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COVID-19 Pandemic Challenges and Impacts on the SDGs 2030: Indian Perspective
Published in Abbas Rajabifard, Greg Foliente, Daniel Paez, COVID-19 Pandemic, Geospatial Information, and Community Resilience, 2021
Saied Pirasteh, Hishmi Jamil Husain, Tammineni Rajitha
We suggest empowering the implementation of geospatial technologies and methods such as Web App and Geo App. However, perhaps India requires speeding up to adopt these approaches. The adoption of spatial approaches such as Web App, Geo App, and smart mapping to spatial epidemiology, disease surveillance, and implementation of health policies in India has great potential for both success and efficacy. It is because India has a large population, ongoing public health challenges, and a growing economy with an emphasis on innovative technologies.
Introduction and Datasets
Published in Andrew B. Lawson, Using R for Bayesian Spatial and Spatio-Temporal Health Modeling, 2021
Bayesian spatial health modeling, sometimes also known as Bayesian disease mapping, has matured to the extent that a range of computational tools exist to enhance end user's ability to analyze and interpret the variations in disease risk commonly found in human and animal populations. This has been enhanced by the easy availability of geographical information systems (GIS) such as ArcGIS or Quantum GIS (QGIS). While a variety of software platforms host specialist programs, there is now a large body of software available for the R programming environment, and given the general accessibility of this free software platform it is advantageous to consider the integration of analyses on this platform. A range of Bayesian modeling software is now available on R which can be used for spatial health modeling. Within R, it is possible to process geo-referenced data, including GIS-based information such as adjacency of regions and polygon neighborhoods. With the standard capabilities of R for descriptive statistics and R's extensive plotting facilities, it is easy to explore geo-referenced data.
Introduction to Genomics
Published in Altuna Akalin, Computational Genomics with R, 2020
Genome browsers contain lots of auxiliary high-throughput data. However, there are many more public high-throughput data sets available and they are certainly not available through genome browsers. Normally, every high-throughput dataset associated with a publication should be deposited in public archives. There are two major public archives we use to deposit data. One of them is Gene Expression Omnibus (GEO) hosted at http://www.ncbi.nlm.nih.gov/geo/, and the other one is European Nucleotide Archive (ENA) hosted at http://www.ebi.ac.uk/ena. These repositories accept high-throughput datasets and users can freely download and use these public data sets for their own research. Many data sets in these repositories are in their raw format, for example, the format the sequencer provides mostly. Some data sets will also have processed data but that is not a norm.
Evaluation of JUN, FN1 and LAMB1 polymorphisms in pterygium in a Chinese Han population
Published in Ophthalmic Genetics, 2022
Xiying Wu, Shiqi Dong, Yuting Xu, Ge Zhu, Ming Yan
The Gene Expression Omnibus (GEO) is an international, public functional genomics data repository for high-throughput microarray and next-generation sequences. The published pterygium gene expression profiles (GSE2513, GSE51995, and GSE83627) were downloaded. In brief, GSE2513and GSE83627 datasets were both based on GPL96 platforms (Affymetrix Human Genome U133A Array, Affymetrix). And GSE2513 was collected from four healthy conjunctiva and eight pterygium samples while GSE83627 was from four donor-matched pterygium and conjunctiva tissues. The mRNA expression data of GSE51995 was based on GPL14550 platforms (Agilent-028004 SurePrint G3 Human GE 8 × 60 K Microarray, Agilent Technologies, Inc.) and from 4 primary pterygium samples and 4 healthy conjunctiva tissues from the same eye. In order to find out the significant DEGs in progression of pterygium, we combined the three datasets and set a unified standard to extract appropriate genes with adjust P value <0.25 and | log 2Fold Change FC| ≥1. Finally, the pooled DEGs were presented with the volcano plot, which was set up using GraphPad Prism 8.0 software. The pooled results were used as candidate DEGs for the following analysis.
Fidelity and utility of GPS loggers as a tool for understanding community participation of older adults
Published in Scandinavian Journal of Occupational Therapy, 2022
After collection, all data were uploaded from portable devices to a password-protected computer and saved without any identifying information. Participants were reminded of the option to exclude specific locations or routes recorded by the GPS logs, which would require researchers to manually delete the trip record from the database. However none made this request, and all files were intact for analysis. Maps created via GPS were retained at a minimum resolution ensure that home addresses of participants were unidentifiable. Participants’ identities were further protected by assigning pseudonyms. Names, home addresses, and phone numbers were maintained in a separate master list and never in conjunction with data. Due to the highly identifiable nature of GPS coordinates, data are reported only in the aggregate and without geo-specific location images to protect confidentiality. Activity logs were labelled by pseudonym, and manually tabulated to assess number and type of destinations. These de-identified hand-written logs were then digitised to be stored with the corresponding data files.
Screening differentially expressed genes between endometriosis and ovarian cancer to find new biomarkers for endometriosis
Published in Annals of Medicine, 2021
The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is a freely available international public repository for next-generation sequencing-based functional genomic datasets and high-throughput microarrays. It also provides users with several web-based tools to query, analyse and visualize data [29], such as GEO2R. Four endometriosis datasets, GSE5108, GSE7305, GSE11691 and GSE25628, and one ovarian cancer dataset, GSE14407 were obtained from GEO. The GSE5108 dataset contained 11 ectopic endometrium samples and 11 eutopic endometrium samples. GSE7305 contained 10 ectopic endometrium samples and 10 normal endometrium samples. GSE11691 contained nine ectopic endometrium samples and nine normal endometrium samples. GSE25628 contained eight ectopic endometrium samples and eight normal endometrium samples. GSE14407 contained 12 normal samples and 12 tumour samples.