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Lead (Pb) poisoning posing a crucial health risk, especially among children, causing devastating damage not only to brain development, but also to kidney function. Thus, an urgent need persists to identify highly effective, safe, and low-toxicity drugs for the treatment of Pb poisoning. The present study focused on exploring the protective effects of Se on Pb-induced nephrotoxicity in weaning rats and human renal tubular epithelial cells, and investigated the possible mechanisms. Forty weaning rats were randomly divided into four groups in vivo : control, Pb-exposed, Pb+Se and Se. Serum creatinine (Cr), urea nitrogen (BUN) and hematoxylin and eosin (H&E) staining were performed to evaluate renal function. The activities of antioxidant enzymes in the kidney tissue were determined. In vitro experiments were performed using human renal tubular epithelial cells (HK-2 cells). The cytotoxicity of Pb and Se was detected by 3-(4,5-dimethylthiazol-2yl)-2, 5-diphenyltetrazolium bromide (MTT) assay. Inverted fluorescence microscope was used to investigate cell morphological changes and the fluorescence intensity of reactive oxygen species (ROS). The oxidative stress parameters were measured by a multi-detection reader. Nuclear factor-erythroid-2-related factor (NRF2) signaling pathways were measured by Western blot and reverse transcription polymerase chain reaction (RT-PCR) in HK-2 cells. We found that Se alleviated Pb-induced kidney injury by relieving oxidative stress and reducing the inflammatory index. Se significantly increased the activity of the antioxidant enzymes glutathione (GSH), superoxide dismutase (SOD) and catalase (CAT), whereas it decreased the excessive release of malondialdehyde (MDA) in the kidneys of weaning rats and HK-2 cells. Additionally, Se enhanced the antioxidant defense systems via activating the NRF2 transcription factor, thereby promoting the to downstream expression of heme oxygenase 1. Furthermore, genes encoding glutamate-cysteine ligase synthetase catalytic (GCLC), glutamate-cysteine ligase synthetase modifier (GCLM) and NADPH quinone oxidoreductase 1 (NQO1), downstream targets of NRF2, formed a positive feedback loop with NRF2 during oxidative stress responses. The MTT assay results revealed a significant decrease in cell viability with Se treatment, and the cytoprotective role of Se was blocked upon knockdown of NRF2 by small interfering RNA (siRNA). MDA activity results also showed that NRF2 knockdown inhibited the NRF2-dependent transcriptional activity of Se. Our findings demonstrate that Se ameliorated Pb-induced nephrotoxicity by reducing oxidative stress both in vivo and in vitro. The molecular mechanism underlying Se's action in Pb-induced kidney injury is related to the activation of the NRF2 transcription factor and the activity of antioxidant enzymes, ultimately suppressing ROS accumulation. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a prevalent progressive disorder. Moxibustion has found widespread use in clinical practice for OA, while its underlying mechanism remains elusive. To investigate whether moxibustion can ameliorate OA by influencing the metabolic processes in OA and to elucidate the specific metabolic mechanisms involved. C57BL/6J WT mice were randomly assigned to one of three groups: the SHAM group, the ACLT group, and the ACLT+M group. In the ACLT+M group, mice underwent moxibustion treatment at acupoints Shenshu (BL23) and Zusanli (ST36) for a continuous period of 28 days, with each session lasting 20 min. We conducted a comprehensive analysis to assess the impact of moxibustion on OA, focusing on pathological changes, intestinal flora composition, and serum metabolites. Moxibustion treatment effectively mitigated OA-related pathological changes. Specifically, moxibustion treatment resulted in the amelioration of articular cartilage damage, synovial inflammation, subchondral bone sclerosis when compared to the ACLT group. Moreover, 16S rDNA sequencing analysis revealed that moxibustion treatment positively influenced the composition of the flora, making it more similar to that of the SHAM group. Notably, moxibustion treatment led to a reduction in the abundance of Ruminococcus and Proteobacteria in the intestine. In addition, non-targeted metabolomics analysis identified 254 significantly different metabolites between the groups. Based on KEGG pathway analysis and the observed impact of moxibustion on OA-related inflammation, moxibustion therapy is closely associated with the cAMP-related signaling pathway. Moxibustion can relieve OA by regulating intestinal flora and via impacting cAMP-related signaling pathway. [Display omitted] • Moxibustion mitigates cartilage destruction, synovial inflammation, and subchondral bone sclerosis. • Metabolomics analysis were used to investigate the effective mechanisms of Moxibustion in treating OA. • Moxibustion promote cartilage protection via impacting cAMP-related signaling pathway. • Our results provide a theoretical basis for OA treatment through the application of Moxibustion. [ABSTRACT FROM AUTHOR]

Winter road maintenance (WRM) has been shown to have significant benefits of improving road safety and reducing traffic delay caused by adverse weather conditions. It has also been suggested that WRM is also beneficial in terms of reducing vehicular air emissions and fuel consumptions because snow and ice on road surface often cause the drivers to reduce their vehicle speeds or to switch to high gears, thus decreasing fuel combustion efficiency. However, there has been very limited information about the underlying relationship, which is important for quantifying this particular benefit of a winter road maintenance program. This research is focused on establishing a quantitative relationship between winter road surface conditions and vehicular air emissions. Speed distribution models are developed for the selected Ontario highways using data from 22 road sites across the province of Ontario, Canada. The vehicular air emissions under different road surface conditions are calculated by coupling the speed models with the engine emission models integrated in the emission estimation model - MOVES. It was found that, on the average, a 10% improvement in road surface conditions could result in approximately 0.6–2% reduction in air emissions. Application of the proposed methodology is demonstrated through a case study to analyse the air emission and energy consumption effects under specific weather events. [ABSTRACT FROM AUTHOR]

