Search bioRxiv⌕ Search

Biology subjects

Rathee, S.

Publications and source records attributed to Rathee, S..

3 recordsLinked to original sources

The role of floral traits in community assembly process at high elevations in Lesser Himalaya

O_LIEcological theory postulates that plant trait research should consider multiple traits related to different organs and/or ontogenetic stages as such traits represent different ecological niche axes. Particularly, floral traits have been suggested to play an important role in assembling plant communities along environmental gradients as they determine the reproductive success, one of the key functions in plants. Yet, the predictive power of floral traits in community assembly research remains largely unverified empirically. C_LIO_LIWe analyzed the predictive power of six floral traits of 139 herbaceous species for inferring community assembly process in twenty-one sites located along an elevation gradient in Lesser Himalaya ranging from 2,000 to 4,000 meters above sea level. The floral trait variability along the gradient was analyzed using community-weighted trait mean (CWM) values and functional diversities (FD) calculated for each of the study communities. C_LIO_LIThe CWM values for onset of flowering and flower display area increased significantly with increasing elevation, whereas specific flower area showed an opposite pattern. In combination with convergence in onset of flowering and specific area (i.e., lower FD values in high elevation sites), these patterns suggest that abiotic filtering and plant-pollinator interactions affected the floral trait composition of the communities studied. Increasing low-temperature stress towards high-elevation sites selected for late-flowering species that produce resource-intensive flowers with larger display areas. C_LIO_LILow pollinator abundancy and activity in high elevation, could also explain why these traits were selected in the study communities. Delayed flowering with increasing elevations might facilitate the phenological overlap of plants and their pollinators, as pollinator activity at higher elevation peaks in the second half of the vegetation period. The dominance of species with low specific flower area and larger display area in high elevation communities were attributed to the increased flower longevity and attraction of pollinators, respectively, to maximize pollination success under pollinator scarcity. C_LIO_LISynthesis. Our study provides empirical support of the recent argument that floral traits contribute considerably to the assembly of plant communities along environmental gradients. Thus, such traits should be included into community assembly research agenda as they represent key growth and survival ecological functions. C_LI

ecology↗

DILIc: An AI-based classifier to search for Drug-Induced Liver Injury literature

Drug-Induced Liver Injury (DILI) is a class of Adverse Drug Reactions (ADR) which causes problems in both clinical and research settings. It is the most frequent cause of acute liver failure in the majority of western countries and is a major cause of attrition of novel drug candidates. Manual trawling of literature for is the main route of deriving information on DILI from research studies. This makes it an inefficient process prone to human error. Therefore, an automatized AI model capable of retrieving DILI-related papers from the huge ocean of literature could be invaluable for the drug discovery community. In this project, we built an artificial intelligence (AI) model combining the power of Natural Language Processing (NLP) and Machine Learning (ML) to address this problem. This model uses NLP to filter out meaningless text (e.g. stopwords) and uses customized functions to extract relevant keywords as singleton, pair, triplet and so on. These keywords are processed by apriori pattern mining algorithm to extract relevant patterns which are used to estimate initial weightings for a ML classifier. Along with pattern importance and frequency, an FDA-approved drug list mentioning DILI adds extra confidence in classification. The combined power of these methods build a DILI classifier (DILIC) with 94.91% cross-validation and 94.14% external validation accuracy. To make DILIC as accessible as possible, including to researchers without coding experience, an R Shiny App capable of classifing single or multiple entries for DILI is developed to enhance ease of user experience and made available at https://researchmind.co.uk/diliclassifier/).

bioinformatics↗

So you want to be a Super Researcher?

Publishing original scientific research is inherent to the work of a researcher. However, the pressure to maintain productivity and scientific impact can lead to research group publishing excessively, negatively affecting the mental health of a researcher. Ph.D. students and early career researchers are particularly susceptible to this pressure due to the inherent vulnerability of their positions. At present, there are no resources that concisely summarise the publication culture of a research group to help the researcher make an informed decision before joining. In this article, we present the Super Researcher app, an R Shiny application(app) with a user-friendly interface. Using text-mining methodology to extract publicly available author data from Scopus, this pilot app has four fundamental functions to provide snapshot information that will help researchers grasp the publication culture of a research group within minutes. The Super Researcher app provides information on: 1) institution data, 2) authors publication, 3) co-author network plots and 4) publication journals. The Super Researcher app is built on R shiny which provides an interactive interface to users. This app utilizes the Big Data framework Apache Spark to mine relevant information from a huge author information database. The authors information is stored and manipulated using both SQL(SQLite) and NoSQL(HBase) databases. Hbase is used for local data storage and manipulation while SQLite feeds data to the R Shiny interface. In this paper, we introduce these functionalities and illustrate how this information can help guide a researcher to select a new Principle Investigator (PI) with better compatibility in terms of publication attitude using a case study. Available: https://researchmind.co.uk/super-researcher/

scientific communication and education↗