msc thesis krishikosh

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Description

KrishiKosh is an institutional repository maintained by the Indian Council of Agricultural Research, New Delhi in which all Agricultural Universities and ICAR Research Institutes upload their publications including research papers, theses & dissertations and institutional publications.

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INFLIBNET's Institutional Repository

Welcome to institutional repository of inflibnet centre, here you can find articles published in all conventional proceedings of inflibnet centre, more over you can also look for various training material,press clippings,news letters etc.

• IR @ INFLIBNET
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Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Krishikosh: A digital repository to disseminate agricultural knowledge

Krishikosh is a versatile open access digital repository catering to the needs of Indian National Agricultural Research and Education System (NARES) and has architecture of centralized hosting of content but decentralized management. The Krishikosh has been designed using open source software Dspace and an efficient Integrated Content Management System (ICMS), which has suitability and configuration to meet the requirements of NARES and created dependable digital repository. Each institution in NARES has been configured as community in Dspace having it’s own collections and logo. Each community and collection can be given independent rights to registered users for uploading and managing the contents. Effectively Krishikosh is a collectively managed, centrally aggregated repository with integrated search facility. Krishikosh is a huge repository of theses, reports, articles etc. Keeping track of all the information is hectic and difficult for the user, which results in low usage of the Krishikosh site. Thus, an application with push notification was developed in order to involve the user with the Krishikosh, by sending messages regarding the new upload. Google Cloud Messaging (GCM) server were utilized for implementation of push notification which take the data from the postgreSQL database, which is most advanced open source database. PHP script is used to connect the java programming in android studio, the database and the GCM server. In this study, usage statistics of Krishikosh were also analyzed.

Chandrasekharan, H, Patle, Sarita, Pandey P S, Mishra A K, Jain A K, Goyal, Shikha, Pandey, Amit, Khemchandani, Usha and Kasrija, Rajkumari. 2012. CeRA - the e- journal Consortium for National Agricultural Research System. Current Science 102 (6): 847–51.

Dspace reference manual (2002), The Dspace Foundation. [ http://www.Dspace.org/ ]

Dspace –Internal Reference Specification (2002), Technology and Architecture.

Michael J. Bass, David Stuve, Robert Tansley, Margret Branschofsky, Peter Breton, Peter Carmichael, Bill Cattey, Dan Chudnov, and Joyce Ng. 2002. Dspace –Internal Reference Specification, Technology and Architecture. Masssachusetts Institute of Technology.

http://wiki.duraspace.org .

http://www.rkmp.co.in/

https://ndl.iitkgp.ac.in/

http://mhrd.gov.in/digital-library

http://onlinelibrary.wiley.com/

http://onlinelibrary.london.ac.uk/

http://shodhganga.inflibnet.ac.in/

Jain A K, Chandrasekharan H and Kumar Rajesh. 2014. Final Report of NAIP sub-project ‘Digital Library and Information Management under NARS (e-GRANTH)’. Indian Agricultural Research Institute, New Delhi, India

Jain A K, Kumar, Amrender, Batra, Kamal, Kapur, Sanjiv. 2016. Reference Manual on Krishikosh - A Repository for NARES. ICAR– Indian Agriculture Research Institute, New Delhi, p 50.

NAIP (National Agricultural Innovation Project). 2014. An Initiative Towards Innovative Agriculture. Final Report, National Agricultural Innovation Project, ICAR, New Delhi

Tyagi R K, Agrawal A, Varshney H, Ravisankar T, Rao K V, Chandrasekharan H, Chandra Prakash M K, Murthy G R K, Jain D K, Singh N, Dhandapani A and Agrawal R C. 2014. Final Report of NAIP sub-project ‘AGROWEB-Digital Dissemination System for Indian Agricultural Research (ADDSIAR)’. National Bureau of Plant Genetic Resources, New Delhi, India.

Copyright (c) 2018 The Indian Journal of Agricultural Sciences

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License .

The copyright of the articles published in The Indian Journal of Agricultural Sciences is vested with the Indian Council of Agricultural Research, which reserves the right to enter into any agreement with any organization in India or abroad, for reprography, photocopying, storage and dissemination of information. The Council has no objection to using the material, provided the information is not being utilized for commercial purposes and wherever the information is being used, proper credit is given to ICAR.

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Director: Dr. Rajarshi Roy Burman Editor: Mr. Ravinder Verma Print ISSN: 0019-5022 Online ISSN: 2394-3319 NAAS Rating (2024): 6.4

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Library Management

ISSN : 0143-5124

Article publication date: 3 June 2014

The purpose of this paper is to provide online access to valuable resource, i.e., rare books, institutional publications, historical reports, theses, success stories, etc. to the researchers, teachers, extension specialists, farmers and scientists working in the ICAR institutes and state agricultural universities.

Design/methodology/approach

Using open source software, the material in the repository was organized into institution-wise collection of documents such as books, journals, theses, reports, articles, etc. The process of digitization including scanning, cleaning, cropping, conversion to text (OCR), conversion to PDF/A, is currently being done at four different centres. So far, more than ten million pages from 19,495 documents have been digitized along with the necessary metadata.

The “KrishiKosh” is a collaborative project which envisages launching an Indian agricultural knowledge portal over the internet and sharing its research information including Electronic Theses and Dissertations (ETD) with the rest of the world using latest information and communication technologies.

Research limitations/implications

Users are able to access any information they want, from anywhere in the world, and at any time over the internet. It also culminated in launching of an Indian agricultural knowledge portal over the internet for sharing its research information including ETD with the rest of the world using the latest information and communication technologies.

Practical implications

The agricultural research community will have access to the vast amount of rare and precious literature to further research and developments in agriculture and allied sectors to achieve self-sufficiency in food production.

Social implications

While the population of the country is heading towards 1.23 billion, Indian agricultural scientists are engaged in increasing the food grain production to address the food security challenge. The importance of agricultural production in the context of global competition has occupied a significant place in the national development plan. It helps to provide right information, in right time at right place to increase the agriculture production.

Originality/value

The Indian agricultural knowledge portal over the internet for sharing its research information including ETD among agriculture policy makers, scientists, etc. using latest ICT. The portal helps to avoid duplication and to acquire more resources to the agriculture knowledge centres.

• Digitization
• Open access
• Institutional repository

Acknowledgements

An earlier version of this paper was presented at the 16th International Symposium on Electronic Theses and Dissertations (ETD), in Hong Kong, on 23-26 September 2013, http://lib.hku.hk/etd2013 /

Veeranjaneyulu, K. (2014), "KrishiKosh: an institutional repository of National Agricultural Research System in India", Library Management , Vol. 35 No. 4/5, pp. 345-354. https://doi.org/10.1108/LM-08-2013-0083

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Code for M.Sc. thesis

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M.sc. thesis: host and graft glial composition in a stem cell transplantation model of parkinson's disease..

This repository consists of a collection of R scripts that were used for my M.Sc. thesis with title "Host and graft composition in a stem cell transplantation model of Parkinson's disease".

As some of the scripts are re-used with different data they need user input (e.g. resolution for UMAP), so running them from the command line is not recommended.

Research Background

Thesis abstract

Parkinson’s disease is characterized by the loss of dopaminergic (DA) neurons in the substantia nigra. This leads to the depletion of dopamine in the striatum and the rise of motor symptoms. Transplantation of stem cell-derived midbrain progenitors has the potential to replace lost DA neurons and achieve functional repair. However, it remains generally unknown how the host environment, and in particular the glial cells, impact the functionality of the transplanted neurons. Glial cells have important roles in providing support and protection for neurons and maintaining brain homeostasis. Thus, further knowledge of the specification of these cell types could provide insight into the impact they have on the transplanted neurons. To investigate this, we performed a meta-analysis combining several single nucleus RNA-sequencing (snRNA-seq) datasets of human neuronal grafts in the striatum and substantia nigra of 6OHDA-lesioned nude rats at four different time points after transplantation. Data analysis was performed mostly in R, following the Seurat workflow with Harmony integration. Our focus was the identification of distinct glial cells present in both host tissue and graft.

