@article{zheng_optimized_2023,

title = {An optimized tri-store system for multi-model data analytics},

author = {Xiuwen Zheng and Subhasis Dasgupta and Arun Kumar and Amarnath Gupta},

url = {https://doi.org/10.48550/arXiv.2305.14391},

doi = {10.48550/ARXIV.2305.14391},

year  = {2023},

date = {2023-01-01},

journal = {CoRR},

volume = {abs/2305.14391},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sahu_agreement_2022,

title = {The agreement between reverse transcriptase-polymerase chain reaction (RT-PCR) and rapid antigen test (RAT) in diagnosing COVID-19},

author = {Rupesh Sahu and Amarnath Gupta and Sumit Rawat and Abhijit Das},

doi = {10.7759/cureus.29266},

issn = {2168-8184},

year  = {2022},

date = {2022-09-01},

journal = {Cureus},

volume = {14},

number = {9},

pages = {e29266},

abstract = {Background False-negative results derived from RT-PCR tests for diagnosing coronavirus disease (COVID-19) have raised questions about whether to consider them the gold standard for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using an imperfect gold standard to assess other diagnostic tests would never let the other tests show better diagnostic performance. The best strategy in such cases is to do an agreement analysis, and this study aims to estimate the agreement between real-time reverse transcriptase-polymerase chain reaction (RT-PCR) and rapid antigen test (RAT) for COVID-19 detection. Methods A retrospective study was done using paired data of individuals tested for COVID-19, both by RT-PCR and RAT, obtained from the virology laboratory of Government Bundelkhand Medical College, Sagar, Madhya Pradesh, India. A sample size of 93 was calculated, and the data were abstracted in a data abstraction sheet. Variables included were results of RT-PCR and RAT, age, gender, presence of symptoms, test kit used, and the time duration between sampling for RT-PCR and RAT. Apart from descriptive statistics, keeping in mind the binary outcome of RT-PCR and RAT, Cohen's kappa was calculated for agreement analysis. A p-value of ¡0.05 was considered significant. Results The data on 100 participants suspected to be infected with COVID-19 (58 male and 42 female) with a mean age of 39.8 (±19.0) years were analysed. The number of discordant pairs was eight. Cohen's kappa showed substantial agreement between RT-PCR and RAT, κ=0.646, (95},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kumar_database_2022,

title = {Database education at UC san diego},

author = {Arun Kumar and Alin Deutsch and Amarnath Gupta and Yannis Papakonstantinou and Babak Salimi and Victor Vianu},

url = {https://doi.org/10.1145/3572751.3572763},

doi = {10.1145/3572751.3572763},

year  = {2022},

date = {2022-01-01},

journal = {SIGMOD Rec.},

volume = {51},

number = {3},

pages = {43–46},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dasgupta_query-driven_2021,

title = {A query-driven system for discovering interesting subgraphs in social media},

author = {Subhasis Dasgupta and Amarnath Gupta},

url = {https://arxiv.org/abs/2102.09120},

year  = {2021},

date = {2021-01-01},

journal = {CoRR},

volume = {abs/2102.09120},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gupta_discovering_2021,

title = {Discovering technology gaps using the IntSight knowledge navigator},

author = {Aurpon Gupta and Subhasis Dasgupta and Snehasis Sinha and Amarnath Gupta},

url = {https://arxiv.org/abs/2109.05142},

year  = {2021},

date = {2021-01-01},

journal = {CoRR},

volume = {abs/2109.05142},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zheng_processing_2021,

title = {Processing analytical queries in the AWESOME polystore [information systems architectures]},

author = {Xiuwen Zheng and Subhasis Dasgupta and Arun Kumar and Amarnath Gupta},

url = {https://arxiv.org/abs/2112.00833},

year  = {2021},

date = {2021-01-01},

journal = {CoRR},

volume = {abs/2112.00833},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dasgupta_ingesting_2019,

title = {Ingesting high-velocity streaming graphs from social media sources},

author = {Subhasis Dasgupta and Aditya Bagchi and Amarnath Gupta},

url = {http://arxiv.org/abs/1905.08337},

year  = {2019},

date = {2019-01-01},

journal = {CoRR},

volume = {abs/1905.08337},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lin_answering_2018,

title = {Answering analytical queries on text data with temporal term histograms},

author = {Kai Lin and Subhasis Dasgupta and Amarnath Gupta},

url = {http://arxiv.org/abs/1809.10856},

year  = {2018},

date = {2018-01-01},

journal = {CoRR},

volume = {abs/1809.10856},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{imam_development_2012,

