Popular Pipelines Research Articles

Protein Retrieval via Integrative Molecular Ensembles (PRIME) through Extended Similarity Indices.

Molecular dynamics (MD) simulations are ideally suited to describe conformational ensembles of biomolecules such as proteins and nucleic acids. Microsecond-long simulations are now routine, facilitated by the emergence of graphical processing units. Clustering, which groups objects based on structural similarity, is typically used to process ensembles, leading to different states, their populations, and the identification of representative structures. A popular pipeline combines hierarchical clustering for clustering and selecting the cluster centroid as representative of the cluster. Here, we propose to improve on this approach, by developing a module-Protein Retrieval via Integrative Molecular Ensembles (PRIME), that consists of tools to improve the prediction of the representative in the most populated cluster using extended continuous similarity. PRIME is integrated with our Molecular Dynamics Analysis with N-ary Clustering Ensembles (MDANCE) package and can be used as a postprocessing tool for arbitrary clustering algorithms, compatible with several MD suites. PRIME predictions produced structures that when aligned to the experimental structure were better superposed (lower RMSD). A further benefit of PRIME is its linear scaling─rather than the traditional O(N2) traditionally associated with comparisons of elements in a set.

Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs

Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs. Our codes and datasets are available at: https://github.com/CurryTang/Graph-LLM .

Plant Transcriptome Analysis with HISAT-StringTie-Ballgown and TopHat-Cufflinks Pipelines.

The development of next-generation sequencing technology has led to a burst of data in a single assay. Management of a large dataset requires high demands on bioinformatic skills and computing resources. Here we present two popular pipelines for RNA-seq data analysis, using open-source software tools HISAT-StringTie-Ballgown and TopHat-Cufflinks. To meet the need of plant scientist, we describe in detail how to perform such comprehensive analysis beginning with raw RNA-seq reads and available reference genome. It allows biologists to align short reads to a reference genome, measure the transcript abundance, and analyze gene differential expression under two or more conditions. We also discuss other RNA-seq tools that are comparable or alternative to this protocol.

Experimental evaluation of the performance of Gpipe parallelism

Pipeline parallelism is the newly proposed model parallelism paradigm for efficiently training giant size Deep Neural Network (DNN) models across multiple accelerators. Gpipe, as a popular pipeline parallelism scheme, has been integrated into the PyTorch framework. Training a model with Gpipe involves choosing a large number of parameters, e.g. determining a DNN model partitioning scheme for the given number of accelerators, selecting the number of GPUs for the training. Therefore, it is crucial to investigate the effects of different Gpipe configurations on the model training performance and assess the scenarios that are suitable for Gpipe. This paper presents a systematic evaluation of Gpipe performance under various settings, including different DNN models, GPU types, GPU numbers, datasets, and model partition strategies. The experiments show several counterintuitive results, i.e. training a DNN model without using Gpipe performs better than using Gpipe, and utilising more GPUs does not guarantee a better performance and sometimes using less GPUs is better while training with Gpipe. Moreover, the test results also show that the GPU type, model size, dataset size, and the DNN model partition scheme clearly influence the training speed. Based on the observation of the evaluation results, the paper proposes a theoretical model to estimate the performance gain ratio while using Gpipe under different setups.

MSSF: A Novel Mutual Structure Shift Feature for Removing Incorrect Keypoint Correspondences between Images

Removing incorrect keypoint correspondences between two images is a fundamental yet challenging task in computer vision. A popular pipeline first computes a feature vector for each correspondence and then trains a binary classifier using these features. In this paper, we propose a novel robust feature to better fulfill the above task. The basic observation is that the relative order of neighboring points around a correct match should be consistent from one view to another, while it may change a lot for an incorrect match. To this end, the feature is designed to measure the bidirectional relative ranking difference for the neighbors of a reference correspondence. To reduce the negative effect of incorrect correspondences in the neighborhood when computing the feature, we propose to combine spatially nearest neighbors with geometrically “good” neighbors. We also design an iterative neighbor weighting strategy, which considers both goodness and correctness of a correspondence, to enhance correct correspondences and suppress incorrect correspondences. As the relative order of neighbors encodes structure information between them, we name the proposed feature the Mutual Structure Shift Feature (MSSF). Finally, we use the proposed features to train a random forest classifier in a supervised manner. Extensive experiments on both raw matching quality and downstream tasks are conducted to verify the performance of the proposed method.

