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Weine J, McGrath C, Dirix P, Buoso S, Kozerke S. CMRsim-A python package for cardiovascular MR simulations incorporating complex motion and flow. Magn Reson Med 2024; 91:2621-2637. [PMID: 38234037 DOI: 10.1002/mrm.30010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 12/15/2023] [Accepted: 12/22/2023] [Indexed: 01/19/2024]
Abstract
PURPOSE To present an open-source MR simulation framework that facilitates the incorporation of complex motion and flow for studying cardiovascular MR (CMR) acquisition and reconstruction. METHODS CMRsim is a Python package that allows simulation of CMR images using dynamic digital phantoms with complex motion as input. Two simulation paradigms are available, namely, numerical and analytical solutions to the Bloch equations, using a common motion representation. Competitive simulation speeds are achieved using TensorFlow for GPU acceleration. To demonstrate the capability of the package, one introductory and two advanced CMR simulation experiments are presented. The latter showcase phase-contrast imaging of turbulent flow downstream of a stenotic section and cardiac diffusion tensor imaging on a contracting left ventricle. Additionally, extensive documentation and example resources are provided. RESULTS The Bloch simulation with turbulent flow using approximately 1.5 million particles and a sequence duration of 710 ms for each of the seven different velocity encodings took a total of 29 min on a NVIDIA Titan RTX GPU. The results show characteristic phase contrast and magnitude modulation present in real data. The analytical simulation of cardiac diffusion tensor imaging with bulk-motion phase sensitivity took approximately 10 s per diffusion-weighted image, including preparation and loading steps. The results exhibit the expected alteration of diffusion metrics due to strain. CONCLUSION CMRsim is the first simulation framework that allows one to feasibly incorporate complex motion, including turbulent flow, to systematically study advanced CMR acquisition and reconstruction approaches. The open-source package features modularity and transparency, facilitating maintainability and extensibility in support of reproducible research.
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Affiliation(s)
- Jonathan Weine
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Charles McGrath
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Pietro Dirix
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Stefano Buoso
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
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van Houdt PJ, Ragunathan S, Berks M, Ahmed Z, Kershaw LE, Gurney-Champion OJ, Tadimalla S, Arvidsson J, Sun Y, Kallehauge J, Dickie B, Lévy S, Bell L, Sourbron S, Thrippleton MJ. Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). Magn Reson Med 2024; 91:1774-1786. [PMID: 37667526 DOI: 10.1002/mrm.29826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/30/2023] [Accepted: 07/25/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework. METHODS A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results. RESULTS The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions. CONCLUSIONS The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Michael Berks
- Quantitative Biomedical Imaging Laboratory, Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Zaki Ahmed
- Corewell Health William Beaumont University Hospital, Diagnostic Radiology, Royal Oak, USA
| | - Lucy E Kershaw
- Edinburgh Imaging and Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Oliver J Gurney-Champion
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Sirisha Tadimalla
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jonathan Arvidsson
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Yu Sun
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jesper Kallehauge
- Aarhus University Hospital, Danish Centre for Particle Therapy, Aarhus, Denmark
- Aarhus University, Department of Clinical Medicine, Aarhus, Denmark
| | - Ben Dickie
- Division of Informatics, Imaging, and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Northern Care Alliance NHS Group, The University of Manchester, Manchester, UK
| | - Simon Lévy
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Laura Bell
- Genentech, Inc, Clinical Imaging Group, South San Francisco, USA
| | - Steven Sourbron
- University of Sheffield, Department of Infection, Immunity and Cardiovascular Disease, Sheffield, UK
| | - Michael J Thrippleton
- University of Edinburgh, Edinburgh Imaging and Centre for Clinical Brain Sciences, Edinburgh, UK
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Romano JD, Truong V, Kumar R, Venkatesan M, Graham BE, Hao Y, Matsumoto N, Li X, Wang Z, Ritchie MD, Shen L, Moore JH. The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research. J Med Internet Res 2024; 26:e46777. [PMID: 38635981 DOI: 10.2196/46777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 06/23/2023] [Accepted: 11/07/2023] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs. OBJECTIVE We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. METHODS We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. RESULTS AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. CONCLUSIONS AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.
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Affiliation(s)
- Joseph D Romano
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Center of Excellence in Environmental Toxicology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Van Truong
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Rachit Kumar
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Mythreye Venkatesan
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Britney E Graham
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Yun Hao
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Nick Matsumoto
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Xi Li
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Zhiping Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Marylyn D Ritchie
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Li Shen
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
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Tecuatl C, Ljungquist B, Ascoli GA. Accelerating the continuous community sharing of digital neuromorphology data. bioRxiv 2024:2024.03.15.585306. [PMID: 38562736 PMCID: PMC10983892 DOI: 10.1101/2024.03.15.585306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The tree-like morphology of neurons and glia is a key cellular determinant of circuit connectivity and metabolic function in the nervous system of essentially all animals. To elucidate the contribution of specific cell types to both physiological and pathological brain states, it is important to access detailed neuroanatomy data for quantitative analysis and computational modeling. NeuroMorpho.Org is the largest online collection of freely available digital neural reconstructions and related metadata and is continuously updated with new uploads. Earlier in the project, we released multiple datasets together yearly, but this process caused an average delay of several months in making the data public. Moreover, in the past 5 years, >80% of invited authors agreed to share their data with the community via NeuroMorpho.Org, up from <20% in the first 5 years of the project. In the same period, the average number of reconstructions per publication increased 600%, creating the need for automatic processing to release more reconstructions in less time. The progressive automation of our pipeline enabled the transition to agile releases of individual datasets as soon as they are ready. The overall time from data identification to public sharing decreased by 63.7%; 78% of the datasets are now released in less than 3 months with an average workflow duration below 40 days. Furthermore, the mean processing time per reconstruction dropped from 3 hours to 2 minutes. With these continuous improvements, NeuroMorpho.Org strives to forge a positive culture of open data. Most importantly, the new, original research enabled through reuse of datasets across the world has a multiplicative effect on science discovery, benefiting both authors and users.
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Affiliation(s)
- Carolina Tecuatl
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Bengt Ljungquist
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Giorgio A. Ascoli
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
- Interdisciplinary Program in Neuroscience; College of Science; George Mason University, Fairfax, VA, USA
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Hsiao Y, Zhang H, Li GX, Deng Y, Yu F, Kahrood HV, Steele JR, Schittenhelm RB, Nesvizhskii AI. Analysis and visualization of quantitative proteomics data using FragPipe-Analyst. bioRxiv 2024:2024.03.05.583643. [PMID: 38496650 PMCID: PMC10942459 DOI: 10.1101/2024.03.05.583643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.
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Affiliation(s)
- Yi Hsiao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Haijian Zhang
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Ginny Xiaohe Li
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yamei Deng
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hossein Valipour Kahrood
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
- Monash Genomics & Bioinformatics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Joel R. Steele
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Ralf B. Schittenhelm
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Alexey I. Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
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Liévin V, Hother CE, Motzfeldt AG, Winther O. Can large language models reason about medical questions? Patterns (N Y) 2024; 5:100943. [PMID: 38487804 PMCID: PMC10935498 DOI: 10.1016/j.patter.2024.100943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/18/2023] [Accepted: 02/02/2024] [Indexed: 03/17/2024]
Abstract
Although large language models often produce impressive outputs, it remains unclear how they perform in real-world scenarios requiring strong reasoning skills and expert domain knowledge. We set out to investigate whether closed- and open-source models (GPT-3.5, Llama 2, etc.) can be applied to answer and reason about difficult real-world-based questions. We focus on three popular medical benchmarks (MedQA-US Medical Licensing Examination [USMLE], MedMCQA, and PubMedQA) and multiple prompting scenarios: chain of thought (CoT; think step by step), few shot, and retrieval augmentation. Based on an expert annotation of the generated CoTs, we found that InstructGPT can often read, reason, and recall expert knowledge. Last, by leveraging advances in prompt engineering (few-shot and ensemble methods), we demonstrated that GPT-3.5 not only yields calibrated predictive distributions but also reaches the passing score on three datasets: MedQA-USMLE (60.2%), MedMCQA (62.7%), and PubMedQA (78.2%). Open-source models are closing the gap: Llama 2 70B also passed the MedQA-USMLE with 62.5% accuracy.
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Affiliation(s)
- Valentin Liévin
- Section for Cognitive Systems, Technical University of Denmark, Anker Engelunds Vej 101, 2800 Kongens Lyngby, Denmark
- FindZebra, Rådvadsvej 36, 2400 Copenhagen, Denmark
| | - Christoffer Egeberg Hother
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Inge Lehmanns Vej 107, 2100 Copenhagen, Denmark
| | - Andreas Geert Motzfeldt
- Section for Cognitive Systems, Technical University of Denmark, Anker Engelunds Vej 101, 2800 Kongens Lyngby, Denmark
| | - Ole Winther
- Section for Cognitive Systems, Technical University of Denmark, Anker Engelunds Vej 101, 2800 Kongens Lyngby, Denmark
- FindZebra, Rådvadsvej 36, 2400 Copenhagen, Denmark
- Center for Genomic Medicine, Copenhagen University Hospital, Rigshospitalet, Ørestads Boulevard 5, 2300 Copenhagen, Denmark
- Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark
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Schütz A, Rami-Merhar B, Schütz-Fuhrmann I, Blauensteiner N, Baumann P, Pöttler T, Mader JK. Retrospective Comparison of Commercially Available Automated Insulin Delivery With Open-Source Automated Insulin Delivery Systems in Type 1 Diabetes. J Diabetes Sci Technol 2024:19322968241230106. [PMID: 38366626 DOI: 10.1177/19322968241230106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
BACKGROUND Automated insulin delivery (AID) systems have shown to improve glycemic control in a range of populations and settings. At the start of this study, only one commercial AID system had entered the Austrian market (MiniMed 670G, Medtronic). However, there is an ever-growing community of people living with type 1 diabetes (PWT1D) using open-source (OS) AID systems. MATERIALS AND METHODS A total of 144 PWT1D who used either the MiniMed 670G (670G) or OS-AID systems routinely for a period of at least three to a maximum of six months, between February 18, 2020 and January 15, 2023, were retrospectively analyzed (116 670G aged from 2.6 to 71.8 years and 28 OS-AID aged from 3.4 to 53.5 years). The goal is to evaluate and compare the quality of glycemic control of commercially available AID and OS-AID systems and to present all data by an in-depth descriptive analysis of the population. No statistical tests were performed. RESULTS The PWT1D using OS-AID systems spent more time in range (TIR)70-180 mg/dL (81.7% vs 73.9%), less time above range (TAR)181-250 mg/dL (11.1% vs 19.6%), less TAR>250 mg/dL (2.5% vs 4.3%), and more time below range (TBR)54-69 mg/dL (2.2% vs 1.7%) than PWT1D using the 670G system. The TBR<54 mg/dL was comparable in both groups (0.3% vs 0.4%). In the OS-AID group, median glucose level and glycated hemoglobin (HbA1c) were lower than in the 670G system group (130 vs 150 mg/dL; 6.2% vs 7.0%). CONCLUSION In conclusion, both groups were able to achieve satisfactory glycemic outcomes independent of age, gender, and diabetes duration. However, the PWT1D using OS-AID systems attained an even better glycemic control with no clinical safety concerns.
