Ambarish Sharan Vidyarthi

Computational Intelligence in Early Diabetes Diagnosis: A Review

The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.

Transformation of ferulic acid to 4-vinyl guaiacol as a major metabolite: a microbial approach

The majority of the flavours and fragrances used worldwide are produced by chemical synthesis at low price. However, consumers prefer natural compounds because of increasing health and nutrition awareness in routine life. Hence, biotransformation is an alternative process to produce natural aroma compounds. Microorganisms have been gradually used more to produce natural aroma compounds with various applications in food, agriculture and pharmaceutical industries. This paper reviews the role of microorganisms in the transformation of ferulic acid to 4-vinyl guaiacol. The microbial processes based on biocatalytic method are discussed in terms of their advantages over chemical synthesis, plant cell cultures and enzyme catalyzed reactions. Thus, the transformation of ferulic acid by microorganisms could have possible use in food, pharmaceutical industry and become an increasingly important platform for the production of natural aroma compounds.

Extracellular facile biosynthesis, characterization and stability of gold nanoparticles by Bacillus licheniformis

Abstract Context: The development of a reliable, eco-friendly process for synthesis of gold nanoparticles (AuNPs) has gained impetus in recent years to counter the drawbacks of chemical and physical methods. Objective: This study illustrates simple, green synthesis of AuNPs in vitro using cell lysate supernatant (CLS) of non-pathogenic bacteria and to investigate its potential antimicrobial activity. Materials and methods: Gold nanoparticles were synthesized by the reduction of precursor AuCl4− ions using the CLS of Bacillus licheniformis at 37°C upon 24 h of incubation. The nanoparticles were characterized for their morphology, particle size, optical absorption, zeta potential, and stability. Further the antimicrobial activity was assayed using cup-plate method. Results: The process of biosynthesis was extracellular and the gold ions were reduced to stable nanogold of average size 38 nm. However, upon storage of AuNPs for longer duration at room temperature stability was influenced in terms of increase in particle size and decrease in zeta potential with respect to as synthesized nanoparticles. SEM micrographs revealed the spherical shape of AuNPs and EDX analysis confirmed the presence of gold in the sample. Also clear zone of inhibition was observed against Bacilllus subtilis MTCC 8364, Pseudomonas aeruginosa MTCC 7925, and Escherichia coli MTCC 1698 confirming the antimicrobial activity of AuNPs. Discussion: The bioprocess under study was simple and less time consuming as compared to other methods as the need for harvesting AuNPs from within the microbial cells via downstream process will be eliminated. Nanoparticles exhibited good stability even in absence of external stabilizing agents. AuNPs showed good antimicrobial activity against several Gram-negative and Gram-positive pathogenic bacteria. Conclusion: The extracellular biosynthesis from CLS may serve as a suitable alternative for large scale synthesis of gold nanoparticles in vitro. The synthesis from lysed bacterial cell strongly suggests that exposure of microbial whole cells to the gold solution for nanoparticle formation is not necessary and that microorganism even in lysed state retained its bioreduction potential. Further the potential of biologically synthesized AuNPs as antimicrobial agents will be of great commercial importance.

Genetic variation among species, races, forms and inbred lines of lac insects belonging to the genus Kerria (Homoptera, Tachardiidae)

The lac insects (Homoptera: Tachardiidae), belonging to the genus Kerria, are commercially exploited for the production of lac. Kerria lacca is the most commonly used species in India. RAPD markers were used for assessing genetic variation in forty-eight lines of Kerria, especially among geographic races, infrasubspecific forms, cultivated lines, inbred lines, etc., of K. lacca. In the 48 lines studied, the 26 RAPD primers generated 173 loci, showing 97.7% polymorphism. By using neighbor-joining, the dendrogram generated from the similarity matrix resolved the lines into basically two clusters and outgroups. The major cluster, comprising 32 lines, included mainly cultivated lines of the rangeeni form, geographic races and inbred lines of K. lacca. The second cluster consisted of eight lines of K. lacca, seven of the kusmi form and one of the rangeeni from the southern state of Karnataka. The remaining eight lines formed a series of outgroups, this including a group of three yellow mutant lines of K. lacca and other species of the Kerria studied, among others. Color mutants always showed distinctive banding patterns compared to their wild-type counterparts from the same population. This study also adds support to the current status of kusmi and rangeeni, as infraspecific forms of K. lacca.

Comparative Analysis of Different DNA-Binding Drugs for Leishmaniasis Cure: A Pharmacoinformatics Approach

Several experiments have been performed to test DNA‐binding drugs to cure Leishmania infection. However, there are no details of pharmacoinformatics study. Herein, we have selected a good number of compounds from experimentally verified studies and performed a comparative analysis based on pharmacoinformatics techniques. In silico docking study was performed to observe the molecular level interactions of these known ligands with the DNA receptor by automated computational docking using Glide. A comparison between the calculated interaction energies and in silico ADME/T study was made. In agreement with drug likeness rules, our study suggests that seco‐hydroxy‐aza‐CBI‐TMI (compound 4b; GScore, −12.058) is a potential molecule for targeting the DNA to cure leishmaniasis.

