A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

Sciatic nerve movement in the deep gluteal space during hip rotations maneuvers.

We hypothesize that the sciatic nerve in the subgluteal space has a specific behavior during internal and external coxofemoral rotation and during isometric contraction of the internal and external rotator muscles of the hip. In 58 healthy volunteers, sciatic nerve behavior was studied by ultrasound during passive internal and external hip rotation movements and during isometric contraction of internal and external rotators. Using MATLAB software, changes in nerve curvature at the beginning and end of each exercise were evaluated for longitudinal catches and axial movement for transverse catches. In the long axis, it was observed that during the passive internal rotation and during the isometric contraction of external rotators, the shape of the curve increased significantly while during the passive external rotation and the isometric contraction of the internal rotators the curvature flattened out. During passive movements in internal rotation, on the short axis, the nerve tended to move laterally and forward, while during external rotation the tendency of the nerve was to move toward a medial and backward position. During the isometric exercises, this displacement was less in the passive movements. Passive movements of hip rotation and isometric contraction of the muscles affect the sciatic nerve in the subgluteal space. Retrotrochanteric pain may be related to both the shear effect of the subgluteus muscles and the endoneural and mechanosensitive aggression to which the sciatic nerve is subjected.

Estimating Player Positions from Padel High-Angle Videos: Accuracy Comparison of Recent Computer Vision Methods.

The estimation of player positions is key for performance analysis in sport. In this paper, we focus on image-based, single-angle, player position estimation in padel. Unlike tennis, the primary camera view in professional padel videos follows a de facto standard, consisting of a high-angle shot at about 7.6 m above the court floor. This camera angle reduces the occlusion impact of the mesh that stands over the glass walls, and offers a convenient view for judging the depth of the ball and the player positions and poses. We evaluate and compare the accuracy of state-of-the-art computer vision methods on a large set of images from both amateur videos and publicly available videos from the major international padel circuit. The methods we analyze include object detection, image segmentation and pose estimation techniques, all of them based on deep convolutional neural networks. We report accuracy and average precision with respect to manually-annotated video frames. The best results are obtained by top-down pose estimation methods, which offer a detection rate of 99.8% and a RMSE below 5 and 12 cm for horizontal/vertical court-space coordinates (deviations from predicted and ground-truth player positions). These results demonstrate the suitability of pose estimation methods based on deep convolutional neural networks for estimating player positions from single-angle padel videos. Immediate applications of this work include the player and team analysis of the large collection of publicly available videos from international circuits, as well as an inexpensive method to get player positional data in amateur padel clubs.

Gemelli-obturator complex in the deep gluteal space: an anatomic and dynamic study.

OBJECTIVE: To investigate the behavior of the sciatic nerve during hip rotation at subgluteal space. MATERIALS AND METHODS: Sonographic examination (high-resolution ultrasound machine at 5.0-14 MHZ) of the gemelli-obturator internus complex following two approaches: (1) a study on cadavers and (2) a study on healthy volunteers. The cadavers were examined in pronation, pelvis-fixed position by forcing internal and external rotations of the hip with the knee in 90° flexion. Healthy volunteers were examined during passive internal and external hip rotation (prone position; lumbar and pelvic regions fixed). Subjects with a history of major trauma, surgery or pathologies affecting the examined regions were excluded. RESULTS: The analysis included eight hemipelvis from six fresh cadavers and 31 healthy volunteers. The anatomical study revealed the presence of connective tissue attaching the sciatic nerve to the structures of the gemellus-obturator system at deep subgluteal space. The amplitude of the nerve curvature during rotating position was significantly greater than during resting position. During passive internal rotation, the sciatic nerve of both cadavers and healthy volunteers transformed from a straight structure to a curved structure tethered at two points as the tendon of the obturator internus contracted downwards. Conversely, external hip rotation caused the nerve to relax. CONCLUSION: Anatomically, the sciatic nerve is closely related to the gemelli-obturator internus complex. This relationship results in a reproducible dynamic behavior of the sciatic nerve during passive hip rotation, which may contribute to explain the pathological mechanisms of the obturator internal gemellus syndrome.

Simulation and study of the geometric parameters in the inguinal area and the genesis of inguinal hernias.

We are interested in studying the genesis of a very common pathology: the human inguinal hernia. How the human inguinal hernia appears is not definitively clear, but it is accepted that it is caused by a combination of mechanical and biochemical alterations, and that muscular simulation plays an important role in this. This study proposes a model to explain how some physical parameters affect the ability to simulate the region dynamically and how these parameters are involved in generating inguinal hernias. We are particularly interested in understanding the mechanical alterations in the inguinal region because little is known about them or how they behave dynamically. Our model corroborates the most important theories regarding the generation of inguinal hernias and is an initial approach to numerically evaluating this affection.

A simulation finite element model for the mechanics of the internal oblique muscle: a defense mechanism against inguinal hernia formation?

Simulation of the human muscular system has multiple applications in biomechanics, biomedicine and in the study of motion in general. Mechanical alterations of the normal functioning in the inguinal area ("inguinal shutter") seems to be involved in the genesis of hernias in adults, but the role of this anatomical mechanisms is poorly understood. A finite element model for the mechanics of the internal oblique muscle allowed creating a dynamic model of the inguinal region applicable to the study of the shutter mechanism as a defence mechanism of contention of the abdominal viscera against development of an inguinal hernia.

A real-time dynamic 3D model of the human inguinal region for surgical education.

A suitable dynamic 3D model that allows the simulation of the inguinal region with real-time performance on a personal computer was developed. A geometric model adjusted to real data was created by means of semiautomatic contour segmentation of anatomic units from the visible human project and data generated from classical anatomic information. A dynamic model included converting muscular units from their continuous geometric representation into a set of voxels and then real-time interaction and performance. The current implementation enables deformation of the realistic model associated with pushing and stretching interaction, allowing immersion in the anatomy of the inguinal structures. The model does not allow simulation of surgical interventions.