Timed Probabilistic Automaton : a bridge between Raven and Song Scope for automatic species recognition

Raven and Song Scope are two automated sound anal-ysis tools based on machine learning technique for en-vironmental monitoring. Many research works have been conducted upon them, however, no or rare explo-ration mentions about the performance and comparison between them. This paper investigates the comparisons from six aspects: theory, software interface, ease of use, detection targets, detection accuracy, and potential application. Through deep exploration one critical gap is identified that there is a lack of approach to detect both syllables and call structures, since Raven only aims to detect syllables while Song Scope targets call structures. Therefore, a Timed Probabilistic Automata (TPA) system is proposed which separates syllables first and clusters them into complex structures after.

Validation tool for traction force microscopy

Traction force microscopy (TFM) is commonly used to estimate cells’ traction forces from the deformation that they cause on their substrate. The accuracy of TFM highly depends on the computational methods used to measure the deformation of the substrate and estimate the forces, and also on the specifics of the experimental set-up. Computer simulations can be used to evaluate the effect of both the computational methods and the experimental set-up without the need to perform numerous experiments. Here, we present one such TFM simulator that addresses several limitations of the existing ones. As a proof of principle, we recreate a TFM experimental set-up, and apply a classic 2D TFM algorithm to recover the forces. In summary, our simulator provides a valuable tool to study the performance, refine experimentally, and guide the extraction of biological conclusions from TFM experiments.

Numerical estimation of 3D mechanical forces exerted by cells on non-linear materials

The exchange of physical forces in both cell-cell and cell-matrix interactions play a significant role in a variety of physiological and pathological processes, such as cell migration, cancer metastasis, inflammation and wound healing. Therefore, great interest exists in accurately quantifying the forces that cells exert on their substrate during migration. Traction Force Microscopy (TFM) is the most widely used method for measuring cell traction forces. Several mathematical techniques have been developed to estimate forces from TFM experiments. However, certain simplifications are commonly assumed, such as linear elasticity of the materials and/or free geometries, which in some cases may lead to inaccurate results. Here, cellular forces are numerically estimated by solving a minimization problem that combines multiple non-linear FEM solutions. Our simulations, free from constraints on the geometrical and the mechanical conditions, show that forces are predicted with higher accuracy than when using the standard approaches.

Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Electromyography and sonomyography analysis of the tibialis anterior: a cross sectional study

Background Foot dorsiflexion plays an essential role in both controlling balance and human gait. Electromyography (EMG) and sonomyography (SMG) can provide information on several aspects of muscle function. The aim was to establish the relationship between the EMG and SMG variables during isotonic contractions of foot dorsiflexors. Methods Twenty-seven healthy young adults performed the foot dorsiflexion test on a device designed ad hoc. EMG variables were maximum peak and area under the curve. Muscular architecture variables were muscle thickness and pennation angle. Descriptive statistical analysis, inferential analysis and a multivariate linear regression model were carried out. The confidence level was established with a statistically significant p-value of less than 0.05. Results The correlation between EMG variables and SMG variables was r = 0.462 (p < 0.05). The linear regression model to the dependent variable “peak normalized tibialis anterior (TA)” from the independent variables “pennation angle and thickness”, was significant (p = 0.002) with an explained variance of R2 = 0.693 and SEE = 0.16. Conclusions There is a significant relationship and degree of contribution between EMG and SMG variables during isotonic contractions of the TA muscle. Our results suggest that EMG and SMG can be feasible tools for monitoring and assessment of foot dorsiflexors. TA muscle parameterization and assessment is relevant in order to know that increased strength accelerates the recovery of lower limb injuries.

Monitoring changes of the tibialis anterior during dorsiflexion with electromyography, sonomyography, dynamometry and kinematic signals

Dorsiflexion (DF) of the foot plays an essential role in both controlling balance and human gait. Electromyography and Sonomyography can provide information on several aspects of muscle function. The aim was to describe a new method for real-time monitoring of muscular activity, as measured using EMG, muscular architecture, as measured using SMG, force, as measured using dynamometry, and kinematic parameters, as measured using IS during isometric and isotonic contractions of the foot DF. The present methodology may be clinically relevant because it involves a reproducible procedure which allows the function and structure of the foot DF to be monitored.

The ENIGMA Consortium: Large-scale collaborative analyses of neuroimaging and genetic data

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

The grumpy stump - ultrasound and stump appendicitis

BACKGROUND: Stump appendicitis, the inflammation of an incompletely removed appendix, is a rare clinical presentation. Sonography can be useful in the diagnosis of the condition; by either directly visualising the inflamed stump, or by identifying signs of peri-caecal inflammation that can raise suspicion of the condition. SUMMARY OF WORK: A potential case of stump appendicitis was identified. This prompted a review of literature focused on the incidence of stump appendicitis, utility of ultrasound to identify an inflamed appendiceal stump, and surgical techniques used in appendectomy. SUMMARY OF RESULTS: Stump appendicitis is rare, with as few as 61 cases identified in literature during the last 60 years1. Of the two common techniques of appendectomy, which are ligation and invagination, the former can sometimes leave a residual stump that acts as a potential lumen for the pathophysiological process of appendicitis to recur. Established sonographic criteria for appendicitis also apply to the residual stump. Sonographic secondary signs that suggest the presence of acute appendicitis2 have also been demonstrated in cases of stump appendicitis3, even in the absence of an identifiable stump. DISCUSSION AND CONCLUSIONS: Appendicitis is usually dismissed in patients with a history of appendectomy. Though uncommon, sonographers should be aware of stump appendicitis in post-appendectomy patients that present with right iliac fossa pain from months to decades later. REFERENCE(S) 1. Subramanian A, Liang MK. A 60-year literature review of stump appendicitis: The need for a critical view. Am J Surg; 2012;203(4):503–7. 2. Reddan T, Corness J, Mengersen K, Harden F. Ultrasound of paediatric appendicitis and its secondary sonographic signs: providing a more meaningful finding. J Med Radiat Sci. 2015;DOI: 10.1002/jmrs.154 3. Martínez Chamorro E, Merina Castilla A, Muñoz Fraile B, Koren Fernández L, Borruel Nacenta S. Stump appendicitis: Preoperative imaging findings in four cases. Abdom Imaging. 2013;38(6):1214–9.