A Comparative Study of CN2 Rule and SVM Algorithm and Prediction of Heart Disease Datasets Using Clustering Algorithms

In this paper, we discuss diagnosis analysis and identification of heart disease using with data mining techniques. The heart disease is a major cause of morbidity and mortality in modern society; it is extremely important but complicated task that should be performed accurately and efficiently. It is an huge amount data of leads medical data to the need for powerful data analysis tools are availability on the data mining technique. They have long to been an concerned with applying for statistical and data mining tools and data mining techniques to improve data analysis on large datasets. In this paper, to proposed system are implemented to find out the heart disease through as to compared with the some data mining techniques are Decision tree, SOM, CN2 Rule and K-Means Clustering the data mining could help in the identification or the prediction of high or low risk of Heart Disease.Keywords: Data Mining, Heart Disease, Decision tree, SOM, CN2 Rule and cluster

Exploring structural variation and gene family architecture with De Novo assemblies of 15 Medicago genomes

Abstract Background Previous studies exploring sequence variation in the model legume, Medicago truncatula, relied on mapping short reads to a single reference. However, read-mapping approaches are inadequate to examine large, diverse gene families or to probe variation in repeat-rich or highly divergent genome regions. De novo sequencing and assembly of M. truncatula genomes enables near-comprehensive discovery of structural variants (SVs), analysis of rapidly evolving gene families, and ultimately, construction of a pan-genome. Results Genome-wide synteny based on 15 de novo M. truncatula assemblies effectively detected different types of SVs indicating that as much as 22% of the genome is involved in large structural changes, altogether affecting 28% of gene models. A total of 63 million base pairs (Mbp) of novel sequence was discovered, expanding the reference genome space for Medicago by 16%. Pan-genome analysis revealed that 42% (180 Mbp) of genomic sequences is missing in one or more accession, while examination of de novo annotated genes identified 67% (50,700) of all ortholog groups as dispensable – estimates comparable to recent studies in rice, maize and soybean. Rapidly evolving gene families typically associated with biotic interactions and stress response were found to be enriched in the accession-specific gene pool. The nucleotide-binding site leucine-rich repeat (NBS-LRR) family, in particular, harbors the highest level of nucleotide diversity, large effect single nucleotide change, protein diversity, and presence/absence variation. However, the leucine-rich repeat (LRR) and heat shock gene families are disproportionately affected by large effect single nucleotide changes and even higher levels of copy number variation. Conclusions Analysis of multiple M. truncatula genomes illustrates the value of de novo assemblies to discover and describe structural variation, something that is often under-estimated when using read-mapping approaches. Comparisons among the de novo assemblies also indicate that different large gene families differ in the architecture of their structural variation

Hybrid assembly with long and short reads improves discovery of gene family expansions

BACKGROUND: Long-read and short-read sequencing technologies offer competing advantages for eukaryotic genome sequencing projects. Combinations of both may be appropriate for surveys of within-species genomic variation. METHODS: We developed a hybrid assembly pipeline called "Alpaca" that can operate on 20X long-read coverage plus about 50X short-insert and 50X long-insert short-read coverage. To preclude collapse of tandem repeats, Alpaca relies on base-call-corrected long reads for contig formation. RESULTS: Compared to two other assembly protocols, Alpaca demonstrated the most reference agreement and repeat capture on the rice genome. On three accessions of the model legume Medicago truncatula, Alpaca generated the most agreement to a conspecific reference and predicted tandemly repeated genes absent from the other assemblies. CONCLUSION: Our results suggest Alpaca is a useful tool for investigating structural and copy number variation within de novo assemblies of sampled populations

An overview of the utilisation of microalgae biomass derived from nutrient recycling of wet market wastewater and slaughterhouse wastewater

Microalgae have high nutritional values for aquatic organisms compared to fish meal, because microalgae cells are rich in proteins, lipids, and carbohydrates. However, the high cost for the commercial production of microalgae biomass using fresh water or artificial media limits its use as fish feed. Few studies have investigated the potential of wet market wastewater and slaughterhouse wastewater for the production of microalgae biomass. Hence, this study aims to highlight the potential of these types of wastewater as an alternative superior medium for microalgae biomass as they contain high levels of nutrients required for microalgae growth. This paper focuses on the benefits of microalgae biomass produced during the phycore-mediation of wet market wastewater and slaughterhouse wastewater as fish feed. The extraction techniques for lipids and proteins as well as the studies conducted on the use of microalgae biomass as fish feed were reviewed. The results showed that microalgae biomass can be used as fish feed due to feed utilisation efficiency, physiological activity, increased resistance for several diseases, improved stress response, and improved protein retention

Preparation and characterization of a novel hybrid hydrogel composite of chitin stabilized graphite: Application for selective and simultaneous electrochemical detection of dihydroxybenzene isomers in water

The development of new and robust sensors for real-time monitoring of environmental pollutants have received much attention. Therefore, in the present work, we have fabricated a simple and robust electrochemical sensor for the simultaneous electrochemical determination of dihydroxybenzene isomers using chitin (CHI) stabilized graphite (GR) hydrogel composite modified electrode. The GR-CHI hydrogel composite was prepared by a simple sonication of raw GR in CHI solution and the as-prepared materials were characterized by range of physicochemical methods. Compared with CHI and GR modified electrodes, the GR-CHI hydrogel composite modified electrode shows an excellent electron transfer ability and enhanced electrocatalytic activity towards hydroquinone (HQ), catechol (CC) and resorcinol (RC). Differential pulse voltammetry was used for the simultaneous determination of HQ, CC and RC. Under optimized conditions, the fabricated electrode detects the HQ, CC and RC in the linear response from 0.2 to 110.6 µM, 0.3 to 110.6 µM and 1.3 to 133.4 µM, respectively. The detection limit for HQ, CC and RC were 0.065 µM, 0.085 µM and 0.35 µM, respectively. The sensor shows its appropriate practicality towards the determination of HQ, CC and RC in different water samples