title = {Development and use of ontologies inside the neuroscience information framework: a practical approach},

author = {Fahim T Imam and Stephen D Larson and Anita Bandrowski and Jeffery S Grethe and Amarnath Gupta and Maryann E Martone},

doi = {10.3389/fgene.2012.00111},

issn = {1664-8021},

year  = {2012},

date = {2012-01-01},

journal = {Front Genet},

volume = {3},

pages = {111},

abstract = {An initiative of the NIH Blueprint for neuroscience research, the Neuroscience Information Framework (NIF) project advances neuroscience by enabling discovery and access to public research data and tools worldwide through an open source, semantically enhanced search portal. One of the critical components for the overall NIF system, the NIF Standardized Ontologies (NIFSTD), provides an extensive collection of standard neuroscience concepts along with their synonyms and relationships. The knowledge models defined in the NIFSTD ontologies enable an effective concept-based search over heterogeneous types of web-accessible information entities in NIF's production system. NIFSTD covers major domains in neuroscience, including diseases, brain anatomy, cell types, sub-cellular anatomy, small molecules, techniques, and resource descriptors. Since the first production release in 2008, NIF has grown significantly in content and functionality, particularly with respect to the ontologies and ontology-based services that drive the NIF system. We present here on the structure, design principles, community engagement, and the current state of NIFSTD ontologies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{imam_development_2012-1,

title = {Development and use of ontologies inside the neuroscience information framework: a practical approach.},

author = {Fahim T. Imam and Stephen D. Larson and Anita Bandrowski and Jeffery S. Grethe and Amarnath Gupta and Maryann E. Martone},

doi = {10.3389/fgene.2012.00111},

issn = {1664-8021},

year  = {2012},

date = {2012-01-01},

journal = {Frontiers in genetics},

volume = {3},

pages = {111},

abstract = {An initiative of the NIH Blueprint for neuroscience research, the Neuroscience Information Framework (NIF) project advances neuroscience by enabling discovery and access to public research data and tools worldwide through an open source, semantically enhanced search portal. One of the critical components for the overall NIF system, the NIF Standardized Ontologies (NIFSTD), provides an extensive collection of standard neuroscience concepts along with their synonyms and relationships. The knowledge models defined in the NIFSTD ontologies enable an effective concept-based search over heterogeneous types of web-accessible information entities in NIF's production system. NIFSTD covers major domains in neuroscience, including diseases, brain anatomy, cell types, sub-cellular anatomy, small molecules, techniques, and resource descriptors. Since the first production release in 2008, NIF has grown significantly in content and functionality, particularly with respect to the ontologies and ontology-based services that drive the NIF system. We present here on the structure, design principles, community engagement, and the current state of NIFSTD ontologies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{su_temporal_2022,

title = {Temporal relation extraction with a graph-based deep biaffine attention model},

author = {Bo-Ying Su and Shang-Ling Hsu and Kuan-Yin Lai and Amarnath Gupta},

url = {https://arxiv.org/abs/2201.06125},

year  = {2022},

date = {2022-01-01},

journal = {CoRR},

volume = {abs/2201.06125},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{maji_pvt_2014,

title = {PVT: an efficient computational procedure to speed up next-generation sequence analysis.},

author = {Ranjan Kumar Maji and Arijita Sarkar and Sunirmal Khatua and Subhasis Dasgupta and Zhumur Ghosh},

doi = {10.1186/1471-2105-15-167},

issn = {1471-2105},

year  = {2014},

date = {2014-06-01},

journal = {BMC bioinformatics},

volume = {15},

pages = {167},

abstract = {BACKGROUND: High-throughput Next-Generation Sequencing (NGS) techniques are advancing genomics and molecular biology research. This technology generates substantially large data which puts up a major challenge to the scientists for an efficient, cost and time effective solution to analyse such data. Further, for the different types of NGS data, there are certain common challenging steps involved in analysing those data. Spliced alignment is one such fundamental step in NGS data analysis which is extremely computational intensive as well as time consuming. There exists serious problem even with the most widely used spliced alignment tools. TopHat is one such widely used spliced alignment tools which although supports multithreading, does not efficiently utilize computational resources in terms of CPU utilization and memory. Here we have introduced PVT (Pipelined Version of TopHat) where we take up a modular approach by breaking TopHat's serial execution into a pipeline of multiple stages, thereby increasing the degree of parallelization and computational resource utilization. Thus we address the discrepancies in TopHat so as to analyze large NGS data efficiently. RESULTS: We analysed the SRA dataset (SRX026839 and SRX026838) consisting of single end reads and SRA data SRR1027730 consisting of paired-end reads. We used TopHat v2.0.8 to analyse these datasets and noted the CPU usage, memory footprint and execution time during spliced alignment. With this basic information, we designed PVT, a pipelined version of TopHat that removes the redundant computational steps during 'spliced alignment' and breaks the job into a pipeline of multiple stages (each comprising of different step(s)) to improve its resource utilization, thus reducing the execution time. CONCLUSIONS: PVT provides an improvement over TopHat for spliced alignment of NGS data analysis. PVT thus resulted in the reduction of the execution time to textasciitilde23},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kozhenkov_biologicalnetworksenabling_2011,

