Integrated Transcriptomics and Metabolomics Analysis of the Fructan Metabolism Response to Low-Temperature Stress in Garlic.

As the main reserve carbohydrate in garlic, fructan contributes to garlic's yield and quality formation. Numerous studies have shown that plant fructan metabolism induces a stress response to adverse environments. However, the transcriptional regulation mechanism of garlic fructan in low-temperature environments is still unknown. In this study, the fructan metabolism of garlic seedlings under low-temperature stress was revealed by transcriptome and metabolome approaches. With the extension of stress time, the number of differentially expressed genes and metabolites increased. Using weighted gene co-expression network analysis (WGCNA), three key enzyme genes related to fructan metabolism were screened (a total of 12 transcripts): sucrose: sucrose 1-fructosyltransferase (1-SST) gene; fructan: fructan 6G fructosyltransferase (6G-FFT) gene; and fructan 1-exohydrolase (1-FEH) gene. Finally, two hub genes were obtained, namely Cluster-4573.161559 (6G-FFT) and Cluster-4573.153574 (1-FEH). The correlation network and metabolic heat map analysis between fructan genes and carbohydrate metabolites indicate that the expression of key enzyme genes in fructan metabolism plays a positive promoting role in the fructan response to low temperatures in garlic. The number of genes associated with the key enzyme of fructan metabolism in trehalose 6-phosphate was the highest, and the accumulation of trehalose 6-phosphate content may mainly depend on the key enzyme genes of fructan metabolism rather than the enzyme genes in its own synthesis pathway. This study not only obtained the key genes of fructan metabolism in garlic seedlings responding to low temperatures but also preliminarily analyzed its regulatory mechanism, providing an important theoretical basis for further elucidating the cold resistance mechanism of garlic fructan metabolism.

A novel approach for automatic segmentation of prostate and its lesion regions on magnetic resonance imaging.

Objective: To develop an accurate and automatic segmentation model based on convolution neural network to segment the prostate and its lesion regions. Methods: Of all 180 subjects, 122 healthy individuals and 58 patients with prostate cancer were included. For each subject, all slices of the prostate were comprised in the DWIs. A novel DCNN is proposed to automatically segment the prostate and its lesion regions. This model is inspired by the U-Net model with the encoding-decoding path as the backbone, importing dense block, attention mechanism techniques, and group norm-Atrous Spatial Pyramidal Pooling. Data augmentation was used to avoid overfitting in training. In the experimental phase, the data set was randomly divided into a training (70%), testing set (30%). four-fold cross-validation methods were used to obtain results for each metric. Results: The proposed model achieved in terms of Iou, Dice score, accuracy, sensitivity, 95% Hausdorff Distance, 86.82%,93.90%, 94.11%, 93.8%,7.84 for the prostate, 79.2%, 89.51%, 88.43%,89.31%,8.39 for lesion region in segmentation. Compared to the state-of-the-art models, FCN, U-Net, U-Net++, and ResU-Net, the segmentation model achieved more promising results. Conclusion: The proposed model yielded excellent performance in accurate and automatic segmentation of the prostate and lesion regions, revealing that the novel deep convolutional neural network could be used in clinical disease treatment and diagnosis.

Identification of the key metabolites and related genes network modules highly associated with the nutrients and taste components among different Pepino (Solanum muricatum) cultivars.

