Browsing by Subject "Software"

Now showing 1 - 10 of 28
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    Accelerating complex modeling workflows in CyberWater using on-demand HPC/Cloud resources
    (IEEE, 2021-09) Li, Feng; Chen, Ranran; Fu, Yuankun; Song, Fengguang; Liang, Yao; Ranawaka, Isuru; Pamidighantam, Sudhakar; Luna, Daniel; Liang, Xu; Computer Information and Graphics Technology, School of Engineering and Technology
    Workflow management systems (WMSs) are commonly used to organize/automate sequences of tasks as workflows to accelerate scientific discoveries. During complex workflow modeling, a local interactive workflow environment is desirable, as users usually rely on their rich, local environments for fast prototyping and refinements before they consider using more powerful computing resources. However, existing WMSs do not simultaneously support local interactive workflow environments and HPC resources. In this paper, we present an on-demand access mechanism to remote HPC resources from desktop/laptop-based workflow management software to compose, monitor and analyze scientific workflows in the CyberWater project. Cyber-Water is an open-data and open-modeling software framework for environmental and water communities. In this work, we extend the open-model, open-data design of CyberWater with on-demand HPC accessing capacity. In particular, we design and implement the LaunchAgent library, which can be integrated into the local desktop environment to allow on-demand usage of remote resources for hydrology-related workflows. LaunchAgent manages authentication to remote resources, prepares the computationally-intensive or data-intensive tasks as batch jobs, submits jobs to remote resources, and monitors the quality of services for the users. LaunchAgent interacts seamlessly with other existing components in CyberWater, which is now able to provide advantages of both feature-rich desktop software experience and increased computation power through on-demand HPC/Cloud usage. In our evaluations, we demonstrate how a hydrology workflow that consists of both local and remote tasks can be constructed and show that the added on-demand HPC/Cloud usage helps speeding up hydrology workflows while allowing intuitive workflow configurations and execution using a desktop graphical user interface.
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    Access to Knowledge in Brazil: New Research on Intellectual Property, Innovation and Development
    (Bloomsbury Academic, 2010) Shaver, Lea
    Access to knowledge is a demand for democratic participation, for global inclusion and for economic justice. It is a reaction to the excessively restrictive international IP regime put in place over the last two decades, which seeks to reassert the public interest in a more balanced information policy. With sponsorship from the Ford Foundation, the Information Society Project at Yale Law School has embarked on a new series of access to knowledge research, in partnership with colleagues in Brazil, China, Egypt, Ethiopia, India, Russia and South Africa. The first book in this series, Access to Knowledge in Brazil, focuses on current issues in intellectual property, innovation and development policy from a Brazilian perspective. Each chapter is authored by scholars from the Fundação Getulio Vargas law schools in São Paolo and Rio de Janeiro and examines a policy area that significantly impacts access to knowledge in the country. These include: exceptions and limitations to copyright, free software and open business models, patent reform and access to medicines, and open innovation in the biotechnology sector.
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    Access to Knowledge in Egypt: New Research on Intellectual Property, Innovation and Development
    (Bloomsbury Academic, 2010) Shaver, Lea; Rizk, Nagla
    The conventional wisdom in Egypt examines the issue of intellectual property solely as a question of policing and enforcement. The high levels of protection indicated by the WTO Agreement on Trade Related Aspects of Intellectual Property Rights are unquestioningly assumed to be desirable. Policy debates - and all too often academic ones as well - focus only on the questions of how to more efficiently tighten IP protection and crack down on piracy. Yet a more critical examination is urgently needed, whereby IP law, policy, and practice are viewed from a development perspective, rather than from an enforcement perspective. This volume takes on this endeavor. It offers the first examination of IP issues in Egypt adopting a multidisciplinary bottom-up approach that aims at maximizing access and contribution to knowledge, and in turn, promoting development. Bringing rigorous empirical research to bear on unquestioned ideologies, the collaborating authors question the conventional wisdom that more IP protection is necessarily better for innovation and development.
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    An Open Source Platform for Computational Histopathology
    (IEEE, 2021) Yu, Xiaxia; Zhao, Bingshuai; Huang, Haofan; Tian, Mu; Zhang, Sai; Song, Hongping; Li, Zengshan; Huang, Kun; Gao, Yi; Biostatistics and Health Data Science, School of Medicine
    Computational histopathology is a fast emerging field which converts the traditional glass slide based department to a new examination platform. Such a paradigm shift also brings the in silico computation to the field. Much research have been presented in the past decades on the algorithm development for pathology image analysis. On the other hand, a comprehensive software platform with advanced visualization and computation capability, large developer community, flexible plugin mechanism, and friendly transnational license, would be extremely beneficial for the entire community. In this work, we present SlicerScope: an open platform for whole slide histopathology image computing based on the highly successful 3D Slicer. We present rationale on the choice of such an architecture, introducing new modules/tools for giga-pixel whole slide image viewing, and four specific analytical modules for qualitative presentation, nucleus level analysis, tissue scale computation, and 3D pathology. The entire software is publicly available at https://slicerscope.github.io/ , facilitating the algorithmic, clinical, and transnational researches.
