoost model demonstrate its effectiveness in capturing the complex nonlinear relationships between process parameters and layer dimensions. This study provides valuable insights for controlling the geometric dimensions of the deposited layers in LAHAM.

Molecular mechanisms behind potentially inferior prognosis of old cholangiocarcinoma (CCA) patients are unclear. Prevalence of interventional targets and the difference between young and old CCA patients are valuable for promising precision medicine. A total of 188 CCA patients with baseline tumor tissue samples were subgrouped into the young (≤45 years) and old (>45 years) sub-cohorts. Somatic and germline mutation profiles, differentially enriched genetic alterations, and actionable genetic alterations were compared. An external dataset was used for the validation of molecular features and the comparison of overall survival (OS). Compared to young patients, KRAS alterations were more common in old patients (P = .04), while FGFR2 fusions were less frequent (P = .05). TERT promoter mutations were exclusively detected in old patients. The external dataset (N = 392) revealed no significant difference in OS between young and old patients; however, old patient-enriched KRAS (hazard ratio [HR]: 1.96, 95% confidence interval [CI]: 1.37–2.80) and TERT alterations (HR: 2.03, 95% CI: 1.22–3.38) were associated with inferior OS. Approximately 38.3% of patients were identified of actionable oncogenic mutations indicative of a potential response to targeted therapy or immunotherapy. Actionable FGFR2 fusions (P = .01) and BRAFV600E (P = .04) mutations were more frequent in young females than old patients. The enrichment of KRAS/TERT alterations in CCA patients over 45 years resulted in inferior OS. Approximately one-third of CCA patients were eligible for targeted therapy or immunotherapy given the actionable mutations carried, especially young females.

Accurately predicting tunnel boring machine (TBM) performance is beneficial for excavation efficiency enhancement and risk mitigation of TBM tunneling. In this paper, we develop a long short-term memory (LSTM) based hybrid intelligent model to predict two key TBM performance parameters (advance rate and cutterhead torque). The model combines the LSTM, BN, Dropout and Dense layers to process the raw data and improve the fitting quality. The features, including the ground formation properties, tunnel route curvature, tunnel location and TBM operational parameters, are divided into historical/real-time time-varying parameters, time-invariant parameters and historical/real-time output prediction data. The effectiveness of the proposed model is verified based on a large monitoring database of the Baimang River Tunnel Project in Shenzhen, south China. We then discuss the influence of the prediction mode, neural network structure and time division interval length of historical data on the prediction accuracy. The significance evaluation of input features shows that the historical output prediction has the largest influence on the prediction accuracy, and the influence of ground properties is secondary. It is also found that the correlations between input features and the output prediction are coincident with their interrelationships with the ground properties and ease of TBM excavation. Finally, it is found that the prediction results are most affected by the total propulsion force followed by the rotation speed of the cutterhead. The established model can provide useful guidance for construction personnel to roughly grasp the possible TBM status from the prediction results when adjusting the operational parameters.

In this paper, a temperature measurement system with NTC (Negative Temperature Coefficient) thermistors was designed. An MCU (Micro Control Unit) primarily operates by converting the voltage value collected by an ADC (Analog-to-Digital Converter) into the resistance value. The temperature value is then calculated, and a DAC (Digital-to-Analog Converter) outputs a current of 4 to 20 mA that is linearly related to the temperature value. The nonlinear characteristics of NTC thermistors pose a challenging problem. The nonlinear characteristics of NTC thermistors were to a great extent solved by using a resistance ratio model. The high precision of the NTC thermistor is obtained by fitting it with the Hoge equation. The results of actual measurements suggest that each module works properly, and the temperature measurement accuracy of 0.067 °C in the range from −40 °C to 120 °C has been achieved. The uncertainty of the output current is analyzed and calculated with the uncertainty of 0.0014 mA. This type of system has broad potential applications in industry fields such as the petrochemical industry.

In semiconductor manufacturing, defect inspection in non-patterned wafer production lines is essential to ensure high-quality integrated circuits. However, in actual production lines, achieving both high efficiency and high sensitivity at the same time is a significant challenge due to their mutual constraints. To achieve a reasonable trade-off between detection efficiency and sensitivity, this paper integrates the time delay integration (TDI) technology into dark-field microscopy. The TDI image sensor is utilized instead of a photomultiplier tube to realize multi-point simultaneous scanning. Experiments illustrate that the increase in the number of TDI stages and reduction in the column fixed pattern noise effectively improve the signal-to-noise ratio of particle defects without sacrificing the detecting efficiency.

This paper presented a real-time millimeter wave radar-based system for tracking vehicle trajectories in a wide area (continuously along a roadway with essentially no length limit in practice). The trajectory tracking results were first validated for single vehicle trajectory tracking using the Real-Time Kinematic positioning technology based on the Beidou satellite navigation systems. The validation showed that the vehicle positions were captured with a mean lateral offset of −0.284 m and a mean longitudinal offset of −0.352 m. The mean estimated speeds were found to have a difference of only −0.048 km/h from the ground truths. The trajectory tracking results were also validated through multi-object tracking using video data from an unmanned aerial vehicle (UAV). Compared with the UAV video footage, the millimeter wave-based system was found to correctly capture around 92% of the total number of vehicles. For the correctly captured vehicles, their positions were found to be within 0.99 m (about a quarter of the width of a regular traffic lane) of the ground truth. In this paper, we also would like to share openly the entire validated datasets which has been recently published online as the TJRD TS platform. Additionally, a demonstration in using the dataset to detect aggressive driving behaviors, such as speeding was presented. It is expected that the open dataset will help enable researchers and practitioners to further explore the behaviors of road users, track the dynamics of safety risks and congestion formation, and evaluate incident impacts in a more microscopic but comprehensive way.

