जर्नल ऑफ़ कंप्यूटर साइंस एंड सिस्टम्स बायोलॉजी

Paracoccidioides brasiliensis is a dimorphic fungus that causes paracoccidioido mycosis (PCM), an endemic mycosis in Latin America. PCM is a chronic, granulomatous, and progressive disease, which has a wide clinical spectrum of manifestations. Although it knows that the main clinical forms are consequences of fungus-host interaction, immune response in PCM is still an open field. The antigenic complexity of P. brasiliensis and the role of most antigens have been poorly explored, thereby decreasing the chances of finding vaccine and therapeutic targets for PCM. Recent results from our group have shown that heat shock protein of 60-kDa from P. brasiliensis strain 18 (Hsp60_Pb18) has a possible detrimental effect on the course of the PCM. Here, we show the molecular model of Hsp60_Pb18 that was generated with the program MODELLER9V8. The model validation was performed using PROCHECK and VERIFY3D. According to the results, the three-dimensional structure of Hsp60_Pb18 is a high reliability model, which displays a remarkable similarity with the three distinct domains of GroEL subunit-equatorial, intermediate and apical domains. This study will provide direction and continuity of studies to characterize Hsp60_ Pb18 and their domains, and thus contribute to the knowledge of the fungus biology and to determine vaccines and/ or therapeutic targets.

Background: Highly complex and computational intensive methods based on Synthetic Minority Over-sampling Technique (SMOTE) and more recently Learning Vector Quantization SMOTE (LVQ-SMOTE) have been proposed for classification problems of imbalanced biomedical data. This works presents a much simpler approach that is not computationally intensive and competes well with existing approaches. It uses principal component analysis (PCA) to generate a pseudo-variable as a linear combination of the features. From this one pseudo-variable, several classification methods are developed that classify directly based on very simple statistics. One method, the Mean Method (MM), classifies cases based on closeness to the means for the two classes from training data sets. When the number of features is very large, a feature reduction (FR) procedure is proposed to reduce misclassifications. In cases where the means of both classes are similar but their spread about their means are different, the Spread Method (SM) is proposed. A unique feature of this method is that one is able to vary the accuracy of classification between the two classes by changing the width of the window for allocation of cases. These proposed methods are found to perform well without the use of over-sampling techniques and multiple-fold cross validation.

Results: The MM or the MM with FR was compared directly to recently published results for LVQ-SMOTE on six (6) data sets and gave better or much better results in every case as measured by adding the percent of true positives to the percent of true negatives. The SM was compared with LVQ-SMOTE on two (2) data sets and operating windows widths were obtained that gave much better results for the SM over LVQ-SMOTE.

Conclusion: Given the simplicity, strengths, and performance of the proposed approach in comparison to current methods, these methods and procedures are recommended for use in classification of imbalanced biomedical data applications.

Web based farm management system is the collection of processes and information that is used to manage various phases of a farm and accessible on the Internet. The paper is aimed to improve the way information disseminates to farmers. It is needed for the development of agriculture to improve the life of farmers. The paper examines the basic visualization of farm in 3D form for Jigawa state farming environment. This means that, how plant of the area will be view virtually. The paper started with examining some bodies that work on agricultural activities based on how they improved in agricultural technology and how to improve the flow of information of agricultural activities through modern channels for sustainable agriculture and rural development. It looks at the design of 3D farmer’s visualization technology and the implementation of the model.

Development of metabolomics has made completion of metabolic map an important issue. Rational suggestion of specific metabolic pathways as candidates for real pathway will promote study to complete metabolic map. Exploring hypothetical metabolic networks containing candidate pathways enable this. Three different hypothetical metabolic networks abbreviated as BRP-dependent network, metabolite-dependent network and CFB network are considered. BRP-dependent and CFB networks are generated by network expansion from seed compounds via balanced reaction pattern (BRP) and via simple cleavage/formation of chemical bonds, respectively. Concept of network expansion, a term introduced by Heinrich’s group, is generalized and a previous approach by Hatzimanikatis and his colleagues for generation of BRP-dependent network equivalent is re-defined in the context of network expansion. Metabolite dependent network is generated from a given set of metabolites based on stoichiometry. Hypothetical metabolites and BRPs are considered to appear in BRP-dependent and metabolite-dependent networks, respectively. Concept of atom network is introduced.

