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Objective: To explore the molecular mechanism of γ-glutamylcysteine (γ-GC) in response to inflammation in vivo and in vitro on regulating the polarization of macrophages. Methods: The expressions of gene or protein were assessed by qPCR and Western blot assays, respectively. Cell viability was investigated by CCK-8 assay. Eight-week-old male BALB/c mice were established to examine the therapeutic effects of γ-GC in vivo. The release of TNF-α and IL-4 was determined by ELISA assay. Macrophages polarization was identified by flow cytometry assay. Results: Our data showed that γ-GC treatment significantly improved the survival, weight loss, and colon tissue damage of IBD mice. Furthermore, we established M1- and M2-polarized macrophages, respectively, and our findings provided evidence that γ-GC switched M1/M2-polarized macrophages through activating AMPK/SIRT1 axis and inhibiting inflammation-related signaling pathway. Conclusion: Collectively, both in vivo and in vitro experiments suggested that γ-GC has the potential to become a promising novel therapeutic dipeptide for the treatment of IBD, which provide new ideas for the treatment of inflammatory diseases in the future. [ABSTRACT FROM AUTHOR]

Vessel big data play a significant role in understanding vessel behaviors and thus facilitating the prosperity of waterway transportation. However, relevant research regarding vessel trajectory recognition in a broad range of narrow channels still lacks, especially using VITS data. The major objective of this paper is to conduct vessel trajectory analysis based on the novel VITS data and examine its availability in inland waterway vessel transportation. An alternate aim is to develop a more comprehensive framework to extract the vessel trajectory of multiple narrow waterways. This paper utilized vessel trajectory information of multiple narrow channels belonging to Yangtze River captured by VITS. Four compression algorithms were conducted. Additionally, the performances of three clustering approaches were evaluated. Speed distribution analysis was also implemented. The results indicated that slide window (SW) algorithm outperforms its other counterparts. Relative to DBSCAN, K-means and hierarchical clustering analysis (HCA) tend to be more capable of balanced classification. This paper is the first to utilize VITS data in vessel trajectory feature extraction and can potentially provide useful insight for vessel trajectory extraction in multiple narrow channels. [ABSTRACT FROM AUTHOR]

Road traffic accidents regarding commercial vehicles have been demonstrated as an important culprit restricting the steady development of the social economy, which are closely related to the distracted behavior of drivers. However, the existing driver's distracted behavior surveillance systems for monitoring and preventing the distracted behavior of drivers still have some shortcomings such as fewer recognition objects and scenarios. This study aims to provide a more comprehensive methodological framework to demonstrate the significance of enlarging the recognition objects, scenarios and types of the existing driver's distracted behavior recognition systems. The driver's posture characteristics were primarily analyzed to provide the basis of the subsequent modeling. Five CNN sub-models were established for different posture categories and to improve the efficiency of recognition, accompanied by a holistic multi-cascaded CNN framework. To suggest the best model, image data sets of commercial vehicle driver postures including 117,410 daytime images and 60,480 night images were trained and tested. The findings demonstrate that compared to the non-cascaded models, both daytime and night cascaded models show better performance. Besides, the night models exhibit worse accuracy and better speed relative to their daytime model counterparts for both non-cascaded and cascaded models. This study could be used to develop countermeasures to improve driver safety and provide helpful information for the design of the driver's real-time monitoring and warning system as well as the automatic driving system. Future research could be implemented to combine the vehicle state parameters with the driver's microscopic behavior to establish a more comprehensive proactive surveillance system. [ABSTRACT FROM AUTHOR]

The clinical management of anaplastic thyroid carcinoma and follicular thyroid carcinoma is challenging and requires an alternative therapeutic strategy. Although atovaquone is an FDA-approved anti-malarial drug, studies has recently demonstrated its anti-cancer activities. In line with these efforts, our study shows that atovaquone is an attractive candidate for thyroid cancer treatment. We show that atovaquone significantly inhibits growth, migration and survival in a concentration-dependent manner in 8505C and FTC113 cells. Mechanistically, atovaquone inhibits mitochondrial complex III activity, leading to mitochondrial respiration inhibition and reduction of ATP production in thyroid cancer cells. The inhibitory effects of atovaquone is reversed in mitochondrial respiration-deficient 8505C ρ0 cells, confirming mitochondrial respiration as the mechanism of atovaquone’s action in thyroid cancer. In addition, atovaquone suppresses phosphorylation of STAT3 in thyroid cancer wildype but not ρ0 cells, demonstrating that STAT3 phosphorylation inhibition by atovaquone is a consequence of mitochondrial respiration inhibition. Notably, we further demonstrate that atovaquone significantly augments doxorubicin’s inhibitory effects via suppressing mitochondrial respiration and STAT3. Our findings suggest that atovaquone can be repurposed for thyroid cancer treatment. Our work also highlights that targeting mitochondrial respiration may represent potential therapeutic strategy in thyroid cancer. [ABSTRACT FROM AUTHOR]

