Abstract

The Standardised Precipitation and Evapotranspiration Index (SPEI) became one of the popular drought indices in the context of increasing temperatures under global warming in recent periods. The SPEI is estimated by fitting a probability distribution for the difference between precipitation (P) and potential evapotranspiration (PET), which represents the climatic water balance. The choice of an inappropriate probability distribution may lead to bias in the index values leading to distorted drought severity. Till date, none of the studies have focused on the suitability of the probability distribution for SPEI over India. The objective of the present study is to compare and evaluate the performance of a group of candidate probability distributions over seven meteorologically homogeneous zones and all over India using high resolution (0.25°) gridded daily precipitation data from India Meteorological Department (IMD). The Kolmogorov–Smirnov (K–S) test was used to test the goodness-of-fit for (P–PET) and Akaike Information Criterion (AIC) was used to obtain the relative distribution rankings for each grid point. The results of the study suggest that Pearson type III distribution has performed better than other distributions, significantly for shorter time scales and slightly for longer time scales, for each meteorological homogeneous zone based on K–S test. Also, for shorter time scales, Pearson type III distribution has been observed to be significantly better based on AIC with 82.89% and 71.91% grid points for 3 and 6 months, respectively. However, the relative ranking by AIC revealed GEV distribution as the best fit for SPEI values all over India for longer time scales with total grid points as 50.26%, and 58.81% for 12- and 24-month time scales respectively. Pearson type III distribution for shorter time scales (3 and 6 months) and GEV distribution for longer time scales (12 and 24 months) have been identified as the best distributions for fitting SPEI for Indian case study. Comparison of GEV based SPEI with remote sensing-based drought severity index (DSI) for drought events indicated concordance for most of regions in India. Also, SPEI is evaluated to test its capability to represent seasonality and its performance has been compared with Standardised Precipitation Anomaly Index (SPAI) which is known to represent seasonality well.

Abstract

Background Drought is a severe environmental stress. It is estimated that about 50% of the world rice production is affected mainly by drought. Apart from conventional breeding strategies to develop drought-tolerant crops, innovative computational approaches may provide insights into the underlying molecular mechanisms of stress response and identify drought-responsive markers. Here we propose a network-based computational approach involving a meta-analytic study of seven drought-tolerant rice genotypes under drought stress. Results Co-expression networks enable large-scale analysis of gene-pair associations and tightly coupled clusters that may represent coordinated biological processes. Considering differentially expressed genes in the co-expressed modules and supplementing external information such as resistance/tolerance QTLs, transcription factors, network-based topological measures, we identify and prioritize drought-adaptive co-expressed gene modules and potential candidate genes. Using the candidate genes that are well-represented across the datasets as ‘seed’ genes, two drought-specific protein-protein interaction networks (PPINs) are constructed with up- and down-regulated genes. Cluster analysis of the up-regulated PPIN revealed ABA signalling pathway as a central process in drought response with a probable crosstalk with energy metabolic processes. Tightly coupled gene clusters representing up-regulation of core cellular respiratory processes and enhanced degradation of branched chain amino acids and cell wall metabolism are identified. Cluster analysis of down-regulated PPIN provides a snapshot of major processes associated with photosynthesis, growth, development and protein synthesis, most of which are shut down during drought. Differential regulation of phytohormones, e.g., jasmonic acid, cell wall metabolism, signalling and posttranslational modifications associated with biotic stress are elucidated. Functional characterization of topologically important, drought-responsive uncharacterized genes that may play a role in important processes such as ABA signalling, calcium signalling, photosynthesis and cell wall metabolism is discussed. Further transgenic studies on these genes may help in elucidating their biological role under stress conditions

Abstract

Network theory is now a method of choice to gain insights in understanding protein structure, folding and function. In combination with molecular dynamics (MD) simulations, it is an invaluable tool with widespread applications such as analyzing subtle conformational changes and flexibility regions in proteins, dynamic correlation analysis across distant regions for allosteric communications, in drug design to reveal alternative binding pockets for drugs, etc. Updated version of NAPS now facilitates network analysis of the complete repertoire of these biomolecules, i.e., proteins, protein–protein/nucleic acid complexes, MD trajectories, and RNA. Various options provided for analysis of MD trajectories include individual network construction and analysis of intermediate time-steps, comparative analysis of these networks, construction and analysis of average network of the ensemble of trajectories and dynamic cross-correlations. For protein–nucleic acid complexes, networks of the whole complex as well as that of the interface can be constructed and analyzed. For analysis of proteins, protein–protein complexes and MD trajectories, network construction based on inter-residue interaction energies with realistic edge-weights obtained from standard force fields is provided to capture the atomistic details. Updated version of NAPS also provides improved visualization features, interactive plots and bulk execution. URL: http://bioinf.iiit.ac.in/NAPS/