Biomass-based energy systems are gaining popularity as a clean and renewable source of energy, and have the potential to be a major contributor to the transition towards a sustainable energy future. In this regard, a novel multi-generation energy system is developed based on the biomass combustion. Wood chip and rice straw biomass is combusted with air agent and triggers an integrated system comprises a gas turbine cycle, a proton exchange membrane electrolyzer, a supercritical carbon dioxide Brayton cycle, and a humidification-dehumidification desalination system. The performances of wood chip and rice straw in triggering the system are compared from environmental and energy indicators viewpoints. The system fueled by wood chip emits lower carbon dioxide emission compared to the system fueled by rice straw (8.294 g/kWh compared to 10.41 g/kWh). However, the rice straw-fueled system results in higher efficiency than the wood chip-fueled system (69.9% compared to 69.1%). The system fueled by wood chip produces 2.68 kg/h of hydrogen while this value if 1.96 kg/h for the system fueled by rice straw. The research validated the possibility of utilizing energy systems that incorporate wood chip and rice straw biomass across various generations and showed the superiority of the wood chip in most cases. [ABSTRACT FROM AUTHOR]

Structural magnetic resonance imaging (sMRI) is an established technique for measuring brain atrophy, and dynamic positron emission tomography with 11 C-Pittsburgh compound B ( 11 C-PIB PET) has the potential to provide both perfusion and amyloid deposition information. It remains unclear, however, how to better combine perfusion, amyloid deposition and morphological information extracted from dynamic 11 C-PIB PET and sMRI with the goal of improving the diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). We adopted a linear sparse support vector machine to build classifiers for distinguishing AD and MCI subjects from cognitively normal (CN) subjects based on different combinations of regional measures extracted from imaging data, including perfusion and amyloid deposition information extracted from early and late frames of 11 C-PIB separately, and gray matter volumetric information extracted from sMRI data. The experimental results demonstrated that the classifier built upon the combination of imaging measures extracted from early and late frames of 11 C-PIB as well as sMRI achieved the highest classification accuracy in both classification studies of AD (100%) and MCI (85%), indicating that multimodality information could aid in the diagnosis of AD and MCI. [ABSTRACT FROM AUTHOR]

A variety of sensor technologies, such as loop detectors, traffic cameras, and radar have been developed for real-time traffic monitoring at intersections most of which are limited to providing link traffic information with few being capable of detecting turning movements. Accurate real-time information on turning movement counts at signalized intersections is a critical requirement for applications such as adaptive traffic signal control. Several attempts have been made in the past to develop algorithms for inferring turning movements at intersections from entry and exit counts; however, the estimation quality of these algorithms varies considerably. This paper introduces a method to improve accuracy and robustness of turning movement estimation at signalized intersections. The new algorithm makes use of signal phase status to minimize the underlying estimation ambiguity. A case study was conducted based on turning movement data obtained from a four-leg signalized intersection to evaluate the performance of the proposed method and compare it with two other existing well-known estimation methods. The results show that the algorithm is accurate, robust and fairly straightforward for real world implementation. [ABSTRACT FROM AUTHOR]

Abstract: This research presents a disaggregated modeling approach for investigating the link between winter road collision occurrence, weather, road surface conditions, traffic exposure, temporal trends and site-specific effects. This approach is unique as it allows for quantification of the safety effects of different winter road maintenance activities at an operational level. Different collision frequency models are calibrated using hourly data collected from 31 different highway routes across Ontario, Canada. It is found that factors such as visibility, precipitation intensity, air temperature, wind speed, exposure, month of the winter season, and storm hour have statistically significant effects on winter road safety. Most importantly, road surface conditions are identified as one of the major contributing factors, representing the first contribution showing the empirical relationship between safety and road surface conditions at such a disaggregate level. The applicability of the modeling framework is demonstrated using several examples, such as quantification of the benefits of alternative maintenance operations and evaluation of the effects of different service standards using safety as a performance measure. [Copyright &y& Elsevier]

Abstract: Sodium selenite can induce the apoptosis of cancer cells, however its mechanism has seldom been studied via proteomics. In this paper, human cervical cancer HeLa cells were investigated by MTT assay and morphological observation to get appropriate selenite concentrations for proteomic study. Results showed that selenite at concentrations larger than 10μmol/L significantly inhibited the viability of HeLa cells. 40μmol/L selenite was in the appropriate range for proteomic study. After 24h treatment with 40μmol/L selenite, total proteins were extracted from the cells and applied to two-dimensional gel electrophoresis (2DE). Those proteins with their expression levels altered at least 2-fold comparing to the control were picked up for protein identification via MALDI-TOF mass spectrometry and further confirmed by Western blot analysis. About 1000 spots were detected by the software in each 2DE gel, among which 13 differentially expressed proteins were identified by mass spectrometry and most of them are relevant to oxidative stress, such as peroxiredoxins, superoxide dismutase, quinolinate phosphoribosyl transferase, and D-dopachrome tautomerase. Meanwhile, reactive oxygen species (ROS) and mitochondrial membrane potential were also detected by flow cytometry and laser confocal scanning microscope. An increase in ROS generation and a decrease in mitochondrial membrane potential were detected in the selenite-treated cells compared with the control, which are consistent with the down-expression of antioxidative proteins in proteomics. Those results indicate that selenite induces the apoptosis of HeLa cells via ROS-mediated mitochondrial pathway. The present study also implies the potentiality of selenium in cervical cancer treatment. [Copyright &y& Elsevier]