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Affiliation(s)
- Anna Schütz
- Department of Pediatric and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Birgit Rami-Merhar
- Department of Pediatric and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Ingrid Schütz-Fuhrmann
- Karl Landsteiner Institute, Endocrinology and Nephrology, Vienna, Austria
- Department of Endocrinology and Nephrology, Clinic Hietzing, Vienna Health Care Group, Vienna, Austria
| | - Nicole Blauensteiner
- Department of Pediatric and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
| | - Petra Baumann
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Tina Pöttler
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Julia K Mader
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
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Kreissner KO, Faller B, Talucci I, Maric HM. MARTin-an open-source platform for microarray analysis. Front Bioinform 2024; 4:1329062. [PMID: 38405547 PMCID: PMC10885354 DOI: 10.3389/fbinf.2024.1329062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/15/2024] [Indexed: 02/27/2024] Open
Abstract
Background: Microarray technology has brought significant advancements to high-throughput analysis, particularly in the comprehensive study of biomolecular interactions involving proteins, peptides, and antibodies, as well as in the fields of gene expression and genotyping. With the ever-increasing volume and intricacy of microarray data, an accurate, reliable and reproducible analysis is essential. Furthermore, there is a high level of variation in the format of microarrays. This not only holds true between different sample types but is also due to differences in the hardware used during the production of the arrays, as well as the personal preferences of the individual users. Therefore, there is a need for transparent, broadly applicable and user-friendly image quantification techniques to extract meaningful information from these complex datasets, while also addressing the challenges posed by specific microarray and imager formats, which can flaw analysis and interpretation. Results: Here we introduce MicroArray Rastering Tool (MARTin), as a versatile tool developed primarily for the analysis of protein and peptide microarrays. Our software provides state-of-the-art methodologies, offering researchers a comprehensive tool for microarray image quantification. MARTin is independent of the microarray platform used and supports various configurations including high-density formats and printed arrays with significant x and y offsets. This is made possible by granting the user the ability to freely customize parts of the application to their specific microarray format. Thanks to built-in features like adaptive filtering and autofit, measurements can be done very efficiently and are highly reproducible. Furthermore, our tool integrates metadata management and integrity check features, providing a straightforward quality control method, along with a ready-to-use interface for in-depth data analysis. This not only promotes good scientific practice in the field of microarray analysis but also enhances the ability to explore and examine the generated data. Conclusion: MARTin has been developed to empower its users with a reliable, efficient, and intuitive tool for peptidomic and proteomic array analysis, thereby facilitating data-driven discovery across disciplines. Our software is an open-source project freely available via the GNU Affero General Public License licence on GitHub.
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Affiliation(s)
- Kai O. Kreissner
- Rudolf Virchow Center for Integrative and Translational Bioimaging, University of Würzburg, Würzburg, Germany
| | | | - Ivan Talucci
- Rudolf Virchow Center for Integrative and Translational Bioimaging, University of Würzburg, Würzburg, Germany
- Department of Neurology, University Hospital Würzburg, Würzburg, Bavaria, Germany
| | - Hans M. Maric
- Rudolf Virchow Center for Integrative and Translational Bioimaging, University of Würzburg, Würzburg, Germany
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George TM, Rastogi M, de Cothi W, Clopath C, Stachenfeld K, Barry C. RatInABox, a toolkit for modelling locomotion and neuronal activity in continuous environments. eLife 2024; 13:e85274. [PMID: 38334473 PMCID: PMC10857787 DOI: 10.7554/elife.85274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/03/2024] [Indexed: 02/10/2024] Open
Abstract
Generating synthetic locomotory and neural data is a useful yet cumbersome step commonly required to study theoretical models of the brain's role in spatial navigation. This process can be time consuming and, without a common framework, makes it difficult to reproduce or compare studies which each generate test data in different ways. In response, we present RatInABox, an open-source Python toolkit designed to model realistic rodent locomotion and generate synthetic neural data from spatially modulated cell types. This software provides users with (i) the ability to construct one- or two-dimensional environments with configurable barriers and visual cues, (ii) a physically realistic random motion model fitted to experimental data, (iii) rapid online calculation of neural data for many of the known self-location or velocity selective cell types in the hippocampal formation (including place cells, grid cells, boundary vector cells, head direction cells) and (iv) a framework for constructing custom cell types, multi-layer network models and data- or policy-controlled motion trajectories. The motion and neural models are spatially and temporally continuous as well as topographically sensitive to boundary conditions and walls. We demonstrate that out-of-the-box parameter settings replicate many aspects of rodent foraging behaviour such as velocity statistics and the tendency of rodents to over-explore walls. Numerous tutorial scripts are provided, including examples where RatInABox is used for decoding position from neural data or to solve a navigational reinforcement learning task. We hope this tool will significantly streamline computational research into the brain's role in navigation.
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Affiliation(s)
- Tom M George
- Sainsbury Wellcome Centre, University College LondonLondonUnited Kingdom
| | - Mehul Rastogi
- Sainsbury Wellcome Centre, University College LondonLondonUnited Kingdom
| | - William de Cothi
- Department of Cell and Developmental Biology, University College LondonLondonUnited Kingdom
| | - Claudia Clopath
- Sainsbury Wellcome Centre, University College LondonLondonUnited Kingdom
- Department of Bioengineering, Imperial College LondonLondonUnited Kingdom
| | | | - Caswell Barry
- Department of Cell and Developmental Biology, University College LondonLondonUnited Kingdom
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Garbo A, Mueller D. Cryologger Ice Tracking Beacon: A Low-Cost, Open-Source Platform for Tracking Icebergs and Ice Islands. Sensors (Basel) 2024; 24:1044. [PMID: 38400203 PMCID: PMC10892840 DOI: 10.3390/s24041044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 01/26/2024] [Accepted: 01/31/2024] [Indexed: 02/25/2024]
Abstract
Icebergs and ice islands (large, tabular icebergs) present a significant hazard to marine vessels and infrastructure at a time when demand for access to Arctic waters is increasing. There is a growing demand for in situ iceberg tracking data to monitor their drift trajectories and improve models used for operational forecasting of ice hazards, yet the high cost of commercial tracking devices often prevents monitoring at optimal spatial and temporal resolutions. Here, we provide a detailed description of the Cryologger Ice Tracking Beacon (ITB), a low-cost, robust, and user-friendly data logger and telemeter for tracking icebergs and ice islands based on the Arduino open-source electronics platform. Designed for deployments of at least 2 years with an hourly sampling interval that is remotely modifiable by the end user, the Cryologger ITB provides long-term measurements of position, temperature, pressure, pitch, roll, heading, and battery voltage. Data are transmitted via the Iridium satellite network at user-specified intervals. We present the results of field campaigns in 2018 and 2019, which saw the deployment of 16 ITBs along the coasts of Greenland and Ellesmere and Baffin islands. The overall success of these ITB deployments has demonstrated that inexpensive, open-source hardware and software can provide a reliable and cost-effective method of monitoring icebergs and ice islands in the polar regions.
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Affiliation(s)
- Adam Garbo
- Water and Ice Research Laboratory, Department of Geography and Environmental Studies, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S5B6, Canada
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11
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Azariah W, Bimo FA, Lin CW, Cheng RG, Nikaein N, Jana R. A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches. Sensors (Basel) 2024; 24:1038. [PMID: 38339755 PMCID: PMC10857264 DOI: 10.3390/s24031038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/19/2024] [Accepted: 01/21/2024] [Indexed: 02/12/2024]
Abstract
The open radio access network (RAN) aims to bring openness and intelligence to the traditional closed and proprietary RAN technology and offer flexibility, performance improvement, and cost-efficiency in the RAN's deployment and operation. This paper provides a comprehensive survey of the Open RAN development. We briefly summarize the RAN evolution history and the state-of-the-art technologies applied to Open RAN. The Open RAN-related projects, activities, and standardization is then discussed. We then summarize the challenges and future research directions required to support the Open RAN. Finally, we discuss some solutions to tackle these issues from the open source perspective.
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Affiliation(s)
- Wilfrid Azariah
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd, Da’an District, Taipei City 106335, Taiwan; (W.A.); (F.A.B.); (C.-W.L.)
| | - Fransiscus Asisi Bimo
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd, Da’an District, Taipei City 106335, Taiwan; (W.A.); (F.A.B.); (C.-W.L.)
| | - Chih-Wei Lin
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd, Da’an District, Taipei City 106335, Taiwan; (W.A.); (F.A.B.); (C.-W.L.)
| | - Ray-Guang Cheng
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd, Da’an District, Taipei City 106335, Taiwan; (W.A.); (F.A.B.); (C.-W.L.)
| | - Navid Nikaein
- Department of Communication Systems, EURECOM, Campus SophiaTech, 450 Route des Chappes, 06410 Biot, France;
| | - Rittwik Jana
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA;
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12
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Weaver RG, White J, Finnegan O, Nelakuditi S, Zhu X, Burkart S, Beets M, Brown T, Pate R, Welk GJ, de Zambotti M, Ghosal R, Wang Y, Armstrong B, Adams EL, Reesor-Oyer L, Pfledderer CD, Bastyr M, von Klinggraeff L, Parker H. A Device Agnostic Approach to Predict Children's Activity from Consumer Wearable Accelerometer Data: A Proof-of-Concept Study. Med Sci Sports Exerc 2024; 56:370-379. [PMID: 37707503 PMCID: PMC10841245 DOI: 10.1249/mss.0000000000003294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
INTRODUCTION This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry. METHODS Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity) were estimated via indirect calorimetry (criterion), and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph. RESULTS Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95% confidence interval (CI), 67.1%-69.3%), 73.0% (95% CI, 71.8%-74.3%), and 66.6% (95% CI, 65.7%-67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%-85.2%), 82.0% (95% CI, 80.6%-83.4%), and 75.3% (95% CI, 74.7%-75.9%), respectively. Apple Watch produced the lowest mean bias (inactive, -4.0 ± 4.5; light activity, 2.1 ± 4.0) and absolute error (inactive, 4.9 ± 3.4; light activity, 3.6 ± 2.7) for inactive and light physical activity minutes. For moderate-to-vigorous physical activity, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent. CONCLUSIONS Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than, a research-grade device, when compared with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.
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Affiliation(s)
| | | | | | | | | | | | | | - Trey Brown
- University of South Carolina, Columbia, SC
| | - Russ Pate
- University of South Carolina, Columbia, SC
| | | | | | | | - Yuan Wang
- University of South Carolina, Columbia, SC
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13
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Hund H, Wettstein R, Kurscheidt M, Schweizer ST, Zilske C, Fegeler C. Interoperability Is a Process - The Data Sharing Framework. Stud Health Technol Inform 2024; 310:28-32. [PMID: 38269759 DOI: 10.3233/shti230921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
Common syntax and data semantics are core components of healthcare interoperability standards. However, interoperable data exchange processes are also needed to enable the integration of existing systems between organizations. While solutions for healthcare delivery processes are available and have been widely adopted, support for processes targeting bio-medical research is limited. Our Data Sharing Framework creates a platform to implement research processes like cohort size estimation, reviews and approvals of research proposals, consent checks, record linkage, pseudonymization and data sharing across organizations. The described framework implements a distributed business process engine for executing BPMN 2.0 processes with synchronization and data exchange using FHIR R4 resources. Our reference implementation has been rolled out to 38 organizations across three research consortia in Germany and is available as open source under the Apache 2.0 license.
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Affiliation(s)
- Hauke Hund
- GECKO Institute, Heilbronn University of Applied Sciences, Heilbronn, Germany
| | - Reto Wettstein
- Institute for Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Simon T Schweizer
- GECKO Institute, Heilbronn University of Applied Sciences, Heilbronn, Germany
| | - Christoph Zilske
- GECKO Institute, Heilbronn University of Applied Sciences, Heilbronn, Germany
| | - Christian Fegeler
- GECKO Institute, Heilbronn University of Applied Sciences, Heilbronn, Germany
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14
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Woelk LM, Kovacevic D, Husseini H, Förster F, Gerlach F, Möckl F, Altfeld M, Guse AH, Diercks BP, Werner R. DARTS: an open-source Python pipeline for Ca 2+ microdomain analysis in live cell imaging data. Front Immunol 2024; 14:1299435. [PMID: 38274810 PMCID: PMC10809147 DOI: 10.3389/fimmu.2023.1299435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024] Open
Abstract
Ca2+ microdomains play a key role in intracellular signaling processes. For instance, they mediate the activation of T cells and, thus, the initial adaptive immune system. They are, however, also of utmost importance for activation of other cells, and a detailed understanding of the dynamics of these spatially localized Ca2+ signals is crucial for a better understanding of the underlying signaling processes. A typical approach to analyze Ca2+ microdomain dynamics is live cell fluorescence microscopy imaging. Experiments usually involve imaging a larger number of cells of different groups (for instance, wild type and knockout cells), followed by a time consuming image and data analysis. With DARTS, we present a modular Python pipeline for efficient Ca2+ microdomain analysis in live cell imaging data. DARTS (Deconvolution, Analysis, Registration, Tracking, and Shape normalization) provides state-of-the-art image postprocessing options like deep learning-based cell detection and tracking, spatio-temporal image deconvolution, and bleaching correction. An integrated automated Ca2+ microdomain detection offers direct access to global statistics like the number of microdomains for cell groups, corresponding signal intensity levels, and the temporal evolution of the measures. With a focus on bead stimulation experiments, DARTS provides a so-called dartboard projection analysis and visualization approach. A dartboard projection covers spatio-temporal normalization of the bead contact areas and cell shape normalization onto a circular template that enables aggregation of the spatiotemporal information of the microdomain detection results for the individual cells of the cell groups of interest. The dartboard visualization allows intuitive interpretation of the spatio-temporal microdomain dynamics at the group level. The application of DARTS is illustrated by three use cases in the context of the formation of initial Ca2+ microdomains after cell stimulation. DARTS is provided as an open-source solution and will be continuously extended upon the feedback of the community. Code available at: 10.5281/zenodo.10459243.
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Affiliation(s)
- Lena-Marie Woelk
- Department of Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dejan Kovacevic
- The Calcium Signalling Group, Department of Biochemistry and Molecular Cell Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hümeyra Husseini
- Department of Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fritz Förster
- Department of Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fynn Gerlach
- The Calcium Signalling Group, Department of Biochemistry and Molecular Cell Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Franziska Möckl
- The Calcium Signalling Group, Department of Biochemistry and Molecular Cell Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marcus Altfeld
- Institute for Immunology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andreas H. Guse
- The Calcium Signalling Group, Department of Biochemistry and Molecular Cell Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Björn-Philipp Diercks
- The Calcium Signalling Group, Department of Biochemistry and Molecular Cell Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - René Werner
- Department of Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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15
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Davis C. The routinization of lay expertise: A diachronic account of the invention and stabilization of an open-source artificial pancreas. Soc Stud Sci 2023:3063127231214237. [PMID: 38152868 DOI: 10.1177/03063127231214237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Embodied health movements (EHMs) advance their agendas by mediating the production, circulation, and revision of biomedical knowledge. To do this, their constituents become lay experts by blending their embodied experience of illness with self-taught technical knowledge. However, it is unclear how lay expertise is routinized within EHMs, and consequently, to what extent it can be made durable in long-term partnerships with credentialed experts. I follow the OpenAPS community-a group of people with type one diabetes who engineered an open-source 'artificial pancreas'-from their inception in the transient #WeAreNotWaiting movement to their research collaborations with endocrinologists and detente with the FDA. I argue that OpenAPS user-contributors formalized their expertise in three steps: First, they broke the OpenAPS algorithm into modules so that prospective users must become experts to assemble it. Second, they lowered this barrier to entry by facilitating the socialization of new user-contributors with a training ritual. And third, they intervened in the strained endocrinologist-patient relationship. These tactics-restricting membership, reproducing expertise, and realigning interests-won the respect of credentialled experts who saw themselves in the OpenAPS community's image. While not all EHMs follow this trajectory, this case demonstrates that lay expertise can mature and assume new institutional forms without relying on commercialization or patronage.
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Affiliation(s)
- Clay Davis
- Northwestern University, Evanston, IL, USA
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16
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Naula Duchi EA, Betancourt Cervantes HA, Yañez Espinosa CR, Rodríguez CA, Garza-Castañon LE, Martínez López JI. Particle Tracking and Micromixing Performance Characterization with a Mobile Device. Sensors (Basel) 2023; 23:9900. [PMID: 38139748 PMCID: PMC10747875 DOI: 10.3390/s23249900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/05/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023]
Abstract
Strategies to stir and mix reagents in microfluid devices have evolved concomitantly with advancements in manufacturing techniques and sensing. While there is a large array of reported designs to combine and homogenize liquids, most of the characterization has been focused on setups with two inlets and one outlet. While this configuration is helpful to directly evaluate the effects of features and parameters on the mixing degree, it does not portray the conditions for experiments that involve more than two substances required to be subsequently combined. In this work, we present a mixing characterization methodology based on particle tracking as an alternative to the most common approach to measure homogeneity using the standard deviation of pixel intensities from a grayscale image. The proposed algorithm is implemented on a free and open-source mobile application (MIQUOD) for Android devices, numerically tested on COMSOL Multiphysics, and experimentally tested on a bidimensional split and recombine micromixer and a three-dimensional micromixer with sinusoidal grooves for different Reynolds numbers and geometrical features for samples with fluids seeded with red, blue, and green microparticles. The application uses concentration field data and particle track data to evaluate up to eleven performance metrics. Furthermore, with the insights from the experimental and numerical data, a mixing index for particles (mp) is proposed to characterize mixing performance for scenarios with multiple input reagents.
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Affiliation(s)
- Edisson A. Naula Duchi
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey 64849, Mexico; (E.A.N.D.); (H.A.B.C.); (C.R.Y.E.); (C.A.R.); (L.E.G.-C.)
| | - Héctor Andrés Betancourt Cervantes
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey 64849, Mexico; (E.A.N.D.); (H.A.B.C.); (C.R.Y.E.); (C.A.R.); (L.E.G.-C.)
| | - Christian Rodrigo Yañez Espinosa
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey 64849, Mexico; (E.A.N.D.); (H.A.B.C.); (C.R.Y.E.); (C.A.R.); (L.E.G.-C.)
| | - Ciro A. Rodríguez
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey 64849, Mexico; (E.A.N.D.); (H.A.B.C.); (C.R.Y.E.); (C.A.R.); (L.E.G.-C.)
- Laboratorio Nacional de Manufactura Aditiva y Digital MADiT, Apodaca 64629, Mexico
| | - Luis E. Garza-Castañon
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey 64849, Mexico; (E.A.N.D.); (H.A.B.C.); (C.R.Y.E.); (C.A.R.); (L.E.G.-C.)
| | - J. Israel Martínez López
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey 64849, Mexico; (E.A.N.D.); (H.A.B.C.); (C.R.Y.E.); (C.A.R.); (L.E.G.-C.)
- Laboratorio Nacional de Manufactura Aditiva y Digital MADiT, Apodaca 64629, Mexico
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Harris NL, Fields CJ, Hokamp K, Just J, Khetani R, Maia J, Ménager H, Munoz-Torres MC, Unni D, Williams J. BOSC 2023, the 24th annual Bioinformatics Open Source Conference. F1000Res 2023; 12:1568. [PMID: 38076297 PMCID: PMC10704065 DOI: 10.12688/f1000research.143015.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
The 24th annual Bioinformatics Open Source Conference ( BOSC 2023) was part of the 2023i conference on Intelligent Systems for Molecular Biology and the European Conference on Computational Biology (ISMB/ECCB 2023). Launched in 2000 and held yearly since, BOSC is the premier meeting covering open-source bioinformatics and open science. Like ISMB 2022, the 2023 meeting was a hybrid conference, with the in-person component hosted in Lyon, France. ISMB/ECCB attracted a near-record number of attendees, with over 2100 in person and about 900 more online. Approximately 200 people participated in BOSC sessions. In addition to 43 talks and 49 posters, BOSC featured two keynotes: Sara El-Gebali, who spoke about "A New Odyssey: Pioneering the Future of Scientific Progress Through Open Collaboration", and Joseph Yracheta, who spoke about "The Dissonance between Scientific Altruism & Capitalist Extraction: The Zero Trust and Federated Data Sovereignty Solution." Once again, a joint session brought together BOSC and the Bio-Ontologies COSI. The conference ended with a panel on Open and Ethical Data Sharing. As in prior years, BOSC was preceded by a CollaborationFest, a collaborative work event that brought together about 40 participants interested in synergistically combining ideas, shaping project plans, developing software, and more.
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Affiliation(s)
- Nomi L. Harris
- Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
| | - Christopher J. Fields
- Carver Biotechnology Center, University of Illinois Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Karsten Hokamp
- Smurfit Institute of Genetics, Trinity College of Dublin, Dublin, D02 PN40, Ireland
| | - Jérémy Just
- Ecole Normale Superieure de Lyon, Lyon, Auvergne-Rhône-Alpes, 69364, France
| | - Radhika Khetani
- Bioinformatics Core, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts, 02115, USA
| | - Jessica Maia
- BD Technologies and Innovation, Research Triangle Park, North Carolina, 27709, USA
| | | | | | - Deepak Unni
- Swiss Institute of Bioinformatics, Basel, 4051, Switzerland
| | - Jason Williams
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA
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18
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Moro AS, Saccenti D, Seccia A, Ferro M, Malgaroli A, Lamanna J. Poke And Delayed Drink Intertemporal Choice Task (POKE-ADDICT): An open-source behavioral apparatus for intertemporal choice testing in rodents. Animal Model Exp Med 2023; 6:619-626. [PMID: 38082507 PMCID: PMC10757207 DOI: 10.1002/ame2.12366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/17/2023] [Indexed: 12/31/2023] Open
Abstract
Advancements in neuroscience research present opportunities and challenges, requiring substantial resources and funding. To address this, we describe here "Poke And Delayed Drink Intertemporal Choice Task (POKE-ADDICT)", an open-source, versatile, and cost-effective apparatus for intertemporal choice testing in rodents. This allows quantification of delay discounting (DD), a cross-species phenomenon observed in decision making which provides valuable insights into higher-order cognitive functioning. In DD, the subjective value of a delayed reward is reduced as a function of the delay for its receipt. Using our apparatus, we implemented an effective intertemporal choice paradigm for the quantification of DD based on an adjusting delayed amount (ADA) algorithm using mango juice as a reward. Our paradigm requires limited training, a few 3D-printed parts and inexpensive electrical components, including a Raspberry Pi control unit. Furthermore, it is compatible with several in vivo procedures and the use of nose pokes instead of levers allows for faster task learning. Besides the main application described here, the apparatus can be further extended to implement other behavioral tests and protocols, including standard operant conditioning. In conclusion, we describe a versatile and cost-effective design based on Raspberry Pi that can support research in animal behavior, decision making and, more specifically, delay discounting.
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Affiliation(s)
- Andrea Stefano Moro
- Department of PsychologySigmund Freud UniversityMilanItaly
- Center for Behavioral Neuroscience and Communication (BNC)Vita‐Salute San Raffaele UniversityMilanItaly
- Transcranial Magnetic Stimulation Unit, Italian Psychotherapy ClinicsMilanItaly
| | - Daniele Saccenti
- Department of PsychologySigmund Freud UniversityMilanItaly
- Transcranial Magnetic Stimulation Unit, Italian Psychotherapy ClinicsMilanItaly
| | - Alessia Seccia
- Center for Behavioral Neuroscience and Communication (BNC)Vita‐Salute San Raffaele UniversityMilanItaly
| | - Mattia Ferro
- Department of PsychologySigmund Freud UniversityMilanItaly
- Center for Behavioral Neuroscience and Communication (BNC)Vita‐Salute San Raffaele UniversityMilanItaly
- Transcranial Magnetic Stimulation Unit, Italian Psychotherapy ClinicsMilanItaly
| | - Antonio Malgaroli
- Center for Behavioral Neuroscience and Communication (BNC)Vita‐Salute San Raffaele UniversityMilanItaly
- Faculty of PsychologyVita‐Salute San Raffaele UniversityMilanItaly
- San Raffaele Turro, IRCCS Ospedale San RaffaeleMilanItaly
| | - Jacopo Lamanna
- Center for Behavioral Neuroscience and Communication (BNC)Vita‐Salute San Raffaele UniversityMilanItaly
- Faculty of PsychologyVita‐Salute San Raffaele UniversityMilanItaly
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Tan KKD, Tsuchida MA, Chacko JV, Gahm NA, Eliceiri KW. Real-time open-source FLIM analysis. Front Bioinform 2023; 3:1286983. [PMID: 38098814 PMCID: PMC10720713 DOI: 10.3389/fbinf.2023.1286983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/08/2023] [Indexed: 12/17/2023] Open
Abstract
Fluorescence lifetime imaging microscopy (FLIM) provides valuable quantitative insights into fluorophores' chemical microenvironment. Due to long computation times and the lack of accessible, open-source real-time analysis toolkits, traditional analysis of FLIM data, particularly with the widely used time-correlated single-photon counting (TCSPC) approach, typically occurs after acquisition. As a result, uncertainties about the quality of FLIM data persist even after collection, frequently necessitating the extension of imaging sessions. Unfortunately, prolonged sessions not only risk missing important biological events but also cause photobleaching and photodamage. We present the first open-source program designed for real-time FLIM analysis during specimen scanning to address these challenges. Our approach combines acquisition with real-time computational and visualization capabilities, allowing us to assess FLIM data quality on the fly. Our open-source real-time FLIM viewer, integrated as a Napari plugin, displays phasor analysis and rapid lifetime determination (RLD) results computed from real-time data transmitted by acquisition software such as the open-source Micro-Manager-based OpenScan package. Our method facilitates early identification of FLIM signatures and data quality assessment by providing preliminary analysis during acquisition. This not only speeds up the imaging process, but it is especially useful when imaging sensitive live biological samples.
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Affiliation(s)
- Kevin K. D. Tan
- Department of Biomedical Engineering, University of Wisconsin, Madison, WI, United States
- Center for Quantitative Cell Imaging, University of Wisconsin, Madison, WI, United States
| | - Mark A. Tsuchida
- Center for Quantitative Cell Imaging, University of Wisconsin, Madison, WI, United States
| | - Jenu V. Chacko
- Center for Quantitative Cell Imaging, University of Wisconsin, Madison, WI, United States
| | - Niklas A. Gahm
- Department of Biomedical Engineering, University of Wisconsin, Madison, WI, United States
- Center for Quantitative Cell Imaging, University of Wisconsin, Madison, WI, United States
- Morgridge Institute for Research, Madison, WI, United States
| | - Kevin W. Eliceiri
- Department of Biomedical Engineering, University of Wisconsin, Madison, WI, United States
- Center for Quantitative Cell Imaging, University of Wisconsin, Madison, WI, United States
- Morgridge Institute for Research, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States
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20
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Jones H, Willis JA, Firth LC, Giachello CNG, Gilestro GF. A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster. eLife 2023; 12:RP86695. [PMID: 37938101 PMCID: PMC10631757 DOI: 10.7554/elife.86695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023] Open
Abstract
Understanding how the brain encodes behaviour is the ultimate goal of neuroscience and the ability to objectively and reproducibly describe and quantify behaviour is a necessary milestone on this path. Recent technological progresses in machine learning and computational power have boosted the development and adoption of systems leveraging on high-resolution video recording to track an animal pose and describe behaviour in all four dimensions. However, the high temporal and spatial resolution that these systems offer must come as a compromise with their throughput and accessibility. Here, we describe coccinella, an open-source reductionist framework combining high-throughput analysis of behaviour using real-time tracking on a distributed mesh of microcomputers (ethoscopes) with resource-lean statistical learning (HCTSA/Catch22). Coccinella is a reductionist system, yet outperforms state-of-the-art alternatives when exploring the pharmacobehaviour in Drosophila melanogaster.
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Affiliation(s)
- Hannah Jones
- Department of Life Sciences, Imperial College LondonLondonUnited Kingdom
| | - Jenny A Willis
- Syngenta, Jealott’s Hill International Research CentreBracknellUnited Kingdom
| | - Lucy C Firth
- Syngenta, Jealott’s Hill International Research CentreBracknellUnited Kingdom
| | - Carlo NG Giachello
- Syngenta, Jealott’s Hill International Research CentreBracknellUnited Kingdom
| | - Giorgio F Gilestro
- Department of Life Sciences, Imperial College LondonLondonUnited Kingdom
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21
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Neis P, Warch D, Hoppe M. Testing and Evaluation of Low-Cost Sensors for Developing Open Smart Campus Systems Based on IoT. Sensors (Basel) 2023; 23:8652. [PMID: 37896746 PMCID: PMC10611299 DOI: 10.3390/s23208652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/14/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023]
Abstract
Urbanization has led to the need for the intelligent management of various urban challenges, from traffic to energy. In this context, smart campuses and buildings emerge as microcosms of smart cities, offering both opportunities and challenges in technology and communication integration. This study sets itself apart by prioritizing sustainable, adaptable, and reusable solutions through an open-source framework and open data protocols. We utilized the Internet of Things (IoT) and cost-effective sensors to capture real-time data for three different use cases: real-time monitoring of visitor counts, room and parking occupancy, and the collection of environment and climate data. Our analysis revealed that the implementation of the utilized hardware and software combination significantly improved the implementation of open smart campus systems, providing a usable visitor information system for students. Moreover, our focus on data privacy and technological versatility offers valuable insights into real-world applicability and limitations. This study contributes a novel framework that not only drives technological advancements but is also readily adaptable, improvable, and reusable across diverse settings, thereby showcasing the untapped potential of smart, sustainable systems.
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Affiliation(s)
- Pascal Neis
- School of Technology, Department of Geoinformatics and Surveying, Mainz University of Applied Sciences, 55128 Mainz, Germany
| | - Dominik Warch
- School of Technology, Department of Geoinformatics and Surveying, Mainz University of Applied Sciences, 55128 Mainz, Germany
| | - Max Hoppe
- School of Technology, Department of Geoinformatics and Surveying, Mainz University of Applied Sciences, 55128 Mainz, Germany
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22
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Sarıyer RM, Edwards AD, Needs SH. Open Hardware for Microfluidics: Exploiting Raspberry Pi Singleboard Computer and Camera Systems for Customisable Laboratory Instrumentation. Biosensors (Basel) 2023; 13:948. [PMID: 37887141 PMCID: PMC10605846 DOI: 10.3390/bios13100948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/18/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023]
Abstract
The integration of Raspberry Pi miniature computer systems with microfluidics has revolutionised the development of low-cost and customizable analytical systems in life science laboratories. This review explores the applications of Raspberry Pi in microfluidics, with a focus on imaging, including microscopy and automated image capture. By leveraging the low cost, flexibility and accessibility of Raspberry Pi components, high-resolution imaging and analysis have been achieved in direct mammalian and bacterial cellular imaging and a plethora of image-based biochemical and molecular assays, from immunoassays, through microbial growth, to nucleic acid methods such as real-time-qPCR. The control of image capture permitted by Raspberry Pi hardware can also be combined with onboard image analysis. Open-source hardware offers an opportunity to develop complex laboratory instrumentation systems at a fraction of the cost of commercial equipment and, importantly, offers an opportunity for complete customisation to meet the users' needs. However, these benefits come with a trade-off: challenges remain for those wishing to incorporate open-source hardware equipment in their own work, including requirements for construction and operator skill, the need for good documentation and the availability of rapid prototyping such as 3D printing plus other components. These advances in open-source hardware have the potential to improve the efficiency, accessibility, and cost-effectiveness of microfluidic-based experiments and applications.
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23
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Laman A, Das D, Priye A. Miniaturized Non-Contact Heating and Transmitted Light Imaging Using an Inexpensive and Modular 3D-Printed Platform for Molecular Diagnostics. Sensors (Basel) 2023; 23:7718. [PMID: 37765775 PMCID: PMC10535971 DOI: 10.3390/s23187718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023]
Abstract
The ability to simultaneously heat and image samples using transmitted light is crucial for several biological applications. However, existing techniques such as heated stage microscopes, thermal cyclers equipped with imaging capabilities, or non-contact heating systems are often bulky, expensive, and complex. This work presents the development and characterization of a Miniaturized Optically-clear Thermal Enclosure (MOTE) system-an open-source, inexpensive, and low-powered modular system-capable of convectively heating samples while simultaneously imaging them with transmitted light. We develop and validate a computational fluid dynamics (CFD) model to design and optimize the heating chamber. The model simulates velocity and temperature profiles within the heating chamber for various chamber materials and sizes. The computational model yielded an optimal chamber dimension capable of achieving a stable temperature ranging from ambient to 95 °C with a spatial discrepancy of less than 1.5 °C, utilizing less than 8.5 W of power. The dual-functionality of the MOTE system, enabling synchronous heating and transmitted light imaging, was demonstrated through the successful execution of paper-based LAMP reactions to detect λ DNA samples in real-time down to 10 copies/µL of the target concentration. The MOTE system offers a promising and flexible platform for various applications, from molecular diagnostics to biochemical analyses, cell biology, genomics, and education.
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Affiliation(s)
- Alex Laman
- Department of Chemical and Environmental Engineering, University of Cincinnati, Cincinnati, OH 45221, USA;
| | - Debayan Das
- Chemical Engineering Department, NIT Durgapur, Mahatma Gandhi Rd., A-Zone, Durgapur 713209, West Bengal, India;
| | - Aashish Priye
- Department of Chemical and Environmental Engineering, University of Cincinnati, Cincinnati, OH 45221, USA;
- Digital Futures, University of Cincinnati, Cincinnati, OH 45221, USA
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24
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Roberts CH, Stott C, Shawe-Taylor M, Chaudhry Z, Lal S, Marks M. Biometric linkage of longitudinally collected electronic case report forms and confirmation of subject identity: an open framework for ODK and related tools. Front Digit Health 2023; 5:1072331. [PMID: 37600479 PMCID: PMC10436742 DOI: 10.3389/fdgth.2023.1072331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 07/25/2023] [Indexed: 08/22/2023] Open
Abstract
The availability of low-cost biometric hardware sensors and software makes it possible to rapidly, affordably and securely sample and store a unique and invariant biological signature (or biometric "template") for the purposes of identification. This has applications in research and trials, particularly for purposes of consent, linkage of case reporting forms collected at different times, and in the confirmation of participant identity for purposes of safety monitoring and adherence to international data laws. More broadly, these methods are applicable to the needs of the billion people who live in resource-restricted settings without identification credentials. The use of mobile electronic data collection software has recently become commonplace in clinical trials, research and actions for public good. A raft of tools based on the open-source ODK project now provide diverse options for data management that work consistently in resource-restricted settings, but none have built-in functionality for capturing biometric templates. In this study, we report the development and validation of a novel open-source app and associated method for capturing and matching biometric fingerprint templates during data collection with the popular data platforms ODK, KoBoToolbox, SurveyCTO, Ona and CommCare. Using data from more than 1,000 fingers, we show that fingerprint templates can be used to link data records with high accuracy. The accuracy of this process increases through the linkage of multiple fingerprints to each data record. By focussing on publishing open-source code and documentation, and by using an affordable (<£50) and mass-produced model of fingerprint sensor, we are able to make this platform freely available to the large global user community that utilises ODK and related data collection systems.
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Affiliation(s)
- Chrissy h. Roberts
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - Marianne Shawe-Taylor
- Hospital for Tropical Diseases, University College London Hospitals NHS Trust, London, United Kingdom
| | - Zain Chaudhry
- Hospital for Tropical Diseases, University College London Hospitals NHS Trust, London, United Kingdom
| | - Sham Lal
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Michael Marks
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Hospital for Tropical Diseases, University College London Hospitals NHS Trust, London, United Kingdom
- Division of Infection and Immunity, University College London, London, United Kingdom
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25
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Benedict J, Cudmore RH. PiE: an open-source pipeline for home cage behavioral analysis. Front Neurosci 2023; 17:1222644. [PMID: 37583418 PMCID: PMC10423934 DOI: 10.3389/fnins.2023.1222644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 07/13/2023] [Indexed: 08/17/2023] Open
Abstract
Over the last two decades a growing number of neuroscience labs are conducting behavioral assays in rodents. The equipment used to collect this behavioral data must effectively limit environmental and experimenter disruptions, to avoid confounding behavior data. Proprietary behavior boxes are expensive, offer limited compatible sensors, and constrain analysis with closed-source hardware and software. Here, we introduce PiE, an open-source, end-to-end, user-configurable, scalable, and inexpensive behavior assay system. The PiE system includes the custom-built behavior box to hold a home cage, as well as software enabling continuous video recording and individual behavior box environmental control. To limit experimental disruptions, the PiE system allows the control and monitoring of all aspects of a behavioral experiment using a remote web browser, including real-time video feeds. To allow experiments to scale up, the PiE system provides a web interface where any number of boxes can be controlled, and video data easily synchronized to a remote location. For the scoring of behavior video data, the PiE system includes a standalone desktop application that streamlines the blinded manual scoring of large datasets with a focus on quality control and assay flexibility. The PiE system is ideal for all types of behavior assays in which video is recorded. Users are free to use individual components of this setup independently, or to use the entire pipeline from data collection to analysis. Alpha testers have included scientists without prior coding experience. An example pipeline is demonstrated with the PiE system enabling the user to record home cage maternal behavior assays, synchronize the resulting data, conduct blinded scoring, and import the data into R for data visualization and analysis.
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Affiliation(s)
- Jessie Benedict
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Robert H. Cudmore
- Department of Physiology and Membrane Biology, University of California-Davis School of Medicine, Davis, CA, United States
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26
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Wierenga RP, Golas S, Ho W, Coley C, Esvelt KM. PyLabRobot: An Open-Source, Hardware Agnostic Interface for Liquid-Handling Robots and Accessories. bioRxiv 2023:2023.07.10.547733. [PMID: 37502883 PMCID: PMC10369895 DOI: 10.1101/2023.07.10.547733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Liquid handling robots are often limited by proprietary programming interfaces that are only compatible with a single type of robot and operating system, restricting method sharing and slowing development. Here we present PyLabRobot, an open-source, cross-platform Python interface capable of programming diverse liquid-handling robots, including Hamilton STARs, Tecan EVOs, and Opentron OT-2s. PyLabRobot provides a universal set of commands and representations for deck layout and labware, enabling the control of diverse accessory devices. The interface is extensible and can work with any robot that manipulates liquids within a Cartesian coordinate system. We validated the system through unit tests and several application demonstrations, including a browser-based simulator, a position calibration tool, and a path-teaching tool for complex movements. PyLabRobot provides a flexible, open, and collaborative programming environment for laboratory automation.
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Affiliation(s)
- Rick P. Wierenga
- Leiden University, Leiden, the Netherlands
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stefan Golas
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Wilson Ho
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Connor Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kevin M. Esvelt
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
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27
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Tsfaty C, Fire M. Malicious source code detection using a translation model. Patterns (N Y) 2023; 4:100773. [PMID: 37521045 PMCID: PMC10382987 DOI: 10.1016/j.patter.2023.100773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 08/01/2023]
Abstract
Modern software development often relies on open-source code sharing. Open-source code reuse, however, allows hackers to access wide developer communities, thereby potentially affecting many products. An increasing number of such "supply chain attacks" have occurred in recent years, taking advantage of open-source software development practices. Here, we introduce the Malicious Source code Detection using a Translation model (MSDT) algorithm. MSDT is a novel deep-learning-based analysis method that detects real-world code injections into source code packages. We have tested MSDT by embedding examples from a dataset of over 600,000 different functions and then applying a clustering algorithm to the resulting embedding vectors to identify malicious functions by detecting outliers. We evaluated MSDT's performance with extensive experiments and demonstrated that MSDT could detect malicious code injections with precision@k values of up to 0.909.
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Affiliation(s)
- Chen Tsfaty
- Department of Software and Information Systems Engineering, Ben-Gurion University, Beer-Sheva 8410501, Israel
| | - Michael Fire
- Department of Software and Information Systems Engineering, Ben-Gurion University, Beer-Sheva 8410501, Israel
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28
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Castillo‐Passi C, Coronado R, Varela‐Mattatall G, Alberola‐López C, Botnar R, Irarrazaval P. KomaMRI.jl: An open-source framework for general MRI simulations with GPU acceleration. Magn Reson Med 2023; 90:329-342. [PMID: 36877139 PMCID: PMC10952765 DOI: 10.1002/mrm.29635] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 02/20/2023] [Accepted: 02/22/2023] [Indexed: 03/07/2023]
Abstract
PURPOSE To develop an open-source, high-performance, easy-to-use, extensible, cross-platform, and general MRI simulation framework (Koma). METHODS Koma was developed using the Julia programming language. Like other MRI simulators, it solves the Bloch equations with CPU and GPU parallelization. The inputs are the scanner parameters, the phantom, and the pulse sequence that is Pulseq-compatible. The raw data is stored in the ISMRMRD format. For the reconstruction, MRIReco.jl is used. A graphical user interface utilizing web technologies was also designed. Two types of experiments were performed: one to compare the quality of the results and the execution speed, and the second to compare its usability. Finally, the use of Koma in quantitative imaging was demonstrated by simulating Magnetic Resonance Fingerprinting (MRF) acquisitions. RESULTS Koma was compared to two well-known open-source MRI simulators, JEMRIS and MRiLab. Highly accurate results (with mean absolute differences below 0.1% compared to JEMRIS) and better GPU performance than MRiLab were demonstrated. In an experiment with students, Koma was proved to be easy to use, eight times faster on personal computers than JEMRIS, and 65% of test subjects recommended it. The potential for designing acquisition and reconstruction techniques was also shown through the simulation of MRF acquisitions, with conclusions that agree with the literature. CONCLUSIONS Koma's speed and flexibility have the potential to make simulations more accessible for education and research. Koma is expected to be used for designing and testing novel pulse sequences before implementing them in the scanner with Pulseq files, and for creating synthetic data to train machine learning models.
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Affiliation(s)
- Carlos Castillo‐Passi
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Institute for Biological and Medical EngineeringPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)Pontificia Universidad Católica de ChileSantiagoChile
| | - Ronal Coronado
- Institute for Biological and Medical EngineeringPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)Pontificia Universidad Católica de ChileSantiagoChile
- Electrical EngineeringPontificia Universidad Católica de ChileSantiagoChile
| | - Gabriel Varela‐Mattatall
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research InstituteWestern UniversityLondonOntarioCanada
- Department of Medical Biophysics, Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada
| | | | - René Botnar
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Institute for Biological and Medical EngineeringPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)Pontificia Universidad Católica de ChileSantiagoChile
| | - Pablo Irarrazaval
- Institute for Biological and Medical EngineeringPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)Pontificia Universidad Católica de ChileSantiagoChile
- Electrical EngineeringPontificia Universidad Católica de ChileSantiagoChile
- Laboratorio de Procesado de ImagenUniversidad de ValladolidValladolidSpain
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29
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Stone H, Heslop D, Lim S, Sarmiento I, Kunasekaran M, MacIntyre CR. Open-Source Intelligence for Detection of Radiological Events and Syndromes Following the Invasion of Ukraine in 2022: Observational Study. JMIR Infodemiology 2023; 3:e39895. [PMID: 37379069 PMCID: PMC10365590 DOI: 10.2196/39895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 01/26/2023] [Accepted: 04/11/2023] [Indexed: 06/29/2023]
Abstract
BACKGROUND On February 25, 2022, Russian forces took control of the Chernobyl power plant after continuous fighting within the Chernobyl exclusion zone. Continual events occurred in the month of March, which raised the risk of potential contamination of previously uncontaminated areas and the potential for impacts on human and environmental health. The disruption of war has caused interruptions to normal preventive activities, and radiation monitoring sensors have been nonfunctional. Open-source intelligence can be informative when formal reporting and data are unavailable. OBJECTIVE This paper aimed to demonstrate the value of open-source intelligence in Ukraine to identify signals of potential radiological events of health significance during the Ukrainian conflict. METHODS Data were collected from search terminology for radiobiological events and acute radiation syndrome detection between February 1 and March 20, 2022, using 2 open-source intelligence (OSINT) systems, EPIWATCH and Epitweetr. RESULTS Both EPIWATCH and Epitweetr identified signals of potential radiobiological events throughout Ukraine, particularly on March 4 in Kyiv, Bucha, and Chernobyl. CONCLUSIONS Open-source data can provide valuable intelligence and early warning about potential radiation hazards in conditions of war, where formal reporting and mitigation may be lacking, to enable timely emergency and public health responses.
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Affiliation(s)
- Haley Stone
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - David Heslop
- School of Population Health, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Samsung Lim
- School of Civil & Environmental Engineering, University of New South Wales, Sydney, Australia
| | - Ines Sarmiento
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - Mohana Kunasekaran
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Kensington, Australia
| | - C Raina MacIntyre
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Kensington, Australia
- College of Public Service & Community Solutions, Arizona State University, Tempe, AZ, United States
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30
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Lauterbur ME, Cavassim MIA, Gladstein AL, Gower G, Pope NS, Tsambos G, Adrion J, Belsare S, Biddanda A, Caudill V, Cury J, Echevarria I, Haller BC, Hasan AR, Huang X, Iasi LNM, Noskova E, Obsteter J, Pavinato VAC, Pearson A, Peede D, Perez MF, Rodrigues MF, Smith CCR, Spence JP, Teterina A, Tittes S, Unneberg P, Vazquez JM, Waples RK, Wohns AW, Wong Y, Baumdicker F, Cartwright RA, Gorjanc G, Gutenkunst RN, Kelleher J, Kern AD, Ragsdale AP, Ralph PL, Schrider DR, Gronau I. Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations. eLife 2023; 12:RP84874. [PMID: 37342968 DOI: 10.7554/elife.84874] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2023] Open
Abstract
Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.
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Affiliation(s)
- M Elise Lauterbur
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, United States
| | - Maria Izabel A Cavassim
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States
| | | | - Graham Gower
- Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Nathaniel S Pope
- Institute of Ecology and Evolution, University of Oregon, Eugene, United States
| | - Georgia Tsambos
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Jeffrey Adrion
- Institute of Ecology and Evolution, University of Oregon, Eugene, United States
- Ancestry DNA, San Francisco, United States
| | - Saurabh Belsare
- Institute of Ecology and Evolution, University of Oregon, Eugene, United States
| | | | - Victoria Caudill
- Institute of Ecology and Evolution, University of Oregon, Eugene, United States
| | - Jean Cury
- Universite Paris-Saclay, CNRS, INRIA, Laboratoire Interdisciplinaire des Sciences du Numerique, Orsay, France
| | | | - Benjamin C Haller
- Department of Computational Biology, Cornell University, Ithaca, United States
| | - Ahmed R Hasan
- Department of Cell and Systems Biology, University of Toronto, Toronto, Canada
- Department of Biology, University of Toronto Mississauga, Mississauga, Canada
| | - Xin Huang
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria
- Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Vienna, Austria
| | | | - Ekaterina Noskova
- Computer Technologies Laboratory, ITMO University, St Petersburg, Russian Federation
| | - Jana Obsteter
- Agricultural Institute of Slovenia, Department of Animal Science, Ljubljana, Slovenia
| | | | - Alice Pearson
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
- Department of Zoology, University of Cambridge, Cambridge, United Kingdom
| | - David Peede
- Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, United States
- Center for Computational Molecular Biology, Brown University, Providence, United States
| | - Manolo F Perez
- Department of Genetics and Evolution, Federal University of Sao Carlos, Sao Carlos, Brazil
| | - Murillo F Rodrigues
- Institute of Ecology and Evolution, University of Oregon, Eugene, United States
| | - Chris C R Smith
- Institute of Ecology and Evolution, University of Oregon, Eugene, United States
| | - Jeffrey P Spence
- Department of Genetics, Stanford University School of Medicine, Stanford, United States
| | - Anastasia Teterina
- Institute of Ecology and Evolution, University of Oregon, Eugene, United States
| | - Silas Tittes
- Institute of Ecology and Evolution, University of Oregon, Eugene, United States
| | - Per Unneberg
- Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Juan Manuel Vazquez
- Department of Integrative Biology, University of California, Berkeley, Berkeley, United States
| | - Ryan K Waples
- Department of Biostatistics, University of Washington, Seattle, United States
| | | | - Yan Wong
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Franz Baumdicker
- Cluster of Excellence - Controlling Microbes to Fight Infections, Eberhard Karls Universit¨at Tubingen, Tubingen, Germany
| | - Reed A Cartwright
- School of Life Sciences and The Biodesign Institute, Arizona State University, Tempe, United States
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
| | - Ryan N Gutenkunst
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, United States
| | - Jerome Kelleher
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Andrew D Kern
- Institute of Ecology and Evolution, University of Oregon, Eugene, United States
| | - Aaron P Ragsdale
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, United States
| | - Peter L Ralph
- Institute of Ecology and Evolution, University of Oregon, Eugene, United States
- Department of Mathematics, University of Oregon, Eugene, United States
| | - Daniel R Schrider
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, United States
| | - Ilan Gronau
- Efi Arazi School of Computer Science, Reichman University, Herzliya, Israel
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31
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Jacoby M, Baumann M, Bischoff T, Mees H, Müller J, Stojanovic L, Volz F. Open-Source Implementations of the Reactive Asset Administration Shell: A Survey. Sensors (Basel) 2023; 23:s23115229. [PMID: 37299956 DOI: 10.3390/s23115229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/25/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023]
Abstract
The use of open-source software is crucial for the digitalization of manufacturing, including the implementation of Digital Twins as envisioned in Industry 4.0. This research paper provides a comprehensive comparison of free and open-source implementations of the reactive Asset Administration Shell (AAS) for creating Digital Twins. A structured search on GitHub and Google Scholar was conducted, leading to the selection of four implementations for detailed analysis. Objective evaluation criteria were defined, and a testing framework was created to test support for the most common AAS model elements and API calls. The results show that all implementations support at least a minimal set of required features while none implement the specification in all details, which highlights the challenges of implementing the AAS specification and the incompatibility between different implementations. This paper is therefore the first attempt at a comprehensive comparison of AAS implementations and identifies potential areas for improvement in future implementations. It also provides valuable insights for software developers and researchers in the field of AAS-based Digital Twins.
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Affiliation(s)
- Michael Jacoby
- Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, 76131 Karlsruhe, Germany
| | - Michael Baumann
- Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, 76131 Karlsruhe, Germany
| | - Tino Bischoff
- Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, 76131 Karlsruhe, Germany
| | - Hans Mees
- Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, 76131 Karlsruhe, Germany
| | - Jens Müller
- Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, 76131 Karlsruhe, Germany
| | - Ljiljana Stojanovic
- Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, 76131 Karlsruhe, Germany
| | - Friedrich Volz
- Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, 76131 Karlsruhe, Germany
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Liu S, Beillas P, Ding L, Wang X. PIPER adult comfort: an open-source full body human body model for seating comfort assessment and its validation under static loading conditions. Front Bioeng Biotechnol 2023; 11:1170768. [PMID: 37324425 PMCID: PMC10267746 DOI: 10.3389/fbioe.2023.1170768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/05/2023] [Indexed: 06/17/2023] Open
Abstract
Introduction: In this paper we introduce an adult-sized FE full-body HBM for seating comfort assessments and present its validation in different static seating conditions in terms of pressure distribution and contact forces. Methods: We morphed the PIPER Child model into a male adult-sized model with the help of different target sources including his body surface scans, and spinal and pelvic bone surfaces and an open sourced full body skeleton. We also introduced soft tissue sliding under the ischial tuberosities (ITs). The initial model was adapted for seating applications with low modulus soft tissue material property and mesh refinements for buttock regions, etc. We compared the contact forces and pressure-related parameters simulated using the adult HBM with those obtained experimentally from the person whose data was used for the model development. Four seat configurations, with the seat pan angle varying from 0° to 15° and seat-to-back angle fixed at 100°, were tested. Results: The adult HBM could correctly simulate the contact forces on the backrest, seat pan, and foot support with an average error of less than 22.3 N and 15.5 N in the horizontal and vertical directions, which is small considering the body weight (785 N). In terms of contact area, peak, and mean pressure, the simulation matched well with the experiment for the seat pan. With soft tissue sliding, higher soft tissue compression was obtained in agreement with the observations from recent MRI studies. Discussion: The present adult model could be used as a reference using a morphing tool as proposed in PIPER. The model will be published openly online as part of the PIPER open-source project (www.PIPER-project.org) to facilitate its reuse and improvement as well as its specific adaptation for different applications.
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Affiliation(s)
- Shenghui Liu
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- Université de Lyon, Université Claude Bernard Lyon 1, Université Gustave Eiffel, LBMC UMR_T 9406, Lyon, France
| | - Philippe Beillas
- Université de Lyon, Université Claude Bernard Lyon 1, Université Gustave Eiffel, LBMC UMR_T 9406, Lyon, France
| | - Li Ding
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Xuguang Wang
- Université de Lyon, Université Claude Bernard Lyon 1, Université Gustave Eiffel, LBMC UMR_T 9406, Lyon, France
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33
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Wodke JAH, Michaelis L, Henkel R. The MeDaX Knowledge Graph Prototype. Stud Health Technol Inform 2023; 302:147-148. [PMID: 37203634 DOI: 10.3233/shti230089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Data sharing is sustainable for several reasons, including minimising economical and human costs or maximising knowledge gain. Still, reuse of biomedical (research) data is often hampered by the diverse technical, juridical, and scientific requirements for biomedical data handling and specifically sharing. We are building a toolbox for automated generation of knowledge graphs (KGs) from diverse sources, for data enrichment, and for data analysis. Into the MeDaX KG prototype, we integrated data from the core data set of the German Medical Informatics Initiative (MII) with ontological and provenance information. This prototype is currently used for internal concept and method testing only. In subsequent versions it will be expanded by including more meta-data and relevant data sources as well as further tools, including a user interface.
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Affiliation(s)
- Judith A H Wodke
- Department of Medical Informatics, University Medicine Greifswald, Germany
| | - Lea Michaelis
- Core Unit Data Integration Center, University Medicine Greifswald, Germany
| | - Ron Henkel
- Department of Medical Informatics, University Medicine Greifswald, Germany
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34
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Sabaté Landman M, Biguri A, Hatamikia S, Boardman R, Aston J, Schönlieb CB. On Krylov methods for large-scale CBCT reconstruction. Phys Med Biol 2023. [PMID: 37192631 DOI: 10.1088/1361-6560/acd616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Krylov subspace methods are a powerful family of iterative solvers for linear systems of equations, which are commonly used for inverse problems due to their intrinsic regularization properties. Moreover, these methods are naturally suited to solve large-scale problems, as they only require matrix-vector products with the system matrix (and its adjoint) to compute approximate solutions, and they display a very fast convergence. 
Even if this class of methods has been widely researched and studied in the numerical linear algebra community, its use in applied medical physics and applied engineering is still very limited. e.g. in realistic large-scale Computed Tomography (CT) problems, and more specifically in Cone Beam CT (CBCT). This work attempts to breach this gap by providing a general framework for the most relevant Krylov subspace methods applied to 3D CT problems, including the most well-known Krylov solvers for non-square systems (CGLS, LSQR, LSMR), possibly in combination with Tikhonov regularization, and methods that incorporate total variation (TV) regularization. This is provided within an open source framework: the Tomographic Iterative GPU-based Reconstruction (TIGRE) toolbox, with the idea of promoting accessibility and reproducibility of the results for the algorithms presented. Finally, numerical results in synthetic and real-world 3D CT applications (medical CBCT and μ-CT datasets) are provided to showcase and compare the different Krylov subspace methods presented in the paper, as well as their suitability for different kinds of problems.
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Affiliation(s)
- Malena Sabaté Landman
- Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Wilberforce Rd, Cambridge, Cambridgeshire, CB3 0WA, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Ander Biguri
- Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Wilberforce Rd, Cambridge, Cambridgeshire, CB3 0WA, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Sepideh Hatamikia
- Austrian Center for Medical Innovation and Technology, Viktor Kaplan-Straße 2/1, Wiener Neustadt, 2700, AUSTRIA
| | - Richard Boardman
- University of Southampton, University Rd, Southampton, Hampshire, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - John Aston
- Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge, Wilberforce Rd, Cambridge, Cambridgeshire, CB2 1TN, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Carola-Bibiane Schönlieb
- DAMTP, University of Cambridge, Office: F0.06, Wilberforce Road, Cambridge, CB3 0WA, Cambridge, Cambridgeshire, CB2 1TN, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Erratum: Ksak: a high-throughput tool for alignment-free phylogenetics. Front Microbiol 2023; 14:1212612. [PMID: 37234540 PMCID: PMC10206615 DOI: 10.3389/fmicb.2023.1212612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/28/2023] Open
Abstract
[This corrects the article DOI: 10.3389/fmicb.2023.1050130.].
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36
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Erel Y, Shannon KA, Chu J, Scott K, Struhl MK, Cao P, Tan X, Hart P, Raz G, Piccolo S, Mei C, Potter C, Jaffe-Dax S, Lew-Williams C, Tenenbaum J, Fairchild K, Bermano A, Liu S. iCatcher+: Robust and Automated Annotation of Infants' and Young Children's Gaze Behavior From Videos Collected in Laboratory, Field, and Online Studies. Adv Methods Pract Psychol Sci 2023; 6:10.1177/25152459221147250. [PMID: 37655047 PMCID: PMC10471135 DOI: 10.1177/25152459221147250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Technological advances in psychological research have enabled large-scale studies of human behavior and streamlined pipelines for automatic processing of data. However, studies of infants and children have not fully reaped these benefits because the behaviors of interest, such as gaze duration and direction, still have to be extracted from video through a laborious process of manual annotation, even when these data are collected online. Recent advances in computer vision raise the possibility of automated annotation of these video data. In this article, we built on a system for automatic gaze annotation in young children, iCatcher, by engineering improvements and then training and testing the system (referred to hereafter as iCatcher+) on three data sets with substantial video and participant variability (214 videos collected in U.S. lab and field sites, 143 videos collected in Senegal field sites, and 265 videos collected via webcams in homes; participant age range = 4 months-3.5 years). When trained on each of these data sets, iCatcher+ performed with near human-level accuracy on held-out videos on distinguishing "LEFT" versus "RIGHT" and "ON" versus "OFF" looking behavior across all data sets. This high performance was achieved at the level of individual frames, experimental trials, and study videos; held across participant demographics (e.g., age, race/ethnicity), participant behavior (e.g., movement, head position), and video characteristics (e.g., luminance); and generalized to a fourth, entirely held-out online data set. We close by discussing next steps required to fully automate the life cycle of online infant and child behavioral studies, representing a key step toward enabling robust and high-throughput developmental research.
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Affiliation(s)
- Yotam Erel
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv-Yafo, Israel
| | | | - Junyi Chu
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Kim Scott
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Melissa Kline Struhl
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Peng Cao
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Xincheng Tan
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
| | - Peter Hart
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Gal Raz
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Sabrina Piccolo
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Catherine Mei
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Christine Potter
- Department of Psychology, The University of Texas at El Paso, El Paso, Texas
| | - Sagi Jaffe-Dax
- The School of Psychological Sciences, Tel Aviv University, Tel Aviv-Yafo, Israel
| | | | - Joshua Tenenbaum
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
- The MIT Quest for Intelligence, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Katherine Fairchild
- The MIT Quest for Intelligence, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Amit Bermano
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Shari Liu
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, Maryland
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37
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Formozov A, Dieter A, Wiegert JS. A flexible and versatile system for multi-color fiber photometry and optogenetic manipulation. Cell Rep Methods 2023; 3:100418. [PMID: 37056369 PMCID: PMC10088095 DOI: 10.1016/j.crmeth.2023.100418] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 12/20/2022] [Accepted: 02/08/2023] [Indexed: 03/09/2023]
Abstract
Here, we present simultaneous fiber photometry recordings and optogenetic stimulation based on a multimode fused fiber coupler for both light delivery and collection without the need for dichroic beam splitters. In combination with a multi-color light source and appropriate optical filters, our approach offers remarkable flexibility in experimental design and facilitates the exploration of new molecular tools in vivo at minimal cost. We demonstrate straightforward re-configuration of the setup to operate with green, red, and near-infrared calcium indicators with or without simultaneous optogenetic stimulation and further explore the multi-color photometry capabilities of the system. The ease of assembly, operation, characterization, and customization of this platform holds the potential to foster the development of experimental strategies for multi-color fused fiber photometry combined with optogenetics far beyond its current state.
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Affiliation(s)
- Andrey Formozov
- Research Group Synaptic Wiring and Information Processing, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- Department of Neurophysiology, MCTN, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
| | - Alexander Dieter
- Research Group Synaptic Wiring and Information Processing, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- Department of Neurophysiology, MCTN, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
| | - J. Simon Wiegert
- Research Group Synaptic Wiring and Information Processing, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- Department of Neurophysiology, MCTN, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany
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38
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Luxem K, Sun JJ, Bradley SP, Krishnan K, Yttri E, Zimmermann J, Pereira TD, Laubach M. Open-source tools for behavioral video analysis: Setup, methods, and best practices. eLife 2023; 12:79305. [PMID: 36951911 PMCID: PMC10036114 DOI: 10.7554/elife.79305] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 03/03/2023] [Indexed: 03/24/2023] Open
Abstract
Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional 'center of mass' tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior.
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Affiliation(s)
- Kevin Luxem
- Cellular Neuroscience, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Jennifer J Sun
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, United States
| | - Sean P Bradley
- Rodent Behavioral Core, National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Keerthi Krishnan
- Department of Biochemistry and Cellular & Molecular Biology, University of Tennessee, Knoxville, United States
| | - Eric Yttri
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, United States
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, United States
| | - Talmo D Pereira
- The Salk Institute of Biological Studies, La Jolla, United States
| | - Mark Laubach
- Department of Neuroscience, American University, Washington D.C., United States
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Kulko RD, Pletl A, Mempel H, Wahl F, Elser B. OpenVNT: An Open Platform for VIS-NIR Technology. Sensors (Basel) 2023; 23:s23063151. [PMID: 36991862 PMCID: PMC10055953 DOI: 10.3390/s23063151] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 02/23/2023] [Accepted: 03/11/2023] [Indexed: 06/12/2023]
Abstract
Spectrometers measure diffuse reflectance and create a "molecular fingerprint" of the material under investigation. Ruggedized, small scale devices for "in-field" use cases exist. Such devices might for example be used by companies in the food supply chain for inward inspection of goods. However, their application for the industrial Internet of Things workflows or scientific research is limited due to their proprietary nature. We propose an open platform for visible and near-infrared technology (OpenVNT), an open platform for capturing, transmitting, and analysing spectral measurements. It is built for use in the field, as it is battery-powered and transmits data wireless. To achieve high accuracy, the OpenVNT instrument contains two spectrometers covering a wavelength range of 400-1700 nm. We conducted a study on white grapes to compare the performance of the OpenVNT instrument against the Felix Instruments F750, an established commercial instrument. Using a refractometer as ground truth, we built and validated models to estimate the Brix value. As a quality measure, we used coefficient of determination of the cross-validation (R2CV) between the instrument estimation and ground truth. With 0.94 for the OpenVNT and 0.97 for the F750, a comparable R2CV was achieved for both instruments. OpenVNT matches the performance of commercially available instruments at one tenth of the price. We provide an open bill of materials, building instructions, firmware, and analysis software to enable research and industrial IOT solutions without the limitations of walled garden platforms.
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Affiliation(s)
- Roman-David Kulko
- Technologie Campus Grafenau, Technische Hochschule Deggendorf, 94481 Grafenau, Germany
| | - Alexander Pletl
- Technologie Campus Grafenau, Technische Hochschule Deggendorf, 94481 Grafenau, Germany
| | - Heike Mempel
- Institut für Gartenbau, Hochschule Weihenstephan-Triesdorf, 85354 Freising, Germany
| | - Florian Wahl
- Technologie Campus Grafenau, Technische Hochschule Deggendorf, 94481 Grafenau, Germany
| | - Benedikt Elser
- Technologie Campus Grafenau, Technische Hochschule Deggendorf, 94481 Grafenau, Germany
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40
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Macri C, Bacchi S, Teoh SC, Lim WY, Lam L, Patel S, Slee M, Casson R, Chan W. Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis. J Med Internet Res 2023; 25:e42789. [PMID: 36881455 PMCID: PMC10031443 DOI: 10.2196/42789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/27/2023] [Accepted: 02/09/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND Strategies to improve the selection of appropriate target journals may reduce delays in disseminating research results. Machine learning is increasingly used in content-based recommender algorithms to guide journal submissions for academic articles. OBJECTIVE We sought to evaluate the performance of open-source artificial intelligence to predict the impact factor or Eigenfactor score tertile using academic article abstracts. METHODS PubMed-indexed articles published between 2016 and 2021 were identified with the Medical Subject Headings (MeSH) terms "ophthalmology," "radiology," and "neurology." Journals, titles, abstracts, author lists, and MeSH terms were collected. Journal impact factor and Eigenfactor scores were sourced from the 2020 Clarivate Journal Citation Report. The journals included in the study were allocated percentile ranks based on impact factor and Eigenfactor scores, compared with other journals that released publications in the same year. All abstracts were preprocessed, which included the removal of the abstract structure, and combined with titles, authors, and MeSH terms as a single input. The input data underwent preprocessing with the inbuilt ktrain Bidirectional Encoder Representations from Transformers (BERT) preprocessing library before analysis with BERT. Before use for logistic regression and XGBoost models, the input data underwent punctuation removal, negation detection, stemming, and conversion into a term frequency-inverse document frequency array. Following this preprocessing, data were randomly split into training and testing data sets with a 3:1 train:test ratio. Models were developed to predict whether a given article would be published in a first, second, or third tertile journal (0-33rd centile, 34th-66th centile, or 67th-100th centile), as ranked either by impact factor or Eigenfactor score. BERT, XGBoost, and logistic regression models were developed on the training data set before evaluation on the hold-out test data set. The primary outcome was overall classification accuracy for the best-performing model in the prediction of accepting journal impact factor tertile. RESULTS There were 10,813 articles from 382 unique journals. The median impact factor and Eigenfactor score were 2.117 (IQR 1.102-2.622) and 0.00247 (IQR 0.00105-0.03), respectively. The BERT model achieved the highest impact factor tertile classification accuracy of 75.0%, followed by an accuracy of 71.6% for XGBoost and 65.4% for logistic regression. Similarly, BERT achieved the highest Eigenfactor score tertile classification accuracy of 73.6%, followed by an accuracy of 71.8% for XGBoost and 65.3% for logistic regression. CONCLUSIONS Open-source artificial intelligence can predict the impact factor and Eigenfactor score of accepting peer-reviewed journals. Further studies are required to examine the effect on publication success and the time-to-publication of such recommender systems.
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Affiliation(s)
- Carmelo Macri
- Discipline of Ophthalmology and Visual Sciences, The University of Adelaide, Adelaide, Australia
| | - Stephen Bacchi
- Department of Ophthalmology, The Royal Adelaide Hospital, Adelaide, Australia
| | - Sheng Chieh Teoh
- Department of Ophthalmology, The Royal Adelaide Hospital, Adelaide, Australia
| | - Wan Yin Lim
- Department of Radiology, The Royal Adelaide Hospital, Adelaide, Australia
| | - Lydia Lam
- Department of Ophthalmology, The Royal Adelaide Hospital, Adelaide, Australia
| | - Sandy Patel
- Department of Radiology, The Royal Adelaide Hospital, Adelaide, Australia
| | - Mark Slee
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Robert Casson
- Department of Ophthalmology, The Royal Adelaide Hospital, Adelaide, Australia
| | - WengOnn Chan
- Department of Ophthalmology, The Royal Adelaide Hospital, Adelaide, Australia
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Kobayashi G, Tanaka KF, Takata N. Pupil Dynamics-derived Sleep Stage Classification of a Head-fixed Mouse Using a Recurrent Neural Network. Keio J Med 2023. [PMID: 36740272 DOI: 10.2302/kjm.2022-0020-OA] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The standard method for sleep state classification is thresholding the amplitudes of electroencephalography (EEG) and electromyography (EMG) data, followed by manual correction by an expert. Although popular, this method has some shortcomings: (1) the time-consuming manual correction by human experts is sometimes a bottleneck hindering sleep studies, (2) EEG electrodes on the skull interfere with wide-field imaging of the cortical activity of a head-fixed mouse under a microscope, (3) invasive surgery to fix the electrodes on the thin mouse skull risks brain tissue injury, and (4) metal electrodes for EEG and EMG recording are difficult to apply to some experimental apparatus such as that for functional magnetic resonance imaging. To overcome these shortcomings, we propose a pupil dynamics-based vigilance state classification method for a head-fixed mouse using a long short-term memory (LSTM) model, a variant of a recurrent neural network, for multi-class labeling of NREM, REM, and WAKE states. For supervisory hypnography, EEG and EMG recording were performed on head-fixed mice. This setup was combined with left eye pupillometry using a USB camera and a markerless tracking toolbox, DeepLabCut. Our open-source LSTM model with feature inputs of pupil diameter, pupil location, pupil velocity, and eyelid opening for 10 s at a 10 Hz sampling rate achieved vigilance state estimation with a higher classification performance (macro F1 score, 0.77; accuracy, 86%) than a feed-forward neural network. Findings from a diverse range of pupillary dynamics implied possible subdivision of the vigilance states defined by EEG and EMG. Pupil dynamics-based hypnography can expand the scope of alternatives for sleep stage scoring of head-fixed mice.
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Bittremieux W, Levitsky L, Pilz M, Sachsenberg T, Huber F, Wang M, Dorrestein PC. Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils. J Proteome Res 2023; 22:625-631. [PMID: 36688502 DOI: 10.1021/acs.jproteome.2c00632] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications to spectrum plotting from online data repositories and assisting data analysis tasks for dozens of other projects. Here, we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.
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Affiliation(s)
- Wout Bittremieux
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium.,Biomedical Informatics Network Antwerpen (biomina), 2020 Antwerp, Belgium
| | - Lev Levitsky
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Matteo Pilz
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, 72076 Tübingen, Germany
| | - Timo Sachsenberg
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, 72076 Tübingen, Germany
| | - Florian Huber
- Centre for Digitalisation and Digitality, University of Applied Sciences Düsseldorf, 40476 Düsseldorf, Germany
| | - Mingxun Wang
- Department of Computer Science, University of California─Riverside, Riverside, California 92507, United States
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, University of California─San Diego, La Jolla, California 92093, United States.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California─San Diego, La Jolla California 92093, United States
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Dubos J, Park SK, Vlasova R, Prieto JC, Styner M. Dmriprep: open-source diffusion MRI quality control framework with graphical user interface. Proc SPIE Int Soc Opt Eng 2023; 12464:124643A. [PMID: 37089869 PMCID: PMC10118003 DOI: 10.1117/12.2654470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
In the last decade, investigating white matter microstructure and connectivity via diffusion MRI (dmri) has become a crucial cornerstone in neuroimaging studies. However, even modern dmri sequences have inherently a low signal-to-noise ratio and long acquisition times, depending on the spatial resolution. Furthermore, many types of artifacts complicate the appropriate analysis of dmri, necessitating appropriate quality control (QC) procedures, including exclusion and/or correction of inappropriate/erroneous dmri data. Our group has been developing and promoting QC procedures and tools to the community to enable appropriate dmri analyses. Since its development in 2011, our DTIPrep QC tool has become a major tool due its ease of use and dmri QC performance. Over the years, novel developments in acquisition and artifact correction methods have led to a need to modernize DTIPrep. Here, we present a novel diffusion MRI analysis environment called dtiplayground with a fully redesigned and significantly enhanced QC module dmriprep, and its graphical user interface dmriprep-ui, building on in-house developed code, FSL and dipy. The user interface is designed to be a unified, user friendly tool for thorough QC of dMRI data.Artifacts addressed by dmriprep include eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, slice-wise and gradient-wise intensity inconsistencies, and susceptibility artifacts. It further provides an user interface for visual QC of gradients and automated tractography. In summary, our work presents a novel open-source framework for modern comprehensive dmri QC.
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Affiliation(s)
- Johanna Dubos
- Dept Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Sang Kyoon Park
- Dept Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Roza Vlasova
- Dept Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | | | - Martin Styner
- Dept Psychiatry, University of North Carolina, Chapel Hill, NC, USA
- Dept Computer Science, University of North Carolina, Chapel Hill, NC, USA
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Juvekar P, Torio E, Bi WL, Bastos DCDA, Golby AJ, Frisken SF. Mapping Resection Progress by Tool-Tip Tracking during Brain Tumor Surgery for Real-Time Estimation of Residual Tumor. Cancers (Basel) 2023; 15:cancers15030825. [PMID: 36765783 PMCID: PMC9913508 DOI: 10.3390/cancers15030825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/21/2023] [Accepted: 01/23/2023] [Indexed: 01/31/2023] Open
Abstract
Surgical resection continues to be the primary initial therapeutic strategy in the treatment of patients with brain tumors. Computerized cranial neuronavigation based on preoperative imaging offers precision guidance during craniotomy and early tumor resection but progressively loses validity with brain shift. Intraoperative MRI (iMRI) and intraoperative ultrasound (iUS) can update the imaging used for guidance and navigation but are limited in terms of temporal and spatial resolution, respectively. We present a system that uses time-stamped tool-tip positions of surgical instruments to generate a map of resection progress with high spatial and temporal accuracy. We evaluate this system and present results from 80 cranial tumor resections. Regions of the preoperative tumor segmentation that are covered by the resection map (True Positive Tracking) and regions of the preoperative tumor segmentation not covered by the resection map (True Negative Tracking) are determined for each case. We compare True Negative Tracking, which estimates the residual tumor, with the actual residual tumor identified using iMRI. We discuss factors that can cause False Positive Tracking and False Negative Tracking, which underestimate and overestimate the residual tumor, respectively. Our method provides good estimates of the residual tumor when there is minimal brain shift, and line-of-sight is maintained. When these conditions are not met, surgeons report that it is still useful for identifying regions of potential residual.
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Affiliation(s)
- Parikshit Juvekar
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence: or (P.J.); (S.F.F.)
| | - Erickson Torio
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Wenya Linda Bi
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dhiego Chaves De Almeida Bastos
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Alexandra J. Golby
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Sarah F. Frisken
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Correspondence: or (P.J.); (S.F.F.)
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Weiler R, Diachenko M, Juarez-Martinez EL, Avramiea AE, Bloem P, Linkenkaer-Hansen K. Robin's Viewer: Using deep-learning predictions to assist EEG annotation. Front Neuroinform 2023; 16:1025847. [PMID: 36844437 PMCID: PMC9951202 DOI: 10.3389/fninf.2022.1025847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 12/20/2022] [Indexed: 02/12/2023] Open
Abstract
Machine learning techniques such as deep learning have been increasingly used to assist EEG annotation, by automating artifact recognition, sleep staging, and seizure detection. In lack of automation, the annotation process is prone to bias, even for trained annotators. On the other hand, completely automated processes do not offer the users the opportunity to inspect the models' output and re-evaluate potential false predictions. As a first step toward addressing these challenges, we developed Robin's Viewer (RV), a Python-based EEG viewer for annotating time-series EEG data. The key feature distinguishing RV from existing EEG viewers is the visualization of output predictions of deep-learning models trained to recognize patterns in EEG data. RV was developed on top of the plotting library Plotly, the app-building framework Dash, and the popular M/EEG analysis toolbox MNE. It is an open-source, platform-independent, interactive web application, which supports common EEG-file formats to facilitate easy integration with other EEG toolboxes. RV includes common features of other EEG viewers, e.g., a view-slider, tools for marking bad channels and transient artifacts, and customizable preprocessing. Altogether, RV is an EEG viewer that combines the predictive power of deep-learning models and the knowledge of scientists and clinicians to optimize EEG annotation. With the training of new deep-learning models, RV could be developed to detect clinical patterns other than artifacts, for example sleep stages and EEG abnormalities.
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Affiliation(s)
- Robin Weiler
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit (VU) Amsterdam, Amsterdam, Netherlands
| | - Marina Diachenko
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit (VU) Amsterdam, Amsterdam, Netherlands
| | - Erika L. Juarez-Martinez
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit (VU) Amsterdam, Amsterdam, Netherlands
| | - Arthur-Ervin Avramiea
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit (VU) Amsterdam, Amsterdam, Netherlands
| | - Peter Bloem
- Department of Computer Science, Vrije Universiteit (VU) Amsterdam, Amsterdam, Netherlands
| | - Klaus Linkenkaer-Hansen
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, Vrije Universiteit (VU) Amsterdam, Amsterdam, Netherlands,*Correspondence: Klaus Linkenkaer-Hansen,
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van Hoof M, Evans N, Inguaggiato G, Marušić A, Gordijn B, Dierickx K, van Zeggeren D, Dunnik H, Gesinn A, Bouter L, Widdershoven G. The Embassy of Good Science - a community driven initiative to promote ethics and integrity in research. Open Res Eur 2023; 2:27. [PMID: 37767226 PMCID: PMC10521075 DOI: 10.12688/openreseurope.14422.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/05/2023] [Indexed: 09/29/2023]
Abstract
The Embassy of Good Science ( https://www.embassy.science) aims to improve research integrity and research ethics by offering an online, open, 'go-to' platform, which brings together information on research integrity and research ethics and makes that information accessible, understandable, and appealing. It effectively organizes and describes research integrity and research ethics guidelines, educational materials, cases, and scenarios. The Embassy is wiki-based, allowing users to add -- when logged in with their ORCID researcher id -- new information, and update and refine existing information. The platform also makes the research integrity and research ethics community visible and more accessible in pages dedicated to relevant initiatives, news and events. Therefore, the Embassy enables researchers to find useful guidance, rules and tools to conduct research responsibly. The platform empowers researchers through increased knowledge and awareness, and through the support of the research integrity and research ethics community. In this article we will discuss the background of this new platform, the way in which it is organized, and how users can contribute.
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Affiliation(s)
- Marc van Hoof
- Department of Ethics, Law and Humanities, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
| | - Natalie Evans
- Department of Ethics, Law and Humanities, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
| | - Giulia Inguaggiato
- Department of Ethics, Law and Humanities, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
| | - Ana Marušić
- Department of Research in Biomedicine and Health, University of Split School of Medicine, Split, Split-Dalmatia, HR-21000, Croatia
| | - Bert Gordijn
- Institute of Ethics, Dublin City University, Dublin, Leinster, 9, Ireland
| | - Kris Dierickx
- Interfaculty Center for Biomedical Ethics and Law, KU Leuven, Leuven, 3000, Belgium
| | | | - Harald Dunnik
- Momkai BV, Amsterdam, Noord-Holland, 1013 NJ, The Netherlands
| | | | - Lex Bouter
- Department of Philosophy, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
| | - Guy Widdershoven
- Department of Ethics, Law and Humanities, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
| | - EnTIRE and VIRT2UE consortia
- Department of Ethics, Law and Humanities, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
- Department of Research in Biomedicine and Health, University of Split School of Medicine, Split, Split-Dalmatia, HR-21000, Croatia
- Institute of Ethics, Dublin City University, Dublin, Leinster, 9, Ireland
- Interfaculty Center for Biomedical Ethics and Law, KU Leuven, Leuven, 3000, Belgium
- Momkai BV, Amsterdam, Noord-Holland, 1013 NJ, The Netherlands
- Gesinn.it, Schwarzenfeld, 92521, Germany
- Department of Philosophy, Amsterdam UMC, Vrije Universiteit, Amsterdam, Noord-Holland, 1081 HV, The Netherlands
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Mureithi M, Ng'aari L, Wasunna B, Kiruthu-Kamamia C, Sande O, Chiwaya GD, Huwa J, Tweya H, Jafa K, Feldacker C. Centering healthcare workers in developing digital health interventions: usability and acceptability of a two-way texting retention intervention in a public HIV clinic in Lilongwe, Malawi. medRxiv 2023:2023.01.09.23284326. [PMID: 36711633 PMCID: PMC9882492 DOI: 10.1101/2023.01.09.23284326] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Background New initiates on antiretroviral therapy (ART) are at high risk of treatment discontinuation, putting their health at risk. In low-resource settings, like Malawi, appropriate digital health applications must fit into local connectivity and resource constraints. Target users' perspectives are critical for app usability, buy-in and optimization. We describe the formative stages of the design of a two-way text-based (2wT) system of tailored reminders and adherence messages for new ART initiates and share results from key informant interviews with HCWs focused on app usability and acceptability. Methods Using a co-creation approach with clients, clinical, technical and evaluation teams and over app development, we held four informal user feedback sessions, a small pilot with 50 clients, and ten key informant (KIIs) to deepen our understanding of healthcare workers (HCWs) needs, acceptability and usability. Results Formative research informed the design of interactive client-to-HCW communication, refining of the language and timing of weekly text blast motivational messages and tailored client-specific visit reminders. Informal feedback from HCW stakeholders also informed educational materials to enhance 2wT client understanding of how to report transfers, request visit date changes and ask questions related to their visits. In KII, HCWs noted their appreciation for the co-creation process, believing that the participatory HCD process and responsive design team enabled the development of a highly acceptable and usable 2wT digital tool. HCWs also suggested future improvements to promote inclusion of clients of varying literacy levels and economic backgrounds as well as integrating with other health platforms to improve uptake of 2wT. Conclusions Inclusion of HCWs increased perceptions of app usability and acceptability among HCWs. HCWs believe that 2wT will improve on-time ART visit attendance and provide valuable early retention in care support. The co-creation approach appears successful in designing an app that will meet HCW needs and, therefore, support client adherence to visits.
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Affiliation(s)
| | | | | | | | | | | | | | - Hannock Tweya
- International Training and Education Center for Health, Department of Global Health, University of Washington, Seattle, Washington, USA
- Department of Global Health, University of Washington, Seattle, Washington, USA
| | | | - Caryl Feldacker
- International Training and Education Center for Health, Department of Global Health, University of Washington, Seattle, Washington, USA
- Department of Global Health, University of Washington, Seattle, Washington, USA
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Liu X, Cheng Z, Xu G, Xie J, Liu X, Ren B, Ai D, Chen Y, Xia LC. Ksak: A high-throughput tool for alignment-free phylogenetics. Front Microbiol 2023; 14:1050130. [PMID: 37065122 PMCID: PMC10098151 DOI: 10.3389/fmicb.2023.1050130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 02/27/2023] [Indexed: 04/18/2023] Open
Abstract
Phylogenetic tools are fundamental to the studies of evolutionary relationships. In this paper, we present Ksak, a novel high-throughput tool for alignment-free phylogenetic analysis. Ksak computes the pairwise distance matrix between molecular sequences, using seven widely accepted k-mer based distance measures. Based on the distance matrix, Ksak constructs the phylogenetic tree with standard algorithms. When benchmarked with a golden standard 16S rRNA dataset, Ksak was found to be the most accurate tool among all five tools compared and was 19% more accurate than ClustalW2, a high-accuracy multiple sequence aligner. Above all, Ksak was tens to hundreds of times faster than ClustalW2, which helps eliminate the computation limit currently encountered in large-scale multiple sequence alignment. Ksak is freely available at https://github.com/labxscut/ksak.
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Affiliation(s)
- Xuemei Liu
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Ziqi Cheng
- Guangzhou Key Laboratory of Molecular Mechanism and Translation in Major Cardiovascular Disease, SunYat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Guohao Xu
- School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China
| | - Jiemin Xie
- School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China
| | - Xudong Liu
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Bozhen Ren
- School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China
| | - Dongmei Ai
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China
| | - Yangxin Chen
- Guangzhou Key Laboratory of Molecular Mechanism and Translation in Major Cardiovascular Disease, SunYat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
- *Correspondence: Li Charlie Xia, ; Yangxin Chen,
| | - Li Charlie Xia
- School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China
- *Correspondence: Li Charlie Xia, ; Yangxin Chen,
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Khanna T, Akkara JD, Bawa V, Sargunam EA. Designing and making an open source, 3D-printed, punctal plug with drug delivery system. Indian J Ophthalmol 2023; 71:297-299. [PMID: 36588257 PMCID: PMC10155558 DOI: 10.4103/ijo.ijo_997_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
With the advancement in the study of keratoconjunctivitis sicca and the scope of its treatment, punctal plugs are being widely used for the therapeutic management of dry eye disease. With the emergence of 3D printing in medicine, 3D printing of punctal plugs that have an inbuilt drug delivery system and also that can be personalized from patient to patient according to their punctum size can be a great therapeutic option. Another benefit of the device is that its printing takes a short period of time and is cost-effective. This study aimed at making an open source design and 3D printing an efficient model of a punctal plug with an inbuilt drug delivery system that can be eventually used for the treatment of various ocular diseases that require frequent drug instillation or blockage of the nasolacrimal pathway. The 3D design for the punctal plug was made using the open source application, FreeCAD, and slicing was done using the application ChituBox. After that, the plugs were printed using the LCD printer Crealty LD-002R. The material used was resin that was compatible with the Crealty LD-002R. Punctal plugs with satisfactory results were printed using the LCD printer. The punctal plugs showed suitable structure and were also easily reproduced in the 3D printer without any complications or setbacks.
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Affiliation(s)
- Twisha Khanna
- Bachelor of Medicine, Bachelor of Surgery, Sri Ramachandra Medical College and Research Institute, Chennai, Tamil Nadu, India
| | - John D Akkara
- Department of Ophthalmology, Sri Ramachandra Medical College and Research Institute, Chennai, Tamil Nadu, India
| | - Vedant Bawa
- Bachelor of Medicine, Bachelor of Surgery, Sri Ramachandra Medical College and Research Institute, Chennai, Tamil Nadu, India
| | - Emmanuel A Sargunam
- Department of Oral and Maxillofacial Surgery, Sri Ramachandra Medical College and Research Institute, Chennai, Tamil Nadu, India
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Xu J, Thevenon G, Chabat T, McCormick M, Li F, Birdsong T, Martin K, Lee Y, Aylward S. Interactive, in-browser cinematic volume rendering of medical images. Comput Methods Biomech Biomed Eng Imaging Vis 2022; 11:1019-1026. [PMID: 37377626 PMCID: PMC10292767 DOI: 10.1080/21681163.2022.2145239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 11/19/2022]
Abstract
The diversity and utility of cinematic volume rendering (CVR) for medical image visualization have grown rapidly in recent years. At the same time, volume rendering on augmented and virtual reality systems is attracting greater interest with the advance of the WebXR standard. This paper introduces CVR extensions to the open-source visualization toolkit (vtk.js) that supports WebXR. This paper also summarizes two studies that were conducted to evaluate the speed and quality of various CVR techniques on a variety of medical data. This work is intended to provide the first open-source solution for CVR that can be used for in-browser rendering as well as for WebXR research and applications. This paper aims to help medical imaging researchers and developers make more informed decision when selecting CVR algorithms for their applications. Our software and this paper also provide a foundation for new research and product development at the intersection of medical imaging, web visualization, XR, and CVR.
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Affiliation(s)
| | | | | | | | | | | | | | - Yueh Lee
- The University of North Carolina at Chapel Hill
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