A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge-Cloud Computing.

With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge-LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance on the computational capacity and the uneven implementation of resources of cloud computing can be successfully mitigated. The system is specifically a simple LPR. Channel pruning was used to reconstruct the backbone layer, reduce the network model parameters, and effectively reduce the GPU resource consumption. By utilizing the computing resources of the Intel second-generation computing stick, the network models were deployed on edge gateways to detect license plates directly. The reliability and effectiveness of the Edge-LPR system were validated through the experimental analysis of the CCPD standard dataset and real-time monitoring dataset from charging stations. The experimental results from the CCPD common dataset demonstrated that the network's total number of parameters was only 0.606 MB, with an impressive accuracy rate of 97%.

One-Dimensional Local Binary Pattern and Common Spatial Pattern Feature Fusion Brain Network for Central Neuropathic Pain.

Central neuropathic pain (CNP) after spinal cord injury (SCI) is related to the plasticity of cerebral cortex. The plasticity of cortex recorded by electroencephalogram (EEG) signal can be used as a biomarker of CNP. To analyze changes in the brain network mechanism under the combined effect of injury and pain or under the effect of pain, this paper mainly studies the changes of brain network functional connectivity in patients with neuropathic pain and without neuropathic pain after SCI. This paper has recorded the EEG with the CNP group after SCI, without the CNP group after SCI, and a healthy control group. Phase-locking value has been used to construct brain network topological connectivity maps. By comparing the brain networks of the two groups of SCI with the healthy group, it has been found that in the [Formula: see text] and [Formula: see text] frequency bands, the injury increases the functional connectivity between the frontal lobe and occipital lobes, temporal, and parietal of the patients. Furthermore, the comparison of brain networks between the group with CNP and the group without CNP after SCI has found that pain has a greater effect on the increased connectivity within the patients' frontal lobes. Motor imagery (MI) data of CNP patients have been used to extract one-dimensional local binary pattern (1D-LBP) and common spatial pattern (CSP) features, the left and right hand movements of the patients' MI have been classified. The proposed LBP-CSP feature method has achieved the highest accuracy of 98.6% and the average accuracy of 91.5%. The results of this study have great clinical significance for the neural rehabilitation and brain-computer interface of CNP patients.

Deep Convolution Generative Adversarial Network-Based Electroencephalogram Data Augmentation for Post-Stroke Rehabilitation with Motor Imagery.

The motor imagery brain-computer interface (MI-BCI) system is currently one of the most advanced rehabilitation technologies, and it can be used to restore the motor function of stroke patients. The deep learning algorithms in the MI-BCI system require lots of training samples, but the electroencephalogram (EEG) data of stroke patients is quite scarce. Therefore, the expansion of EEG data has become an important part of stroke clinical rehabilitation research. In this paper, a deep convolution generative adversarial network (DCGAN) model is proposed to generate artificial EEG data and further expand the scale of the stroke dataset. First, multichannel one-dimensional EEG data is converted into a two-dimensional EEG spectrogram using EEG2Image based on the modified S-transform. Then, DCGAN is used to artificially generate EEG data based on MI. Finally, the validity of the generated artificial EEG data is proved. This paper preliminarily indicates that generating artificial stroke data is a promising strategy, which contributes to the further development of stroke clinical rehabilitation.

Exploration of sleep function connection and classification strategies based on sub-period sleep stages.

Background: As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. Methods: Phase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages. Results: The experimental results have shown that when the number of sub-periods is 30, the α (8-13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%. Conclusion: The proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system.

Rasch analysis of the Brief Symptom Inventory-18 with African American and White students.

Although the United States offers some of the most advanced psychological services in the world, not everyone in the country shares these services equally, resulting in health disparities. Health disparities persist when assessments do not appropriately measure different populations' mental health problems. To address this assessment issue, we conducted principal axis factoring, confirmatory factor analysis, and Rasch analyses to assess the psychometric characteristics of the Brief Symptom Inventory-18 (BSI-18) to evaluate whether the BSI is culturally appropriate for assessing African American students' psychological distress. The dimensional structure of the BSI was first identified and held up under cross-validation with a second sample and a white sample. The measure was unidimensional among African American and white students. Our results suggested BSI in our samples presented characteristics such as low person separation, stability across samples, and little differential item functioning. Most African American and white students identified themselves on the low end of the categories in a 0-4 rating scale, indicating their low endorsement of the items on the BSI. Rasch analyses were completed with the original scale but also collapsing the scale to three points, with some increase in separation and reliability for the collapsed scale. As anticipated, differences in mean BSI scores were found for mental health-related variables. Implications for theory and research on multicultural health scales are discussed as are effects of item skewness on analyses. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Associations of common variants at ALDH2 gene and the risk of stroke in patients with coronary artery diseases undergoing percutaneous coronary intervention.

