检索结果-内蒙古大学图书馆

Mutation of Oryza sativa CORONATINE INSENSITIVE 1b(OsCOI1b) delays leaf senescence

Journal of Integrative Plant Biology 2015年第6期57卷 562-576页

作者： Sang-Hwa Lee Yasuhito Sakuraba Taeyoung Lee Kyu-Won Kim Gynheung An Han Yong Lee Nam-Chon Paek Department of Plant Science Plant Genomics and Breeding InstituteResearch Institute of Agriculture and Life SciencesSeoul National University Interdisciplinary Program in Bioinformatics Seoul National University Department of Plant Molecular Systems Biotechnology Crop Biotech InstituteKyung Hee University Department of Agricultural Biotechnology Seoul National University

Jasmonic acid （JA） functions in plant development, including senescence and immunity. Arabidopsis thaliana CORONATINE INSENSITIVE 1 encodes a JA receptor and functions in the JA‐responsive signaling pathway. The Arabidopsis genome harbors a single COI gene, but the rice （Oryza sativa） genome harbors three COI homologs, OsCOI1a, OsCOI1b, and OsCOI2. Thus, it remains unclear whether each OsCOI has distinct, additive, synergistic, or redundant func-tions in development. Here, we use the oscoi1b‐1 knockout mutants to show that OsCOI1b mainly affects leaf senescence under senescence‐promoting conditions. oscoi1b‐1 mutants stayed green during dark‐induced and natural senescence, with substantial retention of chlorophylls and photosyn-thetic capacity. Furthermore, several senescence‐associated genes were downregulated in oscoi1b‐1 mutants, including homologs of Arabidopsis thaliana ETHYLENE INSENSITIVE 3 and ORESARA 1, important regulators of leaf senescence. These results suggest that crosstalk between JA signaling and ethylene signaling affects leaf senescence. The Arabidopsis coi1‐1 plants containing 35S：OsCOI1a or 35S：OsCOI1b rescued the delayed leaf senescence during dark incubation, sug-gesting that both OsCOI1a and OsCOI1b are required for promoting leaf senescence in rice. oscoi1b‐1 mutants showed significant decreases in spikelet fertility and grain weight, leading to severe reduction of grain yield, indicating that OsCOI1‐mediated JA signaling affects spikelet fertility and grain filling.

关键词： Jasmonic acid leaf senescence OsCOI1b rice stay-green

来源：评论

学校读者我要写书评

暂无评论

Genomic and proteomic databases and applications: A challenge for database technology

引用

9th International Conference on Database Systems for Advanced Applications, DASFAA 2004

作者： Navathe, Shamkant B. Patil, Upen College of Computing Georgia Institute Of Technology AtlantaGA United States School of Biology/ Bioinformatics Program Georgia Institute Of Technology AtlantaGA United States

来源：评论

学校读者我要写书评

暂无评论

A bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

引用

2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Nirwantono, Rudi Assidqi, Khoirunnisa Hidayat, Alam Ahmad Pebrianto Trinugroho, Joko Pardamean, Bens Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Food Biotechnology Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Departement Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site. © 2023 IEEE.

关键词： Amino acids

来源：评论

学校读者我要写书评

暂无评论

Learn2Evade: Learning-based generative model for evading PDF malware classifiers

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2021年第4期2卷 299-313页

作者： Bae, Ho Lee, Younghan Kim, Yohan Hwang, Uiwon Yoon, Sungroh Paek, Yunheung Department of Cyber Security EwhaWomans University Seoul03760 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Interdisciplinary Program in Bioinformatics Interdisciplinary Program in Artificial Intelligence Automation and Systems Research Institute Institute of NewMedia and Communications Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of