Many convolutional neural network (CNN) based approaches for skin cancer classification primarily rely on dermatological images, yielding commendable results in classification accuracy. However, leveraging patient metadata, a crucial source of clinical information for dermatologists, can further enhance accuracy. Current methodologies predominantly employ basic joint fusion structures (FS) and fusion modules (FMs) for multi-modal classification, leaving room for advancement in enhancing accuracy through exploration of more sophisticated FS and FM architectures. Thus, this paper introduces a novel fusion method that integrates dermatological images (dermoscopy images or clinical images) with patient metadata for skin cancer classification, focusing on enhancing FS and FM components. Initially, we propose a joint-individual fusion (JIF) structure that simultaneously learns shared features across multi-modality data while preserving specific characteristics. Subsequently, we introduce a multi-modal fusion attention (MMFA) module designed to amplify the most relevant image and metadata features through a combination of self and mutual attention mechanisms, thereby bolstering the decision-making pipeline. Our study compares the efficacy of the proposed JIF-MMFA method with other state-of-the-art fusion techniques across three distinct public datasets. Results demonstrate that the JIF-MMFA method consistently enhances classification outcomes across various CNN backbones, outperforming alternative fusion methodologies on all three datasets. These findings underscore the effectiveness and robustness of our proposed approach in skin cancer classification. • A novel multi-modal fusion network of dermatological images and patient's metadata is proposed. • Joint-individual fusion (JIF) structure is proposed to learn shared and specific features. • Multi-modal fusion attention (MMFA) module is proposed to integrate image and metadata features. • The proposed JIF-MMFA method outperforms other state-of-the-art fusion methods on public datasets. [ABSTRACT FROM AUTHOR]

This article performed a series of parallel experiments with numerical modeling to reveal key factors affecting the gas adsorption capacity of shale, including shale quality, gas composition and geological conditions. Adsorption experiments for shales with similar OM types and maturities indicate that the OM is the core carrier for natural gas in shale, while the clay mineral has limited effect. The N 2 and CO 2 adsorption results indicate pores less than 3 nm in diameter are the major contributors to the specific surface area for shale, accounting for 80% of the total. In addition, micropores less than 2 nm in diameter are generated in large numbers during the thermal evolution of organic matter, which substantially increases the specific surface area and adsorption capacity. Competitive adsorption experiments prove that shale absorbs more CO 2 than CH 4 , which implies that injection CO 2 could enhance the CH 4 recovery, and further research into N 2 adsorption competitiveness is needed. The Langmuir model simulations indicate the shale gas adsorption occurs via monolayers. Geologically applying the adsorption potential model indicates that the adsorption capacity of shale initially increases before decreasing with increasing depth due to the combined temperature and pressure, which differs from the changing storage capacity pattern for free gases that gradually increase with increasing depth at a constant porosity. These two tendencies cause a mutual conversion between absorbed and free gas that favors shale gas preservation. During the thermal evolution of organic matter, hydrophilic NSO functional groups gradually degrade, reduce the shale humidity and increase the gas adsorption capacity. The shale quality, gas composition and geological conditions all affect the adsorption capacity. Of these factors, the clay minerals and humidity are less important and easily overshadowed by the other factors, such as organic matter abundance. [ABSTRACT FROM AUTHOR]

• A new multi-stage multi-modal learning algorithm for multi-label skin lesion classification. • First stage enables fusion of clinical and dermoscopy images at both feature-level and decision-level. • Second stage effectively integrate the information from image-modality and patients metadata. • Our method can achieve state-of-the-art performance for multi-label skin lesion classification. Skin disease is one of the most common diseases in the world. Deep learning-based methods have achieved excellent skin lesion recognition performance, most of which are based on only dermoscopy images. In recent works that use multi-modality data (patient's meta-data, clinical images, and dermoscopy images), the methods adopt a one-stage fusion approach and only optimize the information fusion at the feature level. These methods do not use information fusion at the decision level and thus cannot fully use the data of all modalities. This work proposes a novel two-stage multi-modal learning algorithm (FusionM4Net) for multi-label skin diseases classification. At the first stage, we construct a FusionNet, which exploits and integrates the representation of clinical and dermoscopy images at the feature level, and then uses a Fusion Scheme 1 to conduct the information fusion at the decision level. At the second stage, to further incorporate the patient's meta-data, we propose a Fusion Scheme 2, which integrates the multi-label predictive information from the first stage and patient's meta-data information to train an SVM cluster. The final diagnosis is formed by the fusion of the predictions from the first and second stages. Our algorithm was evaluated on the seven-point checklist dataset, a well-established multi-modality multi-label skin disease dataset. Without using the patient's meta-data, the proposed FusionM4Net's first stage (FusionM4Net-FS) achieved an average accuracy of 75.7% for multi-classification tasks and 74.9% for diagnostic tasks, which is more accurate than other state-of-the-art methods. By further fusing the patient's meta-data at FusionM4Net's second stage (FusionM4Net-SS), the entire FusionM4Net finally boosts the average accuracy to 77.0% and the diagnostic accuracy to 78.5%, which indicates its robust and excellent classification performance on the label-imbalanced dataset. The corresponding code is available at: https://github.com/pixixiaonaogou/MLSDR. [ABSTRACT FROM AUTHOR]

