Application of data mining techniques in bioinformatics

With the widespread use of databases and the explosive growth in their sizes, there is a need to effectively utilize these massive volumes of data. This is where data mining comes in handy, as it scours the databases for extracting hidden patterns, finding hidden information, decision making and hypothesis testing. Bioinformatics, an upcoming field in today’s world, which involves use of large databases can be effectively searched through data mining techniques to derive useful rules. Based on the type of knowledge that is mined, data mining techniques [1] can be mainly classified into association rules, decision trees and clustering. Until recently, biology lacked the tools to analyze massive repositories of information such as the human genome database [3]. The data mining techniques are effectively used to extract meaningful relationships from these data.Data mining is especially used in microarray analysis which is used to study the activity of different cells under different conditions. Two algorithms under each mining techniques were implemented for a large database and compared with each other. 1. Association Rule Mining: - (a) a priori (b) partition 2. Clustering: - (a) k-means (b) k-medoids 3. Classification Rule Mining:- Decision tree generation using (a) gini index (b) entropy value. Genetic algorithms were applied to association and classification techniques. Further, kmeans and Density Based Spatial Clustering of Applications of Noise (DBSCAN) clustering techniques [1] were applied to a microarray dataset and compared. The microarray dataset was downloaded from internet using the Gene Array Analyzer Software(GAAS).The clustering was done on the basis of the signal color intensity of the genes in the microarray experiment. The following results were obtained:- 1. Association:- For smaller databases, the a priori algorithm works better than partition algorithm and for larger databases partition works better. 2. Clustering:- With respect to the number of interchanges, k-medoids algorithm works better than k-means algorithm. 3. Classification:- The results were similar for both the indices (gini index and entropy value). The application of genetic algorithm improved the efficiency of the association and classification techniques. For the microarray dataset, it was found that DBSCAN is less efficient than k-means when the database is small but for larger database DBSCAN is more accurate and efficient in terms of no. of clusters and time of execution. DBSCAN execution time increases linearly with the increase in database and was much lesser than that of k-means for larger database. Owing to the involvement of large datasets and the need to derive results from them, data mining techniques can be effectively put in use in the field of Bio-informatics [2]. The techniques can be applied to find associations among the genes, cluster similar gene and protein sequences and draw decision trees to classify the genes. Further, the data mining techniques can be made more efficient by applying genetic algorithms which greatly improves the search procedure and reduces the execution time

Comparison of collection methods for Phlebotomus argentipes sand flies to use in a molecular xenomonitoring system for the surveillance of visceral leishmaniasis.

BACKGROUND: The kala-azar elimination programme has resulted in a significant reduction in visceral leishmaniasis (VL) cases across the Indian Subcontinent. To detect any resurgence of transmission, a sensitive cost-effective surveillance system is required. Molecular xenomonitoring (MX), detection of pathogen DNA/RNA in vectors, provides a proxy of human infection in the lymphatic filariasis elimination programme. To determine whether MX can be used for VL surveillance in a low transmission setting, large numbers of the sand fly vector Phlebotomus argentipes are required. This study will determine the best method for capturing P. argentipes females for MX. METHODOLOGY/PRINCIPAL FINDINGS: The field study was performed in two programmatic and two non-programmatic villages in Bihar, India. A total of 48 households (12/village) were recruited. Centers for Disease Control and Prevention light traps (CDC-LTs) were compared with Improved Prokopack (PKP) and mechanical vacuum aspirators (MVA) using standardised methods. Four 12x12 Latin squares, 576 collections, were attempted (12/house, 144/village,192/method). Molecular analyses of collections were conducted to confirm identification of P. argentipes and to detect human and Leishmania DNA. Operational factors, such as time burden, acceptance to householders and RNA preservation, were also considered. A total of 562 collections (97.7%) were completed with 6,809 sand flies captured. Females comprised 49.0% of captures, of which 1,934 (57.9%) were identified as P. argentipes. CDC-LTs collected 4.04 times more P. argentipes females than MVA and 3.62 times more than PKP (p<0.0001 for each). Of 21,735 mosquitoes in the same collections, no significant differences between collection methods were observed. CDC-LTs took less time to install and collect than to perform aspirations and their greater yield compensated for increased sorting time. No significant differences in Leishmania RNA detection and quantitation between methods were observed in experimentally infected sand flies maintained in conditions simulating field conditions. CDC-LTs were favoured by householders. CONCLUSIONS/SIGNIFICANCE: CDC-LTs are the most useful collection tool of those tested for MX surveillance since they collected higher numbers of P. argentipes females without compromising mosquito captures or the preservation of RNA. However, capture rates are still low

