Scholarly Publications

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This community showcases the academic contributions of faculty and researchers at Banaras Hindu University (BHU) and provides a year-wise compilation of publications across disciplines. Institutional Repository BHU

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Now showing 1 - 9 of 9

Comprehensive illustration of transcriptomic and proteomic dataset for mitigation of arsenic toxicity in rice (Oryza sativa L.) by microbial consortium
(Elsevier Inc., 2022) Surabhi Awasthi; Reshu Chauhan; Yuvraj Indoliya; Abhishek Singh Chauhan; Shashank Kumar Mishra; Lalit Agrawal; Sanjay Dwivedi; Shiv Naresh Singh; Suchi Srivastava; Poonam C. Singh; Puneet Singh Chauhan; Debasis Chakrabarty; Sudhakar Srivastava; Rudra Deo Tripathi
The present article represents the data for analysis of microbial consortium (P.putida+C.vulgaris) mediated amelioration of arsenic toxicity in rice plant. In the current study the transcriptome profiling of treated rice root and shoot was performed by illumina sequencing (Platform 2000). To process the reads and to analyse differential gene expression, Fastxtoolkit, NGSQCtoolkit, Bowtie 2 (version 2.1.0), Tophat program (version 2.0.8), Cufflinks and Cuffdiff programs were used. For Proteome profiling, total soluble proteins in shoot of rice plant among different treatments were extracted and separated by 2D poly acrylamide gel electrophoresis (PAGE) and then proteins were identified with the help of MALDI-TOF/TOF. In gel based method of protein identification, the isoelectric focusing machine (IPGphor system,Bio-Rad USA), gel unit (SDS-PAGE) and MALDI-TOF/TOF (4800 proteomic analyzer Applied Biosystem, USA) were used for successful separation and positive identification of proteins. To check the differential abundance of proteins among different treatments, PDQuest software was used for data analysis. For protein identification, Mascot search engine (http://www.matrixscience.com) using NCBIprot/SwissProt databases of rice was used. The analyzed data inferred comprehensive picture of key genes and their respective proteins involved in microbial consortium mediated improved plant growth and amelioration of As induced phyto-toxicity in rice. For the more comprehensive information of data, the related full-length article entitled “Microbial consortium mediated growth promotion and Arsenic reduction in Rice: An integrated transcriptome and proteome profiling” may be accessed. © 2022
Data on optimization of microprojectile bombardment parameters in development of salinity tolerant transgenic lines
(Elsevier Inc., 2020) Susmita Sarangi; Chiranjib Mandal; Sourav Dutta; Pranit Mukherjee; Raju Mondal; S.P. Jeevan Kumar; P. Ray Choudhury; Vijay Pratap Singh; Durgesh Kumar Tripathi; Asit B. Mandal
This data deals with the optimization of microprojectile bombardment particles for efficient genetic transformation in an indica rice involving AmSOD gene for development of salinity tolerant transgenic lines [1]. In this study, various parameters such as effect of genotypes, helium pressure, osmoticum, explants, flight distance, particle size, particle volume, vacuum, carrier DNA and stopping screen properties have been evaluated to determine their role in transformation of indica rice involving AmSOD gene for development of salinity tolerant Pusa Basmati 1 rice variety. To perform the transformation process, plasmid vector pCAMBIA 1305.2 was used, which harbours GUS Plus™ gene, intron from the castor bean catalase gene, pBR322 ori, kanamycin resistant gene and Xho I site. The transformants have been confirmed using slot blot, polymerase chain reaction and Southern hybridization techniques. © 2020 The Authors
Deep phenotyping and genomic data from a nationally representative study on dementia in India
(Nature Research, 2023) Jinkook Lee; Sarah Petrosyan; Pranali Khobragade; Joyita Banerjee; Sandy Chien; Bas Weerman; Alden Gross; Peifeng Hu; Jennifer A. Smith; Wei Zhao; Leon Aksman; Urvashi Jain; G.S. Shanthi; Ravi Kurup; Aruna Raman; Sankha Shubhra Chakrabarti; Indrajeet Singh Gambhir; Mathew Varghese; John P. John; Himanshu Joshi; Parvaiz A. Koul; Debabrata Goswami; Arunansu Talukdar; Rashmi Ranjan Mohanty; Y. Sathyanarayana Raju Yadati; Mekala Padmaja; Lalit Sankhe; Chhaya Rajguru; Monica Gupta; Govind Kumar; Minakshi Dhar; Jorge Jovicich; Andrea Ganna; Mary Ganguli; Prasun Chatterjee; Sunny Singhal; Rishav Bansal; Swati Bajpai; Gaurav Desai; Swaroop Bhatankar; Abhijith R. Rao; Palanimuthu T. Sivakumar; Krishna Prasad Muliyala; Preeti Sinha; Santosh Loganathan; Erik Meijer; Marco Angrisani; Jung Ki Kim; Sharmistha Dey; Perianayagam Arokiasamy; David E. Bloom; Arthur W. Toga; Sharon L. R. Kardia; Kenneth Langa; Eileen M. Crimmins; Aparajit B. Dey