Indoprofen is a non-steroidal anti-inflammatory drug, and has provided insights into treatment of spinal muscular atrophies; however, the treatment effect of indoprofen on sepsis and the precise underlying mechanism remain to be elucidated. This study was carried out to examine the inhibitory effect of indoprofen on high mobility group box 1 (HMGB1)-mediated inflammatory responses in vivo and in vitro. Intraperitoneal injection of indoprofen (20 or 40 mg/kg) at 8 h post-sepsis markedly improved the survival of BALB/c mice and ameliorated multiple-organ injury by blocking the inflammatory responses. In addition, indoprofen partially reduced the HMGB1 level in the serum and in the lung, as well as ameliorated pulmonary edema. Mechanistically, indoprofen potently inhibited the release of HMGB1 following stimulation by lipopolysaccharide (LPS) or polyinosinic:polycytidylic acid (poly I:C), and suppressed recombinant human HMGB1(rhHMGB1)-induced inflammatory responses. It was also found that indoprofen has both cyclooxygenase 2-dependent and -independent inhibitory effects on the proinflammatory effect of HMGB1 in THP-1 cells. Further, the drug reduced rhHMGB1-induced cell surface levels of toll-like receptor 2, toll-like receptor 4, and receptor of advanced glycation end-products in a concentration-dependent manner. Collectively, these data demonstrated that the anti-inflammatory effect of indoprofen in sepsis was associated with HMGB1-mediated inflammatory responses, thus offering a favorable mechanistic basis to support the therapeutic potential of indoprofen for the treatment of lethal sepsis or other inflammatory diseases. [Display omitted] • Indoprofen improves survival and ameliorates multiple-organ injury in septic mice. • Indoprofen attenuates extracellular release and proinflammatory effect of HMGB1. • Indoprofen has a COX2-independent manner on blocking rhHMGB1-induced signal pathways. • Indoprofen reduces rhHMGB1-induced cell surface levels of TLR2/4 and RAGE. [ABSTRACT FROM AUTHOR]

• Build single-vehicle crash severity prediction models with different tree-based and other non-parametric models. • Identified the intrinsic mechanism of how contributing factors determine single-vehicle crash severity. • Utilized Grid-Search method to obtain the best performances based on five crash severity sub-datasets. • Compared the performances of different non-parametric models with training, cross-validation and test datasets. Single-vehicle crashes are more fatality-concentrated and have posed increasing challenges in traffic safety, which is of great research necessity. Tremendous previous studies have conducted relevant analysis with econometric modeling approaches, whereas the ability of non-parametric methods to predict crash severity is still smattering of knowledge. Consequently, the main objective of this paper is to conduct single-vehicle crash severity prediction with different tree-based and non-parameter models. An alternate aim is to identify the intrinsic mechanism of how contributing factors determine single-vehicle crash severity. By virtue of Grid-Search method, this paper conducted fine-tuning of different models to obtain the best performances based on five crash severity sub-datasets. For model evaluation, the accuracy indicators were calculated in training, validation and test sets, respectively. Besides, feature importance extraction was undertaken based on the results of model comparison. The finding indicated that these models didn't exhibit a huge performance difference for crash severity prediction in the same severity level; however, the performances of the models did vary among different datasets, with an average training accuracy of 99.27 %, 96.4 %, 86.98 %, 86.84 %, 71.76 % in fatal injury, severe injury, visible injury, complaint of pain, PDO crash datasets, respectively. Additionally, it was found that in each severity dataset, the indicator urban freeways is a determinant factor that leads to the occurrence of crashes while rural freeways is more related to more severe crashes (i.e., fatal and severe crashes). This paper can provide valuable information for model selection and tuning in accident severity prediction. Future research could consider the influences that temporal instability of contributing features has on the model performances. [ABSTRACT FROM AUTHOR]

• Factors affecting injury severity in rollover crashes were explored. • Random parameters logit model with unobserved heterogeneity in means and variances. • Findings reveal temporal instability of model specifications across individual years. • Aggregate-to-component shift quantified using out-of-sample prediction. • Implications for mitigating the associated driver injury severity. Single-vehicle rollover crashes have been acknowledged as a predominant highway crash type resulting in serious casualties. To investigate the heterogeneous impact of factors determining different injury severity levels in single-vehicle rollover crashes, the random parameters logit model with unobserved heterogeneity in means and variances was employed in this paper. A five-year dataset on single-vehicle rollover crashes, gathered in California from January 1, 2013, to December 31, 2017, was utilized. Driver injury severities that were determined to be outcome variables include no injury, minor injury, and severe injury. Characteristics pertaining to the crash, driver, temporal, vehicle, roadway, and environment were acknowledged as potential determinants. The results showed that the gender indicator specified to minor injury was consistently identified as a significant random parameter in four years' models and the joint five-year model, excluding the 2016 crash model where the night indicator associated with no injury was observed to produce the random effect. Additionally, two series of likelihood ratio tests were conducted to assess the year-to-year and aggregate-to-component temporal stability of model estimation results. Marginal effects of explanatory variables were also calculated and compared to analyze the temporal stability and interpret the results. The findings revealed an overall temporal instability of model specifications across individual years, while there is no significant aggregate-to-component variation. Injury severities were observed to be stably affected by several variables, including improper turn indicator, under the influence of alcohol indicator, old driver indicator, seatbelt indicator, insurance indicator, and airbag indicator. Furthermore, the year-to-year and aggregate-to-component shift was quantified and characterized by calculating the differences in probabilities between within-sample observations and out-of-sample predictions. The overall results imply that continuing to expand and refine the model to incorporate more comprehensive datasets can result in more robust and stable injury severity prediction, thus benefiting in mitigating the associated driver injury severity. [ABSTRACT FROM AUTHOR]