title = {BiologicalNetworksenabling the integration of multi-scale data for the host-pathogen studies.},

author = {Sergey Kozhenkov and Mayya Sedova and Yulia Dubinina and Amarnath Gupta and Animesh Ray and Julia Ponomarenko and Michael Baitaluk},

doi = {10.1186/1752-0509-5-7},

issn = {1752-0509},

year  = {2011},

date = {2011-01-01},

journal = {BMC systems biology},

volume = {5},

pages = {7},

abstract = {BACKGROUND: Understanding of immune response mechanisms of pathogen-infected host requires multi-scale analysis of genome-wide data. Data integration methods have proved useful to the study of biological processes in model organisms, but their systematic application to the study of host immune system response to a pathogen and human disease is still in the initial stage. RESULTS: To study host-pathogen interaction on the systems biology level, an extension to the previously described BiologicalNetworks system is proposed. The developed methods and data integration and querying tools allow simplifying and streamlining the process of integration of diverse experimental data types, including molecular interactions and phylogenetic classifications, genomic sequences and protein structure information, gene expression and virulence data for pathogen-related studies. The data can be integrated from the databases and user's files for both public and private use. CONCLUSIONS: The developed system can be used for the systems-level analysis of host-pathogen interactions, including host molecular pathways that are induced/repressed during the infections, co-expressed genes, and conserved transcription factor binding sites. Previously unknown to be associated with the influenza infection genes were identified and suggested for further investigation as potential drug targets. Developed methods and data are available through the Java application (from BiologicalNetworks program at http://www.biologicalnetworks.org) and web interface (at http://flu.sdsc.edu).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{satish_tolkien_2009,

title = {Tolkien: An event based storytelling system},

author = {Arjun Satish and Ramesh C. Jain and Amarnath Gupta},

url = {http://www.vldb.org/pvldb/vol2/vldb09-947.pdf},

doi = {10.14778/1687553.1687610},

year  = {2009},

date = {2009-01-01},

journal = {Proc. VLDB Endow.},

volume = {2},

number = {2},

pages = {1630–1633},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gupta_proforma_2017,

title = {PROFORMA: Proactive forensics with message analytics},

author = {Amarnath Gupta and Subhasis Dasgupta and Aditya Bagchi},

url = {https://doi.org/10.1109/MSP.2017.4251112},

doi = {10.1109/MSP.2017.4251112},

year  = {2017},

date = {2017-01-01},

journal = {IEEE Secur. Priv.},

volume = {15},

number = {6},

pages = {33–41},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dasgupta_resolving_2015,

title = {Resolving authorization conflicts by ontology views for controlled access to a digital library},

author = {Subhasis Dasgupta and Pinakpani Pal and Chandan Mazumdar and Aditya Bagchi},

url = {https://doi.org/10.1108/JKM-10-2014-0435},

doi = {10.1108/JKM-10-2014-0435},

year  = {2015},

date = {2015-01-01},

journal = {J. Knowl. Manag.},

volume = {19},

number = {1},

pages = {45–59},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dasgupta_controlling_2014,

title = {Controlling access to a digital library ontology - a graph transformation approach},

author = {Subhasis Dasgupta and Aditya Bagchi},

url = {http://perpetualinnovation.net/ojs/index.php/ijngc/article/view/224},

year  = {2014},

date = {2014-01-01},

journal = {Int. J. Next Gener. Comput.},

volume = {5},

number = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{almeida_medication_2025,

title = {Medication experiences in the treatment of opioid use disorders: Insights from Reddit.},

author = {Alexandra Almeida and Mike Conway and David J. Grelotti and Amarnath Gupta and David Frank and Annick Bórquez},

