Influence of crop residues, matric potential and temperature on growth of Exserohilum turcicum an emerging maize pathogen in Argentina

Northern corn leaf blight (NCLB) caused by Exserohilum turcicum is an endemic foliar disease in the Argentinean maize production area. Before applying a control method, it is essential to focus on factors that lead to the survival and conservation of E. turcicum inoculum. However, there is little information about the life cycle of this emerging pathogen in our country. The objective of the present work was to analyse the growth ability of three E. turcicum isolates under different type and conditions of maize, wheat and soybean residues, matric potential, temperature and their interactions. Statistical analysis demonstrated a significant effect of three factors on growth rate: residues-based media, matric potential and temperature. Among them the major effect was produced by matric potential followed by temperature. Although there were no significant differences in the growth rate among different residues types, the range of conditions in which growth occurred was different. According to these results, soybean residues would allow a better survival of E. turcicum inoculum under no-tillage system and providing an inoculum source for maize infections in subsequent year. SIGNIFICANCE AND IMPACT OF THE STUDY: Exserohilum turcicum is an emerging pathogen in Argentina that has caused significant economic losses in different maize growing areas. However, at present there is a lack of information about the life cycle of this fungal pathogen. The present study shows the influence of abiotic factors such as temperature and water potential on the growth of E. turcicum on different crop residues used in rotations with maize under no-till system cultivation. According to our results, soybean residues allowed the pathogen growth in a wider range of conditions compared to wheat and maize residue, providing an inoculum source for maize infections in subsequent season.

Familial history of hypertension-induced impairment on heart rate variability was not observed in strength-trained subjects

Family history of hypertension is an important predictive factor for hypertension and is associated with hemodynamic and autonomic abnormalities. Previous studies reported that strength training might reduce arterial blood pressure (AP), as well as improve heart rate variability (HRV). However, the benefits of strength training in the offspring of hypertensive parents have not been fully evaluated. Here, we analyzed the impact of strength training on hemodynamics and autonomic parameters in offspring of hypertensive subjects. We performed a cross-sectional study with sedentary or physically active offspring of normotensives (S-ON and A-ON) or hypertensives (S-OH and A-OH). We recorded RR interval for analysis of HRV. AP was similar between groups. Sedentary offspring of hypertensives presented impairment of total variance of RR interval, as well as an increase in cardiac sympathovagal balance (S-OH: 4.2±0.7 vs S-ON: 2.8±0.4 and A-ON: 2.4±0.1). In contrast, the strength-trained group with a family history of hypertension did not show such dysfunctions. In conclusion, sedentary offspring of hypertensives, despite displaying no changes in AP, showed reduced HRV, reinforcing the hypothesis that autonomic dysfunctions have been associated with higher risk of hypertension onset. Our findings demonstrated that strength-trained offspring of hypertensives did not present impaired HRV, thus reinforcing the benefits of an active lifestyle in the prevention of early dysfunctions associated with the onset of hypertension in predisposed populations.

Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling

OBJECTIVE

Robotic prosthetic limbs promise to replace mechanical function of lost biological extremities and restore amputees' capacity of moving and interacting with the environment. Despite recent advances in biocompatible electrodes, surgical procedures, and mechatronics, the impact of current solutions is hampered by the lack of intuitive and robust man-machine interfaces.

APPROACH

This work presents a biomimetic interface that synthetizes the musculoskeletal function of an individual's phantom limb as controlled by neural surrogates, i.e. electromyography-derived neural activations. With respect to current approaches based on machine learning, our method employs explicit representations of the musculoskeletal system to reduce the space of feasible solutions in the translation of electromyograms into prosthesis control commands. Electromyograms are mapped onto mechanical forces that belong to a subspace contained within the broader operational space of an individual's musculoskeletal system.

MAIN RESULTS

Our results show that this constraint makes the approach applicable to real-world scenarios and robust to movement artefacts. This stems from the fact that any control command must always exist within the musculoskeletal model operational space and be therefore physiologically plausible. The approach was effective both on intact-limbed individuals and a transradial amputee displaying robust online control of multi-functional prostheses across a large repertoire of challenging tasks.

SIGNIFICANCE

The development and translation of man-machine interfaces that account for an individual's neuromusculoskeletal system creates unprecedented opportunities to understand how disrupted neuro-mechanical processes can be restored or replaced via biomimetic wearable assistive technologies.

Central nervous system modulates the neuromechanical delay in a broad range for the control of muscle force

Force is generated by muscle units according to the neural activation sent by motor neurons. The motor unit is therefore the interface between the neural coding of movement and the musculotendinous system. Here we propose a method to accurately measure the latency between an estimate of the neural drive to muscle and force. Furthermore, we systematically investigate this latency, which we refer to as the neuromechanical delay (NMD), as a function of the rate of force generation. In two experimental sessions, eight men performed isometric finger abduction and ankle dorsiflexion sinusoidal contractions at three frequencies and peak-to-peak amplitudes {0.5, 1, and 1.5 Hz; 1, 5, and 10 of maximal force [%maximal voluntary contraction (MVC)]}, with a mean force of 10% MVC. The discharge timings of motor units of the first dorsal interosseous (FDI) and tibialis anterior (TA) muscle were identified by high-density surface EMG decomposition. The neural drive was estimated as the cumulative discharge timings of the identified motor units. The neural drive predicted 80 ± 0.4% of the force fluctuations and consistently anticipated force by 194.6 ± 55 ms (average across conditions and muscles). The NMD decreased nonlinearly with the rate of force generation ( R² = 0.82 ± 0.07; exponential fitting) with a broad range of values (from 70 to 385 ms) and was 66 ± 0.01 ms shorter for the FDI than TA ( P < 0.001). In conclusion, we provided a method to estimate the delay between the neural control and force generation, and we showed that this delay is muscle-dependent and is modulated within a wide range by the central nervous system. NEW & NOTEWORTHY The motor unit is a neuromechanical interface that converts neural signals into mechanical force with a delay determined by neural and peripheral properties. Classically, this delay has been assessed from the muscle resting level or during electrically elicited contractions. In the present study, we introduce the neuromechanical delay as the latency between the neural drive to muscle and force during variable-force contractions, and we show that it is broadly modulated by the central nervous system.

Electromechanical delay in the tibialis anterior muscle during time-varying ankle dorsiflexion

We evaluated the electromechanical delay (EMD) for the tibialis anterior (TA) muscle during the performance of time-varying ankle dorsiflexions. Subjects were asked to track a sinusoidal trajectory, for a range of amplitudes and frequencies. Motor unit (MU) action potential trains were identified from surface electromyography (EMG) decomposition and summed to generate the cumulative spike train (CST). CST and the exerted force were cross-correlated to identify the delay between the CST and force, which was considered as an estimate of the EMD. The results showed that the EMD decreased logarithmically with the increase in the slope of the force produced.

Estimation of Neuromuscular Primitives from EEG Slow Cortical Potentials in Incomplete Spinal Cord Injury Individuals for a New Class of Brain-Machine Interfaces

One of the current challenges in human motor rehabilitation is the robust application of Brain-Machine Interfaces to assistive technologies such as powered lower limb exoskeletons. Reliable decoding of motor intentions and accurate timing of the robotic device actuation is fundamental to optimally enhance the patient's functional improvement. Several studies show that it may be possible to extract motor intentions from electroencephalographic (EEG) signals. These findings, although notable, suggests that current techniques are still far from being systematically applied to an accurate real-time control of rehabilitation or assistive devices. Here we propose the estimation of spinal primitives of multi-muscle control from EEG, using electromyography (EMG) dimensionality reduction as a solution to increase the robustness of the method. We successfully apply this methodology, both to healthy and incomplete spinal cord injury (SCI) patients, to identify muscle contraction during periodical knee extension from the EEG. We then introduce a novel performance metric, which accurately evaluates muscle primitive activations.

Transplantation of Human Adipose Mesenchymal Stem Cells in Non-Immunosuppressed GRMD Dogs is a Safe Procedure

The possibility to treat Duchenne muscular dystrophy (DMD), a lethal X-linked disorder, through cell therapy with mesenchymal stromal cells (MSCs) has been widely investigated in different animal models. However, some crucial questions need to be addressed before starting human therapeutic trials, particularly regarding its use for genetic disorders. How safe is the procedure? Are there any side effects following mesenchymal stem cell transplantation? To address these questions for DMD the best model is the golden retriever muscular dystrophy dog (GRMD), which is the closest model to the human condition displaying a much longer lifespan than other models. Here we report the follow-up of 5 GRMD dogs, which were repeatedly transplanted with human adipose-derived mesenchymal stromal cells (hASC), derived from different donors. Xenogeneic cell transplantation, which was done without immunosuppression, was well tolerated in all animals with no apparent long-term adverse effect. In the present study, we show that repeated heterologous stem-cell injection is a safe procedure, which is fundamental before starting human clinical trials.

