Birla Institute of Technology and Science

About: Birla Institute of Technology and Science is a education organization based out in Pilāni, Rajasthan, India. It is known for research contribution in the topics: Computer science & Population. The organization has 8897 authors who have published 13947 publications receiving 170008 citations.

Flavonoids: an overview

Abstract: Flavonoids, a group of natural substances with variable phenolic structures, are found in fruits, vegetables, grains, bark, roots, stems, flowers, tea and wine. These natural products are well known for their beneficial effects on health and efforts are being made to isolate the ingredients so called flavonoids. Flavonoids are now considered as an indispensable component in a variety of nutraceutical, pharmaceutical, medicinal and cosmetic applications. This is attributed to their anti-oxidative, anti-inflammatory, anti-mutagenic and anti-carcinogenic properties coupled with their capacity to modulate key cellular enzyme function. Research on flavonoids received an added impulse with the discovery of the low cardiovascular mortality rate and also prevention of CHD. Information on the working mechanisms of flavonoids is still not understood properly. However, it has widely been known for centuries that derivatives of plant origin possess a broad spectrum of biological activity. Current trends of research and development activities on flavonoids relate to isolation, identification, characterisation and functions of flavonoids and finally their applications on health benefits. Molecular docking and knowledge of bioinformatics are also being used to predict potential applications and manufacturing by industry. In the present review, attempts have been made to discuss the current trends of research and development on flavonoids, working mechanisms of flavonoids, flavonoid functions and applications, prediction of flavonoids as potential drugs in preventing chronic diseases and future research directions.

Application of multi-criteria decision making to sustainable energy planning—A review

Abstract: Multi-Criteria Decision Making (MCDM) techniques are gaining popularity in sustainable energy management. The techniques provide solutions to the problems involving conflicting and multiple objectives. Several methods based on weighted averages, priority setting, outranking, fuzzy principles and their combinations are employed for energy planning decisions. A review of more than 90 published papers is presented here to analyze the applicability of various methods discussed. A classification on application areas and the year of application is presented to highlight the trends. It is observed that Analytical Hierarchy Process is the most popular technique followed by outranking techniques PROMETHEE and ELECTRE. Validation of results with multiple methods, development of interactive decision support systems and application of fuzzy methods to tackle uncertainties in the data is observed in the published literature.

Visualizing and interpreting cancer genomics data via the Xena platform.

Abstract: To the Editor — There is a great need for easy-to-use cancer genomics visualization tools for both large public data resources such as TCGA (The Cancer Genome Atlas)1 and the GDC (Genomic Data Commons)2, as well as smaller-scale datasets generated by individual labs. Commonly used interactive visualization tools are either web-based portals or desktop applications. Data portals have a dedicated back end and are a powerful means of viewing centrally hosted resource datasets (for example, Xena’s predecessor, the University of California, Santa Cruz (UCSC) Cancer Browser (currently retired3), cBioPortal4, ICGC (International Cancer Genomics Consortium) Data Portal5, GDC Data Portal2). However, researchers wishing to use a data portal to explore their own data have to either redeploy the entire platform, a difficult task even for bioinformaticians, or upload private data to a server outside the user’s control, a non-starter for protected patient data, such as germline variants (for example, MAGI (Mutation Annotation and Genome Interpretation6), WebMeV7 or Ordino8). Desktop tools can view a user’s own data securely (for example, Integrated Genomics Viewer (IGV)9, Gitools10), but lack well-maintained, prebuilt files for the ever-evolving and expanding public data resources. This dichotomy between data portals and desktop tools highlights the challenge of using a single platform for both large public data and smaller-scale datasets generated by individual labs. Complicating this dichotomy is the expanding amount, and complexity, of cancer genomics data resulting from numerous technological advances, including lower-cost high-throughput sequencing and single-cell-based technologies. Cancer genomics datasets are now being generated using new assays, such as whole-genome sequencing11, DNA methylation whole-genome bisulfite sequencing12 and ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing13). Visualizing and exploring these diverse data modalities is important but challenging, especially as many tools have traditionally specialized in only one or perhaps a few data types. And although these complex datasets generate insights individually, integration with other omics datasets is crucial to help researchers discover and validate findings. UCSC Xena was developed as a high-performance visualization and analysis tool for both large public repositories and private datasets. It was built to scale with the current and future data growth and complexity. Xena’s privacy-aware architecture enables cancer researchers of all computational backgrounds to explore large, diverse datasets. Researchers use the same system to securely explore their own data, together or separately from the public data, all the while keeping private data secure. The system easily supports many tens of thousands of samples and has been tested with up to a million cells. The simple and flexible architecture supports a variety of common and uncommon data types. Xena’s Visual Spreadsheet visualization integrates gene-centric and genomic-coordinate-centric views across multiple data modalities, providing a deep, comprehensive view of genomic events within a cohort of tumors. UCSC Xena (http://xena.ucsc.edu) has two components: the front end Xena Browser and the back end Xena Hubs (Fig. 1). The web-based Xena Browser empowers biologists to explore data across multiple Xena Hubs with a variety of visualizations and analyses. The back end Xena Hubs host genomics data from laptops, public servers, behind a firewall, or in the cloud, and can be public or private (Supplementary Fig. 1). The Xena Browser receives data simultaneously from multiple Xena Hubs and integrates them into a single coherent visualization within the browser. A private Xena Hub is a hub installed on a user’s own computer (Supplementary Fig. 2). It is configured to only respond to requests from the computer’s localhost network interface (that is, http://127.0.0.1). This ensures that the hub only communicates with the computer on which the hub is installed. A public hub is configured to respond to requests from external computers. There are two types of public Xena Hubs (Supplementary Fig. 2). The first type is an open-public hub, which is a public hub accessible by everyone. While we host several open-public hubs (Supplementary Table 1), users can also set up their own as a way to share data. An example of one is the Treehouse Hub set up by the Childhood Cancer Initiative to share pediatric cancer RNA-seq gene expression data (Supplementary Note). The second type W eb s er ve r

China and India lead in greening of the world through land-use management

Abstract: Satellite data show increasing leaf area of vegetation due to direct (human land-use management) and indirect factors (climate change, CO2 fertilization, nitrogen deposition, recovery from natural disturbances, etc.). Among these, climate change and CO2 fertilization effect seem to be the dominant drivers. However, recent satellite data (2000-2017) reveal a greening pattern that is strikingly prominent in China and India, and overlapping with croplands world-wide. China alone accounts for 25% of the global net increase in leaf area with only 6.6% of global vegetated area. The greening in China is from forests (42%) and croplands (32%), but in India is mostly from croplands (82%) with minor contribution from forests (4.4%). China is engineering ambitious programs to conserve and expand forests with the goal of mitigating land degradation, air pollution and climate change. Food production in China and India has increased by over 35% since 2000 mostly due to increasing harvested area through multiple cropping facilitated by fertilizer use and surface/ground-water irrigation. Our results indicate that the direct factor is a key driver of the "Greening Earth", accounting for over a third, and likely more, of the observed net increase in green leaf area. They highlight the need for realistic representation of human land-use practices in Earth system models.

SymPy: Symbolic Computing in Python

Abstract: SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.