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    Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists
    (Springer Nature, 2021) Bulten, Wouter; Balkenhol, Maschenka; Belinga, Jean-Joël Awoumou; Brilhante, Américo; Çakır, Aslı; Egevad, Lars; Eklund, Martin; Farré, Xavier; Geronatsiou, Katerina; Molinié, Vincent; Pereira, Guilherme; Roy, Paromita; Saile, Günter; Salles, Paulo; Schaafsma, Ewout; Tschui, Joëlle; Vos, Anne-Marie; ISUP Pathology Imagebase Expert Panel; van Boven, Hester; Vink, Robert; van der Laak, Jeroen; Hulsbergen-van der Kaa, Christina; Litjens, Geert; Pathology and Laboratory Medicine, School of Medicine
    The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohen's kappa, 0.799 vs. 0.872; p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohen's kappa, 0.733 vs. 0.786; p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.
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    AutoCCS: automated collision cross-section calculation software for ion mobility spectrometry-mass spectrometry
    (Oxford University Press, 2021) Lee, Joon-Yong; Bilbao, Aivett; Conant, Christopher R.; Bloodsworth, Kent J.; Orton, Daniel J.; Zhou, Mowei; Wilson, Jesse W.; Zheng, Xueyun; Webb, Ian K.; Li, Ailin; Hixson, Kim K.; Fjeldsted, John C.; Ibrahim, Yehia M.; Payne, Samuel H.; Jansson, Christer; Smith, Richard D.; Metz, Thomas O.; Chemistry and Chemical Biology, School of Science
    Motivation: Ion mobility spectrometry (IMS) separations are increasingly used in conjunction with mass spectrometry (MS) for separation and characterization of ionized molecular species. Information obtained from IMS measurements includes the ion's collision cross section (CCS), which reflects its size and structure and constitutes a descriptor for distinguishing similar species in mixtures that cannot be separated using conventional approaches. Incorporating CCS into MS-based workflows can improve the specificity and confidence of molecular identification. At present, there is no automated, open-source pipeline for determining CCS of analyte ions in both targeted and untargeted fashion, and intensive user-assisted processing with vendor software and manual evaluation is often required. Results: We present AutoCCS, an open-source software to rapidly determine CCS values from IMS-MS measurements. We conducted various IMS experiments in different formats to demonstrate the flexibility of AutoCCS for automated CCS calculation: (i) stepped-field methods for drift tube-based IMS (DTIMS), (ii) single-field methods for DTIMS (supporting two calibration methods: a standard and a new enhanced method) and (iii) linear calibration for Bruker timsTOF and non-linear calibration methods for traveling wave based-IMS in Waters Synapt and Structures for Lossless Ion Manipulations. We demonstrated that AutoCCS offers an accurate and reproducible determination of CCS for both standard and unknown analyte ions in various IMS-MS platforms, IMS-field methods, ionization modes and collision gases, without requiring manual processing. Availability and implementation: https://github.com/PNNL-Comp-Mass-Spec/AutoCCS. Supplementary information: Supplementary data are available at Bioinformatics online. Demo datasets are publicly available at MassIVE (Dataset ID: MSV000085979).
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    Best practices to evaluate the impact of biomedical research software-metric collection beyond citations
    (Oxford University Press, 2024) Afiaz, Awan; Ivanov, Andrey A.; Chamberlin, John; Hanauer, David; Savonen, Candace L.; Goldman, Mary J.; Morgan, Martin; Reich, Michael; Getka, Alexander; Holmes, Aaron; Pati, Sarthak; Knight, Dan; Boutros, Paul C.; Bakas, Spyridon; Caporaso, J. Gregory; Del Fiol, Guilherme; Hochheiser, Harry; Haas, Brian; Schloss, Patrick D.; Eddy, James A.; Albrecht, Jake; Fedorov, Andrey; Waldron, Levi; Hoffman, Ava M.; Bradshaw, Richard L.; Leek, Jeffrey T.; Wright, Carrie; Pathology and Laboratory Medicine, School of Medicine
    Motivation: Software is vital for the advancement of biology and medicine. Impact evaluations of scientific software have primarily emphasized traditional citation metrics of associated papers, despite these metrics inadequately capturing the dynamic picture of impact and despite challenges with improper citation. Results: To understand how software developers evaluate their tools, we conducted a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We found that although developers realize the value of more extensive metric collection, they find a lack of funding and time hindering. We also investigated software among this community for how often infrastructure that supports more nontraditional metrics were implemented and how this impacted rates of papers describing usage of the software. We found that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seemed to be associated with increased mention rates. Analysing more diverse metrics can enable developers to better understand user engagement, justify continued funding, identify novel use cases, pinpoint improvement areas, and ultimately amplify their software's impact. Challenges are associated, including distorted or misleading metrics, as well as ethical and security concerns. More attention to nuances involved in capturing impact across the spectrum of biomedical software is needed. For funders and developers, we outline guidance based on experience from our community. By considering how we evaluate software, we can empower developers to create tools that more effectively accelerate biological and medical research progress. Availability and implementation: More information about the analysis, as well as access to data and code is available at https://github.com/fhdsl/ITCR_Metrics_manuscript_website.
