A Robust Text Classifier Based on Denoising Deep Neural Network in the Analysis of Big Data

Text classification has always been an interesting issue in the research area of natural language processing (NLP). While entering the era of big data, a good text classifier is critical to achieving NLP for scientific big data analytics. With the ever-increasing size of text data, it has posed important challenges in developing effective algorithm for text classification. Given the success of deep neural network (DNN) in analyzing big data, this article proposes a novel text classifier using DNN, in an effort to improve the computational performance of addressing big text data with hybrid outliers. Specifically, through the use of denoising autoencoder (DAE) and restricted Boltzmann machine (RBM), our proposed method, named denoising deep neural network (DDNN), is able to achieve significant improvement with better performance of antinoise and feature extraction, compared to the traditional text classification algorithms. The simulations on benchmark datasets verify the effectiveness and robustness of our proposed text classifier

Occurrence and fate of PPCPs and correlations with water quality parameters in urban riverine waters of the Pearl River Delta, South China

The occurrence and fate of eight PPCPs was studied in river waters from upstream to downstream of the three rivers in the Pearl River Delta, China. The correlations of PPCP levels and water quality parameters were also investigated. The analytes of the highest concentrations were caffeine, acetaminophen, and ciprofloxacin. Carbamazepine and erythromycin-H2O were detected at the lowest concentrations. The highest concentrations of PPCPs were found in the Shijing River, with 865 ng/L caffeine, 339 ng/L acetaminophen, and 304 ng/L ciprofloxacin. In general, the levels of PPCPs in the Zhujiang River were higher at sites where the metropolitan city Guangzhou is located and decreased from the epicenter along the river. Low levels of PPCPs were generally found in the Beijiang River. Positive correlations were found between PPCP levels, total nitrogen, ammonium nitrogen, and cumulative fluorescence excitation-emission matrix (EEM) volume. Among the four PPCPs evaluated (caffeine, acetaminophen, ciprofloxacin, and sulfamethoxazole), caffeine had the best correlations with the correlation coefficients ranging from 0.62 to 0.98. The prediction of PPCP concentrations at specified locations can be substantially simplified

Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population

Abstract Background Genome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence (WGS) data. However, sequencing thousands of individuals of interest is expensive. Imputation from SNP panels to WGS data is an attractive and less expensive approach to obtain WGS data. The aims of this study were to investigate the accuracy of imputation and to provide insight into the design and execution of genotype imputation. Results We genotyped 450 chickens with a 600 K SNP array, and sequenced 24 key individuals by whole genome re-sequencing. Accuracy of imputation from putative 60 K and 600 K array data to WGS data was 0.620 and 0.812 for Beagle, and 0.810 and 0.914 for FImpute, respectively. By increasing the sequencing cost from 24X to 144X, the imputation accuracy increased from 0.525 to 0.698 for Beagle and from 0.654 to 0.823 for FImpute. With fixed sequence depth (12X), increasing the number of sequenced animals from 1 to 24, improved accuracy from 0.421 to 0.897 for FImpute and from 0.396 to 0.777 for Beagle. Using optimally selected key individuals resulted in a higher imputation accuracy compared with using randomly selected individuals as a reference population for re-sequencing. With fixed reference population size (24), imputation accuracy increased from 0.654 to 0.875 for FImpute and from 0.512 to 0.762 for Beagle as the sequencing depth increased from 1X to 12X. With a given total cost of genotyping, accuracy increased with the size of the reference population for FImpute, but the pattern was not valid for Beagle, which showed the highest accuracy at six fold coverage for the scenarios used in this study. Conclusions In conclusion, we comprehensively investigated the impacts of several key factors on genotype imputation. Generally, increasing sequencing cost gave a higher imputation accuracy. But with a fixed sequencing cost, the optimal imputation enhance the performance of WGP and GWAS. An optimal imputation strategy should take size of reference population, imputation algorithms, marker density, and population structure of the target population and methods to select key individuals into consideration comprehensively. This work sheds additional light on how to design and execute genotype imputation for livestock populations

