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opencc-by-4.0Jan 2020View details →

Raw data for Kierdorf et al, "Muscle function and homeostasis require cytokine inhibition of AKT activity in Drosophila"

This upload contains the raw data corresponding to the publication "Muscle function and homeostasis require cytokine inhibition of AKT activity in Drosophila" by Katrin Kierdorf et al., eLife 2020.

opencc-by-4.0Aug 2021View details →

Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults

There is increasing evidence that including sex as a biological variable is of crucial importance to promote rigorous, repeatable and reproducible science. In spite of this, the body of literature that accounts for the sex of participants in human locomotion studies is small and often produces controversial results. Here, we investigated the modular organization of muscle activation patterns for human locomotion using the concept of muscle synergies with a double purpose: i) uncover possible sex-specific characteristics of motor control and ii) assess whether these are maintained in older age. We recorded electromyographic activities from 13 ipsilateral muscles of the lower limb in young and older adults of both sexes walking (young and old) and running (young) on a treadmill. The data set obtained from the 215 participants was elaborated through non-negative matrix factorization to extract the time-independent (i.e., motor modules) and time-dependent (i.e., motor primitives) coefficients of muscle synergies. We found sparse sex-specific modulations of motor control. Motor modules showed a different contribution of hip extensors, knee extensors and foot dorsiflexors in various synergies. Motor primitives were wider (i.e., lasted longer) in males in the propulsion synergy for walking (but only in young and not in older adults) and in the weight acceptance synergy for running. Moreover, the complexity of motor primitives was similar in younger adults of both sexes, but lower in older females as compared to older males. In essence, our results revealed the existence of small but defined sex-specific differences in the way humans control locomotion and that these strategies are not entirely maintained in older age. In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; c) the touchdown and lift-off timings of the recorded limb, d) the code to process the data. In total, 520 trials from 215 participants are included in the supplementary data set. The file “metadata.dat” is available in ASCII format and contains: <ul> <li>Code: the participant’s code</li> <li>Group: the participant's group (G1=young adults, walking; G2=old adults, walking; G3=young adults, running)</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (walking or running)</li> <li>Speed: the speed at which the recordings were conducted in [m/s]</li> <li>Speed_type: the distinction between fixed (decided by the researchers) or preferred (selected by the participant) speed</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Trials are named like “ID0020_M_YOUNG_TW_01,” where the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number. Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a> The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with one row for each gait cycle and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_ID0020_M_YOUNG_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number. The files containing the raw, filtered, and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_ID0003_F_OLD_TW_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “ID0003” indicate the participant number (in this example the 3rd), the character “F” indicates the sex, the characters “OLD” indicate the age group, the characters “TW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”. The latest version of this code can be found at https://github.com/alesantuz/musclesyneRgies.

opencc-by-4.0Jul 2020View details →

Muscle activation patterns are more constrained and regular in treadmill than in overground human locomotion

The use of motorized treadmills as convenient tools for the study of locomotion has been in vogue for many decades. However, despite the widespread presence of these devices in many scientific and clinical environments, a full consensus on their validity to faithfully substitute free overground locomotion is still missing. Specifically, little information is available on whether and how the neural control of movement is affected when humans walk and run on a treadmill as compared to overground. Here, we made use of linear and nonlinear analysis tools to extract information from electromyographic recordings during walking and running overground and on an instrumented treadmill. We extracted synergistic activation patterns from the muscles of the lower limb via non-negative matrix factorization. We then investigated how the motor modules (or time-invariant muscle weightings) were used in the two locomotion environments. Subsequently, we examined the timing of motor primitives (or time-dependent coefficients of muscle synergies) by calculating their duration, the time of main activation, and their Hurst exponent, a nonlinear metric derived from fractal analysis. We found that motor modules were not influenced by the locomotion environment, while motor primitives resulted overall more regular in treadmill than in overground locomotion, with the main activity of the primitive for propulsion shifted earlier in time. Our results suggest that the spatial and sensory constraints imposed by the treadmill environment forced the central nervous system to adopt a different neural control strategy than that used for free overground locomotion. A data-driven indication that treadmills induce perturbations to the neural control of locomotion.   In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG; e) the muscle synergies extracted via NMF; f) the code to process the data. In total, 120 trials from 30 participants are included in the supplementary data set. The file “metadata.dat” is available in ASCII and RData format and contains: <ul> <li>Code: the participant’s code</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (W=walking, R=running)</li> <li>Environment: to distinguish between overground (O) and treadmill (T)</li> <li>Speed: the speed at which the recordings were conducted in [m/s] (1.4 m/s for walking, 2.8 m/s for running)</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 consist of 21, 29, 29 and 26 cycles, respectively. All the other trials consist of 30 gait cycles. Trials are named like “P0003_OR_01”, where the characters “P0003” indicate the participant number (in this example the 3rd), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data are named, following the same rules, like “FILT_EMG_P0003_OR_01”. Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a> The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with 30 rows (one for each gait cycle) and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P0020_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “P0020” indicate the participant number (in this example the 20th), the characters “TW” indicate the locomotion type and environment (O=overground, T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 only contain 21, 29, 29 and 26 cycles, respectively. The files containing the raw, filtered, and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with 30000 rows (one for each recorded data point) and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with muscle abbreviations that follow those reported above. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P0003_OR_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P0003” indicate the participant number (in this example the 3rd), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P0003_OR_01”. The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData format, in the file named “SYNS.RData”. Each element of this R list represents one trial and contains the factorization rank (list element named “synsR2”), the motor modules (list element named “M”), the motor primitives (list element named “P”), the reconstructed EMG (list element named “Vr”), the number of iterations needed by the NMF algorithm to converge (list element named “iterations”), and the reconstruction quality measured as the coefficient of determination (list element named “R2”). The motor modules and motor primitives are presented as direct output of the factorization and not in any functional order. Motor modules are data frames with 13 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported above, contain the time-independent coefficients (motor modules M), one for each synergy and for each muscle. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives P), one column for each synergy plus the time points (columns are named e.g. “time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed in the methods section to improve user readability. Trials are named like “SYNS_ P0012_OW_01”, where the characters “SYNS” indicate that the trial contains muscle synergy data, the characters “P0012” indicate the participant number (in this example the 12th), the characters “OW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper. All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”.

