Notes

Ageing, the Central Nervous System, as well as Range of motion inside Older Adults: Neural Elements regarding Flexibility Impairment.
Virtual Reality (VR) simulators represent a remarkable educational opportunity in order to acquire and refine surgical practical skills. Nevertheless, there exists no consensus regarding a standard curriculum of simulation-based training. This study introduces an automatic, adaptive curriculum where the training session is real-time scheduled on the basis of the trainee's performances.

An experimental study using the master console of the da Vinci Research Kit (Intuitive Surgical Inc., Sunnyvale, US) was carried out to test this approach. Tasks involving fundamental skills of robotic surgery were designed and simulated in VR. Twelve participants without medical background along with twelve medical residents were randomly and equally divided into two groups a control group, self-managing the training session, and an experimental group, undergoing the proposed adaptive training.

The performances of the experimental users were significantly better with respect to the ones of the control group after training (non-medical p < 0.01; medical p = 0.02). This trend was analogous in the non-medical and medical populations and no significant difference was identified between these two classes (even in the baseline assessment).

The analysis of the learning of the involved surgical skills highlighted how the proposed adaptive training managed to better identify and compensate for the trainee's gaps. The absence of initial difference between the non-medical and medical users underlines that robotic surgical devices require specific training before clinical practice.

This feasibility study could pave the way towards the improvement of simulation-based training curricula.
This feasibility study could pave the way towards the improvement of simulation-based training curricula.
This paper explores the predictive capability of dynamic functional connectivity extracted from functional magnetic resonance imaging (fMRI) of the human brain, in contrast to static connectivity used in past research.

Several state-of-the-art features extracted from static functional connectivity of the brain are employed to predict biological gender and intelligence using publicly available Human Connectome Project (HCP) database. Next, a novel tensor parallel factor (PARAFAC) decomposition model is proposed to decompose sequence of dynamic connectivity matrices into common connectivity components that are orthonormal to each other, common time-courses, and corresponding distinct subject-wise weights. selleckchem The subject-wise loading of the components are employed to predict biological gender and intelligence using a random forest classifier (respectively, regressor) using 5-fold cross-validation.

The results demonstrate that dynamic functional connectivity can indeed classify biological gender with a high accuracy (0.94, where male identification accuracy was 0.87 and female identification accuracy was 0.97). It can also predict intelligence with less normalized mean square error (0.139 for fluid intelligence and 0.031 for fluid ability metrics) compared with other functional connectivity measures (the nearest mean square error were 0.147 and 0.037 for fluid intelligence and fluid ability metrics, respectively, using static connectivity approaches).

Our work is an important milestone for the understanding of non-stationary behavior of hemodynamic blood-oxygen level dependent (BOLD) signal in brain and how they are associated with biological gender and intelligence.

The paper demonstrates that dynamic behavior of brain can contribute substantially towards forming a fingerprint of biological gender and intelligence.
The paper demonstrates that dynamic behavior of brain can contribute substantially towards forming a fingerprint of biological gender and intelligence.
The purpose of this work is to investigate the feasibility of using deep convolutional neural network (CNN) to improve the image quality of a grating-based X-ray differential phase contrast imaging (XPCI) system.

In this work, a novel deep CNN based phase signal extraction and image noise suppression algorithm (named as XP-NET) is developed. The numerical phase phantom, the ex vivo biological specimen and the ACR breast phantom are evaluated via the numerical simulations and experimental studies, separately. Moreover, images are also evaluated under different low radiation levels to verify its dose reduction capability.

Compared with the conventional analytical method, the novel XP-NET algorithm is able to reduce the bias of large DPC signals and hence increasing the DPC signal accuracy by more than 15%. Additionally, the XP-NET is able to reduce DPC image noise by about 50% for low dose DPC imaging tasks.

This proposed novel end-to-end supervised XP-NET has a great potential to improve the DPC signal accuracy, reduce image noise, and preserve object details.

We demonstrate that the deep CNN technique provides a promising approach to improve the grating-based XPCI performance and its dose efficiency in future biomedical applications.
We demonstrate that the deep CNN technique provides a promising approach to improve the grating-based XPCI performance and its dose efficiency in future biomedical applications.
Toward the ultimate goal of cuff-less blood pressure (BP) trend tracking via pulse transit time (PTT) using wearable ballistocardiogram (BCG) signals, we present a unified approach to the gating of wearable BCG and the localization of wearable BCG waves.

We present a unified approach to localize wearable BCG waves suited to various gating and localization reference signals. Our approach gates individual wearable BCG beats and identifies candidate waves in each wearable BCG beat using a fiducial point in a reference signal, and exploits a pre-specified probability distribution of the time interval between the BCG wave and the fiducial point in the reference signal to accurately localize the wave in each wearable BCG beat. We tested the validity of our approach using experimental data collected from 17 healthy volunteers.

We showed that our approach could localize the J wave in the wearable wrist BCG accurately with both the electrocardiogram (ECG) and the wearable wrist photoplethysmogram (PPG) signals as reference, and that the wrist BCG-PPG PTT thus derived exhibited high correlation to BP.
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