Three dimensional structure prediction and ligand-protein interaction study of expansin protein ATEXPA23 from Arabidopsis thaliana L.

Arabidopsis thaliana L. is a small flowering plant that is widely used as a model organism in plant biology. In the present study, we study the peripheral membrane protein ATEXPA23 from Arabidopsis thaliana L. using homology modelling and molecular docking. The microarray analysis shows expression of ATEXPA23 (AT5G39280) protein, which leads to loosening and extension of plant cell walls. This protein is differentially expressed during different stages of plant embryogenesis. It contains one expansin-like CBD domain and one expansin-like EG45 domain. ATEXPA23 belongs to the expansin family in expansin a subfamily. The 3D model after refinement is used to explore the xyloglucan binding characteristics of ATEXPA23 using AutoDock. The docking analysis shows that the surface exposed aromatic amino acid residues Phe 193 and Phe 265 interact with ligand xyloglucan through CH-л interaction. The binding energy values of docking reflect a stable conformation of the docked complex. The interaction of expansin protein with carbohydrate xyloglucan, present in hemicellulose structures of plant cell wall, is thoroughly analysed with cellotetrose and xyloglucan heptasaccharide using electrostatic potential calculation. This CH-л non-covalent interaction predominates on the cellulose-xyloglucan interaction in plant cell wall during cell growth

Development of a Fiber Optic Sensor for Online Monitoring of Thin Coatings

 The thickness measurement of gas, liquid and solid layers is not only important for the basic research on nanoscience but equally valuable in contemporary applied biomedical research. Here, we have developed an optical spectroscopy based technique for the online monitoring of thin films (coatings). A low cost light emitting diode (LED) source combined with a fiber optic bundle and grating based spectrograph have been used to generate white light interferogram. We have monitored online change of refractive index of an air film (~4 μm thickness) with temperature following the change in the intensity profile of the interferogram. A thin film of water between two cover slips (thin glass plates) has also been monitored. We have proposed a schematic for further lowering the cost of the developed instrument for the online monitoring of the coating thickness (semitransparent liquid/gas/solid films) during manufacturing/processing. A brief theoretical analysis on the detection limit of the developed technique has also been discussed in the paper

Anisotropic magneli phase Ti-suboxides in β- cyclodextrin template - Enhanced charge separation upon gold doping

Substoichiometric titanium oxides i.e. Magneli phase (MP) TiOx are attractive due to their conductive nature. However, their synthesis is challenging. In this work, Anisotropic MP- Ti4O7 nanoparticles and Au doped nanocomposites were synthesized using β- cyclodextrin as template. The MP nanomaterials were 20-30 nm in size. The synthesis conditions were mild. These MP- TiOx nanomaterials show efficient charge separation upon light excitation i.e. they (i) act as efficient photocatalysts; (ii) they can be sensitized by a fluorescent dye; (iii) finite element method (FEM) simulations indicate substantial interfacial plasmonic charge generation at the metal-semiconductor interface in the doped nanocomposites.DST FIST Phase II grant of the Department of Chemistry, University of KalyaniUniversity of Kalyani in the form of Personal Research Grant for TeachersDST-INSPIRE, Govt. of India for research fellowship [Ref. No.IF170689], [Ref. No.IF170936

Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy.

Essential gene prediction helps to find minimal genes indispensable for the survival of any organism. Machine learning (ML) algorithms have been useful for the prediction of gene essentiality. However, currently available ML pipelines perform poorly for organisms with limited experimental data. The objective is the development of a new ML pipeline to help in the annotation of essential genes of less explored disease-causing organisms for which minimal experimental data is available. The proposed strategy combines unsupervised feature selection technique, dimension reduction using the Kamada-Kawai algorithm, and semi-supervised ML algorithm employing Laplacian Support Vector Machine (LapSVM) for prediction of essential and non-essential genes from genome-scale metabolic networks using very limited labeled dataset. A novel scoring technique, Semi-Supervised Model Selection Score, equivalent to area under the ROC curve (auROC), has been proposed for the selection of the best model when supervised performance metrics calculation is difficult due to lack of data. The unsupervised feature selection followed by dimension reduction helped to observe a distinct circular pattern in the clustering of essential and non-essential genes. LapSVM then created a curve that dissected this circle for the classification and prediction of essential genes with high accuracy (auROC > 0.85) even with 1% labeled data for model training. After successful validation of this ML pipeline on both Eukaryotes and Prokaryotes that show high accuracy even when the labeled dataset is very limited, this strategy is used for the prediction of essential genes of organisms with inadequate experimentally known data, such as Leishmania sp. Using a graph-based semi-supervised machine learning scheme, a novel integrative approach has been proposed for essential gene prediction that shows universality in application to both Prokaryotes and Eukaryotes with limited labeled data. The essential genes predicted using the pipeline provide an important lead for the prediction of gene essentiality and identification of novel therapeutic targets for antibiotic and vaccine development against disease-causing parasites