Abstract: This research presents a modeling approach to investigate the association of the accident frequency during a snow storm event with road surface conditions, visibility and other influencing factors controlling for traffic exposure. The results have the premise to be applied for evaluating different maintenance strategies using safety as a performance measure. As part of this approach, this research introduces a road surface condition index as a surrogate measure of the commonly used friction measure to capture different road surface conditions. Data from various data sources, such as weather, road condition observations, traffic counts and accidents, are integrated and used to test three event-based models including the Negative Binomial model, the generalized NB model and the zero inflated NB model. These models are compared for their capability to explain differences in accident frequencies between individual snow storms. It was found that the generalized NB model best fits the data, and is most capable of capturing heterogeneity other than excess zeros. Among the main results, it was found that the road surface condition index was statistically significant influencing the accident occurrence. This research is the first showing the empirical relationship between safety and road surface conditions at a disaggregate level (event-based), making it feasible to quantify the safety benefits of alternative maintenance goals and methods. [ABSTRACT FROM AUTHOR]

Abstract: A nonautonomous competitive Lotka–Volterra system is considered in this work. Sufficient conditions on the coefficients are given to guarantee that all but one of the species are driven to extinction. It is shown that these conditions are weaker than those of Montes de Oca and Zeeman [F. Montes de Oca, M.L. Zeeman, Extinction in nonautonomous competitive Lotka–Volterra systems, Proc. Amer. Math. Soc. 124 (1996) 3677–3687]. [Copyright &y& Elsevier]

The use of probe vehicles to provide estimates of link travel times has been suggested as a means of obtaining travel times within signalized networks for use in advanced traveler information systems. Previous research has shown that bias in arrival time distributions of probe vehicles will lead to a systematic bias in the sample estimate of the mean. This paper proposes a methodology for reducing the effect of this bias. The method, based on stratified sampling techniques, requires that vehicle count data be obtained from an in-road loop detector or other traffic surveillance method. The effectiveness of the methodology is illustrated using simulation results for a single intersection approach and for an arterial corridor. The results for the single intersection approach indicate a correlation (R2) between the biased estimate and the population mean of 0.61, and an improved correlation between the proposed estimation method and the population mean of 0.81. Application of the proposed method to the arterial corridor resulted in a reduction in the mean travel time error of approximately 50%, further indicating that the proposed estimation method provides improved accuracy over the typical method of computing the arithmetic mean of the probe reports. [Copyright &y& Elsevier]

This paper discusses a study on the dial-a-ride paratransit scheduling problems arising in paratransit service systems that are subject to tight service time constraints and time-varying, stochastic traffic congestion. Different from existing methodologies, we explicitly incorporate a time-dependent, stochastic travel time model in the problem formulation. A set of recursive relations is first identified to approximate the distribution parameters of arrival times at individual stops of a given route which, coupled with a first-in-first-out (FIFO) assumption, allows us to extend the conventional heuristic algorithms for solving the proposed problem with only marginal increase in computational complexity. Results from a series of numerical experiments on a set of hypothetical problems are described, aiming to illustrate the computational efficiency of the proposed algorithm and the sensitivity of solutions to various model parameters. [Copyright &y& Elsevier]

• Failure of surgery and chemo/radiotherapy in GC treatment, leads to tumor relapse and non-effective outcome. • Immunosurveillance and Immunoescape are important keys for cancer immunotherapy. • Efficient activation of cytotoxic T cells is a vital step in cancer immunotherapy. • Immune check point inhibitors are considered as a therapeutic approach in the treatment of both solid organs and hematologic malignancies. • Despite few controversial and sometimes confusing results received from trials, we are moving forward in solving the puzzle of gastric cancer immunotherapy. Gastric cancer is considered as the third leading cause of deaths and the fifth most common cancers worldwide. Common treatment approaches include chemotherapy, radiation, gastric resection and targeted therapies. The emergence of gastric cancer immunotherapy has already shown some promising results and have altered the therapeutic procedures. Now, different combination therapies as well as novel immunotherapies targeting new molecules have been proposed. Despite ongoing investigations on the therapeutic options and significant advancements in this regard, the disease is poorly prognosed. In fact, limited therapeutic options and delayed diagnosis lead to the progression, dissemination and metastasis of the disease. Current immunotherapies are mostly based on cytotoxic immunocytes, monoclonal antibodies and gene transferred vaccines. The use of Immune checkpoint inhibitors (ICIs) have grown rapidly. In this review, we aimed to explore perspective and progression of different approaches of immunotherapy in the treatment of GC and the clinical outcomes reported so far. We also summarized the tumor immunosurveillance and tumor immunoescape. [ABSTRACT FROM AUTHOR]

• A market-driven, permit-based scheme for managing traffic on single-bottleneck roadways. • An integrated framework imbedding the permit-based traffic management scheme. • A mathematical programming model and a heuristic algorithm for endowment of the permits. • An iterative auction mechanism for optimizing the revenue outcome of the permit-based scheme. • The strategy-proof scheme guaranteeing minimum equilibrium market prices. We study the problem of managing traffic on single-bottleneck roadways using a mobility permit (MP)-based method—a scheme that becomes increasingly relevant to shape the new mobility culture. To identify the main concerns of the involved stakeholders, we present an integrated framework that embeds our MP-based traffic management scheme, in which the mobility permit users are allowed to choose the options best matching their travel needs. Furthermore, to find the most effective market prices, the proposed permit allocation approach is integrated into an iterative auction process to achieve the best equilibrium state in terms of permit prices that is suitable for users, mitigating potential efficiency loss. Our computational results indicate the effectiveness of the proposed scheme as an alternative solution for permit-based mobility management on single-bottleneck roadways. [ABSTRACT FROM AUTHOR]