Bats have a very long evolutionary history and are highly differentiated in their physiological functions. Results of recent studies suggest effects of some host factors (e.g., phylogeny and dietary habit) on their gut microbiota. In this study, we examined the gut microbial compositions of 18 different species of bats. Results showed that Firmicutes, Gammaproteobacteria, and Actinobacteria were dominant in all fecal samples of bats. However, the difference in the diversity of gut microbiota among bats of different phylogenies was notable (p = 0.06). Various species of Firmicutes, Actinobacteria, and Gammaproteobacteria were found to contribute to the majority of variations in gut microbiota of all bats examined, and Aeromonas species were much more abundant in bats that feed on both insects and fish than in those of insectivores. The abundance of various species of Clostridium, Euryarchaeota, and ancient bacterial phyla was found to vary among bats of different phylogenies, and various species of Vibrio varied significantly among bats with different dietary habits. No significant difference in the number of genes involved in various metabolic pathways was detected among bats of different phylogenies, but the abundance of genes involved in 5 metabolic pathways, including transcription; replication, recombination, and repair; amino acid transport and metabolism; and signal transduction mechanisms, was different among bats with different dietary habits. The abundance of genes in 3 metabolic pathways, including those involved in stilbenoid, diarylheptanoid, and gingerol biosynthesis, was found to be different between insectivorous bats and bats that feed on both insects and fish. Results of this study suggest a weak association between dietary habit and gut microbiota in most bats but a notable difference in gut microbiota among bats of different phylogenies.

Safety of drivers in freeway work zones has been a problem. Real-time crash prediction helps prevent crashes before they happen. This paper looks at real-time crash prediction in freeway work zones by using machine learning approaches. Both the Convolutional Neural Network and the Binary Logistic Regression model are introduced. For training and testing the models, crash data and traffic data from several freeways in D7 zone, Los Angeles, California, were used. Crash data were collected from California Highway Patrol Incident System, and traffic data were obtained from the Caltrans Performance Measurement System. Data processing and matching were conducted. Both the two models were trained and tested. Results show that the Convolutional Neural Network performed slightly better over the Binary Logistic Regression model in predicting crashes with a global accuracy of 79.50%. Despite this, the main merit of the Binary Logistic Regression model is that it is able estimate the impact of affecting variables on the probability of crashes and can help identify the factors related to risks in work zones. Machine learning approaches applied in this study perform well in crash prediction. In general, machine learning techniques and reliable real-time crash prediction applications can be promising in helping drivers and transportation engineers make timely responses to potential crashes on freeways.

The temperature of the molten pool in Laser Solid Forming has a direct effect on the dimensional accuracy and mechanical properties of the parts. Accurate prediction of the melt pool temperature is important to ensure the stability of the melt pool temperature and to improve the forming accuracy and quality of the part. In order to accurately predict the melt pool temperature, this study proposes a melt pool temperature prediction method based on particle swarm optimization (PSO) optimised long short-term memory neural network (LSTM). Using IR camera to obtain melt pool temperature data and establish long short-term memory neural network melt pool temperature prediction model based on experimental data. Optimization of the initial learning rate and the number of hidden layer units of the long short-term memory neural network model using the particle swarm optimization algorithm to build a PSO-LSTM model for prediction of melt pool temperature. The results show that the PSO-LSTM prediction model outperforms the long short-term memory neural network and Ridge Regression models in all evaluation indicators and can achieve accurate prediction of melt pool temperature.

Effectively reducing the inflammatory response after spinal cord injury (SCI) is a challenging clinical problem and the subject of active investigation. This study employed a porous scaffold-based three dimensional long-term culture technique to obtain human umbilical cord mesenchymal stem cell (hUC-MSC)-derived Small Extracellular Vesicles (sEVs) (three dimensional culture over time, the “4D-sEVs”). Moreover, the vesicle size, number, and inner protein concentrations of the MSC 4D-sEVs contained altered protein profiles compared with those derived from 2D culture conditions. A proteomics analysis suggested broad changes, especially significant upregulation of Epidermal Growth Factors Receptor (EGFR) and Insulin-like Growth Factor Binding Protein 2 (IGFBP2) in 4D-sEVs compared with 2D-sEVs. The endocytosis of 4D-sEVs allowed for the binding of EGFR and IGFBP2, leading to downstream STAT3 phosphorylation and IL-10 secretion and effective induction of macrophages/microglia polarization from the pro-inflammatory M1 to anti-inflammatory M2 phenotype, both in vitro and in the injured areas of rats with compressive/contusive SCI. The reduction in neuroinflammation after 4D-sEVs delivery to the injury site epicenter led to significant neuroprotection, as evidenced by the number of surviving spinal neurons. Therefore, applying this novel 4D culture-derived Small Extracellular Vesicles could effectively curb the inflammatory response and increase tissue repair after SCI.

Abstract Background Farmers are the integral members of rural communities. In the present study, we determined the association between occupational physical activity (OPA) of farmers and dyslipidaemia. Methods We included 7649 farmers from The China Multi-Ethnic Cohort (CMEC) Study. The working modes of all farmers were divided into four types according to their self-reported seasonal changes in farming work and/or other job changes (1: no change; 2: changing job; 3: seasonal changes; and 4: seasonal and job changes). OPA was self-reported, and the OPA levels in the four groups were classified as Q1, Q2–Q3, and Q4 by quantile. Dyslipidaemia was defined as the presence of abnormalities in lipid indicators. Binary logistic regression was used to estimate the association between OPA and dyslipidaemia. Results Compared with those in the no change group, the participants in other three groups were younger with lower level of education, annual income, and leisure-time physical activity (LTPA). Active OPA could reduce the risk of dyslipidaemia in the no change [men: odds ratios (OR) = 0.21, 95% confidence intervals (CI): 0.07–0.64; women: OR = 0.43, 95% CI: 0.20–0.93] and seasonal change (men: OR = 0.46, 95% CI: 0.27–0.77; women: OR = 0.59, 95% CI: 0.41–0.86) groups. However, in the seasonal and job change group (men: OR = 3.23, 95% CI: 1.06–9.80; women: OR = 3.24, 95% CI: 1.42–7.41), active OPA increased the risk of dyslipidaemia. Conclusions Different OPA levels might lead to differences in association with blood lipid levels. Thus, OPA guidelines must be developed for farmers, especially for those who experience seasonal changes in farming work and job changes.

