DETECTION OF PHISHING WEBSITES USING HYBRID MODEL

Online technologies have revolutionized the modern computing world. Thereare number of users who purchase products online and make payment through variouswebsites. There are multiple websites who ask user to provide sensitive data such asusername, password or credit card details etc. often for malicious reasons. This type ofwebsite is known as phishing website. The phishing website can be detected based on someimportant characteristics like URL (Uniform Resource Locator) and Domain identity.Several approaches have been proposed for detection of phishing websites by extracting thephishing data sets criteria to classify their legitimacy. However, there is no such approachthat can provide better results to the users from phishing attacks. This paper is an attemptto contribute in that area by presenting a hybrid model for classification to detect phishingwebsites with high accuracy and less error rate

PHARMACOGNOSTIC STUDY OF MANSOA ALLIACEA LEAF

Mansoa alliacea Lam. (Family: Bignoniaceae) is a native plant from Amazonian basin in South America. Plant derivatives are used as anti-inflammatory, antioxidant, antiseptic and antibacterial agents. The study was aimed to determine the pharmacognostic and phytochemicals present in Mansoa alliacea. Micro and organoleptic characteristics of fresh and dried leaf samples had been examined. Physicochemical variables had been done by using WHO suggested variables; preliminary phytochemical of leaf sample had been performed to identify the presence of alkaloids, flavonoids, tannins and phenols, and quinones using the ethanolic extract of the leaves of M. alliacea

Healthcare seeking for diarrhoea, malaria and pneumonia among children in four poor rural districts in Sierra Leone in the context of free health care: results of a cross-sectional survey

BACKGROUND: To plan for a community case management (CCM) program after the implementation of the Free Health Care Initiative (FHCI), we assessed health care seeking for children with diarrhoea, malaria and pneumonia in 4 poor rural districts in Sierra Leone. METHODS: In July 2010 we undertook a cross-sectional household cluster survey and qualitative research. Caregivers of children under five years of age were interviewed about healthcare seeking. We evaluated the association of various factors with not seeking health care by obtaining adjusted odds ratios and 95% confidence limits using a multivariable logistic regression model. Focus groups and in-depth interviews of young mothers, fathers and older caregivers in 12 villages explored household recognition and response to child morbidity. RESULTS: The response rate was 93% (n=5951). Over 85% of children were brought for care for all conditions. However, 10.8% of those with diarrhoea, 36.5% of those with presumed pneumonia and 41.0% of those with fever did not receive recommended treatment. In the multivariable models, use of traditional treatments was significantly associated with not seeking outside care for all three conditions. Qualitative data showed that traditional treatments were used due to preferences for locally available treatments and barriers to facility care that remain even after FHCI. CONCLUSION: We found high healthcare seeking rates soon after the FHCI; however, many children do not receive recommended treatment, and some are given traditional treatment instead of seeking outside care. Facility care needs to be improved and the CCM program should target those few children still not accessing care

The evolutionary young miR-1290 favors mitotic exit and differentiation of human neural progenitors through altering the cell cycle proteins.

Regulation of cellular proliferation and differentiation during brain development results from processes requiring several regulatory networks to function in synchrony. MicroRNAs are part of this regulatory system. Although many microRNAs are evolutionarily conserved, recent evolution of such regulatory molecules can enable the acquisition of new means of attaining specialized functions. Here we identify and report the novel expression and functions of a human and higher primate-specific microRNA, miR-1290, in neurons. Using human fetal-derived neural progenitors, SH-SY5Y neuroblastoma cell line and H9-ESC-derived neural progenitors (H9-NPC), we found miR-1290 to be upregulated during neuronal differentiation, using microarray, northern blotting and qRT-PCR. We then conducted knockdown and overexpression experiments to look at the functional consequences of perturbed miR-1290 levels. Knockdown of miR-1290 inhibited differentiation and induced proliferation in differentiated neurons; correspondingly, miR-1290 overexpression in progenitors led to a slowing down of the cell cycle and differentiation to neuronal phenotypes. Consequently, we identified that crucial cell cycle proteins were aberrantly changed in expression level. Therefore, we conclude that miR-1290 is required for maintaining neurons in a differentiated state

Androgen ablation mitigates tolerance to a prostate/prostate cancer-restricted antigen

SummaryTo understand the T cell response to prostate cancer, we created transgenic mice that express a model antigen in a prostate-restricted pattern and crossed these animals to TRAMP mice that develop spontaneous prostate cancer. Adoptive transfer of prostate-specific CD4 T cells shows that, in the absence of prostate cancer, the prostate gland is mostly ignored. Tumorigenesis allows T cell recognition of the prostate gland—but this recognition is tolerogenic, resulting in abortive proliferation and ultimately in hyporesponsiveness at the systemic level. Androgen ablation (the most common treatment for metastatic prostate cancer) was able to mitigate this tolerance—allowing prostate-specific T cells to expand and develop effector function after vaccination. These results suggest that immunotherapy for prostate cancer may be most efficacious when administered after androgen ablation

Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge

Cardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) is a non-invasive method used to evaluate coronary artery disease, as well as evaluating and reconstructing heart and coronary vessel structures. Reconstructed models have a wide array of for educational, training and research applications such as the study of diseased and non-diseased coronary anatomy, machine learning based disease risk prediction and in-silico and in-vitro testing of medical devices. However, coronary arteries are difficult to image due to their small size, location, and movement, causing poor resolution and artefacts. Segmentation of coronary arteries has traditionally focused on semi-automatic methods where a human expert guides the algorithm and corrects errors, which severely limits large-scale applications and integration within clinical systems. International challenges aiming to overcome this barrier have focussed on specific tasks such as centreline extraction, stenosis quantification, and segmentation of specific artery segments only. Here we present the results of the first challenge to develop fully automatic segmentation methods of full coronary artery trees and establish the first large standardized dataset of normal and diseased arteries. This forms a new automated segmentation benchmark allowing the automated processing of CTCAs directly relevant for large-scale and personalized clinical applications

P04-41. Kinetics of Antibody Neutralization and Viral Evolution Following Envelope Vaccination in SIV-infected Rhesus Monkeys

Poster presentation. Conclusion: Our results indicate that env vaccination is associated with an accelerated development of autologous neutralizing antibodies. These antibodies were focused at least in part on the V1 region of Env, since there was selective pressure in this region of the envelope for the evolution of mutational changes

Breeding tomato (Solanum lycopersicum L.) for resistance to biotic and abiotic stresses

Tomato (Solanum lycopersicum L.) is an important vegetable crop cultivated in the tropical and sub-tropical regions of the world. Low productivity in India is due to occurrence of both biotic and abiotic stresses. Among the biotic stresses, tomato leaf curl disease, bacterial wilt, early blight and Groundnut Bud Necrosis Virus disease have become serious production constraints causing considerable yield loss in the major tomato growing areas of the country. Adoption of multiple disease resistant varieties or F1 hybrids would be the most appropriate way to address these diseases. At ICAR-IIHR, Bengaluru systematic breeding strategies were employed to pyramid genes for resistance to early blight, bacterial wilt and tomato leaf curl diseases and to develop advanced breeding lines& F1 hybrids with triple disease resistance. Stable source of resistance to early blight and bi-partite begomo-virus (Tomato Leaf Curl New Delhi Virus) has been identified in Solanum habrochaites LA-1777. Validation with molecular markers linked to tomato leaf curl virus resistance revealed that LA-1777 carryTy2 and other putative resistant genes. Several high yielding dual purpose hybrids were also developed for fresh market and processing with high level of resistance to multiple diseases. Cherry tomato lines have also been bred for high TSS, total carotenoids, total phenols, flavonoids, vitamin C, acidity and lycopene content. IIHR-249-1, IIHR-2101 (Solanum habrochaites LA-1777), IIHR- 2866 and IIHR-2864 recorded high values for quality parameters like total carotenoids, lycopene, vitamin C, total phenols, flavonoids and TSS. Drought tolerant root stock has been developed by an interspecific cross between S. habrochaites LA-1777 and S. lycopersicum (15 SB SB). Resistant sources have also been identified against Tuta absoluta, a serious insect pest reported from major tomato growing areas in the country in recent time. High temperature tolerant breeding lines are in pipe line

Detecting Manipulations in Video

This chapter presents the techniques researched and developed within InVID for the forensic analysis of videos, and the detection and localization of forgeries within User-Generated Videos (UGVs). Following an overview of state-of-the-art video tampering detection techniques, we observed that the bulk of current research is mainly dedicated to frame-based tampering analysis or encoding-based inconsistency characterization. We built upon this existing research, by designing forensics filters aimed to highlight any traces left behind by video tampering, with a focus on identifying disruptions in the temporal aspects of a video. As for many other data analysis domains, deep neural networks show very promising results in tampering detection as well. Thus, following the development of a number of analysis filters aimed to help human users in highlighting inconsistencies in video content, we proceeded to develop a deep learning approach aimed to analyze the outputs of these forensics filters and automatically detect tampered videos. In this chapter, we present our survey of the state of the art with respect to its relevance to the goals of InVID, the forensics filters we developed and their potential role in localizing video forgeries, as well as our deep learning approach for automatic tampering detection. We present experimental results on benchmark and real-world data, and analyze the results. We observe that the proposed method yields promising results compared to the state of the art, especially with respect to the algorithm’s ability to generalize to unknown data taken from the real world. We conclude with the research directions that our work in InVID has opened for the future