16 research outputs found
sktime: A Unified Interface for Machine Learning with Time Series
No full textWe present sktime – a new scikit-learn compatible Python library with a unified interface for machine learning with time series. Time series data gives rise to various distinct but closely related learning tasks, such as forecasting and time series classification, many of which can be solved by reducing them to related simpler tasks. We discuss the main rationale for creating a unified interface, including reduction, as well as the design of sktime’s core API, supported by a clear overview of common time series tasks and reduction approaches
Validity of continuous glucose monitoring for categorizing glycemic responses to diet: implications for use in personalized nutrition
No full textBACKGROUND: Continuous glucose monitor (CGM) devices enable characterization of individuals’ glycemic variation. However, there are concerns about their reliability for categorizing glycemic responses to foods that would limit their potential application in personalized nutrition recommendations. OBJECTIVES: We aimed to evaluate the concordance of 2 simultaneously worn CGM devices in measuring postprandial glycemic responses. METHODS: Within ZOE PREDICT (Personalised Responses to Dietary Composition Trial) 1, 394 participants wore 2 CGM devices simultaneously [n = 360 participants with 2 Abbott Freestyle Libre Pro (FSL) devices; n = 34 participants with both FSL and Dexcom G6] for ≤14 d while consuming standardized (n = 4457) and ad libitum (n = 5738) meals. We examined the CV and correlation of the incremental area under the glucose curve at 2 h (glucose(iAUC0–2 h)). Within-subject meal ranking was assessed using Kendall τ rank correlation. Concordance between paired devices in time in range according to the American Diabetes Association cutoffs (TIR(ADA)) and glucose variability (glucose CV) was also investigated. RESULTS: The CV of glucose(iAUC0–2 h) for standardized meals was 3.7% (IQR: 1.7%–7.1%) for intrabrand device and 12.5% (IQR: 5.1%–24.8%) for interbrand device comparisons. Similar estimates were observed for ad libitum meals, with intrabrand and interbrand device CVs of glucose(iAUC0–2 h) of 4.1% (IQR: 1.8%–7.1%) and 16.6% (IQR: 5.5%–30.7%), respectively. Kendall τ rank correlation showed glucose(iAUC0–2h)-derived meal rankings were agreeable between paired CGM devices (intrabrand: 0.9; IQR: 0.8–0.9; interbrand: 0.7; IQR: 0.5–0.8). Paired CGMs also showed strong concordance for TIR(ADA) with a intrabrand device CV of 4.8% (IQR: 1.9%–9.8%) and an interbrand device CV of 3.2% (IQR: 1.1%–6.2%). CONCLUSIONS: Our data demonstrate strong concordance of CGM devices in monitoring glycemic responses and suggest their potential use in personalized nutrition. This trial was registered at clinicaltrials.gov as NCT03479866
Human Behavior Modeling and Human Behavior-aware Control of Automated Vehicles for Trustworthy Navigation
No full textFirst and foremost, I would like to thank my advisor, Professor Dawn Tilbury,
for her constant guidance and encouragement. She has been extremely helpful in
developing my technical, research, and personal skills and immensely supportive of my ideas and endeavors throughout graduate school. She has been an excellent mentor and has always been there in my time of need, encouraging and boosting my confidence when I needed them the most. I would like to specially thank my committee members and collaborators, Professors Lionel Robert and Jessie Yang, for their support and encouragement, right from the start of my graduate program. The multi-disciplinary nature of the research initiated by these three Professors is what first drew me towards pursuing a Ph.D. I would also like to thank my other committee members Professors Ilya Kolmanovsky and Ram Vasudevan, for providing their support and feedback that
improved the dissertation.
I would like to thank the Department of Mechanical Engineering, Rackham Graduate School, and the University of Michigan for giving me the opportunity to pursue the doctoral degree and providing financial support during my time at the university. In addition, I would like to thank the Toyota Research Institute and the Automotive Research Center for providing financial assistance.
I really appreciate the support I received from the MAVRIC lab members. The
multi-disciplinary culture and environment that the Professors have fostered in the MAVRIC lab have deeply broadened my perspectives. Specically, I would like to thank Hebert Azevedo-Sa. He is usually the first person I discuss my ideas with and has been an excellent critique. I would also like to thank Connor Esterwood, Na Du, Qiaoning Zhang, and Huajing Zhao for the numerous discussions and help with my user studies; especially Connor, who took on a variety of roles to help with my user study|from an engineer to a tailor, to even a hidden driver.
Outside of the University of Michigan, I would like to thank my undergraduate
advisor, Professor Madhu M., and my internship advisor at the Indian Institute of Technology-Madras, Professor Saravanan Gurunathan. They encouraged me to pursue research and provided me with the necessary opportunities. A special thanks to Sajaysurya Ganesh, a close friend, and collaborator in my early research projects, with who I discuss ideas even now.