Road freight transportation (RFT) has received significant attention from the government, industry, and academia because of its high energy consumption and carbon emissions. To reduce the negative effects of the accelerating use of RFT, China has recently been committed to promote green RFT. Efforts made in this direction have mainly focused on supply-side policy and technical measures, while neglecting the importance of demand-side psychological and behavioral strategies. Recent research suggests that drivers play a fundamental role in generating environmental impacts during transport. Thus, the success of green RFT should also be based on a thorough understanding of the drivers' environmental awareness and pro-environmental behavior. In this study, we investigate the current levels of environmental awareness and pro-environmental behavior among China's RFT drivers, and explore the role of environmental awareness as defined by four main components—environmental concern, environmental attitude, environmental knowledge, and behavioral intention—in motivating pro-environmental RFT behavior. We also test the moderating effect of perceived policy effectiveness on the awareness–behavior relationship. Hypotheses are examined using a sample of 243 truck drivers with structural equation modeling and hierarchical regression analysis. The results indicate the existence of an environmental awareness–behavior gap. We determine that the awareness score (3.2) is higher than the behavior score (3.1) because barriers to pro-environmental behavior are stronger than motivators. We also find that environmental concern, attitude, and knowledge indirectly affect pro-environmental behavior via behavioral intention. Moreover, a high level of perceived policy effectiveness facilitates the transformation of awareness into behavior, bridging the awareness–behavior gap. Our study confirms the importance of environmental awareness and effective incentive policies in encouraging pro-environmental RFT behavior. The conclusions will aid researchers' understanding of drivers' environmental awareness and pro-environmental behavior in China's RFT, and compel transport policy makers and managers to implement more effective measures that promote environmentally sustainable RFT. • This study investigated environmental awareness and pro-environmental behavior within China's RFT context. • Drivers' awareness score is higher than their behavior score, indicating a gap. • Environmental concern, attitudes, and knowledge indirectly affect behavior via behavioral intention. • Perceived policy effectiveness helps transform awareness into behavior, thereby bridging the awareness–behavior gap. • The findings are valuable for transport policy makers and managers who are seeking to promote sustainable RFT. [ABSTRACT FROM AUTHOR]

A series of novel non-covalent peptidomimetic proteasome inhibitors possessing bulky group at the C-terminus and N -alkylation at the N -terminus were designed with the aim to increase metabolic stabilities in vivo. All the target compounds were screened for their inhibitory activities against human 20S proteasome, and most analogs exhibited notable potency compared with the positive control bortezomib with IC 50 values lower than 10 nM, which also displayed potent cytotoxic activities against multiple myeloma (MM) cell lines and human acute myeloid leukemia (AML) cells. Furthermore, whole blood stability and in vivo proteasome inhibitory activity experiments of selected compounds were conducted for further evaluation, and the representative compound 43 (IC 50 = 8.39 ± 2.32 nM, RPMI-8226: IC 50 = 15.290 ± 2.281 nM, MM-1S: IC 50 = 9.067 ± 3.103 nM, MV-4-11: IC 50 = 2.464 ± 0.713 nM) revealed a half-life extension of greater than 9-fold (329.21 min VS 36.79 min) and potent proteasome inhibitory activity in vivo. The positive results confirmed the reliability of the metabolism guided optimization strategy, and the analogs discovered are potential leads for exploring new anti -MM drugs. [Display omitted] • A series of non-covalent proteasome inhibitors with bulky group and N -alkylation were synthesized and evaluated. • Compounds 43 and 53 exhibited excellent proteasome inhibitory activities. • Compounds 43 and 53 displayed potent antiproliferative activities against RPMI-8226, MM-1S and MV-4-11 cell lines. • Compound 43 showed high metabolic stability in vitro. [ABSTRACT FROM AUTHOR]

Low-Intensity Pulsed Ultrasound (LIPUS) holds therapeutic potential in promoting skeletal muscle regeneration, a biological process mediated by satellite cells and myoblasts. Despite their central roles in regeneration, the detailed mechanistic of LIPUS influence on satellite cells and myoblasts are not fully underexplored. In the current investigation, we administrated LIPUS treatment to injured skeletal muscles and C2C12 myoblasts over five consecutive days. Muscle samples were collected on days 6 and 30 post-injury for an in-depth histological and molecular assessment, both in vivo and in vitro with immunofluorescence analysis. During the acute injury phase, LIPUS treatment significantly augmented the satellite cell population, concurrently enhancing the number and size of newly formed myofibers whilst reducing fibrosis levels. At 30 days post-injury, the LIPUS-treated group demonstrated a more robust satellite cell pool and a higher myofiber count, suggesting that early LIPUS intervention facilitates satellite cell proliferation and differentiation, thereby promoting long-term recovery. Additionally, LIPUS markedly accelerated C2C12 myoblast differentiation, with observed increases in AMPK phosphorylation in myoblasts, leading to elevated expression of Glut4 and PGC-1α, and subsequent glucose uptake and mitochondrial biogenesis. These findings imply that LIPUS-induced modulation of myoblasts may culminate in enhanced cellular energy availability, laying a theoretical groundwork for employing LIPUS in ameliorating skeletal muscle regeneration post-injury. Utilizing the cardiotoxin (CTX) muscle injury model, we investigated the influence of LIPUS on satellite cell homeostasis and skeletal muscle regeneration. Our findings indicate that LIPUS promotes satellite cell proliferation and differentiation, thereby facilitating skeletal muscle repair. Additionally, in vitro investigations lend credence to the hypothesis that the regulatory effect of LIPUS on satellite cells may be attributed to its capability to enhance cellular energy metabolism. • LIPUS promotes skeletal muscle regeneration by enhancing the activity of satellite cells and myoblasts. • Early LIPUS intervention can promotes long-term recovery by facilitating satellite cell proliferation and differentiation. • LIPUS treatment markedly accelerates C2C12 myoblast differentiation by enhancing cellular energy availability. • The study lay a theoretical foundation forthe clinical application of LIPUS in improving skeletal muscle recovery. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with the problem of how to place hazardous material cars in the train assembly process so that the overall derailment risk can be minimized. The approach considers both the probability of railway cars derailing en route by position as well as the risk associated with additional operations in the rail yard using recent US FRA data. The merits of this car placement model are illustrated through a case study of a railway corridor that connects Los Angeles (CA) to Chicago (IL). The case study demonstrates that the proposed risk minimization strategy could be implemented with minimal rail yard operation cost. [Copyright &y& Elsevier]