Abstract

Diffuse Large B-cell Lymphoma (DLBCL) is a highly heterogeneous cancer of B cells. Apart from cell-of-origin, genetic variations have been observed to contribute towards heterogeneity leading to different pathogenic mechanisms and overall survival outcomes. Various classification schemes have been proposed that may aid in risk stratification and developing new therapeutics for those who fail frontline therapy. This mini review highlights the significance of genetic variations as biomarkers for DLBCL and ease in extending it to clinical setting.

Abstract

Diffuse large B-cell lymphoma (DLBCL) is the aggressive form of haematological malignancies with relapse/refractory in ~ 40% of cases. It mostly develops due to accumulation of various genetic and epigenetic variations that contribute to its aggressiveness. Though large-scale structural alterations have been reported in DLBCL, their functional role in pathogenesis and as potential targets for therapy is not yet well understood. In this study we performed detection and analysis of copy number variations (CNVs) in 11 human DLBCL cell lines (4 activated B-cell–like [ABC] and 7 germinal-centre B-cell–like [GCB]), that serve as model systems for DLBCL cancer cell biology. Significant heterogeneity observed in CNV profiles of these cell lines and poor prognosis associated with ABC subtype indicates the importance of individualized screening for diagnostic and prognostic targets. Functional analysis of key cancer genes exhibiting copy alterations across the cell lines revealed activation/disruption of ten potentially targetable immuno-oncogenic pathways. Genome guided in silico therapy that putatively target these pathways is elucidated. Based on our analysis, five CNV-genes associated with worst survival prognosis are proposed as potential prognostic markers of DLBCL.

Abstract

Modelling the Sommerfeld radiating boundary is a major challenge appearing in the study of many engineering problems involving elastic wave propagation [1]. Particularly, in the finite element analysis of Structure-Soil-Structure-Interaction (SSSI) problems, where the soil modelling has to be truncated at a finite distance. This truncation of the model at finite boundary leads to reflection of radiating elastic waves. The reflected waves from the boundary will affect the solution and may lead to instabilities in the numerical analysis. Therefore, it is necessary to provide an artificial boundary condition that will transmit the outward propagating waves with minimum or negligible reflections. To address the radiating boundary condition problem, researchers have developed various kinds of formulations over few decades, such as: a). Local Absorbing Boundary Conditions (ABC) [2], b). Absorbing Layers techniques which includes Perfectly Matched Layers [3-4], Caughey Absorbing Layer Method (CALM) [5], Absorbing Layers by Increasing Damping (ALID) [6] c). Boundary element method [7], and d). Infinite elements [8-10]. ABC corresponds to the situation where the boundary is supported on infinitesimal dash-pots oriented normal and tangential to the boundary. Though these boundary conditions are very simple, the major drawback is they are inefficient when the wave impinges the boundary in inclined direction. The key property of a PML that distinguishes it from an ordinary absorbing material is that it is designed such that the propagating waves incident upon the PML from a bulk medium do not reflect on the interface. The CALM consists of defining the absorbing layers at the boundaries of the elastic medium under consideration but requires many absorbing layers which requires much computational time. The objective of this study is to present a method for radiating boundary conditions by using modified ABC to take the effect of viscous damping combining with ALID

Abstract

Cobalt oxide particles have emerged as one of the promising metal oxide systems for their unique properties and various applications. This article reports syntheses of nano to micrometer sized morphology-controlled cobalt oxide particles by using the sol-gel synthesis method. The method can produce cobalt oxide particles in gram quantity. We explored the effects of various factors such the metal precursor, capping agent, solvent, calcination temperature, etc. on the size and morphology of the synthesized particles. FTIR and XRD studies confirmed the formation of crystalline Co3O4 particles. Field-emission scanning electron microscopy (FE-SEM) show that particles with sizes ranging from 180 nm to 6 μm and morphologies mainly smoothed surfaced octahedra formed depending on the metal precursor, capping agent, solvent and calcination temperature. The effects of three calcination temperatures have been explored based on the thermal behavior of the particles as studied by the thermogravimetric and differential thermal analysis (TGA/DTA). The powdered particles offer ~50 to ~75 m2 /g Brunner-Emmett-Teller (BET) surface area. The sol-gel technique is rapid, straightforward, and cost-effective and requires low processing temperatures to produce the particles compared to solvothermal methods. The present study shows that a proper choice of reaction conditions in a sol-gel method could be used to produce morphology-controlled cobalt oxide particles of nanometer to micrometer sizes in high yield