In total, we identified 67,787 host cells from 34 samples including a collection of glial cells such as astrocytes, oligodendrocytes, oligodendrocyte progenitor cells (OPCs) and microglia, other immune cells as well as GABAergic neurons such as medium spiny neurons and striatal interneurons. Additional analysis of the glial cells indicates functional homogeneity among astrocytes and oligodendrocytes, but clear regionalisation of the astrocyte population. In addition, distinct subtypes of microglia and OPCs were identified, based on functionality and differentiation status rather than regional specificity. Analysis of graft-derived cells revealed five populations of 87,759 cells from 45 samples consisting of astrocytes, glial progenitor cells (GPCs), vascular leptomeningeal cells (VLMCs), immature and mature neurons, the majority of which expressed DA specific markers. In contrast to the astrocytes of the host environment, no regionalisation was observed alongside functional homogeneity. We believe that knowledge of the specification of these cells could improve the protocols for the generation of a stem cell product for clinical applications.

Tools/Software

All the analysis was performed using R(v4.2.2) and the IDE of choice was Rstudio(v2022.07.02+576). The R packages used were:

• devtools(v2.4.5)
• remotes(v2.4.2)
• BiocManager(v1.30.20)
• Seurat(v4.3.0)
• dplyr(v1.1.0)
• patchwork(v1.1.2)
• harmony(v0.0.1)
• ggplot2(v3.4.2)
• stringr(v1.5.0)
• openxlsx(v4.2.5.2)
• tidyverse(v2.0.0)
• presto(v1.0.0)
• msigdbr(v7.5.1)
• fgsea(v1.24.0)
• enrichplot(v1.18.4)
• RColorBrewer(v.1.1.3)

The file paths used are specific to my machine so they need to be edited for the code to run properly.

There is a number of R scripts that are not run as part of the workflow but are sourced in the R scripts:

• Directories_Packages.R - The main paths and variables are defined in the script and should be modified there.
• snRNA_Functions.R - Contains functions used in the workflow.
• Silhouette scores.R - Calculates the mathematically optimal value for resolution for seurat clustering.

Pre-process of data

As this is a meta-analysis of snRNA-seq data the datasets were provided in different formats, either as a seurat object or a gene matrix so pre-processing was necessary in order to merge the datasets correctly.

The scripts should be run in the following order:

• Setup_SeuratObj.R
• Merge_SeuratObj.R

Analysis of host and graft environment

After merging the data, two new datasets were created; one for the host environment and one for the graft environment.

Initially the data were filtered for low quality cells and then normalised and integrated with HARMONY. Next, the distinct cell types were identified by comparing the highly expressed genes per cluster to well-known canonical markers for cell types in substantia nigra and striatum. After that, the cell types were assigned and some plots and information were extracted. Finally, the glial cell populations of each environment were subset to idnividual seurat object for downstream analysis.

The analysis followed the same steps for the host and graft environment, which are:

• QC_Filtering.R
• NormaliseIntegrate.R
• IdentifyCellTypes.R
• AssignCellTypes.R
• Plots_Stats.R
• SplitObjPerCluster.R

High resolution analysis for glial cell populations

Each glial population identified in the host and graft environments is further analysed with the following sub-workflow. This analysis re-uses some of the previous scripts.

First, the data needed to be normalised and integrated with HARMONY again. Similarly to the host and graft environment analysis, the identification and assignment of cell types withing the glial populations followed after normalisation. Next, the differentially expressed (DE) genes were extracted and gene set enrichment analysis (GSEA) was performed to investigate the functionality of the glial sub-populations.

The scripts used for this should be run as:

• DEanalysis.R
• GO_enrichment_analysis.R