Genomics Portals: integrative web-platform for mining genomics data

<p>Abstract</p> <p>Background</p> <p>A large amount of experimental data generated by modern high-throughput technologies is available through various public repositories. Our knowledge about molecular interaction networks, functional biological pathways and transcriptional regulatory modules is rapidly expanding, and is being organized in lists of functionally related genes. Jointly, these two sources of information hold a tremendous potential for gaining new insights into functioning of living systems.</p> <p>Results</p> <p>Genomics Portals platform integrates access to an extensive knowledge base and a large database of human, mouse, and rat genomics data with basic analytical visualization tools. It provides the context for analyzing and interpreting new experimental data and the tool for effective mining of a large number of publicly available genomics datasets stored in the back-end databases. The uniqueness of this platform lies in the volume and the diversity of genomics data that can be accessed and analyzed (gene expression, ChIP-chip, ChIP-seq, epigenomics, computationally predicted binding sites, etc), and the integration with an extensive knowledge base that can be used in such analysis.</p> <p>Conclusion</p> <p>The integrated access to primary genomics data, functional knowledge and analytical tools makes Genomics Portals platform a unique tool for interpreting results of new genomics experiments and for mining the vast amount of data stored in the Genomics Portals backend databases. Genomics Portals can be accessed and used freely at <url>http://GenomicsPortals.org</url>.</p

Image plagiarism detection using GAN - (Generative Adversarial Network)

In Today’s date plagiarism is a very important aspect because content originality is the client's prior requirement. Many people on the internet use others' images and get publicity while the owner of the image or data won′t get anything out of it. Many users copy the data or image features from the other users and modify it a little bit or create an artificial replica of it. With sufficient computational power and volume of data, the GAN models are capable enough to produce fake images that look very much similar to the real images. These kinds of images are generally not detected by modern plagiarism systems. GAN stands for generative adversarial network. It has two neural networks working inside. The first one is the generator which generates a random image and the second one is the discriminator which identifies whether the image being generated is a real or a fake image. In this paper, we have proposed a system that has been trained on both fake images (GAN Generated images) and real images and will help us in flagging whether the image is plagiarised or a real image

Assessment of Anti-Influenza activity and hemagglutination inhibition of Plumbago indica and Allium sativum extracts

[Background] Human influenza is a seasonal disease associated with significant morbidity and mortality. Anti-flu ayurvedic/herbal medicines have played a significant role in fighting the virus pandemic. Plumbagin and allicin are commonly used ingredients in many therapeutic remedies, either alone or in conjunction with other natural substances. Evidence suggests that these extracts are associated with a variety of pharmacological activities.[Objective] To evaluate anti-influenza activity from Plumbago indica and Allium sativum extract against Influenza A (H1N1)pdm09.[Materials and Methods] Different extraction procedures were used to isolate the active ingredient in the solvent system, and quantitative HPLTC confirms the presence of plumbagin and allicin. The cytotoxicity was carried out on Madin-Darby Canine kidney cells, and the 50% cytotoxic concentration (CC50) values were below 20 mg/mL for both plant extracts. To assess the anti-influenza activity, two assays were employed, simultaneous and posttreatment assay.[Results] A. sativum methanolic and ethanolic extracts showed only 14% reduction in hemagglutination in contrast to P. indica which exhibited 100% reduction in both simultaneous and posttreatment assay at concentrations of 10 mg/mL, 5 mg/mL, and 1 mg/mL.[Conclusions] Our results suggest that P. indica extracts are good candidates for anti-influenza therapy and should be used in medical treatment after further research.The study was funded by Intra-mural fund of Haffkine Institute for Training, Research and Testing, Mumbai, India.Peer reviewe

Sino-Nasal Outcome Test-22: Translation, Cross-cultural Adaptation, and Validation in Local Language

Introduction Quality of life questionnaires have been increasingly used in clinical studies to help estimate the magnitude of problem. Sino-Nasal Outcome Test -22 (SNOT-22) is considered to be a good tool to measure the severity of Sino-Nasal Diseases. As this test is in English, it may be difficult for the local population to express their symptoms correctly. Therefore we have translated and validated the SNOT- 22 test in local Indian language, Marathi. Materials and Methods An early Indian ( Marathi ) version of the SNOT 22 questionnaire was prepared. This was a prospective study,where forty patients with Sino-nasal Diseases confirmed on DNE & CT(PNS) filled the questionnaire. This was repeated after a period of 14 days to retest. For validation the questionnaire was also filled by healthy individuals. Results The mean SNOT-22 score ± SD was 50.17 ± 18.65 (range 10–93) in the initial test, and 49.61 ± 18.40 (range 21–91) in retest in the study group. Cronbach’s alpha was 0.835 and 0.837 at the initial and retest examination respectively, both values were suggesting a good internal consistency. The mean SNOT-22 score ± SD was 13 ± 11.68 in the control group and 49.61 ± 18.40 (range 21–91) in the sino-nasal disease group and proved by Mann- Whitney U test. Conclusion The Marathi SNOT-22 is a valid instrument to assess the symptomatology of patients of Sino-nasal Diseases in Maharashtra

Sino-Nasal Outcome Test-22: Translation, Cross-cultural Adaptation, and Validation in Local Language

Introduction Quality of life questionnaires have been increasingly used in clinical studies to help estimate the magnitude of problem. Sino-Nasal Outcome Test -22 (SNOT-22) is considered to be a good tool to measure the severity of Sino-Nasal Diseases. As this test is in English, it may be difficult for the local population to express their symptoms correctly. Therefore we have translated and validated the SNOT- 22 test in local Indian language, Marathi. Materials and Methods An early Indian ( Marathi ) version of the SNOT 22 questionnaire was prepared. This was a prospective study,where forty patients with Sino-nasal Diseases confirmed on DNE & CT(PNS) filled the questionnaire. This was repeated after a period of 14 days to retest. For validation the questionnaire was also filled by healthy individuals. Results The mean SNOT-22 score ± SD was 50.17 ± 18.65 (range 10–93) in the initial test, and 49.61 ± 18.40 (range 21–91) in retest in the study group. Cronbach’s alpha was 0.835 and 0.837 at the initial and retest examination respectively, both values were suggesting a good internal consistency. The mean SNOT-22 score ± SD was 13 ± 11.68 in the control group and 49.61 ± 18.40 (range 21–91) in the sino-nasal disease group and proved by Mann- Whitney U test. Conclusion The Marathi SNOT-22 is a valid instrument to assess the symptomatology of patients of Sino-nasal Diseases in Maharashtra

Generalized random set framework for functional enrichment analysis using primary genomics datasets

Motivation: Functional enrichment analysis using primary genomics datasets is an emerging approach to complement established methods for functional enrichment based on predefined lists of functionally related genes. Currently used methods depend on creating lists of ‘significant’ and ‘non-significant’ genes based on ad hoc significance cutoffs. This can lead to loss of statistical power and can introduce biases affecting the interpretation of experimental results

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-