This project helps to improve Performance of Distributed Cooperative Caching by choosing appropriate object replacement algorithms in the Social wireless networks (SWNET). E-Object caching in such SWNETs are shown to be able to minimize the Object provisioning and maintenance cost which is based on the pricing and service dependences among different stakeholders including network communication service providers, Object providers (Server) and End users. Here we consider replacement of objects with respect to two dimensions. One is Frequency and another is Recency (freshness) factor of object requests. The efficiency of the algorithm lies in choosing which items to discard to make room for the new ones. So we use the concept of knapsack problem with respect to frequency and freshness of the objects. So nodes maintain their cache memories with more frequent and latest objects.

Objective: Estimating the effective population size (Ne) is crucial to understanding how populations evolved, expanded or shrunk. One possible approach is to compare DNA diversity, so as to obtain an average Ne over many past generations; however as the population sizes change over time, another possibility is to describe this change. Linkage Disequilibrium (LD) patterns contain information about these changes, and, whenever a large number of densely linked markers are available, can be used to monitor fluctuating population size through time. Here, we present a new R package, NeON that has been designed to explore population’s LD patterns to reconstruct two key parameters of human evolution: the effective population size and the divergence time between populations.

Methods: NeON starts with binary or pairwise-LD PLINK files, and allows (a) to assign a genetic map position using HapMap (NCBI release 36 or 37) (b) to calculate the effective population size over time exploiting the relationship between Ne and the average squared correlation coefficient of LD (r2LD) within predefined recombination distance categories, and (c) to calculate the confidence interval about Ne based on the observed variation of the estimator across chromosomes; the outputs of the functions are both numerical and graphical. This package also offers the possibility to estimate the divergence time between populations given the Ne values calculated from the within-population LD data and a matrix of between-populations FST. These routines can be adapted to any species whenever genetic map positions are available.

Results and Conclusion: The functions contained in the R package NeON provide reliable estimates of effective population sizes of human chromosomes from LD patterns of genome-wide SNPs data, as it is shown here for the populations contained in the CEPH panel. The NeON package enables to accommodate variable numbers of individuals, populations and genetic markers, allowing analyzing those using standard personal computers.

Set-up models are used for a wide variety of dental procedures, from bridge and crown elaboration, maxillofacial surgery planning and for the indirect bonding of lingual brackets. The first step in the construction of a set-up model is the sectioning of the malocclusion model. Manually sectioning malocclusion models is a laborious, often imprecise process than generates a large amount of dust. In this article we present the JAEL system-a joystick-controlled, remote-operated machine which quickly, precisely and cleanly sections malocclusion models for use in procedures such as prosthetic appliances, maxillofacial surgical predictions and the indirect bonding of lingual brackets.

Objective: Understanding functions of microRNAs (or miRNAs), particularly their effects on protein degradation, is biologically important. Emerging technologies, including the reverse-phase protein array (RPPA) for quantifying protein concentration and RNA-seq for quantifying miRNA expression, provide a unique opportunity to study miRNA-protein regulatory mechanisms. One naïve way to analyze such data is to directly examine the correlation between the raw miRNA measurements and protein concentrations estimated from RPPA. However, the uncertainty associated with protein concentration estimates is ignored, which may lead to less accurate results and significant power loss.

Methods: We propose an integrated nonlinear hierarchical model for detecting miRNA targets through original RPPA intensity data. This model is fitted within a maximum likelihood framework and the correlation test between miRNA and protein is assessed using Wald tests. We compare this model and the simple method through extensive simulation studies and a real dataset from the Cancer Genome Atlas (TCGA) project.

Results: This integrated method is shown to have consistently higher power than the simple method, especially when sample sizes are limited and when the RPPA intensity levels are close to the boundaries of imaging limits.

Conclusions: Our proposed method is powerful in detecting miRNA’s protein target through RPPA. We recommend this method in practice.