Enhanced Light Field Reconstruction by Combining Disparity and Texture Information in PSVs via Disparity-Guided Fusion

Dense Light Fields (LFs) can be reconstructed based on a few sparse views. One popular pipeline is the explicit-depth-based pipeline, which first estimates disparity maps using the existing sparse views and then produces the object texture of the target views by warping the relevant information from the given sparse views. However, due to errors in disparity estimation, such a solution often loses object details. Such problems, particularly in regions of small disparity, can be sorted out by estimating the texture without disparity estimation using the so-called implicit-depth-based pipeline. This work integrates both pipelines. It will adapt to the disparity status in the local region and automatically adjust the LFs produced by both pipelines through disparity-guided fusion. In addition, plane-sweep volumes (PSVs) are exploited for handling the given sparse view information, which can reliably decouple the disparity and texture information into different dimensions. In this way, it can best supply the relevant information to the explicit-depth-based and implicit-depth-based pipelines, respectively. The proposed solution shows superiority in various LF reconstruction tasks in our experiments.

Stress Triaxial Constraint and Fracture Toughness Properties of X90 Pipeline Steel

The X90 pipeline steel with high-strength and high-toughness become the most popular pipeline steel. Due to the stress triaxial constraint and fracture toughness properties are the key factors for the stable work of pipeline steel, the research on the fracture toughness of X90 is a great significance to promote the engineering application of high-strength pipeline steel. In order to investigate the stress triaxial constraint and fracture toughness properties of X90 pipeline steel, the experimental rules with different grooves size are proposed using the common toughness experiment and the corresponding numerical models are established in this paper. The resistance curves and fracture toughness of each type of specimens are obtained and compared with that of finite element analysis. Furthermore, the stress distribution, J-integral distribution and stress triaxial constraint of the specimen are analyzed, as well as the influence of side grooves size on the determination of fracture toughness is also discussed. The results obtained from the study will provide a reference to the fracture toughness evaluation research and application of X90 pipeline steel.

Evolutionary Genome Mining for the Discovery and Engineering of Natural Product Biosynthesis.

Genome mining has become an invaluable tool in natural products research to quickly identify and characterize the biosynthetic pathways that assemble secondary or specialized metabolites. Recently, evolutionary principles have been incorporated into genome mining strategies in an effort to better assess and prioritize novelty and understand their chemical diversification for engineering purposes. Here, we provide an introduction to the principles underlying evolutionary genome mining, including bioinformatic strategies and natural product biosynthetic databases. We introduce workflows for traditional genome mining, focusing on the popular pipeline antiSMASH, and methods to predict enzyme substrate specificity from genomic information. We then provide an in-depth discussion of evolutionary genome mining workflows, including EvoMining, CORASON, ARTS, and others, as adopted by our group for the discovery and prioritization of natural products biosynthetic gene clusters and their products.

Improved prior selection using semantics in maximum a posteriori for few-shot learning

Few-shot learning is to recognize novel concepts with few labeled samples. Recently, significant progress has been made to address the overfitting caused by data scarcity, especially those on modeling the distribution of novel categories given a single point. However, they often deeply rely on the prior knowledge from base set, which is generally hard to define, and its selection can easily bias the learning. A popular pipeline is to pretrain a feature extractor with base set and generate statistics from them as prior information. Since pretrained feature extractor cannot extract accurate representations for categories have never seen, and there is only 1 or 5 support images from novel categories, making it hard to acquire accurate priors, especially when they are far away from the class center. To address these issues, in this paper, we base our network on Maximum a posteriori (MAP), proposing a strategy for better prior selection from base set. We specially introduce semantic information, which are learned from unsupervised text corpora and easily available, to alleviate biases caused by unrepresentative support samples. Our intuition is that when the support from visual information is biased, semantics can provide strong prior knowledge to assist learning. Experimental results on four few-shot benchmarks also show that it outperforms the state-of-the-art methods by a large margin, improves around 2.08%∼12.68% than the best results in each dataset on both 1- and 5-shot tasks.

