Differential Regulatory Analysis Based on Coexpression Network in Cancer Research

With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene coexpression network (GCN) increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.

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Towards the elimination of occupational cancers in the Russian Federation: cancer research for cancer prevention (Part 1)

Russian Journal of Occupational Health and Industrial Ecology ◽

10.31089/1026-9428-2019-2-104-106 ◽

2019 ◽

pp. 104-106

Author(s):

J. Schüz ◽

A. Olsson

Keyword(s):

Cancer Research ◽

Russian Federation ◽

Large Scale ◽

Cancer Control ◽

Occupational Exposures ◽

Cancer Registration ◽

Occupational Cancer ◽

Control Plan ◽

The Russian Federation ◽

Incident Cases

Cancer is increasing worldwide. Th e Russian Federation is no exception in this regard with an increase of the total number of new cases predicted to rise from 529,062 in 2018 to 587,622 in 2040. Th e present high burden and increase in incident cases at the same time increases the pressure on healthcare infrastructure and related costs. Th us, primary and secondary prevention of cancer becomes essential. Occupational cancers related to exposure at the workplace are among the preventable cancer burden, due to the modifi ability of the risk through minimisation of occupational exposures and adequate worker protection. For the Russian Federation, some 20,000 cancers each year may be att ributable to occupation, but systematic recording is currently lacking. As information is also lacking on the absolute eff ect of various occupational carcinogens in the Russian workforce due to lack of large-scale epidemiological studies and because for many suspected occupational carcinogens the evidence may become stronger, the true burden may in fact be higher. Th e Russian Federation appears particularly suitable for research into occupational cancer given the sizable workforce, the heavy industr ialisation as well as the good documentation and workplace surveillance over time, so that results are both informative for the situation in the Russian Federation and on a global scale. Five challenging but not unfeasible steps of nationwide population-based cancer registration, development of a legal framework for record linkage of registries and data collections, recording of occupational cancers, large scale epidemiological occupational cancer research and rigorous implementation of worker protection on known carcinogens, lead the way to a continuously updated cancer control plan that includes the elimination of occupational cancer in the Russian Federation.

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Molecular Epidemiology and Biomarkers in Etiologic Cancer Research: The New in Light of the Old

10.31234/osf.io/tcdfb ◽

2020 ◽

Author(s):

Lungwani Muungo

Keyword(s):

Gene Expression ◽

Cancer Research ◽

Molecular Epidemiology ◽

Exposure Assessment ◽

Large Scale ◽

First Generation ◽

Cancer Epidemiology ◽

Policy Changes ◽

The Past ◽

New Generation

The purpose of this review is to evaluate progress inmolecular epidemiology over the past 24 years in canceretiology and prevention to draw lessons for futureresearch incorporating the new generation of biomarkers.Molecular epidemiology was introduced inthe study of cancer in the early 1980s, with theexpectation that it would help overcome some majorlimitations of epidemiology and facilitate cancerprevention. The expectation was that biomarkerswould improve exposure assessment, document earlychanges preceding disease, and identify subgroupsin the population with greater susceptibility to cancer,thereby increasing the ability of epidemiologic studiesto identify causes and elucidate mechanisms incarcinogenesis. The first generation of biomarkers hasindeed contributed to our understanding of riskandsusceptibility related largely to genotoxic carcinogens.Consequently, interventions and policy changes havebeen mounted to reduce riskfrom several importantenvironmental carcinogens. Several new and promisingbiomarkers are now becoming available for epidemiologicstudies, thanks to the development of highthroughputtechnologies and theoretical advances inbiology. These include toxicogenomics, alterations ingene methylation and gene expression, proteomics, andmetabonomics, which allow large-scale studies, includingdiscovery-oriented as well as hypothesis-testinginvestigations. However, most of these newer biomarkershave not been adequately validated, and theirrole in the causal paradigm is not clear. There is a needfor their systematic validation using principles andcriteria established over the past several decades inmolecular cancer epidemiology.

