BACTERICIDAL ACTIVITY OF DIFFERENT PARTS OF AZADIRACHTA INDICA ON PROBIOTIC MICROBES

ABSTRACTObjective: The purpose of the present study was to investigate the bactericidal activity of different parts of Azadirachta indica plant against probioticmicrobes such as Bacillus clausii, Lactobacillus acidophilus, Lactobacillus rhamnosus, Lactobacillus casei and Saccharomyces boulardii that were foundto be present in these commercial available probiotic products such as yoghurt, probase, enterogermina and various other types of fermented food. Methods: Bactericidal activity of different parts of Azadirachta indica plant considering leaves, barks and seeds were observed by agar well diffusionmethod by calculating inhibition length. Phytochemical screening was done in aqueous and methanolic extracts of leaves, barks and seed of Azadirachtaindica plant in order to reveal the presence of secondary metabolite in the defined extracts.Results: The present study has revealed the presence of maximum bactericidal activity in its aqueous extracts of leaves as compared to the bark and seed.Conclusion: This can be concluded from the present study to recommend to utilize the bark and seed as traditional herbal remedies that has shownless amount of bactericidal activity against the probiotic microbes.Keywords: Probiotic microbes, Phytochemical screening, Bactericidal activity, Agar well-diffusion method, Azadirachta indica

Corporate Philanthropy: A Systematic Review

A systematic review of the corporate philanthropy literature is conducted. A sample of 60 academic articles was created and analyzed. The sample was examined to (1) develop a definition of corporate philanthropy contrasting it with related concepts; (2) review how corporate philanthropy has been examined theoretically; (3) review how it has been operationalized and determine commonly examined control, independent and dependent variables; (4) the societal implications of corporate philanthropy and (5) identify gaps in the literature and areas for future research. Findings suggest there is little cohesion in the literature regarding a standard definition, wide use of theories to situate corporate philanthropy, and several narrow conceptualizations with opportunities for an empirical and theoretical investigation to enhance the understanding of corporate philanthropy. The gaps identified in the literature review consist of (1) the further study of corporate philanthropy as an independent variable to determine the impacts of corporate action; (2) whether there is a certain amount of optimality associated with corporate donations; (3) whether there are cultural limitations to the findings of attitude towards corporate philanthropy, and (4) a fuller study of the risks and/or benefits posed by corporate philanthropy to society

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in many machine learning tasks, including but not limited to image classification, text mining, and speech processing. However, recent research on DNNs has indicated ever-increasing concern on the robustness to adversarial examples, especially for security-critical tasks such as traffic sign identification for autonomous driving. Studies have unveiled the vulnerability of a well-trained DNN by demonstrating the ability of generating barely noticeable (to both human and machines) adversarial images that lead to misclassification. Furthermore, researchers have shown that these adversarial images are highly transferable by simply training and attacking a substitute model built upon the target model, known as a black-box attack to DNNs. Similar to the setting of training substitute models, in this paper we propose an effective black-box attack that also only has access to the input (images) and the output (confidence scores) of a targeted DNN. However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating adversarial examples. We use zeroth order stochastic coordinate descent along with dimension reduction, hierarchical attack and importance sampling techniques to efficiently attack black-box models. By exploiting zeroth order optimization, improved attacks to the targeted DNN can be accomplished, sparing the need for training substitute models and avoiding the loss in attack transferability. Experimental results on MNIST, CIFAR10 and ImageNet show that the proposed ZOO attack is as effective as the state-of-the-art white-box attack and significantly outperforms existing black-box attacks via substitute models.Comment: Accepted by 10th ACM Workshop on Artificial Intelligence and Security (AISEC) with the 24th ACM Conference on Computer and Communications Security (CCS