Atrial Fibrillation (A-fib) is a common cardiac rhythm problem in the population these days in which irregular heartbeat leads to blood clots, heart failure, stroke, and other significant clinical complications. Researchers have found that the atrial fat can lead to AF in most patients. To develop an automated method for detecting the epicardial fat present in the atrium using a Convolutional Neural Network. Cardiac Computed Tomography (CT) images of ten patients were pre-processed to remove the unwanted structure around the heart. An automated pixel value masking was done to locate the epicardial fat in the atrium and a 3D view of the heart was constructed for correct visualization of the location of the fat. A fast and fully automated Convolutional Neural Network (CNN) was applied to detect the atrial epicardial fat through feature selection from the CT images. We achieved 89.22% accuracy, 90.18% sensitivity, and 88.52% specificity in the detection of atrial epicardial fat using our CNN architecture. Our results showed that this CNN-based method can be helpful in atrial epicardial fat detection. Since Deep learning techniques add robustness, rapidness, and reliability, this study provides an unutilized way to detect the atrial fat tissue.

Heart disease has a higher fatality rate than any other disease. Increased Atrial fat on the left atrium has been discovered to cause Atrial Fibrillation (AF) in most patients. AF can put one’s life at risk and eventually lead to death. AF might worsen over time; therefore, it is crucial to have an early diagnosis and treatment. To evaluate the left atrium fat tissue pattern using Radon descriptor-based machine learning. This study developed a bridge between the Radon transform framework and machine learning to distinguish two distinct patterns. Motivated by a Radon descriptor-based machine learning approach, the patches of eight patients from CT images of the heart were used and categorized into “epicardial fat tissue” and “nonfat tissue” groups. The 10 feature vectors are extracted from each big patch using Radon descriptors and then fed into a traditional machine learning model. The results show that the proposed methodology discriminates between fat tissues and nonfat tissues clearly. KNN has shown the best performance with 96.77% specificity, 98.28% sensitivity, and 97.50% accuracy. To our knowledge, this study is the first attempt to provide a Radon transform-based machine learning method to distinguish between fat tissue and nonfat tissue on the left atrium. Our proposed research method could be potentially used in advanced interventions.

Bacillus megaterium isolated from poultry farm soil was identified by standard biochemical tests and screened for the production of serine protease. Production of serine protease was done using 5 different medias by varying the type of amino acid added. The purification was done by salt precipitation, dialysis and DEAEcellulose ion exchange chromatography. The proline containing media obtained the highest fold purification out of the five different medias (leucine, lysine, proline, tryptophan and methionine cotaining media). The enzyme showedan optimal activity at the temperature 37°C and the pH 6 which are known as its optimum temperature and pH respectively. The enzyme was proved as a Mn2+ dependent serine protease as it was activated by Mn2+ ions and inhibited by PMSF. The molecular weight of the enzyme was determined by SDS-PAGE technique as around 30kDa. It showed an excellent detergent activity on the blood stains and a very good stability in presence of locally available detrgents. The enzyme acted on the keratin protein of the chicken feather and showed a degrading capacity on the protein. So it was proved that the recently studied serine protease has a keratinase activity also. From these datas I conclude that the protease isolated from Bacillus megaterium is a Mn2+ dependent serine protease which has both keratinase and detergent activity.

The study “Analysis of Chinese Toy Market in India with special reference to Toy Sellers: A study of Delhi NCR” is an attempt to analyse the impact of Chinese toys available in Indian market on Indian Toy Industry and to discuss the various issues related to the availability of chineses toys in Indian Market in views of toy sellers in India.Indiais well known in world market as it has approximately 1250 games and toys exporters, manufacturers, and suppliers. Yet toys made in China have confined by its unique features, techniques and low pricing as over 70 per cent of all toys sold in Indiaan market are coming from China which is somehow upsetting the Indian traders of toys. China is a big fan of electronic toys and games because they give new ways to teach youngsters while having fun. This is somehow, creating hurdule for Indian Toy Industry in India.

Imidazolium ionic liquids containing second-generation MacMillan catalysts were synthesized and evaluated as organocatalysts (10 mol%) for enantioselective Friedel-Crafts reaction between N-benzylindole and crotonaldehyde using trifluoroacetic acid as a co-catalyst (10 mol%) at À 60 °C, the corresponding product was obtained in 87 % yield with 89 % ee. The scope and limitation of organocatalyst were studied using substituted indoles and different α, β-unsaturated aldehydes, and enantioselective alkylated indoles were obtained in 43-95 % yields with 58-90 % ee's. The second-generation modified MacMillan catalyst 6 was successfully recovered and reused up to four cycles and a significant drop in yields and ee was observed after the third recycle.