In Vivo Neuromechanics: Decoding Causal Motor Neuron Behavior with Resulting Musculoskeletal Function

Human motor function emerges from the interaction between the neuromuscular and the musculoskeletal systems. Despite the knowledge of the mechanisms underlying neural and mechanical functions, there is no relevant understanding of the neuro-mechanical interplay in the neuro-musculo-skeletal system. This currently represents the major challenge to the understanding of human movement. We address this challenge by proposing a paradigm for investigating spinal motor neuron contribution to skeletal joint mechanical function in the intact human in vivo. We employ multi-muscle spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate α-motor neuron discharges across five lumbosacral segments in the human spinal cord. We use complete α-motor neuron discharge series to drive forward subject-specific models of the musculoskeletal system in open-loop with no corrective feedback. We perform validation tests where mechanical moments are estimated with no knowledge of reference data over unseen conditions. This enables accurate blinded estimation of ankle function purely from motor neuron information. Remarkably, this enables observing causal associations between spinal motor neuron activity and joint moment control. We provide a new class of neural data-driven musculoskeletal modeling formulations for bridging between movement neural and mechanical levels in vivo with implications for understanding motor physiology, pathology, and recovery.

Clinical course of isolated distal deep vein thrombosis in patients with active cancer: a multicenter cohort study

Essentials Isolated distal deep vein thrombosis (IDDVT) is frequently associated with cancer. No study has specifically evaluated the long-term clinical course of cancer-associated IDDVT. Patients with cancer-associated IDDVT are at very high risk of symptomatic recurrence and death. We observed low rates of major bleeding during anticoagulation.

SUMMARY

Background Although isolated distal deep vein thrombosis (IDDVT) is frequently associated with cancer, no study has specifically evaluated the long-term clinical course of IDDVT in this setting. Aim To provide data on the rate of recurrent venous thromboembolism (VTE), major bleeding events and death in IDDVT patients with active cancer. Patients and Methods Consecutive patients with active cancer and an objective IDDVT diagnosis (January 2011 to September 2014) were included from our files. We collected information on baseline characteristics, IDDVT location and extension, VTE risk factors, and type and duration of anticoagulant treatment. Results A total of 308 patients (mean age 66.2 [standard deviation (SD), 13.2 years]; 57.1% female) with symptomatic IDDVT and a solid (n = 261) or hematologic (n = 47) cancer were included at 13 centers. Cancer was metastatic in 148 (48.1%) patients. All but three (99.0%) patients received anticoagulant therapy, which consisted of low-molecular-weight heparin in 288 (93.5%) patients. Vitamin K antagonists were used for the long-term treatment in 46 (14.9%) patients, whereas all others continued the initial parenteral agent for a mean treatment duration of 4.2 months (SD, 4.6 months). During a total follow-up of 355.8 patient-years (mean, 13.9 months), there were 47 recurrent objectively diagnosed VTEs for an incidence rate of 13.2 events per 100 patient-years. During anticoagulant treatment, the annual incidence of major bleeding was 2.0 per 100 patient-years. Conclusions Cancer patients with IDDVT have a high risk of VTE recurrence. Additional studies are warranted to investigate the optimal intensity and duration of anticoagulant treatment for these patients.

Closed-loop EMG-informed model-based analysis of human musculoskeletal mechanics on rough terrains

This work aims at estimating the musculoskeletal forces acting in the human lower extremity during locomotion on rough terrains. We employ computational models of the human neuro-musculoskeletal system that are informed by multi-modal movement data including foot-ground reaction forces, 3D marker trajectories and lower extremity electromyograms (EMG). Data were recorded from one healthy subject locomoting on rough grounds realized using foam rubber blocks of different heights. Blocks arrangement was randomized across all locomotion trials to prevent adaptation to specific ground morphology. Data were used to generate subject-specific models that matched an individual's anthropometry and force-generating capacity. EMGs enabled capturing subject- and ground-specific muscle activation patterns employed for walking on the rough grounds. This allowed integrating realistic activation patterns in the forward dynamic simulations of the musculoskeletal system. The ability to accurately predict the joint mechanical forces necessary to walk on different terrains have implications for our understanding of human movement but also for developing intuitive human machine interfaces for wearable exoskeletons or prosthetic limbs that can seamlessly adapt to different mechanical demands matching biological limb performance.