Last but not least, I would like to thank my family and friends for supporting me during the past several years. My friends at Ann Arbor made life away from home much easier; they are like my second family. A long list of people from my Master's and Ph.D. programs at the University of Michigan has played an essential role in my graduate experience. Still, I would like to especially thank Sandipp Krishnan Ravi, Subramaniam Balakrishna, Rahasudha Kannan, and Paavai Pari for all their love and support. I will fondly remember my time at the University of Michigan and in Ann Arbor because of all of the people I encountered, the friends I made, and the experiences I had. My parents, wife, and extended family have all been incredibly supportive of the pursuit of my degree, and I am eternally grateful for their love and guidance.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169640/1/jskumaar_1.pd
Precision Nutrition and Reliability of Continuous Glucose Monitors: Insights From the PREDICT Study
No full textAuthor Correction: Attributes and predictors of long COVID
No full textIn the version of this article initially published, linkage of the following authors to affiliation 3 (Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK) was incorrect: Benjamin Murray, Thomas Varsavsky, Mark S. Graham, Kerstin Klaser, Michela Antonelli, Liane S. Canas, Erika Molteni, Marc Modat, M. Jorge Cardoso and Sebastien Ourselin. The correct linkage is to affiliation 1 (School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK). The error has been corrected in the HTML and PDF versions of the article.</p
Gut micro-organisms associated with health, nutrition and dietary interventions
No full textThe incidence of cardiometabolic diseases is increasing globally, and both poor diet and the human gut microbiome have been implicated1. However, the field lacks large-scale, comprehensive studies exploring these links in diverse populations2. Here, in over 34,000 US and UK participants with metagenomic, diet, anthropometric and host health data, we identified known and yet-to-be-cultured gut microbiome species associated significantly with different diets and risk factors. We developed a ranking of species most favourably and unfavourably associated with human health markers, called the ‘ZOE Microbiome Health Ranking 2025’. This system showed strong and reproducible associations between the ranking of microbial species and both body mass index and host disease conditions on more than 7,800 additional public samples. In an additional 746 people from two dietary interventional clinical trials, favourably ranked species increased in abundance and prevalence, and unfavourably ranked species reduced over time. In conclusion, these analyses provide strong support for the association of both diet and microbiome with health markers, and the summary system can be used to inform the basis for future causal and mechanistic studies. It should be emphasized, however, that causal inference is not possible without prospective cohort studies and interventional clinical trials
sktime/sktime: v0.30.0
No full text<h2>What's Changed</h2>
<p>Maintenance release and some breaking changes.</p>
<ul>
<li>python 3.8 is no longer supported given end of life in October</li>
<li>major rework of time series annotation, anomalies, changepoints, segmentation API</li>
<li>scheduled deprecations and change actions</li>
</ul>
<p>For last larger feature update, see <a href="https://github.com/sktime/sktime/releases/tag/v0.29.1">0.29.1</a>.</p>
<p>Please see our <a href="https://www.sktime.net/en/latest/changelog.html">changelog</a> for a description of all changes.</p>
<h2>All Contributors</h2>
<p>@Alex-JG3,
@fkiraly,
@gareth-brown-86,
@geetu040,
@yarnabrina</p>
<h2>New Contributors</h2>
<ul>
<li>@gareth-brown-86 made their first contribution in https://github.com/sktime/sktime/pull/6521</li>
</ul>
<p><strong>Full Changelog</strong>: https://github.com/sktime/sktime/compare/v0.29.1...v0.30.0</p>
sktime/sktime: v0.29.0
No full text<h2>What's Changed</h2>
<p>Maintenance release with scheduled deprecations and change actions.
For last larger feature update, see <a href="https://github.com/sktime/sktime/releases/tag/v0.28.1">0.28.1</a>.</p>
<p>Please see our <a href="https://www.sktime.net/en/latest/changelog.html">changelog</a> for a description of all changes.</p>
<h2>All Contributors</h2>
<p>@fkiraly,
@geetu040,
@yarnabrina</p>
<p><strong>Full Changelog</strong>: https://github.com/sktime/sktime/compare/v0.28.1...v0.29.0</p>
Human postprandial responses to food and potential for precision nutrition
No full textMetabolic responses to food influence risk of cardiometabolic disease, but large-scale high-resolution studies are lacking. We recruited n = 1,002 twins and unrelated healthy adults in the United Kingdom to the PREDICT 1 study and assessed postprandial metabolic responses in a clinical setting and at home. We observed large inter-individual variability (as measured by the population coefficient of variation (s.d./mean, %)) in postprandial responses of blood triglyceride (103%), glucose (68%) and insulin (59%) following identical meals. Person-specific factors, such as gut microbiome, had a greater influence (7.1% of variance) than did meal macronutrients (3.6%) for postprandial lipemia, but not for postprandial glycemia (6.0% and 15.4%, respectively); genetic variants had a modest impact on predictions (9.5% for glucose, 0.8% for triglyceride, 0.2% for C-peptide). Findings were independently validated in a US cohort (n = 100 people). We developed a machine-learning model that predicted both triglyceride (r = 0.47) and glycemic (r = 0.77) responses to food intake. These findings may be informative for developing personalized diet strategies. The ClinicalTrials.gov registration identifier is NCT03479866