131I-Evans blue: evaluation of necrosis targeting property and preliminary assessment of the mechanism in animal models

Necrosis is a form of cell death, which is related to various serious diseases such as cardiovascular disease, cancer, and neurodegeneration. Necrosis-avid agents (NAAs) selectively accumulated in the necrotic tissues can be used for imaging and/or therapy of related diseases. The aim of this study was to preliminarily investigate necrosis avidity of 131I-evans blue (131I-EB) and its mechanism. The biodistribution of 131I-EB at 24 h after intravenous administration showed that the radioactivity ratio of necrotic to viable tissue was 3.41 in the liver and 11.82 in the muscle as determined by γ counting in model rats. Autoradiography and histological staining displayed preferential uptake of 131I-EB in necrotic tissues. In vitro nuclear extracts from necrotic cells exhibited 82.3% of the uptake in nuclei at 15 min, as well as 79.2% of the uptake at 2 h after 131I-EB incubation. The DNA binding study demonstrated that evans blue (EB) has strong binding affinity with calf-thymus DNA (CT-DNA) (Ksv=5.08×105 L/(mol/L)). Furthermore, the accumulation of 131I-EB in necrotic muscle was efficiently blocked by an excess amount of unlabeled EB. In conclusion, 131I-EB can not only detect necrosis by binding the DNA released from necrotic cells, but also image necrotic tissues generated from the disease clinically. KEY WORDS: 131I-Evans blue, Necrosis avidity, Radioactivity, DNA binding, Necrosis imagin

Characterization of the Transcriptome and Proteome of Brassica napus Reveals the Close Relation between DW871 Dwarfing Phenotype and Stalk Tissue

Rapeseed is a significant oil-bearing cash crop. As a hybrid crop, Brassica napus L. produces a high yield, but it also has drawbacks such as a tall stalk, easy lodging, and is not suitable for mechanized production. To address these concerns, we created the DW871 rapeseed dwarf variety, which has a high yield, high oil content, and is suitable for mechanized production. To fully comprehend the dwarfing mechanism of DW871 and provide a theoretical foundation for future applications of the variety, we used transcriptome and proteome sequencing to identify genes and proteins associated with the dwarfing phenotype, using homologous high-stalk material HW871 as a control. By RNA-seq and iTRAQ, we discovered 8665 DEGs and 50 DAPs. Comprehensive transcription and translation level analysis revealed 25 correlations, 23 of which have the same expression trend, involving monolignin synthesis, pectin-lignin assembly, lignification, glucose modification, cell wall composition and architecture, cell morphology, vascular bundle development, and stalk tissue composition and architecture. As a result of these results, we can formulate a hypothesis about the DW871 dwarfing phenotype: plant hormone signal transduction, such as IAA and BRs, is linked to the formation of dwarf phenotypes, and metabolic pathways related to lignin synthesis, such as phenylpropane biosynthesis, also play a role. Our works will contribute to a better understanding of the genes and proteins involved in the rapeseed dwarf phenotype, and we will propose new insights into the dwarfing mechanism of Brassica napus L

Additional file 1: of Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population

Table S1. A pedigree-based genetic relationship matrix among the 24 key individuals. Table S2 A summary of the sequencing and assembly of 24 key individuals. Table S3 Number of identified variants in the chicken genome with GATK. Table S4 The results of the SNP calling for chromosomes (chr1, chr3, chr6, and chr28) with different sequencing depth under the different total cost of genotyping, respectively. (XLSX 25 kb

Additional file 1: of Improving accuracy of genomic prediction by genetic architecture based priors in a Bayesian model

Two tables containing unbiasedness of genomic prediction of three traits in Germany cattle population and that of 17 traits in the loblolly pine population can are provided as supporting information. (DOCX 20 kb