Formation of Multiple Groups of Mobile Robots Using Sliding Mode Control

Formation control of multiple groups of agents finds application in large area navigation by generating different geometric patterns and shapes, and also in carrying large objects. In this paper, Centroid Based Transformation (CBT) \cite{c39}, has been applied to decompose the combined dynamics of wheeled mobile robots (WMRs) into three subsystems: intra and inter group shape dynamics, and the dynamics of the centroid. Separate controllers have been designed for each subsystem. The gains of the controllers are such chosen that the overall system becomes singularly perturbed system. Then sliding mode controllers are designed on the singularly perturbed system to drive the subsystems on sliding surfaces in finite time. Negative gradient of a potential based function has been added to the sliding surface to ensure collision avoidance among the robots in finite time. The efficacy of the proposed controller is established through simulation results.Comment: 8 pages, 5 figure

A Computational Approach for Identifying Plant-Based Foods for Addressing Vitamin Deficiency Diseases

Vitamins are nutrients that are essential to human health, and deficiencies have been shown to cause severe diseases. In this study, a computational approach was used to identify vitamin deficiency diseases and plant-based foods with vitamin content. Data from the United States Department of Agriculture Standard Reference (SR27), National Library of Medicine\u27s Medical Subject Headings and MEDLINE, and Wikipedia were combined to identify vitamin deficiency diseases and vitamin content of plant-based foods. A total of 41,584 vitamin-disease associations were identified from MEDLINE-indexed articles as well as from entries in Wikipedia. The SR27 identified 1912 foods that contained at least one vitamin, with an average of 1276 foods per vitamin. Vitamin B12 and D contained the fewest number of foods (n=135 and 70, respectively). The results of this study establish the foundation for developing a process to link vitamin deficiency diseases to vitamin-rich foods

Grand challenges in biodiversity informatics

Author Posting. © The Author, 2007. This is the author's version of the work. It is posted here by permission of KH Biotech Services Pte for personal use, not for redistribution. The definitive version was published in Asia-Pacific Biotech News 11(1): 15-18.The exponentially growing array of biological data has necessitated the development of a new information management domain, biodiversity informatics. It is one of the newest members of the ‘informatics’ sub-disciplines, which all generally focus on the management of information through the application of advanced technologies. Like other informatics sub-disciplines, biodiversity informatics depends on fundamental computer science and information science principles to facilitate the management of heterogeneous data. Biodiversity informatics distinguishes itself as being the most focused on biological knowledge dating back to the earliest dates of recorded history – while most biological or biomedical informatics studies focus on organizing and studying information spanning less than 100 years, the scope of biodiversity informatics spans the age of the Earth. Biodiversity informatics is also concerned with the widest range of disparate data types – including climatology, epidemiology, geography, and taxonomy. To this end, many informatics principles can readily be incorporated into biodiversity informatics; however, there are equally as many challenges that will require creative solutions. Here, several such challenges are presented in an effort to lay a framework for the types of issues that will define the future of biodiversity informatics and, in turn, the future of biology and biomedicine

Biodiversity informatics : organizing and linking information across the spectrum of life

This article has been accepted for publication in Briefings in Bioinformatics © 2007 The Author Published by Oxford University Press. All rights reserved. This is a pre-print, electronic version of an article published in Briefings in Bioinformatics 8 (2007) 347-357, doi:10.1093/bib/bbm037Biological knowledge can be inferred from three major levels of information: molecules, organisms, and ecologies. Bioinformatics is an established field that has made significant advances in the development of systems and techniques to organize contemporary molecular data; biodiversity informatics is an emerging discipline that strives to develop methods to organize knowledge at the organismal level extending back to the earliest dates of recorded natural history. Furthermore, while bioinformatics studies generally focus on detailed examinations of key “model” organisms, biodiversity informatics aims to develop over-arching hypotheses that span the entire tree of life. Biodiversity informatics is presented here as a discipline that unifies biological information from a range of contemporary and historical sources across the spectrum of life using organisms as the linking thread. The present review primarily focuses on the use of organism names as a universal meta-data element to link and integrate biodiversity data across a range of data sources

Structural network analysis of biological networks for assessment of potential disease model organisms

AbstractModel organisms provide opportunities to design research experiments focused on disease-related processes (e.g., using genetically engineered populations that produce phenotypes of interest). For some diseases, there may be non-obvious model organisms that can help in the study of underlying disease factors. In this study, an approach is presented that leverages knowledge about human diseases and associated biological interactions networks to identify potential model organisms for a given disease category. The approach starts with the identification of functional and interaction patterns of diseases within genetic pathways. Next, these characteristic patterns are matched to interaction networks of candidate model organisms to identify similar subsystems that have characteristic patterns for diseases of interest. The quality of a candidate model organism is then determined by the degree to which the identified subsystems match genetic pathways from validated knowledge. The results of this study suggest that non-obvious model organisms may be identified through the proposed approach

TaxonGrab: Extracting Taxonomic Names From Text

Identification of organism names in biological texts is essential for the management of archival resources to facilitate comparative biological investigation. Because organism nomenclature conforms closely to prescribed rules, automated techniques may be useful for identifying organism names from existing documents, and may also support the completion of comprehensive indices of taxonomic names; such comprehensive lists are not yet available. Using a combination of contextual rules and a language lexicon, we have developed a set of simple computational techniques for extracting taxonomic names from biological text. Our proposed method consistently performs at greater than 96% Precision and 94% Recall, and at a much higher speed than manual extraction techniques. An implementation of the described method is available as a Web based tool written in PHP. Additionally, the PHP source code is available from SourceForge: http://sourceforge.net/projects/taxongrab, and the project website is http://research.amnh.org/informatics/taxlit/apps/

Identifying Phytochemicals from Biomedical Literature Utilizing Semantic Knowledge Sources

Chemicals derived from plants (phytochemicals) are major concepts of interest in the study of medicinal plants. To date, efforts to catalogue and organize phytochemical knowledge have resorted to manual approaches. This study explored the potential to leverage publicly accessible semantic knowledge sources for identifying possible phytochemicals. Within the context of this feasibility study, putative phytochemicals were identified for more than 4,000 plants from the Medical Subject Headings Supplementary Concept Records and the Semantic MEDLINE Database. An examination of phytochemicals identified for five selected plant species using the method developed here reveals that there is a disparity in electronically catalogued phytochemical knowledge compared to information from Dr. Duke’s Phytochemical and Ethnobotanical Databases maintained by the United States Department of Agriculture. The results therefore suggest that semantic knowledge sources for biomedicine can be utilized as a source for identifying potential phytochemicals and thus contribute to the overall curation of plant phytochemical knowledge