Management of Gout

Gout is an important health concern for old age people. People who are affected this disease are mostly at 40 or older. Physicians prescribed many drugs for the management of gout i.e. Uric acid lowering agent, NSAIDs etc. This study was designed to develop best medication plan to manage the gout. This study was carried out from May 2014 to September 2014.For this purpose I visited many hospitals and clinics to check the patients record and medication prescribed, also ask to patients which drug response to best and read different literatures. Then I made a plan of medication for the management of gout which is effective and economic for the patients. Keywords: Gout, Management/Treatmen

The Ingalls-Thomas Bijections

Given a finite acyclic quiver Q with path algebra kQ, Ingalls and Thomas have exhibited a bijection between the set of Morita equivalence classes of support-tilting modules and the set of thick subcategories of mod kQ and they have collected a large number of further bijections with these sets. We add some additional bijections and show that all these bijections hold for arbitrary hereditary artin algebras. The proofs presented here seem to be of interest also in the special case of the path algebra of a quiver.Comment: This is a modified version of an appendix which was written for the paper "The numbers of support-tilting modules for a Dynkin algebra" (see arXiv:1403.5827v1

Probiotic Yeast: Mode of Action and Its Effects on Ruminant Nutrition

The main purpose of yeast supplementation is to treat rumen microbial dysbiosis which may enhance the nutrient utilization leading to enhanced animal growth and productivity. Yeast improves rumen ecosystem by two ways: by direct production of digestive enzymes and growth stimulator and by promoting the growth and function of beneficial microbiota. Yeasts have potential to produce metabolites, which stimulate the growth, like rumen acetogens and antimicrobial compounds which inhibit potential pathogens. The yeast probiotic impact on animals depend on different interacting factors including animal breed, supplemented dose, type, diet, strain, physiological stage and feeding system. In the situation of a high feed cost all over the world, probiotic yeast gives a useful nutritional strategy which allows increasing diet digestibility and consequently enhances the performance in ruminants in cost-effective manner. Many yeast culture-based products are commercially available worldwide, but their effectiveness as probiotic dietary supplement in a particular breed is mostly questionable. Therefore, exploration of the new indigenous probiotic strain is of great interest in this context. The probiotic strains of same ecological origin may be more compatible with rumen microbiome giving maximum outputs. Moreover, the breed specific probiotic yeast is an economical and viable option for farmers to overcome the effects of malnutrition

機械的乳化及びマイクロチャネル乳化を利用したビタミン・ファイトケミカルを内包したマイクロ・ナノエマルションの作製及び特性評価

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 鍋谷 浩志, 東京大学教授 岡田 謙介, 東京大学准教授 斎藤 幸恵, 東京大学准教授 荒木 徹也, 国立研究開発法人農業・食品産業技術総合研究機構主任研究員 小林 功University of Tokyo(東京大学

Deep reinforcement learning based Evasion Generative Adversarial Network for botnet detection

Botnet detectors based on machine learning are potential targets for adversarial evasion attacks. Several research works employ adversarial training with samples generated from generative adversarial nets (GANs) to make the botnet detectors adept at recognising adversarial evasions. However, the synthetic evasions may not follow the original semantics of the input samples. This paper proposes a novel GAN model leveraged with deep reinforcement learning (DRL) to explore semantic aware samples and simultaneously harden its detection. A DRL agent is used to attack the discriminator of the GAN that acts as a botnet detector. The agent trains the discriminator on the crafted perturbations during the GAN training, which helps the GAN generator converge earlier than the case without DRL. We name this model RELEVAGAN, i.e. [“relieve a GAN” or deep REinforcement Learning-based Evasion Generative Adversarial Network] because, with the help of DRL, it minimises the GAN's job by letting its generator explore the evasion samples within the semantic limits. During the GAN training, the attacks are conducted to adjust the discriminator weights for learning crafted perturbations by the agent. RELEVAGAN does not require adversarial training for the ML classifiers since it can act as an adversarial semantic-aware botnet detection model. The code will be available at https://github.com/rhr407/RELEVAGAN

Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that the p values were statistically significant relative to all of the other classifiers (p < 0.005) using HbO signals

Antibacterial activity of the venom of Heterometrus xanthopus

Heterometrus xanthopus (Scorpion) is one of the most venomous and ancient arthropods. Its venom contains anti-microbial peptides like hadrurin, scorpine, Pandinin 1, and Pandinin 2 that are able to effectively kill multidrug-resistant pathogens. The present study was conducted to evaluate the anti-bacterial activity of H. xanthopus venom. Six Gram-positive and Gram-negative bacterial strains were tested against 1/100, 1/10, and 1/1 fractions of distilled water diluted and crude venom. 1/100 and 1/10 dilutions were not successful in any of the six bacterial strains studied while the 1/1 dilution was effective on Bacillus subtilis ATCC 6633, Salmonella typhimurium ATCC 14028, and Pseudomonas aeruginosa ATCC 27853 with highest zone of inhibition were obtained on B. subtilis. Crude venom was effective against Enterococcus faecalis ATCC 14506, B. subtilis, S. typhimurium, and P. aeruginosa. The most effective results were observed on B. subtilis