FlavorDB: a database of flavor molecules.

Flavor is an expression of olfactory and gustatory sensations experienced through a multitude of chemical processes triggered by molecules. Beyond their key role in defining taste and smell, flavor molecules also regulate metabolic processes with consequences to health. Such molecules present in natural sources have been an integral part of human history with limited success in attempts to create synthetic alternatives. Given their utility in various spheres of life such as food and fragrances, it is valuable to have a repository of flavor molecules, their natural sources, physicochemical properties, and sensory responses. FlavorDB (http://cosylab.iiitd.edu.in/flavordb) comprises of 25,595 flavor molecules representing an array of tastes and odors. Among these 2254 molecules are associated with 936 natural ingredients belonging to 34 categories. The dynamic, user-friendly interface of the resource facilitates exploration of flavor molecules for divergent applications: finding molecules matching a desired flavor or structure; exploring molecules of an ingredient; discovering novel food pairings; finding the molecular essence of food ingredients; associating chemical features with a flavor and more. Data-driven studies based on FlavorDB can pave the way for an improved understanding of flavor mechanisms.

El-MAVEN: A Fast, Robust, and User-Friendly Mass Spectrometry Data Processing Engine for Metabolomics.

Analysis of large metabolomic datasets is becoming commonplace with the increased realization of the role that metabolites play in biology and pathophysiology. While there are many open-source analysis tools to extract peaks from liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and tandem mass spectrometry (LC-MS/MS) data, these tools are not very interactive and are suboptimal when a large number of samples are to be analyzed. El-MAVEN is an open-source analysis platform that extends MAVEN and provides fast, powerful, and interactive analysis capabilities especially for datasets containing over 100 samples. The El-MAVEN workflow is easy to use with just four steps from loading data to exporting of the results. Advanced analysis and software techniques such as multiprocessing, machine learning, and reduction of memory leaks are implemented so as to provide a seamless and interactive user experience. Results from El-MAVEN can be exported in a range of formats allowing continued analysis on other platforms. Additionally, El-MAVEN is also fully integrated with Polly™, a cloud-based analysis platform that provides a range of tools for flux analysis and integrative-omics analysis. El-MAVEN is a powerful tool that enables fast and efficient analysis of large metabolomic datasets to accelerate the process of gaining insight from raw data.

Assessment of Under Nutrition Using Composite Index of Anthropometric Failure (CIAF) amongst Toddlers Residing in Urban Slums of Raipur City, Chhattisgarh, India.

INTRODUCTION: Several indicators have been used for measurement of under nutrition in the past. They are overlapping and none individually provide a comprehensive number of under nourished in the community. The effort has been to discuss the use of an alternative indicator of malnutrition - the composite index of anthropometric failure (CIAF). AIM: To study the prevalence of under nutrition of Toddlers using CIAF and compare the prevalence of under nutrition obtained by primitive indicators and CIAF. MATERIALS AND METHODS: Cross-sectional community based study was carried out in urban slums of Raipur (C.G) during Jan 01,2014 to Sept 30, 2014 using sample size of 602. Slums were selected by multistage random sampling and the subjects were selected by convenient sampling, i.e. starting from a random point house to house survey was carried out until desired number of subjects (According to PPS) were covered assuming that slum population is evenly distributed. Attendant of Toddlers were interviewed with semi structured proforma and Height and Weight were measured by measuring tape and Salter's weighing machine respectively. Informed consent was obtained. MS excel was used for data analysis after compilation. RESULTS: Girls and boys were 50% each. By CIAF the prevalence of under nutrition was found to be 62.1% while, Underweight, Stunting and Wasting showed it to be 45.2%, 46.6% and 17.8% respectively. CONCLUSION: Primitive indices under estimate the burden of under nutrition and CIAF should be used a screening tool for assessing under nutrition.