Water Disaster Types and Water Control Measures of Hanxing Coal Mine Area

AbstractHanxing coal mine area is a typical karst high-water deposit. With further exploration of under-group coal seams, various threats of water disasters came. Coal water disasters can be divided into five types through comprehensive study on geological and hydro-geological conditions of the coal mine area. The five kinds of water disasters are surfer water disaster, coal roof aquifer water disaster, coal floor high-pressure Ordovician karst water disaster, goaf water disaster and karst collapse column water disaster. For all types of water disasters, corresponding water control measures were proposed and these water control measures are of great theoretical and realistic significance to the mine safety and high-level decision in Hanxing coal mine area

TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images

In the remote sensing field, Change Detection (CD) aims to identify and localize the changed regions from dual-phase images over the same places. Recently, it has achieved great progress with the advances of deep learning. However, current methods generally deliver incomplete CD regions and irregular CD boundaries due to the limited representation ability of the extracted visual features. To relieve these issues, in this work we propose a novel Transformer-based learning framework named TransY-Net for remote sensing image CD, which improves the feature extraction from a global view and combines multi-level visual features in a pyramid manner. More specifically, the proposed framework first utilizes the advantages of Transformers in long-range dependency modeling. It can help to learn more discriminative global-level features and obtain complete CD regions. Then, we introduce a novel pyramid structure to aggregate multi-level visual features from Transformers for feature enhancement. The pyramid structure grafted with a Progressive Attention Module (PAM) can improve the feature representation ability with additional inter-dependencies through spatial and channel attentions. Finally, to better train the whole framework, we utilize the deeply-supervised learning with multiple boundary-aware loss functions. Extensive experiments demonstrate that our proposed method achieves a new state-of-the-art performance on four optical and two SAR image CD benchmarks. The source code is released at https://github.com/Drchip61/TransYNet.Comment: This work is accepted by TGRS2023. It is an extension of our ACCV2022 paper and arXiv:2210.0075

Immune infiltration analysis reveals immune cell signatures in salivary gland tissue of primary Sjögren’s syndrome

IntroductionMouse models are the basis for primary Sjögren’s syndrome (pSS) research. However, the depth of comparisons between mice and humans in salivary gland (SG) immune cells remains limited.MethodsThe gene expression profiles of SGs from normal subjects and pSS patients were downloaded from the Gene Expression Comprehensive Database. The proportion of infiltrating immune cell subsets was then assessed by cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT). An experimental Sjögren’s syndrome (ESS) mouse model was successfully constructed using SG protein. Based on mouse SG tissue RNA-Seq data, the seq-ImmuCC model was used to quantitatively analyze the compositional ratios of 10 immune cells in pSS patients and mouse model SG tissues.ResultsComputed and obtained 31 human data samples using the CIBERSORT deconvolution method. The immune cell infiltration results showed that, compared to normal human SG tissue, the content of gamma delta T cells was significantly different from naive CD4+ T cells and significantly increased, while the plasma cell content decreased. Principal component analysis indicated differences in immune cell infiltration between pSS patients and normal subjects. Meanwhile, for ESS model mouse data analysis, we found that the proportion of macrophages increased, while the proportion of CD4+ T cells, B cells, and monocytes decreased. Furthermore, we found that the proportion of monocytes was decreased, while the proportion of macrophages was increased in the SG tissues of pSS patients and model mice. The infiltration of CD4+ T, CD8+ T, and B cells also showed some differences.DiscussionWe comprehensively analyzed SG immune infiltration in pSS patients and model mice. We demonstrated conserved and nonconserved aspects of the immune system in mice and humans at the level of immune cells to help explain the primary regulation of immune mechanisms during the development of Sjögren’s syndrome

Optical sensors using chaotic correlation fiber loop ring down

We have proposed a novel optical sensor scheme based on chaotic correlation fiber loop ring down (CCFLRD). In contrast to the well-known FLRD spectroscopy, where pulsed laser is injected to fiber loop and ring down time is measured, the proposed CCFLRD uses a chaotic laser to drive a fiber loop and measures autocorrelation coefficient ring down time of chaotic laser. The fundamental difference enables us to avoid using long fiber loop as required in pulsed FLRD, and thus generates higher sensitivity. A strain sensor has been developed to validate the CCFLRD concept. Theoretical and experiment results demonstrate that the proposed method is able to enhance sensitivity by more than two orders of magnitude comparing to the existing FLRD method. We believe the proposed method could find great potential applications for chemical, medical, and physical sensing

Marked methylation changes in intestinal genes during the perinatal period of preterm neonates

BACKGROUND: The serious feeding- and microbiota-associated intestinal disease, necrotizing enterocolitis (NEC), occurs mainly in infants born prematurely (5-10% of all newborns) and most frequently after formula-feeding. We hypothesized that changes in gene methylation is involved in the prenatal maturation of the intestine and its response to the first days of formula feeding, potentially leading to NEC in preterm pigs used as models for preterm infants. RESULTS: Reduced Representation Bisulfite Sequencing (RRBS) was used to assess if changes in intestinal DNA methylation are associated with formula-induced NEC outbreak and advancing age from 10 days before birth to 4 days after birth. Selected key genes with differentially methylated gene regions (DMRs) between groups were further validated by HiSeq-based bisulfite sequencing PCR and RT-qPCR to assess methylation and expression levels. Consistent with the maturation of many intestinal functions in the perinatal period, methylation level of most genes decreased with advancing pre- and postnatal age. The highest number of DMRs was identified between the newborn and 4 d-old preterm pigs. There were few intestinal DMR differences between unaffected pigs and pigs with initial evidence of NEC. In the 4 d-old formula-fed preterm pigs, four genes associated with intestinal metabolism (CYP2W1, GPR146, TOP1MT, CEND1) showed significant hyper-methylation in their promoter CGIs, and thus, down-regulated transcription. Methylation-driven down-regulation of such genes may predispose the immature intestine to later metabolic dysfunctions and severe NEC lesions. CONCLUSIONS: Pre- and postnatal changes in intestinal DNA methylation may contribute to high NEC sensitivity in preterm neonates. Optimizing gene methylation changes via environmental stimuli (e.g. diet, nutrition, gut microbiota), may help to make immature newborn infants more resistant to gut dysfunctions, both short and long term. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-716) contains supplementary material, which is available to authorized users

Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain

The goal of precision oncology is to select more effective treatments or beneficial drugs for patients. The transcription of ‘‘hidden responders’’ which precision oncology often fails to identify for patients is important for revealing responsive molecular states. Recently, a RAS pathway activation detection method based on machine learning and a nature-inspired deep RAS activation pan-cancer has been proposed. However, we note that the activating gene variations found in KRAS, HRAS and NRAS vary substantially across cancers. Besides, the ability of a machine learning classifier to detect which KRAS, HRAS and NRAS gain of function mutations or copy number alterations causes the RAS pathway activation is not clear. Here, we proposed a deep neural network framework for deciphering and identifying pan-cancer RAS pathway activation (DIPRAS). DIPRAS brings a new insight into deciphering and identifying the pan-cancer RAS pathway activation from a deeper perspective. In addition, we further revealed the identification and characterization of RAS aberrant pathway activity through gene ontological enrichment and pathological analysis. The source code is available by the URL https://github.com/zhaoyw456/DIPRAS

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO