Lightweight Encrytion Scheme against Flow Analysis in Multi-Hop Wireless Network Based on Network Coding

Traffic analysis is a major issue faced in multi-hop wireless networks (MWN) in the case of privacy preservation. Network coding is essential in achieving greater capacity for any network and we extend this network coding for privacy preservation in multi-hop networks as it offers coding and mixing functions at intermediate nodes. Certain existing privacy preserving methods like onion routing can be employed here. Applying homomorphic encryption on Global Encoding Vectors(GEV’s), our method offers confidentiality and privacy preserving features. Only the sink has capability of decrypting the message content by inverting the GEV. Here, we focus on the privacy issue in order to prevent traffic analysis and flow tracing and achieve source anonymity in MWNs. Source anonymity refers to carrying the communication through the network maintaining the secrecy of the source node. Energy consumption when compared with the existing system was found to be reduced. Simulative evaluation by NS2 shows the efficiency of the system. Keywords: MWN, Privacy preservation, NS2, GEV

A Multi-task Network to Detect Junctions in Retinal Vasculature

Junctions in the retinal vasculature are key points to be able to extract its topology, but they vary in appearance, depending on vessel density, width and branching/crossing angles. The complexity of junction patterns is usually accompanied by a scarcity of labels, which discourages the usage of very deep networks for their detection. We propose a multi-task network, generating labels for vessel interior, centerline, edges and junction patterns, to provide additional information to facilitate junction detection. After the initial detection of potential junctions in junction-selective probability maps, candidate locations are re-examined in centerline probability maps to verify if they connect at least 3 branches. The experiments on the DRIVE and IOSTAR showed that our method outperformed a recent study in which a popular deep network was trained as a classifier to find junctions. Moreover, the proposed approach is applicable to unseen datasets with the same degree of success, after training it only once.Comment: MICCAI 2018 Camera Ready Versio

Haplotype-based stratification of Huntington's disease

Huntington’s disease (HD) is an autosomal dominant neurodegenerative disease caused by expansion of a CAG trinucleotide repeat in HTT, resulting in an extended polyglutamine tract in huntingtin. We and others have previously determined that the HD-causing expansion occurs on multiple different haplotype backbones, reflecting more than one ancestral origin of the same type of mutation. In view of the therapeutic potential of mutant allele-specific gene silencing, we have compared and integrated two major systems of HTT haplotype definition, combining data from 74 sequence variants to identify the most frequent disease-associated and control chromosome backbones and revealing that there is potential for additional resolution of HD haplotypes. We have used the large collection of 4078 heterozygous HD subjects analyzed in our recent genome-wide association study of HD age at onset to estimate the frequency of these haplotypes in European subjects, finding that common genetic variation at HTT can distinguish the normal and CAG-expanded chromosomes for more than 95% of European HD individuals. As a resource for the HD research community, we have also determined the haplotypes present in a series of publicly available HD subject-derived fibroblasts, induced pluripotent cells, and embryonic stem cells in order to facilitate efforts to develop inclusive methods of allele-specific HTT silencing applicable to most HD patients. Our data providing genetic guidance for therapeutic gene-based targeting will significantly contribute to the developments of rational treatments and implementation of precision medicine in HD

Haplotype-based stratification of Huntington's disease

Huntington’s disease (HD) is an autosomal dominant neurodegenerative disease caused by expansion of a CAG trinucleotide repeat in HTT, resulting in an extended polyglutamine tract in huntingtin. We and others have previously determined that the HD-causing expansion occurs on multiple different haplotype backbones, reflecting more than one ancestral origin of the same type of mutation. In view of the therapeutic potential of mutant allele-specific gene silencing, we have compared and integrated two major systems of HTT haplotype definition, combining data from 74 sequence variants to identify the most frequent disease-associated and control chromosome backbones and revealing that there is potential for additional resolution of HD haplotypes. We have used the large collection of 4078 heterozygous HD subjects analyzed in our recent genome-wide association study of HD age at onset to estimate the frequency of these haplotypes in European subjects, finding that common genetic variation at HTT can distinguish the normal and CAG-expanded chromosomes for more than 95% of European HD individuals. As a resource for the HD research community, we have also determined the haplotypes present in a series of publicly available HD subject-derived fibroblasts, induced pluripotent cells, and embryonic stem cells in order to facilitate efforts to develop inclusive methods of allele-specific HTT silencing applicable to most HD patients. Our data providing genetic guidance for therapeutic gene-based targeting will significantly contribute to the developments of rational treatments and implementation of precision medicine in HD

Hybrid manifold smoothing and label propagation technique for Kannada handwritten character recognition

Handwritten character recognition is one of the classical problems in the field of image classification. Supervised learning techniques using deep learning models are highly effective in their application to handwritten character recognition. However, they require a large dataset of labeled samples to achieve good accuracies. Recent supervised learning techniques for Kannada handwritten character recognition have state of the art accuracy and perform well over a large range of input variations. In this work, a framework is proposed for the Kannada language that incorporates techniques from semi-supervised learning. The framework uses features extracted from a convolutional neural network backbone and uses regularization to improve the trained features and label propagation to classify previously unseen characters. The episodic learning framework is used to validate the framework. Twenty-four classes are used for pre-training, 12 classes are used for testing and 11 classes are used for validation. Fine-tuning is tested using one example per unseen class and five examples per unseen class. Through experimentation the components of the network are implemented in Python using the Pytorch library. It is shown that the accuracy obtained 99.13% make this framework competitive with the currently available supervised learning counterparts, despite the large reduction in the number of labeled samples available for the novel classes

Identification of Genetic Factors that Modify Clinical Onset of Huntington’s Disease

SummaryAs a Mendelian neurodegenerative disorder, the genetic risk of Huntington’s disease (HD) is conferred entirely by an HTT CAG repeat expansion whose length is the primary determinant of the rate of pathogenesis leading to disease onset. To investigate the pathogenic process that precedes disease, we used genome-wide association (GWA) analysis to identify loci harboring genetic variations that alter the age at neurological onset of HD. A chromosome 15 locus displays two independent effects that accelerate or delay onset by 6.1 years and 1.4 years, respectively, whereas a chromosome 8 locus hastens onset by 1.6 years. Association at MLH1 and pathway analysis of the full GWA results support a role for DNA handling and repair mechanisms in altering the course of HD. Our findings demonstrate that HD disease modification in humans occurs in nature and offer a genetic route to identifying in-human validated therapeutic targets in this and other Mendelian disorders.PaperCli

The Top 100 questions for the sustainable intensification of agriculture in India’s rainfed drylands

India has the largest area of rainfed dryland agriculture globally, with a variety of distinct types of farming systems producing most of its coarse cereals, food legumes, minor millets, and large amounts of livestock. All these are vital for national and regional food and nutritional security. Yet, the rainfed drylands have been relatively neglected in mainstream agricultural and rural development policy. As a result, significant social-ecological challenges overlap in these landscapes: endemic poverty, malnutrition and land degradation. Sustainable intensification of dryland agriculture is essential for helping to address these challenges, particularly in the context of accelerating climate change. In this paper, we present 100 questions that point to the most important knowledge gaps and research priorities. If addressed, these would facilitate and inform sustainable intensification in Indian rainfed drylands, leading to improved agricultural production and enhanced ecosystem services. The horizon scanning method used to produce these questions brought together experts and practitioners involved in a broad range of disciplines and sectors. This exercise resulted in a consolidated set of questions covering the agricultural drylands, organized into 13 themes. Together, these represent a collective programme for new cross- and multi-disciplinary research on sustainable intensification in the Indian rainfed drylands