Complete Genomic Sequence Analysis of a Sugarcane Streak Mosaic Virus Isolate from Yunnan Province of China.

In recent years, the sugarcane streak mosaic virus (SCSMV) has been the primary pathogen of sugarcane mosaic disease in southern China. In this study, the complete genome of a sugarcane mosaic sample (named YN-21) from Kaiyuan City, Yunnan Province, was amplified and sequenced. By comparing the amino acid sequences of YN-21 and 15 other SCSMV isolates from the NCBI database, the protease recognition site of SCSMV was determined. YN-21 had the highest nucleotide and amino acid identities of 97.66% and 99.30%, respectively, in comparison with the SCSMV isolate (JF488066). The P1 had the highest variability of 83.38-99.72% in the amino acid sequence, and 6K2 was the most conserved, with 97.92-100% amino acid sequence identity. A phylogenetic analysis of nucleotide and amino acid sequences clustered the 16 SCSMV isolates into two groups. All the Chinese isolates were clustered into the same group, and YN-21 was closely related to the Yunnan and Hainan isolates in China. Recombination analysis showed no major recombination sites in YN-21. Selective pressure analysis showed that the dN/dS values of 11 proteins of SCSMV were less than 1, all of which were undergoing negative selection. These results can provide practical guidance for monitoring SCSMV epidemics and genetics.

Self-selected running gait modifications reduce acute impact loading, awkwardness, and effort.

Impact loading has been associated with running-related injuries, and gait retraining has been suggested as a means of reducing impact loading and lowering the risk of injury. However, gait retraining can lead to increased perceived awkwardness and effort. The influence of specifically trained and self-selected running gait modifications on acute impact loading, perceived awkwardness and effort is currently unclear. Sixteen habitual rearfoot/midfoot runners performed forefoot strike pattern, increased step rate, anterior trunk lean and self-selected running gait modifications on an instrumented treadmill based on real-time biofeedback. Impact loading, perceived awkwardness and effort scores were compared among the four gait retraining conditions. Self-selected gait modification reduced vertical average loading rate (VALR) by 25.3%, vertical instantaneous loading rate (VILR) by 27.0%, vertical impact peak (VIP) by 16.8% as compared with baseline. Forefoot strike pattern reduced VALR, VILR and peak tibial acceleration. Increased step rate reduced VALR. Anterior trunk lean did not reduce any impact loading. Self-selected gait modification was perceived as less awkward and require less effort than the specifically trained gait modification (p < 0.05). These findings suggest that self-selected gait modification could be a more natural and less effortful strategy than specifically trained gait modification to reduce acute impact loading, while the clinical significance remains unknown.

Foot strike pattern, step rate, and trunk posture combined gait modifications to reduce impact loading during running.

Elevated impact loading can be detrimental to runners as it has been linked to the increased risk of tibial stress fracture and plantar fasciitis. The objective of this study was to investigate the combined effects of foot strike pattern, step rate, and anterior trunk lean gait modifications on impact loading in runners. Nineteen healthy runners performed 12 separate gait modification trials involving: three foot strike patterns (rearfoot, midfoot, and forefoot strike), two step rates (natural and 10% increased), and two anterior trunk lean postures (natural and 10-degree increased flexion). Overall, forefoot strike combined with increased step rate led to the lowest impact loading rates, and rearfoot strike combined with anterior trunk lean led to the highest impact loading rates. In addition, there were interaction effects between foot strike pattern and step rate on awkwardness and effort, such that it was both more natural and easier to transition to a combined gait modification involving forefoot strike and increased step rate than to an isolated gait modification involving either forefoot strike or increased step rate. These findings could help to inform gait modifications for runners to reduce impact loading and associated injury risks.

Configurable, wearable sensing and vibrotactile feedback system for real-time postural balance and gait training: proof-of-concept.

BACKGROUND: Postural balance and gait training is important for treating persons with functional impairments, however current systems are generally not portable and are unable to train different types of movements. METHODS: This paper describes a proof-of-concept design of a configurable, wearable sensing and feedback system for real-time postural balance and gait training targeted for home-based treatments and other portable usage. Sensing and vibrotactile feedback are performed via eight distributed, wireless nodes or "Dots" (size: 22.5 × 20.5 × 15.0 mm, weight: 12.0 g) that can each be configured for sensing and/or feedback according to movement training requirements. In the first experiment, four healthy older adults were trained to reduce medial-lateral (M/L) trunk tilt while performing balance exercises. When trunk tilt deviated too far from vertical (estimated via a sensing Dot on the lower spine), vibrotactile feedback (via feedback Dots placed on the left and right sides of the lower torso) cued participants to move away from the vibration and back toward the vertical no feedback zone to correct their posture. A second experiment was conducted with the same wearable system to train six healthy older adults to alter their foot progression angle in real-time by internally or externally rotating their feet while walking. Foot progression angle was estimated via a sensing Dot adhered to the dorsal side of the foot, and vibrotactile feedback was provided via feedback Dots placed on the medial and lateral sides of the mid-shank cued participants to internally or externally rotate their foot away from vibration. RESULTS: In the first experiment, the wearable system enabled participants to significantly reduce trunk tilt and increase the amount of time inside the no feedback zone. In the second experiment, all participants were able to adopt new gait patterns of internal and external foot rotation within two minutes of real-time training with the wearable system. CONCLUSION: These results suggest that the configurable, wearable sensing and feedback system is portable and effective for different types of real-time human movement training and thus may be suitable for home-based or clinic-based rehabilitation applications.

