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Rice blast caused by Magnaporthe oryzae continues to be a major constraint in rice production worldwide. Rice is one of the staple crops in India and rice blast causes huge economic losses. Interestingly, the Indian subcontinent is the centre for origin and diversity of rice as well as the Magnaporthe species complex. Secondary metabolites are known to play important role in pathogenesis and M. oryzae has high potential of genes involved in secondary metabolism but, unfortunately most of them remain uncharacterized. In the present study, we analysed the draft genome assemblies of M. click here oryzae strains isolated from different parts of India, for putative secondary metabolite key gene (SMKG) clusters encoding polyketide synthases, non-ribosomal peptide synthetases, diterpene cyclases and dimethylallyl tryptophan synthase. Based on the complete genome sequence of 70-15 strain and its previous reports of identified SMKGs, we have identified the key genes for the interrogated strains. Expression analysis of these genes amongst different strains indicates how they have evolved depending on the host and environmental conditions. To our knowledge, this study is first of its kind where the secondary metabolism genes and their role in functional adaptation were studied across several strains of M. oryzae.

The purpose of this study was to better understand conflicting findings in the literature regarding the adjustment of siblings of children with cancer by examining, in a single sample, differences in patterns of results as a function of reporter and comparator used (i.e., population norms, demographically matched classmates).

Self- and parent-report standardized measures of depression, anxiety, and behavioral problems were collected for 67 siblings and 67 demographically matched classmates. Comparisons were made to norms and controls.

Siblings consistently demonstrated poorer psychosocial functioning than their demographically matched peers across all measures but their scores did not differ from norms. A significantly greater percentage of siblings fell outside the normal range than that expected in the general population for parent-reported total and internalizing problems, but not for externalizing problems or the self-report measures.

Findings regarding the psychological adjustment of siblings of children with cancer differ according to the research methods used. It is important to use rigorous methods such as demographically matched peer comparisons when investigating the impact of childhood cancer on siblings.

Findings regarding the psychological adjustment of siblings of children with cancer differ according to the research methods used. It is important to use rigorous methods such as demographically matched peer comparisons when investigating the impact of childhood cancer on siblings.Uncompetitive antagonists of the N-methyl d-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable for both new medication development and preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. We present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds are still required.We study how surface phenomena can change the interface geometry in liquid-liquid two-phase systems with periodic boundary conditions. Without any curvature effect on surface tension, planar (slab), cylindrical, and spherical structures are successively obtained as a function of the total composition and elongation of the box, in accordance with molecular dynamics simulations for a water/heptane system. The curvature effects described by Tolman relationship desymmetrize the phase diagram by stabilizing a concavity but it leads to inconsistencies with high curvature. Helfrich model partially resolves this and predicts the possible presence of shells reflecting a frustrated system.Utilizing the exact diagonalization (ED) method, we find that excitons cannot form in π-conjugated molecules such as anthracene, phenanthrene, and pyrene when the electron-electron interaction is governed by the Rytova-Keldysh (RK) potential. Within the Pariser-Parr-Pople (PPP) model, however, the excitons may survive only in the presence of a weak screening effect. Either way, the optical gap is seen to be insensitive to the dielectric environment owing to the opposite contributions from the excitonic effect and quasiparticle correction. Furthermore, the latter two are shown to exhibit almost the same behavior in all three molecules when the screening parameter varies.Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis largely owing to its inefficient diagnosis, rapid progress, and tenacious drug resistance. Here, we aimed to analyze the expressive patterns of proteins and phosphorylation in PDAC tissue samples and compare them to normal pancreatic tissue to investigate the underlying mechanisms and to reveal potential protein targets for diagnosis and drug development. Liquid chromatography coupled to mass spectrometry (LC-MS) based proteomics and phosphoproteomics analyses were performed using 20 pairs of patient-derived PDAC tissue and normal pancreatic tissue samples. Protein identification and quantification were conducted using MaxQuant software. Bioinformatics analysis was used to retrieve PDAC-relevant pathways and gene ontology (GO) terms. 4985 proteins and 3643 phosphoproteins were identified with high confidence; of these, 322 proteins and 235 phosphoproteins were dysregulated in PDAC. Several pathways, including several extracellular matrix-related pathways, were found to be strongly associated with PDAC.