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.

Spectral unmixing based on nonnegative matrix factorization with local smoothness constraint

Spectral unmixing (SU) is an emerging problem in the remote sensing image processing. Since both the endmember signatures and their abundances have nonnegative values, it is a natural choice to employ the attractive nonnegative matrix factorization (NMF) methods to solve this problem. Motivated by that the abundances are sparse, the NMF with local smoothness constraint (NMF-LSC) is proposed in this paper. In the proposed method, the smoothness constraint is utilized to impose the sparseness, instead of the traditional L1-norm which is restricted by the underlying column-sum-to-one requirement of the to the abundance matrix. Simulations show the advantages of our algorithm over the compared methods.