Figure 6 in Effects of in vivo exposures to nanoparticles (Al O , CuO, TiO ) on the activities of ATPases in the gill and muscle of freshwater mussel (Unio tigridis)

Figure 6. Effects of NPs on Ca-ATPase activity in the muscle of mussels after 14 days. * indicates significant (p <0.05) differences compared to control.

Figure 4 in Effects of in vivo exposures to nanoparticles (Al O , CuO, TiO ) on the activities of ATPases in the gill and muscle of freshwater mussel (Unio tigridis)

Figure 4. Effects of NPs on Ca-ATPase activity in the gill of mussels after 14 days. * indicates significant (p <0.05) differences compared to control.

Figure 3 in Effects of in vivo exposures to nanoparticles (Al O , CuO, TiO ) on the activities of ATPases in the gill and muscle of freshwater mussel (Unio tigridis)

Figure 3. Effects of NPs on Mg-ATPase activity in the gill of mussels after 14 days. * indicates significant (p <0.05) differences compared to control.

Figure 5 in Effects of in vivo exposures to nanoparticles (Al O , CuO, TiO ) on the activities of ATPases in the gill and muscle of freshwater mussel (Unio tigridis)

Figure 5. Effects of NPs on Mg-ATPase activity in the muscle of mussels after 14 days. * indicates significant (p <0.05) differences compared to control.

Figure 1 in Effects of in vivo exposures to nanoparticles (Al O , CuO, TiO ) on the activities of ATPases in the gill and muscle of freshwater mussel (Unio tigridis)

Figure 1. TEM images of Al O (a), CuO (b), and TiO (c) nanoparticles in stock solutions (Canli and Canli, 2020).

opencc-zeroMar 2020View details →

dryad36/100

Neural ensemble reactivation in REM and SWS coordinate with muscle activity to promote rapid motor skill learning

Neural activity patterns of recent experiences are reactivated during sleep in structures critical for memory storage, including hippocampus and neocortex. This reactivation process is thought to aid memory consolidation. Although synaptic rearrangement dynamics following learning involve an interplay between slow-wave sleep (SWS) and rapid eye movement sleep (REM), most physiological evidence implicates SWS directly following experience as a preferred window for reactivation. Here we show that reactivation occurs in both REM and SWS, and that coordination of REM and SWS activation on the same day is associated with rapid learning of a motor skill. We performed 6-hour recordings from cells in rats' motor cortex as they were trained daily on a skilled reaching task. In addition to SWS following training, reactivation occurred in REM, primarily during the pre-task rest period, and REM and SWS reactivation occurred on the same day in rats that acquired the skill rapidly. Both pre-task REM and posttask SWS activation were coordinated with muscle activity during sleep, suggesting a functional role for reactivation in skill learning. Our results provide the first demonstration that reactivation in REM sleep occurs during motor skill learning, and that coordinated reactivation in both sleep states on the same day, although at different times, is beneficial for skill learning.

opencc-zeroDec 2015View details →

Dataset for: Spinal motion and muscle activity during active trunk movements - comparing sheep and humans adopting upright and quadrupedal postures

