Angiosperms of Sendirakillai Sacred Grove (SSG), Cuddalore District, Tamil Nadu, India

We provide a checklist of Angiosperm alpha diversity of Sendirakillai Sacred Grove (SSG), a community conserved Tropical Dry Evergreen Forest (TDEF) fragment located on the Coromandel Coast of Cuddalore district (11°44’24” N, 79°47’24” E), Tamil Nadu, South India. Plant specimens were collected either with flowers or fruits and were identified and confirmed with available regional floras, revisions and monographs. In the present study, we have enumerated a total of 180 species and 2 varieties belonging to 151 genera distributed in 66 families from 29 orders according to Angiosperm Phylogeny Group III Classification. More than 30% of the total flora is represented by six families namely Fabaceae (14), Rubiaceae (12), Cyperaceae (10), Apocynaceae (8), Poaceae (8) and Euphorbiaceae (7). Three endemic species to India and three species that are confined to peninsular India and Sri Lanka are recorded from the sacred grove. Threats to the biodiversity of sacred grove are identified and conservation strategies are proposed

Antimicrobial Susceptibility Pattern of Uropathogenic Bacteria in RMMC Hospital of Chidambaram

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

Small Molecule Inhibitors of the BfrB-Bfd Interaction Decrease Pseudomonas aeruginosa Fitness and Potentiate Fluoroquinolone Activity

© 2019 American Chemical Society. All rights reserved. The iron storage protein bacterioferritin (BfrB) is central to bacterial iron homeostasis. The mobilization of iron from BfrB, which requires binding by a cognate ferredoxin (Bfd), is essential to the regulation of cytosolic iron levels in P. aeruginosa. This paper describes the structure-guided development of small molecule inhibitors of the BfrB-Bfd protein-protein interaction. The process was initiated by screening a fragment library and followed by obtaining the structure of a fragment hit bound to BfrB. The structural insights were used to develop a series of 4-(benzylamino)- A nd 4-((3-phenylpropyl)amino)-isoindoline-1,3-dione analogs that selectively bind BfrB at the Bfd binding site. Challenging P. aeruginosa cells with the 4-substituted isoindoline analogs revealed a dose-dependent growth phenotype. Further investigation determined that the analogs elicit a pyoverdin hyperproduction phenotype that is consistent with blockade of the BfrB-Bfd interaction and ensuing irreversible accumulation of iron in BfrB, with concomitant depletion of iron in the cytosol. The irreversible accumulation of iron in BfrB prompted by the 4-substituted isoindoline analogs was confirmed by visualization of BfrB-iron in P. aeruginosa cell lysates separated on native PAGE gels and stained for iron with Ferene S. Challenging P. aeruginosa cultures with a combination of commercial fluoroquinolone and our isoindoline analogs results in significantly lower cell survival relative to treatment with either antibiotic or analog alone. Collectively, these findings furnish proof of concept for the usefulness of small molecule probes designed to dysregulate bacterial iron homeostasis by targeting a protein-protein interaction pivotal for iron storage in the bacterial cell

Discrimination of outer membrane proteins with improved performance

<p>Abstract</p> <p>Background</p> <p>Outer membrane proteins (OMPs) perform diverse functional roles in Gram-negative bacteria. Identification of outer membrane proteins is an important task.</p> <p>Results</p> <p>This paper presents a method for distinguishing outer membrane proteins (OMPs) from non-OMPs (that is, globular proteins and inner membrane proteins (IMPs)). First, we calculated the average residue compositions of OMPs, globular proteins and IMPs separately using a training set. Then for each protein from the test set, its distances to the three groups were calculated based on residue composition using a weighted Euclidean distance (WED) approach. Proteins from the test set were classified into OMP versus non-OMP classes based on the least distance. The proposed method can distinguish between OMPs and non-OMPs with 91.0% accuracy and 0.639 Matthews correlation coefficient (MCC). We then improved the method by including homologous sequences into the calculation of residue composition and using a feature-selection method to select the single residue and di-peptides that were useful for OMP prediction. The final method achieves an accuracy of 96.8% with 0.859 MCC. In direct comparisons, the proposed method outperforms previously published methods.</p> <p>Conclusion</p> <p>The proposed method can identify OMPs with improved performance. It will be very helpful to the discovery of OMPs in a genome scale.</p

MicroRNA interactome analysis predicts post-transcriptional regulation of ADRB2 and PPP3R1 in the hypercholesterolemic myocardium

