EFFECTS OF AQUEOUS EXTRACT OF GLYCYRRHIZA GLABRA LINN. AND DIOSMETIN ON MODULATION OF SPATIAL MEMORY THROUGH ACETYLCHOLINESTERASE AND BRAIN-DERIVED NEUROTROPHIC FACTOR IN ETHANOL-INDUCED COGNITIVE IMPAIRMENT MODEL RATS

Objective: The objective of this research was to evaluate the cognitive impairment due to excessive consumption of alcohol and memory enhancement action of Glycyrrhiza glabra Linn. (AEGGL) and diosmetin (Dm). Methods: In this study, 36 adult male Wistar rats were divided into the six groups (n=6) and eight-arm radial maze, narrow beam test, and open field behavior parameters were assessed on day 1, 10, and 21. After the 21 days of experiment, animals were sacrificed, and blood samples were collected for serum acetylcholinesterase (AChE) and brain-derived neurotrophic factor (BDNF) estimation. We have also analyzed the morphology of CA3 region of the hippocampus. Results: The results of this study suggested that AEGGL and Dm treatment could be the potential drugs for ethanol-induced cognitive impairment. Conclusion: Ethanol-induced cognitive impairment was recovered by AEGGL and Dm treatment, we suggested that this might be due to anticholinesterase activity and increased synthesis of BDNF levels in the brain. Further, researches are warranted to understand the exact mechanism of action of drugs

Effect of the Cryogenically Treated Copper Nozzle Used in Plasma Arc Machining of S235 Steel

The investigation was carried out by making use of the design of experiments method in order to achieve its objective, which was to study wear analysis in relation to a cryogenically treated nozzle that was utilized in plasma arc machining. Kerf width and surface roughness are two output characteristics that are key variables in deciding the quality of the cut and the efficiency of the operation. Both of these metrics are outputted by the process. While machining S235 steel, an investigation into the impact that nozzle treatment has on various quality metrics is currently under way. The examination is carried out with the arc voltage, the cutting speed, and the gas pressure, all serving as important components. A cryogenic treatment of the nozzle material using liquid nitrogen at a temperature of −194°C has been attempted in an effort to increase the life of the nozzle. Machining is performed using two different nozzle conditions, such as cryogenically treated and cryogenically untreated, with regard to the input parameter combinations that have been selected. To have a better understanding of the wear behavior of nozzles, an image from a scanning electron microscope is studied. Because of the treatment, the production of wear tracks in the direction that gas flow takes has been drastically decreased. This, in turn, has increased the cutting efficiency by decreasing the amount of arc current that was necessary. In addition, a grey relational analysis is carried out in order to find the best possible machining settings in both conditions. The parameters that were optimized for a nozzle that had been cryogenically treated were 6 bar of gas pressure, 120 amperes of arc current, and 1800 of cutting speed per minute. The use of cryogenic treatment resulted in a reduction of surface roughness by 0.4670 µm and a narrowing of the kerf width by 0.96 mm. It is clear from the SEM pictures of untreated and cryogenically treated nozzles that thermal distortion and wear in the nozzle tip area are minimized to a greater extent in the treated nozzle. This is evidenced by the fact that the treated nozzle has a more uniform appearance

Rapid serodiagnosis of leptospirosis by latex agglutination test and flow-through assay

Purpose: Diagnosis of leptospirosis facilitates patient management and initiation of therapy. The microscopic agglutination test (MAT) is the serological test used in reference laboratories because of its high degree of sensitivity and specificity. But the results are not available quickly for patient management. In the present study, in order to develop a simple, rapid immunodiagnostic assay, one of the outer membrane proteins (OMPs), recombinant LipL41 (rLipL41) has been utilised in latex agglutination test (LAT) and flow-through assay. Methods: Part of LipL41 gene was expressed in Escherichia coli system and purified. The rLipL41 antigen of pathogenic Leptospira interrogans serovar Icterohaemorrhagiae, which is conserved in all pathogenic Leptospira spp. was used as capture antigen in the LAT and flow-through test. Both tests are very rapid and could be completed within 5 minutes. The sensitivity and specificity of rLipL41 was assessed and evaluated in LAT and flow-through assay in comparison with standard MAT. Results: The sensitivity and specificity of the LAT were 89.70 and 90.45% and flow-through assay were 89.09 and 77.70%, respectively. Conclusions: The developed LAT and flow-through assays were simple, rapid and economical for the detection of leptospira infection and suitable for large-scale screening of samples in endemic areas without any sophisticated equipment

Poly(ethylene chlorotrifluoroethylene) membrane formation via thermally induced phase separation (TIPS)