Vehicle trajectory data is in high demand for transportation research due to its rich detail. Lane information is an important aspect of trajectory data, which is typically obtained using sensors such as cameras or LiDAR, which are able to extract road lane features. However, some sensors for trajectory tracking (e.g., MMW radar sensors) are unable to provide lane information. Vehicle detection and trajectory tracking systems based on these sensing technologies can integrate with lane information through manual calibration during initial installation, but this process is labor-intensive and requires frequent recalibration as the sensors gradually become deviated by wind and vibration. This has posed a challenge for trajectory tracking, particularly for real-time applications. To address this challenge, this paper proposes a method for estimating lane-level road geometrics using microscopic trajectory data. The method involves segmenting the trajectory points using direction vectors and clustering them and fitting a series of cluster center points. The mean error (ME) of the distance between the estimated result and the ground truth reference is used to measure the accuracy of the lane-level road geometrics estimation in different conditions. Results show that when the average trajectory data includes at least approximately 30 points per meter in each segment, the ME is always less than 0.1 m. The method has also been tested on MMW wave radar data and found to be effective. This demonstrates the feasibility of our approach for dynamic calibration of road alignment in vehicle trajectory tracking systems.

Previous studies on pedestrian crossing have mostly focused on pedestrian crossing decisions; while as an important behavioral aspect, the pedestrian crossing process, i.e., their motions during the entire crossing process, has been narrowly studied. Understanding how pedestrian moves across the street during their entire crossing process helps identify risky movements and reasons for such movements, which can further help in the implementation of effective countermeasures. Therefore, this paper proposed a new and easily applied approach for investigating and understanding the pattern of the pedestrian crossing process at crosswalks based on vision-based trajectory tracking technology and UAV (unmanned aerial vehicle) data. This study uses UAV for collecting video data which is timesaving and has a sufficient coverage area, compared to other methods. For trajectory extraction, the vision-based Deep-SORT-Yolov5 architecture is applied. An improved DBSCAN (density-based spatial clustering of applications with noise) algorithm is introduced for clustering and identification of patterns of pedestrian crossing processes based on their trajectories. This approach is tested via a case study involving six marked crosswalks in Shanghai, China. By using the proposed method, different crossing patterns are extracted and compared. The results show reasonable outputs of trajectory patterns, which reasonably explain the potential instincts of the pedestrians and affecting factors on the behavior of the pedestrian crossing process. Suggestions are made based on the results. This paper contributes to a more comprehensive safety analysis of pedestrian crossings by considering the pedestrian crossing process. The model, along with the UAV-based trajectory observation method, provides an easily-applied and low-cost way of traffic data collection for the purpose of pedestrian safety evaluation.

Salmonella, one of the major causes of foodborne infections, can cause bacterial foodborne illness. We investigated the serotype distribution, multidrug resistance (MDR), and β-lactamase resistance genes of human Salmonella isolates collected from clinical specimens in Guizhou, China, between 2013 and 2018. A total of 363 Salmonella isolates were collected from clinical specimens at 17 surveillance hospitals. Twenty-four serotypes were identified by sliding agglutination test. S. Enteritidis (33.9%), Salmonella 4,[5],12:i:- (24.0%), S. Typhimurium (16.3%), S. London (6.3%), and S. Derby (3.9%) were the top five serotypes. In 2018, the most common serotype changed from S. Enteritidis to S. Typhimurium. Among the 363 Salmonella isolates, 97.5% of isolates were resistant to at least one class of antimicrobial agents. For cephalosporins, ceftriaxone had the highest resistance rate of 10.5%, and cefepime and cefoxitin were 8.0% and 2.2%, respectively. Three hundred and one (82.9%) Salmonella isolates showed MDR. Salmonella 4,[5],12:i:- had the highest MDR rate with 94.2%, followed by S. London (91.3%) and S. Typhimurium (88.1%). Multidrug resistance rates of Salmonella isolates in Guizhou from 2013 to 2017 increased from 75.8% to 86.7%. Sixteen isolates (4.4%) showed extensive drug resistance. One hundred thirty-four antimicrobial resistance patterns were found. Two hundred and forty-one (66.4%) isolates carried at least one β-lactamase resistance gene. The blaTEM gene (61.2%) was the most prevalent resistant gene in all Salmonella isolates, followed by the blaCTX-M gene (6.1%) and blaOXA-1 gene (4.1%). Our findings showed that the MDR rate of Salmonella isolates from Guizhou province increased year by year. Therefore, systematic and long-term surveillance on MDR Salmonella isolates from clinical patients should be further strengthened.

Evidence shows that chaperone-mediated autophagy (CMA) is involved in cancer cell pathogenesis and progression. However, the potential role of CMA in breast cancer angiogenesis remains unknown. We first manipulated CMA activity by knockdown and overexpressing of lysosome-associated membrane protein type 2A (LAMP2A) in MDA-MB-231, MDA-MB-436, T47D and MCF7 cells. We found that the tube formation, migration and proliferation abilities of human umbilical vein endothelial cells (HUVECs) were inhibited after cocultured with tumor-conditioned medium from breast cancer cells of LAMP2A knockdown. While the above changes were promoted after cocultured with tumor-conditioned medium from breast cancer cells of LAMP2A overexpression. Moreover, we found that CMA could promote VEGFA expression in breast cancer cells and in xenograft model through upregulating lactate production. Finally, we found that lactate regulation in breast cancer cells is hexokinase 2 (HK2) dependent, and knockdown of HK2 can significantly reduce the ability of CMA-mediated tube formation capacity of HUVECs. Collectively, these results indicate that CMA could promote breast cancer angiogenesis via regulation of HK2-dependent aerobic glycolysis, which may serve as an attractive target for breast cancer therapies.

Abstract Here we report a breakthrough in the fabrication of a long lifetime transmon qubit. We use tantalum films as the base superconductor. By using a dry etching process, we obtained transmon qubits with a best T 1 lifetime of 503 μs. As a comparison, we also fabricated transmon qubits with other popular materials, including niobium and aluminum, under the same design and fabrication processes. After characterizing their coherence properties, we found that qubits prepared with tantalum films have the best performance. Since the dry etching process is stable and highly anisotropic, it is much more suitable for fabricating complex scalable quantum circuits, when compared to wet etching. As a result, the current breakthrough indicates that the dry etching process of tantalum film is a promising approach to fabricate medium- or large-scale superconducting quantum circuits with a much longer lifetime, meeting the requirements for building practical quantum computers.

An improper Z-increment in laser solid forming can result in fluctuations in the off-focus amount during the manufacturing procedure, thereby exerting an influence on the precision and quality of the fabricated component. To solve this problem, this study proposes a closed-loop control system for a Z-increment based on machine vision monitoring. Real-time monitoring of the precise cladding height is accomplished by constructing a paraxial monitoring system, utilizing edge detection technology and an inverse perspective transformation model. This system enables the continuous assessment of the cladding height, which serves as a control signal for the regulation of the Z-increments in real-time. This ensures the maintenance of a constant off-focus amount throughout the manufacturing process. The experimental findings indicate that the proposed approach yields a maximum relative error of 1.664% in determining the cladding layer height, thereby enabling accurate detection of this parameter. Moreover, the real-time adjustment of the Z-increment quantities results in reduced standard deviations of individual cladding layer heights, and the height of the cladding layer increases. This proactive adjustment significantly enhances the stability of the manufacturing process and improves the utilization of powder material. This study can, therefore, provide effective guidance for process control and product optimization in laser solid forming.

Existing diagnosis methods for bearing faults often neglect the temporal correlation of signals, resulting in easy loss of crucial information. Moreover, these methods struggle to adapt to complex working conditions for bearing fault feature extraction. To address these issues, this paper proposes an intelligent diagnosis method for compound faults in metro traction motor bearings. This method combines multisignal fusion, Markov transition field (MTF), and an optimized deep residual network (ResNet) to enhance the accuracy and effectiveness of diagnosis in the presence of complex working conditions. At the outset, the acquired vibration and acoustic emission signals are encoded into two-dimensional color feature images with temporal relevance by Markov transition field. Subsequently, the image features are extracted and fused into a set of comprehensive feature images with the aid of the image fusion framework based on a convolutional neural network (IFCNN). Afterwards, samples representing different fault types are presented as inputs to the optimized ResNet model during the training phase. Through this process, the model’s ability to achieve intelligent diagnosis of compound faults in variable working conditions is realized. The results of the experimental analysis verify that the proposed method can effectively extract comprehensive fault features while working in complex conditions, enhancing the efficiency of the detection process and achieving a high accuracy rate for the diagnosis of compound faults.

Abstract Men who have sex with men (MSM) are potentially at high risk for HIV infection, their HIV prevalence far exceeds the national prevalence rate. There are also a number of MSM who have sex with women, mostly unprotected, which can transmit HIV to their female sexual partners and even to the next generation. The purpose of this article is to evaluate the prevalence of heterosexual behaviors in Chinese men who have sex with men and the status of condom utilization as well as HIV infection among MSM with heterosexual behaviors, so as to further improve the investigation and prevention and control of AIDS in MSM population. We systematically searched China National Knowledge Infrastructure, Wanfang Data, VIP Database for Chinese Technical Calligraphy (VIP), Pubmed, and Embase following certain retrieval strategies to find relevant articles published from January 1, 2015 to November 18, 2019, The useful information extracted from qualified articles, Stata 15.1 and Review Manager 5.3 were employed for further meta analysis. The estimated prevalence of heterosexual behaviors among MSM in the past year was 19.0% (95% CI 17.0%, 22.0%). The estimated condom utilization rate of the last heterosexual behavior among MSM and condom adherence rate of heterosexual behaviors among MSM were 51.0% (95% CI 44.0%, 58.0%) and 31.0% (95% CI 25.0%, 38.0%), respectively. And the pooled HIV prevalence in MSM with heterosexual behaviors was 9% (95% CI 6%, 13%). The sensitivity analysis showed that the results were stable. No publication bias was found by Egger’s test. There is a high proportion of MSM with heterosexual behaviors and low condom utilization in China. HIV prevalence in MSM with heterosexual behaviors is also high. Therefore, adequate attention should be given to this particular group and measures should be taken in order to reduce the risk of transmission of HIV from subpopulations to the general population.

