Childhood wheeze phenotypes show less than expected growth in FEV1 across adolescence

Better characterization of childhood wheeze phenotypes using newer statistical methods provides a basis for addressing the heterogeneity of childhood asthma. Outcomes of these phenotypes beyond childhood are unknown.

Neuronal classification and distribution in the central nervous system of the female mud crab, Scylla olivacea

The mud crab, Scylla olivacea, is one of the most economically valuable marine species in Southeast Asian countries. However, commercial cultivation is disadvantaged by reduced reproductive capacity in captivity. Therefore, an understanding of the general and detailed anatomy of central nervous system (CNS) is required before investigating the distribution and functions of neurotransmitters, neurohormones, and other biomolecules, involved with reproduction. We found that the anatomical structure of the brain is similar to other crabs. However, the ventral nerve cord (VNC) is unlike other caridian and dendrobrachiate decapods, as the subesophageal (SEG), thoracic and abdominal ganglia are fused, due to the reduction of abdominal segments and the tail. Neurons in clusters within the CNS varied in sizes, and we found that there were five distinct size classes (i.e., very small globuli, small, medium, large, and giant). Clusters in the brain and SEG contained mainly very small globuli and small-sized neurons, whereas, the VNC contained small-, medium-, large-, and giant-sized neurons. We postulate that the different sized neurons are involved in different functions. Microsc. Res. Tech. 77:189–200, 2014. © 2013 Wiley Periodicals, Inc.

Evaluation of neck muscle size: long-term reliability and comparison of methods

Although it is important for prospective studies, the reliability of quantitative measures of cervical muscle size on magnetic resonance imaging is not well established. The aim of the current work was to assess the long-term reliability of measurements of cervical muscle size. In addition, we examined the utility of selecting specific sub-regions of muscles at each vertebral level, averaging between sides of the body, and pooling muscles into larger groups. Axial scans from the base of skull to the third thoracic vertebra were performed in 20 healthy male subjects at baseline and 1.5 years later. We evaluated the semi-spinalis capitis, splenius capitis, spinalis cervicis, longus capitis, longus colli, levator scapulae, sternocleidomastoid, anterior scalenes and middle with posterior scalenes. Bland-Altman analysis showed all measurements to be repeatable between testing-days. Reliability was typically best when entire muscle volume was measured (co-efficients of variation (CVs): 3.3-8.1% depending on muscle). However, when the size of the muscle was assessed at specific vertebral levels, similar measurement precision was achieved (CVs: 2.7-7.6%). A median of 4-6 images were measured at the specific vertebral levels versus 18-37 images for entire muscle volume. This would represent considerable time saving. Based on the findings we also recommend measuring both sides of the body and calculating an average value. Pooling specific muscles into the deep neck flexors (CV: 3.5%) and neck extensors (CV: 2.7%) can serve to reduce variability further. The results of the current study help to establish outcome measures for interventional studies and for sample size estimation.

Influence of prolonged bed-rest on spectral and temporal electromyographic motor control characteristics of the superficial lumbo-pelvic musculature

Little is known about the motor control of the lumbo-pelvic musculature in microgravity and its simulation (bed-rest). Analysis of spectral and temporal electromyographic variables can provide information on motor control relevant for normal function. This study examined the effect of 56-days of bed-rest with 1-year follow-up in 10 male subjects on the median frequency and the activation timing in surface electromyographic recordings from five superficial lumbo-pelvic muscles during a repetitive knee movement task. Trunk fat mass (from whole body-composition measurements) and movement accuracy as possible explanatory factors were included. Increased median frequency was observed in the lumbar erector spinae starting late in bed-rest, but this was not seen in its synergist, the thoracic erector spinae (p<.0001). These changes persisted up to 1-year after bed-rest and were independent of changes in body-composition or movement accuracy. Analysis suggested decreases of median frequency (p<.0001) in the abdominal and gluteal muscles to result from increased (p<.01) trunk fat levels during and after bed-rest. No changes in lumbo-pelvic muscle activation timing were seen. The results suggest that bed-rest particularly affects the shorter lumbar erector spinae and that the temporal sequencing of superficial lumbo-pelvic muscle activation is relatively robust.

Classification of team sport activities using a single wearable tracking device

Wearable tracking devices incorporating accelerometers and gyroscopes are increasingly being used for activity analysis in sports. However, minimal research exists relating to their ability to classify common activities. The purpose of this study was to determine whether data obtained from a single wearable tracking device can be used to classify team sport-related activities. Seventy-six non-elite sporting participants were tested during a simulated team sport circuit (involving stationary, walking, jogging, running, changing direction, counter-movement jumping, jumping for distance and tackling activities) in a laboratory setting. A MinimaxX S4 wearable tracking device was worn below the neck, in-line and dorsal to the first to fifth thoracic vertebrae of the spine, with tri-axial accelerometer and gyroscope data collected at 100Hz. Multiple time domain, frequency domain and custom features were extracted from each sensor using 0.5, 1.0, and 1.5s movement capture durations. Features were further screened using a combination of ANOVA and Lasso methods. Relevant features were used to classify the eight activities performed using the Random Forest (RF), Support Vector Machine (SVM) and Logistic Model Tree (LMT) algorithms. The LMT (79-92% classification accuracy) outperformed RF (32-43%) and SVM algorithms (27-40%), obtaining strongest performance using the full model (accelerometer and gyroscope inputs). Processing time can be reduced through feature selection methods (range 1.5-30.2%), however a trade-off exists between classification accuracy and processing time. Movement capture duration also had little impact on classification accuracy or processing time. In sporting scenarios where wearable tracking devices are employed, it is both possible and feasible to accurately classify team sport-related activities.