Regularization Methods for High-Dimensional Inference

High dimensionality is a common problem in statistical inference, and is becoming more prevalent in modern data analysis settings. While often data of interest may have a large -- often unmanageable -- dimension, modifications to various well-known techniques can be made to improve performance and aid interpretation. We typically assume that although predictors lie in a high-dimensional ambient space, they have a lower-dimensional structure that can be exploited through either prior knowledge or estimation. In performing regression, the structure in the predictors can be taken into account implicitly through regularization. In the case where the underlying structure in the predictors is known, using knowledge of this structure can yield improvements in prediction. We approach this problem through regularization using a known projection based on knowledge of the structure of the Grassmannian. Using this projection, we can obtain improvements over many classical and recent techniques in both regression and classification problems with only minor modification to a typical least squares problem. The structure of the predictors can also be taken into account explicitly through methods of dimension reduction. We often wish to have a lower-dimensional representation of our data in order to build potentially more interpretable models or to explore possible connections between predictors. In many problems, we are faced with data that does not have a similar distribution between estimating the model parameters and performing prediction. This results in problems when estimating a lower-dimensional structure of the predictors, as it may change. We pose methods for estimating a linear dimension reduction that will take into account these discrepancies between data distributions, while also incorporating as much of the information as possible in the data into construction of the predictor structure. These methods are built on regularized maximum likelihood and yield improvements in many cases of regression and classification, including those cases in which predictor dimension changes between training and testing

Research Article A New Informatics Framework for Evaluating the Codon Usage Metrics, Evolutionary Models and Phylogeographic Reconstruction of Tomato Yellow Leaf Curl Virus (TYLCV) in Different Regions of Asian Countries

Not AvailableTomato yellow leaf curl virus (TYLCV) is a major devastating viral disease, majorly affecting the tomato production globally. The disease is majorly transmitted by the Whitefly. The Begomovirus (TYLCV) having a six major protein coding genes, among them the C1/AC1 is evidently associated with viral replication. Owing to immense role of C1/AC1 gene, the present study is an initial effort to elucidate the factors shaping the codon usage bias and evolutionary pattern of TYLCV-C1/AC1 gene in five major Asian countries. Based on publicly available nucleotide sequence data the Codon usage pattern, Evolutionary and Phylogeographic reconstruction was carried out. The study revealed the presence of significant variation between the codon bias indices in all the selected regions. Implying that the codon usage pattern indices (eNC, CAI, RCDI, GRAVY, Aromo) are seriously affected by selection and mutational pressure, taking a supremacy in shaping the codon usage bias of viral gene. Further, the tMRCA age was 1853, 1939, 1855, 1944, 1828 for China, India, Iran, Oman and South Korea, respectively for TYLCV-C1/AC1 gene. The integrated analysis of Codon usage bias, Evolutionary rate and Phylogeography analysis in viruses signifies the positive role of selection and mutational pressure among the selected regions for TYLCV (C1/AC1) gene.Not Availabl

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Not AvailableAfrican swine fever virus (ASFV) belongs to the family of Asfarviridae to the genus Asfivirus. ASF virus causes hemorrhage illness with a high mortality rate and hence, commercial loss in the swine community. The ASFV has been categorized by variation in codon usage that is caused by high mutation rates and natural selection. The evolution is caused mainly due to the mutation pressure and regulating the protein gene expression. Based on publicly accessible nucleotide sequences of the ASFV and its host (pig & tick), codon usage bias analysis was performed since an approved effective vaccination is not available to date, it is very important to analyze the codon usage bias of the p30, p54, and p72 proteins of ASFV to produce an effective and efficient vaccine to control the disease. Even though the codon usage bias analyses have been evaluated earlier, the evaluation of the codon usage pattern specific to p30, p54, and p72 of ASFV is inadequate. In all the protein-coding sequences, nucleotide base and codons terminating with base T were most frequent and the mean effective number of codons (Nc) was high, indicating the presence of codon usage bias. The GC contents and dinucleotide frequencies also indicated the codon usage bias of the ASFV pig and tick. The Nc plot, parity plot, neutrality plot analysis, revealed natural selection, as well as mutation pressure, were the major constraints in altering the codon bias of ASF virus. codon usage bias analysis was performed with no substantial differences in codon usage of the ASFV in pig and tickNot Availabl