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High-Rate Transition Metal-based Cathode Materials for Battery-Supercapacitor Hybrid Devices

Nanoscale Advances ◽

10.1039/d1na00523e ◽

2021 ◽

Author(s):

Cong Wang ◽

Zehao Song ◽

Pei Shi ◽

Lin Lv ◽

Houzhao Wan ◽

...

Keyword(s):

Large Scale ◽

Rapid Development ◽

Electronic Devices ◽

High Rate ◽

Specific Power ◽

Energy Storage Devices ◽

Storage Devices ◽

Electrochemical Devices ◽

Hybrid Devices ◽

Scale Grid

With the rapid development of portable electronic devices, electric vehicles and large-scale grid energy storage devices, it needs to reinforce specific energy and specific power of related electrochemical devices meeting...

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High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops

International Journal of Molecular Sciences ◽

10.3390/ijms22158266 ◽

2021 ◽

Vol 22 (15) ◽

pp. 8266

Author(s):

Minsu Kim ◽

Chaewon Lee ◽

Subin Hong ◽

Song Lim Kim ◽

Jeong-Ho Baek ◽

...

Keyword(s):

Drought Stress ◽

High Throughput ◽

Large Scale ◽

Crop Yields ◽

Plant Traits ◽

Rapid Development ◽

Plant Responses ◽

Phenotypic Data ◽

Agricultural Technologies ◽

High Throughput Phenotyping

Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.

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Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-020-00218-2 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Junshu Wang ◽

Guoming Zhang ◽

Wei Wang ◽

Ka Zhang ◽

Yehua Sheng

Keyword(s):

Cloud Computing ◽

Large Scale ◽

Medical Service ◽

Rapid Development ◽

Medical Knowledge ◽

Language Models ◽

Computing Environment ◽

Computing Power ◽

Cloud Computing Environment ◽

Proposed Model

AbstractWith the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

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KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

Frontiers in Genetics ◽

10.3389/fgene.2021.811158 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yuran Jia ◽

Shan Huang ◽

Tianjiao Zhang

Keyword(s):

Dna Binding ◽

Prediction Accuracy ◽

Large Scale ◽

Protein Identification ◽

Feature Fusion ◽

Binding Protein ◽

Rapid Development ◽

Dna Binding Protein ◽

Feature Extraction Method ◽

Independent Test Dataset

DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.

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Quick and efficient approach to develop genomic resources in orphan species: Application in Lavandula angustifolia

PLoS ONE ◽

10.1371/journal.pone.0243853 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243853

Author(s):

Berline Fopa Fomeju ◽

Dominique Brunel ◽

Aurélie Bérard ◽

Jean-Baptiste Rivoal ◽

Philippe Gallois ◽

...

Keyword(s):

Large Scale ◽

De Novo ◽

Rapid Development ◽

Genetic Distances ◽

Lavandula Angustifolia ◽

Distance Analysis ◽

Alternative Medicines ◽

Dna And Rna ◽

Snp Development ◽

High Level

Next-Generation Sequencing (NGS) technologies, by reducing the cost and increasing the throughput of sequencing, have opened doors to generate genomic data in a range of previously poorly studied species. In this study, we propose a method for the rapid development of a large-scale molecular resources for orphan species. We studied as an example the true lavender (Lavandula angustifolia Mill.), a perennial sub-shrub plant native from the Mediterranean region and whose essential oil have numerous applications in cosmetics, pharmaceuticals, and alternative medicines. The heterozygous clone “Maillette” was used as a reference for DNA and RNA sequencing. We first built a reference Unigene, compound of coding sequences, thanks to de novo RNA-seq assembly. Then, we reconstructed the complete genes sequences (with introns and exons) using an Unigene-guided DNA-seq assembly approach. This aimed to maximize the possibilities of finding polymorphism between genetically close individuals despite the lack of a reference genome. Finally, we used these resources for SNP mining within a collection of 16 commercial lavender clones and tested the SNP within the scope of a genetic distance analysis. We obtained a cleaned reference of 8, 030 functionally in silico annotated genes. We found 359K polymorphic sites and observed a high SNP frequency (mean of 1 SNP per 90 bp) and a high level of heterozygosity (more than 60% of heterozygous SNP per genotype). On overall, we found similar genetic distances between pairs of clones, which is probably related to the out-crossing nature of the species and the restricted area of cultivation. The proposed method is transferable to other orphan species, requires little bioinformatics resources and can be realized within a year. This is also the first reported large-scale SNP development on Lavandula angustifolia. All the genomics resources developed herein are publicly available and provide a rich pool of molecular resources to explore and exploit lavender genetic diversity in breeding programs.