Purpose – Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps. Design/methodology/approach – This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing. Findings – Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction. Research limitations/implications – The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations. Practical implications – The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications. Originality/value – This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

Road matching is the prerequisite for integrating different data to obtain a complete, correct road network. The growth of multi-source data calls for the advancement of matching methods. There are many methods of road matching, ranging from traditional methods using threshold or weighted judgment to methods based on statistical and optimization models. This article provides an optimal matching method by relaxation to min-cost network flow. The matching problem is considered an optimization problem, i.e., finding a plan to match features in two datasets to minimize the total difference. The optimization-based matching method can overcome traditional method’s suboptimal nature. Referencing more road information than the other methods make our method superior in its flexibility and applicability to different matching relationships. Additionally, the iterative relaxation by angle, flow, and shortest path-based constraints is introduced to reduce ambiguous matching. We demonstrate the model in real-world experiments. Our method achieves a better result than the other four methods: it has a more stable performance for matching pairs of more complicated correspondence relations and easily confused situations. The average matching precision, recall, and f-score were 0.985 in two test sites.

BackgroundStudies that address the changing characteristics of diseases are of great importance for preventing and controlling the occurrence and development of diseases and for improving health. However, studies of the epidemiological characteristics of noncommunicable diseases (NCDs) and malignant tumors (MTs) of the residents in Guiyang, China, are lacking. ObjectiveThe aim of this study was to evaluate the prevalences of NCDs and MTs in residents of Guiyang, Guizhou Province, China, and analyze differences among ages, genders, and regions. MethodsA multistage stratified cluster sampling method was used. Based on the inclusion and exclusion criteria, 81,517 individuals were selected for the study. Of these, 77,381 (94.9%) participants completed the study. Structured questionnaires were used to collect information on demographic characteristics, NCDs, and MTs. The chi-square test (with 95% confidence intervals) was used to analyze differences in disease prevalence among genders, ages, and geographical regions. ResultsThe major chronic NCDs of Guiyang residents are obesity, hypertension, and diabetes. MTs in women are mostly breast cancer, cervical cancer, and endometrial cancer, whereas in men, MTs are mainly lung cancer, rectal cancer, and gastric cancer. The prevalences of hypertension and diabetes in women are higher than in men, but the prevalences of lung cancer and gastric cancer in men are higher than in women. The epidemiological characteristics of individuals in different life stages are dissimilar. In terms of regional distribution, the prevalences of the above diseases in the Baiyun and Yunyan districts of Guiyang are relatively high. ConclusionsSeveral NCDs (obesity, hypertension, and diabetes) and MTs (women: breast cancer, cervical cancer, and endometrial cancer; men: lung cancer, rectal cancer, and gastric cancer) should be the focus for the prevention and control of chronic diseases in the future. In particular, the Baiyun and Yunyan districts of Guiyang are the important regions to emphasize.

To study the cell proliferation and wound healing activity of polysaccharides from Ganoderma amboinense (GAMPS), the polysaccharide was extracted by water extraction and alcohol precipitation method, and its monosaccharide composition and molecular weight were analyzed. The effects of different concentrations of GAMPS on the cell proliferation were determined by cell survival rate test, and the wound healing ability of GAMPS to NIH/3T3 cells was detected. The preliminary evaluation of the antioxidant ability of GAMPS was conducted by the oxygen radical absorbance capacity (ORAC). The results showed that the GAMPS was composed of glucose, mannose, and galactose at a molar ratio of 67.62:14.07:7.50, and the weight-average molecular weights were 5.439 × 106 and 1.704 × 105 g/mol by using high-performance gel-permeation chromatography-multiple angle laser scatter (HPGPC-MALS) analysis. GAMPS (0.2 μg/μL) showed the strongest proliferation ability to THP-1 cells, with cell survival rate of 178.7%. The wound healing effect of GAMPS (0.1 μg/μL) was obvious on NIH/3T3 and 3.75 μg/μL of GAMPS showed the strongest total antioxidant ability. All the results indicate that GAMPS promotes cell proliferation, and has cell wound healing effect and strong antioxidant activity. The results provide theoretical foundation for the development and utilization of GAMPS.

Polystyrene microplastics (PS-MPs, particle size

Car-following (CF) behavior is one of the most important driving behaviors. Accurately understanding and modeling CF behavior is essential for traffic flow simulation and user-acceptable advanced driving assistance systems (ADASs). In previous decades, CF models were calibrated based on drivers or trajectories, with short-term changes ignored. Recent studies have indicated that these changes could be caused by occasional irritations or regular switches of driving modes, but there is still a lack of specific understanding of driving modes and how these modes affect simulation accuracy in the reproduction of CF behavior. This paper explored the existence of driving modes and the quantified modeling influence of driving modes. Specifically, we first extracted 4000 high-resolution CF events of 40 drivers from large-scale naturalistic driving data for the discovery of underlying driving modes. Then, we introduced a novel multivariate time series method, Toeplitz Inverse Covariance-based Clustering (TICC), to achieve the segmentation and classification extraction of different driving modes. Finally, calibrated by the CF dataset, the proper cluster number of the driving mode was determined, and a comparison of driving-mode-based modeling (DMBM) and driver-based modeling (DBM) was conducted. The results showed that the driving process could be viewed as five core driving modes, and the DMBM has the potential to bring upwards of a 13% accuracy improvement with fewer parameters.

A water lubricating axial piston pump (WLPP) is the core power component of a green and environmentally friendly water hydraulic system. The friction and wear of the friction pairs of a WLPP are the key factors that restrict its development. In order to explore the friction and wear mechanism of materials, the tribological properties of CFRPEEK against 316L and 1Cr17Ni2 under water lubrication were investigated in a friction testing machine and an axial piston pump, respectively. An environmental scanning electron microscope (ESEM), confocal laser scanning microscopy and a surface profiler were used to analyze the morphology of the samples. In a friction testing machine, two different metals are paired with CFRPEEK, and the friction coefficient and wear rate barely show any differences. The wear rate of CFRPEEK is two orders of magnitude higher than that of metal. In the WLPP, 316L can hardly be paired with CFRPEEK, while 1Cr17Ni2 works well. The wear of 1Cr17Ni2 in the WLPP is greater than that of CFRPEEK. The high-pressure water film lubrication friction pairs cause the wear of the metal and show the difference in these two test methods. The wear mechanism is mainly abrasive wear. Improving the wear resistance of metals is very important for the development of WLPP.