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Not AvailableTime space between waves of Covid-19 is linked to selection of most appropriate virus mutant(s)/variant(s), in the Spike (S) gene, generated during massive and error prone replication of the virus in the human populations that are either naive or partially immune due to infection immunity or vaccinal immunity, having higher transmissibility with immune-escape feature and increased pathogenicity. The mutations in the S gene have been found to be progressive to resist prevalent virus neutralizing antibodies in the hosts. The first mutation of significance was D (Asp) to G (Gly) at the position 614 of the Spike protein, over the original Wuhan strain/virus. Subsequently, with in a short time span of less than 18 months since December 2019, many Variants/mutants of SARS-CoV-2, viz., B.1.1.7, B.1.427, B.1.1.28.2, B.1.429, B.1.617, B.1.351, and P.1 have been found associated with the current waves of the Covid-19 pandemic in different countries. The latest significant S variants identified in the second wave in India and elsewhere are B.1.617 and its Kappa (k), Delta (8) and Delta plus (8+; Lys to Asn at position 417) strains, in the order of appearance. The lambda (X) variant is prevalent in South America. The P.2 variant B.1.1.28.2 was antigenically dominant over B.1 D614G virus. The k and 8 variants of the S gene were partially resistant to neutralization by existing vaccinal antibodies, and the 8 variant has been feared to ignite third wave in many countries of Americas, Asia and Europe. The SARS-CoV-2 possibly has ‘Quasispecies’ structure, like the transboundary animal pathogen Foot and Mouth Disease (FMD) virus, and continuous forward mutations in the S gene has been the cause of the fact that "previous immunity acquired during the course of the natural infection (or vaccination) is not fully competent to protect against subsequent mutants selected (in the nature) that leads to new waves of the disease". The waves of Covid-19 have been guided by emergence of sequential antigenic variants, e.g. B.1 D614G virus ^B.1 8 mutant ^B.1 8+ mutant. Intermittent/inter-wave drop in incidence of the infection between the Covid-19 waves could be attributed to very short term (less than 3 months?) infection/vaccination immunity that is breached by the successive antigenic mutant (s) selected. Vaccination and virus circulation happening simultaneously, can facilitate faster selection of neutralization-escape mutants, which is now evident and may lead to new waves of the disease/infection at short intervals of less than 6 months. All variants described here are antigenically dissimilar, whereas the current Covid-19 vaccines are monovalent. Therefore, it may be essential and beneficial to have multivalent vaccines or with higher antigenic mass in monovalent vaccines. Further, it is required that nasal swabs of a certain percentage of vaccinates be collected at the time of vaccination and preserved for retrospect analysis of their infection status, that in turn will reveal the infection status of the population in villages/cities/districts/State.Not Availabl

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Not AvailableLeptospirosis is an emerging disease for which culture and identification are partly unresolved. In fact, 16S rRNA-based sequencing is the most widely used PCR methodology that can detect such uncultivable pathogens. However, this assay has some limitations linked to potential problems of contamination, which hampers diagnosis. To overcome this, we have a simple PCR strategy involving targeting of the gene encoding the RNA polymerase β subunit (rpoB), a highly conserved enzyme. The sequence of the Leptospira rpoB gene was determined and compared with the published sequence. Our findings have significant implications for the development of a new tool for the identification of spirochetes, especially if clinical samples are contaminated or when the infecting strain is uncultivable. The consistent use of PCR has improved the early diagnosis of Leptospirosis but the limitation is that it cannot provide information on the infecting Leptospira strain which provides important epidemiological data.Not Availabl

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Not AvailableIn this paper, we describe the synthesis of novel class of pseudo-peptides derived by coupling an amino acid with a heterocyclic moiety containing free amine group using suitable coupling agents. The synthesized compounds were characterized using spectral ((1)H NMR, (13)C NMR and MS) techniques. Preliminary pharmacological assays for Leptospirosis were studied by test tube dilution (TDT) and micro dilution technique (MDT). In particular, all the analyses led to the conclusion that the synthesized compound inhibiting the Leptospira a causal organism of Leptospirosis.Not Availabl

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Not AvailableIncreasing demand of highly potent non-toxic drugs in medicine is opening up opportunities for studying the usage of nanoparticles in the treatment of various disease conditions. Proliferation of research works on silver nanoparticles pertinent to this area is driven by the useful electrochemical properties of silver embedded in various host lattices and their versatility in a broad range of applications in biosciences. In this work, we report a method for the green synthesis of silver nanoparticles (AgNPs) using Areca catechu leaf extract. The nanoparticles prepared using this method were characterized using various spectroscopic and microscopic techniques. Spherical morphology and crystalline nature of the nanoparticles were evident from these analyses. Oxygen scavenging activity of the AgNPs and its application in the treatment of various multidrug resistant bacterial diseases caused by E. coli, P. aeruginosa, K. pneumoniae, S. typhi, P. vulgaris and fungal diseases of A. niger and F. oxysporium were studied in detail. The radical scavenging activity of AgNPs were found to be significantly high in 1,1-Diphenyl-2-picrylhydrazylradical (DPPH), and 2,2’Azinobis-3-ethylbenzthiazoline-6-sulfonic acid (ABTS) assays, which were further validated by high reducing capacity observed in phosphomolybdenum and reducing power assays. Furthermore, the AgNPs exhibited significant bactericidal and fungicidal properties against multidrug resistant strains.Not Availabl

