CoupleNet: Coupling Global Structure with Local Parts for Object Detection

The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly available.Comment: Accepted by ICCV 201

Cultura organizacional y desempeño laboral en los trabajadores de una empresa minera de Ica

La presente tesis parte de la necesidad de entender la asociación entre la Cultura Organizacional y Desempeño Laboral en una empresa minera de capitales chinos, con trabajadores de nacionalidad peruana y china; con el objetivo de ser una referencia para la comunidad académica, se van a describir aspectos importantes de la cultura china y aspectos de la cultura peruana, tanto las similitudes como las diferencias significativas que se manifiestan durante la jornada laboral. Para encontrar una medida cuantitativa de la asociación entre la cultura organizacional de la empresa y el desempeño laboral de los trabajadores se midió la correlación entre estas dos variables, usando como instrumento de medición una encuesta aplicada a una muestra de 322 trabajadores, a través del coeficiente de correlación de Spearman

A Ligation-PCR Approach for Generating Gene Replacement Constructs in Magnaporthe grisea

The conventional approach for generating gene replacement constructs involves several sequence-specific cloning steps and is time-consuming. A ligation-PCR approach was developed to efficiently generate gene replacement constructs. Two vectors useful for this ligation-PCR approach and another vector suitable for improving the efficiency of knockout mutant screens were constructed

MICRAT: A Novel Algorithm for Inferring Gene Regulatory Networks Using Time Series Gene Expression Data

Background: Reconstruction of gene regulatory networks (GRNs), also known as reverse engineering of GRNs, aims to infer the potential regulation relationships between genes. With the development of biotechnology, such as gene chip microarray and RNA-sequencing, the high-throughput data generated provide us with more opportunities to infer the gene-gene interaction relationships using gene expression data and hence understand the underlying mechanism of biological processes. Gene regulatory networks are known to exhibit a multiplicity of interaction mechanisms which include functional and non-functional, and linear and non-linear relationships. Meanwhile, the regulatory interactions between genes and gene products are not spontaneous since various processes involved in producing fully functional and measurable concentrations of transcriptional factors/proteins lead to a delay in gene regulation. Many different approaches for reconstructing GRNs have been proposed, but the existing GRN inference approaches such as probabilistic Boolean networks and dynamic Bayesian networks have various limitations and relatively low accuracy. Inferring GRNs from time series microarray data or RNA-sequencing data remains a very challenging inverse problem due to its nonlinearity, high dimensionality, sparse and noisy data, and significant computational cost, which motivates us to develop more effective inference methods. Results: We developed a novel algorithm, MICRAT (Maximal Information coefficient with Conditional Relative Average entropy and Time-series mutual information), for inferring GRNs from time series gene expression data. Maximal information coefficient (MIC) is an effective measure of dependence for two-variable relationships. It captures a wide range of associations, both functional and non-functional, and thus has good performance on measuring the dependence between two genes. Our approach mainly includes two procedures. Firstly, it employs maximal information coefficient for constructing an undirected graph to represent the underlying relationships between genes. Secondly, it directs the edges in the undirected graph for inferring regulators and their targets. In this procedure, the conditional relative average entropies of each pair of nodes (or genes) are employed to indicate the directions of edges. Since the time delay might exist in the expression of regulators and target genes, time series mutual information is combined to cooperatively direct the edges for inferring the potential regulators and their targets. We evaluated the performance of MICRAT by applying it to synthetic datasets as well as real gene expression data and compare with other GRN inference methods. We inferred five 10-gene and five 100-gene networks from the DREAM4 challenge that were generated using the gene expression simulator GeneNetWeaver (GNW). MICRAT was also used to reconstruct GRNs on real gene expression data including part of the DNA-damaged response pathway (SOS DNA repair network) and experimental dataset in E. Coli. The results showed that MICRAT significantly improved the inference accuracy, compared to other inference methods, such as TDBN, etc. Conclusion: In this work, a novel algorithm, MICRAT, for inferring GRNs from time series gene expression data was proposed by taking into account dependence and time delay of expressions of a regulator and its target genes. This approach employed maximal information coefficients for reconstructing an undirected graph to represent the underlying relationships between genes. The edges were directed by combining conditional relative average entropy with time course mutual information of pairs of genes. The proposed algorithm was evaluated on the benchmark GRNs provided by the DREAM4 challenge and part of the real SOS DNA repair network in E. Coli. The experimental study showed that our approach was comparable to other methods on 10-gene datasets and outperformed other methods on 100-gene datasets in GRN inference from time series datasets

Reservoir Characterization during Underbalanced Drilling of Horizontal Wells Based on Real-Time Data Monitoring

In this work, a methodology for characterizing reservoir pore pressure and permeability during underbalanced drilling of horizontal wells was presented. The methodology utilizes a transient multiphase wellbore flow model that is extended with a transient well influx analytical model during underbalanced drilling of horizontal wells. The effects of the density behavior of drilling fluid and wellbore heat transfer are considered in our wellbore flow model. Based on Kneissl’s methodology, an improved method with a different testing procedure was used to estimate the reservoir pore pressure by introducing fluctuations in the bottom hole pressure. To acquire timely basic data for reservoir characterization, a dedicated fully automated control real-time data monitoring system was established. The methodology is applied to a realistic case, and the results indicate that the estimated reservoir pore pressure and permeability fit well to the truth values from well test after drilling. The results also show that the real-time data monitoring system is operational and can provide accurate and complete data set in real time for reservoir characterization. The methodology can handle reservoir characterization during underbalanced drilling of horizontal wells

Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks

The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named DeepMethyl to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/

Multiobjective Optimization of PID Controller of PMSM

PID controller is used in most of the current-speed closed-loop control of permanent magnet synchronous motors (PMSM) servo system. However, Kp, Ki, and Kd of PID are difficult to tune due to the multiple objectives. In order to obtain the optimal PID parameters, we adopt a NSGA-II to optimize the PID parameters in this paper. According to the practical requirement, several objective functions are defined. NSGA-II can search the optimal parameters according to the objective functions with better robustness. This approach provides a more theoretical basis for the optimization of PID parameters than the aggregation function method. The simulation results indicate that the system is valid, and the NSGA-II can obtain the Pareto front of PID parameters

Associations between the platelet/high-density lipoprotein cholesterol ratio and likelihood of nephrolithiasis: a cross-sectional analysis in United States adults

AimsThe primary objective of this study was to investigate the relationship between the platelet/high-density lipoprotein cholesterol ratio (PHR) and the prevalence of nephrolithiasis within the adult population of the United States.MethodsThe data used in this study were obtained from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2018. The analysis included a non-pregnant population aged 20 years or older, providing proper PHR index and nephrolithiasis data. The research utilized subgroup analyses and weighted univariate and multivariable logistic regression to evaluate the independent association between the PHR and the susceptibility to nephrolithiasis.ResultsThe study comprised 30,899 participants with an average PHR value of 19.30 ± 0.11. The overall prevalence rate of nephrolithiasis was estimated at 9.98% with an increase in the higher PHR tertiles (T1, 8.49%; T2, 10.11%; T3, 11.38%, P < 0.0001). An elevated PHR level was closely linked with a higher susceptibility to nephrolithiasis. Compared with patients in T1, and after adjusting for potential confounders in model 2, the corresponding odds ratio for nephrolithiasis in T3 was 1.48 (95% CI: 1.06 to 2.08), with a P-value = 0.02. The results of the interaction tests revealed a significant impact of chronic kidney disease on the relationship between PHR and nephrolithiasis. Furthermore, the restricted cubic spline analyses exhibited a positive, non-linear correlation between PHR and the risk of nephrolithiasis.ConclusionA convenient biomarker, the PHR, was independently associated with nephrolithiasis and could be a novel biomarker in predicting occurrence in clinical decision