Development and Evaluation of Active Case Detection Methods to Support Visceral Leishmaniasis Elimination in India.

As India moves toward the elimination of visceral leishmaniasis (VL) as a public health problem, comprehensive timely case detection has become increasingly important, in order to reduce the period of infectivity and control outbreaks. During the 2000s, localized research studies suggested that a large percentage of VL cases were never reported in government data. However, assessments conducted from 2013 to 2015 indicated that 85% or more of confirmed cases were eventually captured and reported in surveillance data, albeit with significant delays before diagnosis. Based on methods developed during these assessments, the CARE India team evolved new strategies for active case detection (ACD), applicable at large scale while being sufficiently effective in reducing time to diagnosis. Active case searches are triggered by the report of a confirmed VL case, and comprise two major search mechanisms: 1) case identification based on the index case's knowledge of other known VL cases and searches in nearby houses (snowballing); and 2) sustained contact over time with a range of private providers, both formal and informal. Simultaneously, house-to-house searches were conducted in 142 villages of 47 blocks during this period. We analyzed data from 5030 VL patients reported in Bihar from January 2018 through July 2019. Of these 3033 were detected passively and 1997 via ACD (15 (0.8%) via house-to-house and 1982 (99.2%) by light touch ACD methods). We constructed multinomial logistic regression models comparing time intervals to diagnosis (30-59, 60-89 and ≥90 days with =90 days compared to the referent of <30 days for ACD vs PCD were 0.88, 0.56 and 0.42 respectively. These ACD strategies not only reduce time to diagnosis, and thus risk of transmission, but also ensure that there is a double check on the proportion of cases actually getting captured. Such a process can supplement passive case detection efforts that must go on, possibly perpetually, even after elimination as a public health problem is achieved

Tracking an Underwater Target with Unknown Measurement Noise Statistics Using Variational Bayesian Filters

This paper considers a bearings-only tracking problem using noisy measurements of unknown noise statistics from a passive sensor. It is assumed that the process and measurement noise follows the Gaussian distribution where the measurement noise has an unknown non-zero mean and unknown covariance. Here an adaptive nonlinear filtering technique is proposed where the joint distribution of the measurement noise mean and its covariance are considered to be following normal inverse Wishart distribution (NIW). Using the variational Bayesian (VB) method the estimation technique is derived with optimized tuning parameters i.e, the confidence parameter and the initial degree of freedom of the measurement noise mean and the covariance, respectively. The proposed filtering technique is compared with the adaptive filtering techniques based on maximum likelihood and maximum aposteriori in terms of root mean square error in position and velocity, bias norm, average normalized estimation error squared, percentage of track loss, and relative execution time. Both adaptive filtering techniques are implemented using the traditional Gaussian approximate filters and are applied to a bearings-only tracking problem illustrated with moderately nonlinear and highly nonlinear scenarios to track a target following a nearly straight line path. Two cases are considered for each scenario, one when the measurement noise covariance is static and another when the measurement noise covariance is varying linearly with the distance between the target and the ownship. In this work, the proposed adaptive filters using the VB approach are found to be superior to their corresponding adaptive filters based on the maximum aposteriori and the maximum likelihood at the expense of higher computation cost.Comment: 24 pages, 26 figure