There is considerable knowledge about plant compounds that produce flavor, scent, and aroma. Aside from the similarities, however, groups of plant-produced nutrients and taste components have little in common with each other. Network analysis holds promise for metabolic gene discovery, which is especially important in plant systems where metabolic networks are not yet fully resolved. To bridge this gap, we propose a joint model of gene regulation and metabolic reactions in two different pepino varieties. Differential metabolomics analysis is carried out for detection of eventual interaction of compound. We adopted a multi-omics approach to profile the transcriptome and metabolome analyze differences in phenolic acids, flavonoids, organic acids, lipids, alkaloids, and sugars between LOF and SRF. The two most predominant classes of metabolites are phenolic acids and lipids in pepino. Overall results show enrichment in most DEGs was carbohydrate and biosynthesis of secondary metabolites pathway. Results of DEMs predominantly comprised N-p-coumaroyl agmatine and tryptamine, and significant differences were observed in their expression between LOF and SRF. Integrated DEMs and DEGs specific networks were constructed by combining two types of networks: transcriptional regulatory networks composed of interactions between DEMs and the regulated genes, and pepino metabolite-metabolite interaction networks. Newly discovered features, such as DEGs (USPA, UBE2 and DELLA) involved in the production of secondary metabolites are found in coregulated gene clusters. Moreover, lipid metabolites were most involved in DEMs correlations by OPLS-DA while identifying a significant number of DEGs co-regulated by SENP1, HMGCS et al. These results further that the metabolite discrepancies result from characterized the nutrients and taste components between two pepino genotype. Among the possible causes of the differences between species in pepino metabolite concentrations is co-regulated by these DEGs, continue to suggest that novel features of metabolite biosynthetic pathway remain to be uncovered. Finally, the integrated metabolome and transcriptome analyses have revealed that many important metabolic pathways are regulated at the transcriptional level. The metabolites content differences observed among varieties of the same species mainly originates from different regulated genes and enzymes expression. Overall, this study provides new insights into the underlying causes of differences in the plant metabolites and suggests that genetic data can be used to improve its nutrients and taste components.

Effects of light spectrum on the morphophysiology and gene expression of lateral branching in Pepino (Solanum muricatum).

In the present study, we determined the morphological and physiological indicators of Pepino to elucidate its lateral branching responses to different light qualities using a full-spectrum lamp (F) as the control and eight different light ratios using blue light (B) and red light (R). In addition, correlation analysis revealed that the gene expression patterns correlated with lateral branching under various light treatments. Compared with the F treatment, the R treatment increased the plant height and inhibited the elongation of lateral branches, in contrast with the B treatment. The number of lateral branches did not change significantly under different light quality treatments. Moreover, correlation analysis showed that the ratio of blue light was significantly positively correlated with the length of lateral branches and significantly negatively correlated with plant height, aboveground dry weight, and other indicators. We conducted transcriptome sequencing of the sites of lateral branching at three periods under different light quality treatments. The gene related to photodynamic response, cryptochrome (CRY), was the most highly expressed under B treatment, negatively regulated lateral branch length, and positively correlated with plant height. Branched 1, a lateral branch regulation gene, was upregulated under R treatment and inhibited branching. Overall, the red light facilitated internode elongation, leaf area expansion, plant dry weight increase, and inhibition of lateral branching. Soluble sugar content increased, and the lateral branches elongated under blue light. Different light qualities regulated lateral branching by mediating different pathways involving strigolactones and CRY. Our findings laid a foundation for further clarifying the response mechanism of Pepino seedlings to light and provided a theoretical reference for elucidating the regulation of different light qualities on the lateral branching of Pepino.

Pepino (Solanum muricatum) Metabolic Profiles and Soil Nutrient Association Analysis in Three Growing Sites on the Loess Plateau of Northwestern China.

Different soil nutrients affect the accumulation characteristics of plant metabolites. To investigate the differences among the metabolites of pepino grown in greenhouses on the Loess Plateau in northwest China, we investigated the main soil nutrients and their correlation with metabolites. A total of 269 pepino metabolites were identified using UPLC-QTOF-MS to detect metabolites in fruits from three major pepino growing regions and analyze their differential distribution characteristics. A total of 99 of these substances differed among pepino fruits from the three areas, and the main classes of the differential metabolites were, in order of number: amino acids and derivatives, nucleotides and derivatives, organic acids, alkaloids, vitamins, saccharides and alcohols, phenolic acids, lipids and others. An environmental factor analysis identified soil nutrients as the most significant differentiator. Five soil nutrient indicators: TN (total nitrogen), TP (total phosphorus), AP (available phosphorus), AK (available potassium), and OM (organic matter), exhibited significant differences in three growing sites. Metabolite and soil nutrient association analysis using redundancy analysis (RDA) and the Mantel test indicated that TN and OM contributed to the accumulation of amino acids and derivatives, nucleotides and derivatives, and alkaloids while inhibiting organic acids, vitamins coagulation biosynthesis. Moreover, AP and TP were associated with the highest accumulation of saccharides and, alcohols, phenolic acids. Consequently, differences in soil nutrients were reflected in pepino metabolite variability. This study clarified the metabolite variability and the relationship between pepino and soil nutrients in the main planting areas of northwest China. It provides a theoretical basis for the subsequent development of Pepino's nutritional value and cultivation management.