Abstract: This paper describes a study on willingness to pay (WTP) and public acceptability for High-Occupancy/Toll (HOT) lanes using empirical evidence from Toronto, Ontario, Canada. From a stated preference survey of more than 250 drivers, we estimate mean willingness to pay values under various trip conditions and for various traveler characteristics. The study provides statistically significant evidence on the relationships between willingness to pay and the improvement in travel speeds in HOT lanes, the length of the trip, and the urgency of on-time arrival. Furthermore, our study confirms several literature findings from previous studies on the relationship between travelers'' willingness to pay and income as well as prior experience with HOT lanes. Some of the findings are qualitatively validated on the basis of the observed travel behavior in choosing tolled facilities over untolled facilities during periods of heightened congestion and urgency. [Copyright &y& Elsevier]

Abstract: Ranking a group of candidate sites and selecting from it the high-risk locations or hotspots for detailed engineering study and countermeasure evaluation is the first step in a transport safety improvement program. Past studies have however mainly focused on the task of applying appropriate methods for ranking locations, with few focusing on the issue of how to define selection methods or threshold rules for hotspot identification. The primary goal of this paper is to introduce a multiple testing-based approach to the problem of selecting hotspots. Following the recent developments in the literature, two testing procedures are studied under a Bayesian framework: Bayesian test with weights (BTW) and a Bayesian test controlling for the posterior false discovery rate (FDR) or false negative rate (FNR). The hypotheses tests are implemented on the basis of two random effect or Bayesian models, namely, the hierarchical Poisson/Gamma or Negative Binomial model and the hierarchical Poisson/Lognormal model. A dataset of highway–railway grade crossings is used as an application example to illustrate the proposed procedures incorporating both the posterior distribution of accident frequency and the posterior distribution of ranks. Results on the effects of various decision parameters used in hotspot identification procedures are discussed. [Copyright &y& Elsevier]

Abstract: Frequently transportation engineers are required to make difficult safety investment decisions in the face of uncertainty concerning the cost-effectiveness of different countermeasures. For certain types of highway–railway grade crossings, this problem is further aggravated due to the lack of observed before and after collision data that reflects the impact of specific countermeasures. This study proposes a Bayesian data fusion method as an attempt to overcome these challenges. In this framework, we make use of previous research findings on the effectiveness of a given countermeasure, which could vary by jurisdictions and operating conditions to obtain a priori inference on its expected effects. We then use locally calibrated models, which are valid for a specific jurisdiction, to develop the current best estimates regarding the countermeasure effects. By using a Bayesian framework, these two sources are integrated to obtain the posterior distribution of the countermeasure effectiveness. As a result, the outputs provide information not only of the expected collision response to a specific countermeasure but also its variance and corresponding probability distribution for a range of likely values. Examples from Canadian highway–railway grade crossing data are used to illustrate the proposed methodology and the specific effects of prior knowledge and data likelihood on the combined estimates of countermeasure effects. [Copyright &y& Elsevier]

• Deep learning models were employed to predict train delays. • Train operations were modeled as sequences. • Interactions were captured from train groups in the prediction model. • The proposed model shows satisfactory performance on different railway lines. This paper presents a carefully designed train delay prediction model, called FCLL-Net, which combines a fully-connected neural network (FCNN) and two long short-term memory (LSTM) components, to capture operational interactions. The performance of FCLL-Net is tested using data from two high speed railway lines in China. The results show that FCLL-Net has significantly improved prediction performance, over 9.4% on both lines, in terms of the selected absolute and relative metrics compared to the commonly used state-of-the-art models. Additionally, the sensitivity analysis demonstrates that interactions of train operations and weather-related features are of great significance to consider in delay prediction models. [ABSTRACT FROM AUTHOR]

