Taxonomic, distributional and biological study of the genus Agrilus (Coleoptera: Buprestidae). Part III

Zootaxa ◽  
2021 ◽  
Vol 4963 (1) ◽  
pp. 58-90
Author(s):  
EDUARD JENDEK ◽  
OTO NAKLÁDAL
Keyword(s):  
Biological Data ◽  
Biological Information ◽  
Biological Study

Two hundred and eighteen taxa of the genus Agrilus (Coleoptera, Buprestidae) mostly from the Palaearctic and Oriental regions are studied and their taxonomic, nomenclatural, distributional or biological data are updated. For some species, the biological information is supplemented by images from the field, or distributional data by maps showing the whole range of the species. The synonymy of following Agrilus is updated: A. blairi Bourgoin, 1925 (A. telawensis Fisher, 1935 syn. nov.); A. lancifer Deyrolle, 1864 (A. perakianus Kerremans, 1900 syn. nov.); A. lestagei Théry, 1930 (A. collartianus Descarpentries & Villiers, 1963 syn. nov.); A. lugubris Kerremans, 1914 (A. achilleus Obenberger, 1935 syn. nov.); A. ocularis Deyrolle, 1864 (A. capitatus Deyrolle, 1864 syn. nov.), A. bidentellus Obenberger, 1924 syn. nov.) and Agrilus velatus Kerremans, 1912 (A. kuchingensis Tôyama, 1987 syn. nov.). The invalid synonyms A. myrmido Kerremans, 1912; A. myrmidonius Obenberger, 1936 and A. miwai Théry, 1936 are moved from the synonymy of A. achilleus Obenberger, 1935 to synonymy of A. lugubris Kerremans, 1914. A. massanensis Schaefer, 1955 is downgraded to subspecies A. pratensis massanensis Schaefer, 1955 status nov. The specific name samyi Baudon, 1968 is resurrected from the synonymy of A. suturaalba Deyrolle, 1864 and revalidated as Agrilus samyi Baudon, 1968 (nomen revalidatum).

Taxonomic, distributional and biological study of the genus Agrilus (Coleoptera: Buprestidae). Part II

Zootaxa ◽  
2019 ◽  
Vol 4554 (2) ◽  
pp. 401
Author(s):  
EDUARD JENDEK ◽  
OTO NAKLÁDAL
Keyword(s):  
Russian Far East ◽  
Far East ◽  
Biological Data ◽  
Biological Information ◽  
Biological Study ◽  
Palaearctic Species ◽  
The Russian Far East

Two hundred and twenty-six species of the genus Agrilus (Coleoptera, Buprestidae) mostly from the Palaearctic and Oriental regions are studied and their taxonomic, nomenclatural, distributional or biological data are updated. For some species, the biological information is supplemented by images from the field, and distributional data by maps showing the whole range of the species. Two west palaearctic species Agrilus angustulus (Illiger, 1803) and A. graminis Kiesenwetter, 1857 are recorded from the Russian Far East. The following three new junior subjective synonyms are proposed. Agrilus alexeevi Bellamy, 1998 syn. nov. (synonym of A. relegatus Curletti, 1990); A. bicoloratus Abeille de Perrin, 1894 syn. nov. (synonym of A. angustulus (Illiger, 1803)) and A. splendidicollis Fairmaire, 1889 syn. nov. (synonym of A. sinensis Thomson, 1879). 

scholarly journals A Non-negative Matrix Factorization Based Method for Identifying Essential Proteins

2021 ◽  
Author(s):  
Zhihong Zhang ◽  
Sai Hu ◽  
Wei Yan ◽  
Bihai Zhao ◽  
Lei Wang
Keyword(s):  
Protein Interaction ◽  
Matrix Factorization ◽  
Biological Data ◽  
Protein Domain ◽  
Biological Information ◽  
Ppi Network ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Non Negative Matrix Factorization

Abstract BackgroundIdentification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, various different computational methods have been proposed to identify essential proteins based on protein-protein interaction (PPI) networks. However, there has been reliable evidence that a huge amount of false negatives and false positives exist in PPI data. Therefore, it is necessary to reduce the influence of false data on accuracy of essential proteins prediction by integrating multi-source biological information with PPI networks.ResultsIn this paper, we proposed a non-negative matrix factorization and multiple biological information based model (NDM) for identifying essential proteins. The first stage in this progress was to construct a weighted PPI network by combing the information of protein domain, protein complex and the topology characteristic of the original PPI network. Then, the non-negative matrix factorization technique was used to reconstruct an optimized PPI network with whole enough weight of edges. In the final stage, the ranking score of each protein was computed by the PageRank algorithm in which the initial scores were calculated with homologous and subcellular localization information. In order to verify the effectiveness of the NDM method, we compared the NDM with other state-of-the-art essential proteins prediction methods. The comparison of the results obtained from different methods indicated that our NDM model has better performance in predicting essential proteins.ConclusionEmploying the non-negative matrix factorization and integrating multi-source biological data can effectively improve quality of the PPI network, which resulted in the led to optimization of the performance essential proteins identification. This will also provide a new perspective for other prediction based on protein-protein interaction networks.

THE TYPES OF BIOLOGICAL DATABASES (BIOBANKS)

2021 ◽  
pp. 136-148
Author(s):  
Elena S. Boltanova ◽  
◽  
Maria P. Imekova ◽  
Keyword(s):  
Biological Samples ◽  
Local Level ◽  
Legal Regulation ◽  
Biological Data ◽  
Biological Information ◽  
Biological Databases ◽  
Legal Regime ◽  
Eu Countries ◽  
Legal Method ◽  
The Eu

In the world, it is customary to create biological databases of different species. And initially, the databases for the investigation of crimes were widespread. However, later, when their potential and benefits, including for medicine, were assessed, the databases for other areas appeared. Russia was no exception in this regard. Although, in our country, unlike foreign states, the activities of biological databases based on purposes other than the disclosure of crimes are practically not regulated in any way. This article deals with the analysis of legal regulation of biobanks in the Russian Federation and abroad. Special attention is paid to the classification of biobanks. The purpose of the study is to determine the feasibility in the legislative regulation of their activities, as well as the patterns in such a regulation. To achieve this goal, the authors studied extensive regulatory material, which included EU directives and national regulations of the EU member states. The methodological basis of the study was the general scientific and private scientific meth-ods of research. Of course, such private scientific research methods as the comparative-legal method and the formal legal method have been widely used. Due to the comparative legal analysis, it is established that the EU countries have a high level of legislative activity in terms of determining the legal regime of biological databases. All countries recognize the specifics of such a legal regime, which can largely be explained by a special legal nature of biological samples and biological data. In this regard, the following issues related to the activities of biological databases are reflected everywhere in the EU countries at the level of law: the procedure for their creation; the procedure for receiving, processing, storing and transmitting biological samples and the data obtained on their basis; the rights and obligations of database creators and persons who have provided their biological samples and biological data about themselves; a set of measures aimed at protecting the rights and interests of donors and third parties, etc. As it seems, a similar approach to the regulation of the activities of biological bases estab-lished not for the investigation of crimes should be implemented by Russia. At the same time, special attention should be paid to the research of biological databases. In the Russian Federa-tion, they are created, as a rule, at the local level. Their main drawback is that they are sepa-rate sources of limited biological information, functioning independently of each other while comprehensive (concentrated in one place) information can bring invaluable benefits and advantages for Russian science and medicine as a whole. However, this requires the estab-lishment of an appropriate legal framework.

Influence of Genomic and Other Biological Data Sets in the Understanding of Protein Structures, Functions and Interactions

2011 ◽  
Vol 2 (1) ◽  
pp. 24-44
Author(s):  
N. Srinivasan ◽  
G. Agarwal ◽  
R. M. Bhaskara ◽  
R. Gadkari ◽  
O. Krishnadev ◽  
...  
Keyword(s):  
Protein Structures ◽  
Biological Properties ◽  
Biological Data ◽  
Biological Information ◽  
Biological Databases ◽  
Homology Detection ◽  
Putative Gene ◽  
Remote Homology ◽  
Strategic Integration ◽  
Remote Homology Detection

In the post-genomic era, biological databases are growing at a tremendous rate. Despite rapid accumulation of biological information, functions and other biological properties of many putative gene products of various organisms remain either unknown or obscure. This paper examines how strategic integration of large biological databases and combinations of various biological information helps address some of the fundamental questions on protein structure, function and interactions. New developments in function recognition by remote homology detection and strategic use of sequence databases aid recognition of functions of newly discovered proteins. Knowledge of 3-D structures and combined use of sequences and 3-D structures of homologous protein domains expands the ability of remote homology detection enormously. The authors also demonstrate how combined consideration of functions of individual domains of multi-domain proteins helps in recognizing gross biological attributes. This paper also discusses a few cases of combining disparate biological datasets or combination of disparate biological information in obtaining new insights about protein-protein interactions across a host and a pathogen. Finally, the authors discuss how combinations of low resolution structural data, obtained using cryoEM studies, of gigantic multi-component assemblies, and atomic level 3-D structures of the components is effective in inferring finer features in the assembly.

scholarly journals Current analytical technologies and bioinformatic resources for plant metabolomics data