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Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

10.1101/553040 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ning Wang ◽

Andrew E. Teschendorff

Keyword(s):

Transcription Factors ◽

Single Cell ◽

Cell Fate ◽

Regulatory Networks ◽

Large Scale ◽

Single Cells ◽

Differential Expression Analysis ◽

Dropout Rate ◽

Rna Seq ◽

Regulatory Activity

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

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Application and analysis of image modeling based on UAV

E3S Web of Conferences ◽

10.1051/e3sconf/202020601026 ◽

2020 ◽

Vol 206 ◽

pp. 01026

Author(s):

Shuheng Wang ◽

Xuefeng Niu ◽

Chunyu Zhu ◽

Xiang Wu ◽

Shi Liu

Keyword(s):

Large Scale ◽

Rapid Development ◽

Line Drawing ◽

Geographic Information ◽

Good Effect ◽

Image Modeling ◽

Rapid Acquisition ◽

Ground Control Points ◽

Information Products ◽

Economic Construction

As one of the important geographic information products, orthophotos play an extremely important role in the field of geographic information. With the rapid development of China’s economic construction and the continuous improvement of photogrammetry technology, traditional orthophoto has been replaced by real orthophoto to meet the requirements of large-scale accurate mapping. This article discusses the method of generating real shot images, uses drone images, and uses Zhanwei New Village in Wuhan as the survey area to lay out the ground control points. Based on Pix4D modeling, the air and three encryptions are completed and based on high-precision DSM Quickly generate real radiography products. The research in this paper shows that the corrected real image can eliminate the phenomenon of blocking the wall and the problem of occlusion. It has a good effect and can be used in the field of line drawing maps. It provides a simple and quick solution for the rapid acquisition of orthophotos in the field of photogrammetry.

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Prediction Study of Tianjin Port Throughput Based on GM (1, 1) Extended Model

Journal of Physics Conference Series ◽

10.1088/1742-6596/2066/1/012089 ◽

2021 ◽

Vol 2066 (1) ◽

pp. 012089

Author(s):

Yahui Wang ◽

Zhuoyi Zhang

Keyword(s):

Large Scale ◽

Raw Materials ◽

Rapid Development ◽

Development Planning ◽

Average Error ◽

Extended Model ◽

Container Transportation ◽

Important Indicator ◽

Change Trend ◽

Cargo Throughput

Abstract Tianjin Port is the largest comprehensive main hub port and one of the main transshipment ports for energy and raw materials transportation in northern China. It has freight business with many countries. At the same time, Tianjin Port is the first port to carry out international maritime container transportation in China’s coastal areas. Tianjin Port was built in the 1950s, and the container business has been started since 1973, In recent years, with the rapid development of large-scale, intensive and intelligent container ships in Tianjin Port, cargo throughput is an important indicator in the comprehensive evaluation of port development, which represents the development level of a port. At the same time, it also brings new tasks to the navigation guarantee work, in particular, it puts forward systematic requirements for port and wharf construction, navigation aids layout, navigation aids efficiency display and navigation aids base layout. The annual throughput of port cargo or container is one of the bases of world ports. As an output index, port enterprises, shipping companies, navigation guarantee departments and shipping economic analysis departments attach great importance to it. Therefore, the prediction of Tianjin Port cargo throughput can provide reference for Tianjin Port’s next development planning, waterway use and navigation guarantee planning and layout, navigation aids setting, wharf construction, route mapping, etc. the article constructs of Tianjin Port. The average error is 0.29%, and the prediction accuracy is first class. This model can better predict the change trend of cargo for Tianjin Port, which is a better way to analyze the change trend for Tianjin port.

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