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Article: A Serverless Tool for Platform Agnostic Computational Experiment Management.

Kiar, Gregory / Brown, Shawn T / Glatard, Tristan / Evans, Alan C

2019 Volume 13, Page(s) 12

Abstract: Neuroscience has been carried into the domain of big data and high performance computing (HPC) on the backs of initiatives in data collection and an increasingly compute-intensive tools. While managing HPC experiments requires considerable technical ... ...

Abstract	Neuroscience has been carried into the domain of big data and high performance computing (HPC) on the backs of initiatives in data collection and an increasingly compute-intensive tools. While managing HPC experiments requires considerable technical acumen, platforms, and standards have been developed to ease this burden on scientists. While web-portals make resources widely accessible, data organizations such as the Brain Imaging Data Structure and tool description languages such as Boutiques provide researchers with a foothold to tackle these problems using their own datasets, pipelines, and environments. While these standards lower the barrier to adoption of HPC and cloud systems for neuroscience applications, they still require the consolidation of disparate domain-specific knowledge. We present Clowdr, a lightweight tool to launch experiments on HPC systems and clouds, record rich execution records, and enable the accessible sharing and re-launch of experimental summaries and results. Clowdr uniquely sits between web platforms and bare-metal applications for experiment management by preserving the flexibility of do-it-yourself solutions while providing a low barrier for developing, deploying and disseminating neuroscientific analysis.
Language	English
Publishing date	2019-03-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452979-5
ISSN	1662-5196
ISSN	1662-5196
DOI	10.3389/fninf.2019.00012
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Unless changes are made in Benin, multiple storage and transport bottlenecks may prevent vaccines from reaching the population.

Brown, Shawn T / Lee, Bruce Y

Vaccine

2014 Volume 32, Issue 21, Page(s) 2518–2519

MeSH term(s)	Benin ; Computer Simulation ; Drug Storage ; Efficiency, Organizational ; Models, Theoretical ; Software ; Transportation ; Vaccines/supply & distribution
Chemical Substances	Vaccines
Language	English
Publishing date	2014-05-01
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	605674-x
ISSN	1873-2518 ; 0264-410X
ISSN (online)	1873-2518
ISSN	0264-410X
DOI	10.1016/j.vaccine.2014.02.060
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Book ; Online: Creating a Discipline-specific Commons for Infectious Disease Epidemiology

Wagner, Michael M. / Hogan, William / Levander, John / Darr, Adam / Diller, Matt / Sibilla, Max / Sperringer, Jr., Alexander T. Loiacono. Terence / Brown, Shawn T.

2023

Abstract: Objective: To create a commons for infectious disease (ID) epidemiology in which epidemiologists, public health officers, data producers, and software developers can not only share data and software, but receive assistance in improving their ... ...

Abstract	Objective: To create a commons for infectious disease (ID) epidemiology in which epidemiologists, public health officers, data producers, and software developers can not only share data and software, but receive assistance in improving their interoperability. Materials and Methods: We represented 586 datasets, 54 software, and 24 data formats in OWL 2 and then used logical queries to infer potentially interoperable combinations of software and datasets, as well as statistics about the FAIRness of the collection. We represented the objects in DATS 2.2 and a software metadata schema of our own design. We used these representations as the basis for the Content, Search, FAIR-o-meter, and Workflow pages that constitute the MIDAS Digital Commons. Results: Interoperability was limited by lack of standardization of input and output formats of software. When formats existed, they were human-readable specifications (22/24; 92%); only 3 formats (13%) had machine-readable specifications. Nevertheless, logical search of a triple store based on named data formats was able to identify scores of potentially interoperable combinations of software and datasets. Discussion: We improved the findability and availability of a sample of software and datasets and developed metrics for assessing interoperability. The barriers to interoperability included poor documentation of software input/output formats and little attention to standardization of most types of data in this field. Conclusion: Centralizing and formalizing the representation of digital objects within a commons promotes FAIRness, enables its measurement over time and the identification of potentially interoperable combinations of data and software. Comment: 12 pages, 6 figures
Keywords	Computer Science - Software Engineering ; Computer Science - Artificial Intelligence
Subject code	020
Publishing date	2023-11-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Performance benefits of Intel(R) OptaneTM DC persistent memory for the parallel processing of large neuroimaging data

Hayot-Sasson, Valerie / Brown, Shawn T / Glatard, Tristan

2019

Abstract: Open-access neuroimaging datasets have reached petabyte scale, and continue to grow. The ability to leverage the entirety of these datasets is limited to a restricted number of labs with both the capacity and infrastructure to process the data. Whereas ... ...

Abstract	Open-access neuroimaging datasets have reached petabyte scale, and continue to grow. The ability to leverage the entirety of these datasets is limited to a restricted number of labs with both the capacity and infrastructure to process the data. Whereas Big Data engines have significantly reduced application performance penalties with respect to data movement, their applied strategies (e.g. data locality, in-memory computing and lazy evaluation) are not necessarily practical within neuroimaging workflows where intermediary results may need to be materialized to shared storage for post-processing analysis. In this paper we evaluate the performance advantage brought by Intel(R) OptaneTM DC persistent memory for the processing of large neuroimaging datasets using the two available configurations modes: Memory mode and App Direct mode. We employ a synthetic algorithm on the 76 GiB and 603 GiB BigBrain, as well as apply a standard neuroimaging application on the Consortium for Reliability and Reproducibility (CoRR) dataset using 25 and 96 parallel processes in both cases. Our results show that the performance of applications leveraging persistent memory is superior to that of other storage devices,with the exception of DRAM. This is the case in both Memory and App Direct mode and irrespective of the amount of data and parallelism. Furthermore, persistent memory in App Direct mode is believed to benefit from the use of DRAM as a cache for writing when output data is significantly smaller than available memory. We believe the use of persistent memory will be beneficial to both neuroimaging applications running on HPC or visualization of large, high-resolution images.
Keywords	Computer Science - Performance
Subject code	006 ; 004
Publishing date	2019-12-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Map of different vaccine supply chain efficiency measures.

Haidari, Leila A / Brown, Shawn T / Wedlock, Patrick / Lee, Bruce Y

Vaccine

2017 Volume 35, Issue 1, Page(s) 199–200

Language	English
Publishing date	2017-01-03
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	605674-x
ISSN	1873-2518 ; 0264-410X
ISSN (online)	1873-2518
ISSN	0264-410X
DOI	10.1016/j.vaccine.2016.11.025
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Book ; Online: Performance Evaluation of Big Data Processing Strategies for Neuroimaging

Hayot-Sasson, Valérie / Brown, Shawn T / Glatard, Tristan

2018

Abstract: Neuroimaging datasets are rapidly growing in size as a result of advancements in image acquisition methods, open-science and data sharing. However, the adoption of Big Data processing strategies by neuroimaging processing engines remains limited. Here, ... ...

Abstract	Neuroimaging datasets are rapidly growing in size as a result of advancements in image acquisition methods, open-science and data sharing. However, the adoption of Big Data processing strategies by neuroimaging processing engines remains limited. Here, we evaluate three Big Data processing strategies (in-memory computing, data locality and lazy evaluation) on typical neuroimaging use cases, represented by the BigBrain dataset. We contrast these various strategies using Apache Spark and Nipype as our representative Big Data and neuroimaging processing engines, on Dell EMC's Top-500 cluster. Big Data thresholds were modelled by comparing the data-write rate of the application to the filesystem bandwidth and number of concurrent processes. This model acknowledges the fact that page caching provided by the Linux kernel is critical to the performance of Big Data applications. Results show that in-memory computing alone speeds-up executions by a factor of up to 1.6, whereas when combined with data locality, this factor reaches 5.3. Lazy evaluation strategies were found to increase the likelihood of cache hits, further improving processing time. Such important speed-up values are likely to be observed on typical image processing operations performed on images of size larger than 75GB. A ballpark speculation from our model showed that in-memory computing alone will not speed-up current functional MRI analyses unless coupled with data locality and processing around 280 subjects concurrently. Furthermore, we observe that emulating in-memory computing using in-memory file systems (tmpfs) does not reach the performance of an in-memory engine, presumably due to swapping to disk and the lack of data cleanup. We conclude that Big Data processing strategies are worth developing for neuroimaging applications.
Keywords	Computer Science - Distributed ; Parallel ; and Cluster Computing
Subject code	004
Publishing date	2018-12-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Comparing perturbation models for evaluating stability of neuroimaging pipelines.

Kiar, Gregory / de Oliveira Castro, Pablo / Rioux, Pierre / Petit, Eric / Brown, Shawn T / Evans, Alan C / Glatard, Tristan

The international journal of high performance computing applications

2020 Volume 34, Issue 5, Page(s) 491–501

Abstract: With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary ... ...

Abstract	With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary across domains, tools, data sets, and computational infrastructures, but numerical instabilities are thought to be a core contributor. In neuroimaging, unexpected deviations have been observed when varying operating systems, software implementations, or adding negligible quantities of noise. In the field of numerical analysis, these issues have recently been explored through Monte Carlo Arithmetic, a method involving the instrumentation of floating-point operations with probabilistic noise injections at a target precision. Exploring multiple simulations in this context allows the characterization of the result space for a given tool or operation. In this article, we compare various perturbation models to introduce instabilities within a typical neuroimaging pipeline, including (i) targeted noise, (ii) Monte Carlo Arithmetic, and (iii) operating system variation, to identify the significance and quality of their impact on the resulting derivatives. We demonstrate that even low-order models in neuroimaging such as the structural connectome estimation pipeline evaluated here are sensitive to numerical instabilities, suggesting that stability is a relevant axis upon which tools are compared, alongside more traditional criteria such as biological feasibility, computational efficiency, or, when possible, accuracy. Heterogeneity was observed across participants which clearly illustrates a strong interaction between the tool and data set being processed, requiring that the stability of a given tool be evaluated with respect to a given cohort. We identify use cases for each perturbation method tested, including quality assurance, pipeline error detection, and local sensitivity analysis, and make recommendations for the evaluation of stability in a practical and analytically focused setting. Identifying how these relationships and recommendations scale to higher order computational tools, distinct data sets, and their implication on biological feasibility remain exciting avenues for future work.
Language	English
Publishing date	2020-05-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	2017480-9
ISSN	1741-2846 ; 1094-3420
ISSN (online)	1741-2846
ISSN	1094-3420
DOI	10.1177/1094342020926237
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Article ; Online: Exploring the potential public health benefits of universal influenza vaccine.

DePasse, Jay V / Nowalk, Mary Patricia / Smith, Kenneth J / Raviotta, Jonathan M / Shim, Eunha / Zimmerman, Richard K / Brown, Shawn T

Human vaccines & immunotherapeutics

2019 Volume 15, Issue 12, Page(s) 2919–2926

Abstract: ... ...

Abstract	Background
MeSH term(s)	Adolescent ; Adult ; Child ; Child, Preschool ; Humans ; Immunity, Herd ; Infant ; Infant, Newborn ; Influenza Vaccines/administration & dosage ; Influenza, Human/prevention & control ; Middle Aged ; Public Health ; Systems Analysis ; Vaccination ; Young Adult
Chemical Substances	Influenza Vaccines
Language	English
Publishing date	2019-06-18
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2664176-8
ISSN	2164-554X ; 2164-5515
ISSN (online)	2164-554X
ISSN	2164-5515
DOI	10.1080/21645515.2019.1619406
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Article ; Online: Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses.

Bhagwat, Nikhil / Barry, Amadou / Dickie, Erin W / Brown, Shawn T / Devenyi, Gabriel A / Hatano, Koji / DuPre, Elizabeth / Dagher, Alain / Chakravarty, Mallar / Greenwood, Celia M T / Misic, Bratislav / Kennedy, David N / Poline, Jean-Baptiste

GigaScience

2021 Volume 10, Issue 1

Abstract: Background: The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or ... ...

Abstract	Background: The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance. Methods: Here we use an open structural MRI dataset (ABIDE), supplemented with the Human Connectome Project, to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of (i) software tool (e.g., ANTS, CIVET, FreeSurfer), (ii) cortical parcellation (Desikan-Killiany-Tourville, Destrieux, Glasser), and (iii) quality control procedure (manual, automatic). We divide our statistical analyses by (i) method type, i.e., task-free (unsupervised) versus task-driven (supervised); and (ii) inference objective, i.e., neurobiological group differences versus individual prediction. Results: Results show that software, parcellation, and quality control significantly affect task-driven neurobiological inference. Additionally, software selection strongly affects neurobiological (i.e. group) and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks. Conclusions: This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.
MeSH term(s)	Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Neuroimaging ; Reproducibility of Results ; Software
Language	English
Publishing date	2021-02-08
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2708999-X
ISSN	2047-217X ; 2047-217X
ISSN (online)	2047-217X
ISSN	2047-217X
DOI	10.1093/gigascience/giaa155
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Article ; Online: When are solar refrigerators less costly than on-grid refrigerators: A simulation modeling study.

Haidari, Leila A / Brown, Shawn T / Wedlock, Patrick / Connor, Diana L / Spiker, Marie / Lee, Bruce Y

Vaccine

2017 Volume 35, Issue 17, Page(s) 2224–2228

Abstract: Background: Gavi recommends solar refrigerators for vaccine storage in areas with less than eight hours of electricity per day, and WHO guidelines are more conservative. The question remains: Can solar refrigerators provide value where electrical ... ...

Abstract	Background: Gavi recommends solar refrigerators for vaccine storage in areas with less than eight hours of electricity per day, and WHO guidelines are more conservative. The question remains: Can solar refrigerators provide value where electrical outages are less frequent? Methods: Using a HERMES-generated computational model of the Mozambique routine immunization supply chain, we simulated the use of solar versus electric mains-powered refrigerators (hereafter referred to as "electric refrigerators") at different locations in the supply chain under various circumstances. Results: At their current price premium, the annual cost of each solar refrigerator is 132% more than each electric refrigerator at the district level and 241% more at health facilities. Solar refrigerators provided savings over electric refrigerators when one-day electrical outages occurred more than five times per year at either the district level or the health facilities, even when the electric refrigerator holdover time exceeded the duration of the outage. Two-day outages occurring more than three times per year at the district level or more than twice per year at the health facilities also caused solar refrigerators to be cost saving. Lowering the annual cost of a solar refrigerator to 75% more than an electric refrigerator allowed solar refrigerators to be cost saving at either level when one-day outages occurred more than once per year, or when two-day outages occurred more than once per year at the district level or even once per year at the health facilities. Conclusion: Our study supports WHO and Gavi guidelines. In fact, solar refrigerators may provide savings in total cost per dose administered over electrical refrigerators when electrical outages are less frequent. Our study identified the frequency and duration at which electrical outages need to occur for solar refrigerators to provide savings in total cost per dose administered over electric refrigerators at different solar refrigerator prices.
Language	English
Publishing date	2017-04-19
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	605674-x
ISSN	1873-2518 ; 0264-410X
ISSN (online)	1873-2518
ISSN	0264-410X
DOI	10.1016/j.vaccine.2016.11.103
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