2021 ◽  
Author(s):  
Chigateri M. Vinay ◽  
Sanjay Kannath Udayamanoharan ◽  
Navya Prabhu Basrur ◽  
Bobby Paul ◽  
Padmalatha S. Rai
Keyword(s):  
Inorganic Compounds ◽  
Biological Data ◽  
Biological Information ◽  
Metabolomics Data ◽  
Primary And Secondary Metabolites ◽  
Chemical Structures ◽  
Plant Metabolomics ◽  
Wide Range ◽  
Analytical Tools

AbstractPlant metabolome as the downstream product in the biological information of flow starting from genomics is highly complex, and dynamically produces a wide range of primary and secondary metabolites, including ionic inorganic compounds, hydrophilic carbohydrates, amino acids, organic compounds, and compounds associated with hydrophobic lipids. The complex metabolites present in biological samples bring challenges to analytical tools for separating and characterization of the metabolites. Analytical tools such as nuclear magnetic resonance (NMR) and mass spectrometry have recently facilitated the separation, characterization, and quantification of diverse chemical structures. The massive amount of data generated from these analytical tools need to be handled using fast and accurate bioinformatics tools and databases. In this review, we focused on plant metabolomics data acquisition using various analytical tools and freely available workflows from raw data to meaningful biological data to help biologists and chemists to move at the same pace as computational biologists.

scholarly journals Bioinformatics: scope and challenges in aquaculture research of Bangladesh- a review

2021 ◽  
Vol 10 (2) ◽  
pp. 137-145
Author(s):  
KMS Rana ◽  
K Ahammad ◽  
MA Salam
Keyword(s):  
Health Management ◽  
National Level ◽  
Work Force ◽  
Disease Diagnosis ◽  
Software Tools ◽  
Biological Data ◽  
Biological Information ◽  
Biological Research ◽  
Future Research ◽  
Data Set

Bioinformatics is one of the ongoing trends of biological research integrating gene based information and computational technology to produce new knowledge. It works to synthesize complex biological information from multiomics data (results of high throughput technologies) by employing a number of bioinformatics tools (software). User convenience and availability are the determining factors of these tools being widely used in bioinformatics research. BLAST, FASTA (FAST-All), EMBOSS, ClustalW, RasMol and Protein Explorer, Cn3D, Swiss PDB viewer, Hex, Vega, Bioeditor etc. are commonly operated bioinformatics software tools in fisheries and aquaculture research. By default, these software tools mine and analyze a vast biological data set using the available databases. However, aquaculture scientists can use bioinformatics for genomic data manipulation, genome annotation and expression profiling, molecular folding, modeling, and design as well as generating biological network and system biology. Therefore, they can contribute in specified fields of aquaculture such as disease diagnosis and aquatic health management, fish nutritional aspects and culture-able strain development. Although having huge prospects, Bangladesh is still in infancy of applying bioinformatics in aquaculture research with limited resources. Research council at national level should be formed to bring all the enthusiastic scientists and skilled manpower under a single umbrella and facilitate to contribute in a collaborative platform. Besides, fully-fledged bioinformatics degree should be launched at University levels to produce knowledgeable and trained work force for future research. This review was attempted to shed light on bioinformatics, as young integrated field of bio-computational research, and its significance in aquaculture research of Bangladesh. Int. J. Agril. Res. Innov. Tech. 10(2): 137-145, December 2020

scholarly journals Using Google Earth to improve management of threatened limestone karst ecosystems in Peninsular Malaysia

2016 ◽  
Author(s):  
Thor-Seng Liew ◽  
Liz Price ◽  
Gopalasamy Reuben Clements
Keyword(s):  
Distribution Patterns ◽  
Forest Degradation ◽  
Peninsular Malaysia ◽  
Google Earth ◽  
Biological Data ◽  
Biological Information ◽  
Karst Ecosystems ◽  
Conservation Prioritisation ◽  
Degree Of Isolation ◽  
Limestone Hill

AbstractBiodiversity conservation is now about prioritisation, especially in a world with limited resources and so many habitats and species in need of protection. However, we cannot prioritise effectively if historical and current information on a particular habitat or species remains scattered. Several good platforms have been created to help users to find, use and create biodiversity information. However, good platforms for sharing habitat information for threatened ecosystems are still lacking. Limestone hills are an example of threatened ecosystems that harbour unique biodiversity, but are facing intensifying anthropogenic disturbances. As limestone is a vital resource for the construction industry, it is not possible to completely halt forest degradation and quarrying in developing countries such as Malaysia, where 445 limestone hills have been recorded in the peninsula to date. As such, there is an urgent need to identify which hills must be prioritised for conservation. To make decisions based on sound science, collating spatial and biological information on limestone hills into a publicly accessible database is critical. Here, we compile Malaysia’s first limestone hill GIS map for 445 limestone hills in the peninsula based on information from geological reports and scientific literature. To assist in conservation prioritisation efforts, we quantify characteristics of limestone hills in terms of size, degree of isolation, and spatial distribution patterns and also assessed the degree of habitat disturbance of each limestone hill in terms of buffer area forest degradation and quarrying activity. All this information is stored in a KMZ file and can be accessed through the Google Earth interface. This product should not be viewed as a final output containing basic limestone hill information. Rather, this database is a foundational platform for users to collect, store, update and manipulate spatial and biological data from limestone hills to better inform decisions regarding their management.