Abstract Background Cardiovascular risk factors in Chinese ethnic minority groups are rarely reported. Objective To quantify the cardiovascular risk factors in Miao Chinese adults and to examine the association of health behaviors and metabolic risk factors with dyslipidemia. Methods A cross-sectional analysis was conducted using baseline data from the China Multi-Ethnic Cohort (CMEC) study. A representative sample of 5559 Miao participants aged 30 to 79 years were surveyed and given physical and laboratory exams. The proportion of behavioral and metabolic risk factors were described in ethnic Miao adults. Logistic regression was utilized to evaluate the odds ratio (OR) and 95% confidence interval (CI) of the association between health behaviors and metabolic risk factors with dyslipidemia. Results In Miao Chinese adults, the prevalence of dyslipidemia was 32.8%. After multivariate adjustment, subjects with poor waist-to-hip ratio (WHR), body mass index (BMI), fasting blood glucose (FBG) and blood pressure (BP) were more likely to have higher risk of triglycerides (TG) abnormality, regardless of gender and age. Furthermore, the strongly association was detected between poor WHR and low density lipoprotein cholesterol (LDL-C) abnormality (adjusted OR = 5.24, 95%CI: 2.42–11.34) in the older subgroup (≥ 60 years). Males who current smoking were an independent risk factor only for high density lipoprotein cholesterol (HDL-C) abnormality (adjusted OR = 1.44, 95%CI: 1.05–1.99). However, in the subgroup age, current smoker were at greater risk of high TG and low HDL-C. Males with regular drinking were less likely to be high LDL-C (adjusted OR = 0.51, 95%CI: 0.32–0.81). Conclusions The present findings indicated that Miao adults with metabolic risk factors were at greater risk of dyslipidemia.

Abstract Background Commercial xylose purification produces xylose mother liquor (XML) as a major byproduct, which has become an inexpensive and abundant carbon source. A portion of this XML has been used to produce low-value-added products such as caramel but the remainder often ends up as an organic pollutant. This has become an issue of industrial concern. In this study, a uracil-deficient Candida tropicalis strain was engineered to efficiently convert XML to the commercially useful product xylitol. Results The xylitol dehydrogenase gene was deleted to block the conversion of xylitol to xylulose. Then, an NADPH regeneration system was added through heterologous expression of the Yarrowia lipolytica genes encoding 6-phosphate-gluconic acid dehydrogenase and 6-phosphate-glucose dehydrogenase. After process optimization, the engineered strain, C. tropicalis XZX-B4ZG, produced 97.10 g L− 1 xylitol in 120 h from 300 g L− 1 XML in a 5-L fermenter. The xylitol production rate was 0.82 g L− 1 h− 1 and the conversion rate was 92.40 %. Conclusions In conclusion, this study performed a combination of metabolic engineering and process optimizing in C. tropicalis to enhance xylitol production from XML. The use of C. tropicalis XZX-B4ZG, therefore, provided a convenient method to transform the industrial by-product XML into the useful material xylitol.

Soil arbuscular mycorrhizal (AM) fungi play crucial roles in facilitating agroecosystem functioning. However, little is known about their performances in either community composition or ecological function as affected by fertilization with different carbon (C) input levels. Here, an eight-year field experiment established in a sandy loam soil at Northern China, including four treatments of non-fertilization (Control), and NPK-balanced fertilization with no C (F), with relatively low C input level (LCF), and with relatively high C input level (HCF), was adopted to assess soil AM fungal community composition using the Illumina sequencing. Ten genera with the dominance of Glomus followed by Paraglomus and Claroideoglomus were identified, and the communities varied significantly between control and fertilized soils. Long-term fertilization, notably LCF and HCF, increased soil AM fungal diversity, alkaline phosphatase (ALP) activity, and glomalin-related soil protein (GRSP) concentration, which were closely correlated to soil organic C and/or pH. In addition, LCF rather than HCF significantly increased both Chao1 and Shannon indices of AM fungi relative to F, while HCF rather than LCF significantly decreased not only the mycorrhizal colonization relative to control but also the ratio of total GRSP to soil organic C relative to F, suggesting that LCF is better with respect to promoting AM fungal diversity and maintaining their ecological vitality. Our study demonstrated the great value of moderate C input in conserving soil AM fungi and exploring their services.

The identification of spacial noise correlation is of critical importance in developing error-corrected quantum devices, but it has barely been studied so far. In this work, we utilize an effective method called qubit motion, to efficiently determine the noise correlations between any pair of qubits in a 7-qubit superconducting quantum system. The noise correlations between the same pairs of qubits are also investigated when the qubits are at distinct operating frequencies. What’s more, in this multi-qubit system with the presence of noise correlations, we demonstrate the enhancing effect of qubit motion on the coherence of logic qubits, and we propose a Motion-CPMG operation sequence to more efficiently protect the logic state from decoherence, which is experimentally demonstrated to extend the coherence time of logic qubits by nearly one order of magnitude.

For the problem of Panchromatic and multispectral remote sensing image fusion, we propose a remote sensing image fusion algorithm based on nonnegative dictionary learning. The basic idea of the algorithm is to use the panchromatic image with high spatial resolution to learn the high-low resolution dictionary pair, and to improve the fusion effect of remote sensing image by combining the nonnegativity of the image. Firstly, high resolution dictionary and low resolution dictionary are learned from high spatial resolution panchromatic image by nonnegative dictionary learning technology. Then multispectral image is sparsely represented by low resolution dictionary to obtain coefficient matrix. Finally, using coefficient matrix and high resolution dictionary, high resolution multispectral image is reconstructed. Compared with state-of-the-art methods, the proposed algorithm can get high spatial resolution and well preserve spectral information of multispectral image. Our experimental results of real QUICKBIRD and IKONOS remote sensing image fusion validate the effectiveness of the proposed algorithm.