Robust Real-Time Musculoskeletal Modeling Driven by Electromyograms

OBJECTIVE

Current clinical biomechanics involves lengthy data acquisition and time-consuming offline analyses with biomechanical models not operating in real-time for man-machine interfacing. We developed a method that enables online analysis of neuromusculoskeletal function in vivo in the intact human.

METHODS

We used electromyography (EMG)-driven musculoskeletal modeling to simulate all transformations from muscle excitation onset (EMGs) to mechanical moment production around multiple lower-limb degrees of freedom (DOFs). We developed a calibration algorithm that enables adjusting musculoskeletal model parameters specifically to an individual's anthropometry and force-generating capacity. We incorporated the modeling paradigm into a computationally efficient, generic framework that can be interfaced in real-time with any movement data collection system.

RESULTS

The framework demonstrated the ability of computing forces in 13 lower-limb muscle-tendon units and resulting moments about three joint DOFs simultaneously in real-time. Remarkably, it was capable of extrapolating beyond calibration conditions, i.e., predicting accurate joint moments during six unseen tasks and one unseen DOF.

CONCLUSION

The proposed framework can dramatically reduce evaluation latency in current clinical biomechanics and open up new avenues for establishing prompt and personalized treatments, as well as for establishing natural interfaces between patients and rehabilitation systems.

SIGNIFICANCE

The integration of EMG with numerical modeling will enable simulating realistic neuromuscular strategies in conditions including muscular/orthopedic deficit, which could not be robustly simulated via pure modeling formulations. This will enable translation to clinical settings and development of healthcare technologies including real-time bio-feedback of internal mechanical forces and direct patient-machine interfacing.

RAW 264.7 co-cultured with ultra-high molecular weight polyethylene particles spontaneously differentiate into osteoclasts: an in vitro model of periprosthetic osteolysis

Wear-particle osteolysis affects prosthesis survival leading to implant loosening up to 70% of revisions. Therapeutic strategies are increasing, however alternative testing methods to experimentally evaluate such treatments are lacking. The aim of this study was to reproduce an in vitro osteolysis model recapitulating the events that, starting from the exposure of macrophages to polyethylene, lead to the establishment of osteoclastogenesis and inflammation. Responses to polyethylene, at 3 and 7 days, in a macrophage cell line, RAW 264.7, were determined by DNA quantification, immunofluorescence, pit assay, gene expression, cytokine production and NF-kB activation. Results showed that 3 days exposure to particles could induce a significant production of Tumor Necrosis Factor alpha (p < 0.0005) and Prostaglandin E₂ (p < 0.005) compared to controls. Particles also induced macrophages to spontaneously differentiate into mature and active osteoclasts, in terms of identification of multinucleated cells by Phalloidin staining and by the analysis of osteoclast-specific gene markers. In particular, at 3 days polyethylene induced a significant up-regulation of Nuclear Factor of Activated T-cells, cytoplasmic 1, Receptor Activator of Nuclear factor Kappa-B and Receptor Activator of Nuclear Factor Kappa-B Ligand genes (p < 0.0005) compared to controls. At protein level, the particles induced a significant increase of Receptor Activator of Nuclear Factor Kappa-B Ligand at day 7 over controls (p < 0.0005). Osteoclasts were capable to resorb bone even in absence of differentiating factors. The possible mechanism, beside spontaneous osteoclastogenesis mediated by wear debris, was identified in an autocrine up-regulation of Receptor activator of nuclear factor kappa-B ligand gene expression and protein synthesis. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 510-520, 2017.

Human-like compliant locomotion: state of the art of robotic implementations

This review paper provides a synthetic yet critical overview of the key biomechanical principles of human bipedal walking and their current implementation in robotic platforms. We describe the functional role of human joints, addressing in particular the relevance of the compliant properties of the different degrees of freedom throughout the gait cycle. We focused on three basic functional units involved in locomotion, i.e. the ankle-foot complex, the knee, and the hip-pelvis complex, and their relevance to whole-body performance. We present an extensive review of the current implementations of these mechanisms into robotic platforms, discussing their potentialities and limitations from the functional and energetic perspectives. We specifically targeted humanoid robots, but also revised evidence from the field of lower-limb prosthetics, which presents innovative solutions still unexploited in the current humanoids. Finally, we identified the main critical aspects of the process of translating human principles into actual machines, providing a number of relevant challenges that should be addressed in future research.