The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is a nationally representative in-depth study of cognitive aging and dementia. We present a publicly available dataset of harmonized cognitive measures of 4,096 adults 60 years of age and older in India, collected across 18 states and union territories. Blood samples were obtained to carry out whole blood and serum-based assays. Results are included in a venous blood specimen datafile that can be linked to the Harmonized LASI-DAD dataset. A global screening array of 960 LASI-DAD respondents is also publicly available for download, in addition to neuroimaging data on 137 LASI-DAD participants. Altogether, these datasets provide comprehensive information on older adults in India that allow researchers to further understand risk factors associated with cognitive impairment and dementia. © 2023, The Author(s).
Global Roadkill Data: a dataset on terrestrial vertebrate mortality caused by collision with vehicles
(Nature Research, 2025) Clara Grilo; Tomé Neves; Jennifer L. Bates; Aliza Le Roux; Pablo Medrano-Vizcaíno; Mattia Quaranta; Inês Silva; Kylie Soanes; Yun Wang; Éric Guinard; Savvas Zotos; Ludmila Zoel; Jairo José Zocche; Juli Luft Zenere; Glauco Zeferino; Antonio Carlos da Silva Zanzini; Caroline Zank; Sarah Shazwani Zakaria; Răzvan Zaharia; Débora Regina Yogui; Muhammad Syafiq Yahya; Pochamoni Bharath Simha Yadav; Marcelo Hideki Shigaki Yabu; Donggul Woo; Marina Winter; Samual T. Williams; Yuya Watari; Mengistu Wale; Ioannis N. Vogiatzakis; Ana Paula Vidotto-Magnoni; Vinícius Rozendo Vianna; Guillermo Velo-Antón; Nadines Velázquez; Aayush Vasava; Yntze van der Hoek; Edgar A. van der Grift; Francesco Valerio; Juan De Dios Valdez Leal; Viral Vadodariya; Pantiya Utsa; Ricardo Keichi Umetsu; Giedrius Trakimas; Paolo Tizzani; Bill Thompson; Weldemariam Tesfahunegny; Leonardo Teófilo Teles; Gustavo Tejera; Fernanda Zimmermann Teixeira; Mekonen Teferi; Jiraporn Teampanpong; Syoko Tamanaha; Hilary M. Swarts; Lourens Hendrik Swanepoel; Thilina D. Surasinghe; Warong Suksavate; Alexandru Strugariu; Grace Stonecipher; Mitch A. Sternberg; Hayley J. Stannard; Cǎtǎlin Răzvan Stanciu; Ariel G. Spanowicz; Wellington Fernandes de Souza; Marina de Souza; Ronald Sosa; Mahmood Soofi; Daniel V. Slomp; Eduardo Araujo da Silva; Daniel Silva; André Luiz Ferreira da Silva; Antonio Guilherme Cândido da Silva; Neftalí Sillero; Rodrigo Sidooski; Bibek Raj Shrestha; Bhuvan Keshar Sharma; Lorena Sforza; Margareth Lumy Sekiama; Andrés Eloy Seijas; Jiří Sedoník; David C. Seburn; Greta Schmidt; Christopher M. Schalk; Shauna Sayers; Tommaso Savini; Sema Sarıkurt; José Hernán Sarasola; Xavier Santos; Adryhano Dos Santos; Manuel Santiago-Plata; Palanisamy Santhoshkumar; Mariela Santacruz; Arya Sanjar; Arockianathan ANAth An Samson; Martin Šálek; Jaime A. Salas; Erica Naomi Saito; Tiberiu Constantin Sahlean; Joel C. Saenz; Enrique Sacramento; João Vinícius Sachet; Guilherme Augusto Munhoz de Sá
Roadkill is widely recognized as one of the primary negative effects of roads on many wildlife species and also has socioeconomic impacts when they result in accidents. A comprehensive dataset of roadkill locations is essential to evaluate the factors contributing to roadkill risk and to enhance our comprehension of its impact on wildlife populations and socioeconomic dimensions. We undertook a compilation of roadkill records, encompassing both published and unpublished data gathered from road surveys or opportunistic sources. GLOBAL ROADKILL DATA includes 208,570 roadkill records of terrestrial vertebrates from 54 countries across six continents, encompassing data collected between 1971 and 2024. This dataset serves to minimise the collection of redundant data and acts as a valuable resource for local and macro scale analysis regarding rates of roadkill, road- and landscape-related features associated with risk of roadkill, vulnerability of species to road traffic, and populations at risk of local extinction. The objective of this dataset is to promote scientific progress in infrastructure ecology and terrestrial vertebrate conservation while limiting the socio-economic costs. © The Author(s) 2025.
High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma
(Nature Research, 2024) Nisha Chaudhary; Arpita Rai; Aakash Madhav Rao; Md Imam Faizan; Jeyaseelan Augustine; Akhilanand Chaurasia; Deepika Mishra; Akhilesh Chandra; Varnit Chauhan; Tanveer Ahmad
Oral cancer is a global health challenge with a difficult histopathological diagnosis. The accurate histopathological interpretation of oral cancer tissue samples remains difficult. However, early diagnosis is very challenging due to a lack of experienced pathologists and inter- observer variability in diagnosis. The application of artificial intelligence (deep learning algorithms) for oral cancer histology images is very promising for rapid diagnosis. However, it requires a quality annotated dataset to build AI models. We present ORCHID (ORal Cancer Histology Image Database), a specialized database generated to advance research in AI-based histology image analytics of oral cancer and precancer. The ORCHID database is an extensive multicenter collection of high-resolution images captured at 1000X effective magnification (100X objective lens), encapsulating various oral cancer and precancer categories, such as oral submucous fibrosis (OSMF) and oral squamous cell carcinoma (OSCC). Additionally, it also contains grade-level sub-classifications for OSCC, such as well- differentiated (WD), moderately-differentiated (MD), and poorly-differentiated (PD). The database seeks to aid in developing innovative artificial intelligence-based rapid diagnostics for OSMF and OSCC, along with subtypes. © The Author(s) 2024.
Insight in the transcriptome data of hairy root disease-causing bacterium-Agrobacterium rhizogenes
(Elsevier Inc., 2020) Akhilesh Yadav; Hariom Verma; Waquar Akhter Ansari; Asha Lata Singh; Major Singh
Agrobacterium rhizogenes induce the production of the hairy root through the transformation of plant genomes. In this article, we executed the transcriptome of A. rhizogenes through RNA-sequencing. RNA-sequencing of A. rhizogenes generated a total of 2.6 Gb raw data with a 75 bp paired-end sequence. The raw data has been submitted to the SRA database of NCBI with accession number SRR5641651. Reads were generated 2946 unigenes and all unigenes were annotated in the database. The length of transcripts ranged from 90 to 6369 bp, with a median transcript length of 968. The transcripts were annotated through the number of databases to obtain information about SSRs, SNPs, Gene Ontology, Transcription factors, and pathways analysis. © 2020 The Author(s)
Large freshwater-influx-induced salinity gradient and diagenetic changes in the northern Indian Ocean dominate the stable oxygen isotopic variation in Globigerinoides ruber
(Copernicus Publications, 2023) Rajeev Saraswat; Thejasino Suokhrie; Dinesh K. Naik; Dharmendra P. Singh; Syed M. Saalim; Mohd Salman; Gavendra Kumar; Sudhira R. Bhadra; Mahyar Mohtadi; Sujata R. Kurtarkar; Abhayanand S. Maurya
The application of stable oxygen isotopic ratio of surface-dwelling planktic foraminifera Globigerinoides ruber (white variety; δ18Oruber) to reconstruct past hydrological changes requires a precise understanding of the effect of ambient parameters on δ18Oruber. The northern Indian Ocean, with its huge freshwater influx and being a part of the Indo-Pacific Warm Pool, provides a unique setting to understand the effect of both the freshwater-influx-induced salinity and temperature on δ18Oruber. Here, we use a total of 400 surface samples (252 from this work and 148 from previous studies), covering the entire salinity end-member region, to assess the effect of freshwater-influx-induced seawater salinity and temperature on δ18Oruber in the northern Indian Ocean. The analysed surface δ18Oruber mimics the expected δ18O calcite estimated from the modern seawater parameters (temperature, salinity, and seawater δ18O) very well. We report a large diagenetic overprinting of δ18Oruber in the surface sediments, with an increase of 0.18‰ per kilometre increase in water depth. The freshwater-influx-induced salinity exerts the major control on δ18Oruber (R2Combining double low line0.63) in the northern Indian Ocean, with an increase of 0.29‰ per unit increase in salinity. The relationship between temperature- and salinity-corrected δ18Oruber (δ18Oruber-δ18Osw) in the northern Indian Ocean [TCombining double low line-0.59 (δ18Oruber-δ18Osw)+26.40] is different than reported previously, based on the global compilation of plankton tow δ18Oruber data. The revised equations will help create a better palaeoclimatic reconstruction from the northern Indian Ocean by using the stable oxygen isotopic ratio. The entire data set (newly generated and previously published) used in this work is available both as a Supplement to this article and at PANGAEA (10.1594/PANGAEA.945401; Saraswat et al., 2022). © 2023 Rajeev Saraswat et al.