Ventilator-associated pneumonia: Its incidence, the risk factor and drug resistance pattern in a tertiary care hospital

Background: Ventilator-associated pneumonia (VAP) is an infection of the lung that develops 48 h or longer after mechanical ventilation. Objectives: The present study was aimed to find out the bacteriological profile of VAP along with the resistance pattern of bacteriological isolates. Materials and Methods: A prospective observational study was conducted from January 2013 to May 2014 among 791 patients admitted in critical care units of our tertiary care hospital. After selection by applying inclusion and exclusion criteria endotracheal aspirates were collected from ventilated patients. Samples were subjected to further processing by Gram-staining, culture, biochemical testing and antibiogram. Results : Out of 791 patients admitted in intensive care unit in this tertiary care hospital with VAP 540 (68.2%) patients were culture positive. Pseudomonas aeruginosa was most commonly isolated pathogen of both early onset and late onset VAP. In early VAP Acinetobacter baumannii showed 62.5% metallo-beta-lactamase (MBL) positivity. P. aeruginosa showed 27.5% MBL positivity, whereas in late onset VAP, 71.4% A. baumannii isolates and 75.8% P. aeruginosa isolates showed MBL positivity, respectively. Conclusion : Simple prevention of aspiration, sterilization of equipments, hand washing of personnel can reduce VAP in hospital care setting

Three dimensional structure prediction and ligand-protein interaction study of expansin protein ATEXPA23 from Arabidopsis thaliana L.

20-27Arabidopsis thaliana L. is a small flowering plant that is widely used as a model organism in plant biology. In the present study, we study the peripheral membrane protein ATEXPA23 from Arabidopsis thaliana L. using homology modelling and molecular docking. The microarray analysis shows expression of ATEXPA23 (AT5G39280) protein, which leads to loosening and extension of plant cell walls. This protein is differentially expressed during different stages of plant embryogenesis. It contains one expansin-like CBD domain and one expansin-like EG45 domain. ATEXPA23 belongs to the expansin family in expansin a subfamily. The 3D model after refinement is used to explore the xyloglucan binding characteristics of ATEXPA23 using AutoDock. The docking analysis shows that the surface exposed aromatic amino acid residues Phe 193 and Phe 265 interact with ligand xyloglucan through CH-л interaction. The binding energy values of docking reflect a stable conformation of the docked complex. The interaction of expansin protein with carbohydrate xyloglucan, present in hemicellulose structures of plant cell wall, is thoroughly analysed with cellotetrose and xyloglucan heptasaccharide using electrostatic potential calculation. This CH-л non-covalent interaction predominates on the cellulose-xyloglucan interaction in plant cell wall during cell growth

Dehydration of alcohols by pervaporation using hydrophilic polyether urethane membranes

219-227This study presents pervaporation separation of methanol-water (MW), ethanol-water (EW) and isopropanol-water (IW) mixtures through a series of polyethylene glycol (PEG) based polyether urethane membranes. Five polyether urethane (PUPEG) membranes were developed by using PEGs of increasing molecular weight (PEG₂₀₀, PEG₄₀₀, PEG₆₀₀, PEG₁₀₀₀ and PEG₄₀₀₀). PUPEG membranes showed selectivity towards water, which increased with increasing molecular weight as well as increasing polymer chain length of the membrane. PUPEG membrane prepared using PEG₄₀₀₀ showed highest separation performance in dehydrating water from three alcohol-water mixtures. A pervaporation flux of 0.48 kg/m².h was achieved with 94.5% water in permeates from 20% water present in isopropanol-water mixture using PUPEG₄₀₀₀ membrane. All PUPEG membranes showed water separation performances; flux and separation factor for water varied in the order: MW <EW<IW mixtures