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Purpose Age-related macular degeneration (AMD), a multifactorial disease with variable phenotypic presentation, was associated with 52 single nucleotide polymorphisms (SNPs) at 34 loci in a genome-wide association study (GWAS). These genetic variants could modulate different biological pathways involved in AMD, contributing to phenotypic variability. To better understand the effects of these SNPs, we performed a deep phenotype association study (DeePAS) in the Age-Related Eye Disease Study 2 (AREDS2), followed by replication using AREDS participants, to identify genotype associations with AMD and non-AMD ocular and systemic phenotypes. Design Cohort study. Participants AREDS and AREDS2 participants. Methods AREDS2 participants (discovery cohort) had detailed phenotyping for AMD; other eye conditions; cardiovascular, neurologic, gastrointestinal, and endocrine disease; cognitive function; serum nutrient levels; and others (total of 139 AMD and non-AMD phenotypes). Genotypes of the 52 GWAS SNPs were obtained. The DeePAS was performed by correlating the 52 SNPs to all phenotypes using logistic and linear regression models. Associations that reached Bonferroni-corrected statistical significance were replicated in AREDS. Main Outcome Measures Genotype–phenotype associations. Results A total of 1776 AREDS2 participants had 5 years follow-up; 1435 AREDS participants had 10 years. The DeePAS revealed a significant association of the rs3750846 SNP at the ARMS2/HTRA1 locus with subretinal/sub–retinal pigment epithelial (RPE) hemorrhage related to neovascular AMD (odds ratio 1.55 [95% confidence interval 1.31–1.84], P = 2.67 × 10 −7 ). This novel association remained significant after conditioning on participants with neovascular AMD ( P = 2.42 × 10 −4 ). Carriers of rs3750846 had poorer visual acuity during follow-up ( P = 6.82 × 10 −7 ) and were more likely to have a first-degree relative with AMD ( P = 5.38 × 10 −6 ). Two SNPs at the CFH locus, rs10922109 and rs570618, were associated with the drusen area in the Early Treatment Diabetic Retinopathy Study Report (ETDRS) grid ( P = 2.29 × 10 −11 and P = 3.20 × 10 −9 , respectively) and the center subfield ( P = 1.24 × 10 −9 and P = 6.68 × 10 −8 , respectively). SNP rs570618 was additionally associated with the presence of calcified drusen ( P = 5.38 × 10 −6 ). Except for positive family history of AMD with rs3750846, all genotype–phenotype associations were significantly replicated in AREDS. No pleiotropic associations were identified. Conclusions The association of the SNP at the ARMS2/HTRA1 locus with subretinal/sub-RPE hemorrhage and poorer visual acuity and of SNPs at the CFH locus with drusen area may provide new insights in pathophysiological pathways underlying different stages of AMD.

Yield improvement, yield ramp, and defect screening have been major areas of concern for the semiconductor industry as technology nodes have advanced. Much effort has been focused on capturing the defects missed by traditional stuck-at and transition delay fault model based testing. A majority of these un-modeled defects stems from features inside a standard cell or between two adjacent standard cells. Traditionally, critical area has been used as the manufacturability guideline to determine opens and shorts that should be targeted for test. This paper motivates a new paradigm — a design-for-manufacturability (DFM) hotspot-aware fault model to target intra-cell and inter-cell defects. The basic objective behind this approach is to bring in knowledge of manufacturing vulnerability in design layouts to weigh likelihood of occurrence of systematic defects. Recent technologies have standard cells much smaller than the lithography-driven optical diameter which means the cell's feature context is a key driver for DFM-driven fault sensitivity. This paper describes a novel automated flow for cell characterization that can be used to create patterns at the cell boundary for DFM-aware faults. The paper presents ATPG results for different DFM-aware faults, and analyzes the coverage gaps. Finally, the paper ends with a comparison with the cell-aware and dual-cell-aware fault models, and describes relative advantages and application scenarios.

In Ad-hoc networks, routing is complex as nodes are mobile due to which paths are destroyed and generated again and again. The difficulty in MANET is to find a feasible path between communicating nodes as routing is non-deterministic polynomial hard which is due to mobile nature of nodes. Many past works focused on finding a feasible path from a source to destination by considering only a single parameter i.e. the shortest path using hop counts only. This causes path failure due to high node mobility and computed path which may not be available for longer time. So, nodes start path discovery in order to establish connection between communicating nodes. To minimize this overhead, we propose a novel approach for routing in MANET by using Cuckoo Search algorithm (CS) on Ad-hoc Multipath Distance Vector (AOMDV), called CS-AOMDV. This computes the paths with high fitness values so that data packets can be transferred successfully from source to destination by considering routing load in terms of delay, energy efficiency metric and hop count. The cuckoo search algorithm is integrated with AOMDV protocol during the Route Reply (RRPLY) process, where multiple routes are available and maintained at source node having higher fitness values. Through simulation using ns-2, we have observed that our proposed routing algorithm outperforms over AOMDV in terms of delay, packet loss, packet delivery ratio and throughput.

With the exponential increase in the amount of digital information over the internet, online shops, online music, video and image libraries, search engines and recommendation system have become the most convenient ways to find relevant information within a short time. In the recent times, deep learning's advances have gained significant attention in the field of speech recognition, image processing and natural language processing. Meanwhile, several recent studies have shown the utility of deep learning in the area of recommendation systems and information retrieval as well. In this short review, we cover the recent advances made in the field of recommendation using various variants of deep learning technology. We organize the review in three parts: Collaborative system, Content based system and Hybrid system. The review also discusses the contribution of deep learning integrated recommendation systems into several application domains. The review concludes by discussion of the impact of deep learning in recommendation system in various domain and whether deep learning has shown any significant improvement over the conventional systems for recommendation. Finally, we also provide future directions of research which are possible based on the current state of use of deep learning in recommendation systems., Comment: 6 pages, 1 figure, 1 table, "Published with International Journal of Computer Applications (IJCA)"

Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system 'accuracy' remains a challenge and identify several additional common difficulties and potential research directions including (i) the 'scalability' issue due to the increasing need of mining information from millions of full-text articles, (ii) the 'interoperability' issue of integrating various text-mining systems into existing curation workflows and (iii) the 'reusability' issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.

The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text — found in biomedical publications and clinical notes — is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.

Objective Identifying disease-mutation relationships is a significant challenge in the advancement of precision medicine. The aim of this work is to design a tool that automates the extraction of disease-related mutations from biomedical text to advance database curation for the support of precision medicine. Materials and Methods We developed a machine-learning (ML) based method to automatically identify the mutations mentioned in the biomedical literature related to a particular disease. In order to predict a relationship between the mutation and the target disease, several features, such as statistical features, distance features, and sentiment features, were constructed. Our ML model was trained with a pre-labeled dataset consisting of manually curated information about mutation-disease associations. The model was subsequently used to extract disease-related mutations from larger biomedical literature corpora. Results The performance of the proposed approach was assessed using a benchmarking dataset. Results show that our proposed approach gains significant improvement over the previous state of the art and obtains F-measures of 0.880 and 0.845 for prostate and breast cancer mutations, respectively. Discussion To demonstrate its utility, we applied our approach to all abstracts in PubMed for 3 diseases (including a non-cancer disease). The mutations extracted were then manually validated against human-curated databases. The validation results show that the proposed approach is useful in a real-world setting to extract uncurated disease mutations from the biomedical literature. Conclusions The proposed approach improves the state of the art for mutation-disease extraction from text. It is scalable and generalizable to identify mutations for any disease at a PubMed scale.

Diabetes mellitus (DM) is reaching possibly epidemic proportions in India. The degree of disease and destruction due to diabetes and its potential complications are enormous, and originated a significant health care burden on both households and society. The concerning factor is that diabetes is now being proven to be linked with a number of complications and to be occurring at a comparatively younger age in the country. In India, the migration of people from rural to urban areas and corresponding modification in lifestyle are all moving the degree of diabetes. Deficiency of knowledge about diabetes causes untimely death among the population at large. Therefore, acquiring a proficiency that should spread awareness about diabetes may affect the people in India. In this work, a mobile/android application based solution to overcome the deficiency of awareness about diabetes has been shown. The application uses novel machine learning techniques to predict diabetes levels for the users. At the same time, the system also provides knowledge about diabetes and some suggestions on the disease. A comparative analysis of four machine learning (ML) algorithms were performed. The Decision Tree (DT) classifier outperforms amongst the 4 ML algorithms. Hence, DT classifier is used to design the machinery for the mobile application for diabetes prediction using real world dataset collected from a reputed hospital in the Chhattisgarh state of India.