Validity of FitBit, Jawbone UP, Nike+ and other wearable devices for level and stair walking.

BACKGROUND: Increased physical activity can provide numerous health benefits. The relationship between physical activity and health assumes reliable activity measurements including step count and distance traveled. This study assessed step count and distance accuracy for Nike+ FuelBand, Jawbone UP 24, Fitbit One, Fitbit Flex, Fitbit Zip, Garmin Vivofit, Yamax CW-701, and Omron HJ-321 during level, upstairs, and downstairs walking in healthy adults. METHODS: Forty subjects walked on flat ground (400m), upstairs (176 steps), and downstairs (176 steps), and a subset of 10 subjects performed treadmill walking trials to assess the influence of walking speed on accuracy. Activity monitor measured step count and distance values were compared with actual step count (determined from video recordings) and distance to determine accuracy. RESULTS: For level walking, step count errors in Yamax CW-701, Fitbit Zip, Fitbit One, Omron HJ-321, and Jawbone UP 24 were within 1% and distance errors in Fitbit Zip and Yamax CW-701 were within 5%. Garmin Vivofit and Omron HJ-321 were the most accurate in estimating step count for stairs with errors less than 4%. An important finding is that all activity monitors overestimated distance for stair walking by at least 45%. CONCLUSION: In general, there were not accuracy differences among activity monitors for stair walking. Accuracy did not change between moderate and fast walking speeds, though slow walking increased errors for some activity monitors. Nike+ FuelBand was the least accurate step count estimator during all walking tasks. Caution should be taken when interpreting step count and distance estimates for activities involving stairs.

Novel Foot Progression Angle Algorithm Estimation via Foot-Worn, Magneto-Inertial Sensing.

OBJECTIVE: The foot progression angle (FPA) is an important clinical measurement but currently can only be computed while walking in a laboratory with a marker-based motion capture system. This paper proposes a novel FPA estimation algorithm based on a single integrated sensor unit, consisting of an accelerometer, gyroscope, and magnetometer, worn on the foot. METHODS: The algorithm introduces a real-time heading vector with a complementary filter and utilizes a gradient descent method and zero-velocity update correction. Validation testing was performed by comparing FPA estimation from the wearable sensor with the standard FPAs computed from a marker-based motion capture system. Subjects performed nine walking trials of 2.5 min each on a treadmill. During each trial, subjects walked at one speed out of three options (1.0, 1.2, and 1.4 m/s) and walked with one gait pattern out of three options (normal, toe-in, and toe-out). RESULTS: The algorithm estimated FPA to within 0.2 ° of error or less for each walking conditions. CONCLUSION: A novel FPA algorithm has been introduced and described based on a single foot-worn sensor unit, and validation testing showed that FPA estimation was accurate for different walking speeds and foot angles. SIGNIFICANCE: This study enables future wearable systems gait research to assess or train walking patterns outside a laboratory setting in natural walking environments.

Muscle force modification strategies are not consistent for gait retraining to reduce the knee adduction moment in individuals with knee osteoarthritis.

While gait retraining paradigms that alter knee loads typically focus on modifying kinematics, the underlying muscle force modifications responsible for these kinematic changes remain largely unknown. As humans are generally thought to select uniform gait muscle patterns such as strategies based on fatigue cost functions or energy minimization, we hypothesized that a kinematic gait change known to reduce the knee adduction moment (i.e. toe-in gait) would be accompanied by a uniform muscle force modification strategy for individuals with symptomatic knee osteoarthritis. Ten subjects with self-reported knee pain and radiographic evidence of medial compartment knee osteoarthritis performed normal gait and toe-in gait modification walking trials. Two hundred muscle-actuated dynamic simulations (10 steps for normal gait and 10 steps from toe-in gait for each subject) were performed to determine muscle forces for each gait. Results showed that subjects internally rotated their feet during toe-in gait, which decreased the foot progression angle by 7° (p<0.01) and reduced the first peak knee adduction moment by 20% (p<0.01). While significant muscle force modifications were evidenced within individuals, there were no consistent muscle force modifications across all subjects. It may be that self-selected muscle pattern changes are not uniform for gait modification particularly for individuals with knee pain. Future studies focused on altering knee loads should not assume consistent muscle force modifications for a given kinematic gait change across subjects and should consider muscle forces in addition to kinematics in gait retraining paradigms.