This study presents a tool, Neuronize, for building realistic three-dimensional models of neuronal cells from the morphological information extracted through computer-aided tracing applications. Neuronize consists of a set of methods designed to build 3D neural meshes that approximate the cell membrane at different resolution levels, allowing a balance to be reached between the complexity and the quality of the final model. The main contribution of the present study is the proposal of a novel approach to build a realistic and accurate 3D shape of the soma from the incomplete information stored in the digitally traced neuron, which usually consists of a 2D cell body contour. This technique is based on the deformation of an initial shape driven by the position and thickness of the first order dendrites. The addition of a set of spines along the dendrites completes the model, building a final 3D neuronal cell suitable for its visualization in a wide range of 3D environments.

Gaining a better understanding of the human brain continues to be one of the greatest challenges for science, largely because of the overwhelming complexity of the brain and the difficulty of analyzing the features and behavior of dense neural networks. Regarding analysis, 3D visualization has proven to be a useful tool for the evaluation of complex systems. However, the large number of neurons in non-trivial circuits, together with their intricate geometry, make the visualization of a neuronal scenario an extremely challenging computational problem. Previous work in this area dealt with the generation of 3D polygonal meshes that approximated the cells’ overall anatomy, but did not attempt to deal with the extremely high storage and computational cost required to manage a complex scene. This paper presents NeuroTessMesh, a tool specifically designed to cope with many of the problems associated with the visualization of neural circuits that are comprised of large numbers of cells. Additionally, this method facilitates the recovery and visualization of the 3D geometry of cells included in databases such as NeuroMorpho, and provides the tools needed to approximate missing information such as the soma’s morphology. This method takes as its only input the available compact, yet incomplete, morphological tracings of the cells as acquired by neuroscientists. It uses a multiresolution approach that combines an initial, coarse mesh generation with subsequent on-the-fly adaptive mesh refinement stages using tessellation shaders. For the coarse mesh generation, a novel approach, based on the Finite Element Method, allows approximation of the 3D shape of the soma from its incomplete description. Subsequently, the adaptive refinement process performed in the graphic card generates meshes that provide good visual quality geometries at a reasonable computational cost, both in terms of memory and rendering time. All the described techniques have been integrated into NeuroTessMesh, available to the scientific community, to generate, visualize and save the adaptive resolution meshes.

And is designed for both real and simulated settings. The validity of the scale was tested using a virtual reality shoulder arthroscopy simulator. Methods: The study consisted of two parts. In the first part, an Imperial Global Arthroscopy Rating Scale for assessing technical performance was developed using a Delphi method. Application of this scale required installing a dual-camera system to synchronously record the simulator screen and body movements of trainees to allow an assessment that is independent of time and place. The scale includes aspects such as efficient portal positioning, angles of instrument insertion, proficiency in handling the arthroscope and adequately manipulating the camera, and triangulation skills. In the second part of the study, a validation study was conducted. Two experienced arthroscopic surgeons, blinded to the identities and experience of the participants, each assessed forty-nine subjects performing three different tests using the Imperial Global Arthroscopy Rating Scale. Results were analyzed using two-way analysis of variance with measures of absolute agreement. The intraclass correlation coefficient was calculated for each test to assess inter-rater reliability. Results: The scale demonstrated high internal consistency (Cronbach alpha, 0.918). The intraclass correlation coefficient demonstrated high agreement between the assessors: 0.91 (p < 0.001). Construct validity was evaluated using Kruskal-Wallis one-way analysis of variance (chi-square test, 29.826; p < 0.001), demonstrating that the Imperial Global Arthroscopy Rating Scale distinguishes significantly between subjects with different levels of experience utilizing a virtual reality simulator. Conclusions: The Imperial Global Arthroscopy Rating Scale has a high internal consistency and excellent inter-rater reliability and offers an approach for assessing technical performance in basic arthroscopy on a virtual reality simulator. Clinical Relevance: The Imperial Global Arthroscopy Rating Scale provides detailed information on surgical skills. Although it requires further validation in the operating room, this scale, which is independent of time and place, offers a robust and reliable method for assessing arthroscopic technical skills....

Symptoms of Attention Deficit Hyperactivity Disorder (ADHD) include excessive activity, difficulty sustaining attention, and inability to act in a reflective manner. Early diagnosis and treatment of ADHD is key but may be influenced by the observation and communication skills of caregivers, and the experience of the medical professional. Attempts to obtain additional measures to support the medical diagnosis, such as reaction time when performing a task, can be found in the literature. We propose an information recording system that allows to study in detail the behavior shown by children already diagnosed with ADHD during a car driving video game. We continuously record the participants’ activity throughout the task and calculate the error committed. Studying the trajectory graphs, some children showed uniform patterns, others lost attention from one point onwards, and others alternated attention/inattention intervals. Results show a dependence between the age of the children and their performance. Moreover, by analyzing the positions by age over time using clustering, we show that it is possible to classify children according to their performance. Future studies will examine whether this detailed information about each child’s performance pattern can be used to fine-tune treatment.

Hypothesis testing has been pointed out as one of the statistical topics in which students present more misconceptions. In this article, an approach based on the divide-and-conquer methodology is proposed to facilitate its learning. The proposed strategy is designed to sequentially explain and evaluate the different concepts involved in hypothesis testing, ensuring that a new concept is not presented until the previous one has been fully assimilated. The proposed approach, which contains several gamification elements (i.e., points or a leader-board), is implemented into an application via a modern game engine. The usefulness of the proposed approach was assessed in an experiment in which 89 first-year students enrolled in the Statistics course within the Industrial Engineering degree participated. Based on the results of a test aimed at evaluating the acquired knowledge, it was observed that students who used the developed application based on the proposed approach obtained statistically significant higher scores than those that attended a traditional class (p-value < 0.001), regardless of whether they used the learning tool before or after the traditional class. In addition, the responses provided by the students who participated in the study to a test of satisfaction showed their high satisfaction with the application and their interest in the promotion of these tools. However, despite the good results, they also considered that these learning tools should be considered as a complement to the master class rather than a replacement.