Multiscale musculoskeletal modelling, data-model fusion and electromyography-informed modelling

This paper proposes methods and technologies that advance the state of the art for modelling the musculoskeletal system across the spatial and temporal scales; and storing these using efficient ontologies and tools. We present population-based modelling as an efficient method to rapidly generate individual morphology from only a few measurements and to learn from the ever-increasing supply of imaging data available. We present multiscale methods for continuum muscle and bone models; and efficient mechanostatistical methods, both continuum and particle-based, to bridge the scales. Finally, we examine both the importance that muscles play in bone remodelling stimuli and the latest muscle force prediction methods that use electromyography-assisted modelling techniques to compute musculoskeletal forces that best reflect the underlying neuromuscular activity. Our proposal is that, in order to have a clinically relevant virtual physiological human, (i) bone and muscle mechanics must be considered together; (ii) models should be trained on population data to permit rapid generation and use underlying principal modes that describe both muscle patterns and morphology; and (iii) these tools need to be available in an open-source repository so that the scientific community may use, personalize and contribute to the database of models.

Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies

OBJECTIVES

The development of neurorehabilitation technologies requires the profound understanding of the mechanisms underlying an individual's motor ability and impairment. A major factor limiting this understanding is the difficulty of bridging between events taking place at the neurophysiologic level (i.e., motor neuron firings) with those emerging at the musculoskeletal level (i.e. joint actuation), in vivo in the intact moving human. This review presents emerging model-based methodologies for filling this gap that are promising for developing clinically viable technologies.

METHODS

We provide a design overview of musculoskeletal modeling formulations driven by recordings of neuromuscular activity with a critical comparison to alternative model-free approaches in the context of neurorehabilitation technologies. We present advanced electromyography-based techniques for interfacing with the human nervous system and model-based techniques for translating the extracted neural information into estimates of motor function.

RESULTS

We introduce representative application areas where modeling is relevant for accessing neuromuscular variables that could not be measured experimentally. We then show how these variables are used for designing personalized rehabilitation interventions, biologically inspired limbs, and human-machine interfaces.

CONCLUSION

The ability of using electrophysiological recordings to inform biomechanical models enables accessing a broader range of neuromechanical variables than analyzing electrophysiological data or movement data individually. This enables understanding the neuromechanical interplay underlying in vivo movement function, pathology, and robot-assisted motor recovery.

SIGNIFICANCE

Filling the gap between our understandings of movement neural and mechanical functions is central for addressing one of the major challenges in neurorehabilitation: personalizing current technologies and interventions to an individual's anatomy and impairment.

Estimating EMG signals to drive neuromusculoskeletal models in cyclic rehabilitation movements

A main challenge in the development of robotic rehabilitation devices is how to understand patient's intentions and adapt to his/her current neuro-physiological capabilities. A promising approach is the use of electromyographic (EMG) signals which reflect the actual activation of the muscles during the movement and, thus, are a direct representation of user's movement intention. However, EMGs acquisition is a complex procedure, requiring trained therapists and, therefore, solutions based on EMG signals are not easily integrable in devices for home-rehabilitation. This work investigates the effectiveness of a subject- and task-specific EMG model in estimating EMG signals in cyclic plantar-dorsiflexion movements. Then, the outputs of this model are used to drive CEINMS toolbox, a state-of-the-art EMG-driven neuromusculoskeletal model able to predict joint torques and muscle forces. Preliminary results show that the proposed methodology preserves the accuracy of the estimates values.

MOtoNMS: A MATLAB toolbox to process motion data for neuromusculoskeletal modeling and simulation

BACKGROUND

Neuromusculoskeletal modeling and simulation enable investigation of the neuromusculoskeletal system and its role in human movement dynamics. These methods are progressively introduced into daily clinical practice. However, a major factor limiting this translation is the lack of robust tools for the pre-processing of experimental movement data for their use in neuromusculoskeletal modeling software.

RESULTS

This paper presents MOtoNMS (matlab MOtion data elaboration TOolbox for NeuroMusculoSkeletal applications), a toolbox freely available to the community, that aims to fill this lack. MOtoNMS processes experimental data from different motion analysis devices and generates input data for neuromusculoskeletal modeling and simulation software, such as OpenSim and CEINMS (Calibrated EMG-Informed NMS Modelling Toolbox). MOtoNMS implements commonly required processing steps and its generic architecture simplifies the integration of new user-defined processing components. MOtoNMS allows users to setup their laboratory configurations and processing procedures through user-friendly graphical interfaces, without requiring advanced computer skills. Finally, configuration choices can be stored enabling the full reproduction of the processing steps. MOtoNMS is released under GNU General Public License and it is available at the SimTK website and from the GitHub repository. Motion data collected at four institutions demonstrate that, despite differences in laboratory instrumentation and procedures, MOtoNMS succeeds in processing data and producing consistent inputs for OpenSim and CEINMS.