Identifying the RNA signatures of coronary artery disease from combined lncRNA and mRNA expression profiles

Coronary artery disease (CAD) is the most common cardiovascular disease. CAD research has greatly progressed during the past decade. mRNA is a traditional and popular pipeline to investigate various disease, including CAD. Compared with mRNA, lncRNA has better stability and thus may serve as a better disease indicator in blood. Investigating potential CAD-related lncRNAs and mRNAs will greatly contribute to the diagnosis and treatment of CAD. In this study, a computational analysis was conducted on patients with CAD by using a comprehensive transcription dataset with combined mRNA and lncRNA expression data. Several machine learning algorithms, including feature selection methods and classification algorithms, were applied to screen for the most CAD-related RNA molecules. Decision rules were also reported to provide a quantitative description about the effect of these RNA molecules on CAD progression. These new findings (CAD-related RNA molecules and rules) can help understand mRNA and lncRNA expression levels in CAD

Low‐pressure pipeline irrigation technology in China*

ABSTRACT/RÉSUMÉ (ABSTRACT for English and RÉSUMÉ for French)This paper introduces the development of low‐pressure pipeline irrigation technology in China, including relevant technical standards. It illustrates closed, open and semi‐closed pipeline types and provides successful examples of application. It also presents popular pipeline materials, including PE, PVC‐U, PVC‐O, steel reinforced polyethylene and reinforced concrete, as well as their features and range of application. In addition, it discusses problems existing in low‐pressure pipeline irrigation schemes, such as pipeline damage caused by negative pressure, and pipeline blockage resulting from airlocks. © 2020 John Wiley & Sons, Ltd.

Bayesian Transfer Learning for Object Detection in Optical Remote Sensing Images

In the literature of object detection in optical remote sensing images, a popular pipeline is first modifying an off-the-shelf deep neural network, then initializing the modified network by pretrained weights on a source data set, and finally fine-tuning the network on a target data set. The procedure works well in practice but might not make full use of underlying knowledge implied by pretrained weights. In this article, we propose a novel method, referred to as Fisher regularization, for efficient knowledge transferring. Based on Bayes’ theorem, the method stores underlying knowledge into a Fisher information matrix and fine-tunes parameters based on the knowledge. The proposed method would not introduce extra parameters and is less sensitive to hyperparameters than classical weight decay. Experiments on NWPUVHR-10 and DOTA data sets show that the proposed method is effective and works well with different object detectors.

USE OF RENEWABLE SOURCES OF ENERGY FOR NUTRITION OF OWN REQUIREMENTS OF OIL PIPELINE

The development of the modern world directly depends on the development of transport infrastructure. One of the types of transportation is the pipeline system, they are designed mainly for the delivery of liquid from one region to another, the most popular pipelines are oil pipelines and gas pipelines. Water pipelines are usually located in densely populated areas, while oil pipelines are often located in hard-to-reach areas, for example, in the regions of the Far North. As a rule, such territories do not have a centralized power supply, which creates additional difficulties in ensuring their functioning. Generally, diesel generators are the source of electricity in such regions. Based on the analysis of the oil transportation cycle, the authors proposed a certain way to increase its energy efficiency and increase reliability. The aim of this work is increasing energy efficiency and reliability of electrical equipment for own needs in oil pipelines. To achieve this goal, it is proposed to replace diesel generators with plants using renewable energy sources. In particular, it is proposed to use wind-solar installations. These installations are supposed to be used as the main power sources, while diesel generators will be used as stand-by sources of energy. The article provides a feasibility study for this event, as well as specific technical solutions for its implementation. First of all, this event will reduce fuel costs. Also, this will increase the category of reliability of the emergency valve from the third to the first.