Background: Low concentrations of S100B have neurotrophic effects and can promote nerve growth and repair, which plays an essential role in the pathophysiological and histopathological alterations of major depressive disorder (MDD) during disease development. Studies have shown that plasma S100B levels are altered in patients with MDD. In this study, we investigated whether the plasma S100B levels in MDD differ between genders. Methods: We studied 235 healthy controls (HCs) (90 males and 145 females) and 185 MDD patients (65 males and 120 females). Plasma S100B levels were detected via multifactor assay. The Mahalanobis distance method was used to detect the outliers of plasma S100B levels in the HC and MDD groups. The Kolmogorov–Smirnov test was used to test the normality of six groups of S100B samples. The Mann–Whitney test and Scheirer-Ray-Hare test were used for the comparison of S100B between diagnoses and genders, and the presence of a relationship between plasma S100B levels and demographic details or clinical traits was assessed using Spearman correlation analysis. Results: All individuals in the HC group had plasma S100B levels that were significantly greater than those in the MDD group. In the MDD group, males presented significantly higher plasma S100B levels than females. In the male group, the plasma S100B levels in the HC group were significantly higher than those in the MDD group, while in the female group, no significant difference was found between the HC and MDD groups. In the male MDD subgroup, there was a positive correlation between plasma S100B levels and years of education. In the female MDD subgroup, there were negative correlations between plasma S100B levels and age and suicidal ideation. Conclusions: In summary, plasma S100B levels vary with gender and are decreased in MDD patients, which may be related to pathological alterations in glial cells. [ABSTRACT FROM AUTHOR]

• The "linkage" role of micro-kinetic parameters between road characteristics and accidents was discovered. • The longitudinal kinetic characteristics of tires can reflect the risk of rear-end accidents. • Demonstrates changes in vehicle operating on high-risk segments through micro-kinetic parameters. • Proposed a relatively novel modeling method. • Reveals the reliability of micro-kinetic parameters in the field of accident analysis. Freeway accidents are a leading cause of death in China, which also triggers substantial economic loss and an emotional burden to society. However, the internal mechanism of how microscopic kinetic parameters of vehicles influenced by road characteristics determine the occurrence of different types of accidents has not been explicitly studied. This research aimed to explore the "link role" of tire microscopic kinetic parameters in road characteristic variables and traffic accidents to aid in facilitating the traffic design and management, and thus to prevent traffic accident. Method: A mountain freeway in Zhejiang Province, China was used as the research object and the data used in this paper were obtained through a real-time vehicle experiment. Multiple estimation models, including the standard ordered logit (SOL) model, fixed parameters logit (FPL) model, and random parameters logit (RPL) model were established. Results: The findings show that road characteristics will affect the longitudinal kinetic characteristics of the vehicle and, consequently, map the level of risk of rear-end accidents. Driving compensation effects were also identified in this paper (i.e., the drivers tend to be more cautious in complicated driving circumstances). Another finding relating to the mountain freeway is that different tunnel characteristics (e.g., tunnel entrance and tunnel exit) have different effects on different types of traffic accidents. Practical Applications: The framework proposed in this article can provide new insight for researchers to enlarge the research subjects of both explanatory and outcome variables in accident analysis. Future research could be implemented to consider more driving conditions. [ABSTRACT FROM AUTHOR]

• The deep learning technology approaches have been applied in the skin lesion segmentation. • A novel image-based separable-Unet network with stochastic weights averaging was proposed. • The proposed system in this paper exhibits stronger segmentation performance compared to the state-of-the-art with a lower computation cost. • To the best of our knowledge, this report for the first time describes the over-fitted problem of easy examples dominant as a problem of falling bad local optima, and introduce the stochastic weights averaging method to solve the problem, which can further boost our model to obtain SOTA performance. • Compared with existing methods, the proposed method can achieve both excellent performance in accuracy and real-time. Efficient segmentation of skin lesion in dermoscopy images can improve the classification accuracy of skin diseases, which provides a powerful approach for the dermatologists in examining pigmented skin lesions. However, the segmentation is challenging due to the low contrast of skin lesions from a captured image, fuzzy and indistinct lesion boundaries, huge variety of interclass variation of melanomas, the existence of artifacts, etc. In this work, an efficient and accurate melanoma region segmentation method is proposed for computer-aided diagnostic systems. A skin lesion segmentation (SLS) method based on the separable-Unet with stochastic weight averaging is proposed in this work. Specifically, the proposed Separable-Unet framework takes advantage of the separable convolutional block and U-Net architectures, which can extremely capture the context feature channel correlation and higher semantic feature information to enhance the pixel-level discriminative representation capability of fully convolutional networks (FCN). Further, considering that the over-fitting is a local optimum (or sub-optimum) problem, a scheme based on stochastic weight averaging is introduced, which can obtain much broader optimum and better generalization. The proposed method is evaluated in three publicly available datasets. The experimental results showed that the proposed approach segmented the skin lesions with an average Dice coefficient of 93.03% and Jaccard index of 89.25% for the International Skin Imaging Collaboration (ISIC) 2016 Skin Lesion Challenge (SLC) dataset, 86.93% and 79.26% for the ISIC 2017 SLC, and 94.13% and 89.40% for the PH2 dataset, respectively. The proposed approach is compared with other state-of-the-art methods, and the results demonstrate that the proposed approach outperforms them for SLS on both melanoma and non-melanoma cases. Segmentation of a potential lesion with the proposed approach in a dermoscopy image requires less than 0.05 s of processing time, which is roughly 30 times faster than the second best method (regarding the value of Jaccard index) for the ISIC 2017 dataset with the same hardware configuration. We concluded that using the separable convolutional block and U-Net architectures with stochastic weight averaging strategy could enable to obtain better pixel-level discriminative representation capability. Moreover, the considerably decreased computation time suggests that the proposed approach has potential for practical computer-aided diagnose systems, besides provides a segmentation for the specific analysis with improved segmentation performance. [ABSTRACT FROM AUTHOR]

