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 Using Google Earth to Improve the Management of Threatened Limestone Karst Ecosystems in Peninsular Malaysia

2016 ◽  
Vol 9 (2) ◽  
pp. 903-920 ◽  
Author(s):  
Thor-Seng Liew ◽  
Liz Price ◽  
Gopalasamy Reuben Clements
Keyword(s):  
Peninsular Malaysia ◽  
Google Earth ◽  
Karst Ecosystems

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).

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 Monitoring Approach for Tropical Coniferous Forest Degradation Using Remote Sensing and Field Data

2020 ◽  
Vol 12 (16) ◽  
pp. 2531
Author(s):  
Efraín Duarte ◽  
Juan A. Barrera ◽  
Francis Dube ◽  
Fabio Casco ◽  
Alexander J. Hernández ◽  
...  
Keyword(s):  
Coniferous Forest ◽  
Forest Degradation ◽  
Google Earth ◽  
Carbon Stocks ◽  
Insufficient Information ◽  
Degraded Area ◽  
Margin Of Error ◽  
Definition Of ◽  
Reporting And Verification ◽  
High Uncertainty

Current estimates of CO2 emissions from forest degradation are generally based on insufficient information and are characterized by high uncertainty, while a global definition of ‘forest degradation’ is currently being discussed in the scientific arena. This study proposes an automated approach to monitor degradation using a Landsat time series. The methodology was developed using the Google Earth Engine (GEE) and applied in a pine forest area of the Dominican Republic. Land cover change mapping was conducted using the random forest (RF) algorithm and resulted in a cumulative overall accuracy of 92.8%. Forest degradation was mapped with a 70.7% user accuracy and a 91.3% producer accuracy. Estimates of the degraded area had a margin of error of 10.8%. A number of 344 Landsat collections, corresponding to the period from 1990 to 2018, were used in the analysis. Additionally, 51 sample plots from a forest inventory were used. The carbon stocks and emissions from forest degradation were estimated using the RF algorithm with an R2 of 0.78. GEE proved to be an appropriate tool to monitor the degradation of tropical forests, and the methodology developed herein is a robust, reliable, and replicable tool that could be used to estimate forest degradation and improve monitoring, reporting, and verification (MRV) systems under the reducing emissions from deforestation and forest degradation (REDD+) mechanism.

Study on Global Burned Forest Areas Based on Landsat Data

2020 ◽  
Vol 86 (8) ◽  
pp. 503-508
Author(s):  
Zhaoming Zhang ◽  
Tengfei Long ◽  
Guojin He ◽  
Mingyue Wei ◽  
Chao Tang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Forest Cover ◽  
Time Series Data ◽  
Global Climate ◽  
Distribution Patterns ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
Series Data ◽  
Burned Area ◽  
Burned Forest

Forests are an extremely valuable natural resource for human development. Satellite remote sensing technology has been widely used in global and regional forest monitoring and management. Accurate data on forest degradation and disturbances due to forest fire is important to understand forest ecosystem health and forest cover conditions. For a long time, satellite-based global burned area products were only available at coarse native spatial resolution, which was difficult for detecting small and highly fragmented fires. In order to analyze global burned forest areas at finer spatial resolution, in this study a novel, multi-year 30 meter resolution global burned forest area product was generated and released based on Landsat time series data. Statistics indicate that in 2000, 2005, 2010, 2015, and 2018 the total area of burned forest land in the world was 94.14 million hm2, 96.65 million hm2, 59.52 million hm2, 76.42 million hm2, and 83.70 million hm2, respectively, with an average value of 82.09 million hm2. Spatial distribution patterns of global burned forest areas were investigated across different continents and climatic domains. It was found that burned forest areas were mainly distributed in Africa and Oceania, which accounted for approximately 73.85% and 6.81% of the globe, respectively. By climatic domain, the largest burned forest areas occurred in the tropics, with proportions between 88.44% and 95.05% of the world's total during the study period. Multi-year dynamic analysis shows the global burned forest areas varied considerably due to global climate anomalies, e.g., the La Niña phenomenon.

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.

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