doi = {10.1111/add.70022},

issn = {1360-0443 0965-2140},

year  = {2025},

date = {2025-03-01},

journal = {Addiction (Abingdon, England)},

abstract = {BACKGROUND AND AIMS: Better understanding the challenges faced by patients on medications for opioid use disorder (MOUD), including methadone and buprenorphine, is critical to increasing their use/retention. Social media platforms such as Reddit offer a space for patients to share their experiences with medications. We aimed to identify and characterize challenges faced by patients taking MOUD through analysis of discussions from the r/Methadone and r/suboxone subreddits. DESIGN, SETTING AND PARTICIPANTS: Mixed methods study applying natural language processing methods to 37 278 posts from both subreddits from their origin in 2011 until 31 December 2022. Independent topic analyses used Correlated Topic Models to extract the main themes discussed. MEASUREMENTS: We labeled, validated and grouped the topics into macro classes and computed topic shares. We interpreted and compared topics across subreddits informed by the patient-centered medication experience framework. FINDINGS: We found 27 and 34 challenge-related topics for the r/Methadone and r/suboxone subreddits, respectively. Topics were grouped into three macro-topics: (i) healthcare-related issues, including misunderstandings/confusion around appointments, prescriptions, bottle checks, telehealth technology and health insurance coverage; (ii) medication-related issues, including withdrawal, cravings, dosage, side effects, mixing with other medications/drugs; and (iii) treatment discontinuation, including tapering protocols. Patients conveyed highly specialized knowledge about dosage and tapering strategies and spoke from experience. Key differences between r/Methadone and r/suboxone were driven by their dispensing requirements (clinic-based vs. take-home), with 20.05},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dasgupta_miscon_2025,

title = {MISCON: a mission-driven conversational consultant for pre-venture entrepreneurs in food deserts},

author = {Subhasis Dasgupta and Hans Taparia and Laura Schmidt and Amarnath Gupta},

url = {https://doi.org/10.48550/arXiv.2501.14954},

doi = {10.48550/ARXIV.2501.14954},

year  = {2025},

date = {2025-01-01},

journal = {CoRR},

volume = {abs/2501.14954},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{almeida_use_2024,

title = {The use of natural language processing methods in reddit to investigate opioid use: Scoping review},

author = {Alexandra Almeida and Thomas Patton and Mike Conway and Amarnath Gupta and Steffanie A Strathdee and Annick Bórquez},

doi = {10.2196/51156},

issn = {2564-1891},

year  = {2024},

date = {2024-09-01},

journal = {JMIR Infodemiology},

volume = {4},

pages = {e51156},

abstract = {BACKGROUND: The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis.nnOBJECTIVE: We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use.nnMETHODS: We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations.nnRESULTS: In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zheng_news_2021,

title = {News meets microblog: Hashtag annotation via retriever-generator},

author = {Xiuwen Zheng and Dheeraj Mekala and Amarnath Gupta and Jingbo Shang},

url = {https://arxiv.org/abs/2104.08723},

year  = {2021},

date = {2021-01-01},

journal = {CoRR},

volume = {abs/2104.08723},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bhattacharya_towards_2020,

title = {Towards finding the best-fit distribution for OSN data},

author = {Subhayan Bhattacharya and Sankhamita Sinha and Sarbani Roy and Amarnath Gupta},

url = {https://doi.org/10.1007/s11227-020-03232-y},

doi = {10.1007/S11227-020-03232-Y},

year  = {2020},

date = {2020-01-01},

journal = {J. Supercomput.},

volume = {76},

number = {12},

pages = {9882–9900},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jain_building_2013,

title = {Building social life networks},

author = {Ramesh C. Jain and Laleh Jalali and Siripen Pongpaichet and Amarnath Gupta},

url = {http://sites.computer.org/debull/A13sept/p91.pdf},

year  = {2013},

date = {2013-01-01},

journal = {IEEE Data Eng. Bull.},

volume = {36},

number = {3},

pages = {91–98},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{grethe_mediator_2009,

title = {Mediator infrastructure for information integration and semantic data integration environment for biomedical research.},

author = {Jeffrey S. Grethe and Edward Ross and David Little and Brian Sanders and Amarnath Gupta and Vadim Astakhov},

doi = {10.1007/978-1-59745-524-4_3},

issn = {1064-3745},

year  = {2009},

date = {2009-01-01},

journal = {Methods in molecular biology (Clifton, N.J.)},

volume = {569},

pages = {33–53},

abstract = {This paper presents current progress in the development of semantic data integration environment which is a part of the Biomedical Informatics Research Network (BIRN; http://www.nbirn.net) project. BIRN is sponsored by the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). A goal is the development of a cyberinfrastructure for biomedical research that supports advance data acquisition, data storage, data management, data integration, data mining, data visualization, and other computing and information processing services over the Internet. Each participating institution maintains storage of their experimental or computationally derived data. Mediator-based data integration system performs semantic integration over the databases to enable researchers to perform analyses based on larger and broader datasets than would be available from any single institution's data. This paper describes recent revision of the system architecture, implementation, and capabilities of the semantically based data integration environment for BIRN.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bug_data_2008,

