Abstract
Analysis of locomotion is often used as a measure for impairment and recovery following experimental peripheral nerve injury. Compared to rodents, sheep offer several advantages for studying peripheral nerve regeneration. In the present study, we compared for the first time, two-dimensional (2D) and three-dimensional (3D) hindlimb kinematics during obstacle avoidance in the ovine model. This study obtained kinematic data to serve as a template for an objective assessment of the ankle joint motion in future studies of common peroneal nerve (CP) injury and repair in the ovine model. The strategy used by the sheep to bring the hindlimb over a moderately high obstacle, set to 10% of its hindlimb length, was pronounced knee, ankle and metatarsophalangeal flexion when approaching and clearing the obstacle. Despite the overall time course kinematic patterns about the hip, knee, ankle, and metatarsophalangeal were identical, we found significant differences between values of the 2D and 3D joint angular motion. Our results showed that the most apparent changes that occurred during the gait cycle were for the ankle (2D-measured STANCEmax: 157 +/- 2.4 degrees vs. 3D-measured STANCEmax: 151 +/- 1.2 degrees; P<.05) and metatarsophalangeal joints (2D-measured STANCEmin: 151 +/- 2.2 degrees vs. 3D-measured STANCEmin: 162 +/- 2.2 degrees; P<.01 and 2D-measured TO: 163 +/- 4.9 degrees vs. 3D-measured TO: 177 +/- 1.4 degrees; P<.05), whereas the hip and knee joints were much less affected. Data and techniques described here are useful for an objective assessment of altered gait after CP injury and repairin an ovine model.

Abstract
Road safety is crucial in the design of autonomous vehicles, so safety and performance tests must be carried out regularly. Visualization applications of data acquired in these tests are critical to helping the continuous progress of autonomous driving. Currently, there is an application that helps in visualizing these data through a web platform, called Kratos Visualization. This application was developed using the WebGL library, to help companies that carry out experiments with autonomous vehicles to visualize and validate the data that they collect. These data represent different sequences of autonomous driving experiences and can contain a lot of information about the vehicle and objects in the environment around it. The work that led to this paper aims to explore the visualization of these data with the help of virtual reality. To strengthen the visualization of autonomous driving data, an application was created named Kratos VR. Designed with the A-Frame framework to work in any browser, this application contains the same functionalities of Kratos Visualization and new virtual reality features. Performance tests were carried out to evaluate the application. These tests allowed us to conclude that virtual reality can be successfully used to effectively visualize Advanced Driving Assist Systems (ADAS) and Autonomous Driving (AD) data and that the developed application provides a solid basis for future virtual reality applications in this field.

Abstract
Background Blood cancers (BCs) are responsible for over 720 K yearly deaths worldwide. Their prevalence and mortality-rate uphold the relevance of research related to BCs. Despite the availability of different resources establishing Disease-Disease Associations (DDAs), the knowledge is scattered and not accessible in a straightforward way to the scientific community. Here, we propose SicknessMiner, a biomedical Text-Mining (TM) approach towards the centralization of DDAs. Our methodology encompasses Named Entity Recognition (NER) and Named Entity Normalization (NEN) steps, and the DDAs retrieved were compared to the DisGeNET resource for qualitative and quantitative comparison. Results We obtained the DDAs via co-mention using our SicknessMiner or gene- or variant-disease similarity on DisGeNET. SicknessMiner was able to retrieve around 92% of the DisGeNET results and nearly 15% of the SicknessMiner results were specific to our pipeline. Conclusions SicknessMiner is a valuable tool to extract disease-disease relationship from RAW input corpus.

Abstract
Multiple Sclerosis (MS) is a chronic and inflammatory disorder that causes degeneration of axons in brain white matter and spinal cord. Magnetic Resonance Imaging (MRI) is extensively used to identify MS lesions and evaluate the progression of the disease, but the manual identification and quantification of lesions are time consuming and error-prone tasks. Thus, automated Deep Learning methods, in special Convolutional Neural Networks (CNNs), are becoming popular to segment medical images. It has been noticed that the performance of those methods tends to decrease when applied to MRI acquired under different protocols. The aim of this work is to statistically evaluate the possible influence of domain adaptation during the training process of CNNs models for segmenting MS lesions in MRI. The segmentation models were tested on MRIs (FLAIR and T1) of 20 patients diagnosed with Multiple Sclerosis. The set of segmented images of each different model was compared statistically, through the metrics Dice Similarity Coefficient (DSC), Predictive Positive Value (PPV) and Absolute Volume Difference (AVD). The results indicate that the domain adapted training can improve the performance of automatic segmentation methods, by CNNs, and have great potential to be used in medical clinics in the future.

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