16S rRNA基因在微生物生态学中的应用：现状和问题

PDF HTML阅读 XML下载 导出引用 引用提醒 16S rRNA基因在微生物生态学中的应用 DOI: 10.5846/stxb201306181726 作者: 作者单位: 中科院成都生物研究所应用与环境微生物研究中心 中国科学院大学,中科院成都生物研究所应用与环境微生物研究中心,中科院成都生物研究所应用与环境微生物研究中心,中科院成都生物研究所应用与环境微生物研究中心,中科院成都生物研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点基础研究发展规划资助项目(2013CB733502); 国家自然科学基金资助项目(41371268, 31300447) The applications of the 16S rRNA gene in microbial ecology: current situation and problems Author: Affiliation: Key Laboratory of Environmental and Applied Microbiology of Chinese Academy of Sciences,Key Laboratory of Environmental and Applied Microbiology of Chinese Academy of Sciences,Key Laboratory of Environmental and Applied Microbiology of Chinese Academy of Sciences,Key Laboratory of Environmental and Applied Microbiology of Chinese Academy of Sciences,Chendu Institute of Biology of Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:16S rRNA (Small subunit ribosomal RNA)基因是对原核微生物进行系统进化分类研究时最常用的分子标志物 (Biomarker),广泛应用于微生物生态学研究中。近些年来随着高通量测序技术及数据分析方法等的不断进步,大量基于16S rRNA基因的研究使得微生物生态学得到了快速发展,然而使用16S rRNA基因作为分子标志物时也存在诸多问题,比如水平基因转移、多拷贝的异质性、基因扩增效率的差异、数据分析方法的选择等,这些问题影响了微生物群落组成和多样性分析时的准确性。对当前使用16S rRNA基因分析微生物群落组成和多样性的进展情况做一总结,重点讨论当前存在的主要问题以及各种分析方法的发展,尤其是与高通量测序技术有关的实验和数据处理问题。 Abstract:The 16S rRNA (small subunit ribosomal RNA) gene is a universal marker for phylogenetic reconstructions to approximate the tree of life owing to its presence in all prokaryotes and its high conservation. Sequencing of 16S rRNA genes amplified directly from environmental samples is commonly used to study microbial community composition and diversity. Great advances in pyrosequencing technology and bioinformatics in recent years enable us to obtain sequence data from large-scale environmental samples efficiently and cost-effectively. However, some critical problems need to be addressed when the 16S rRNA gene is used for microbial diversity studies, such as horizontal gene transfer (HGT), intragenomic heterogeneity, PCR amplification efficiency, and sequencing data analysis. In this review, we summarize the state-of-the-art applications of 16S rRNA gene as a biomarker for microbial ecology studies, and introduce current pyrosequencing techniques and bioinformatics for large-scale data analysis. This review focuses on four aspects. (i) We introduce the structure and properties of the 16S rRNA gene, e.g. the primary and secondary structure, HGT and heterogeneities of 16S rRNA genes. Based on current available microbial genomes, multi-copy and intragenomic heterogeneities of 16S rRNA genes are recognized. These phenomena may seriously bias the estimations of microbial diversity in environmental samples. Some online tools and databases used for analysis of the 16S rRNA gene sequencing data are also introduced. These tools are used to predict horizontal gene transfer, secondary structure, and to align and classify 16S rRNA gene sequences. (ii) We introduce some 16S rRNA-based techniques commonly used in microbial ecology studies, such as fingerprinting profiling, hybridization, microarray, and high throughput pyrosequencing methods. We compare the advantages and limitations of various methods and recommend how to use them properly based on a specific target. Different methods have different resolutions and detection limitations. Low-resolution profiling methods potentially miss some important information and make it difficult to detail the phylogenetic composition of an environmental sample. Pyrosequencing technique is highly recommended in the future for microbial ecology study. Several sequencing platforms, e.g. Roche 454, Ion Torrent and MiSeq, are compared. (iii) We evaluate the biases that may be introduced during sample preparation and PCR procedures, e.g. DNA extraction, primer selection, PCR optimization, PCR product purification, and data analysis. Amplicon sequencing method suffers from a high level of sequencing and amplification artifacts. It is important to select OTU (operational taxonomic units) classification and chimera removing algorithms. In this case, the Uchime and Uparse are recommended for microbial amplicon pyrosequencing reads. (iv) We introduce some bioinformatics tools for pyrosequencing data analysis, such as chimera check and diversity index calculation. The most popular pipelines for pyrosequencing data analysis include RDP, QIIME and Mothur. In order to link ecological questions with microbial composition data, the methods of ecological statistics must be employed to build the relationships of microbial datasets with environmental variables. Here, we introduce some multiple statistical methods, e.g. PCA and UniFrac analysis. Based on these analyses, microbial data based on 16S rRNA sequencing are linked to the environmental variables, and fundamental ecological questions are addressed. Finally, we recommend researchers to consider these problems systematically when using 16S rRNA-based techniques in microbial ecology study. 参考文献 相似文献 引证文献