• Machine learning models were proven to be incapable of unexpected situation prediction. • Support vector regression and Kalman filter were combined for train running time prediction. • The Kalman filter substantially improved the performance of the machine learning models. • The proposed method conforms to the timeliness and high accuracy requirements of real-time prediction. This study aims to propose a hybrid model that comprises support vector regression (SVR) and a Kalman filter (KF) to improve the train running time prediction accuracy of machine learning models during railway disruptions. The SVR was trained using offline data, whereas the KF updated the SVR prediction using real-time information. Thus, the hybrid model mitigates the time-consuming online training of machine learning models and their inability to reflect real-time information when using offline training. To obtain a high-performance prediction model, four key SVR parameters were first optimized based on cross-validation. Then, SVR predictions were evaluated using the mean absolute and percentage errors of the test datasets by considering the trains that suffered disruptions. The results from this evaluation show that the SVR notably outperformed other benchmark models but was unable to provide satisfactory predictions under unexpected situations. Next, we applied the KF to update the SVR prediction using real-time information and conducted model performance evaluation of the predictions based on the hybrid model. The corresponding results show that the KF significantly improved the SVR prediction accuracy under unexpected disruption situations. Furthermore, using offline training, along with the KF instead of online training, substantially reduced the computational time. [ABSTRACT FROM AUTHOR]

• A human brain tau PET template in MNI space is constructed for AD researches. • The template performs well in the spatial normalization and voxel-wise comparison. • This template is convenient and fits in most clinical settings. • This tau PET template is a supplement to human brain templates in SPM. • This tau PET template is proven practical in AD researches. Positron emission tomography (PET) imaging techniques of tau retention in the human brain are important for mechanistic studies of Alzheimer's disease (AD). However, the method for effectively conducting voxel-wise analysis on tau PET images still needs to be improved. In the present study, we introduced a tau PET template for the human brain in Montreal Neurological Institute (MNI) space for the convenient and reliable voxel-wise analysis of tau PET images in AD studies. Twenty-four AD patients and 22 controls were used to construct the tau PET template, and an additional 22 subjects (11 AD patients and 11 controls) were enrolled to evaluate the performance of the template. Thirty regions (28 cortical and 2 subcortical regions) throughout the brain were used to evaluate the accuracy of the tau PET template. A significant relationship (R2 = 0.848, P < 0.001) was found between the standardized uptake value ratios (SUVRs) obtained by the tau PET template and magnetic resonance imaging (MRI)-aided approach, and the paired-sample t -test showed no significant difference (P = 0.62) between the values. These two approaches revealed consistent brain regions with high tau retention. The tau PET template was comparable with the traditional MRI-aided strategy. Furthermore, compared to the MRI-aided approach, the tau PET template was more convenient and easier to use. More importantly, in most clinical settings, AD patients who underwent PET/computed tomography (CT) typically do not have MR images, in which case the traditional MRI-aided approach would not be applicable. Our tau PET template overcame this deficiency and may serve as a useful tool in AD research. This tau PET template performed well and may serve as a useful tool in future AD studies. [ABSTRACT FROM AUTHOR]

• Three factors are ascertained to measure the effects of disruptions. • Real-time prediction requirements are particularly considered in the model. • The model shows high accuracy in predicting the effects of disruptions. • The model shows strong generalizability on two different high-speed railway lines. Based on the Bayesian network (BN) paradigm, we propose a hybrid model to predict the three main consequences of disruptions and disturbances during train operations, namely, the primary delay (L), the number of affected trains (N), and the total delay times (T). To obtain an effective BN structure, we first analyze the dependencies of the involved factors on each station and among adjacent stations, given domain knowledge and expertise about operational characteristics. We then put forward four candidate BN structures, integrating expert knowledge, the interdependencies learned from real-world data, and real-time prediction and operational requirements. Next, we train the candidate structures based on a 5-fold cross-validation method, using the operational data from Wuhan-Guangzhou (W-G) and Xiamen-Shenzhen (X-S) high-speed railway (HSR) lines in China. The best performing structure is nominated to predict the consequences of disruptions and disturbances in the two HSR lines. Comparisons results show that the proposed model outperforms three other commonly used predictive models, reaching an average prediction accuracy of 96.6%, 74.8%, and 91.0% on the W-G HSR line, and 94.8%, 91.1%, and 87.9% on the X-S HSR line for variables L , N , and T , respectively. [ABSTRACT FROM AUTHOR]

• A cleaner vanadium extraction process with ultrasonic-H 2 O 2 synergistic acid leaching was developed. • Under the new process, vanadium recovery and H 2 O 2 decomposition rate increased by 63.30% and 106.38%. • The ultrasonic process requires only 50% of the energy consumption of conventional conditions. Residual vanadium accounts for about 1% of calcification roasted-acid leaching tailings after industrial vanadium extraction. Vanadium pollution is primarily induced by the stacking of tailings. In this study, the conventional leaching process based on sulfuric acid is compared with the ultrasound-H 2 O 2 synergistic enhanced process in the extraction of residual vanadium from calcification roasted-acid leaching tailings. Besides, the experimental process parameters are also optimized. Under the optimal conditions, the vanadium leaching rate increases by 63.64%, and the energy consumption decreases by 50%. The experimental enhancement mechanism is clarified by exploring the decomposition patterns of H 2 O 2 under ultrasound in combination with the characterization results based on electron paramagnetic resonance (EPR). Under the action of ultrasound, H 2 O 2 is effectively decomposed into •OH radicals to provide higher oxidation potential, which accelerates the reaction process. Through the characterization and analysis of the samples before and after the experiment, it is found that H 2 O 2 enhances the cavitation effect of ultrasound. This effectively destroys the composite oxide structure on the surface of tailing particles, thus increasing the reaction area. In conclusion, the ultrasound-H 2 O 2 synergistic vanadium leaching process exhibits broad application prospects in the management and recovery of industrial tailings. [ABSTRACT FROM AUTHOR]