scholarly journals A New approximate matching compression algorithm for DNA sequences

2019 ◽  
Author(s):  
J.M. Lázaro-Guevara ◽  
K.M. Garrido
Keyword(s):  
Dna Sequences ◽  
High Speed ◽  
Biological Data ◽  
Compression Algorithm ◽  
Dna Bases ◽  
Biological Information ◽  
Massive Data ◽  
Compression Algorithms ◽  
Approximate Matching ◽  
Internet Connections

1.AbstractUndeveloped countries like Guatemala, where access to high-speed internet connections is limited, downloading and sharing Biological information of thousands of Mega Bits is a huge problem for the beginning and development of Bioinformatics. Based on that information is an urgent necessity to find a better way to share this biological data. There is when the compression algorithms become relevant. With all this information in mind, born the idea of creating a new algorithm using redundancy and approximate selection.Methods:Using the probability given by the transition matrix of the three-word tuple and relative frequencies. Calculating the relative and total frequencies given by the permutation formula (nr) and compressing 6 bits of information into 1 implementing the ASCII table code (0…255 characters, 28), using clusters of 102 DNA bases compacted into 17 string sequences. For decompressing, the inverse process must be done, except that the triplets must be selected randomly (or use a matrix dictionary, 4102).Conclusion:The compression algorithm has a better compression ratio than LZW and Huffman’s algorithm. However, the time needed for decompressing makes this algorithm incompatible for massive data. The functionality as MD5sum need more research but is a promising helpful tool for DNA checking.

Bioinformatics

2018 ◽  
pp. 26-47 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Interdisciplinary Research ◽  
Research Area ◽  
Product Line ◽  
Biological Data ◽  
Biological Information ◽  
Clinical Bioinformatics ◽  
Software Product ◽  
Fundamental Biology ◽  
Insight Into ◽  
Chemical Cross Linking

This chapter describes the overview of bioinformatics; bioinformatics, data mining, and data visualization; bioinformatics and secretome analysis; bioinformatics, mass spectrometry, and chemical cross-linking reagents; bioinformatics and Software Product Line (SPL); bioinformatics and protein kinase; bioinformatics and MicroRNAs (miRNAs); and clinical bioinformatics and cancer. Bioinformatics is the application of computer technology to the management and analysis of biological data. Bioinformatics is an interdisciplinary research area that is the interface between biology and computer science. The primary goal of bioinformatics is to reveal the wealth of biological information hidden in the large amounts of data and obtain a clearer insight into the fundamental biology of organisms. Bioinformatics entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve the formal and practical problems arising from the management and analysis of biological data.

scholarly journals Prediction of protein-protein interactions using one-class classification methods and integrating diverse biological data

2007 ◽  
Vol 4 (3) ◽  
pp. 208-223 ◽  
Author(s):  
José A. Reyes ◽  
David Gilbert
Keyword(s):  
Performance Evaluation ◽  
Protein Interactions ◽  
Biological Data ◽  
Biological Information ◽  
Machine Learning Techniques ◽  
Protein Protein Interactions ◽  
Comparative Performance ◽  
Learning Techniques ◽  
Imbalanced Class ◽  
One Class Classification

Summary This research addresses the problem of prediction of protein-protein interactions (PPI) when integrating diverse kinds of biological information. This task has been commonly viewed as a binary classification problem (whether any two proteins do or do not interact) and several different machine learning techniques have been employed to solve this task. However the nature of the data creates two major problems which can affect results. These are firstly imbalanced class problems due to the number of positive examples (pairs of proteins which really interact) being much smaller than the number of negative ones. Secondly the selection of negative examples can be based on some unreliable assumptions which could introduce some bias in the classification results.Here we propose the use of one-class classification (OCC) methods to deal with the task of prediction of PPI. OCC methods utilise examples of just one class to generate a predictive model which consequently is independent of the kind of negative examples selected; additionally these approaches are known to cope with imbalanced class problems. We have designed and carried out a performance evaluation study of several OCC methods for this task, and have found that the Parzen density estimation approach outperforms the rest. We also undertook a comparative performance evaluation between the Parzen OCC method and several conventional learning techniques, considering different scenarios, for example varying the number of negative examples used for training purposes. We found that the Parzen OCC method in general performs competitively with traditional approaches and in many situations outperforms them. Finally we evaluated the ability of the Parzen OCC approach to predict new potential PPI targets, and validated these results by searching for biological evidence in the literature.

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