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Not AvailableAnthrax is a one of the zoonotic diseases existing in India. Early detection of anthrax outbreaks is crucial for minimizing anthrax morbidity and death, as well as the risk of anthrax transmission in the population. Objective of the present research is to develop a disease prediction model by employing Machine-Learning techniques to assess the risk of anthrax analogous to the impact of changes in precipitation level that can benefit as an early warning system for detecting future anthrax outbreaks among livestock across Karnataka. By considering the disease incidence data during 2000 to 2019, livestock population data and the ecological parameters, the machine learning model was successful in identifying the next outbreak susceptible areas and the parameters that contribute significantly to the disease outbreak. Machine learning model was developed by R statistical software version 3.1.3 using different data mining regression and classification models viz., GLM, GAM, MARS, FDA, CT, SVM, NB, ADA, RF, GBM and ANN. Disease incidence data was collected from Department of animal husbandry, Bengaluru, Karnataka. Disease incidence data was divided in two groups based on average annual precipitation above and below normal (1151mm) for the risk assessment and study the impact of changes in precipitation level. Data with average annual-precipitation above normal was predicted with high risk in the north, northern east and the state's southern region. Whereas data with average annual-precipitation below normal was predicted with high risk in south, northern east and the state's central region. Cohen's Kappa, ROC curve, True Skill Statistics (TSS), and ACCURACY was used to assess the models performance. Further, this model can be intensified and validated using the anthrax outbreak data available at national level which will be useful for policymakers to formulate control strategiesNot Availabl

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Not AvailableKyasanur Forest Disease was ϐirst evolved in the Kyasanur forest, Karnataka. The transmission of the virus has occurred from the monkey to the human by the tick vector. On the early day of viral spread, the disease was restricted to the surrounded region of Kyasanur forest, Shimoga district. But in the present days, the disease has been spreading to neighboring districts and states as well. So, this study involves estimation of codon bias among the gene C, gene E, gene prM, and gene NS5 of the KFD virus and rate of evolution with phylogenetic analysis. The codon usage analysis has revealed the moderate codon bias among all the selected genes and the role of mutation pressure in genesC and E and natural selection in genes- prM and NS5. Also, the tMRCA age was 1942, 1982, 1975, and 1931 of genes- C, E, prM, and NS5, respectively, of the KFD virus. The integrated analysis of codon usage bias and evolutionary rate analysis signiϐies that both mutational pressure and natural selection among the selected genes of the KFD virusNot Availabl

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Not AvailableAnthrax is an ancient and acuteillness that affects alarge quantity of animal species and is caused by a bacterium Bacillus anthracis, which is a rod-shaped, gram-positive and spore-forming bacterium. Virulent forms of B.anthracishas two large pathogenicity related plasmids pXO1 and pXO2. pXO1 has the different anthrax toxin genes cya, lef, and pagA where as pXO2 has the genes accountable for capsule synthesis and degradation, capA, capB, capC, and capD. B. anthracis express its pathogenic activity mostly over the capsule and the manufacture of a toxic compound involving three proteins known as edema factor (EF), lethal factor (LF) and protective antigen (PA). These two enormous plasmids of B.anthracisare crucial for full pathogenicity, exclusion of either of the plasmids extremely weakens the malignity of B. anthracis. In the current study we conducted the relative analysis of the codon usage and nucleotide bias of virulent genes subsist in pXO1 plasmid of B.anthracis. Codon usage bias not only plays a substantial role at the extent of gene expression, but also supports to improve the efficacy and accurateness of translation. Codon usage pattern analysis of B. anthracisgenome is essential for understanding the evolutionary characteristicsin the different species. To examine the codon usage arrangement of theB.anthracisgenome, Nucleotide sequences of the virulent genes viz cya, lef and pag were collected from National Center for Biotechnology Information (NCBI). The correlations between GC3s, whole GC content, Effective No. of Codons (ENC), Codon Adaptation Index (CAI), Codon Bias Index (CBI), Frequency of Optimal Codons (FOP), General average hydropathicity (Gravy) and Aromaticity (Aroma), of the selected genes were determined. The ENC-plot i.e., ENc values vs GC3s, Pr2 plot i.e., relationship between A3 / (A3 +T3) and G3 / (G3 +C3), Neutrality plot i.e., GC12 versus GC3s, and the RSCU of the genes, all shows codon usage bias existence in all the virulent genes subsists in pXO1 plasmid of B.anthracis genome. These results expresses the codon usage bias existing in the pXO1 plasmid’s virulent genes of B.anthracis genome could be utilized for further exploration on their evolutionary analysis as in design of primers, design of transgenes, determine of origin of species as well as prediction of gene expression level and gene functionNot Availabl