20 research outputs found

    Standardization of optimum sieve size for maximizing seed quality in Amaranthus (Amaranthus tricolor L.)

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    Vegetables have a critical function in human health and nutritional security. Vegetables are considered an essential building block of any diet. Out of the leafy vegetables, Amaranthus is the most popular and salable vegetable consumed by people all over India. Seed processing experiment was undertaken in Amaranthus tricolor (CO 2) by using the sieves placed inside the mechanical seed shaker (Gyratory sieve shaker) to improve the quality of Amaranthus seeds. The seeds of Amaranthus were size graded with seed shaker attached with various sieve size of BSS 18 X18 (R), BSS 20 X 20 (R), BSS 22 X 22 (R) and BSP 22 X 22 (P). During processing, the machine could be adjusted for 2, 3, 4 and 5 minutes with an oscillating speed of 1440 rpm. The separated seeds were evaluated for seed quality characteristics such as seed germination percentage, shoot length, root length, dry matter production, 1000 seed weight and seed recovery percentage. The results revealed that the sieve size of BSS 22 X 22 (R=retained) mesh sieve had the better quality seeds with a maximum recovery of 67.41 g and when it is operated for a period of 5 minutes. The germination percentage was improved from 77 % to 95 % with  1000 seed weight of 73.21 mg, and the observed recovery was 56 per cent with the vigour index of 1145. Hence, BSS 22 X 22 retained mesh sieve with a duration of 5 minutes could be recommended as an optimum sieve size for grading Amaranthus seeds for improving the seed quality

    Antimicrobial Susceptibility Pattern of Uropathogenic Bacteria in RMMC Hospital of Chidambaram

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    Background: In every year millions of people were affected by the Urinary Tract Infection. It was creating a serious health issue. Aim: The present study was to analysis of the uropathogenic bacteria in patients were attended RMMC Hospital and their antibiotic resistance pattern, in vitro detection of haemolysis virulent factor of uropathogenic. Material and Methods: All urine samples were tested by the standard microbiological procedure. Kirby-Bauer method used for the Antibiotic Susceptibility Test according to the CLSI guidelines. Commercially available antibiotics were used. Blood Agar used for the detection of haemolysis. Results: A total of 261 urine samples were included in this study. We isolated a total of 103 positive cultures. 12% of Gram-positive, 83% of Gram-negative bacteria and 3% of Candida fungi. Escherichia coli was the most predominant bacteria (54%) followed by Klebsiella sp (15%), Staphylococcus aureus (12%), Pseudomonas aeruginosa (12%), Proteus (1%) and fungi Candida (3%). Mostly female patients’ sample were analysed and the inpatient higher majority than the outpatients. Conclusion: Escherichia coli are the common bacteria to cause of UTI. Nowadays most of the uropathogens are to resistance to the overall antibiotics. This kind of reactions creating the life-threatening of humans. Keywords: Antibiotic, Antibiotic Susceptibility Test, Uropathogens, Resistance, Haemolysi

    The P72R Polymorphism in R248Q/W p53 Mutants Modifies the Mutant Effect on Epithelial to Mesenchymal Transition Phenotype and Cell Invasion via CXCL1 Expression

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    High-grade serous carcinoma (HGSC), the most lethal subtype of epithelial ovarian cancer (EOC), is characterized by widespread TP53 mutations (\u3e90%), most of which are missense mutations (\u3e70%). The objective of this study was to investigate differential transcriptional targets affected by a common germline P72R SNP (rs1042522) in two p53 hotspot mutants, R248Q and R248W, and identify the mechanism through which the P72R SNP affects the neomorphic properties of these mutants. Using isogenic cell line models, transcriptomic analysis, xenografts, and patient data, we found that the P72R SNP modifies the effect of p53 hotspot mutants on cellular morphology and invasion properties. Most importantly, RNA sequencing studies identified CXCL1 a critical factor that is differentially affected by P72R SNP in R248Q and R248W mutants and is responsible for differences in cellular morphology and functional properties observed in these p53 mutants. We show that the mutants with the P72 SNP promote a reversion of the EMT phenotype to epithelial characteristics, whereas its R72 counterpart promotes a mesenchymal transition via the chemokine CXCL1. These studies reveal a new role of the P72R SNP in modulating the neomorphic properties of p53 mutants via CXCL1, which has significant implications for tumor invasion and metastasis

    Blinded predictions and post-hoc analysis of the second solubility challenge data : exploring training data and feature set selection for machine and deep learning models

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    Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state-of-the-art, the American Chemical Society organised a “Second Solubility Challenge” in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019, but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms, and were trained on a relatively small dataset of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility datasets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge datasets, with the best model, a graph convolutional neural network, resulting in a RMSE of 0.86 log units. Critical analysis of the models reveal systematic di↵erences between the performance of models using certain feature sets and training datasets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy, but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modelling complex chemical spaces from sparse training datasets

    MIBiG 3.0 : a community-driven effort to annotate experimentally validated biosynthetic gene clusters

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    With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/
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