Motor Output Variability Impairs Driving Ability in Older Adults: Reply to Stinchcombe, Dickerson, Weaver, and Bedard

Driving is a complex skill, as indicated by Stinchcombe and colleagues in their letter. It requires the integration of sensory inputs, cognitive processing, and motor execution. Although our title is broad, we clearly indicate that our findings only address a single component of driving, namely reactive driving. We also indicate that these findings are based on a simulated task and recommend that future studies should examine the contribution of motor output variability to on-road driving performance (see Considerations in the Discussion section). Thus, we share the consideration of Stinchcombe and colleagues that the current results only address a small portion of the driving complexity

AccEq-DRT: Planning Demand-Responsive Transit to reduce inequality of accessibility

Accessibility measures how well a location is connected to surrounding opportunities. We focus on accessibility provided by Public Transit (PT). There is an evident inequality in the distribution of accessibility between city centers or close to main transportation corridors and suburbs. In the latter, poor PT service leads to a chronic car-dependency. Demand-Responsive Transit (DRT) is better suited for low-density areas than conventional fixed-route PT. However, its potential to tackle accessibility inequality has not yet been exploited. On the contrary, planning DRT without care to inequality (as in the methods proposed so far) can further improve the accessibility gap in urban areas. To the best of our knowledge this paper is the first to propose a DRT planning strategy, which we call AccEq-DRT, aimed at reducing accessibility inequality, while ensuring overall efficiency. To this aim, we combine a graph representation of conventional PT and a Continuous Approximation (CA) model of DRT. The two are combined in the same multi-layer graph, on which we compute accessibility. We then devise a scoring function to estimate the need of each area for an improvement, appropriately weighting population density and accessibility. Finally, we provide a bilevel optimization method, where the upper level is a heuristic to allocate DRT buses, guided by the scoring function, and the lower level performs traffic assignment. Numerical results in a simplified model of Montreal show that inequality, measured with the Atkinson index, is reduced by up to 34\%. Keywords: DRT Public, Transportation, Accessibility, Continuous Approximation, Network DesignComment: 15 page

Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions

This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starting from a known glucose value, the fused glucose prediction is computed. A new loss function is introduced to make the DCP model learn to react faster to changes in glucose variations. The algorithm has been tested on 10 \textit{in-silico} type-1 diabetic children from the T1DMS software. Three initial predictors have been used: a Gaussian process regressor, a feed-forward neural network and an extreme learning machine model. The DCP and two other fusion algorithms have been evaluated at a prediction horizon of 120 minutes with the root-mean-squared error of the prediction, the root-mean-squared error of the predicted variation, and the continuous glucose-error grid analysis. By making a successful trade-off between prediction accuracy and predicted-variation accuracy, the DCP, alongside with its specifically designed loss function, improves the clinical acceptability of the predictions, and therefore the safety of the model for diabetic people

Sex Differences in Spatial Accuracy Relate to the Neural Activation of Antagonistic Muscles in Young Adults

Sex is an important physiological variable of behavior, but its effect on motor control remains poorly understood. Some evidence suggests that women exhibit greater variability during constant contractions and poorer accuracy during goal-directed tasks. However, it remains unclear whether motor output variability or altered muscle activation impairs accuracy in women. Here, we examine sex differences in endpoint accuracy during ankle goal-directed movements and the activity of the antagonistic muscles. Ten women (23.1 ± 5.1 years) and 10 men (23 ± 3.7 years) aimed to match a target (9° in 180 ms) with ankle dorsiflexion. Participants performed 50 trials and we recorded the endpoint accuracy and the electromyographic (EMG) activity of the primary agonist (Tibialis Anterior; TA) and antagonist (Soleus; SOL) muscles. Women exhibited greater spatial inaccuracy (Position error: t = −2.65, P = 0.016) but not temporal inaccuracy relative to men. The motor output variability was similar for the two sexes (P \u3e 0.2). The spatial inaccuracy in women was related to greater variability in the coordination of the antagonistic muscles (R 2 0.19, P = 0.03). These findings suggest that women are spatially less accurate than men during fast goal-directed movements likely due to an altered activation of the antagonistic muscles

Editorial: Exploiting wheat biodiversity and agricultural practices for tackling the effects of climate change

Editorial: Exploiting wheat biodiversity and agricultural practices for tackling the effects of climate chang

Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children

Research in diabetes, especially when it comes to building data-driven models to forecast future glucose values, is hindered by the sensitive nature of the data. Because researchers do not share the same data between studies, progress is hard to assess. This paper aims at comparing the most promising algorithms in the field, namely Feedforward Neural Networks (FFNN), Long Short-Term Memory (LSTM) Recurrent Neural Networks, Extreme Learning Machines (ELM), Support Vector Regression (SVR) and Gaussian Processes (GP). They are personalized and trained on a population of 10 virtual children from the Type 1 Diabetes Metabolic Simulator software to predict future glucose values at a prediction horizon of 30 minutes. The performances of the models are evaluated using the Root Mean Squared Error (RMSE) and the Continuous Glucose-Error Grid Analysis (CG-EGA). While most of the models end up having low RMSE, the GP model with a Dot-Product kernel (GP-DP), a novel usage in the context of glucose prediction, has the lowest. Despite having good RMSE values, we show that the models do not necessarily exhibit a good clinical acceptability, measured by the CG-EGA. Only the LSTM, SVR and GP-DP models have overall acceptable results, each of them performing best in one of the glycemia regions

Robust Line Detection in Historical Church Registers

For being able to automatically acquire information recorded in church registers and other historical scriptures, the text of such documents needs to be segmented prior to automatic reading. Segmentation of old handwritten scriptures is difficult for two main reasons