Impacts of irrigation with treated livestock wastewater on the accumulation characteristic of ARGs in the farmland soil: a case study in Hohhot, China.

Irrigation with treated livestock wastewater (TWW) is a promising strategy for reusing resources. However, TWW irrigation might introduce antibiotic resistant genes (ARGs) into the soil, posing environmental risks associated with antibiotic resistance. This study focuses on investigating the influence of irrigation amounts and duration on the fate of ARGs and identifies key factors driving their changes. The results showed that there were 13 ARGs in TWW, while only 5 ARGs were detected in irrigated soil. That is some introduced ARGs from TWW could not persistently exist in the soil. After 1-year irrigation, an increase in irrigation amount from 0.016 t/m2 to 0.048 t/m2 significantly enhanced the abundance of tetC by 29.81%, while ermB and sul2 decreased by 45.37% and 76.47%, respectively (p < 0.01). After 2-year irrigation, the abundance of tetC, ermB, ermF, dfrA1, and total ARGs significantly increased (p < 0.05) when the irrigation amount increased. The abundances of ARGs after 2-year irrigation were found to be 2.5-34.4 times higher than 1 year. Obviously, the irrigation years intensified the positive correlation between ARGs abundance and irrigation amount. TetC and ermF were the dominant genes resulting in the accumulation of ARGs. TWW irrigation increased the content of organic matter and total nitrogen in the soil, which affected microbial community structure. The changes of the potential host were the determining factors driving the ARGs abundance. Our study demonstrated that continuous TWW irrigation for 2 years led to a substantial accumulation of ARGs in soil.

APA-Scan: detection and visualization of 3'-UTR alternative polyadenylation with RNA-seq and 3'-end-seq data.

BACKGROUND: The eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3'-untranslated region (3'-UTR) of mRNA produces transcripts with shorter or longer 3'-UTR. Often, 3'-UTR serves as a binding platform for microRNAs and RNA-binding proteins, which affect the fate of the mRNA transcript. Thus, 3'-UTR APA is known to modulate translation and provides a mean to regulate gene expression at the post-transcriptional level. Current bioinformatics pipelines have limited capability in profiling 3'-UTR APA events due to incomplete annotations and a low-resolution analyzing power: widely available bioinformatics pipelines do not reference actionable polyadenylation (cleavage) sites but simulate 3'-UTR APA only using RNA-seq read coverage, causing false positive identifications. To overcome these limitations, we developed APA-Scan, a robust program that identifies 3'-UTR APA events and visualizes the RNA-seq short-read coverage with gene annotations. METHODS: APA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3'-UTR transcripts in the RNA-seq data. APA-Scan works in three major steps: (i) calculate the read coverage of the 3'-UTR regions of genes; (ii) identify the potential APA sites and evaluate the significance of the events among two biological conditions; (iii) graphical representation of user specific event with 3'-UTR annotation and read coverage on the 3'-UTR regions. APA-Scan is implemented in Python3. Source code and a comprehensive user's manual are freely available at https://github.com/compbiolabucf/APA-Scan . RESULT: APA-Scan was applied to both simulated and real RNA-seq datasets and compared with two widely used baselines DaPars and APAtrap. In simulation APA-Scan significantly improved the accuracy of 3'-UTR APA identification compared to the other baselines. The performance of APA-Scan was also validated by 3'-end-seq data and qPCR on mouse embryonic fibroblast cells. The experiments confirm that APA-Scan can detect unannotated 3'-UTR APA events and improve genome annotation. CONCLUSION: APA-Scan is a comprehensive computational pipeline to detect transcriptome-wide 3'-UTR APA events. The pipeline integrates both RNA-seq and 3'-end-seq data information and can efficiently identify the significant events with a high-resolution short reads coverage plots.

A Progressive Subnetwork Searching Framework for Dynamic Inference.