Recent research has shown that a small perturbation to an input may forcibly change the prediction of a machine learning (ML) model. Such variants are commonly referred to as adversarial examples. Early studies have focused mostly on ML models for image processing and expanded to other applications, including those for malware classification. In this article, we focus on the problem of finding adversarial examples against ML-based portable document format (PDF) malware *** deem that our problem is more challenging than those againstMLmodels for image processing because of the highly complex data structure of PDF and of an additional constraint that the generated PDF should exhibit malicious behavior. To resolve our problem, we propose a variant of generative adversarial networks that generate evasive variant PDF malware (without any crash), which can be classified as benign by various existing classifiers yetmaintaining the original malicious behavior. Our model exploits the target classifier as the second discriminator to rapidly generate an evasive variant PDF with our new feature selection process that includes unique features extracted from malicious PDF files. We evaluate our technique against three representative PDF malware classifiers (Hidost 13, Hidost 16, and PDFrate-v2) and further examine its effectiveness with AntiVirus engines from VirusTotal. To the best of our knowledge, our work is the first to analyze the performance against the commercial AntiVirus engines. Our model finds, with great speed, evasive variants for all selected seeds against state-of-The-Art PDF malware classifiers and raises a serious security concern in the presence of adversaries. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

bioinformatics in the undergraduate curriculum: Opportunities for computer science educators

Bioinformatics in the undergraduate curriculum: Opportunitie...

引用

Proceedings of the Thirty-Fifth SIGCSE Technical Symposium on Computer Science Education

作者： Burhans, Debra T. DeJongh, Matt Doom, Travis E. LeBlanc, Mark Computer Science Department Bioinformatics Program Canisius College Buffalo NY Computer Science Department Hope College Holland MI Computer Science Department BioMedical Science Program Wright State University Dayton OH Department of Math Wheaton College Norton MA

ISBN: (纸本)1581137982

Biology has become an increasingly data-driven science. Modern experimental techniques, including automated DNA sequencing, gene expression micro arrays, and X-ray crystallography are producing molecular data at a rate that has made traditional data analysis methods impractical. Computational methods are becoming an increasingly important aspect of the evaluation and analysis of experimental data in molecular biology. bioinformatics is the term coined for the new field that merges biology and computer science to manage and analyze this data, with the ultimate goal of understanding and modeling living systems. The emergence of bioinformatics provides new challenges and opportunities for computer science educators. This panel assembles four individuals who collectively have experience teaching bioinformatics at both liberal arts colleges and universities, and who also have industry experience in bioinformatics, to discuss various approaches to incorporating bioinformatics into the undergraduate curriculum.

关键词： Computer science

来源：评论

学校读者我要写书评

暂无评论

Systematic Literature Review of Transformer Model Implementations in Detecting Depression 6

Systematic Literature Review of Transformer Model Implementa...

引用

6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Nanggala, Kenjovan Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities. © 2023 IEEE.

关键词： deep learning major depressive disorder multimodal transformer unimodal

来源：评论

学校读者我要写书评

暂无评论

GranatumX:A Community-engaging, Modularized, and Flexible Webtool for Single-cell Data Analysis

引用

Genomics, Proteomics & bioinformatics 2021年第3期19卷 452-460页

作者： David G.Garmire Xun Zhu Aravind Mantravadi Qianhui Huang Breck Yunits Yu Liu Thomas Wolfgruber Olivier Poirion Tianying Zhao Cédric Arisdakessian Stefan Stanojevic Lana X.Garmire Department of Electrical Engineering&Computer Science University of MichiganAnn ArborMI 48109USA Epidemiology Program University of Hawaii Cancer CenterHonoluluHI 96813USA Department of Biostatistics University of MichiganAnn ArborMI 48109USA Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48105USA Molecular Biosciences and Bioengineering Graduate Program University of Hawaii at ManoaHonoluluHI 96822USA

We present GranatumX,a next-generation software environment for single-cell RNA sequencing(scRNA-seq)data *** is inspired by the interactive webtool *** enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical *** also offers software developers the opportunity to rapidly promote their own tools with others in customizable *** architecture of GranatumX allows for easy inclusion of plugin modules,named Gboxes,which wrap around bioinformatics tools written in various programming languages and on various *** can be run on the cloud or private servers and generate reproducible *** is a community-engaging,flexible,and evolving software ecosystem for scRNA-seq analysis,connecting developers with bench *** is freely accessible at http://***/granatumx/app.