title = {Data federation in the Biomedical Informatics Research Network: tools for semantic annotation and query of distributed multiscale brain data.},

author = {William Bug and Vadim Astahkov and Jyl Boline and Christine Fennema-Notestine and Jeffrey S. Grethe and Amarnath Gupta and David N. Kennedy and Daniel L. Rubin and Brian Sanders and Jessica A. Turner and Maryann E. Martone},

issn = {1942-597X 1559-4076},

year  = {2008},

date = {2008-11-01},

journal = {AMIA ... Annual Symposium proceedings. AMIA Symposium},

pages = {1220},

abstract = {The broadly defined mission of the Biomedical Informatics Research Network (BIRN, www.nbirn.net) is to better understand the causes human disease and the specific ways in which animal models inform that understanding. To construct the community-wide infrastructure for gathering, organizing and managing this knowledge, BIRN is developing a federated architecture for linking multiple databases across sites contributing data and knowledge. Navigating across these distributed data sources requires a shared semantic scheme and supporting software framework to actively link the disparate repositories. At the core of this knowledge organization is BIRNLex, a formally-represented ontology facilitating data exchange. Source curators enable database interoperability by mapping their schema and data to BIRNLex semantic classes thereby providing a means to cast BIRNLex-based queries against specific data sources in the federation. We will illustrate use of the source registration, term mapping, and query tools.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bug_nifstd_2008,

title = {The NIFSTD and BIRNLex vocabularies: building comprehensive ontologies for neuroscience.},

author = {William J. Bug and Giorgio A. Ascoli and Jeffrey S. Grethe and Amarnath Gupta and Christine Fennema-Notestine and Angela R. Laird and Stephen D. Larson and Daniel Rubin and Gordon M. Shepherd and Jessica A. Turner and Maryann E. Martone},

doi = {10.1007/s12021-008-9032-z},

issn = {1559-0089 1539-2791},

year  = {2008},

date = {2008-09-01},

journal = {Neuroinformatics},

volume = {6},

number = {3},

pages = {175–194},

abstract = {A critical component of the Neuroscience Information Framework (NIF) project is a consistent, flexible terminology for describing and retrieving neuroscience-relevant resources. Although the original NIF specification called for a loosely structured controlled vocabulary for describing neuroscience resources, as the NIF system evolved, the requirement for a formally structured ontology for neuroscience with sufficient granularity to describe and access a diverse collection of information became obvious. This requirement led to the NIF standardized (NIFSTD) ontology, a comprehensive collection of common neuroscience domain terminologies woven into an ontologically consistent, unified representation of the biomedical domains typically used to describe neuroscience data (e.g., anatomy, cell types, techniques), as well as digital resources (tools, databases) being created throughout the neuroscience community. NIFSTD builds upon a structure established by the BIRNLex, a lexicon of concepts covering clinical neuroimaging research developed by the Biomedical Informatics Research Network (BIRN) project. Each distinct domain module is represented using the Web Ontology Language (OWL). As much as has been practical, NIFSTD reuses existing community ontologies that cover the required biomedical domains, building the more specific concepts required to annotate NIF resources. By following this principle, an extensive vocabulary was assembled in a relatively short period of time for NIF information annotation, organization, and retrieval, in a form that promotes easy extension and modification. We report here on the structure of the NIFSTD, and its predecessor BIRNLex, the principles followed in its construction and provide examples of its use within NIF.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gupta_federated_2008,

title = {Federated access to heterogeneous information resources in the Neuroscience Information Framework (NIF).},

author = {Amarnath Gupta and William Bug and Luis Marenco and Xufei Qian and Christopher Condit and Arun Rangarajan and Hans Michael Müller and Perry L. Miller and Brian Sanders and Jeffrey S. Grethe and Vadim Astakhov and Gordon Shepherd and Paul W. Sternberg and Maryann E. Martone},