Limited data are available about the role of common variants at the aldehyde dehydrogenase 2 gene (ALDH2) on the clinical outcome in Chinese patients with coronary heart disease (CHD) undergoing percutaneous coronary intervention (PCI). In the present study, a total of 1089 patients were consecutively enrolled from January 2012 and July 2013. Six common variants at ALDH2 gene, including rs2339840, rs4648328, rs4767939, rs11066028, rs16941669, and rs671, were selected to test the associations of those polymorphisms with the cardiovascular outcome in patients with CHD after PCI. The clinical endpoints included cardiovascular death, nonfatal myocardial infarction, and nonfatal stroke. The composite of clinical endpoints was defined as the primary endpoint, and every endpoint alone was considered as the secondary endpoints. The median follow-up time was 38.27 months. Our results showed that the common variant rs2339840 was independently associated with a lower risk of stroke in patients with CHD after PCI (codominant model, HR = 0.32, 95% CI, 0.11-0.91, P = .074 for heterozygotes; HR = 0.25, 95% CI, 0.06-1.14, P = .033 for homozygotes; dominant model, HR = 0.32, 95% CI, 0.14-0.74, P = .007). However, no significant associations were found between other 5 single nucleotide polymorphisms (SNPs) and the clinical endpoints. For the first time, the common variant rs2339840 was reported to be a protective factor against stroke in CHD patients with PCI.

Characterization of the microbial communities along the gastrointestinal tract of sheep by 454 pyrosequencing analysis.

OBJECTIVE: The gastrointestinal tract of sheep contain complex microbial communities that influence numerous aspects of the sheep's health and development. The objective of this study was to analyze the composition and diversity of the microbiota in the gastrointestinal tract sections (rumen, reticulum, omasum, abomasum, duodenum, jejunum, ileum, cecum, colon, and rectum) of sheep. METHODS: This analysis was performed by 454 pyrosequencing using the V3-V6 region of the 16S rRNA genes. Samples were collected from five healthy, small tailed Han sheep aged 10 months, obtained at market. The bacterial composition of sheep gastrointestinal microbiota was investigated at the phylum, class, order, family, genus, and species levels. RESULTS: The dominant bacterial phyla in the entire gastrointestinal sections were Firmicutes, Bacteroidetes, and Proteobacteria. In the stomach, the three most dominant genera in the sheep were Prevotella, unclassified Lachnospiraceae, and Butyrivibrio. In the small intestine, the three most dominant genera in the sheep were Escherichia, unclassified Lachnospiraceae, and Ruminococcus. In the large intestine, the three most dominant genera in the sheep were Ruminococcus, unclassified Ruminococcaceae, and Prevotella. R. flavefaciens, B. fibrisolvens, and S. ruminantium were three most dominant species in the sheep gastrointestinal tract. Principal Coordinates Analysis showed that the microbial communities from each gastrointestinal section could be separated into three groups according to similarity of community composition: stomach (rumen, reticulum, omasum, and abomasum), small intestine (duodenum, jejunum, and ileum), and large intestine (cecum, colon, and rectum). CONCLUSION: This is the first study to characterize the entire gastrointestinal microbiota in sheep by use of 16S rRNA gene amplicon pyrosequencing, expanding our knowledge of the gastrointestinal bacterial community of sheep.

A novel nonsense mutation in LMNA gene identified by Exome Sequencing in an atrial fibrillation family.

Genetic factor plays an important role in cardiac arrhythmias. Several loci have been identified associated with this disease. However, they only explained parts of it and more genes and loci remain to be identified. In present study, we recruited a four generation family from the north of China. Four members of this family were diagnosed with atrial fibrillation by electrocardiogram (ECG). We used Exome Sequencing and Sanger sequencing to explore the candidate mutation for cardiac arrhythmia in this family. A nonsense mutation (c.G1494A, p.Trp498Ter) in the LMNA gene were identified as the candidate mutation. This variant is a novel mutation and has not yet been reported for any actual databases. This novel mutation co-segregated exactly with the disease in this family. Meanwhile, it was not detected in 524 control subjects of matched ancestry. According to structural model prediction, the mutation is expected to affect the Lamin Tail Domain (LTD) of lamin A/C protein. So the nonsense mutation discovered in the family probably was a novel mutation associated with familial atrial fibrillation. This discovery expands the mutation spectrum of LMNA and indicates the importance of LMNA in AF.

