Engineering Features from Raw Sensor Data to Analyse Player Movements during Competition.

Research in field sports often involves analysis of running performance profiles of players during competitive games with individual, per-position, and time-related descriptive statistics. Data are acquired through wearable technologies, which generally capture simple data points, which in the case of many team-based sports are times, latitudes, and longitudes. While the data capture is simple and in relatively high volumes, the raw data are unsuited to any form of analysis or machine learning functions. The main goal of this research is to develop a multistep feature engineering framework that delivers the transformation of sequential data into feature sets more suited to machine learning applications.

Evaluation of the passive permeability of antidepressants through pore-suspended lipid bilayer.

HYPOTHESIS: The antidepressant drug imipramine, and its metabolite desipramine show different extents of interaction with, and passive permeation through, cellular membrane models, with the effects depending on the membrane composition. Through multimodal interrogation, we can observe that the drugs have a direct impact on the physicochemical properties of the membrane, that may play a role in their pharmacokinetics. EXPERIMENTS: Microcavity pore-suspended lipid bilayers (MSLBs) of four different compositions, each with a different headgroup charge namely; zwitterionic dioleoylphosphatidylcholine (DOPC), mixed DOPC and negatively charged dioleoylphosphatidylglycerol (DOPG) (3:1), mixed DOPC and positively charged dioleoyltrimethylammoniumpropane (DOTAP) (3:1), and with increasing complex composition mimicking blood-brain-barrier (BBB) were prepared on gold and polydimethylsiloxane (PDMS) substrates using a Langmuir-Blodgett-vesicle fusion method. The molecular interaction and permeation of antidepressants, imipramine, and its metabolite desipramine with the lipid bilayers were evaluated using highly sensitive label-free electrochemical impedance spectroscopy (EIS) and surface-enhanced Raman spectroscopy (SERS). Drug-induced membrane packing/fluidity alterations were assessed using fluorescence lifetime imaging (FLIM) and fluorescence lifetime correlation spectroscopy (FLCS) of MSLB over microfluidic PDMS array. FINDINGS: Using EIS to evaluate in real-time membrane admittance changes, we found that imipramine greatly increases the ion permeability of negatively charged DOPC:DOPG (3:1) membranes. The effect was observed also at neutral (DOPC) and to a lesser extent at positively charged DOPC:DOTAP(3:1) membranes. In contrast, desipramine had a much weaker impact on ion permeability across all bilayer compositions. Temporal capacitance data show that desipramine intercalates at negatively charged membrane thereby increasing the thickness of the membrane. The overall kinetics of the imipramine permeation is higher than that of desipramine. This was confirmed using SERS, which also provides an evaluation of drug passive permeation based on arrival time across the membrane. Using FLCS, we found that imipramine increases the lipid membrane fluidity, whereas desipramine lowers it, with the exception of the negatively charged membrane. A translocation rate pharmacokinetics model was established for the first time at the MSLB platform by real-time monitoring of the variation in membrane resistance of pristine DOPC and blood-brain-barrier (BBB) membrane.

Air Temperature and Relative Humidity Datasets from an Urban Meteorological Network in the City Area of Novi Sad (Serbia).

This data article describes two groups of datasets which capture, firstly - 10-minutes air temperature (Ta) and relative humidity (RH) data from 27 urban and non-urban sites over a period of 3.5 years covering 2014-2018; and secondly - hourly Ta data from 12 urban sites over a period of 2 years covering 2016 and 2017. Both datasets are from urban meteorological network located in the Novi Sad city (Serbia). These datasets have 2 different types of information in the collection: one type provides details about the monitoring sites at which the Ta and RH sensors are placed, while the second type contains Ta and RH data at all sensor locations. In all, the 10-minutes dataset contains about 185,000 instances of Ta and RH data, and the hourly datasets contain 17,544 instances of Ta data. The 10-minutes datasets were not quality controlled, but the hourly Ta data has been cleaned and gap-filled so there are 24 measures at each site for each day. There are multiple potential uses, where this data can be applied. It can provide insights in understanding intra-urban and inter-urban research, urban climate modeling on local or micro scales, heat-related public health investigations and urban environment inquiries. It can also be used in machine learning experiments, for example, to test the accuracy of classification algorithms or to build and validate spatio-temporal machine learning functions, either for classification purposes or for gap filling. These datasets are directly citable through its DOIs and available for download from the Zenodo platform or from the Fair Micromet Portal.

Moving well-being well: Using machine learning to explore the relationship between physical literacy and well-being in children.

Physical literacy provides a foundation for lifelong engagement in physical activity, resulting in positive health outcomes. Direct pathways between physical literacy and health have not yet been investigated thoroughly. Associations between physical literacy and well-being in children (n = 1073, mean age 10.86 ± 1.20 years) were analysed using machine learning. Motor competence (TGMD-3 and BOT-2) and health-related fitness (PACER and plank) were assessed in the physical competence domain. Motivation (adapted-Behavioural Regulation in Exercise Questionnaire) and confidence (modified-Physical Activity Self-Efficacy Scale) were assessed in the affective domain. Well-being was measured using the KIDSCREEN-27. Accuracy of predicting well-being from physical literacy was investigated using five machine learning classifiers (decision tree, random forest, XGBoost, AdaBoost, k-nearest neighbour) in the full sample and across subgroups (sex, socioeconomic status [SES], age). XGBoost predicted well-being from physical literacy with an accuracy of 87% in the full sample. Predictive accuracy was lowest in low SES participants. Contribution of physical literacy features differed substantially across subgroups. Physical literacy predicts well-being in children but the relative contribution of physical literacy features to well-being differs substantially between subgroups.