doi = {10.1007/s12021-008-9033-y},

issn = {1559-0089 1539-2791},

year  = {2008},

date = {2008-09-01},

journal = {Neuroinformatics},

volume = {6},

number = {3},

pages = {205–217},

abstract = {The overarching goal of the NIF (Neuroscience Information Framework) project is to be a one-stop-shop for Neuroscience. This paper provides a technical overview of how the system is designed. The technical goal of the first version of the NIF system was to develop an information system that a neuroscientist can use to locate relevant information from a wide variety of information sources by simple keyword queries. Although the user would provide only keywords to retrieve information, the NIF system is designed to treat them as concepts whose meanings are interpreted by the system. Thus, a search for term should find a record containing synonyms of the term. The system is targeted to find information from web pages, publications, databases, web sites built upon databases, XML documents and any other modality in which such information may be published. We have designed a system to achieve this functionality. A central element in the system is an ontology called NIFSTD (for NIF Standard) constructed by amalgamating a number of known and newly developed ontologies. NIFSTD is used by our ontology management module, called OntoQuest to perform ontology-based search over data sources. The NIF architecture currently provides three different mechanisms for searching heterogeneous data sources including relational databases, web sites, XML documents and full text of publications. Version 1.0 of the NIF system is currently in beta test and may be accessed through http://nif.nih.gov.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gardner_neuroscience_2008,

title = {The neuroscience information framework: a data and knowledge environment for neuroscience.},

author = {Daniel Gardner and Huda Akil and Giorgio A. Ascoli and Douglas M. Bowden and William Bug and Duncan E. Donohue and David H. Goldberg and Bernice Grafstein and Jeffrey S. Grethe and Amarnath Gupta and Maryam Halavi and David N. Kennedy and Luis Marenco and Maryann E. Martone and Perry L. Miller and Hans-Michael Müller and Adrian Robert and Gordon M. Shepherd and Paul W. Sternberg and David C. Van Essen and Robert W. Williams},

doi = {10.1007/s12021-008-9024-z},

issn = {1559-0089 1539-2791},

year  = {2008},

date = {2008-09-01},

journal = {Neuroinformatics},

volume = {6},

number = {3},

pages = {149–160},

abstract = {With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience's Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov , http://neurogateway.org , and other sites as they come on line.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{martone_cell_2008,

title = {The cell centered database project: an update on building community resources for managing and sharing 3D imaging data.},

author = {Maryann E. Martone and Joshua Tran and Willy W. Wong and Joy Sargis and Lisa Fong and Stephen Larson and Stephan P. Lamont and Amarnath Gupta and Mark H. Ellisman},

doi = {10.1016/j.jsb.2007.10.003},

issn = {1095-8657 1047-8477},

year  = {2008},

date = {2008-03-01},

journal = {Journal of structural biology},

volume = {161},

number = {3},

pages = {220–231},

abstract = {Databases have become integral parts of data management, dissemination, and mining in biology. At the Second Annual Conference on Electron Tomography, held in Amsterdam in 2001, we proposed that electron tomography data should be shared in a manner analogous to structural data at the protein and sequence scales. At that time, we outlined our progress in creating a database to bring together cell level imaging data across scales, The Cell Centered Database (CCDB). The CCDB was formally launched in 2002 as an on-line repository of high-resolution 3D light and electron microscopic reconstructions of cells and subcellular structures. It contains 2D, 3D, and 4D structural and protein distribution information from confocal, multiphoton, and electron microscopy, including correlated light and electron microscopy. Many of the data sets are derived from electron tomography of cells and tissues. In the 5 years since its debut, we have moved the CCDB from a prototype to a stable resource and expanded the scope of the project to include data management and knowledge engineering. Here, we provide an update on the CCDB and how it is used by the scientific community. We also describe our work in developing additional knowledge tools, e.g., ontologies, for annotation and query of electron microscopic data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{liu_medsman_2008,

title = {MedSMan: a live multimedia stream querying system},

author = {Bin Liu and Amarnath Gupta and Ramesh C. Jain},

url = {https://doi.org/10.1007/s11042-007-0177-9},

doi = {10.1007/S11042-007-0177-9},

year  = {2008},

date = {2008-01-01},

journal = {Multim. Tools Appl.},

volume = {38},

number = {2},

pages = {209–232},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{larson_formal_2007,

title = {A formal ontology of subcellular neuroanatomy.},

author = {Stephen D. Larson and Lisa L. Fong and Amarnath Gupta and Christopher Condit and William J. Bug and Maryann E. Martone},