Risky behavior analysis for cross-border drivers: A logit model and qualitative comparative analysis of odds of fault and injury vulnerability in Guangdong, Hong Kong and Macau.

INTRODUCTION: Due to globalization and the acceleration of cross-border exchanges, cross-border risk behaviors have received widespread attention. Previous research has concluded that foreign cross-border drivers engage in relatively more risk-taking behavior patterns and are likely to experience a higher crash rate or be more inclined to cause severe crashes. However, there is little evidence on the comparison of drivers who belong to the same ethnic group driving across within-country borders. METHOD: Based on the cross-border motor-vehicle crash reports in 2006-2010 from the Road Traffic Accident Database of the China Ministry of Public Security, this paper examines the risk factors of being at fault and getting killed or seriously injured in cross-border traffic crashes and casual paths toward crash liability and injury severity for Hong Kong and Macao drivers driving in the Chinese mainland. RESULTS: There are extremely complex factors behind drivers from Hong Kong and Macao causing at-fault crashes or sustaining fatal and serious injuries in the Chinese mainland. Factors such as gender, age, illumination, and weather conditions do not individually affect the risk of driver at-fault crashes or severe casualties in the crashes among Hong Kong and Macao drivers driving in the Chinese mainland. Nonetheless, collectively, these factors influence them along with different vehicle types, roads, and environmental factors. CONCLUSIONS: This paper provides more theoretical findings for understanding the compound effect of multiple risk factors involving cross-border at-fault crashes or serious casualties. The conclusions of this research are valuable as representative references for cross-border risk management policies. PRACTICAL APPLICATIONS: To reduce the effects of different factors on cross-border risky driving behaviors and/or injurious crashes, various measures should be focused on, including specialized driver training, enhancement of the roads/environment, development of effective road safety campaigns, and directives facilitating cross-border cooperation in the field of road safety.

A bibliometric of publication trends in medical image segmentation: Quantitative and qualitative analysis.

PURPOSE: Medical images are important in diagnosing disease and treatment planning. Computer algorithms that describe anatomical structures that highlight regions of interest and remove unnecessary information are collectively known as medical image segmentation algorithms. The quality of these algorithms will directly affect the performance of the following processing steps. There are many studies about the algorithms of medical image segmentation and their applications, but none involved a bibliometric of medical image segmentation. METHODS: This bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates. RESULTS: The analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning-based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network-based algorithm was the research hotspots and frontiers. CONCLUSIONS: Through this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network-based medical image segmentation.

Transcriptome-wide and expression analysis of the NAC gene family in pepino (Solanum muricatum) during drought stress.