Abstract

In recent times, our healthcare system is being challenged by many drug-resistant microorganisms and ageing-associated diseases for which we do not have any drugs or drugs with poor therapeutic profile. With pharmaceutical technological advancements, increasing computational power and growth of related biomedical fields, there have been dramatic increase in the number of drugs approved in general, but still way behind in drug discovery for certain class of diseases. Now, we have access to bigger genomics database, better biophysical methods, and knowledge about chemical space with which we should be able to easily explore and predict synthetically feasible compounds for the lead optimization process. In this chapter, we discuss the limitations and highlights of currently available computational methods used for protein–ligand binding affinities estimation and this includes force-field, ab initio electronic structure theory and machine learning approaches. Since the electronic structure-based approach cannot be applied to systems of larger length scale, the free energy methods based on this employ certain approximations, and these have been discussed in detail in this chapter. Recently, the methods based on electronic structure theory and machine learning approaches also are successfully being used to compute protein–ligand binding affinities and other pharmacokinetic and pharmacodynamic properties and so have greater potential to take forward computer-aided drug discovery to newer heights

Abstract

Hydroxymethylbilane synthase (HMBS), the third enzyme in the heme biosynthesis pathway, catalyzes the formation of 1-hydroxymethylbilane (HMB) by a stepwise polymerization of four molecules of porphobilinogen (PBG) using the dipyrromethane (DPM) cofactor. The mechanism by which HMBS polymerizes four units of PBG has not been elucidated to date. In vitro and in silico studies on HMBS have suggested certain residues with catalytic importance, but their specific role in the catalysis is unclear. To understand the catalytic mechanism of HMBS, quantum mechanical (QM) calculations were performed on model systems obtained from the active site of the human HMBS enzyme. The addition of one molecule of PBG to the DPM cofactor is carried out in four steps: (1) protonation of the substrate, PBG; (2) deamination of PBG; (3) electrophilic addition of the deaminated substrate to the terminal pyrrole ring of the enzyme-bound DPM cofactor and (4) deprotonation of the carbon atom at the a-position of the second ring of DPM. Based on the energy profiles from the QM calculations on cluster models, R26 is proposed to be the best suitable proton donor to the PBG moiety, which aids in the deamination of the substrate. During the electrophilic addition step, the intermediate formed is stabilized by the carboxylate side chain of the D99 residue. In the final deprotonation step, an extra proton from the second ring of DPM is transferred to R26 via the carboxylate side chain of D99, thus completing one cycle of the catalytic mechanism. The residues in the cluster model seem to play an important role in obtaining accurate energy barriers. All the stationary points along the reaction pathway have been characterized using QM calculations. The rate limiting step for the complete mechanism is found to be the deamination of the PBG moiety. The results of this study provide a detailed understanding of the catalytic mechanism and would help design future studies aimed at modulating the activity of HMBS

Abstract

Motivated by the persistent anomalies in the semileptonic B-meson decays, we investigate the competency of LHC data to constrain the RDðÞ -favored parameter space in a charge −1=3 scalar leptoquark (S1) model. We consider some scenarios with one large free coupling to accommodate the RDðÞ anomalies. As a result, some of them dominantly yield nonresonant ττ and τν events at the LHC through the t-channel S1 exchange. So far, no experiment has searched for leptoquarks using these signatures and the relevant resonant leptoquark searches are yet to put any strong exclusion limit on the parameter space. We recast the latest ττ and τν resonance search data to obtain new exclusion limits. The nonresonant processes strongly interfere (destructively in our case) with the Standard Model background and play the determining role in setting the exclusion limits. To obtain precise limits, we include non-negligible effects coming from the subdominant (resonant) pair and inclusive single leptoquark productions systematically in our analysis. To deal with large destructive interference, we make use of the transverse mass distributions from the experiments in our statistical analysis. In addition, we also recast the relevant direct search results to obtain the most stringent collider bounds on these scenarios to date. These are independent bounds and are competitive to other known bounds. Finally, we indicate how one can adopt these bounds to a wide class of models with S1 that are proposed to accommodate the RDðÞ anomalies.