Systematic screening of long intergenic noncoding RNAs expressed during chicken embryogenesis

ABSTRACT: Long noncoding RNAs (lncRNAs) have emerged as important regulators of many biological processes, including embryogenesis and development. To provide a systematic analysis of lncRNAs expressed during chicken embryogenesis, we used Iso-Seq and RNA-Seq to identify potential lncRNAs at embryonic stages from d 1 to d 8 of incubation: sequential stages covering gastrulation, somitogenesis, and organogenesis. The data characterized an expanded landscape of lncRNAs, yielding 45,410 distinct lncRNAs (31,282 genes). Amongst these, a set of 13,141 filtered intergenic lncRNAs (lincRNAs) transcribed from 9803 lincRNA gene loci, of which, 66.5% were novel, were further analyzed. These lincRNAs were found to share many characteristics with mammalian lincRNAs, including relatively short lengths, fewer exons, lower expression levels, and stage-specific expression patterns. Functional studies motivated by “guilt-by-association” associated individual lincRNAs with specific GO functions, providing an important resource for future studies of lincRNA function. Most importantly, a weighted gene co-expression network analysis suggested that genes of the brown module were specifically associated with the day 2 stage. LincRNAs within this module were co-expressed with proteins involved in hematopoiesis and lipid metabolism. This study presents the systematic identification of lincRNAs in developing chicken embryos and will serve as a powerful resource for the study of lincRNA functions

The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics - A Review

In the challenging downhole environment, drilling tools are normally subject to high temperature, severe vibration, and other harsh operation conditions. The drilling activities generate massive field data, namely field reliability big data (FRBD), which includes downhole operation, environment, failure, degradation, and dynamic data. Field reliability big data has large size, high variety, and extreme complexity. FRBD presents abundant opportunities and great challenges for drilling tool reliability analytics. Consequently, as one of the key factors to affect drilling tool reliability, the downhole vibration factor plays an essential role in the reliability analytics based on FRBD. This paper reviews the important parameters of downhole drilling operations, examines the mode, physical and reliability impact of downhole vibration, and presents the features of reliability big data analytics. Specifically, this paper explores the application of vibration factor in reliability big data analytics covering tool lifetime/failure prediction, prognostics/diagnostics, condition monitoring (CM), and maintenance planning and optimization. Furthermore, the authors highlight the future research about how to better apply the downhole vibration factor in reliability big data analytics to further improve tool reliability and optimize maintenance planning

Expression of the phosphorylated MEK5 protein is associated with TNM staging of colorectal cancer

<p>Abstract</p> <p>Background</p> <p>Activation of MEK5 in many cancers is associated with carcinogenesis through aberrant cell proliferation. In this study, we determined the level of phosphorylated MEK5 (pMEK5) expression in human colorectal cancer (CRC) tissues and correlated it with clinicopathologic data.</p> <p>Methods</p> <p>pMEK5 expression was examined by immunohistochemistry in a tissue microarray (TMA) containing 335 clinicopathologic characterized CRC cases and 80 cases of nontumor colorectal tissues. pMEK5 expression of 19 cases of primary CRC lesions and paired with normal mucosa was examined by Western blotting. The relationship between pMEK5 expression in CRC and clinicopathologic parameters, and the association of pMEK5 expression with CRC survival were analyzed respectively.</p> <p>Results</p> <p>pMEK5 expression was significantly higher in CRC tissues (185 out of 335, 55.2%) than in normal tissues (6 out of 80, 7.5%; <it>P </it>< 0.001). Western blotting demonstrated that pMEK5 expression was upregulated in 12 of 19 CRC tissues (62.1%) compared to the corresponding adjacent nontumor colorectal tissues. Overexpression of pMEK5 in CRC tissues was significantly correlated to the depth of invasion (<it>P </it>= 0.001), lymph node metastasis (<it>P </it>< 0.001), distant metastasis (<it>P </it>< 0.001) and high preoperative CEA level (<it>P </it>< 0.001). Consistently, the pMEK5 level in CRC tissues was increased following stage progression of the disease (<it>P </it>< 0.001). Analysis of the survival curves showed a significantly worse 5-year disease-free (<it>P </it>= 0.002) and 5-year overall survival rate (<it>P </it>< 0.001) for patients whose tumors overexpressed pMEK5. However, in multivariate analysis, pMEK5 was not an independent prognostic factor for CRC (DFS: <it>P </it>= 0.139; OS: <it>P </it>= 0.071).</p> <p>Conclusions</p> <p>pMEK5 expression is correlated with the staging of CRC and its expression might be helpful to the TNM staging system of CRC.</p

Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning

The safe and comfortable operation of high-speed trains has attracted extensive attention. With the operation of the train, the performance of high-speed train bogie components inevitably degrades and eventually leads to failures. At present, it is a common method to achieve performance degradation estimation of bogie components by processing high-speed train vibration signals and analyzing the information contained in the signals. In the face of complex signals, the usage of information theory, such as information entropy, to achieve performance degradation estimations is not satisfactory, and recent studies have more often used deep learning methods instead of traditional methods, such as information theory or signal processing, to obtain higher estimation accuracy. However, current research is more focused on the estimation for a certain component of the bogie and does not consider the bogie as a whole system to accomplish the performance degradation estimation task for several key components at the same time. In this paper, based on soft parameter sharing multi-task deep learning, a multi-task and multi-scale convolutional neural network is proposed to realize performance degradation state estimations of key components of a high-speed train bogie. Firstly, the structure takes into account the multi-scale characteristics of high-speed train vibration signals and uses a multi-scale convolution structure to better extract the key features of the signal. Secondly, considering that the vibration signal of high-speed trains contains the information of all components, the soft parameter sharing method is adopted to realize feature sharing in the depth structure and improve the utilization of information. The effectiveness and superiority of the structure proposed by the experiment is a feasible scheme for improving the performance degradation estimation of a high-speed train bogie

Global Investigation of Cytochrome P450 Genes in the Chicken Genome

Cytochrome P450 (CYP) superfamily enzymes are broadly involved in a variety of physiological and toxicological processes. However, genome-wide analysis of this superfamily has never been investigated in the chicken genome. In this study, genome-wide analyses identified 45 chicken CYPs (cCYPs) from the chicken genome, and their classification and evolutionary relationships were investigated by phylogenetic, conserved protein motif, and gene structure analyses. The comprehensive evolutionary data revealed several remarkable characteristics of cCYPs, including the highly divergent and rapid evolution of the cCYPs, and the loss of cCYP2AF in the chicken genome. Furthermore, the cCYP expression profile was investigated by RNA-sequencing. The differential expression of cCYPs in developing embryos revealed the involvement of cCYPs in embryonic development. The significantly regulated cCYPs suggested its potential role in hepatic metabolism. Additionally, 11 cCYPs, including cCYP2AC1, cCYP2C23a, and cCYP2C23b, were identified as estrogen-responsive genes, which indicates that these cCYPs are involved in the estrogen-signaling pathway. Meanwhile, an expression profile analysis highlights the divergent role of different cCYPs. These data expand our view of the phylogeny and evolution of cCYPs, provide evolutionary insight, and can help elucidate the roles of cCYPs in physiological and toxicological processes in chicken

A Low-Altitude Flight Conflict Detection Algorithm Based on a Multilevel Grid Spatiotemporal Index

Flight conflict detection is fundamental to flight dispatch, trajectory planning, and flight safety control. An ever-increasing aircraft population and higher speeds, particularly the emergence of hypersonic/supersonic aircrafts, are challenging the timeliness and accuracy of flight conflict detection. Traditional trajectory conflict detection algorithms rely on traversing multivariate equations of every two trajectories, in order to yield the conflict result and involve extensive computation and high algorithmic complexity; these algorithms are often unable to provide the flight conflict solutions required quickly enough. In this paper, we present a novel, low-altitude flight conflict detection algorithm, based on the multi-level grid spatiotemporal index, that transforms the traditional trajectory-traversing multivariate conflict computation into a grid conflict state query of distributed grid databases. Essentially, this is a method of exchanging "storage space" for "computational time". First, we build the spatiotemporal subdivision and encoding model based on the airspace. The model describes the geometries of the trajectories, low-altitude obstacles, or dangerous fields and identifies the grid with grid codes. Next, we design a database table structure of the grid and create a grid database. Finally, we establish a multilevel grid spatiotemporal index, design a query optimization scheme, and examine the flight conflict detection results from the grid database. Experimental verification confirms that the computation efficiency of our algorithm is one order of magnitude higher than those of traditional methods. Our algorithm can perform real-time (dynamic/static) conflict detection on both individual aircraft and aircraft flying in formation with more efficient trajectory planning and airspace utilization

Data in support of the discovery of alternative splicing variants of quail LEPR and the evolutionary conservation of qLEPRl by nucleotide and amino acid sequences alignment

Leptin receptor (LEPR) belongs to the class I cytokine receptor superfamily which share common structural features and signal transduction pathways. Although multiple LEPR isoforms, which are derived from one gene, were identified in mammals, they were rarely found in avian except the long LEPR. Four alternative splicing variants of quail LEPR (qLEPR) had been cloned and sequenced for the first time (Wang et al., 2015 [1]). To define patterns of the four splicing variants (qLEPRl, qLEPR-a, qLEPR-b and qLEPR-c) and locate the conserved regions of qLEPRl, this data article provides nucleotide sequence alignment of qLEPR and amino acid sequence alignment of representative vertebrate LEPR. The detailed analysis was shown in [1]. Keywords: Quail LEPR, Alternative splicing, Evolutionary conservation, Sequence alignmen