Deep neural network (DNN) model compression is a popular and important optimization method for efficient and fast hardware acceleration. However, the compressed model is usually fixed, without the capability to tune the computing complexity (i.e., latency in hardware) on-the-fly, depending on dynamic latency requirements, workloads, and computing hardware resource allocation. To address this challenge, dynamic DNN with run-time adaption of computing structures has been constructed through training with a cross-entropy objective function consisting of multiple subnets sampled from the supernet. Our investigations in this work show that the performance of dynamic inference highly relies on the quality of subnet sampling. To construct a dynamic DNN with multiple high-quality subnets, we propose a progressive subnetwork searching framework, which is embedded with several proposed new techniques, including trainable noise ranking, channel-group sampling, selective fine-tuning, and subnet filtering. Our proposed framework empowers the target dynamic DNN with higher accuracy for all the subnets compared with prior works on both the Canadian Institute for Advanced Research dataset with 10 classes (CIFAR-10) and ImageNet datasets. Specifically, compared with United States-Neural Network (US-NN), our method achieves 0.9% average accuracy gain for Alexnet, 2.5% for ResNet18, 1.1% for Visual Geometry Group (VGG)11, and 0.58% for MobileNetv1, on the ImageNet dataset, respectively. Moreover, to demonstrate run-time tuning of computing latency of dynamic DNN in real computing system, we have deployed our constructed dynamic networks into Nvidia Titan graphics processing unit (GPU) and Intel Xeon central processing unit (CPU), showing great improvement over prior works. The code is available at https://github.com/ASU-ESIC-FAN-Lab/Dynamic-inference.

Non-Structured DNN Weight Pruning-Is It Beneficial in Any Platform?

Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or SRAM operations. It motivates the intensive research on model compression with two main approaches. Weight pruning leverages the redundancy in the number of weights and can be performed in a non-structured, which has higher flexibility and pruning rate but incurs index accesses due to irregular weights, or structured manner, which preserves the full matrix structure with a lower pruning rate. Weight quantization leverages the redundancy in the number of bits in weights. Compared to pruning, quantization is much more hardware-friendly and has become a "must-do" step for FPGA and ASIC implementations. Thus, any evaluation of the effectiveness of pruning should be on top of quantization. The key open question is, with quantization, what kind of pruning (non-structured versus structured) is most beneficial? This question is fundamental because the answer will determine the design aspects that we should really focus on to avoid the diminishing return of certain optimizations. This article provides a definitive answer to the question for the first time. First, we build ADMM-NN-S by extending and enhancing ADMM-NN, a recently proposed joint weight pruning and quantization framework, with the algorithmic supports for structured pruning, dynamic ADMM regulation, and masked mapping and retraining. Second, we develop a methodology for fair and fundamental comparison of non-structured and structured pruning in terms of both storage and computation efficiency. Our results show that ADMM-NN-S consistently outperforms the prior art: 1) it achieves 348× , 36× , and 8× overall weight pruning on LeNet-5, AlexNet, and ResNet-50, respectively, with (almost) zero accuracy loss and 2) we demonstrate the first fully binarized (for all layers) DNNs can be lossless in accuracy in many cases. These results provide a strong baseline and credibility of our study. Based on the proposed comparison framework, with the same accuracy and quantization, the results show that non-structured pruning is not competitive in terms of both storage and computation efficiency. Thus, we conclude that structured pruning has a greater potential compared to non-structured pruning. We encourage the community to focus on studying the DNN inference acceleration with structured sparsity.

Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells.

Microbes and viruses are known to alter host transcriptomes by means of infection. In light of recent challenges posed by the COVID-19 pandemic, a deeper understanding of the disease at the transcriptome level is needed. However, research about transcriptome reprogramming by post-transcriptional regulation is very limited. In this study, computational methods developed by our lab were applied to RNA-seq data to detect transcript variants (i.e., alternative splicing (AS) and alternative polyadenylation (APA) events). The RNA-seq data were obtained from a publicly available source, and they consist of mock-treated and SARS-CoV-2 infected (COVID-19) lung alveolar (A549) cells. Data analysis results show that more AS events are found in SARS-CoV-2 infected cells than in mock-treated cells, whereas fewer APA events are detected in SARS-CoV-2 infected cells. A combination of conventional differential gene expression analysis and transcript variants analysis revealed that most of the genes with transcript variants are not differentially expressed. This indicates that no strong correlation exists between differential gene expression and the AS/APA events in the mock-treated or SARS-CoV-2 infected samples. These genes with transcript variants can be applied as another layer of molecular signatures for COVID-19 studies. In addition, the transcript variants are enriched in important biological pathways that were not detected in the studies that only focused on differential gene expression analysis. Therefore, the pathways may lead to new molecular mechanisms of SARS-CoV-2 pathogenesis.