Datasets used for the manuscript "Spinal motion and muscle activity during active trunk movements - comparing sheep and humans adopting upright and quadrupedal postures", accepted in PLOS ONE

opencc-by-4.0Jan 2017View details →

Spatial variation and inconsistency between estimates of onset of muscle activation from EMG and ultrasound

Study abstract: Delayed onset of muscle activation is a descriptor of impaired motor control. Activation onset can be estimated from electromyography (EMG)-registered muscle excitation and from ultrasound-registered muscle motion, which enables non-invasive measurements in deep muscles. However, in voluntary activation, EMG- and ultrasound-detected activation onsets may not correspond. To evaluate this, ten healthy men performed isometric elbow flexion at 20% to 70% of their maximal force. Utilising a multi-channel electrode transparent to ultrasound, EMG and M(otion)-mode ultrasound were recorded simultaneously over the biceps brachii muscle. The time intervals between automated and visually estimated activation onsets were correlated with the regional variation of EMG and muscle motion onset, contraction level and speed. Automated and visual onsets indicated variable time intervals between EMG- and motion onset, median (interquartile range) 96 (121) ms and 48 (72) ms, respectively. In 17% of trials (computed analysis) or 23% (visual analysis), motion onset was detected before local EMG onset. Multi-channel EMG and M-mode ultrasound revealed regional differences in activation onset, which decreased with higher contraction speed (Spearman ρ≥0.45, P<0.001). In voluntary activation the heterogeneous motor unit recruitment together with immediate motion transmission may explain the high variation of the time intervals between local EMG- and ultrasound-detected activation onset. Data description: EMG data: folder includes the EMG, torque and synchronization signal data as .otb files. The respective program can be downloaded without costs from http://www.otbioelettronica.it/index.php?lang=en. Data consist of two series (Misome2 and Misome3) of isometric trials at different force levels. The first three digits refer to the subject number. M-mode ultrasound data: folder includes the M-mode clips of all recorded trials in .tvd format. The respective program can be downloaded for free at http://www.telemedultrasound.com/download/software-downloads/?lang=en. In addition, images of activation onset in DICOM format are provided. The data are sorted for subjects and series (Misome2 and Misome3). The following explanation refers to the filenames of the DICOM images. Usually, the M-mode trace started at the left side synchronously with the synch signal. In this case the rightmost frame of the trace is missing to indicate that the left edge represents the start of the trace. If the file name includes “ons50”, the frame includes the 50. frame = the rightmost frame, still starting with the synch signal. If the filename includes on100, the rightmost frame is the 100. frame and the duration of 50 frames must be added to the visible onset time. Filenames that include “basel” refer to frames that represent a proper delineation of the baseline. Excel data sheet: Data sheet that includes the computed and visual EMG, M-mode ultrasound and torque onsets, the rate of torque development and the differences between the different types of onsets. The column headers explain the data type, most comprehensively in the first computed data sheet. The colors facilitate the orientation with blue columns referring to torque onsets and green columns referring to ultrasound onsets. The violet EMG channels are those in vicinity to the ultrasound beam. In the second version of each sheet the 5% slowest trials are separated.

opencc-by-4.0Dec 2020View details →

Long short-term memory (LSTM) recurrent neural network for muscle activity detection

Background: The accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.   Methods: First, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager–Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis. Results: The proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR. Conclusions: The presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/ distinction of muscle activity from background noise in sEMG signals.

dryad36/100

The force response of muscles to activation and length perturbations depends on length history

Recent studies have demonstrated that muscle force is not determined solely by activation under dynamic conditions, and that length history has an important role in determining dynamic muscle force. Yet, the mechanisms for how muscle force is produced under dynamic conditions remain unclear. To explore how muscle force production is determined under dynamic conditions, we investigated the effects of muscle stiffness, activation, and length perturbations on muscle force. First, submaximal isometric contraction was established for whole soleus muscles. Next, the muscles were actively shortened at three velocities. During active shortening, we measured muscle stiffness at L0 and the force response to time-varying activation and length perturbations. We found that muscle stiffness increased with activation but decreased as shortening velocity increased. The slope of the relationship between maximum force and activation amplitude differed significantly among shortening velocities. Also, the intercept and slope of the relationship between length perturbation amplitude and maximum force decreased with shortening velocities. As shortening velocities were related with muscle stiffness, the results suggest that length history determines muscle stiffness and the history-dependent muscle stiffness influences the contribution of activation to muscle force and the contribution of length perturbations to muscle force. A three-parameter viscoelastic model that included a linear spring and linear damper in parallel with tunable history-dependent spring stiffness proportional to measured muscle stiffness predicted history-dependent muscle force with high accuracy. The results and simulations support the hypothesis that muscle force under dynamic conditions can be accurately predicted as the force response of a history-dependent viscoelastic material to length perturbations.

opencc-zeroJun 2022View details →

opencc-by-4.0Feb 2018View details →

Muscle activity, ground reaction forces and pointing performance during postural control tasks in healthy adults

To investigate the muscle coordination during postural control, we recorded muscle activity and postural dynamics in healthy human adults. Fourteen participants performed postural tasks in which postural stability and pointing behaviour was varied. The data set contains electromyography of 36 muscles distributed across the body and ground reaction forces recorded during postural control tasks. A full factorial design was used. Stability was either not challenged or challenged in the anterior-posterior or medial-lateral direction. In addition, participants were asked to either relax their arms or to perform an unimanual or a bimanual pointing task. In the pointing task, participants held a laser pointer and pointed it on a target in front of them. Pointing performance was recorded using a video recording of the laser beam on the target area. InformationData.pdf – Description of data acquisition and file structure EMG.zip – EMG data FP.zip – Force plate data Video.zip – Video feed

opencc-by-4.0Jun 2018View details →

Segmenting Mechanomyography Measures of Muscle Activity Phases Using Inertial Data

This dataset contains the data used in our manuscript titled "Segmenting Mechanomyography Measures of Muscle Activity Phases Using Inertial Data". Data structure is explained in the README.txt file located at the top-level of the dataset. Manuscript title in the README.txt file and contained in the title of the zip file are of a previous working title. Please contact corresponding author Richard B. Woodward for any questions.

dryad36/100

FGF-2-dependent signaling activated in aged human skeletal muscle promotes intramuscular adipogenesis

Aged skeletal muscle is markedly affected by fatty muscle infiltration and strategies to reduce the occurrence of intramuscular adipocytes are urgently needed. Here, we show that fibroblast growth factor-2 (FGF-2) not only stimulates muscle growth, but also promotes intramuscular adipogenesis. Using multiple screening assays upstream and downstream of microRNA (miR)-29a signaling, we located the secreted protein and adipogenic inhibitor SPARC to an FGF-2 signaling pathway that is conserved between skeletal muscle cells from mice and humans and that is activated in skeletal muscle of aged mice and humans. FGF-2 induces the miR-29a/SPARC axis through transcriptional activation of FRA-1, which binds and activates an evolutionary conserved AP-1 site element proximal in the miR-29a promoter. Genetic deletions in muscle cells and AAV-mediated overexpression of FGF-2 or SPARC in mouse skeletal muscle revealed that this axis regulates differentiation of fibro/adipogenic progenitors in vitro and intramuscular adipose tissue (IMAT) formation in vivo. Skeletal muscle from human donors aged > 75 years versus < 55 years showed activation of FGF-2-dependent signaling and increased IMAT. Thus, our data highlights a disparate role of FGF-2 in adult skeletal muscle and reveals a novel pathway to combat fat accumulation in aged human skeletal muscle.

opencc-zeroAug 2021View details →

opencc-by-4.0Sep 2021View details →

Determining voluntary activation in synergistic muscles: new insights from a mechanomyographic approach

Aim: We propose a new approach based on the mechanomyographic (MMG) signal to detect the voluntary activation (VA) of the synergistic superficial heads of quadriceps muscle. We  hypothesised that, after a fatiguing task, the changes in MMG signal of each quadriceps’ head would correlate with the level of VA of the whole quadriceps. Methods: Twenty-five men [age:25(1) yrs; body mass:77(2) kg; stature:1.81(0.02) m] underwent a unilateral single-leg quadriceps exercise up to failure. Before and after the intervention, VA was assessed by the interpolated twitch technique via nerve stimulation during and after a maximum voluntary contraction (MVC). Along with force, the MMG signal was recorded from vastus lateralis, vastus medialis, and rectus femoris muscles. The MMG peak-to-peak was calculated and the voluntary activation index (VAMMG) as the superimposed/potentiated MMG peak-to-peak ratio, was determined from the MMG signal for each head.  Results: VAMMG presented very-high reliability (ICC:0.975-0.998) and sensitivity (MDC95%: 0.4%-4.26%). Fatigue reduced MVC and VA in both the exercised [-17(5)%, ES:-0.92 and -7(3)%, ES:-1.90 for MVC and VA, respectively] and the contralateral limb [-15(7)%, ES:-0.87 and -7(3)%, ES:-2.32 for MVC and VA, respectively]. VAMMG decreased in both the exercised [~-9(6)%, ES:-1.00] and the contralateral limb [~-5(2)%, ES:-1.12], with larger VAMMG decrements occurring in vastus medialis in the exercised limb only. Moderate to very high correlations were found between VAMMGand VA (R= 0.503-0.886) before and after fatigue.  Conclusions: VAMMG may be implemented to assess VA and could provide further information when different synergistic muscle heads are involved in a fatiguing task.