ICan: an optimized ion-current-based quantification procedure with enhanced quantitative accuracy and sensitivity in biomarker discovery.

The rapidly expanding availability of high-resolution mass spectrometry has substantially enhanced the ion-current-based relative quantification techniques. Despite the increasing interest in ion-current-based methods, quantitative sensitivity, accuracy, and false discovery rate remain the major concerns; consequently, comprehensive evaluation and development in these regards are urgently needed. Here we describe an integrated, new procedure for data normalization and protein ratio estimation, termed ICan, for improved ion-current-based analysis of data generated by high-resolution mass spectrometry (MS). ICan achieved significantly better accuracy and precision, and lower false-positive rate for discovering altered proteins, over current popular pipelines. A spiked-in experiment was used to evaluate the performance of ICan to detect small changes. In this study E. coli extracts were spiked with moderate-abundance proteins from human plasma (MAP, enriched by IgY14-SuperMix procedure) at two different levels to set a small change of 1.5-fold. Forty-five (92%, with an average ratio of 1.71 ± 0.13) of 49 identified MAP protein (i.e., the true positives) and none of the reference proteins (1.0-fold) were determined as significantly altered proteins, with cutoff thresholds of ≥1.3-fold change and p ≤ 0.05. This is the first study to evaluate and prove competitive performance of the ion-current-based approach for assigning significance to proteins with small changes. By comparison, other methods showed remarkably inferior performance. ICan can be broadly applicable to reliable and sensitive proteomic survey of multiple biological samples with the use of high-resolution MS. Moreover, many key features evaluated and optimized here such as normalization, protein ratio determination, and statistical analyses are also valuable for data analysis by isotope-labeling methods.

Analysis pipelines and packages for Infinium HumanMethylation450 BeadChip (450k) data

The Illumina HumanMethylation450 BeadChip has become a popular platform for interrogating DNA methylation in epigenome-wide association studies (EWAS) and related projects as well as resource efforts such as the International Cancer Genome Consortium (ICGC) and the International Human Epigenome Consortium (IHEC). This has resulted in an exponential increase of 450k data in recent years and triggered the development of numerous integrated analysis pipelines and stand-alone packages. This review will introduce and discuss the currently most popular pipelines and packages and is particularly aimed at new 450k users.

Low Power R4SDC Pipelined FFT Processor Architecture

When the real-time signal processing is required pipelined FFT is the suitable option because of its high throughput and low power demands. A number of FFT architectures are there. Radix-4 single delay commutator (R4SDC) architecture is researched in this paper. R4SDC is the most popular pipeline FFT architectures, because of its efficient use of butterflies and multipliers. In this a low power technique for the pipeline FFT architecture is discussed. In this, Conventional R4SDC architecture, complex multiplier, and multiplier-less architecture based on common sub-expression technique are implemented and compared for 16, 64 and 256-point FFT architectures. A new type of multiplier algorithm called Multiplier-less architecture is implemented and compared with the carry save array, Wallace and Conventional complex multiplier (NBW).

Improvement of RNA-Seq precision with MapAl

We here introduce MapAl, a novel tool allowing the reliable assessment of double the total number of transcripts compared to other popular pipelines. Pawel P. Łabaj , Bryan E. Linggi , H. Steven Wiley , and David P. Kreil 1,3 1 Chair of Bioinformatics, Boku University Vienna, Austria 2 EMSL, Pacific Northwest National Laboratory, Richland, U.S.A. 3 School of Life Sciences, The University of Warwick, U.K.