doi = {10.3389/neuro.11.003.2007},

issn = {1662-5196},

year  = {2007},

date = {2007-01-01},

journal = {Frontiers in neuroinformatics},

volume = {1},

pages = {3},

abstract = {The complexity of the nervous system requires high-resolution microscopy to resolve the detailed 3D structure of nerve cells and supracellular domains. The analysis of such imaging data to extract cellular surfaces and cell components often requires the combination of expert human knowledge with carefully engineered software tools. In an effort to make better tools to assist humans in this endeavor, create a more accessible and permanent record of their data, and to aid the process of constructing complex and detailed computational models, we have created a core of formalized knowledge about the structure of the nervous system and have integrated that core into several software applications. In this paper, we describe the structure and content of a formal ontology whose scope is the subcellular anatomy of the nervous system (SAO), covering nerve cells, their parts, and interactions between these parts. Many applications of this ontology to image annotation, content-based retrieval of structural data, and integration of shared data across scales and researchers are also described.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{marenco_interoperability_2007,

title = {Interoperability across neuroscience databases.},

author = {Luis Marenco and Prakash Nadkarni and Maryann Martone and Amarnath Gupta},

doi = {10.1007/978-1-59745-520-6_2},

issn = {1064-3745},

year  = {2007},

date = {2007-01-01},

journal = {Methods in molecular biology (Clifton, N.J.)},

volume = {401},

pages = {23–36},

abstract = {Data interoperability between well-defined domains is currently performed by leveraging Web services. In the biosciences, more specifically in neuroscience, robust data interoperability is more difficult to achieve due to data heterogeneity, continuous domain changes, and the constant creation of new semantic data models (Nadkarni et al., J Am Med Inform Assoc 6, 478-93, 1999; Miller et al., J Am Med Inform Assoc 8, 34-48, 2001; Gardner et al., J Am Med Inform Assoc 8, 17-33, 2001). Data heterogeneity in neurosciences is primarily due to its multidisciplinary nature. This results in a compelling need to integrate all available neuroscience information to improve our understanding of the brain. Researchers associated with neuroscience initiatives such as the human brain project (HBP) (Koslow and Huerta, Neuroinformatics: An Overview of the Human Brain Project, 1997), the Bioinformatics Research Network (BIRN), and the Neuroinformatics Information Framework (NIF) are exploring mechanisms to allow robust interoperability between these continuously evolving neuroscience databases. To accomplish this goal, it is crucial to orchestrate technologies such as database mediators, metadata repositories, semantic metadata annotations, and ontological services. This chapter introduces the importance of database interoperability in neurosciences. We also describe current data sharing and integration mechanisms in genera. We conclude with data integration in bioscience and present approaches on neuroscience data sharing.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{larson_formal_2007-1,

title = {A formal ontology of subcellular neuroanatomy},

author = {Stephen D. Larson and Lisa Fong and Amarnath Gupta and Christopher Condit and William J. Bug and Maryann E. Martone},

url = {https://doi.org/10.3389/neuro.11.003.2007},

doi = {10.3389/NEURO.11.003.2007},

year  = {2007},

date = {2007-01-01},

journal = {Frontiers Neuroinformatics},

volume = {1},

pages = {3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{baitaluk_pathsys_2006,

title = {PathSys: integrating molecular interaction graphs for systems biology},

author = {Michael Baitaluk and Xufei Qian and Shubhada Godbole and Alpan Raval and Animesh Ray and Amarnath Gupta},

url = {https://doi.org/10.1186/1471-2105-7-55},

doi = {10.1186/1471-2105-7-55},

year  = {2006},

date = {2006-01-01},

journal = {BMC Bioinform.},

volume = {7},

pages = {55},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{baitaluk_biologicalnetworks_2006,

title = {BiologicalNetworks: visualization and analysis tool for systems biology},

author = {Michael Baitaluk and Mayya Sedova and Animesh Ray and Amarnath Gupta},

url = {https://doi.org/10.1093/nar/gkl308},

doi = {10.1093/NAR/GKL308},

year  = {2006},

date = {2006-01-01},

journal = {Nucleic Acids Res.},

volume = {34},

number = {Web-Server-Issue},

pages = {466–471},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gupta_report_2006,

title = {Report on the 2nd international workshop on data integration in the life sciences: (DILS'05)},

author = {Amarnath Gupta and Bertram Ludäscher and Louiqa Raschid},

url = {https://doi.org/10.1145/1147376.1147386},

doi = {10.1145/1147376.1147386},

year  = {2006},

date = {2006-01-01},

journal = {SIGMOD Rec.},

volume = {35},

number = {2},

pages = {56–58},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gupta_kinetics_2005,

title = {Kinetics of phase separation in systems exhibiting simple coacervation.},