Solanum muricatum (Pepino) is an increasingly popular solanaceous crop and is tolerant of drought conditions. In this study, 71 NAC transcription factor family genes of S. muricatum were selected to provide a theoretical basis for subsequent in-depth study of their regulatory roles in the response to biological and abiotic stresses, and were subjected to whole-genome analysis. The NAC sequences obtained by transcriptome sequencing were subjected to bioinformatics prediction and analysis. Three concentration gradient drought stresses were applied to the plants, and the target gene sequences were analyzed by qPCR to determine their expression under drought stress. The results showed that the S. muricatum NAC family contains 71 genes, 47 of which have conserved domains. The protein sequence length, molecular weight, hydrophilicity, aliphatic index and isoelectric point of these transcription factors were predicted and analyzed. Phylogenetic analysis showed that the S. muricatum NAC gene family is divided into seven subfamilies. Some NAC genes of S. muricatum are closely related to the NAC genes of Solanaceae crops such as tomato, pepper and potato. The seedlings of S. muricatum were grown under different gradients of drought stress conditions and qPCR was used to analyze the NAC expression in roots, stems, leaves and flowers. The results showed that 13 genes did not respond to drought stress while 58 NAC genes of S. muricatum that responded to drought stress had obvious tissue expression specificity. The overall expression levels in the root were found to be high. The number of genes at extremely significant expression levels was very large, with significant polarization. Seven NAC genes with significant responses were selected to analyze their expression trend in the different drought stress gradients. It was found that genes with the same expression trend also had the same or part of the same conserved domain. Seven SmNACs that may play an important role in drought stress were selected for NAC amino acid sequence alignment of Solanaceae crops. Four had strong similarity to other Solanaceae NAC amino acid sequences, and SmNAC has high homology with the Solanum pennellii. The NAC transcription factor family genes of S. muricatum showed strong structural conservation. Under drought stress, the expression of NAC transcription factor family genes of S. muricatum changed significantly, which actively responded to and participated in the regulation process of drought stress, thereby laying foundations for subsequent in-depth research of the specific functions of NAC transcription factor family genes of S. muricatum.

ADA-SR: Activity detection and analysis using security robots for reliable workplace safety.

BACKGROUND: Human-Robot Interaction (HRI) has become a prominent solution to improve the robustness of real-time service provisioning through assisted functions for day-to-day activities. The application of the robotic system in security services helps to improve the precision of event detection and environmental monitoring with ease. OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process. RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset. CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall.

Analysis of Traffic Crashes Caused by Motorcyclists Running Red Lights in Guangdong Province of China.

Motorcycles are among the primary means of transport in China, and the phenomenon of motorcyclists running red lights is becoming increasingly prevalent. Based on the traffic crash data for 2006-2010 in Guangdong Province, China, fixed- and random-parameter logit models are used to study the characteristics of motorcyclists, vehicles, roads, and environments involved in red light violations and injury severity resulting from motorcyclists' running red lights in China. Certain factors that affect the probability of motorcyclists running red lights are identified. For instance, while the likelihood of violating red light signals during dark conditions is lower than during light conditions for both car drivers and pedestrians, motorcyclists have significantly increased probability of a red light violation during dark conditions. For the resulting severe casualties in red-light-running crashes, poor visibility is a common risk factor for motorcyclists and car drivers experiencing severe injury. Regarding the relationship between red light violations and the severity of injuries in crashes caused by motorcyclists running red lights, this study indicated that driving direction and time period have inconsistent effects on the probability of red light violations and the severity of injuries. On the one hand, the likelihood of red light violations when a motorcycle rider is turning left/right is higher than when going straight, but this turning factor has a nonsignificant impact on the severity of injuries; on the other hand, reversing, making a U-turn and changing lanes have nonsignificant effects on the probability of motorcyclists' red light violations in contrast to going straight, but have a very significant impact on the severity of injuries. Moreover, the likelihood of red light violations during the early morning is higher than off-peak hours, but this time factor has a negative impact on the severity of injuries. Measures including road safety educational programs for targeted groups and focused enforcement of traffic policy and regulations are suggested to reduce the number of crashes and the severity of injuries resulting from motorcyclists running red lights.

Complete chloroplast genome sequence of turnip (Brassica rapa. ssp. rapa): genome structure and phylogenetic analysis.