Facile fabrication of functionalised Zr co-ordinated MOF: Antibiotic adsorption and insightful physiochemical characterization

Excessive utilization and discharge of life-saving drugs into the aquatic environment possess a serious threat to human health as well as its surrounding ecosystem. Henceforth, their removal from the water bodies is crucial which is achieved through an economical and competent adsorption process. The surface charge plays a pivotal role in deciding the adsorption capacity of materials. In the present work, UiO-66 and UiO-66-NH2 adsorbent, environment-friendly porous functional solid Metal-organic frameworks (MOFs) were prepared via the hydrothermal method. The as-prepared adsorbents depict notable adsorption towards different antibiotics as model pollutants, because of their hierarchical structure which provides more adsorption sites and electrostatic interaction due to opposite surface charges as examined through pHPZC analysis. The UiO-66-NH2 exhibited the best adsorption for 20 ppm Norfloxacin, i.e., 93 % removal in 2 h at pH 5 with an excellent recycling ability and the adsorption kinetics followed the pseudo-second-order model. Moreover, the adsorption strength of UiO-66-NH2 was also assessed towards Ciprofloxacin and Oxytetracycline hydrochloride pharmaceutical drugs. This study illustrates conceptual designs that guide the preparation of safe and stable adsorbents and broaden the applicability of UiO-66-based MOFs for environmental pollutant removal

<span style="font-size:15.0pt;mso-bidi-font-size: 12.0pt;mso-bidi-font-weight:bold" lang="EN-GB">Evaluation of ‘cattle’ and ‘Indian Bison’ type antigens of <i style="mso-bidi-font-style:normal">Mycobacterium avium </i>subspecies <i style="mso-bidi-font-style:normal">paratuberculosis</i> for diagnosis of Bovine Johne’s Disease using ‘indigenous ELISA’ and AGPT </span>

1182-1185Two antigens (‘cattle’ type and ‘Indian Bison’ type) of <i style="mso-bidi-font-style: normal">Mycobacterium avium subspecies paratuberculosis were evaluated for diagnosis of Johne’s disease (JD) in a gaushala (cattle herd). Of the 160 cows of Sahiwal and <i style="mso-bidi-font-style: normal">Hariana breeds screened, 81 (50.6%) tested positive in ELISA and 66 (41.8%) in AGPT test. Using the two antigens, 33.5% tested positive in both the tests while 41.1% tested negative. Exclusively, only 8.2% tested positive in ELISA while 17.1% tested positive in AGPT. Two antigens together detected 58.9% prevalence of MAP in the gaushala. Individually, indigenous ELISA using antigen from native source of MAP proved superior to AGPT in the diagnosis of JD in cows. </span

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Not AvailableThe major hindrance in the development and sustainability of aquaculture industry is the occurrence of various diseases in the farming systems. Today, preventive and management measures are central concern to overcome such outbreak of diseases. Immunostimulants are considered as an effective tool for enhancing immune status of cultured organisms. Among different immunostimulants used in aquaculture practices, β-glucan is one of the promising immunostimulant, which is a homopolysaccharide of glucose molecule linked by the glycoside bond. It forms the major constituents of cell wall of some plants, fungi, bacteria, mushroom, yeast, and seaweeds. Major attention on β-glucan was captivated with the gain in knowledge on its receptors and the mechanism of action. The receptor present inside the animal body recognizes and binds to β-glucan, which in turn renders the animal with high resistance and enhanced immune response. This review highlights β-glucan as an immunostimulant, its effective dosages, and route of administration and furthermore provides an outline on role of β-glucan in enhancing growth, survival, and protection against infectious pathogens pertaining to fishes and shellfishes. Study also summarizes the effect of β-glucan on its receptors, recognition of proteins, immune-related enzymes, immune-related gene expression and their mechanisms of action.ICA