Atmospheric nitrogen deposition has large eco-environmental effects such as ocean acidification, eutrophication in coastal areas. However, knowledge of the source and the pathway of N deposition in coastal areas is limited, especially during tropical storms, hindering the accurate quantification of how anthropogenic activities influence the ocean ecosystem. In this study, the Nested Air Quality Prediction Modeling System was used to investigate the wet deposition of N induced by typhoon Hagupit over eastern coastal China from an in- and below-cloud process perspective. Our results reveal for the first time an enhancement mechanism of N deposition related to the ‘pumping effect’ of the typhoon. Different from the non-typhoon conditions, air pollutants in the typhoon-affected regions were pumped into the higher altitudes and deposited via the in-cloud scavenging process in the moving path of the typhoon-affected regions. This study updates our understanding of the source–receptor relationship on atmospheric wet deposition caused by tropical cyclones.

With the aim of investigating the cladding geometry characteristics by a wide-band fiber laser with coaxial rectangular nozzle, and optimizing the powder efficiency and deposition speed for economy efficiency, Fe-based alloy powder was deposited on AISI 1045 substrate by a 3000 W fiber laser in this study. Laser power (P), scan speed (V), and powder feed rate (F) were selected for a factorial design. The effects of the three process parameters on the geometry characteristics and economic efficiency of single tracks were statistically analyzed, and a linear regression model was established between the combined parameters and the relevant characteristics (including track height, ratio of track width to height, powder efficiency, and deposition speed). A process map was developed with the track shape and key economic indexes as boundaries. A flat-top feature of the track profile was found and can be utilized to achieve good cladding evenness. The process map showed that the powder efficiency and deposition speed were higher than 50% and 20 mm3/s, respectively, when selecting process parameters in the as-built operation window.

Nontyphoidal Salmonella is a bacterial and foodborne pathogen that poses a severe public health threat. However, the genetic diversity of different serogroups of nontyphoidal Salmonella in Guizhou is unknown. This study aimed to obtain the RNA secondary structure of the typical direct repeat sequences, the characteristics of clustered regularly interspaced short palindromic repeats (CRISPR) genotypes, and the genetic diversity of different serogroups of nontyphoidal Salmonella strains. The 342 nontyphoidal Salmonella strains were collected from nine cities (prefectures) of Guizhou province during 2013–2018, serotyped by slide agglutination, and examined the molecular genotypes by CRISPR method. The strains were divided into five serogroups. The dominant serogroup was group B (47.08%), followed by group D1 (36.55%). One hundred and thirty-five CRISPR genotypes were detected with 108 novel spacer sequences amongst 981 unique spacer sequences. The diversity of nontyphoidal Salmonella CRISPR loci was not only the deletion, duplication, or point mutation of spacer sequences but also the acquisition of new spacer sequences to form novel genotypes. The CRISPR genotyping was an effective typing method that could reveal the genetic diversity of different nontyphoidal Salmonella serotypes except for S. Enteritidis.

A hydrofoil is a fundamental structure in fluid machinery, and it is widely applied to the fields of propellers, blades of axial flow pumps and underwater machinery. To reveal that the geometric structure of the leading-edge of a hydrofoil is the mechanism that affects the transient cavitating flow, we regard the three fish-type leading-edge structures of mackerel, sturgeon and small yellow croaker as the research objects and use high-precision non-contact 3D scanners to establish three bionic hydrofoils (Mac./Stu./Cro.). We use large eddy simulation to simulate the transient cavitating flow of hydrofoils numerically and compare and analyze their lift–drag characteristics, the transient behavior of unsteady cavitation and the vortex evolution. The numerical simulation results are in good agreement with the experimental results. The warping of leading-edge structure will cause a change in lift–drag characteristics, and the Cro. hydrofoil has a good lift-to-drag ratio. When the leading-edge structure is tilted upward (Cro. hydrofoil), the position of the attached cavity will move forward, which will accelerate the cavitation evolution and improve the velocity fluctuation of the trailing edge. When the leading-edge structure is tilted downward (Stu. hydrofoil), the change in the vortex stretching and dilatation terms will be complex, and the influence area of the vortex will widen.

BackgroundEchinococcus multilocularis causes alveolar echinococcosis (AE), a rising zoonotic disease in the northern hemisphere. Treatment of this fatal disease is limited to chemotherapy using benzimidazoles and surgical intervention, with frequent disease recurrence in cases without radical surgery. Elucidating the molecular mechanisms underlying E. multilocularis infections and host-parasite interactions ultimately aids developing novel therapeutic options. This study explored an involvement of unfolded protein response (UPR) and endoplasmic reticulum-stress (ERS) during E. multilocularis infection in mice.MethodsE. multilocularis- and mock-infected C57BL/6 mice were subdivided into vehicle, albendazole (ABZ) and anti-programmed death ligand 1 (αPD-L1) treated groups. To mimic a chronic infection, treatments of mice started six weeks post i.p. infection and continued for another eight weeks. Liver tissue was then collected to examine inflammatory cytokines and the expression of UPR- and ERS-related genes.ResultsE. multilocularis infection led to an upregulation of UPR- and ERS-related proteins in the liver, including ATF6, CHOP, GRP78, ERp72, H6PD and calreticulin, whilst PERK and its target eIF2α were not affected, and IRE1α and ATF4 were downregulated. ABZ treatment in E. multilocularis infected mice reversed, or at least tended to reverse, these protein expression changes to levels seen in mock-infected mice. Furthermore, ABZ treatment reversed the elevated levels of interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α and interferon (IFN)-γ in the liver of infected mice. Similar to ABZ, αPD-L1 immune-treatment tended to reverse the increased CHOP and decreased ATF4 and IRE1α expression levels.Conclusions and significanceAE caused chronic inflammation, UPR activation and ERS in mice. The E. multilocularis-induced inflammation and consecutive ERS was ameliorated by ABZ and αPD-L1 treatment, indicating their effectiveness to inhibit parasite proliferation and downregulate its activity status. Neither ABZ nor αPD-L1 themselves affected UPR in control mice. Further research is needed to elucidate the link between inflammation, UPR and ERS, and if these pathways offer potential for improved therapies of patients with AE.

