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1. ROTDIF-web and ALTENS: GenApp-based Science Gateways for Biomolecular Nuclear Magnetic Resonance (NMR) Data Analysis and Structure Modeling

   https://doi.org/10.17605/OSF.IO/T4GKH  

   Chen, Yuexi ; Jeong, Cheol ; Savelyev, Alexey ; Krueger, Susan ; Curtis, Joseph E ; Brookes, Emre H ; Fushman, David ( September 2019 , Gateways 2019)

   Proteins and nucleic acids participate in essentially every biochemical process in living organisms, and the elucidation of their structure and motions is essential for our understanding how these molecular machines perform their function. Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful versatile technique that provides critical information on the molecular structure and dynamics. Spin-relaxation data are used to determine the overall rotational diffusion and local motions of biological macromolecules, while residual dipolar couplings (RDCs) reveal local and long-range structural architecture of these molecules and their complexes. This information allows researchers to refine structures of proteins and nucleic acids and providesmore »restraints for molecular docking. Several software packages have been developed by NMR researchers in order to tackle the complicated experimental data analysis and structure modeling. However, many of them are offline packages or command-line applications that require users to set up the run time environment and also to possess certain programming skills, which inevitably limits accessibility of this software to a broad scientific community. Here we present new science gateways designed for NMR/structural biology community that address these current limitations in NMR data analysis. Using the GenApp technology for scientific gateways (https://genapp.rocks), we successfully transformed ROTDIF and ALTENS, two offline packages for bio-NMR data analysis, into science gateways that provide advanced computational functionalities, cloud-based data management, and interactive 2D and 3D plotting and visualizations. Furthermore, these gateways are integrated with molecular structure visualization tools (Jmol) and with gateways/engines (SASSIE-web) capable of generating huge computer-simulated structural ensembles of proteins and nucleic acids. This enables researchers to seamlessly incorporate conformational ensembles into the analysis in order to adequately take into account structural heterogeneity and dynamic nature of biological macromolecules. ROTDIF-web offers a versatile set of integrated modules/tools for determining and predicting molecular rotational diffusion tensors and model-free characterization of bond dynamics in biomacromolecules and for docking of molecular complexes driven by the information extracted from NMR relaxation data. ALTENS allows characterization of the molecular alignment under anisotropic conditions, which enables researchers to obtain accurate local and long-range bond-vector restraints for refining 3-D structures of macromolecules and their complexes. We will describe our experience bringing our programs into GenApp and illustrate the use of these gateways for specific examples of protein systems of high biological significance. We expect these gateways to be useful to structural biologists and biophysicists as well as NMR community and to stimulate other researchers to share their scientific software in a similar way.« less
2. Recommender-as-a-Service with Chatbot Guided Domain-science Knowledge Discovery in a Science Gateway

   https://doi.org/10.1002/cpe.6080  

   Vekaria, K. ; Calyam, P. ; Sivarathri, S. ; Wang, S. ; Zhang, Y. ; Pandey, A. ; Chen, C. ; Xu, D. ; Joshi, T. ; Nair, S.S. ( January 2020 , Wiley Concurrency and Computation: Practice and Experience)

   Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration ofmore »recommenders in science gateways in order to spur research productivity,we present a novel “OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons).« less
3. Measuring success for a future vision: Defining impact in science gateways/virtual research environments

   https://doi.org/10.1002/cpe.6099  

   Calyam, Prasad ; Wilkins‐Diehr, Nancy ; Miller, Mark ; Brookes, Emre H. ; Arora, Ritu ; Chourasia, Amit ; Jennewein, Douglas M. ; Nandigam, Viswanath ; Drew LaMar, M. ; Cleveland, Sean B. ; et al ( December 2020 , Concurrency and Computation: Practice and Experience)

   Walker, D. ; Stankovski, V. ; Kalyanam, R. (Ed.)

   Scholars worldwide leverage science gateways/virtual research environments (VREs) for a wide variety of research and education endeavors spanning diverse scientific fields. Evaluating the value of a given science gateway/VRE to its constituent community is critical in obtaining the financial and human resources necessary to sustain operations and increase adoption in the user community. In this article, we feature a variety of exemplar science gateways/VREs and detail how they define impact in terms of, for example, their purpose, operation principles, and size of user base. Further, the exemplars recognize that their science gateways/VREs will continuously evolve with technological advancements and standardsmore »in cloud computing platforms, web service architectures, data management tools and cybersecurity. Correspondingly, we present a number of technology advances that could be incorporated in next‐generation science gateways/VREs to enhance their scope and scale of their operations for greater success/impact. The exemplars are selected from owners of science gateways in the Science Gateways Community Institute (SGCI) clientele in the United States, and from the owners of VREs in the International Virtual Research Environment Interest Group (VRE‐IG) of the Research Data Alliance. Thus, community‐driven best practices and technology advances are compiled from diverse expert groups with an international perspective to envisage futuristic science gateway/VRE innovations.« less
4. Recent Advances in the Temple University Digital Pathology Corpus

   https://doi.org/10.1109/SPMB47826.2019.9037859  

   Hunt, I. ; Husain, S. ; Simon, J. ; Obeid, I. ; Picone, J. ( December 2019 , IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad ; Picone, Joseph ; Selesnick, Ivan (Ed.)

   The Neural Engineering Data Consortium (NEDC) is developing a large open source database of high-resolution digital pathology images known as the Temple University Digital Pathology Corpus (TUDP) [1]. Our long-term goal is to release one million images. We expect to release the first 100,000 image corpus by December 2020. The data is being acquired at the Department of Pathology at Temple University Hospital (TUH) using a Leica Biosystems Aperio AT2 scanner [2] and consists entirely of clinical pathology images. More information about the data and the project can be found in Shawki et al. [3]. We currently have a Nationalmore »Science Foundation (NSF) planning grant [4] to explore how best the community can leverage this resource. One goal of this poster presentation is to stimulate community-wide discussions about this project and determine how this valuable resource can best meet the needs of the public. The computing infrastructure required to support this database is extensive [5] and includes two HIPAA-secure computer networks, dual petabyte file servers, and Aperio’s eSlide Manager (eSM) software [6]. We currently have digitized over 50,000 slides from 2,846 patients and 2,942 clinical cases. There is an average of 12.4 slides per patient and 10.5 slides per case with one report per case. The data is organized by tissue type as shown below: Filenames: tudp/v1.0.0/svs/gastro/000001/00123456/2015_03_05/0s15_12345/0s15_12345_0a001_00123456_lvl0001_s000.svs tudp/v1.0.0/svs/gastro/000001/00123456/2015_03_05/0s15_12345/0s15_12345_00123456.docx Explanation: tudp: root directory of the corpus v1.0.0: version number of the release svs: the image data type gastro: the type of tissue 000001: six-digit sequence number used to control directory complexity 00123456: 8-digit patient MRN 2015_03_05: the date the specimen was captured 0s15_12345: the clinical case name 0s15_12345_0a001_00123456_lvl0001_s000.svs: the actual image filename consisting of a repeat of the case name, a site code (e.g., 0a001), the type and depth of the cut (e.g., lvl0001) and a token number (e.g., s000) 0s15_12345_00123456.docx: the filename for the corresponding case report We currently recognize fifteen tissue types in the first installment of the corpus. The raw image data is stored in Aperio’s “.svs” format, which is a multi-layered compressed JPEG format [3,7]. Pathology reports containing a summary of how a pathologist interpreted the slide are also provided in a flat text file format. A more complete summary of the demographics of this pilot corpus will be presented at the conference. Another goal of this poster presentation is to share our experiences with the larger community since many of these details have not been adequately documented in scientific publications. There are quite a few obstacles in collecting this data that have slowed down the process and need to be discussed publicly. Our backlog of slides dates back to 1997, meaning there are a lot that need to be sifted through and discarded for peeling or cracking. Additionally, during scanning a slide can get stuck, stalling a scan session for hours, resulting in a significant loss of productivity. Over the past two years, we have accumulated significant experience with how to scan a diverse inventory of slides using the Aperio AT2 high-volume scanner. We have been working closely with the vendor to resolve many problems associated with the use of this scanner for research purposes. This scanning project began in January of 2018 when the scanner was first installed. The scanning process was slow at first since there was a learning curve with how the scanner worked and how to obtain samples from the hospital. From its start date until May of 2019 ~20,000 slides we scanned. In the past 6 months from May to November we have tripled that number and how hold ~60,000 slides in our database. This dramatic increase in productivity was due to additional undergraduate staff members and an emphasis on efficient workflow. The Aperio AT2 scans 400 slides a day, requiring at least eight hours of scan time. The efficiency of these scans can vary greatly. When our team first started, approximately 5% of slides failed the scanning process due to focal point errors. We have been able to reduce that to 1% through a variety of means: (1) best practices regarding daily and monthly recalibrations, (2) tweaking the software such as the tissue finder parameter settings, and (3) experience with how to clean and prep slides so they scan properly. Nevertheless, this is not a completely automated process, making it very difficult to reach our production targets. With a staff of three undergraduate workers spending a total of 30 hours per week, we find it difficult to scan more than 2,000 slides per week using a single scanner (400 slides per night x 5 nights per week). The main limitation in achieving this level of production is the lack of a completely automated scanning process, it takes a couple of hours to sort, clean and load slides. We have streamlined all other aspects of the workflow required to database the scanned slides so that there are no additional bottlenecks. To bridge the gap between hospital operations and research, we are using Aperio’s eSM software. Our goal is to provide pathologists access to high quality digital images of their patients’ slides. eSM is a secure website that holds the images with their metadata labels, patient report, and path to where the image is located on our file server. Although eSM includes significant infrastructure to import slides into the database using barcodes, TUH does not currently support barcode use. Therefore, we manage the data using a mixture of Python scripts and manual import functions available in eSM. The database and associated tools are based on proprietary formats developed by Aperio, making this another important point of community-wide discussion on how best to disseminate such information. Our near-term goal for the TUDP Corpus is to release 100,000 slides by December 2020. We hope to continue data collection over the next decade until we reach one million slides. We are creating two pilot corpora using the first 50,000 slides we have collected. The first corpus consists of 500 slides with a marker stain and another 500 without it. This set was designed to let people debug their basic deep learning processing flow on these high-resolution images. We discuss our preliminary experiments on this corpus and the challenges in processing these high-resolution images using deep learning in [3]. We are able to achieve a mean sensitivity of 99.0% for slides with pen marks, and 98.9% for slides without marks, using a multistage deep learning algorithm. While this dataset was very useful in initial debugging, we are in the midst of creating a new, more challenging pilot corpus using actual tissue samples annotated by experts. The task will be to detect ductal carcinoma (DCIS) or invasive breast cancer tissue. There will be approximately 1,000 images per class in this corpus. Based on the number of features annotated, we can train on a two class problem of DCIS or benign, or increase the difficulty by increasing the classes to include DCIS, benign, stroma, pink tissue, non-neoplastic etc. Those interested in the corpus or in participating in community-wide discussions should join our listserv, nedc_tuh_dpath@googlegroups.com, to be kept informed of the latest developments in this project. You can learn more from our project website: https://www.isip.piconepress.com/projects/nsf_dpath.« less
5. ARENA: The Augmented Reality Edge Networking Architecture

