Ground effect models for rotorcraft/ship dynamic interface study

Issued as Progress letter [nos. 1-28], and Final report, Project no. E-16-M07Final report has author: J.V.R. PrasadFinal report has title: Ground effect models for rotorcraft/ship dynamic interface stud

Testing embedded system through optimal mining technique (OMT) based on multi-input domain

Testing embedded systems must be done carefully particularly in the significant regions of the embedded systems. Inputs from an embedded system can happen in multiple order and many relationships can exist among the input sequences. Consideration of the sequences and the relationships among the sequences is one of the most important considerations that must be tested to find the expected behavior of the embedded systems. On the other hand combinatorial approaches help determining fewer test cases that are quite enough to test the embedded systems exhaustively. In this paper, an Optimal Mining Technique that considers multi-input domain which is based on built-in combinatorial approaches has been presented. The method exploits multi-input sequences and the relationships that exist among multi-input vectors. The technique has been used for testing an embedded system that monitors and controls the temperature within the Nuclear reactors

Multiple Feature Fuzzy c-means Clustering Algorithm for Segmentation of Microarray Images

Microarray technology allows the simultaneous monitoring of thousands of genes. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, segmentation and intensity extraction are the three important steps in microarray image analysis. Clustering algorithms have been used for microarray image segmentation with an advantage that they are not restricted to a particular shape and size for the spots. Instead of using single feature clustering algorithm, this paper presents multiple feature clustering algorithm with three features for each pixel such as pixel intensity, distance from the center of the spot and median of surrounding pixels. In all the traditional clustering algorithms, number of clusters and initial centroids are randomly selected and often specified by the user.  In this paper, a new algorithm based on empirical mode decomposition algorithm for the histogram of the input image will generate the number of clusters and initial centroids required for clustering.   It overcomes the shortage of random initialization in traditional clustering and achieves high computational speed by reducing the number of iterations. The experimental results show that multiple feature Fuzzy C-means has segmented the microarray image more accurately than other algorithms

A New Method of Gridding for Spot Detection in Microarray Images

A Deoxyribonucleic Acid (DNA) microarray is a collection of microscopic DNA spots attached to a solid surface, such as glass, plastic or silicon chip forming an array. The analysis of DNA microarray images allows the identification of gene expressions to draw biological conclusions for applications ranging from genetic profiling to diagnosis of cancer. The DNA microarray image analysis includes three tasks: gridding, segmentation and intensity extraction. The gridding process is usually divided into two main steps: sub-gridding and spot detection. In this paper, a fully automatic approach to detect the location of spots is proposed. Each spot is associated with a gene and contains the pixels that indicate the level of expression of that particular gene. After gridding, the image is segmented using fuzzy c-means clustering algorithm for separation of spots from the background pixels.  The result of the experiment shows that the method presented in this paper is accurate and automatic without human intervention and parameter presetting. Keywords: Microarray Image, Mathematical Morphology, Image Processin

POLYPHARMACY INDUCED DRUG INTERACTIONS, ADVERSE DRUG REACTIONS (ADR) AND MEDICATION ERRORS IN TERTIARY CARE SOUTH INDIAN HOSPITAL

Objective: To study the pattern of drug interactions (DI) in our hospital and to identify whether it is associated with polypharmacy. To determine the level of severity of potential drug-drug interactions (PDDI), to detect, monitor and prevention of ADRs in the hospitalized patients and to identify the medication errors (ME). Methods: A prospective interventional study was conducted in a 300 bedded tertiary care South Indian hospital for a period of 6 mo. Prescriptions were analysed for PDDI using Micromedex software 2.2. The causality and severity of ADRs were assessed by using Naranjo’s, WHO UMC Scales and Hart wigs severity scales. ME was identified by review of patient drug charts. Results: Total 190 prescriptions were analyzed, in which 1028 drug interactions were seen. Out of which 718 were DDI, 198 DFI, 100 DEI, and 12 DTI were observed. More number of DI was seen in cardiovascular drugs, antibiotics followed by antacids and antiulcer agents. A total of 52 ADRs were identified in 43 patients. Diuretics, cardiovascular drugs were associated with a higher incidence of ADRs followed by Anti-Diabetic agents. 58 ME was seen in 190 prescriptions, among them omission error, prescribing errors and Wrong dose error was seen. Conclusion: Clinical pharmacist plays a potential role in the health care system in assisting the physician i.e. modifying the number of drugs taken, number of doses taken, medication adherence, identification of drug interactions, preventing, monitoring and detection of ADRs and identifying the medication errors

Influence of seed treatment and packaging materials on seed longevity of cluster bean [Cyamopsistetra gonoloba (L.) Taub.]

An experiment was conducted to investigate the influence of packaging materials and seed treatments on storability of cluster bean under ambient conditions of Bengaluru. The experiment consisted of six treatments viz. control (T1), bavistin @ 2 g kg-1 (T2), spinosad @ 0.04 ml/kg (T3), neem leaf powder @ 1:20 ratio (T4), Acorus calamus@ 10 g kg-1 (T5) and cow dung powder @ 10 g kg-1 (T6) and three packaging materials viz., cloth bag (C1) super grain bag (C2) and poly lined cloth bag (C3). Treated seed samples were stored in three containers under ambient storage conditions up to the duration of which seeds maintain minimum seed certification standards and samples were drawn at bimonthly intervals for ascertaining the seed quality parameters. The study suggested that seed treat-ment could be useful to prolong the storage life of cluster bean seeds. The seeds treated with spinosad (0.04 ml/kg) and stored in super-grain bag were better for maintenance of higher seed quality parameters [germination (80.00%), root length (11.70 cm), shoot length (13.60 cm), mean seedling dry weight (152 mg), seedling vigour index I and II (2024&12140) and TDH activity (1.224) with low electrical conductivity (0.368 dSm-1)] up to 18 months under ambient conditions of Bengaluru (room temperature). Super-grain bag proved to be better storage container with higher seed quality attributes viz., germination (72.38 %), seedling vigour index-I (1726), total dehydrogenase activity (1.201) and other seed quality parameters compared to cloth bag. The study suggested that use of appropriate packaging material and seed treatment could be useful to prolong the storage life of cluster bean seeds

Privacy Preserving and Time Series Analysis of Medical Dataset using Deep Feature Selection

A significant category of medical data that includes rich temporal and spatial information is time-series medical imaging. Since then, experts in a variety of domains, including clinical picture analysis, have been actively participating in the rapidly emerging subject of profound learning. This paper discusses profound learning processes and their applicability to clinical picture examination and mainly focused common machine learning techniques in the field of computer vision and how deep learning has transformed ML, ML models for deep learning and applications of deep learning to clinical image analysis. In fact, even before the term "deep learning" was coined, a variety of clinical picture investigation concerns, including harm and non-harm grouping, harm type characterisation, harm or organ division, and injury recognition, were addressed using picture input machine learning (PIML). Deep learning is predicted to be the key innovation for clinical picture examination in the upcoming few years. Picture input ML, including profound learning, is an exceptionally powerful, flexible, higher-throughput innovation that can raise the current level of execution in clinical picture examination. "Profound learning" or picture input ML, in clinical picture examination is a quickly developing, promising field. Picture input ML is supposed to turn into a significant field in clinical picture examination in the following couple of many years

Challenges for Opioid Receptor Nomenclature

Recent developments in the study of the structure and function of opioid receptors raise significant challenges for the definition of individual receptor types and the development of a nomenclature that precisely describes isoforms that may subserve different functions in vivo. Presentations at the 2013 meeting of the International Narcotics Research Conference in Cairns, Australia, considered some of the new discoveries that are now unravelling the complexities of opioid receptor signalling. Variable processing of opioid receptor messenger RNAs may lead to the presence of several isoforms of the μ receptor. Each opioid receptor type can function either as a monomer or as part of a homo- or heterodimer or higher multimer. Additionally, recent evidence points to the existence of agonist bias in the signal transduction pathways activated through μ receptors, and to the presence of regulatory allosteric sites on the receptors. This brief review summarizes the recent discoveries that raise challenges for receptor definition and the characterization of signal transduction pathways activated by specific receptor forms. LINKED ARTICLES: This article is part of a themed section on Opioids: New Pathways to Functional Selectivity. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2015.172.issue-2.NHMRC 104596

Nutritional status and socioeconomic empowerment of fisherwomen in the coastal ecosystem of Andhra Pradesh, Karnataka, Kerala and Tamil Nadu, India

This study was carried out in the coastal areas of Andhra Pradesh, Karnataka, Kerala and Tamil Nadu. From these states, 13 districts and 28 villages were selected. A total of 5,744 households were covered. Cereal consumption was highest in Andhra Pradesh followed by Kerala, Karnataka and Tamil Nadu. Pulse consumption was high in Kerala when compared to other states. The overall energy intake of the fisherwomen was 1,827 kcal/day; protein intake was 50.6 gm/day; carbohydrate intake was 343.5 gm/day; and fat intake was 27 gm/day. The mean intake of micronutrients was less than the recommended dietary allowance. The mean body mass index was 21.3. The nutritional status of the women was: 49% normal; 17% low normal; 10.5% mildly malnourished; 4% moderately malnourished; and 2.9% severely malnourished. About 11.5% of the fisherwomen were overweight and 4.6% were obese. A subsample of 915 women was clinically observed: 34.8% were diagnosed with angular stomatitis; 31 % with cheelosis; 42.8% with bleeding gums; and 44.2% with dry skin. Some 72% of the women were anemic. An assessment ofthe socioeconomic status indicated that very few households (15.4%) maintained livestock for income generation. About 60% of the fisherwomen carried out post-harvest activities to earn income. Food expenditure comprised 60.7% of the earned income contribvting to the major share of the spending. Debt servicing was a serious problem faced by 44.9% of the respondents who had procured loans mostly from non-institutional sources