An efficient rhodium-catalyzed direct C-H activation for the synthesis of isoquinolone with 1,4,2-dioxazol-5-ones as oxidizing directing groups have been developed, which featured mild reaction conditions, short reaction time and satisfactory yield. An efficient approach to isoquinolone have been developed, which featured in completing expeditiously under mild conditions with satisfactory yield. Image 1 [ABSTRACT FROM AUTHOR]

Hypervirulent carbapenem-resistant Klebsiella pneumoniae (hv-CRKP) has been widely reported and poses a global threat. However, the comprehensive genetic structure of ST11-KL64 hv-CRKP and the possible evolutionary mechanisms from a genetic structure perspective of this high-risk clone remain unclear. Here, a bla KPC-2 - bla NDM-1 -positive ST11-KL64 hv-CRKP isolate was obtained from a human bloodstream infection (BSI). Whole-genome sequencing and bioinformatics analyses revealed that it contained a fusion plasmid, pKPTCM2–1. pKPTCM2–1 is a conjugative plasmid composed of an oriT -positive pLVPK-like virulence plasmid and a type IV secretion system-produced bla NDM-1 -bearing IncX3 plasmid mediated by IS 26 -based co-integration. This progress generated 8-bp target site duplications (TGAAAACC) on both sides. The fusion plasmid possessed self-transferability and could be transferred to bla KPC-2 -harboring ST11-KL64 CRKP to form the ST11-KL64 hv-CRKP clone. The pLVPK-like-positive ST11-KL64 strain exhibited virulence levels similar to those of the typical hypervirulent K. pneumoniae NTUH-2044. The mutation, Tet(A) (A276S), which was believed to lead to tigecycline resistance was observed. Overall, this high-risk clone has emerged as a tremendous threat in fatal BSIs and thus, targeted surveillance is an urgent need to contain the hv-CRKP clones. • One bla KPC-2 - bla NDM-1 -positive ST11-KL64 hv-CRKP in human bloodstream infection. • oriT -positive pLVPK-like virulence plasmid and bla NDM-1 -bearing IncX3 plasmid fusion. • One mutation of Tet(A) (A276S) maybe lead to tigecycline resistance. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a methodology to estimate and/or update SPF parameters under limited data conditions. [•] An extensive simulation exercise is developed to demonstrate the robustness of the suggested method. [•] Results clearly show the appropriateness of the presented technique. [•] This study contributes to unification of SPF updating process and understanding of comparative aspects of a large number of priors. [ABSTRACT FROM AUTHOR]

Abstract: Problem: This paper aims to address two related issues when applying hierarchical Bayesian models for road safety analysis, namely: (a) how to incorporate available information from previous studies or past experiences in the (hyper) prior distributions for model parameters and (b) what are the potential benefits of incorporating past evidence on the results of a road safety analysis when working with scarce accident data (i.e., when calibrating models with crash datasets characterized by a very low average number of accidents and a small number of sites). Method: A simulation framework was developed to evaluate the performance of alternative hyper-priors including informative and non-informative Gamma, Pareto, as well as Uniform distributions. Based on this simulation framework, different data scenarios (i.e., number of observations and years of data) were defined and tested using crash data collected at 3-legged rural intersections in California and crash data collected for rural 4-lane highway segments in Texas. Results: This study shows how the accuracy of model parameter estimates (inverse dispersion parameter) is considerably improved when incorporating past evidence, in particular when working with the small number of observations and crash data with low mean. The results also illustrates that when the sample size (more than 100 sites) and the number of years of crash data is relatively large, neither the incorporation of past experience nor the choice of the hyper-prior distribution may affect the final results of a traffic safety analysis. Conclusions: As a potential solution to the problem of low sample mean and small sample size, this paper suggests some practical guidance on how to incorporate past evidence into informative hyper-priors. By combining evidence from past studies and data available, the model parameter estimates can significantly be improved. The effect of prior choice seems to be less important on the hotspot identification. Impact on Industry: The results show the benefits of incorporating prior information when working with limited crash data in road safety studies. [Copyright &y& Elsevier]

Abstract: In this paper, we aim to identify the different factors that influence injury severity of highway vehicle occupants, in particular drivers, involved in a vehicle-train collision at highway-railway grade crossings. The commonly used approach to modeling vehicle occupant injury severity is the traditional ordered response model that assumes the effect of various exogenous factors on injury severity to be constant across all accidents. The current research effort attempts to address this issue by applying an innovative latent segmentation based ordered logit model to evaluate the effects of various factors on the injury severity of vehicle drivers. In this model, the highway-railway crossings are assigned probabilistically to different segments based on their attributes with a separate injury severity component for each segment. The validity and strength of the formulated collision consequence model is tested using the US Federal Railroad Administration database which includes inventory data of all the railroad crossings in the US and collision data at these highway railway crossings from 1997 to 2006. The model estimation results clearly highlight the existence of risk segmentation within the affected grade crossing population by the presence of active warning devices, presence of permanent structure near the crossing and roadway type. The key factors influencing injury severity include driver age, time of the accident, presence of snow and/or rain, vehicle role in the crash and motorist action prior to the crash. [Copyright &y& Elsevier]

Abstract: Train derailments are important safety concerns, and they become increasingly so when dangerous goods (DG) are involved. One way to reduce the risk of DG derailments is through effective DG railway car placement along the train consist. This paper investigates the relationship between DG railway car placement and derailment for different route attributes and DG shipments. A model is presented for estimating the probability of derailment by position, based on the estimated point of derailment (POD) and the number of cars derailing. A DG placement model that considers in-transit derailment risk is shown to provide a sound scientific basis for effective DG marshalling in conventional rail hump yard operations. [Copyright &y& Elsevier]