Development of Morphologically engineered Flower-like Hafnium-Doped ZnO with Experimental and DFT Validation for Low-Temperature and Ultrasensitive Detection of NOX Gas

This is the final version. Available on open access from the American Chemical Society via the DOI in this recordSubstitutional doping and different nanostructures of ZnO have rendered it an effective sensor for the detection of volatile organic compounds in real-time atmosphere. However, the low selectivity of ZnO sensors limits their applications. Herein, hafnium (Hf)-doped ZnO (Hf-ZnO) nanostructures are developed by the hydrothermal method for high selectivity of hazardous NOX gas in the atmosphere, substantially portraying the role of doping concentration on the enhancement of structural, optical, and sensing behavior. ZnO microspheres with 5% Hf doping showed excellent sensing and detected 22 parts per billion (ppb) NOX gas in the atmosphere, within 24 s, which is much faster than ZnO (90 s), and rendered superior sensing ability (S = 67) at a low temperature (100 °C) compared to ZnO (S = 40). The sensor revealed exceptional stability under humid air (S = 55 at 70% RH), suggesting a potential of 5% Hf-ZnO as a new stable sensing material. Density functional theory (DFT) and other characterization analyses revealed that the high sensing activity of 5% Hf-ZnO is attributed to the accessibility of more adsorption sites arising due to charge distortion, increased oxygen vacancies concentration, Lewis acid base, porous morphology, small particle size (5 nm), and strong bond interaction amidst NO2 molecule with ZnO-Hf-Ovacancy sites, resulting from the substitution of the host cation (Zn2+) with doping cation (Hf4+).Korea government (Ministry of Education)Engineering and Physical Sciences Research Council (EPSRC

Thermal analysis of water in reinforced plasma-polymerised poly(2-hydroxyethyl acrylate) hydrogels

Thermal analysis of water in reinforce hydrogels of plasma-polymerised poly(2-hydroxyethyl acrylate) (plPHEA) grafted onto macroporous poly(methyl methacrylate) (PMMA) are explained in a simple thermodynamic framework based on the transition diagram. Water in bulk PHEA was also analysed for comparison with plPHEA. These two hydrophilic polymers were prepared with a broad range of water mass fractions from 0.05 to 0.72. Thermal transition diagrams of water/PHEA and water/plPHEA were determined showing less undercooling of water crystallisation in plPHEA than in PHEA. Kinetics of water crystallisation for high and low water contents were studied in both hydrophilic systems following several thermal treatments. Water crystallises much faster in plPHEA than in PHEA for high water contents. For low water contents, crystallisation becomes possible holding at 30 degrees C for some time due to water segregation in both PHEA systems. However, much less water is segregated from the water/plPHEA mixture due to the influence of the hydrophobic component.This work was supported by a Marie Curie Host Fellowship and by the Spanish Science and Technology Ministry through the MAT2001-2678-C02-01 and MAT2002-04239-C03-03 projects. CIBER-BBN is an initiative funded by the VI National R&D&i Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund.Serrano Aroca, Á.; Monleón Pradas, M.; Gómez Ribelles, JL.; Rault, J. (2015). Thermal analysis of water in reinforced plasma-polymerised poly(2-hydroxyethyl acrylate) hydrogels. European Polymer Journal. 72:523-534. https://doi.org/10.1016/j.eurpolymj.2015.05.032S5235347

The Usefulness of Gridded Climate Data Products in Characterizing Climate Variability and Assessing Crop Production

A sparse rain gauge network in dryland regions has been a major challenge for accessing high-quality observed data needed to understand variability and trends in climate. Gridded estimates of weather parameters produced through data assimilation algorithms that integrate satellite and irregularly distributed on-ground observations from multiple observing networks are a potential alternative. Questions remain about the application of such climate data sources for assessing climate variability and crop productivity. This study assessed the usefulness and limitations of gridded data from four different sources i.e. AgMERRA, CHIRPS, NASA Power, and TAMSAT in estimating climate impacts on crop productivity using Agricultural Production Systems Simulator (APSIM). The study used data for 11 locations from Africa and India. The agreement between these data sets and observed data both in the amount and distribution of rainfall was evaluated before and after bias correction statistically. A deviation of more than 100 mm per season was observed in 13%, 20%, 25%, and 40% of the seasons in CHIRPS, AgMERRA, NASA Power, and TAMSAT data sets respectively. The differences were reduced significantly when data sets were bias-corrected. The number of rainy days is better estimated by TAMSAT and CHIRPS with a deviation of 4% and 6% respectively while AgMERRA and NASA Power overestimated by 28% and 67% respectively. The influence of these differences on crop growth and productivity was estimated by simulating maize yields with APSIM. Simulated crop yields with all gridded data sets were poorly correlated with observed data. The normalized root-mean-square error (NRMSE) of maize yield simulated with observed and gridded data was 30% for all locations with TAMSAT data. When yields were simulated with data after bias correction using the linear scaling technique, results were slightly improved. The results of our study thus indicate that the gridded data sets are usefully applied for characterizing climate variability, i.e. trends and seasonality in rainfall, however their use in driving crop model simulations of smallholder farm level production should be carefully interpreted