Poly(ethylene chlorotrifluoroethylene) (ECTFE) is a 1:1 alternating copolymer of ethylene and chlorotrifluoroethylene that offers excellent resistance in chemically and thermally challenging environments. ECTFE membranes with a variety of microstructures have been fabricated via thermally induced phase separation (TIPS) with dibutyl phthalate (DBP) as the diluent. A continuous flat sheet extrusion apparatus with a double rotating drum was used that permitted controlling both the casting solution thickness and axial tension on the nascent membrane. Initial compositions of ECTFE/DBP solutions in the liquid–liquid region of the binary phase diagram were chosen, resulting in membranes with an interconnected pore structure. The effects of several important process parameters were studied to determine their effect on the structure and properties of the membrane. The parameters evaluated included the initial ECTFE concentration, cooling rate, membrane thickness, co-extrusion of diluent, and stretching of the nascent membrane. The resulting membranes were characterized using SEM, porometry, and permeation measurements. For the range of process parameters studied, ECTFE membranes exhibited a decrease in surface porosity with increasing initial polymer concentration and cooling rate. The effect of membrane thickness on the permeation flux was not significant. Co-extrusion of diluent increased the surface porosity and eliminated the dense skin that was otherwise present under rapid cooling conditions. Subsequent stretching of the nascent membrane resulted in a more open structure and a significant increase in the permeation flux

CSSP (Consensus Secondary Structure Prediction): a web-based server for structural biologists

Sequence-structure correlation studies are important in deciphering the relationships between various structural aspects, which may shed light on the protein-folding problem. The first step of this process is the prediction of secondary structure for a protein sequence of unknown three-dimensional structure. To this end, a web server has been created to predict the consensus secondary structure using well known algorithms from the literature. Furthermore, the server allows users to see the occurrence of predicted secondary structural elements in other structure and sequence databases and to visualize predicted helices as a helical wheel plot. The web server is accessible at http://bioserver1.physics.iisc.ernet.in/cssp/

Grey relational analysis and surface texture analysis of Al-based metal matrix composites

The investigation found that the incorporation of MoS2 particles as reinforcement in Al–4% Mg material results in an increase in density and microhardness. The composite with 4% Mg and 6% MoS2 exhibited the highest percentage increase in the density up to 8.04%, and the highest percentage increase in the microhardness value of up to 33% as compared to the pure Aluminium. The pin-on-disc wear test revealed that the wear loss of the Al–Mg alloy is reduced by adding MoS2 particles as reinforcement. The wear loss decreases by 16.03% for the composite with 4% Mg and 6% MoS2. SEM analysis showed that the composite with higher MoS2 content (6%) exhibits a smoother worn surface. However, WEDM machining of the composite materials at higher levels of peak current, pulse on time, and gap voltage resulted in poor surface quality with more craters and micro voids. The sliding distance (m) was found to be the most significant parameter for wear output parameters, contributing to 57.05%–59.08% of the materials in samples 1 to 5. Similarly, the pulse on time (μs) was the most significant parameter for WEDM output parameters, contributing 46.29%–80.33% for the materials in samples 1 to 5. The study concluded that the PM route is an effective method for producing Al–Mg–MoS2 composites, which can improve the wear resistance of the Al–Mg alloy, and that WEDM parameters need to be optimized to achieve a better surface finish

Czekanowsky Hypergraph-Based Deep Learning Classifier for Precision Cyclone Forecasting

Early prediction of cyclones helps reduce deaths and damage to properties worldwide. With the advancement in satellite imaging technology, obtaining atmospheric images and remotely sensed objects such as cyclones is possible using different modalities. Such images are handy for weather prediction, specifically for forecasting cyclonic storms. However, it has a few limitations for achieving higher prediction accuracy with minimal time. A novel technique, proposed by Czekanowsky Dice Hypergraphic Extended Kalman Momentum Filterization based Bivariate Correlative Deep Structure Learning classification (CDHEKMF-BCDSLC), is introduced to achieve better cyclone prediction accuracy. Initially, the multiple satellite images are gathered from cyclone datasets—the proposed technique comprises four processing steps: segmentation, preprocessing, feature extraction, and classification. At first, Czekanowsky dices the Intensity threshold-based Interval Hypergraph (CDIT-IH) model using a Segmentation process. It minimized to perform cyclone prediction time. After that, preprocessing is applied to a novel invariant extended Kalman momentum filter designed to improve image contrast. Next, Bivariate correlative spatiotemporal feature extraction is performed on each image pixel intensity to extract features over time and location. Finally, a multidimensional deep belief network classification model is applied for accurate cyclone prediction. A multidimensional deep belief network classification model is a machine learning technique that consists of multiple layers for learning the given input (i.e., Spatiotemporal features). This process increases the prediction accuracy. Experimental results reveal that the proposed technique noticeably predicts cyclone conditional variants using prediction accuracy, precision, recall, F-measure, and prediction time concerning the number of cyclone images. The Quantitative results show that the proposed technique achieves 6% better accuracy with 17% minimum prediction time, 5% improved precision and f- f-measure, and 4% recall when compared to the state-of-the-art methods