T-BFA: Targeted Bit-Flip Adversarial Weight Attack.

Traditional Deep Neural Network (DNN) security is mostly related to the well-known adversarial input example attack.Recently, another dimension of adversarial attack, namely, attack on DNN weight parameters, has been shown to be very powerful. Asa representative one, the Bit-Flip based adversarial weight Attack (BFA) injects an extremely small amount of faults into weight parameters to hijack the executing DNN function. Prior works of BFA focus on un-targeted attacks that can hack all inputs into a random output class by flipping a very small number of weight bits stored in computer memory. This paper proposes the first work oftargetedBFA based (T-BFA) adversarial weight attack on DNNs, which can intentionally mislead selected inputs to a target output class. The objective is achieved by identifying the weight bits that are highly associated with classification of a targeted output through a class-dependent weight bit searching algorithm. Our proposed T-BFA performance is successfully demonstrated on multiple DNN architectures for image classification tasks. For example, by merely flipping 27 out of 88 million weight bits of ResNet-18, our T-BFA can misclassify all the images from Hen class into Goose class (i.e., 100% attack success rate) in ImageNet dataset, while maintaining 59.35% validation accuracy.

AS-Quant: Detection and Visualization of Alternative Splicing Events with RNA-seq Data.

(1) Background: A simplistic understanding of the central dogma falls short in correlating the number of genes in the genome to the number of proteins in the proteome. Post-transcriptional alternative splicing contributes to the complexity of the proteome and is critical in understanding gene expression. mRNA-sequencing (RNA-seq) has been widely used to study the transcriptome and provides opportunity to detect alternative splicing events among different biological conditions. Despite the popularity of studying transcriptome variants with RNA-seq, few efficient and user-friendly bioinformatics tools have been developed for the genome-wide detection and visualization of alternative splicing events. (2) Results: We propose AS-Quant, (Alternative Splicing Quantitation), a robust program to identify alternative splicing events from RNA-seq data. We then extended AS-Quant to visualize the splicing events with short-read coverage plots along with complete gene annotation. The tool works in three major steps: (i) calculate the read coverage of the potential spliced exons and the corresponding gene; (ii) categorize the events into five different categories according to the annotation, and assess the significance of the events between two biological conditions; (iii) generate the short reads coverage plot for user specified splicing events. Our extensive experiments on simulated and real datasets demonstrate that AS-Quant outperforms the other three widely used baselines, SUPPA2, rMATS, and diffSplice for detecting alternative splicing events. Moreover, the significant alternative splicing events identified by AS-Quant between two biological contexts were validated by RT-PCR experiment. (3) Availability: AS-Quant is implemented in Python 3.0. Source code and a comprehensive user's manual are freely available online.

Pseudomonas pratensis sp. nov., Isolated from Grassland Soil from Inner Mongolia, China.

A novel bacterial strain, designated MHJ-10JT, was isolated from a soil sample obtained from a grassland in Inner Mongolia, China. MHJ-10JT strain could grow at 4-37 °C (optimum: 30 °C) and pH 4-9 (optimum: pH 6), as well as in the presence of 0-6% NaCl (optimum: 1%). Cells of strain MHJ-10JT are Gram-negative, rod-shaped, and motile. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain MHJ-10JT was most closely related to Pseudomonas lutea OK2T (98.5% 16S rRNA gene sequence similarity). The values of the average nucleotide identities (ANI) and digital DNA-DNA hybridization (dDDH) between strain MHJ-10JT and its related species were all below 80.5% and 24.4%, respectively, which are significantly lower than the thresholds of 95% for ANI and 70% for DDH for species delineation. The genomic G + C content of the MHJ-10JT strain is 64.8 mol%. Based on the phenotypic, genotypic, chemotaxonomic, and phylogenetic analyses, strain MHJ-10JT can be assigned to the genus Pseudomonas. In this study, we propose that strain MHJ-10JT be classified as a novel species belonging to the genus Pseudomonas with the species name Pseudomonas pratensis sp. nov. The type strain of the proposed novel species is MHJ-10JT (= KCTC 82206T = CGMCC 17322T).

Lysobacter alkalisoli sp. nov., a chitin-degrading strain isolated from saline-alkaline soil.

Strains of Lysobacter, thought to play vital roles in the environment for their high enzyme production capacity, are ubiquitous in various ecosystems. During an analysis of bacterial diversity in saline soil, a Gram-stain-negative, aerobic, chitin-degrading bacterial strain, designated SJ-36T, was isolated from saline-alkaline soil sampled at Tumd Right Banner, Inner Mongolia, PR China. Strain SJ-36T grew at 4-40 °C (optimum, 30 °C), pH 5.0-10.0 (optimum, pH 7.0-8.0) and 0-6 % NaCl (optimum, 1.0 %). Oxidase and catalase activities were positive. A phylogenetic tree based on 16S rRNA gene sequences and the phylogenomic tree both showed that strain SJ-36T formed a tight clade with Lysobacter maris KMU-14T (sharing 97.6 % 16S rRNA gene similarity) and Lysobacter aestuarii S2-CT (97.8 %). The major polar lipids of strain SJ-36T were phosphatidylethanolamine, diphosphatidylglycerol, phosphatidylglycerol, two unidentified lipids and one unidentified phospholipid. The major fatty acids were iso-C15 : 0 (37.5 %), summed feature 9 (14.0 %; iso-C17 : 1ω9c and/or C16 : 0 10-methyl) and iso-C11 : 0 (10.6 %). Q-8 was the predominant ubiquinone. Its genomic DNA G+C content was 66.6 mol%. The average nucleotide identity values of strain SJ-36T to L. maris KMU-14T, L. aestuarii S2-CT and other type strains were 81.5, 79.1 and <79.0 %, respectively. The results of physiological, phenotypic and phylogenetic characterizations allowed the discrimination of strain SJ-36T from its phylogenetic relatives. Lysobacter alkalisoli sp. nov. is therefore proposed with strain SJ-36T (=CGMCC 1.16756T=KCTC 43039T) as the type strain.

Network-based multi-task learning models for biomarker selection and cancer outcome prediction.

MOTIVATION: Detecting cancer gene expression and transcriptome changes with mRNA-sequencing or array-based data are important for understanding the molecular mechanisms underlying carcinogenesis and cellular events during cancer progression. In previous studies, the differentially expressed genes were detected across patients in one cancer type. These studies ignored the role of mRNA expression changes in driving tumorigenic mechanisms that are either universal or specific in different tumor types. To address the problem, we introduce two network-based multi-task learning frameworks, NetML and NetSML, to discover common differentially expressed genes shared across different cancer types as well as differentially expressed genes specific to each cancer type. The proposed frameworks consider the common latent gene co-expression modules and gene-sample biclusters underlying the multiple cancer datasets to learn the knowledge crossing different tumor types. RESULTS: Large-scale experiments on simulations and real cancer high-throughput datasets validate that the proposed network-based multi-task learning frameworks perform better sample classification compared with the models without the knowledge sharing across different cancer types. The common and cancer-specific molecular signatures detected by multi-task learning frameworks on The Cancer Genome Atlas ovarian, breast and prostate cancer datasets are correlated with the known marker genes and enriched in cancer-relevant Kyoto Encyclopedia of Genes and Genome pathways and gene ontology terms. AVAILABILITY AND IMPLEMENTATION: Source code is available at: https://github.com/compbiolabucf/NetML. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hierarchical Temporal Memory Based on Spin-Neurons and Resistive Memory for Energy-Efficient Brain-Inspired Computing.

Hierarchical temporal memory (HTM) tries to mimic the computing in cerebral neocortex. It identifies spatial and temporal patterns in the input for making inferences. This may require a large number of computationally expensive tasks, such as dot product evaluations. Nanodevices that can provide direct mapping for such primitives are of great interest. In this paper, we propose that the computing blocks for HTM can be mapped using low-voltage, magnetometallic spin-neurons combined with an emerging resistive crossbar network, which involves a comprehensive design at algorithm, architecture, circuit, and device levels. Simulation results show the possibility of more than 200× lower energy as compared with a 45-nm CMOS ASIC design.