CONCLUSIONS

MOtoNMS fills the gap between motion analysis and neuromusculoskeletal modeling and simulation. Its support to several devices, a complete implementation of the pre-processing procedures, its simple extensibility, the available user interfaces, and its free availability can boost the translation of neuromusculoskeletal methods in daily and clinical practice.

CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks

Personalized neuromusculoskeletal (NMS) models can represent the neurological, physiological, and anatomical characteristics of an individual and can be used to estimate the forces generated inside the human body. Currently, publicly available software to calculate muscle forces are restricted to static and dynamic optimisation methods, or limited to isometric tasks only. We have created and made freely available for the research community the Calibrated EMG-Informed NMS Modelling Toolbox (CEINMS), an OpenSim plug-in that enables investigators to predict different neural control solutions for the same musculoskeletal geometry and measured movements. CEINMS comprises EMG-driven and EMG-informed algorithms that have been previously published and tested. It operates on dynamic skeletal models possessing any number of degrees of freedom and musculotendon units and can be calibrated to the individual to predict measured joint moments and EMG patterns. In this paper we describe the components of CEINMS and its integration with OpenSim. We then analyse how EMG-driven, EMG-assisted, and static optimisation neural control solutions affect the estimated joint moments, muscle forces, and muscle excitations, including muscle co-contraction.

Revealing the diversity of Cloeodes Traver, 1938 (Ephemeroptera: Baetidae) in the Neotropics: description of eleven new species from Brazilian mountain ranges

In the present work, based on material from distinct mountain ranges in Brazil, 11 new species of Cloeodes with hind wings or hind wing pads are described, illustrated and discussed. Among the new species, in C. aiuruoca, C. amantykyra, C. atlanticus, C. boldrinii and C. ioachimi, the apex of the fore femora in the nymphs is extremely projected (a characteristic previously found only in two species of the genus). Cloeodes guara and C. tracheatus share the presence of unusual large and dark gills. Cloeodes melanotarsus is readily distinguished by conspicuous blackish tarsi, C. lucifer by the bright yellow coloration of tergum I, and C. xyrognathos by blade-like incisors. Cloeodes magnus, besides being the largest species of the genus, with a body size reaching 12 mm, possess short maxillary palp. Comments on the presence of C. irvingi and C. opacus in Brazil are also provided. An interactive online key is provided for the nymphs of all the species of Cloeodes in which hind wing pads are present.

A predictive model of muscle excitations based on muscle modularity for a large repertoire of human locomotion conditions

Humans can efficiently walk across a large variety of terrains and locomotion conditions with little or no mental effort. It has been hypothesized that the nervous system simplifies neuromuscular control by using muscle synergies, thus organizing multi-muscle activity into a small number of coordinative co-activation modules. In the present study we investigated how muscle modularity is structured across a large repertoire of locomotion conditions including five different speeds and five different ground elevations. For this we have used the non-negative matrix factorization technique in order to explain EMG experimental data with a low-dimensional set of four motor components. In this context each motor components is composed of a non-negative factor and the associated muscle weightings. Furthermore, we have investigated if the proposed descriptive analysis of muscle modularity could be translated into a predictive model that could: (1) Estimate how motor components modulate across locomotion speeds and ground elevations. This implies not only estimating the non-negative factors temporal characteristics, but also the associated muscle weighting variations. (2) Estimate how the resulting muscle excitations modulate across novel locomotion conditions and subjects. The results showed three major distinctive features of muscle modularity: (1) the number of motor components was preserved across all locomotion conditions, (2) the non-negative factors were consistent in shape and timing across all locomotion conditions, and (3) the muscle weightings were modulated as distinctive functions of locomotion speed and ground elevation. Results also showed that the developed predictive model was able to reproduce well the muscle modularity of un-modeled data, i.e., novel subjects and conditions. Muscle weightings were reconstructed with a cross-correlation factor greater than 70% and a root mean square error less than 0.10. Furthermore, the generated muscle excitations matched well the experimental excitation with a cross-correlation factor greater than 85% and a root mean square error less than 0.09. The ability of synthetizing the neuromuscular mechanisms underlying human locomotion across a variety of locomotion conditions will enable solutions in the field of neurorehabilitation technologies and control of bipedal artificial systems. Open-access of the model implementation is provided for further analysis at https://simtk.org/home/p-mep/.