author = {Amarnath Gupta and H. B. Bohidar},

doi = {10.1103/PhysRevE.72.011507},

issn = {1539-3755},

year  = {2005},

date = {2005-07-01},

journal = {Physical review. E, Statistical, nonlinear, and soft matter physics},

volume = {72},

number = {1 Pt 1},

pages = {011507},

abstract = {The kinetics of phase separation of a homogeneous polyelectrolytic solution into a dense polymer-rich coacervate and the dilute supernatant phase is discussed through statistical thermodynamics. It has been shown that the coacervate phase is associated with higher internal pressure, consequently giving rise to syneresis. Physical conditions for phase separations has been deduced explicitly which reveals that sigma(2)/qrt[I] > or = constant (where sigma is polyelectrolyte charge density and I is solution ionic strength), consistent with experimental observations. In the lattice model, r is the number of sites occupied by the polymer having a volume critical fraction psi(2c), it was found that phase separation would ensue when sigma(3)r > or = (64/9 alpha(2)) [psi(2c)/(1 - omega(2c))(2)], which reduces to (sigma(3)r/psi(2c)) > or = (64/9 alpha(2)) approximately 0.45 at 20 degrees C for psi(2c) < 1. The separation kinetics mimics a spinodal decomposition process. Rate of release of supernatant due to syneresis was found to be independent of the initial coacervate mass. Syneresis results are discussed in the context of temporal evolution of self-organization in polymer melts through Avrami model.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gupta_flory_2005,

title = {Flory temperature and upper critical solution temperature of gelatin solutions.},

author = {Amarnath Gupta and B. Mohanty and H. B. Bohidar},

doi = {10.1021/bm0492430},

issn = {1525-7797},

year  = {2005},

date = {2005-06-01},

journal = {Biomacromolecules},

volume = {6},

number = {3},

pages = {1623–1627},

abstract = {The Flory temperatures (theta) measured by turbidity experiments performed on gelatin solutions were found to be 12 +/- 0.3, 13 +/- 0.3, 14 +/- 0.3, 14.5 +/- 0.3, and 15 +/- 0.3 degrees C for salt concentrations 0.1, 0.075, 0.05, 0.025, and 0 M (NaCl), respectively. Estimated persistence length (l(p)) of this weakly charged polyelectrolyte could be deduced from the Benoit and Doty (J. Phys. Chem. 1953, 57, 958) relationship with the approximation that this biopolymer assumes a compact near-globular shape at Flory temperature, implying l(p) = 9(R(h))(2)/(5L(m)), where L(m) is the contour length and R(h) is the hydrodynamic radius. It was found that l(p) approximately 2.2 +/- 0.2 nm at room temperature (20 degrees C), invariant of salt concentration. The Flory expansion factor (alpha= R(h)(T)/R(h)(theta) = 1.5+/-0.2) was found to be almost constant. theta-Composition for this biopolymer was deduced from turbidimitric titration of aqueous gelatin solutions with the alcohols methanol, ethanol, 2-propanol, and tert-butyl alcohol. It appears that hydrophobic interactions play a crucial role in causing chain collapse at theta-temperature and composition.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{martone_e-neuroscience_2004,

title = {E-neuroscience: challenges and triumphs in integrating distributed data from molecules to brains.},

author = {Maryann E. Martone and Amarnath Gupta and Mark H. Ellisman},

doi = {10.1038/nn1229},

issn = {1097-6256},

year  = {2004},

date = {2004-05-01},

journal = {Nature neuroscience},

volume = {7},

number = {5},

pages = {467–472},

abstract = {Imaging, from magnetic resonance imaging (MRI) to localization of specific macromolecules by microscopies, has been one of the driving forces behind neuroinformatics efforts of the past decade. Many web-accessible resources have been created, ranging from simple data collections to highly structured databases. Although many challenges remain in adapting neuroscience to the new electronic forum envisioned by neuroinformatics proponents, these efforts have succeeded in formalizing the requirements for effective data sharing and data integration across multiple sources. In this perspective, we discuss the importance of spatial systems and ontologies for proper modeling of neuroscience data and their use in a large-scale data integration effort, the Biomedical Informatics Research Network (BIRN).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{chagoyen_designing_2004,

title = {Designing and executing scientific workflows with a programmable integrator},

author = {Monica Chagoyen and M. Erdem Kurul and Pedro A. Alarcón and José Mar'ıa Carazo and Amarnath Gupta},

url = {https://doi.org/10.1093/bioinformatics/bth209},

doi = {10.1093/BIOINFORMATICS/BTH209},

year  = {2004},

date = {2004-01-01},

journal = {Bioinform.},

volume = {20},

number = {13},

pages = {2092–2100},

keywords = {},

pubstate = {published},

tppubtype = {article}

}