Java-Based Diabetes Type 2 Prediction Tool for Better Diagnosis

BACKGROUND The concept of classification of clinical data can be utilized in the development of an effective diagnosis system by taking the advantage of computational intelligence. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important problem in neural networks. Unfortunately, although several classification studies have been carried out with significant performance, many of the current methods often fail to reach out to patients. Graphical user interface-enabled tools need to be developed through which medical practitioners can simply enter the health profiles of their patients and receive an instant diabetes prediction with an acceptable degree of confidence. METHODS In this study, the neural network approach was used for a dataset of 768 persons from a Pima Indian population living near Phoenix, AZ. A neural network mixture of experts model was trained with these data using the expectation-minimization algorithm. RESULTS The mixture of experts method was used to train the algorithm with 97% accuracy. A graphical user interface was developed that would work in conjunction with the trained network to provide the output in a presentable format. CONCLUSIONS This study provides a machine-implementable approach that can be used by physicians and patients to minimize the extent of error in diagnosis. The authors are hopeful that replication of results of this study in other populations may lead to improved diagnosis. Physicians can simply enter the health profile of patients and get the diagnosis for diabetes type 2.

SWIFT MODELLER v2.0: a platform-independent GUI for homology modeling

SWIFT MODELLER v2.0 is a platform-independent Java-based graphical user interface to MODELLER. It provides an interactive homology modeling solution by automating the formatting, scripting, and data extraction processes, meaning that the user only needs to paste in the protein target sequence as input. SWIFT MODELLER v2.0 takes a step-by-step approach where the flow of the software screens depicts steps in the homology modeling protocol. Ramachandran plots and DOPE profile graphs are sketched and displayed for in-depth model analysis, along with an embedded Jmol viewer for 3D visualization of the constructed model. SWIFT MODELLER v2.0 is functional on all Linux-based and Microsoft Windows operating systems for which MODELLER has been developed. The software is available as freeware at http://www.bitmesra.ac.in/swift-modeller/swift.htm.

Comparative multivariate analysis of codon and amino acid usage in three Leishmania genomes.

Multivariate analysis of codon and amino acid usage was performed for three Leishmania species, including L. donovani, L. infantum and L. major. It was revealed that all three species are under mutational bias and translational selection. Lower GC12 and higher GC3S in all three parasites suggests that the ancestral highly expressed genes (HEGs), compared to lowly expressed genes (LEGs), might have been rich in AT-content. This also suggests that there must have been a faster rate of evolution under GC-bias in LEGs. It was observed from the estimation of synonymous/non-synonymous substitutions in HEGs that the HEG dataset of L. donovani is much closer to L. major evolutionarily. This is also supported by the higher dN value as compared to dS between L. donovani and L. major, suggesting the conservation of synonymous codon positions between these two species and the role of translational selection in shaping the composition of protein-coding genes.

In silico analysis of motifs in promoters of Differentially Expressed Genes in rice (Oryza sativa L.) under anoxia

The aim of this study was to characterise the molecular mechanisms of transcriptional regulation of Differentially Expressed Genes (DEGs) in rice coleoptiles under anoxia by identifying motifs that are common in the promoter region of co-regulated genes. Un-changed DEGs (<2 fold and >-2), up-regulated DEGs (>or=2 fold) and down-regulated DEGs (<or=-2 fold) were separated in three different data sets. Their gene promoters were extracted from eukaryotic promoter database. Statistically significant consensus promoter motifs were detected by in silico method. A significant variation in the number of promoter motifs, consensus promoter motif and their sequences between UR-DEGs and DR-DEGs were detected that might be responsible for their related expression.

Enhancement of Chlorella vulgaris cell density: Shake flask and bench-top photobioreactor studies to identify and control limiting factors

Low cell density is a major bottleneck in any microalgal bioprocess that prevents the large scale exploitation of this potential bioresource from commercialization of commodities like biofuels. Control of factors limiting growth is the key to enhancing cell density. Factors limiting photoautotrophic growth of C. vulgaris were identified and controlled to a possible extent. Limiting CO2-transfer rate, light attenuation, scarcity of nutrients, and high pH compounded to retard growth gradually in the basal medium. Analysis of the maximum feasible CO2 mass-transfer rate and CO2 fixation rates enabled the assessment of CO2-limited growth without on-line estimation of dissolved CO2. Growth (1.4×108 cells mL−1, 12.6 g dry wt L−1) was extensively enhanced when limiting factors were staved in a customized 250mL stirred-tank photobioreactor. Scaling the culture 8 times with constant kLa (volumetric mass-transfer coefficient) and Rei (impeller Reynolds number) resulted in reduction of biomass titer by 80% because of light attenuation.