• Investigated the influence mechanisms of toll way crashes involving distracted and non-distracted driving. • Random parameters logit models with heterogeneity in the means and variances are estimated. • Differences between distracted and non-distracted driving injury severities are compared. • Temporal shifts and non-transferability in parameter estimations are explored. Distracted driving is a prominent cause of traffic crashes and may increase the severity of collisions. Due to the larger speeds on toll ways, distracted driving crashes are more severe than on other types of roads, making it worthwhile to investigate. This study examined the variation in the influence of factors affecting injury severity in crashes involving distracted and non-distracted driving, as well as the change over time, using crash data from Florida toll ways from the 2017 to 2019. A series of random parameters logit models with heterogeneity in the means and variances were developed to analyze different driver-injury severities (no injury, minor injury, and severe injury) in crashes involving distracted and non-distracted driving. In addition, likelihood ratio tests were conducted to determine whether model parameters differed between different driver behaviors (distracted/non-distracted driving) and among years. Several factors potentially impacting injury severities were studied, including driver, crash, vehicle, roadway, environment, temporal, and others. Significant disparities were observed between the contributing factors of the severity of crashes involving distracted and non-distracted driving. Results showed that considerable differences were also observed in the severity of injuries caused by two types of crashes and distracted driving resulted in more serious crashes than non-distracted driving. Despite model results indicated that factors influencing injury severity altered over time, several factors, such as motorcycle involvement and commercial car involvement, still exhibited relative temporal stability in non-distracted driving crashes over the three years. Temporal instability and non-transferability were also captured by out-of-sample predictions to verify the temporal shifts of contributing variables from year to year. This study is useful for distinguishing the influence mechanisms between the two types of crashes involving distracted and non-distracted driving, and the results can be applied for safety countermeasures development. [ABSTRACT FROM AUTHOR]

The exit areas of urban freeways always experience serious traffic safety and congestion problems. As a basic task, vehicle trajectory data are difficult to extract by traditional manual counting method because of the complicated weaving flow and large traffic volume at the exit areas of urban freeways. This paper presents a novel vehicle trajectory extraction composite framework combining YOLOv4 vehicle detection algorithm, SORT vehicle tracking algorithm and KD-Tree trajectory data reconstruction algorithm (YSKT algorithms framework). An unmanned aerial vehicle (UAV) was used to collect traffic videos of urban freeways exit areas, and the YSKT algorithms framework was adopted to extract vehicle trajectory data from the collected traffic videos. According to the test results of 4 traffic video samples, around 95% of complete vehicle trajectories in the videos could be extracted. Furthermore, basic traffic flow characteristic parameters, traffic efficiency parameters and traffic safety parameters were calculated and analyzed according to the extracted vehicle trajectory data, which was expected to help researchers analyze traffic problems in this kind of road segment in future studies. [ABSTRACT FROM AUTHOR]

Background: The glial cell line-derived neurotrophic factor (GDNF) has an important role in neurons and is closely associated with psychiatric disorders. The development of bipolar disorder (BD) may differ between genders. Existing studies have shown that plasma GDNF levels are altered in patients with BD. In this study, we investigate whether the GDNF levels in patients with BD differ in terms of gender.Methods: Participants were divided into the BD group (n = 76, with 26 males and 50 females) and healthy control (HC) group (n = 89, with 35 males and 54 females). Plasma GDNF levels were detected via multifactor assay. Clinical symptoms of patients with BD were collected and assessed using the Hamilton Depression-17 Inventory, Hamilton Anxiety-17 Inventory, Young's Mania Rating Scale, and Brief Psychiatric Rating Scale.Results: The GDNF levels were significantly higher in all participants in the HC group (F = 4.262, p < 0.05) compared with those in the BD group. In the HC group, the males (t = 4.814, p < 0.001) presented significantly higher levels than the females. The plasma GDNF levels in males in the BD group (t = 3.022, p < 0.05) were significantly lower than those in males in the HC group.Conclusion: Differences in plasma GDNF levels are associated with the gender of patients with BD. [ABSTRACT FROM AUTHOR]

Alcohol abuse is a major cause of alcoholic liver disease (ALD) and can result in fibrosis and cirrhosis. γ‐glutamylcysteine (γ‐GC) is a precursor of glutathione (GSH) with antioxidant and anti‐inflammatory properties. Our research aimed to explore the protective impact of γ‐GC on ALD and its potential mechanisms of efficiency in vitro and in vivo. L02 cells were pretreated with γ‐GC (20, 40, and 80 μM) for 2 h and exposed to ethanol for 24 h. Cell viability, apoptosis, oxidative stress, and inflammatory levels were measured. The expression of protein cleaved caspase‐3 and cleaved PARP and flow cytometry results indicated that γ‐GC decreases apoptosis on L02 cells after ethanol treatment. Moreover, γ‐GC also attenuated oxidative stress and mitochondrial damage in hepatocytes caused by ethanol via increasing cellular GSH, SOD activity, and mitochondrial membrane potential. In vivo experiments, γ‐GC effectively reduced the AST, ALT, and TG levels in mice. The inflammation of ALD was alleviated by γ‐GC both in vivo and in vitro. Additionally, histopathological examination demonstrated that γ‐GC treatment lessened lipid droplet formation and inflammatory damage. In conclusion, these results showed that γ‐GC has anti‐inflammatory and anti‐apoptotic effects on ALD because it could help hepatocytes maintain sufficient GSH levels to combat the excess reactive oxygen species (ROS) generated during ethanol metabolism. Practical applications: Alcohol intake is the fifth highest risk factor for premature death and disability among all risk variables. However, few medicines are both safe and effective for the treatment of ALD. As a direct precursor of GSH, γ‐GC has a broad variety of potential antioxidant and anti‐inflammatory applications for the treatment of numerous medical conditions. In conclusion, these results showed that γ‐GC could protect cells from ALD via suppressing oxidative stress, alleviating inflammation, and preventing apoptosis. [ABSTRACT FROM AUTHOR]