Abstract: Impulsivity is a pathological hallmark of drug addiction. However, little is known about the neuropsychological underpinnings of this impaired impulsive control network on drug addiction. Twenty two abstinent heroin dependent (HD) subjects and 15 cognitively normal (CN) subjects participated in this study. Resting-state functional connectivity MRI was employed to measure abnormalities in the intrinsic amygdala functional connectivity (iAFC) network activity and the Barratt Impulsive Scale, 11th version was used to measure impulsivity. Linear regression analysis was applied to detect the neural constructs underlying impulsivity by correlating iAFC network activity with impulsive scores. In the HD group, higher impulsivity scores and significantly enhanced iAFC network activity were found, especially in bilateral thalamus, right insula, and inferior frontal gyrus. Markedly decreased anticorrelated iAFC network activity was seen in the left precuneus, and even switched to positive correlation pattern in right precuneus, relative to the CN group. The iAFC network strengths in the HD group were positively correlated with impulsivity in the right subcallosal gyrus, insula, thalamus and posterior cingulate cortex, and negatively correlated in left fusiform area. In the CN group, the left pre-somamotor area-amygdala connectivity was positively correlated, and right orbital frontal cortex-amygdala and precuneus-amygdala connectivity were negatively correlated with impulsivity scores. Our study demonstrates different constructs of the impulsive network in HD and CN subjects. Altered iAFC network connectivity in HD subjects may contribute to the loss of impulsive control. This further facilitates our understanding of the neural underpinnings of behavior dysfunction in addiction. [Copyright &y& Elsevier]

• An integrated framework is proposed to predict lane-changing risk. • Driving intentions are recognized using LSTM neural network. • LGBM algorithm achieves a higher lane-changing risk prediction accuracy. • Feature importance analysis is conducted using LGBM classifier. Proactive lane-changing (LC) risk prediction can assist driver's LC decision-making to ensure driving safety. However, most previous studies on LC risk prediction did not consider the driver's intention recognition, which made it difficult to guarantee the timeliness and practicability of LC risk prediction. Moreover, the difference in driving risks and its influencing factors between LC to left lane (LCL) and LC to right lane (LCR) have rarely been investigated. To bridge the above research gaps, this study proposes a proactive LC risk prediction framework which integrates the LC intention recognition module and LC risk prediction module. The Long Short-term Memory (LSTM) neural network with time-series input was employed to recognize the driver's LC intention. The Light Gradient Boosting Machine (LGBM) algorithm was then applied to predict the LC risk. Feature importance analysis was lastly conducted to obtain the key features that affect the LC risk. The highD trajectory dataset was used for framework validation. Results show that the recognition accuracy of the driver's LCL, LCR and lane-keeping (LK) intentions based on the proposed LSTM model are 97%, 96% and 97%, respectively. Meanwhile, the LGBM algorithm outperforms other machine learning algorithms in LC risk prediction. The results from feature importance analysis show that the interaction characteristics of the LC vehicle and its preceding vehicle in the current lane have the greatest impact on the LC risk. The proposed framework could potentially be implemented in advanced driver-assistance system (ADAS) or autonomous driving system for improved driving safety. [ABSTRACT FROM AUTHOR]

Abstract: In probe-based traffic monitoring systems, traffic conditions can be inferred based on the position data of a set of periodically polled probe vehicles. In such systems, the two consecutive polled positions do not necessarily correspond to the end points of individual links. Obtaining estimates of travel time at the individual link level requires the total traversal time (which is equal to the polling interval duration) be decomposed. This paper presents an algorithm for solving the problem of decomposing the traversal time to times taken to traverse individual road segments on the route. The proposed algorithm assumes minimal information about the network, namely network topography (i.e. links and nodes) and the free flow speed of each link. Unlike existing deterministic methods, the proposed solution algorithm defines a likelihood function that is maximized to solve for the most likely travel time for each road segment on the traversed route. The proposed scheme is evaluated using simulated data and compared to a benchmark deterministic method. The evaluation results suggest that the proposed method outperforms the bench mark method and on average improves the accuracy of the estimated link travel times by up to 90%. [Copyright &y& Elsevier]

• A hybrid deep learning model was developed for delay propagation patterns. • Train timetables were modeled as images. • A cost-sensitive technique was used to address the data imbalance challenge. • The proposed model shows satisfactory performance on different situations. As a vital component of train operational control, train delay propagation pattern discovery is critically important for both railway controllers and passengers. In this study, we present a carefully designed deep learning model, called FCF-Net, that comprises fully connected neural networks (FCNN) and convolutional neural networks (CNN) for train delay propagation pattern recognition in railway systems. FCF-Net first uses a CNN component that handles train timetables as images to capture interactions of train events and an FCNN component to capture the influence of non-operational features separately; then it uses another FCNN component to combinedly learn the dependencies between operational and non-operational features. In addition, considering the imbalance of train delay data, a cost-sensitive technique that assigns different misclassification costs for different class was used to better deal with the imbalanced data. The main goal of the FCF-Net is to realize efficient and accurate train delay propagation pattern recognition by mining potential knowledge from train operation data. The predictive and computational performance of the model was tested and evaluated on data from two high-speed railway lines with different operational features in China. The results show that FCF-Net, once trained with sufficient data, outperforms conventional deep learning with common loss and other state-of-the-art deep learning models for train delay propagation pattern recognition, indicating its capability in knowledge discovery from train operation data. In addition, the computational results show that FCF-Net exhibits more efficient training process than existing state-of-the-art deep learning models. [ABSTRACT FROM AUTHOR]