Impacts of common variants in ALDH2 on coronary artery disease patients.

Genome-wide association studies (GWAS) have identified Aldehyde dehydrogenase 2 (ALDH2) as a susceptibility locus for coronary artery disease (CAD) previously. However, the impacts of common variants in this gene on CAD and its outcomes have not been extensively studied. This study explored the association between the Tagging SNPs in ALDH2 and CAD as well as its main outcomes. Six common variants in ALDH2 were selected as tagging SNPs and two cohorts containing 7296 individuals were genotyped to investigate the impacts of ALDH2 on CAD and its main outcomes. The results show that the variant rs671 in ALDH2 is associated with an increased risk of CAD in southern Chinese (OR=1.26, 95%CI: 1.07-1.48, p=0.004), while not in northern Chinese (OR=1.00, 95%CI: 0.86-1.50, p=0.94). Meanwhile, we find that rs671 genotypes may not influence the outcomes of CAD (HR=1.11, 95%CI: 0.892-1.38, p=0.346). Additionally, we also tested the effect of rs671 genotype on CAD severity, while no significant association was found between them. In the subgroup analysis, the results revealed that rs671 were significantly associated with CAD (OR=1.24, 95%CI: 1.11-1.38, p<0.001) in non-alcoholic subjects. Overall, our findings indicate that the associations between rs671 in ALDH2 and CAD are regional disparity, and rs671 genotypes may not influence the main outcomes of CAD.

Genotyping on ALDH2: comparison of four different technologies.

OBJECTIVES: This study aimed to compare the accuracy and performance of four genotyping methods for detecting single nucleotide polymorphisms (SNPs) in aldehyde dehydrogenase-2 (ALDH2), which is the principal enzyme involved in alcohol metabolism. DESIGN AND METHODS: We genotyped rs671 of ALDH2 in 96 coronary heart disease (CHD) patients with four methods including high resolution melting analysis (HRM), TaqMan allelic discrimination assay (TaqMan), allele-specific PCR (AS-PCR) and pyrosequencing. Meanwhile, we compared the accuracy and performance of these methods. RESULTS: All selected patients were successfully genotyped with referred methods. The results of these four assays showed 100% concordant results and had 100% accuracy as verified by Sanger sequencing. CONCLUSIONS: All of the referred methods can be used for genotyping ALDH2 rs671 with the same accuracy compared to Sanger sequencing. In small size of clinical samples, HRM and AS-PCR outperform over others due to their lower cost and less hands-on operation, which are suitable for clinical application.

Rapid molecular genetic diagnosis of hypertrophic cardiomyopathy by semiconductor sequencing.

BACKGROUND: Rapidly determining the complex genetic basis of Hypertrophic cardiomyopathy (HCM) is vital to better understanding and optimally managing this common polygenetic cardiovascular disease. METHODS: A rapid custom Ion-amplicon-resequencing assay, covering 30 commonly affected genes of HCM, was developed and validated in 120 unrelated patients with HCM to facilitate genetic diagnosis of this disease. With this HCM-specific panel and only 20 ng of input genomic DNA, physicians can, for the first time, go from blood samples to variants within a single day. RESULTS: On average, this approach gained 595628 mapped reads per sample, 95.51% reads on target (64.06 kb), 490-fold base coverage depth and 93.24% uniformity of base coverage in CDS regions of the 30 HCM genes. After validation, we detected underlying pathogenic variants in 87% (104 of 120) samples. Tested seven randomly selected HCM genes in eight samples by Sanger sequencing, the sensitivity and false-positive-rate of this HCM panel was 100% and 5%, respectively. CONCLUSIONS: This Ion amplicon HCM resequencing assay provides a currently most rapid, comprehensive, cost-effective and reliable measure for genetic diagnosis of HCM in routinely obtained samples.

Association between telomere length and type 2 diabetes mellitus: a meta-analysis.

BACKGROUND: Several epidemiological studies have examined the association between shortened telomere length and type 2 diabetes mellitus (T2DM), while the results remained conflicting. We conducted a meta-analysis to derive a more precise estimation of the relationship between them. METHODS: We systematically reviewed the databases of PubMed, EMBASE, and Web of Science for all studies on the association between telomere length and T2DM. We conducted this study assessed by STATA 11.0. Data were summarized using random-effects or fixed-effects meta-analysis. The heterogeneity and publication bias among studies were examined by using χ(2)-based Q statistic test and Egger's test, respectively. RESULTS: Nine cohorts consisting of 5759 cases and 6518 controls were selected into the meta-analysis. The results indicated that shortened telomere length was significantly associated with T2DM risk (OR: 1.291; 95% CI: 1.112, 1.498; P<0.001) with heterogeneity (I(2) = 71.6%). When three cohorts responsible for the heterogeneity were excluded, the pooled OR for the remaining cohorts indicated a significant association between shortened telomere length and T2DM (OR: 1.117; 95% CI: 1.002, 1.246; P = 0.045) without heterogeneity. CONCLUSION: We found a statistically significant association between shortened telomere length and T2DM.

Large scale meta-analyses of fasting plasma glucose raising variants in GCK, GCKR, MTNR1B and G6PC2 and their impacts on type 2 diabetes mellitus risk.

BACKGROUND: The evidence that the variants GCK rs1799884, GCKR rs780094, MTNR1B rs10830963 and G6PC2 rs560887, which are related to fasting plasma glucose levels, increase the risk of type 2 diabetes mellitus (T2DM) is contradictory. We therefore performed a meta-analysis to derive a more precise estimation of the association between these polymorphisms and T2DM. METHODS: All the publications examining the associations of these variants with risk of T2DM were retrieved from the MEDLINE and EMBASE databases. Using the data from the retrieved articles, we computed summary estimates of the associations of the four variants with T2DM risk. We also examined the studies for heterogeneity, as well as for bias of the publications. RESULTS: A total of 113,025 T2DM patients and 199,997 controls from 38 articles were included in the meta-analysis. Overall, the pooled results indicated that GCK (rs1799884), GCKR (rs780094) and MTNR1B (rs10830963) were significantly associated with T2DM susceptibility (OR, 1.04; 95%CI, 1.01-1.08; OR, 1.08; 95%CI, 1.05-1.12 and OR, 1.05; 95%CI, 1.02-1.08, respectively). After stratification by ethnicity, significant associations for the GCK, MTNR1B and G6PC2 variants were detected only in Caucasians (OR, 1.09; 95%CI, 1.02-1.16; OR, 1.10; 95%CI, 1.08-1.13 and OR, 0.97; 95%CI, 0.95-0.99, respectively), but not in Asians (OR, 1.02, 95% CI 0.98-1.05; OR, 1.01; 95%CI, 0.98-1.04 and OR, 1.12; 95%CI, 0.91-1.32, respectively). CONCLUSIONS: Our meta-analyses demonstrated that GCKR rs780094 variant confers high cross-ethnicity risk for the development of T2DM, while significant associations between GCK, MTNR1B and G6PC2 variants and T2DM risk are limited to Caucasians.

Common variants in KCNQ1 confer increased risk of type 2 diabetes and contribute to the diabetic epidemic in East Asians: a replication and meta-analysis.

We aimed to evaluate the effect of four common variants (rs2237892, rs2283228, rs2237895, and rs2237897) in KCNQ1 on susceptibility of type 2 diabetes (T2D) by performing a case-control study as well as a comprehensive meta-analysis. We genotyped these four variants in two sets of Chinese Han population, comprising a total of 2533 type 2 diabetic patients and 2643 nondiabetic controls. We also performed a meta-analysis of our results with published studies in East Asians, meanwhile assessing the population attributable risk (PAR) of these variants. By combining our case-control sets, a total of 45,204 T2D cases and 42,832 controls were included in the meta-analyses. The per-allele ORs ranged from 1.24 to 1.33, and the PARs ranged from 15.8% to 31.8%, with SNP rs2237892 being the most widely studied (16 articles containing a total of 38,338 cases and 35,907 controls), showing strongest association (per-allele OR: 1.33, 95% CI: 1.28-1.39) and indicating the highest PAR (31.8%). This study confirmed the strong association between common variants in KCNQ1 and risk of T2D. Variants in KCNQ1 were among the leading genetic factors contributing to the overall burden of T2D in East Asians.