Accurate real-time obstacle detection of coal mine driverless electric locomotive based on ODEL-YOLOv5s.

The accurate identification and real-time detection of obstacles have been considered the premise to ensure the safe operation of coal mine driverless electric locomotives. The harsh coal mine roadway environment leads to low detection accuracy of obstacles based on traditional detection methods such as LiDAR and machine learning, and these traditional obstacle detection methods lead to slower detection speeds due to excessive computational reasoning. To address the above-mentioned problems, we propose a deep learning-based ODEL-YOLOv5s detection model based on the conventional YOLOv5s. In this work, several data augmentation methods are introduced to increase the diversity of obstacle features in the dataset images. An attention mechanism is introduced to the neck of the model to improve the focus of the model on obstacle features. The three-scale prediction of the model is increased to a four-scale prediction to improve the detection ability of the model for small obstacles. We also optimize the localization loss function and non-maximum suppression method of the model to improve the regression accuracy and reduce the redundancy of the prediction boxes. The experimental results show that the mean average precision (mAP) of the proposed ODEL-YOLOv5s model is increased from 95.2 to 98.9% compared to the conventional YOLOv5s, the average precision of small obstacle rock is increased from 89.2 to 97.9%, the detection speed of the model is 60.2 FPS, and it has better detection performance compared with other detection models, which can provide technical support for obstacle identification and real-time detection of coal mine driverless electric locomotives.

Stability and Transformation of Metabolic Syndrome in Adolescents: A Prospective Assessment in Relation to the Change of Cardiometabolic Risk Factors.

Underlying pathophysiological mechanisms drive excessive clustering of cardiometabolic risk factors, causing metabolic syndrome (MetS). MetS status may transform as adolescents transition to young adulthood. This study investigated the latent clustering structure and its stability for MetS during adolescence, and assessed the anthropometric and clinical metabolic determinants for MetS transformation. A community-based representative adolescent cohort (n = 1516) was evaluated for MetS using four diagnostic criteria, and was followed for 2.2 years to identify new-onset MetS. The clustering structure underlying cardiometabolic parameters was stable across adolescence; both comprised a fat-blood pressure (BP)-glucose three-factor structure (total variance explained: 68.8% and 69.7% at baseline and follow-up, respectively). Among adolescents with MetS-negative at baseline, 3.2-4.4% had incident MetS after 2.2 years. Among adolescents with MetS-positive at baseline, 52.0-61.9% experienced MetS remission, and 38.1-48.0% experienced MetS persistence. Increased systolic BP (SBP) was associated with a high MetS incidence risk, while decreased levels of SBP and glucose were associated with MetS remission. Compared with adolescents with a normal metabolic status at baseline, those with an initial abdominal obesity and increased triglycerides level had a 15.0- and 5.7-fold greater risk for persistent abnormality, respectively. Abdominal obesity and low high-density lipoprotein cholesterol are two abnormal MetS components that highly persist during adolescence, and are the intervention targets for reducing the future risk of cardiometabolic disorders.

Transcriptional signature of human pro-inflammatory TH17 cells identifies reduced IL10 gene expression in multiple sclerosis.

We have previously reported the molecular signature of murine pathogenic TH17 cells that induce experimental autoimmune encephalomyelitis (EAE) in animals. Here we show that human peripheral blood IFN-γ+IL-17+ (TH1/17) and IFN-γ-IL-17+ (TH17) CD4+ T cells display distinct transcriptional profiles in high-throughput transcription analyses. Compared to TH17 cells, TH1/17 cells have gene signatures with marked similarity to mouse pathogenic TH17 cells. Assessing 15 representative signature genes in patients with multiple sclerosis, we find that TH1/17 cells have elevated expression of CXCR3 and reduced expression of IFNG, CCL3, CLL4, GZMB, and IL10 compared to healthy controls. Moreover, higher expression of IL10 in TH17 cells is found in clinically stable vs. active patients. Our results define the molecular signature of human pro-inflammatory TH17 cells, which can be used to both identify pathogenic TH17 cells and to measure the effect of treatment on TH17 cells in human autoimmune diseases.

REST and stress resistance in ageing and Alzheimer's disease.