Third revision of the global surface seawater dimethyl sulfide climatology (DMS-Rev3)
(Copernicus Publications, 2022) Shrivardhan Hulswar; Rafel Simó; Martí Galí; Thomas G. Bell; Arancha Lana; Swaleha Inamdar; Paul R. Halloran; George Manville; Anoop Sharad Mahajan
This paper presents an updated estimation of the bottom-up global surface seawater dimethyl sulfide (DMS) climatology. This update, called DMS-Rev3, is the third of its kind and includes five significant changes from the last climatology, L11 (Lana et al., 2011), that was released about a decade ago. The first change is the inclusion of new observations that have become available over the last decade, creating a database of 873 539 observations leading to an ∼18-fold increase in raw data as compared to the last estimation. The second is significant improvements in data handling, processing, and filtering, to avoid biases due to different observation frequencies which result from different measurement techniques. Thirdly, we incorporate the dynamic seasonal changes observed in the geographic boundaries of the ocean biogeochemical provinces. The fourth change involves the refinement of the interpolation algorithm used to fill in the missing data. Lastly, an upgraded smoothing algorithm based on observed DMS variability length scales (VLS) helps to reproduce a more realistic distribution of the DMS concentration data. The results show that DMS-Rev3 estimates the global annual mean DMS concentration to be ∼2.26 nM (2.39 nM without a sea-ice mask), i.e., about 4 % lower than the previous bottom-up L11 climatology. However, significant regional differences of more than 100 % as compared to L11 are observed. The global sea-to-air flux of DMS is estimated at ∼27.1 TgS yr-1, which is about 4 % lower than L11, although, like the DMS distribution, large regional differences were observed. The largest changes are observed in high concentration regions such as the polar oceans, although oceanic regions that were under-sampled in the past also show large differences between revisions of the climatology. Finally, DMS-Rev3 reduces the previously observed patchiness in high productivity regions. Copyright: © 2022 Shrivardhan Hulswar et al.
Varietal dataset of nutritionally important Lablab purpureus (L.)Sweet from Eastern Uttar Pradesh, India
(Elsevier Inc., 2019) Ajeet Singh; P.C. Abhilash
Legumes are one of the important crops for food and nutritional security. According to the International Treaty on Plant Genetic Resources for Food and Agriculture, the collection and documentation of promising germplasms are essential for creating the global database and also for facilitating the global exchange for crop improvement and further exploitation. Presented here are varietal dataset of an agriculturally important legume, Lablab purpureus (L.)Sweet, collected from eastern Uttar Pradesh of North India. Extensive field surveys were conducted for studying the occurrence and distribution of L. purpureus in six districts of eastern Uttar Pradesh (Ballia, Ghazipur, Jaunpur, Mirzapur, Sonebhadra and Varanasi)and germplasms of promising varieties were collected, and cultivated for further characterization. Dataset provides the morphological traits such as variation in stem colour, leaf size, flower colour, pod colour, pod size, seed size, seed weight etc. of fourteen different varieties of L. purpureus grown in the field gene bank maintained by authors at Rajgarh block of Mirzapur district, eastern Uttar Pradesh, India. Additionally, national and global distribution maps of L. purpureus was prepared using ArcGIS platform. © 2019 The Author(s)