关键词： Single-cell RNA sequencing Analysis Pipeline Webtool Module

来源：评论

学校读者我要写书评

暂无评论

Empirical normalization for quadratic discriminant analysis and classifying cancer subtypes

Empirical normalization for quadratic discriminant analysis ...

引用

10th International Conference on Machine Learning and Applications, ICMLA 2011

作者： Kon, Mark A. Nikolaev, Nikolay Dept. of Mathematics and Statistics Program in Bioinformatics Boston University Boston MA 02215 United States Dept. of Mathematics and Statistics Boston University Boston MA 02215 United States

ISBN: (纸本)9780769546070

We introduce a new discriminant analysis method (Empirical Discriminant Analysis or EDA) for binary classification in machine learning. Given a dataset of feature vectors, this method defines an empirical feature map transforming the training and test data into new data with components having Gaussian empirical distributions. This map is an empirical version of the Gaussian copula used in probability and mathematical finance. The purpose is to form a feature mapped dataset as close as possible to Gaussian, after which standard quadratic discriminants can be used for classification. We discuss this method in general, and apply it to some datasets in computational biology. © 2011 IEEE.

关键词： Discriminant analysis

来源：评论

学校读者我要写书评

暂无评论

A Systematic Literature Review of CNN Approaches in Classifying Brain Tumor 6

A Systematic Literature Review of CNN Approaches in Classify...

引用

6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Paulindino, Aedentrisa Yasmanda Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

The ability of Convolutional Neural Networks (CNNs) to accurately discriminate between normal and tumorous brain tissues has been promising. The review focuses on the different CNN models, pre-processing methods, data augmentation, and Transfer Learning (TL) strategies used in this research. This Systematic Literature Review (SLR) collected the data from Google Scholar. The results of this study indicate that open-source datasets from Kaggle and Brain MRI Images for Brain Tumor Detection are the most used datasets. However, limited data and imbalanced class problems remain common challenges across various datasets. To overcome those challenges, using a larger dataset, oversampling, Generative Adversarial Network (GAN), federated learning, and Self-Supervised Learning (SSL) to handle the imbalance are the potential solution. Additionally, popular CNN architectures for brain tumor classification extensively use pre-trained models such as VGG16, VGG19, DenseNet121, DenseNet201, GoogleNet, ResNet-50, and Inception-v3. TL strategies are preferred, allowing CNNs to leverage knowledge from large datasets, improving generalization even with limited labeled data. © 2023 IEEE.

关键词： brain tumor classification cnn mri pre-trained transfer learning

来源：评论

学校读者我要写书评

暂无评论

A Systematic Literature Review of Deep Learning Application in Multiclass Anomaly Detection for Chest Medical Imaging 6

A Systematic Literature Review of Deep Learning Application ...

引用

6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Philips Pardamean, Bens Elwirehardja, Gregorius Natanael Isnan, Mahmud Bina Nusantara University Binus Graduate Program - Master of Compuer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia

ISBN: (纸本)9798350345162

Medical imaging abnormality detection is challenging, but deep learning approaches have shown promise. This paper reviews the current state of the art in deep learning approaches for detecting abnormalities in chest medical imaging. To discover the trends, opportunities, and challenges associated with this field, 18 studies were selected from Google Scholar based on their titles, abstracts, and contents for extensive review to answer two research questions. The study found that the National Institutes of Health (NIH) Chest X-ray 14 dataset is the most used dataset for this task. Most research uses a single-modal approach, considering only image data as input, with X-ray being the more popular instrument. There are 8 out of 18 studies leverage the transfer learning approach, with ResN et50 being the most popular network. MobileNetV2 has demonstrated competitive results compared to more robust networks. Preprocessing techniques such as image enhancement and data augmentation are leveraged by 61.1 % of the reviewed studies and are shown to improve model performance. © 2023 IEEE.

关键词： abnormality detection attention mechanisms chest x-ray CT images deep learning transfer learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：