Human neurons are functional over an entire lifetime, yet the mechanisms that preserve function and protect against neurodegeneration during ageing are unknown. Here we show that induction of the repressor element 1-silencing transcription factor (REST; also known as neuron-restrictive silencer factor, NRSF) is a universal feature of normal ageing in human cortical and hippocampal neurons. REST is lost, however, in mild cognitive impairment and Alzheimer's disease. Chromatin immunoprecipitation with deep sequencing and expression analysis show that REST represses genes that promote cell death and Alzheimer's disease pathology, and induces the expression of stress response genes. Moreover, REST potently protects neurons from oxidative stress and amyloid ß-protein toxicity, and conditional deletion of REST in the mouse brain leads to age-related neurodegeneration. A functional orthologue of REST, Caenorhabditis elegans SPR-4, also protects against oxidative stress and amyloid ß-protein toxicity. During normal ageing, REST is induced in part by cell non-autonomous Wnt signalling. However, in Alzheimer's disease, frontotemporal dementia and dementia with Lewy bodies, REST is lost from the nucleus and appears in autophagosomes together with pathological misfolded proteins. Finally, REST levels during ageing are closely correlated with cognitive preservation and longevity. Thus, the activation state of REST may distinguish neuroprotection from neurodegeneration in the ageing brain.

Genome-wide association studies.

A host of data on genetic variation from the Human Genome and International HapMap projects, and advances in high-throughput genotyping technologies, have made genome-wide association (GWA) studies technically feasible. GWA studies help in the discovery and quantification of the genetic components of disease risks, many of which have not been unveiled before and have opened a new avenue to understanding disease, treatment, and prevention. This chapter presents an overview of GWA, an important tool for discovering regions of the genome that harbor common genetic variants to confer susceptibility for various diseases or health outcomes in the post-Human Genome Project era. A tutorial on how to conduct a GWA study and some practical challenges specifically related to the GWA design is presented, followed by a detailed GWA case study involving the identification of loci associated with glioma as an example and an illustration of current technologies.

Gene set enrichment analysis: performance evaluation and usage guidelines.

A central goal of biology is understanding and describing the molecular basis of plasticity: the sets of genes that are combinatorially selected by exogenous and endogenous environmental changes, and the relations among the genes. The most viable current approach to this problem consists of determining whether sets of genes are connected by some common theme, e.g. genes from the same pathway are overrepresented among those whose differential expression in response to a perturbation is most pronounced. There are many approaches to this problem, and the results they produce show a fair amount of dispersion, but they all fall within a common framework consisting of a few basic components. We critically review these components, suggest best practices for carrying out each step, and propose a voting method for meeting the challenge of assessing different methods on a large number of experimental data sets in the absence of a gold standard.

Combinations of newly confirmed Glioma-Associated loci link regions on chromosomes 1 and 9 to increased disease risk.

BACKGROUND: Glioblastoma multiforme (GBM) tends to occur between the ages of 45 and 70. This relatively early onset and its poor prognosis make the impact of GBM on public health far greater than would be suggested by its relatively low frequency. Tissue and blood samples have now been collected for a number of populations, and predisposing alleles have been sought by several different genome-wide association (GWA) studies. The Cancer Genome Atlas (TCGA) at NIH has also collected a considerable amount of data. Because of the low concordance between the results obtained using different populations, only 14 predisposing single nucleotide polymorphism (SNP) candidates in five genomic regions have been replicated in two or more studies. The purpose of this paper is to present an improved approach to biomarker identification. METHODS: Association analysis was performed with control of population stratifications using the EIGENSTRAT package, under the null hypothesis of "no association between GBM and control SNP genotypes," based on an additive inheritance model. Genes that are strongly correlated with identified SNPs were determined by linkage disequilibrium (LD) or expression quantitative trait locus (eQTL) analysis. A new approach that combines meta-analysis and pathway enrichment analysis identified additional genes. RESULTS: (i) A meta-analysis of SNP data from TCGA and the Adult Glioma Study identifies 12 predisposing SNP candidates, seven of which are reported for the first time. These SNPs fall in five genomic regions (5p15.33, 9p21.3, 1p21.2, 3q26.2 and 7p15.3), three of which have not been previously reported. (ii) 25 genes are strongly correlated with these 12 SNPs, eight of which are known to be cancer-associated. (iii) The relative risk for GBM is highest for risk allele combinations on chromosomes 1 and 9. (iv) A combined meta-analysis/pathway analysis identified an additional four genes. All of these have been identified as cancer-related, but have not been previously associated with glioma. (v) Some SNPs that do not occur reproducibly across populations are in reproducible (invariant) pathways, suggesting that they affect the same biological process, and that population discordance can be partially resolved by evaluating processes rather than genes. CONCLUSION: We have uncovered 29 glioma-associated gene candidates; 12 of them known to be cancer related (p = 1. 4 × 10-6), providing additional statistical support for the relevance of the new candidates. This additional information on risk loci is potentially important for identifying Caucasian individuals at risk for glioma, and for assessing relative risk.

Identification of functional modules that correlate with phenotypic difference: the influence of network topology.

One of the important challenges to post-genomic biology is relating observed phenotypic alterations to the underlying collective alterations in genes. Current inferential methods, however, invariably omit large bodies of information on the relationships between genes. We present a method that takes account of such information - expressed in terms of the topology of a correlation network - and we apply the method in the context of current procedures for gene set enrichment analysis.