Tailored countermeasures that may significantly improve road traffic safety can be proposed and implemented if the relationship between various associated factors and aggressive driving is well understood. However, this relationship remains unknown, as driving behavior is complex, and the interrelationships among variables are not easy to identify. Considering this situation, this paper constructed a model based on a structural equation model (SEM) and factor analysis (FA), which is a multivariate statistical analysis technique used to analyze structural relationships. The model is applied in a case study using data from the Shanghai Naturalistic Driving Study. In the case study, 16 variables were grouped into five latent factors in the SEM, and the model fits the data well. Compared with other variables, the results show that age had the most significant positive impact on aggressive driving behavior (older drivers exhibited high aggressive driving frequency). Adverse weather negatively impacted driver behavior (lower speed and high longitude acceleration), which in turn negatively affected aggressive driving behavior. In addition, the results show that driver factors (such as age and sex) were the main factors influencing vehicle use (such as hard acceleration), and the environment was the main factor determining risky scenarios, where safety-critical situations increase. This paper provides a reference for defining and determining aggressive driving and a model for exploring the relationship between driving safety factors and aggressive driving, which can be used in real-world applications for improving driving safety with applications in advanced driver-assistance (ADAS) and traffic enforcement safety control systems.

With the high development of sports, football has attracted more and more attention from the public. However, the hot competition has made football players undergo high-intensity training, and the risk of injury caused by training is also increasing. Lateral incision knee ACL injury is one of the most common types of injuries in football players, which has a serious impact on the athlete’s physiology and daily training. The comprehensive, multi-level, convenient and fast medical system brought by the medical Internet of Things has become the growing demand of the medical industry. Based on the medical Internet of Things, this paper studies the rehabilitation nursing of football players’ ACL injury by lateral cutting and running combined with optical microscope. In this paper, 36 male and female football players were selected for group experiments, and the landing and peak torque indexes of the experimental group and the control group of male and female athletes were analyzed under the observation of an optical microscope. Group (P

Salmonella enterica serovar 4,[5],12:i:-, a monophasic variant of Salmonella Typhimurium lacking the phase 2 flagellin, is one of the common serotypes causing Salmonellosis worldwide. However, information on Salmonella serovar 4,[5],12:i:- from Guizhou Province has lacked so far. This study aimed to investigate the antimicrobial resistance, the presence of antimicrobial resistance genes and virulence genes, and characterize the MLST genotypes of Salmonella serovar 4,[5],12:i:- isolates from Guizhou province, China. We collected 363 non-typhoid Salmonella (NTS) isolates of Guizhou from 2013 to 2018. Biochemical identification, serogroups testing, and specific multiplex polymerase chain reaction (mPCR) assay were conducted to identify Salmonella 4,[5],12:i:- isolates. Isolates were determined the antimicrobial resistance by the micro broth dilution method, detected the presence of antimicrobial resistance genes and virulence genes by PCR, and examined the molecular genotyping by Multilocus sequence typing (MLST). Eighty-seven Salmonella 4,[5],12:i:- isolates were detected, accounting for 23.9% (87/363) of the total NTS isolates. All Salmonella 4,[5],12:i:- isolates showed highly resistant to sulfaoxazole (93.1%), streptomycin (90.8%), ampicillin (88.5%), tetracycline (86.2%) and doxycycline (86.2%). A high proportion (94.2%) of multi-drug resistance (MDR) isolates were found. Most (83.9%) Salmonella 4,[5],12:i:- isolates carried four antimicrobial resistance genes, especially blaTEM-1, strA-strB, sul2, and tetB genes. Salmonella 4,[5],12:i:- isolates showed a high rate of invA, sseL, mgtC, siiE, sopB, gipA, gtgB, sspH1, and sspH2 (72.4%~98.9%). On the contrary, none of the isolates were detected the spvC and pefA genes. MLST analysis revealed three sequence types (STs), and ST34 (97.7%) was the dominant sequence type. This study is the first report of Salmonella 4,[5],12:i:- in humans from Guizhou province, China. The data might be useful for rational antimicrobial usage against Salmonella 4,[5],12:i:- infections, risk management, and public health strategies in Guizhou.

Our previous study indicated that intravenous vitamin C (IVC) treatment concurrent with modulated electrohyperthermia (mEHT) was safe and improved the quality of life (QoL) of non-small-cell lung cancer (NSCLC) patients. The aim of this trial was to further verify the efficacy of the above combination therapy in previously treated patients with refractory advanced (stage IIIb or IV) NSCLC. A total of 97 patients were randomized to receive IVC and mEHT plus best supportive care (BSC) (n = 49 in the active arm, receiving 1 g/kg * d IVC concurrently with mEHT, three times a week for 25 treatments in total) or BSC alone (n = 48 in the control arm). After a median follow-up of 24 months, progression-free survival (PFS) and overall survival (OS) were significantly prolonged by combination therapy compared to BSC alone (PFS: 3 months vs 1.85 months, P