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1. Water Data Explorer: An Open-Source Web Application and Python Library for Water Resources Data Discovery

   https://doi.org/10.3390/w13131850  

   Bustamante, Giovanni Romero; Nelson, Everett James; Ames, Daniel P.; Williams, Gustavious P.; Jones, Norman L.; Boldrini, Enrico; Chernov, Igor; Sanchez Lozano, Jorge Luis (July 2021, Water)

   null (Ed.)

   We present the design and development of an open-source web application called Water Data Explorer (WDE), designed to retrieve water resources observation and model data from data catalogs that follow the WaterOneFlow and WaterML Service-Oriented Architecture standards. WDE is a fully customizable web application built using the Tethys Platform development environment. As it is open source, it can be deployed on the web servers of international government agencies, non-governmental organizations, research teams, and others. Water Data Explorer provides uniform access to international data catalogs, such as the Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI) Hydrologic Information System (HIS) and the World Meteorological Organization (WMO) Hydrological Observing System (WHOS), as well as to local data catalogs that support the WaterOneFlow and WaterML standards. WDE supports data discovery, visualization, downloading, and basic data interpolation. It can be customized for different regions by modifying the user interface (i.e., localization), as well as by including pre-defined data catalogs and data sources. Access to WDE functionality is provided by a new open-source Python package called “Pywaterml” which provides programmable access to WDE methods to discover, visualize, download, and interpolate data. We present two case studies that access the CUAHSI HIS and WHOS catalogs and demonstrate regional customization, data discovery from WaterOneFlow web services, data visualization of time series observations, and data downloading. 

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2. An artificial intelligence accelerated virtual screening platform for drug discovery

   https://doi.org/10.1038/s41467-024-52061-7  

   Zhou, Guangfeng; Rusnac, Domnita-Valeria; Park, Hahnbeom; Canzani, Daniele; Nguyen, Hai_Minh; Stewart, Lance; Bush, Matthew_F; Nguyen, Phuong_Tran; Wulff, Heike; Yarov-Yarovoy, Vladimir; et al (September 2024, Nature Communications)

   Abstract Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel Na_V1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to Na_V1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery. 

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3. Platform Agnostic Streaming Data Application Performance Models

   https://doi.org/10.1109/RSDHA54838.2021.00008  

   Faber, Clayton J.; Plano, Tom; Kodali, Samatha; Xiao, Zhili; Dwaraki, Abhishek; Buhler, Jeremy D.; Chamberlain, Roger D.; Cabrera, Anthony M. (November 2021, Proc. of IEEE/ACM Workshop on Redefining Scalability for Diversely Heterogeneous Architectures (RSDHA))
4. iMicrobe: Tools and data-driven discovery platform for the microbiome sciences

   https://doi.org/10.1093/gigascience/giz083  

   Youens-Clark, Ken; Bomhoff, Matt; Ponsero, Alise_J; Wood-Charlson, Elisha_M; Lynch, Joshua; Choi, Illyoung; Hartman, John_H; Hurwitz, Bonnie_L (July 2019, GigaScience)

   Abstract BackgroundScientists have amassed a wealth of microbiome datasets, making it possible to study microbes in biotic and abiotic systems on a population or planetary scale; however, this potential has not been fully realized given that the tools, datasets, and computation are available in diverse repositories and locations. To address this challenge, we developed iMicrobe.us, a community-driven microbiome data marketplace and tool exchange for users to integrate their own data and tools with those from the broader community. FindingsThe iMicrobe platform brings together analysis tools and microbiome datasets by leveraging National Science Foundation–supported cyberinfrastructure and computing resources from CyVerse, Agave, and XSEDE. The primary purpose of iMicrobe is to provide users with a freely available, web-based platform to (1) maintain and share project data, metadata, and analysis products, (2) search for related public datasets, and (3) use and publish bioinformatics tools that run on highly scalable computing resources. Analysis tools are implemented in containers that encapsulate complex software dependencies and run on freely available XSEDE resources via the Agave API, which can retrieve datasets from the CyVerse Data Store or any web-accessible location (e.g., FTP, HTTP). ConclusionsiMicrobe promotes data integration, sharing, and community-driven tool development by making open source data and tools accessible to the research community in a web-based platform. 

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5. Amulet: An Energy-Efficient, Multi-Application Wearable Platform

   https://doi.org/10.1145/2994551.2994554  

   Hester, Josiah; Lord, Sarah; Halter, Ryan; Kotz, David; Sorber, Jacob; Peters, Travis; Yun, Tianlong; Peterson, Ronald; Skinner, Joseph; Golla, Bhargav; et al (October 2016, Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems)