Cardiovascular Dynamics in Crocodylus porosus Breathing Air and During Voluntary Aerobic Dives

Pressure records from the heart and outflow vessels of the heart of Crocodylus porosus resolve previously conflicting results, showing that left aortic filling via the foramen of Panizza may occur during both cardiac diastole and systole. Filling of the left aorta during diastole, identified by the asynchrony and comparative shape of pressure events in the left and right aortae, is reconciled more easily with the anatomy, which suggests that the foramen would be occluded by opening of the pocket valves at the base of the right aorta during systole. Filling during systole, indicated when pressure traces in the left and right aortae could be superimposed, was associated with lower systemic pressures, which may occur at the end of a voluntary aerobic dive or can be induced by lowering water temperature or during a long forced dive. To explain this flexibility, we propose that the foramen of Panizza is of variable calibre. The presence of a 'right-left' shunt, in which increased right ventricular pressure leads to blood being diverted from the lungs and exiting the right ventricle via the left aorta, was found to be a frequent though not obligate correlate of voluntary aerobic dives. This contrasts with the previous concept of the shunt as a correlate of diving bradycardia. The magnitude of the shunt is difficult to assess but is likely to be relatively small. This information has allowed some new insights into the functional significance of the complex anatomy of the crocodilian heart and major blood vessels

Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People

In the context of time-series forecasting, we propose a LSTM-based recurrent neural network architecture and loss function that enhance the stability of the predictions. In particular, the loss function penalizes the model, not only on the prediction error (mean-squared error), but also on the predicted variation error. We apply this idea to the prediction of future glucose values in diabetes, which is a delicate task as unstable predictions can leave the patient in doubt and make him/her take the wrong action, threatening his/her life. The study is conducted on type 1 and type 2 diabetic people, with a focus on predictions made 30-minutes ahead of time. First, we confirm the superiority, in the context of glucose prediction, of the LSTM model by comparing it to other state-of-the-art models (Extreme Learning Machine, Gaussian Process regressor, Support Vector Regressor). Then, we show the importance of making stable predictions by smoothing the predictions made by the models, resulting in an overall improvement of the clinical acceptability of the models at the cost in a slight loss in prediction accuracy. Finally, we show that the proposed approach, outperforms all baseline results. More precisely, it trades a loss of 4.3\% in the prediction accuracy for an improvement of the clinical acceptability of 27.1\%. When compared to the moving average post-processing method, we show that the trade-off is more efficient with our approach

Computational assessment of insulin secretion and insulin sensitivity from 2-h oral glucose tolerance tests for clinical use for type 2 diabetes

In type 2 diabetes mellitus, glucose homeostasis is tightly maintained through insulin secretion and insulin sensitivity. Therefore, finding an accurate method to assess insulin secretion and sensitivity using clinically available data would enhance the quality of diabetic medical care. In an effort to find such a method, we developed a computational approach to derive indices of these factors using a 2-h oral glucose tolerance test (OGTT). To evaluate our method, clinical data from subjects who received an OGTT and a glucose clamp test were examined. Our insulin secretion index was significantly correlated with an analogous index obtained from a hyperglycemic clamp test (r = 0.90, n = 46, p < 0.001). Our insulin sensitivity index sensitivity was also significantly correlated with an analogous index obtained from a hyperinsulinemic-euglycemic clamp test (r = 0.56, n = 79, p < 0.001). These results suggest that our method can potentially provide an accurate and convenient tool toward improving the management of diabetes in clinical practice by assessing insulin secretion and insulin sensitivity

Direct observations of a surface eigenmode of the dayside magnetopause

The abrupt boundary between a magnetosphere and the surrounding plasma, the magnetopause, has long been known to support surface waves. It was proposed that impulses acting on the boundary might lead to a trapping of these waves on the dayside by the ionosphere, resulting in a standing wave or eigenmode of the magnetopause surface. No direct observational evidence of this has been found to date and searches for indirect evidence have proved inconclusive, leading to speculation that this mechanism might not occur. By using fortuitous multipoint spacecraft observations during a rare isolated fast plasma jet impinging on the boundary, here we show that the resulting magnetopause motion and magnetospheric ultra-low frequency waves at well-defined frequencies are in agreement with and can only be explained by the magnetopause surface eigenmode. We therefore show through direct observations that this mechanism, which should impact upon the magnetospheric system globally, does in fact occur