Little is known about the molecular mechanism including microRNAs (miRNA) in hypercholesterolemia-induced cardiac dysfunction. We aimed to explore novel hypercholesterolemia-induced pathway alterations in the heart by an unbiased approach based on miRNA omics, target prediction and validation. With miRNA microarray we identified forty-seven upregulated and ten downregulated miRNAs in hypercholesterolemic rat hearts compared to the normocholesterolemic group. Eleven mRNAs with at least 4 interacting upregulated miRNAs were selected by a network theoretical approach, out of which 3 mRNAs (beta-2 adrenergic receptor [Adrb2], calcineurin B type 1 [Ppp3r1] and calcium/calmodulin-dependent serine protein kinase [Cask]) were validated with qRT-PCR and Western blot. In hypercholesterolemic hearts, the expression of Adrb2 mRNA was significantly decreased. ADRB2 and PPP3R1 protein were significantly downregulated in hypercholesterolemic hearts. The direct interaction of Adrb2 with upregulated miRNAs was demonstrated by luciferase reporter assay. Gene ontology analysis revealed that the majority of the predicted mRNA changes may contribute to the hypercholesterolemia-induced cardiac dysfunction. In summary, the present unbiased target prediction approach based on global cardiac miRNA expression profiling revealed for the first time in the literature that both the mRNA and protein product of Adrb2 and PPP3R1 protein are decreased in the hypercholesterolemic heart

Paddy Yield Prediction in Tamilnadu Delta Region Using MLR-LSTM Model

Crop yield forecasting has been well studied in recent decades and is significant in protecting food security. Crop growth is a complex phenomenon that depends on various factors. Machine learning and deep learning trends have emerged as important innovations in this field. We propose to utilize crop, weather, and soil data from agricultural datasets to evaluate yield prediction behavior. Paddy being a staple food crop in India is chosen for this research. In this paper, we propose hybrid architecture for paddy yield prediction, namely, MLR-LSTM, which combines Multiple Linear Regression and Long Short-Term Memory to utilize their complementary nature. The results are compared with traditional machine learning methods such as Support vector machine, Long short-term memory and Random forest method. Evaluation metrics such as Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), F1 score, Recall, and Precision are used to evaluate the hybrid method and traditional models. The results obtained from the proposed hybrid method indicates that the hybrid model delivers better R2, RMSE, MAE, MSE values of 0.93, 0.1549, 0.199, and 0.024 respectively

Ensemble Feature Selection Framework for Paddy Yield Prediction in Cauvery Basin using Machine Learning Classifiers

AbstractMachine learning technique involves a significant amount of time and the model performance reduces in multi-dimensional datasets because of redundant features. Feature selection is a significant step in machine learning and involves the selection of a subset of relevant data feature from larger data feature, which further enhances model performance by simplification. Crop yield prediction is a significant application of machine learning, and feature selection plays a crucial role in it. To predict paddy yield accurately, weather, soil, and crop attributes are essential factors. Therefore, feature selection techniques are employed to identify relevant and non-redundant attributes from a larger dataset, which simplifies the prediction model. In this study, an ensemble feature selection method is proposed that selects an optimized subset of attributes by combining various attribute subsets based on mutual information between attributes and between attributes and the class. The proposed ensemble approach is validated using five classification techniques, including K-nearest neighbor, Random Forest, Support Vector Machine, Naive Bayes, and Bagging. Several evaluation metrics such as Accuracy, Error rate, Kappa, Precision, Recall, Specificity, and F1 score are used to assess the performance of the ensemble approach and compare it with other feature selection techniques. The experimental results indicate that the proposed ensemble approach with the Random Forest classifier outperforms other classifiers, with Accuracy and Error rate values of 0.9491 and 0.0509, respectively

A Multi-Hop Dynamic Path-Selection (MHDP) Algorithm for the Augmented Lifetime of Wireless Sensor Networks

Abstract In Wireless Sensor Networks (WSN), the lifetime of sensors is the crucial issue. Numerous schemes are proposed to augment the life time of sensors based on the wide range of parameters. In majority of the cases, the center of attraction will be the nodes&apos; lifetime enhancement and routing. In the scenario of cluster based WSN, multi-hop mode of communication reduces the communication cast by increasing average delay and also increases the routing overhead. In this proposed scheme, two ideas are introduced to overcome the delay and routing overhead. To achieve the higher degree in the lifetime of the nodes, the residual energy (remaining energy) of the nodes for multi-hop node choice is taken into consideration first. Then the modification in the routing protocol is evolved (Multi-Hop Dynamic Path-Selection Algorithm-MHDP). A dynamic path updating is initiated in frequent interval based on nodes residual energy to avoid the data loss due to path extrication and also to avoid the early dying of nodes due to elevation of data forwarding. The proposed method improves network&apos;s lifetime significantly. The diminution in the average delay and increment in the lifetime of network are also accomplished. The MHDP offers 50% delay lesser than clustering. The average residual energy is 20% higher than clustering and 10% higher than multi-hop clustering. The proposed method improves network lifetime by 40% than clustering and 30% than multi-hop clustering which is considerably much better than the preceding methods

Kinetics of Base-catalysed Iodination of Substituted Acetonaphthones

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