Turnip (Brassica rapa. ssp. rapa) is considered worldwide to be one of the most important leaf and root vegetable crops in the Brassicaceae family. However, to date, few chloroplast (cp) genomic resources have been reported for this genus. Here, we determined the complete cp genome sequences of Brassica rapa ssp. rapa. A 153,621 bp quadripartite cycle without any gap was obtained with a large single-copy region (LSC) of 83,512 bp, a small single-copy region (SSC) of 17,683 bp, and two inverted repeat (IR), IRa and IRb of 26,213 bp. A total of 132 genes were identified, including 87 protein-coding genes (PCG), 37 transfer RNA (tRNA), and 8 ribosomal RNA (rRNA). The phylogenetic analysis of ten other crops selected showed that the turnip was most closely related to the Brassica rapa.

Overloading among crash-involved vehicles in China: identification of factors associated with overloading and crash severity.

OBJECTIVE: Motor vehicle overloading is correlated with the possibility of road crash occurrence and severity. Although overloading of motor vehicles is pervasive in developing nations, few empirical analyses have been performed on factors that might influence the occurrence of overloading. This study aims to address this shortcoming by seeking evidence from several years of crash data from Guangdong province, China. METHODS: Data on overloading and other factors are extracted for crash-involved vehicles from traffic crash records for 2006-2010 provided by the Traffic Management Bureau in Guangdong province. Logistic regression is applied to identify risk factors for overloading in crash-involved vehicles and within these crashes to identify factors contributing to greater crash severity. Driver, vehicle, road and environmental characteristics and violation types are considered in the regression models. In addition to the basic logistic models, association analysis is employed to identify the potential interactions among different risk factors during fitting the logistic models of overloading and severity. RESULTS: Crash-involved vehicles driven by males from rural households and in an unsafe condition are more likely to be overloaded and to be involved in higher severity overloaded vehicle crashes. If overloaded vehicles speed, the risk of severe traffic crash casualties increases. Young drivers (aged under 25 years) in mountainous areas are more likely to be involved in higher severity overloaded vehicle crashes. CONCLUSIONS: This study identifies several factors associated with overloading in crash-involved vehicles and with higher severity overloading crashes and provides an important reference for future research on those specific risk factors.

The effect of fatigue driving on injury severity considering the endogeneity.

INTRODUCTION: Fatigue driving is one of the most risky driving-related behaviors and represented a significant social and economic cost to the community. Several studies have already examined the relationship between fatigue driving behavior and traffic injury severity from different aspects. However, fatigue driving and injury severity in traffic crash may share some common influential factors. Ignoring the impact of these common factors will lead to endogeneity problem and result in biased parameter estimation. METHOD: Based on 38,564 crash records during 2006-2011 in Guangdong province, China, we apply a bivariate endogenous binary-ordered probit model to examine the relationship between fatigue driving and injury severity considering endogeneity of fatigue driving. We also explore the difference of influential factors between commercial and non-commercial vehicle drivers. RESULTS: This study identifies several common observed influential factors of fatigue driving propensity and fatal injury propensity and reveals a substantial and significant negative correlation of unobserved factors between them. CONCLUSIONS: The influence of fatigue driving on injury severity is significantly underestimated if the endogeneity of fatigue driving on fatal injury propensity is ignored. Factors such as vehicle insurance and road types not only affect fatal injury propensity, but also fatigue driving propensity. PRACTICAL APPLICATIONS: The findings in this study can help better understand how those factors affect fatigue driving and injury severity, and contributes to more efficient policy for preventing the harmfulness of fatigue-related crashes.

Understanding factors associated with misclassification of fatigue-related accidents in police record.

INTRODUCTION: Fatigue is one of the riskiest causes of traffic accidents threatening road safety. Due to lack of proper criteria, the identification of fatigue-related accidents by police officers largely depends on inferential evidence and their own experience. As a result, many fatigue-related accidents are misclassified and the harmfulness of fatigue on road safety is misestimated. METHOD: In this paper, a joint model framework is introduced to analyze factors contributing to misclassification of a fatigue-related accident in police reports. Association rule data mining technique is employed to identify the potential interactions of factors, and logistic regression models are applied to analyze factors that hinder police officers' identification of fatigue-related accidents. Using the fatigue-related crash records from Guangdong Province during 2005-2014, factors contributing to the false positive and false negative detection of the fatigue-related accident have been identified and compared. RESULTS: Some variables and interactions were identified to have significant impacts on fatigue-related accident detection. CONCLUSIONS: Based on the results, it can be inferred that the stereotype of certain groups of drivers, crash types, and roadway conditions affects police officers' judgment on fatigue-related accidents. PRACTICAL APPLICATIONS: This finding can provide useful information for training police officers and build better criteria for fatigue identification.

