Time-restricted feeding improves adaptation to chronically alternating light-dark cycles

Disturbance of the circadian clock has been associated with increased risk of cardio-metabolic disorders. Previous studies showed that optimal timing of food intake can improve metabolic health. We hypothesized that time-restricted feeding could be a strategy to minimize long term adverse metabolic health effects of shift work and jetlag. In this study, we exposed female FVB mice to weekly alternating light-dark cycles (i.e. 12 h shifts) combined with ad libitum feeding, dark phase feeding or feeding at a fixed clock time, in the original dark phase. In contrast to our expectations, long-term disturbance of the circadian clock had only modest effects on metabolic parameters. Mice fed at a fixed time showed a delayed adaptation compared to ad libitum fed animals, in terms of the similarity in 24 h rhythm of core body temperature, in weeks when food was only available in the light phase. This was accompanied by increased plasma triglyceride levels and decreased energy expenditure, indicating a less favorable metabolic state. On the other hand, dark phase feeding accelerated adaptation of core body temperature and activity rhythms, however, did not improve the metabolic state of animals compared to ad libitum feeding. Taken together, restricting food intake to the active dark phase enhanced adaptation to shifts in the light-dark schedule, without significantly affecting metabolic parameters

Forecasting time series in healthcare with Gaussian processes and Dynamic Time Warping based subset selection

Modelling real-world time series can be challenging in the absence of sufficient data. Limited data in healthcare, can arise for several reasons, namely when the number of subjects is insufficient or the observed time series is irregularly sampled at a very low sampling frequency. This is especially true when attempting to develop personalised models, as there are typically few data points available for training from an individual subject. Furthermore, the need for early prediction (as is often the case in healthcare applications) amplifies the problem of limited availability of data. This article proposes a novel personalised technique that can be learned in the absence of sufficient data for early prediction in time series. Our novelty lies in the development of a subset selection approach to select time series that share temporal similarities with the time series of interest, commonly known as the test time series. Then, a Gaussian processes-based model is learned using the existing test data and the chosen subset to produce personalised predictions for the test subject. We will conduct experiments with univariate and multivariate data from real-world healthcare applications to show that our strategy outperforms the state-of-the-art by around 20%

Estimating a surface area and/or volume of a body or a body part of a subject

According to an aspect, there is provided a computer-implemented method for estimating a surface area and/or a volume of a body or a body part of a subject. The method comprises obtaining at least one image, wherein the at least one image includes a face of the subject; processing the at least one image to determine values for one or more facial image parameters for the face of the subject; determining values for one or more characteristics of the subject, wherein the one or more characteristics comprises one or more of age of the subject, weight of the subject, height of the subject and gender of the subject; using a facial parametric model and the determined values for the one or more facial image parameters to determine values for one or more facial shape parameters for the face of the subject, wherein the facial parametric model relates specific values for one or more facial image parameters to a respective 3D representation of a face having respective values for the one or more facial shape parameters; using a prediction model with the determined values for the one or more characteristics and the determined values of the one or more facial shape parameters to predict a 3D representation of the full body of the subject; and analyzing the predicted 3D representation of the full body of the subject to estimate the surface area and/or the volume of the body or body part of the subject. A corresponding apparatus and computer program product is also provided.</p

Feature selection for unbiased imputation of missing values : a case study in healthcare

Datasets in healthcare are plagued with incomplete information. Imputation is a common method to deal with missing data where the basic idea is to substitute some reasonable guess for each missing value and then continue with the analysis as if there were no missing data. However unbiased predictions based on imputed datasets can only be guaranteed when the missing mechanism is completely independent of the observed or missing data. Often, this promise is broken in healthcare dataset acquisition due to unintentional errors or response bias of the interviewees. We highlight this issue by studying extensively on an annual health survey dataset on infant mortality prediction and provide a systematic testing for such assumption. We identify such biased features using an empirical approach and show the impact of wrongful inclusion of these features on the predictive performance.Clinical relevance— We show that blind analysis along with plug and play imputation of healthcare data is a potential pitfall that clinicians and researchers want to avoid in finding important markers of disease