Using artificial intelligence to automate meat cut identification from the semimembranosus muscle on beef boning lines.

The identification of different meat cuts for labeling and quality control on production lines is still largely a manual process. As a result, it is a labor-intensive exercise with the potential for not only error but also bacterial cross-contamination. Artificial intelligence is used in many disciplines to identify objects within images, but these approaches usually require a considerable volume of images for training and validation. The objective of this study was to identify five different meat cuts from images and weights collected by a trained operator within the working environment of a commercial Irish beef plant. Individual cut images and weights from 7,987 meats cuts extracted from semimembranosus muscles (i.e., Topside muscle), post editing, were available. A variety of classical neural networks and a novel Ensemble machine learning approaches were then tasked with identifying each individual meat cut; performance of the approaches was dictated by accuracy (the percentage of correct predictions), precision (the ratio of correctly predicted objects relative to the number of objects identified as positive), and recall (also known as true positive rate or sensitivity). A novel Ensemble approach outperformed a selection of the classical neural networks including convolutional neural network and residual network. The accuracy, precision, and recall for the novel Ensemble method were 99.13%, 99.00%, and 98.00%, respectively, while that of the next best method were 98.00%, 98.00%, and 95.00%, respectively. The Ensemble approach, which requires relatively few gold-standard measures, can readily be deployed under normal abattoir conditions; the strategy could also be evaluated in the cuts from other primals or indeed other species.

A Methodology for Validating Diversity in Synthetic Time Series Generation.

In order for researchers to deliver robust evaluations of time series models, it often requires high volumes of data to ensure the appropriate level of rigor in testing. However, for many researchers, the lack of time series presents a barrier to a deeper evaluation. While researchers have developed and used synthetic datasets, the development of this data requires a methodological approach to testing the entire dataset against a set of metrics which capture the diversity of the dataset. Unless researchers are confident that their test datasets display a broad set of time series characteristics, it may favor one type of predictive model over another. This can have the effect of undermining the evaluation of new predictive methods. In this paper, we present a new approach to generating and evaluating a high number of time series data. The construction algorithm and validation framework are described in detail, together with an analysis of the level of diversity present in the synthetic dataset.

Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits.

The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e., ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e., feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.

Physiological Profile and Activity Pattern of Minor Gaelic Football Players.

Cullen, BD, Roantree, M, McCarren, A, Kelly, DT, O'Connor, PL, Hughes, SM, Daly, PG, and Moyna1, NM. Physiological profile and activity pattern of minor Gaelic football players. J Strength Cond Res 31(7): 1811-1820, 2017-The purpose of this study was to evaluate the physiological profile and activity pattern in club- and county-level under-18 (U-18) Gaelic football players relative to playing position. Participants (n = 85) were analyzed during 17 official 15-a-side matches using global positioning system technology (SPI Pro X II; GPSports Systems, Canberra, Australia) and heart rate (HR) telemetry. During the second part of this study, 63 participants underwent an incremental treadmill test to assess their maximal oxygen uptake (V[Combining Dot Above]o2max) and peak HR (HRmax). Players covered a mean distance of 5,774 ± 737 m during a full 60-minute match. The mean %HRmax and %V[Combining Dot Above]O2max observed during the match play were 81.6 ± 4.3% and 70.1 ± 7.75%, respectively. The playing level had no effect on the distance covered, player movement patterns, or %HRmax observed during match play. Midfield players covered significantly greater distance than defenders (p = 0.033). Playing position had no effect on %HRmax or the frequency of sprinting or high-intensity running during match play. The frequency of jogging, cruise running, striding (p = 0.000), and walking (p = 0.003) was greater in the midfield position than in the forward position. Time had a significant effect (F(1,39) = 33.512, p-value = 0.000, and (Equation is included in full-text article.)= 0.462) on distance covered and %HRmax, both of which showed a reduction between playing periods. Gaelic football is predominantly characterized by low-to-moderate intensity activity interspersed with periods of high-intensity running. The information provided may be used as a framework for coaches in the design and prescription of training strategies. Positional specific training may be warranted given the comparatively greater demands observed in the midfield playing position. Replicating the demands of match play in training may reduce the decline in distance covered and %HRmax observed during the second half of match play.

Mapping longitudinal studies to risk factors in an ontology for dementia.

A common activity carried out by healthcare professionals is to test various hypotheses on longitudinal study data in an effort to develop new and more reliable algorithms that might determine the possibility of developing certain illnesses. The INnovative, Midlife INtervention for Dementia Deterrence project provides input from a number of European dementia experts to identify the most accurate model of inter-related risk factors which can yield a personalized dementia-risk quotient and profile. This model is then validated against the large population-based prospective Maastricht Aging Study dataset. As part of this overall goal, the research presented in this article demonstrates how we can automate the process of mapping modifiable risk factors against large sections of the aging study and thus use information technology to provide more powerful query interfaces.