   https://doi.org/10.1109/ISMAR52148.2021.00065  

   Pereira, Nuno ; Rowe, Anthony ; Farb, Michael W ; Liang, Ivan ; Lu, Edward ; Riebling, Eric ( October 2021 , 2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR))

   Many have predicted the future of the Web to be the integration of Web content with the real-world through technologies such as Augmented Reality (AR). This has led to the rise of Extended Reality (XR) Web Browsers used to shorten the long AR application development and deployment cycle of native applications especially across different platforms. As XR Browsers mature, we face new challenges related to collaborative and multi-user applications that span users, devices, and machines. These collaborative XR applications require: (1) networking support for scaling to many users, (2) mechanisms for content access control and application isolation, and (3) themore »ability to host application logic near clients or data sources to reduce application latency. In this paper, we present the design and evaluation of the AR Edge Networking Architecture (ARENA) which is a platform that simplifies building and hosting collaborative XR applications on WebXR capable browsers. ARENA provides a number of critical components including: a hierarchical geospatial directory service that connects users to nearby servers and content, a token-based authentication system for controlling user access to content, and an application/service runtime supervisor that can dispatch programs across any network connected device. All of the content within ARENA exists as endpoints in a PubSub scene graph model that is synchronized across all users. We evaluate ARENA in terms of client performance as well as benchmark end-to-end response-time as load on the system scales. We show the ability to horizontally scale the system to Internet-scale with scenes containing hundreds of users and latencies on the order of tens of milliseconds. Finally, we highlight projects built using ARENA and showcase how our approach dramatically simplifies collaborative multi-user XR development compared to monolithic approaches.« less