Traffic accidents involving fatigue driving and their extent of casualties.

The rapid progress of motorization has increased the number of traffic-related casualties. Although fatigue driving is a major cause of traffic accidents, the public remains not rather aware of its potential harmfulness. Fatigue driving has been termed as a "silent killer." Thus, a thorough study of traffic accidents and the risk factors associated with fatigue-related casualties is of utmost importance. In this study, we analyze traffic accident data for the period 2006-2010 in Guangdong Province, China. The study data were extracted from the traffic accident database of China's Public Security Department. A logistic regression model is used to assess the effect of driver characteristics, type of vehicles, road conditions, and environmental factors on fatigue-related traffic accident occurrence and severity. On the one hand, male drivers, trucks, driving during midnight to dawn, and morning rush hours are identified as risk factors of fatigue-related crashes but do not necessarily result in severe casualties. Driving at night without street-lights contributes to fatigue-related crashes and severe casualties. On the other hand, while factors such as less experienced drivers, unsafe vehicle status, slippery roads, driving at night with street-lights, and weekends do not have significant effect on fatigue-related crashes, yet accidents associated with these factors are likely to have severe casualties. The empirical results of the present study have important policy implications on the reduction of fatigue-related crashes as well as their severity.

Analyzing fault and severity in pedestrian-motor vehicle accidents in China.

The number of pedestrian-motor vehicle accidents and pedestrian deaths in China surged in recent years. However, a large scale empirical research on pedestrian traffic crashes in China is lacking. In this study, we identify significant risk factors associated with fault and severity in pedestrian-motor vehicle accidents. Risk factors in several different dimensions, including pedestrian, driver, vehicle, road and environmental factors, are considered. We analyze 6967 pedestrian traffic accident reports for the period 2006-2010 in Guangdong Province, China. These data, obtained from the Guangdong Provincial Security Department, are extracted from the Traffic Management Sector-Specific Incident Case Data Report. Pedestrian traffic crashes have a unique inevitability and particular high risk, due to pedestrians' fragility, slow movement and lack of lighting equipment. The empirical analysis of the present study has the following policy implications. First, traffic crashes in which pedestrians are at fault are more likely to cause serious injuries or death, suggesting that relevant agencies should pay attention to measures that prevent pedestrians from violating traffic rules. Second, both the attention to elderly pedestrians, male and experienced drivers, the penalty to drunk driving, speeding, driving without a driver's license and other violation behaviors should be strengthened. Third, vehicle safety inspections and safety training sessions for truck drivers should be reinforced. Fourth, improving the road conditions and road lighting at night are important measures in reducing the probability of accident casualties. Fifth, specific road safety campaigns in rural areas, and education programs especially for young children and teens should be developed and promoted. Moreover, we reveal a country-specific factor, hukou, which has significant effect on the severity in pedestrian accidents due to the discrepancy in the level of social insurance/security, suggesting that equal social security level among urban and rural people should be set up. In addition, establishing a comprehensive liability distribution system for non-urban areas and roadways will be conducive to both pedestrians' and drivers' voluntary compliance with traffic rules.

Motivational intensity modulates attentional scope: evidence from behavioral and ERP studies.

Previous studies have found that affective states with high motivational intensity narrow attentional scope, whereas affective states with low motivational intensity broaden attentional scope. This conclusion, however, is based on fragmented evidence based on several separate studies. The present study tests this conclusion within a single study using both behavioral (Experiment 1) and neurophysiological (Experiment 2) measures. Experiment 1 showed that individuals had the global precedence effect in the neutral affective state. However, the global precedence effect was reduced for affective states with high motivational intensity, whereas the global precedence effect was not significantly enhanced for those with low motivational intensity. Experiment 2 replicated these results with event-related potential (ERP) recording. ERP results showed that affective states with high motivational intensity induced smaller N2 and greater late positive potential (LPP) amplitudes than low motivational intensity and neutral affective states. However, no differences were found between the low motivational intensity and neutral affective states. Furthermore, smaller LPP predicted the tendency a global attentional focus in the frontal and central areas and larger LPP predicted a narrowed focus in the frontal area. The findings suggested that high motivational intensity of affective states can affect attentional scope.

Traffic violations in Guangdong Province of China: speeding and drunk driving.

The number of speeding- and drunk driving-related injuries in China surged in the years immediately preceding 2004 and then began to decline. However, the percent decrease in the number of speeding and drunk driving incidents (decrease by 22%) is not proportional to the corresponding percent decrease in number of automobile accident-related injuries (decrease by 47%) from the year 2004 to 2010 (Traffic Management Bureau, Ministry of Public Security, Annual Statistical Reports on Road Traffic Accidents). Earlier studies have established traffic violations as one of the major risks threatening road safety. In this study, we examine in greater detail two important types of traffic violation events, speeding and drunk driving, and attempt to identify significant risk factors associated with these types of traffic violations. Risk factors in several different dimensions, including driver, vehicle, road and environmental factors, are considered. We analyze the speeding (N=11,055) and drunk driving (N=10,035) data for the period 2006-2010 in Guangdong Province, China. These data, obtained from the Guangdong Provincial Security Department, are extracted from the Traffic Management Sector-Specific Incident Case Data Report and are the only comprehensive and official source of traffic accident data in China. Significant risk factors associating with speeding and drunk driving are identified. We find that several factors are associated with a significantly higher probability of both speeding and drunk driving, particularly male drivers, private vehicles, the lack of street lighting at night and poor visibility. The impact of other specific and unique risk factors for either speeding or drunk driving, such as hukou, road type/grades, commercial vehicles, compulsory third party insurance and vehicle safety status, also require particular attention. Legislative or regulatory measures targeting different vehicle types and/or driver groups with respect to the various driver, vehicle, road and environmental risk factors can subsequently be devised to reduce the speeding and drunk driving rates. As the country with the highest number of traffic accident fatalities in the world, applying these findings in workable legislation and enforcement to reduce speeding and drunk driving rates will save tens of thousands of lives.

Risk factors associated with traffic violations and accident severity in China.

With the recent economic boom in China, vehicle volume and the number of traffic accident fatalities have become the highest in the world. Meanwhile, traffic accidents have become the leading cause of death in China. Systematically analyzing road safety data from different perspectives and applying empirical methods/implementing proper measures to reduce the fatality rate will be an urgent and challenging task for China in the coming years. In this study, we analyze the traffic accident data for the period 2006-2010 in Guangdong Province, China. These data, extracted from the Traffic Management Sector-Specific Incident Case Data Report, are the only officially available and reliable source of traffic accident data (with a sample size>7000 per year). In particular, we focus on two outcome measures: traffic violations and accident severity. Human, vehicle, road and environmental risk factors are considered. First, the results establish the role of traffic violations as one of the major risks threatening road safety. An immediate implication is: if the traffic violation rate could be reduced or controlled successfully, then the rate of serious injuries and fatalities would be reduced accordingly. Second, specific risk factors associated with traffic violations and accident severity are determined. Accordingly, to reduce traffic accident incidence and fatality rates, measures such as traffic regulations and legislation-targeting different vehicle types/driver groups with respect to the various human, vehicle and environment risk factors-are needed. Such measures could include road safety programs for targeted driver groups, focused enforcement of traffic regulations and road/transport facility improvements. Data analysis results arising from this study will shed lights on the development of similar (adjusted) measures to reduce traffic violations and/or accident fatalities and injuries, and to promote road safety in other regions.