Synthetic jet cooling using asymmetric acoustic dipoles

In two earlier papers the principles and experimental results have been discussed for a typical embodiment of synthetic jet cooling technology: an acoustic dipole cooler comprised of a standard loudspeaker in a housing provided with two pipes. The current paper shows experimental and numerical results for another type: the asymmetric dipole. Basically, this type consists of a loudspeaker with a minimal volume attached to it with one or more holes with or without pipes. Results for driving power and noise are presented for a number of actuators covering a large parameter space: frequency, pipe dimensions and driving voltage were varied over a large range. A relatively simple acoustic model extended to include separation losses matched the experimental results very well. The results indicate promising heat transfer performance with minimal noise combined with a large degree of freedom

PREgDICT : early prediction of gestational weight gain for pregnancy care

Excessive or inadequate Gestational Weight Gain (GWG) is considered to not only put the mothers, but also the infants at increased risks with a number of adverse outcomes. In this paper, we use self-reported weight measurements from the early days of pregnancy to predict and classify the end-of-pregnancy weight gain into an underweight, normal or obese category in accordance with the Institute of Medicine recommended guidelines. Self-reported weight measurements suffer from issues such as lack of enough data and non-uniformity. We propose and compare two novel parametric and non-parametric approaches that utilise self-training data along with population data to tackle limited data availability. We, dynamically find the subset of closest time series from the population weight-gain data to a given subject. Then, a non-parametric Gaussian Process (GP) regression model, learnt on the selected subset is used to forecast the self-reported weight measurements of given subject. Our novel approach produces mean absolute error (MAE) of 2.572 kgs in forecasting end-of-pregnancy weight gain and achieves weight-category-classification accuracy of 63.75% mid-way through the pregnancy, whereas a state-of-the-art approach is only 53.75% accurate and produces high MAE of 16.22 kgs. Our method ensures reliable prediction of the end-of-pregnancy weight gain using few data points and can assist in early intervention that can prevent gaining or losing excessive weight during pregnancy

Gestational weight gain prediction using privacy preserving federated learning

Gestational weight gain prediction in expecting women is associated with multiple risks. Manageable interventions can be devised if the weight gain can be predicted as early as possible. However, training the model to predict such weight gain requires access to centrally stored privacy sensitive weight data. Federated learning can help mitigate this problem by sending local copies of trained models instead of raw data and aggregate them at the central server. In this paper, we present a privacy preserving federated learning approach where the participating users collaboratively learn and update the global model. Furthermore, we show that this model updation can be done incrementally without having the need to store the local updates eternally. Our proposed model achieves a mean absolute error of 4.455 kgs whilst preserving privacy against 2.572 kgs achieved in a centralised approach utilising individual training data until day 140.Clinical relevance— Privacy preserving training of machine learning algorithm for early gestational weight gain prediction with minor tradeoff to performance

Demo Abstract: Privacy preserving Pregnancy Weight Gain Management

Early gestational weight gain prediction can help expecting women overcome several associated risks. However, training the model requires access to centrally stored privacy sensitive weight and other meta-data. In this demo, we present a privacy preserving federated learning approach where we train a global weight gain prediction model by aggregating client models trained locally on their personal data. We showcase a software data-exploration tool that exhibits local model generation, sharing and updating across users and server for proposed collaborative learning. Our proposed model predicts the final weight category with 61.3% accuracy on day 140, with a 8.8% compromise on the centralized training accuracy.status: publishe

A personalized Bayesian approach for early intervention in gestational weight gain management toward pregnancy care

Pre-pregnancy body mass index and weight gain management are associated with pregnancy outcomes in expecting women. Poor gestational weight gain (GWG) management could increase the risk of adverse complications. These risks can be alleviated by lifestyle-based interventions if an undesired GWG trend is detected early on in the pregnancy. Current literature lacks analysis of gestational weight gain data and tracking the pregnancy over time. In this work, we collected longitudinal gestational weight gain data from women during their pregnancy and model their weight measurements to predict the end-of-pregnancy weight gain and classify it in accordance with the medically recommended guidelines. The measurement frequency of the weights is often very variable such that segments of data can be missing and the need to predict early utilising few data points complicates data modelling. We propose a Bayesian approach to forecast weight gain while effectively dealing with the limited data availability for early prediction. We validate on diverse populations from Europe and China. We show that utilising individual's data only up to mid-way through the pregnancy, our approach produces mean absolute errors of 2.45 kgs and 2.82 kgs in forecasting end-of-pregnancy weight gain on these populations respectively, whereas the best of state-of-the-art yields 8.17 and 6.60 kgs on respective populations. The proposed method can serve as a tool to keep track of an individual's pregnancy and achieve GWG goals, thus supporting the prevention of excessive or insufficient weight gain during pregnancy