Statistical Machine Learning for Automatic Assessment of Physical Activity Intensity Using Multi-axial Accelerometry and Heart Rate

This work explores the automatic recognition of physical activity intensity patterns from multi-axial accelerometry and heart rate signals. Data collection was carried out in free-living conditions and in three controlled gymnasium circuits, for a total amount of 179.80 h of data divided into: sedentary situations (65.5%), light-to-moderate activity (17.6%) and vigorous exercise (16.9%). The proposed machine learning algorithms comprise the following steps: time-domain feature definition, standardization and PCA projection, unsupervised clustering (by k-means and GMM) and a HMM to account for long-term temporal trends. Performance was evaluated by 30 runs of a 10-fold cross-validation. Both k-means and GMM-based approaches yielded high overall accuracy (86.97% and 85.03%, respectively) and, given the imbalance of the dataset, meritorious F-measures (up to 77.88%) for non-sedentary cases. Classification errors tended to be concentrated around transients, what constrains their practical impact. Hence, we consider our proposal to be suitable for 24 h-based monitoring of physical activity in ambulatory scenarios and a first step towards intensity-specific energy expenditure estimators

Position- and orientation-independent activity of the Schizosaccharomyces pombe meiotic recombination hot spot M26

The activity of the M26 meiotic recombination hot spot of Schizosaccharomyces pombe depends on the presence of the heptamer 5′-ATGACGT-3′. Transplacement of DNA fragments containing the ade6-M26 gene to other chromosomal loci has previously demonstrated that the heptamer functions in some, but not all, transplacements, suggesting that hot spot activity depends on chromosomal context. In this study, hot spot activity was tested in the absence of gross DNA changes by using site-directed mutagenesis to create the heptamer sequence at novel locations in the genome. When created by mutagenesis of 1–4 bp in the ade6 and ura4 genes, the heptamer was active as a recombination hot spot, in an orientation-independent manner, at all locations tested. Thus, the heptamer sequence can create an active hot spot in other chromosomal contexts, provided that the gross chromosomal structure is not altered; this result is consistent with the hypothesis that a specific higher-order chromatin structure is required for M26 hot spot activity.

Synergy between chronic corticosterone and sodium azide treatments in producing a spatial learning deficit and inhibiting cytochrome oxidase activity.

Previously, we developed a rat model of persistent mitochondrial dysfunction based upon the chronic partial inhibition of the mitochondrial enzyme cytochrome oxidase (EC 1.9.3.1). Continuous systemic infusion of sodium azide at approximately 1 mg/kg per hr inhibited cytochrome oxidase activity and produced a spatial learning deficit. In other laboratories, glucocorticoids have been reported to exacerbate neuronal damage from various acute metabolic insults. Therefore, we tested the hypothesis that corticosterone, the primary glucocorticoid in the rat, would potentiate the sodium azide-induced learning deficit. To this end, we first identified nonimpairing doses of sodium azide (approximately 0.75 mg/kg per hr) and corticosterone (100-mg pellet, 3-week sustained-release). We now report that chronic co-administration of these individually nonimpairing treatments produced a severe learning deficit. Moreover, the low dose of corticosterone, which did not elevate serum corticosterone, acted synergistically with sodium azide to inhibit cytochrome oxidase activity. The latter result represents a previously unidentified effect of glucocorticoids that provides a candidate mechanism for glucocorticoid potentiation of neurotoxicity induced by metabolic insult. These results may have the clinical implication of expanding the definition of hypercortisolism in patient populations with compromised oxidative metabolism. Furthermore, they suggest that glucocorticoid treatment may contribute to pathology in disease or trauma conditions that involve metabolic insult.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.

Employee Orientation: An Organizational and Experiential Learning Approach

Employee orientation problems for a resort chain were studied and addressed through action research. The implemented solution leveraged experiential learning to foster employee initiative and problem solving to instill a culture of learning, improve customer satisfaction and increase employee retention. Business results were achieved but learner/ management reaction was mixed.

The influence of interhemispheric connections on ongoing and evoked orientation preference maps and spiking activity in the cat primary visual cortex

In the primary visual cortex, neurons with similar physiological features are clustered together in columns extending through all six cortical layers. These columns form modular orientation preference maps. Long-range lateral fibers are associated to the structure of orientation maps since they do not connect columns randomly; they rather cluster in regular intervals and interconnect predominantly columns of neurons responding to similar stimulus features. Single orientation preference maps – the joint activation of domains preferring the same orientation - were observed to emerge spontaneously and it was speculated whether this structured ongoing activation could be caused by the underlying patchy lateral connectivity. Since long-range lateral connections share many features, i.e. clustering, orientation selectivity, with visual inter-hemispheric connections (VIC) through the corpus callosum we used the latter as a model for long-range lateral connectivity. In order to address the question of how the lateral connectivity contributes to spontaneously generated maps of one hemisphere we investigated how these maps react to the deactivation of VICs originating from the contralateral hemisphere. To this end, we performed experiments in eight adult cats. We recorded voltage-sensitive dye (VSD) imaging and electrophysiological spiking activity in one brain hemisphere while reversible deactivating the other hemisphere with a cooling technique. In order to compare ongoing activity with evoked activity patterns we first presented oriented gratings as visual stimuli. Gratings had 8 different orientations distributed equally between 0º and 180º. VSD imaged frames obtained during ongoing activity conditions were then compared to the averaged evoked single orientation maps in three different states: baseline, cooling and recovery. Kohonen self-organizing maps were also used as a means of analysis without prior assumption (like the averaged single condition maps) on ongoing activity. We also evaluated if cooling had a differential effect on evoked and ongoing spiking activity of single units. We found that deactivating VICs caused no spatial disruption on the structure of either evoked or ongoing activity maps. The frequency with which a cardinally preferring (0º or 90º) map would emerge, however, decreased significantly for ongoing but not for evoked activity. The same result was found by training self-organizing maps with recorded data as input. Spiking activity of cardinally preferring units also decreased significantly for ongoing when compared to evoked activity. Based on our results we came to the following conclusions: 1) VICs are not a determinant factor of ongoing map structure. Maps continued to be spontaneously generated with the same quality, probably by a combination of ongoing activity from local recurrent connections, thalamocortical loop and feedback connections. 2) VICs account for a cardinal bias in the temporal sequence of ongoing activity patterns, i.e. deactivating VIC decreases the probability of cardinal maps to emerge spontaneously. 3) Inter- and intrahemispheric long-range connections might serve as a grid preparing primary visual cortex for likely junctions in a larger visual environment encompassing the two hemifields.

The influence of interhemispheric connections on ongoing and evoked orientation preference maps and spiking activity in the cat primary visual cortex

In the primary visual cortex, neurons with similar physiological features are clustered together in columns extending through all six cortical layers. These columns form modular orientation preference maps. Long-range lateral fibers are associated to the structure of orientation maps since they do not connect columns randomly; they rather cluster in regular intervals and interconnect predominantly columns of neurons responding to similar stimulus features. Single orientation preference maps – the joint activation of domains preferring the same orientation - were observed to emerge spontaneously and it was speculated whether this structured ongoing activation could be caused by the underlying patchy lateral connectivity. Since long-range lateral connections share many features, i.e. clustering, orientation selectivity, with visual inter-hemispheric connections (VIC) through the corpus callosum we used the latter as a model for long-range lateral connectivity. In order to address the question of how the lateral connectivity contributes to spontaneously generated maps of one hemisphere we investigated how these maps react to the deactivation of VICs originating from the contralateral hemisphere. To this end, we performed experiments in eight adult cats. We recorded voltage-sensitive dye (VSD) imaging and electrophysiological spiking activity in one brain hemisphere while reversible deactivating the other hemisphere with a cooling technique. In order to compare ongoing activity with evoked activity patterns we first presented oriented gratings as visual stimuli. Gratings had 8 different orientations distributed equally between 0º and 180º. VSD imaged frames obtained during ongoing activity conditions were then compared to the averaged evoked single orientation maps in three different states: baseline, cooling and recovery. Kohonen self-organizing maps were also used as a means of analysis without prior assumption (like the averaged single condition maps) on ongoing activity. We also evaluated if cooling had a differential effect on evoked and ongoing spiking activity of single units. We found that deactivating VICs caused no spatial disruption on the structure of either evoked or ongoing activity maps. The frequency with which a cardinally preferring (0º or 90º) map would emerge, however, decreased significantly for ongoing but not for evoked activity. The same result was found by training self-organizing maps with recorded data as input. Spiking activity of cardinally preferring units also decreased significantly for ongoing when compared to evoked activity. Based on our results we came to the following conclusions: 1) VICs are not a determinant factor of ongoing map structure. Maps continued to be spontaneously generated with the same quality, probably by a combination of ongoing activity from local recurrent connections, thalamocortical loop and feedback connections. 2) VICs account for a cardinal bias in the temporal sequence of ongoing activity patterns, i.e. deactivating VIC decreases the probability of cardinal maps to emerge spontaneously. 3) Inter- and intrahemispheric long-range connections might serve as a grid preparing primary visual cortex for likely junctions in a larger visual environment encompassing the two hemifields.

Activity-based life cycle costing model for cost estimation and monitoring – Case: manned and autonomous dry bulk carrier

Recent developments in automation, robotics and artificial intelligence have given a push to a wider usage of these technologies in recent years, and nowadays, driverless transport systems are already state-of-the-art on certain legs of transportation. This has given a push for the maritime industry to join the advancement. The case organisation, AAWA initiative, is a joint industry-academia research consortium with the objective of developing readiness for the first commercial autonomous solutions, exploiting state-of-the-art autonomous and remote technology. The initiative develops both autonomous and remote operation technology for navigation, machinery, and all on-board operating systems. The aim of this study is to develop a model with which to estimate and forecast the operational costs, and thus enable comparisons between manned and autonomous cargo vessels. The building process of the model is also described and discussed. Furthermore, the model’s aim is to track and identify the critical success factors of the chosen ship design, and to enable monitoring and tracking of the incurred operational costs as the life cycle of the vessel progresses. The study adopts the constructive research approach, as the aim is to develop a construct to meet the needs of a case organisation. Data has been collected through discussions and meeting with consortium members and researchers, as well as through written and internal communications material. The model itself is built using activity-based life cycle costing, which enables both realistic cost estimation and forecasting, as well as the identification of critical success factors due to the process-orientation adopted from activity-based costing and the statistical nature of Monte Carlo simulation techniques. As the model was able to meet the multiple aims set for it, and the case organisation was satisfied with it, it could be argued that activity-based life cycle costing is the method with which to conduct cost estimation and forecasting in the case of autonomous cargo vessels. The model was able to perform the cost analysis and forecasting, as well as to trace the critical success factors. Later on, it also enabled, albeit hypothetically, monitoring and tracking of the incurred costs. By collecting costs this way, it was argued that the activity-based LCC model is able facilitate learning from and continuous improvement of the autonomous vessel. As with the building process of the model, an individual approach was chosen, while still using the implementation and model building steps presented in existing literature. This was due to two factors: the nature of the model and – perhaps even more importantly – the nature of the case organisation. Furthermore, the loosely organised network structure means that knowing the case organisation and its aims is of great importance when conducting a constructive research.

A machine learning approach to measure and monitor physical activity in children to help fight overweight and obesity

Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical activity levels are issued by most governments as part of public health measures. As such, reliable measurement of physical activity for regulatory purposes is vital. This has lead research to explore standards for achieving this using wearable technology and artificial neural networks that produce classifications for specific physical activity events. Applied from a very early age, the ubiquitous capture of physical activity data using mobile and wearable technology may help us to understand how we can combat childhood obesity and the impact that this has in later life. A supervised machine learning approach is adopted in this paper that utilizes data obtained from accelerometer sensors worn by children in free-living environments. The paper presents a set of activities and features suitable for measuring physical activity and evaluates the use of a Multilayer Perceptron neural network to classify physical activities by activity type. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 96 % with 95 % for sensitivity, 99 % for specificity and a kappa value of 94 % when three and four feature combinations were used.

Scaling-up an efficacious school-based physical activity intervention: study protocol for the 'Internet-based professional learning to help teachers support activity in youth' (iPLAY) cluster randomized controlled trial and scale-up implementation evaluat

BACKGROUND: Despite the health benefits of regular physical activity, most children are insufficiently active. Schools are ideally placed to promote physical activity; however, many do not provide children with sufficient in-school activity or ensure they have the skills and motivation to be active beyond the school setting. The aim of this project is to modify, scale up and evaluate the effectiveness of an intervention previously shown to be efficacious in improving children's physical activity, fundamental movement skills and cardiorespiratory fitness. The 'Internet-based Professional Learning to help teachers support Activity in Youth' (iPLAY) study will focus largely on online delivery to enhance translational capacity.

METHODS/DESIGN: The intervention will be implemented at school and teacher levels, and will include six components: (i) quality physical education and school sport, (ii) classroom movement breaks, (iii) physically active homework, (iv) active playgrounds, (v) community physical activity links and (vi) parent/caregiver engagement. Experienced physical education teachers will deliver professional learning workshops and follow-up, individualized mentoring to primary teachers (i.e., Kindergarten - Year 6). These activities will be supported by online learning and resources. Teachers will then deliver the iPLAY intervention components in their schools. We will evaluate iPLAY in two complementary studies in primary schools across New South Wales (NSW), Australia. A cluster randomized controlled trial (RCT), involving a representative sample of 20 schools within NSW (1:1 allocation at the school level to intervention and attention control conditions), will assess effectiveness and cost-effectiveness at 12 and 24 months. Students' cardiorespiratory fitness will be the primary outcome in this trial. Key secondary outcomes will include students' moderate-to-vigorous physical activity (via accelerometers), fundamental movement skill proficiency, enjoyment of physical education and sport, cognitive control, performance on standardized tests of numeracy and literacy, and cost-effectiveness. A scale-up implementation study guided by the RE-AIM framework will evaluate the reach, effectiveness, adoption, implementation, and maintenance of the intervention when delivered in 160 primary schools in urban and regional areas of NSW.

DISCUSSION: This project will provide the evidence and a framework for government to guide physical activity promotion throughout NSW primary schools and a potential model for adoption in other states and countries.