• Three counting models are used to build violation performance functions, and NB-2 is better suited. • DUI violations present temporal instability varying months and day of the week. • Violations frequently occur on pleasing weather days with clear visibility, slight breeze, and good air quality. • The worst-case scenarios for DUI violations are sunny holidays and Friday with lower temperatures. • More speeding violations occur on sunny holidays with improved visibility. Traffic violations and crashes are inherently associated. Analysis of traffic violation frequency is a prerequisite for improvements in crash prevention and corresponding countermeasures. One of the essential works in the field of traffic violations relates to the exploration of the correlations between a certain violation type (e.g., speeding or safety belt use) and its causal factors (e.g., demographics and road types). Till now, the effects of spatiotemporal and meteorological factors on severe traffic violations, a general term for dangerous driving behaviors, have not been fully considered. Using the dataset consisting of daily severe traffic violations and meteorological conditions during 12 months in Jiangsu Province, China, violation performance functions were developed for three violation types (total violations, driving under the influence, and speeding) based on three models (Poisson regression, zero-inflated Poisson regression, and negative binomial model). The findings indicate that the negative binomial model has a better performance for traffic violation frequency estimation. Additionally, elastic analysis for three violation types relying on the negative binomial model was conducted to present the relationships between the explanatory variables and the expected violation frequency. The effects of spatiotemporal factors have revealed that the violation situations are significantly different in varying cities and the frequency of drunk driving shows a significant time instability. It is also found that rainy days will generate a decrease in the possibility of violation occurrence. With regard to temperature, a significant negative effect is found and the decrease in temperature will bring about an increase in violation frequency. Besides, traffic violation frequency is significantly increased during holidays with comfortable weather conditions. The conclusion of this study can provide insightful suggestions for the department of traffic enforcement to adjust the patrol plans according to the specified periods (weeks, months, or holidays) and weather conditions. Special rectification actions and targeted educational activities are also advised to be put forward simultaneously. [ABSTRACT FROM AUTHOR]

Investigating the relationship between the months and traffic crashes is a foremost task for the safety improvement of mountainous freeways. Taking a mountainous freeway located in China as an example, this paper proposed a combined modeling framework to identify the relationships between months and different crash types. K-means and Apriori were initially used to extract the monthly distribution patterns of different types of crashes. A graphical approach and a risk calculation equation were developed to assess the output of K-means and Apriori. Then, using the assessment results as the input, a logistic regression model was constructed to quantify the effects of each month on crashes. The results indicate that the monthly distribution patterns of different crash types are inconsistent, i.e., for a specific month, the high risk of a certain crash type may be covered up if experts only focus on the total number of crashes. Moreover, when identified as high-risk months by K-means and Apriori, the crash-proneness will significantly increase several times than months identified as high-risk by only one of K-means and Apriori, thereby illustrating the superior performance of the mix-method. The conclusions can assist local relevant organizations in formulating strategies for preventing different types of traffic crashes in different months (e.g., the risk of rear-end crashes in August, the risk of fixed-object hitting crashes in February, and the risk of overturning crashes in October) and provide a methodological reference for relevant studies in other regions. [ABSTRACT FROM AUTHOR]

Background: L-theanine, a non-protein amino acid was found principally in the green tea, has been previously shown to exhibit potent anti-obesity property and hepatoprotective effect. Herein, we investigated the effects of L-theanine on alleviating nonalcoholic hepatic steatosis in vitro and in vivo, and explored the underlying molecular mechanism. Methods: In vitro, HepG2 and AML12 cells were treated with 500 μM oleic acid (OA) or treated with OA accompanied by L-theanine. In vivo, C57BL/6J mice were fed with normal control diet (NCD), high‐fat diet (HFD), or HFD along with L-theanine for 16 weeks. The levels of triglycerides (TG), accumulation of lipid droplets and the expression of genes related to hepatocyte lipid metabolic pathways were detected in vitro and in vivo. Results: Our data indicated that, in vivo, L-theanine significantly reduced body weight, hepatic steatosis, serum levels of alanine transaminase (ALT), aspartate transaminase (AST), TG and LDL cholesterol (LDL-C) in HFD-induced nonalcoholic fatty liver disease (NAFLD) mice. In vitro, L-theanine also significantly alleviated OA induced hepatocytes steatosis. Mechanic studies showed that L-theanine significantly inhibited the nucleus translocation of sterol regulatory element binding protein 1c (SREBP-1c) through AMPK-mTOR signaling pathway, thereby contributing to the reduction of fatty acid synthesis. We also identified that L-theanine enhanced fatty acid β-oxidation by increasing the expression of peroxisome proliferator–activated receptor α (PPARα) and carnitine palmitoyltransferase-1 A (CPT1A) through AMP-activated protein kinase (AMPK). Furthermore, our study indicated that L-theanine can active AMPK through its upstream kinase Calmodulin-dependent protein kinase kinase-β (CaMKKβ). Conclusions: Taken together, our findings suggested that L-theanine alleviates nonalcoholic hepatic steatosis by regulating hepatocyte lipid metabolic pathways via the CaMKKβ-AMPK signaling pathway. [ABSTRACT FROM AUTHOR]

The tensile performance of ductile tubes can be enhanced by the application of fillers. Research studies on the mechanical performance of filled tensile tubes have mainly focused on experiments and numerical simulations on concrete-filled steel tube (CFST) components, while the effects of factors such as size, section pattern, and material of filled tensile tubes on their performance have rarely been studied. In this research, the effects of size, section pattern, and material on the tensile performance of filled tubes have been evaluated through theoretical studies, simulations, and experiments. The tensile strength reinforcement and deformation weakening coefficients of filled circular thin-walled tubes corresponding to hollow tubes were theoretically deduced, and the influencing factors of the two were parametrically evaluated. Tensile performances of filled tubes with circular and square sections were compared with each other through numerical methods. In the current research, the circular section was optimized and prestressed circular hollow support section was proposed. Tensile fracture tests were performed on circular thin-walled tubes made of six different materials to determine material effects on the tensile performance of these structures. It was also found that metallic materials with good ductility significantly enhanced the tensile performance, fracture toughness, and energy consumption of test components containing prestressed filler. [ABSTRACT FROM AUTHOR]

Despite the broad application of advanced fleet management systems (FMSs) in third-party logistics (3PL) companies, there is a marginally limited understanding of how to employ them to enhance transport energy efficiency. In a case study of a Chinese 3PL company, this paper analyzed data obtained from the online FMS to assess drivers' awareness of fuel-saving factors. A questionnaire was primarily designed to investigate the drivers' awareness of fuel-saving factors based on the reliability and validity test. Then, Extreme Gradient Boosting (XGBoost), a machine learning algorithm, was utilized to explore the intrinsic impacts of various factors on fuel consumption with the outputs providing the evaluation basis for individual awareness of the drivers. The results show a significant deviation in the driver's awareness of fuel-saving factors, among which the three indicators of engine speed, idling condition, and rolling without engine load are seriously underestimated, while the indicators related to the environment are seriously overestimated due to social expectations. In addition, the average speed was found to be the most important fuel-saving indicator besides the load. Based on these findings, this paper recommends that the 3PL companies choose a route with more freeways when planning, and the mileage should be controlled within 800 km as far as possible. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the antitumor effect of citrate on prostate cancer and its underlying mechanism. CCK-8 and Colony formation assay were performed to detect the anti-proliferative effect of citrate on prostate cancer. Flow cytometry analysis was conducted to investigate the pro-apoptosis effect of citrate on prostate cancer. Immunofluorescence assay was taken to detect whether citrate induced autophagy in prostate cancer. Western blot and Immunohistochemical assay were performed to explore the underlying mechanism by which citrate activates autophagic death in prostate cancer cells. Xenograft tumorigenicity assay was conducted to explore whether citrate suppressed the growth of xenograft prostate tumors in vivo. We found citrate could significantly induce apoptosis and autophagy of prostate cancer cells in vitro and in vivo. Furthermore, treatment with autophagy inhibitor (chloroquine) drastically suppresses the apoptosis rate of prostate cancer induced by citrate. Based on the Ca2+-chelating property of citrate, the further study suggested that citrate activates autophagic cell death in prostate cancer cells via downregulation CaMKII/AKT/mTOR pathway. Finally, citrate suppresses the growth of xenograft prostate tumors without remarkable toxicity in mice. Our study elucidated a novel molecular mechanism about the anti-cancer activities of citrate. That citrate activates autophagic cell death of prostate cancer via downregulation CaMKII/AKT/mTOR pathway and without remarkable toxicity in mice. This study suggests that citrate might be a promising therapeutic agent for the treatment of prostate cancer. • Citrate suppresses the growth of xenograft prostate tumors without remarkable toxicity in mice. • Citrate could activate autophagic death of prostate cancer cells. • Citrate activates autophagic death in prostate cancer cells via downregulation CaMKII/AKT/mTOR pathway. [ABSTRACT FROM AUTHOR]

Chemotherapy resistance of breast cancer cells is one of the major factors affecting patient survival rate. Heat shock protein 27 (Hsp27) is a member of the small heat shock protein family that has been reported to be associated with chemotherapy resistance in tumor cells, but the exact mechanism is not fully understood. Here, we explored the regulation of Hsp27 in adriamycin-resistant pathological conditions of breast cancer in vitro and in vivo. We found that overexpression of Hsp27 in MCF-7 breast cancer cells reversed DNA damage induced by adriamycin, and thereby reduced subsequent cell apoptosis. Non-phosphorylated Hsp27 accelerated ubiquitin-mediated degradation of c-Myc under normal physiological conditions. After stimulation with adriamycin, Hsp27 was phosphorylated and translocated from the cytoplasm into the nucleus, where phosphorylated Hsp27 upregulated c-Myc and Nijmegen breakage syndrome 1 (NBS1) protein levels thus leading to ATM activation. We further showed that phosphorylated Hsp27 promoted c-Myc nuclear import and stabilization by regulating T58/S62 phosphorylation of c-Myc through a protein phosphatase 2A (PP2A)-dependent mechanism. Collectively, the data presented in this study demonstrate that Hsp27, in its phosphorylation state, plays a critical role in adriamycin-resistant pathological conditions of breast cancer cells. [Display omitted] • Phosphorylated Hsp27 (p-Hsp27) contributed to ADR resistance. • p-Hsp27 facilitated ADR resistance by the c-Myc-NBS1-ATM signaling pathway. • p-Hsp27 promoted c-Myc translocation by increasing Ser62 phosphorylation of c-Myc. • p-Hsp27 maintained c-Myc stabilization by reducing Thr58 phosphorylation of c-Myc. • p-Hsp27 regulated T58/S62 phosphorylation of c-Myc through PP2A-dependent mechanism. [ABSTRACT FROM AUTHOR]

Improving the safety and efficiency of offshore connection of pontoon bridge modules and avoiding connection failures or collision from their relative motion due to waves is currently an important study for landing operations. Thus, this paper proposes an online prediction method for the relative motion of the offshore pontoon bridge module connection based on a long short-term memory (LSTM) deep learning architecture. The developed scheme processes the motion response data from the wave tank to de-noise and segment them, employs the sample data obtained for training and testing, and generates a prediction model operating under various working conditions. Through extensive experiments, we verify that without requiring any information on the module and waves, our method attains a high forecast accuracy and provides a decision basis for the offshore connection of the pontoon bridge modules. • This paper proposes an online prediction method using a deep learning architecture for the connection of pontoon bridge modules. • The wavelet denoising method is implemented for data treatment of the pontoon bridge module connection with the measured data. • According to the difference in prediction accuracy in different stages, the required sample data is analyzed. • The proposed prediction model achieves satisfactory pontoon bridge module offshore Connection prediction performance. [ABSTRACT FROM AUTHOR]

Crash severity prediction has been raised as a key problem in traffic accident studies. Thus, to progress in this area, in this study, a thorough artificial neural network combined with an improved metaheuristic algorithm was developed and tested in terms of its structure, training function, factor analysis, and comparative results. Data from I5, an interstate highway in the Washington State during the period of 2011–2015, were used for fitting and prediction, and after setting the theoretical three-layer neural network (NN), an improved Particle Swarm Optimization (PSO) method with adaptive inertial weight was offered to optimize the NN, and finally, a comparison among different adaptive strategies was conducted. The results showed that although the algorithms produced almost the same accuracy in their predictions, a backpropagation method combined with a nonlinear inertial weight setting in PSO produced fast global and accurate local optimal searching, thereby demonstrating a better understanding of the entire model explanation, which could best fit the model, and at last, the factor analysis showed that non-road-related factors, particularly vehicle-related factors, are more important than road-related variables. The method developed in this study can be applied to a big data analysis of traffic accidents and be used as a fast-useful tool for policy makers and traffic safety researchers. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is a very prevalent male-specific malignancy; most PCa patients eventually die as a result of metastasis. L-theanine (C7H14N2O3), a nonprotein amino acid derivative from green tea leaves, has been demonstrated to act as an anticarcinogen through proapoptotic and antiproliferative effects. However, the antimetastatic effect of L-theanine in tumor cells and its underlying mechanism are still unclear. Here, we found that L-theanine could suppress invasion, migration, and increase cell-cell adhesion of prostate cancer cells in vitro and in vivo. We also found that L-theanine could inhibit the epithelial-mesenchymal transition process in PCa. Our study revealed that L-theanine could downregulate MMP9, N-cadherin, Vimentin, Snail, and upregulate E-cadherin. Furthermore, L-theanine suppressed the transcription of MMP9 and Snail by significantly inhibiting the ERK/NF-κB signaling pathway and the binding activity of p65 to the promoter regions of MMP9 and Snail. All of these findings suggest that L-theanine has therapeutic potential for metastatic PCa and may be considered a promising candidate for antimetastatic therapy of prostate cancer. [ABSTRACT FROM AUTHOR]

• The remarkable relationship between the kinetic data and the crash risk was proved. • Kinetic data is generated by the vehicle in real time while normal driving. • A new method for high crash risk identification based on kinetic parameters was proposed. • It can be effectively used to identify road design defects related to crash risk. • Investigated the feasibility of kinetic parameters as crash surrogate events. With the development and maturation of vehicle-based data acquisition technology, in-vehicle data is increasingly being used to explore road safety. This paper reports on research that analyzed the real-time tire force data (kinetic response) obtained from vehicle kinetic experiments, and constructed a new approach for identifying the high-risk of crashes on freeway segments with horizontal curvature. First, the road was divided into 1km units. Then, taking into account the characteristics of freeway alignment, each segment with horizontal curve was selected as the object of subsequent analysis. Automotive instrumentation was used to obtain a measure of tire force in the course of normal driving. The entire data set was preprocessed according to rate of change and the density of the data was reduced. By defining the outliers of the kinetic data and conducting factor analysis, two representative crash risk indicators of longitudinal and lateral stability were obtained. Negative binomial regression model (NBR model) and random effects negative binomial regression model (RENBR model) were constructed and jointly applied based on the new indicators to predict the risk value of horizontal curve segments. The method showed good prediction performance (71.8 %) for high-risk road segments with design flaws, but the predicted effect for low-risk road segments was not ideal. This study not only illustrated the effectiveness of in-vehicle data in assessing road crash risk by coupling multiple kinetic parameters, but also provided support for freeway safety research using surrogate measures of risk when there is a lack of crash statistics. [ABSTRACT FROM AUTHOR]

Globally, the use of electric vehicles, and in particular the use of electric buses, has been increasing. The city of Nanjing leads China in the adoption of electric buses, supported by city policies and infrastructure. To lower costs and provide a better service, vehicle selection is crucial, however, existing selection methods are limited. Accordingly, Nanjing Bus Company developed a test method based on road tests to select a bus. This paper presents a detailed description of the test method and a case study of its application. The method included an organization structure, selection of eight test vehicles (four 10 m length, four 8 m length) from four brands (a total of 32 test vehicles), selection of indicators and selection of routes. Data was collected from repeated drives by 65 drivers over an 8-week period. Indicators included power consumption, charging duration, failure duration and driving distance. It is concluded that the road test method designed and conducted by the Nanjing Bus Company provides a good framework for the selection of pure electric buses. Furthermore, subsequent experience with selected buses supports the validity and value of the model. [ABSTRACT FROM AUTHOR]