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    CentFlow: Centrality-Based Flow Balancing and Traffic Distribution for Higher Network Utilization
    (IEEE, 2017) Challa, R.; Jeon, S.; Kim, D. S.; Choo, H.; Electrical and Computer Engineering, School of Engineering and Technology
    Next-generation networks (NGNs) are embracing two key principles of software defined networking (SDN) paradigm functional segregation of control and forwarding plane, and logical centralization of the control plane. A centralized control enhances the network management significantly by regulating the traffic distribution dynamically and effectively. An eagle-eye view of the entire topology opens up the opportunity for an SDN controller to refine the routing. Optimizing the network utilization in terms of throughput is majorly dependent on the routing decisions. Open Shortest Path First (OSPF) and Intermediate System to Intermediate System (IS-IS) are well-known traditional link state routing protocols proven with operation over operator networks for a long time. However, these classical protocols deployed distributively fall short of expectation in addressing the current routing issues due to the lack of a holistic view of the network topology and situation, whereas handling enormous traffic and user quality of experience (QoE) requirements are getting critical. IP routing in NGN is widely expected to be supported by SDN to enhance the network utilization in terms of throughput. We propose a novel routing algorithm-CentFlow, for an SDN domain to boost up the network utilization. The proposed weight functions in CentFlow achieve smart traffic distribution by detecting highly utilized nodes depending on the centrality measures and the temporal node degree that changes based on node utilization. Furthermore, the frequently selected edges are penalized thereby augmenting the flow balancing and dispersion. CentFlow reaps greater benefits on an SDN controller than the classical OSPF due to its comprehensive view of the network. Experimental results show that CentFlow enhances the utilization of up to 62% of nodes and 49% of links, respectively, compared to an existing Dijkstra algorithm-based routing scheme in SDN. Furthermore, nearly 6.5% more flows are processed networ- wide.
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    CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
    (American Chemical Society, 2024) Hwang, Wonmuk; Austin, Steven L.; Blondel, Arnaud; Boittier, Eric D.; Boresch, Stefan; Buck, Matthias; Buckner, Joshua; Caflisch, Amedeo; Chang, Hao-Ting; Cheng, Xi; Choi, Yeol Kyo; Chu, Jhih-Wei; Crowley, Michael F.; Cui, Qiang; Damjanovic, Ana; Deng, Yuqing; Devereux, Mike; Ding, Xinqiang; Feig, Michael F.; Gao, Jiali; Glowacki, David R.; Gonzales, James E., II; Hamaneh, Mehdi Bagerhi; Harder, Edward D.; Hayes, Ryan L.; Huang, Jing; Huang, Yandong; Hudson, Phillip S.; Im, Wonpil; Islam, Shahidul M.; Jiang, Wei; Jones, Michael R.; Käser, Silvan; Kearns, Fiona L.; Kern, Nathan R.; Klauda, Jeffery B.; Lazaridis, Themis; Lee, Jinhyuk; Lemkul, Justin A.; Liu, Xiaorong; Luo, Yun; MacKerell, Alexander D., Jr.; Major, Dan T.; Meuwly, Markus; Nam, Kwangho; Nilsson, Lennart; Ovchinnikov, Victor; Paci, Emanuele; Park, Soohyung; Pastor, Richard W.; Pittman, Amanda R.; Post, Carol Beth; Prasad, Samarjeet; Pu, Jingzhi; Qi, Yifei; Rathinavelan, Thenmalarchelvi; Roe, Daniel R.; Roux, Benoit; Rowley, Christopher N.; Shen, Jana; Simmonett, Andrew C.; Sodt, Alexander J.; Töpfer, Kai; Upadhyay, Meenu; van der Vaart, Arjan; Vazquez-Salazar, Luis Itza; Venable, Richard M.; Warrensford, Luke C.; Woodcock, H. Lee; Wu, Yujin; Brooks, Charles L., III; Brooks, Bernard R.; Karplus, Martin; Chemistry and Chemical Biology, School of Science
    Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.
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    Critical assessment of protein intrinsic disorder prediction
    (Springer Nature, 2021) Necci, Marco; Piovesan, Damiano; CAID Predictors; DisProt Curators; Tosatto, Silvio C. E.; Biochemistry and Molecular Biology, School of Medicine
    Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude.