In Silico Characterization of Tomato leaf curl Joydebpur virus (ToLCJV) DNA-A Proteins

We retrieved six protein sequences of Tomato leaf curl Joydebpur virus (ToLCJV) DNA-A [FJ345402] from GenBank-NCBI (ACJ03821, ACJ03822, ACJ03823, ACJ03824, ACJ03825 and ACJ03826) which were used for computational modeling structure prediction. Ramachandran plot of ACJ03826-AC4 had maximum 73.3% and ACJ03822-AV1 had 71% residues in core region therefore these models cannot be placed in a good quality category. ACJ03824-AC2 had only 18.6% residues in core and 13.6% residues in disallowed regions and therefore it was the least stable protein. Verify-3D graph profile scores for selected ToLCJV proteins were greater than zero. Therefore all the verify-3D graph corresponds to an acceptable environment for the model. Findings of the present study provide a base for docking and In-Silico anti-Begomoviral compound designing

Isolation of Cellulose-Degrading Bacteria and Determination of Their Cellulolytic Potential

Eight isolates of cellulose-degrading bacteria (CDB) were isolated from four different invertebrates (termite, snail, caterpillar, and bookworm) by enriching the basal culture medium with filter paper as substrate for cellulose degradation. To indicate the cellulase activity of the organisms, diameter of clear zone around the colony and hydrolytic value on cellulose Congo Red agar media were measured. CDB 8 and CDB 10 exhibited the maximum zone of clearance around the colony with diameter of 45 and 50 mm and with the hydrolytic value of 9 and 9.8, respectively. The enzyme assays for two enzymes, filter paper cellulase (FPC), and cellulase (endoglucanase), were examined by methods recommended by the International Union of Pure and Applied Chemistry (IUPAC). The extracellular cellulase activities ranged from 0.012 to 0.196 IU/mL for FPC and 0.162 to 0.400 IU/mL for endoglucanase assay. All the cultures were also further tested for their capacity to degrade filter paper by gravimetric method. The maximum filter paper degradation percentage was estimated to be 65.7 for CDB 8. Selected bacterial isolates CDB 2, 7, 8, and 10 were co-cultured with Saccharomyces cerevisiae for simultaneous saccharification and fermentation. Ethanol production was positively tested after five days of incubation with acidified potassium dichromate

ASASSN-14dq: A fast-declining type II-P Supernova in a low-luminosity host galaxy

Optical broadband (UBVRI) photometric and low-resolution spectroscopic observations of the type II-P supernova (SN) ASASSN-14dq are presented. ASASSN-14dq exploded in a low-luminosity/metallicity host galaxy UGC 11860, the signatures of which are present as weak iron lines in the photospheric phase spectra. The SN has a plateau duration of $\sim\,$90 d, with a plateau decline rate of 1.38 $\rm mag\ (100 d)^{-1}$ in V-band which is higher than most type II-P SNe. ASASSN-14dq is a luminous type II-P SN with a peak $V$-band absolute magnitude of -17.7$\,\pm\,$0.2 mag. The light curve of ASASSN-14dq indicates it to be a fast-declining type II-P SN, making it a transitional event between the type II-P and II-L SNe. The empirical relation between the steepness parameter and $\rm ^{56}Ni$ mass for type II SNe was rebuilt with the help of well-sampled light curves from the literature. A $\rm ^{56}Ni$ mass of $\sim\,$0.029 M$_{\odot}$ was estimated for ASASSN-14dq, which is slightly lower than the expected $\rm ^{56}Ni$ mass for a luminous type II-P SN. Using analytical light curve modelling, a progenitor radius of $\rm \sim3.6\times10^{13}$ cm, an ejecta mass of $\rm \sim10\ M_{\odot}$ and a total energy of $\rm \sim\,1.8\times 10^{51}$ ergs was estimated for this event. The photospheric velocity evolution of ASASSN-14dq resembles a type II-P SN, but the Balmer features (H$\alpha$ and H$\beta$) show relatively slow velocity evolution. The high-velocity H$\alpha$ feature in the plateau phase, the asymmetric H$\alpha$ emission line profile in the nebular phase and the inferred outburst parameters indicate an interaction of the SN ejecta with the circumstellar material (CSM).Comment: 28 pages, 29 figures, Accepted in MNRA

Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here, we perform a genome-wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients\u27 response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers

DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression approach -- eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in samples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging resistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework --termed INCISOR-- for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these interactions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics

Application of Tikhonov Regularized Methods to Image Deblurring Problem

We consider the monotone inclusion problems in real Hilbert spaces. Proximal splitting algorithms are very popular technique to solve it and generally achieve weak convergence under mild assumptions. Researchers assume strong conditions like strong convexity or strong monotonicity on the considered operators to prove strong convergence of the algorithms. Mann iteration method and normal S-iteration method are popular methods to solve fixed point problems. We propose a new common fixed point algorithm based on normal S-iteration method {using Tikhonov regularization }to find common fixed point of nonexpansive operators and prove strong convergence of the generated sequence to the set of common fixed points without assuming strong convexity and strong monotonicity. Based on the proposed fixed point algorithm, we propose a forward-backward-type algorithm and a Douglas-Rachford algorithm in connection with Tikhonov regularization to find the solution of monotone inclusion problems. Further, we consider the complexly structured monotone inclusion problems which are very popular these days. We also propose a strongly convergent forward-backward-type primal-dual algorithm and a Douglas-Rachford-type primal-dual algorithm to solve the monotone inclusion problems. Finally, we conduct a numerical experiment to solve image deblurring problems

A RETROSPECTIVE STUDY ON UTILITY OF FINE NEEDLE ASPIRATION CYTOLOGY (FNAC) IN THE DIAGNOSIS OF SOFT TISSUE TUMORS AND TUMOR-LIKE LESIONS.

Background Examining soft tissue tumors and tumor-like lesions is an everyday use for the minimally invasive diagnostic method known as Fine Needle Aspiration Cytology (FNAC). The purpose of this research was to evaluate FNAC's value in the diagnosis of these lesions in Bihar, India.   Methods Patients with soft tissue tumors and tumor-like lesions who presented to a tertiary care hospital in Bihar over a set period were included in a retrospective review of FNAC records. FNAC samples were analyzed by board-certified cytopathologists, and pertinent clinical and pathological data were obtained. Histopathological findings, surgical results, and follow-up information were all associated. Results A total of 200 patients were included in the study. In most cases, FNAC yielded useful diagnostic information with high sensitivity and specificity for determining the nature of soft tissue lesions. This method helped doctors distinguish between benign and malignant tumors, locate specific tumor kinds, grade them, and develop effective treatment plans. In Bihar, lipomas, fibromas, and synovial sarcomas were the most frequent soft tissue tumors. Conclusion  FNAC has emerged as a helpful first step in Bihar when diagnosing and staging soft tissue tumors and tumor-like lesions. It could distinguish between benign and malignant tumors with a high degree of accuracy, which aided in making informed treatment decisions. FNAC can play a crucial role in settings with limited resources by preventing unneeded operations and allowing for more targeted therapies. Recommendations Improvements in patient care can be achieved through better diagnosis and more targeted treatment by incorporating FNAC into clinical practice