Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject’s self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot–ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject’s walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject’s walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject’s walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations.

Neuromusculoskeletal disorders affecting walking ability are often difficult to manage, in part due to limited understanding of how a patient’s lower extremity muscle excitations contribute to the patient’s lower extremity joint moments. To assist in the study of these disorders, researchers have developed electromyography (EMG) driven neuromusculoskeletal models utilizing scaled generic musculoskeletal geometry. While these models can predict individual muscle contributions to lower extremity joint moments during walking, the accuracy of the predictions can be hindered by errors in the scaled geometry. This study presents a novel EMG-driven modeling method that automatically adjusts surrogate representations of the patient’s musculoskeletal geometry to improve prediction of lower extremity joint moments during walking. In addition to commonly adjusted neuromusculoskeletal model parameters, the proposed method adjusts model parameters defining muscle-tendon lengths, velocities, and moment arms. We evaluated our EMG-driven modeling method using data collected from a high-functioning hemiparetic subject walking on an instrumented treadmill at speeds ranging from 0.4 to 0.8 m/s. EMG-driven model parameter values were calibrated to match inverse dynamic moments for five degrees of freedom in each leg while keeping musculoskeletal geometry close to that of an initial scaled musculoskeletal model. We found that our EMG-driven modeling method incorporating automated adjustment of musculoskeletal geometry predicted net joint moments during walking more accurately than did the same method without geometric adjustments. Geometric adjustments improved moment prediction errors by 25% on average and up to 52%, with the largest improvements occurring at the hip. Predicted adjustments to musculoskeletal geometry were comparable to errors reported in the literature between scaled generic geometric models and measurements made from imaging data. Our results demonstrate that with appropriate experimental data, joint moment predictions for walking generated by an EMG-driven model can be improved significantly when automated adjustment of musculoskeletal geometry is included in the model calibration process.

Inclusion modes in complexes with a-and byclodextrins in water have been investigated by NMR spectroscopy at 400 or 500 MHz, and compared with structures obtained by computer-aided molecular modelling and with calorimetric data. The NOEs observed on 0-and m-aryl protons upon irradiation of either H3 or H5 inside the CD cavity indicate for all phenols an inclusion mode with the hydroxy group at the wide cavity end, and an increasingly deep immersion for phenol or phenolate with iodine compared with this nitro group, aspara-substituent. This is found to be in line with the complexationinduced NMR shifts. Adamantane-l-carboxylate is indicated by distinct NOEs to be fully immersed into the PCD cavity; the corresponding complex with a-CD shows contact only at the wider rim and a tilted conformation which allows formation of a hydrogen bond between the guest COO-and the 2-OH group of the CD. The same conformation is found by CHARMm calculations, including simulations in a water box. The results, together with some A@ values derived from N M R titrations, are in line with data from calorimetric studies. These show for complexes with tight fit (in a-CD) large enthalpies of up to 43 W mol-l as the predominating driving force against sizeable entropy disadvantages (TAS" < -24 kJ mol-'), particularly for guest molecules of higher electron density and/or polarizibility. These observations point to predominating dispersive interactions. In contrast, inclusion in the wider fLCD cavity suffers less from entropy disadvantage (TAS" < -11 W mol-*); the binding, however, is still dominated by AH", pointing to predominant cohesive and not entropic hydrophobic forces.

Mechanical loading is believed to be a critical factor in the development and treatment of knee osteoarthritis. However, the contact forces to which the knee articular surfaces are subjected during daily activities cannot be measured clinically. Thus, the ability to predict internal knee contact forces accurately using external measures (i.e., external knee loads and muscle EMG signals) would be clinically valuable. This study quantifies how well external knee load and EMG measures predict internal knee contact forces during gait. A single subject with a force-measuring tibial prosthesis and post-operative valgus alignment performed four gait patterns (normal, medial thrust, walking pole, and trunk sway) to induce a wide range of external and internal knee joint loads. Linear regression analyses were performed to assess how much of the variability in internal contact forces was accounted for by variability in the external measures. Though the different gait patterns successfully induced significant changes in the external and internal quantities, changes in external measures were generally weak indicators of changes in total, medial, and lateral contact force. Our results suggest that when total contact force may be changing, caution should be exercised when inferring changes in knee contact forces based on observed changes in external knee load and EMG measures. Advances in musculoskeletal modeling methods may be needed for accurate estimation of in vivo knee contact forces.

Stroke is a leading cause of long-term disability worldwide and often impairs walking ability. To improve recovery of walking function post-stroke, researchers have investigated the use of treatments such as fast functional electrical stimulation (FastFES). During FastFES treatments, individuals post-stroke walk on a treadmill at their fastest comfortable speed while electrical stimulation is delivered to two muscles of the paretic ankle, ideally to improve paretic leg propulsion and toe clearance. However, muscle selection and stimulation timing are currently standardized based on clinical intuition and a one-size-fits-all approach, which may explain in part why some patients respond to FastFES training while others do not. This study explores how personalized neuromusculoskeletal models could potentially be used to enable individual-specific selection of target muscles and stimulation timing to address unique functional limitations of individual patients post-stroke. Treadmill gait data, including EMG, surface marker positions, and ground reactions, were collected from an individual post-stroke who was a non-responder to FastFES treatment. The patient's gait data were used to personalize key aspects of a full-body neuromusculoskeletal walking model, including lower-body joint functional axes, lower-body muscle force generating properties, deformable foot-ground contact properties, and paretic and non-paretic leg neural control properties. The personalized model was utilized within a direct collocation optimal control framework to reproduce the patient's unstimulated treadmill gait data (verification problem) and to generate three stimulated walking predictions that sought to minimize inter-limb propulsive force asymmetry (prediction problems). The three predictions used: (1) Standard muscle selection (gastrocnemius and tibialis anterior) with standard stimulation timing, (2) Standard muscle selection with optimized stimulation timing, and (3) Optimized muscle selection (soleus and semimembranosus) with optimized stimulation timing. Relative to unstimulated walking, the optimal control problems predicted a 41% reduction in propulsive force asymmetry for scenario (1), a 45% reduction for scenario (2), and a 64% reduction for scenario (3), suggesting that non-standard muscle selection may be superior for this patient. Despite these predicted improvements, kinematic symmetry was not noticeably improved for any of the walking predictions. These results suggest that personalized neuromusculoskeletal models may be able to predict personalized FastFES training prescriptions that could improve propulsive force symmetry, though inclusion of kinematic requirements would be necessary to improve kinematic symmetry as well.

The coevolution of interacting species can lead to co-dependent mutualists. Little is known about the effect of selection on partners within verses apart from the association. Here, we determined the effect of selection on bacteria (Xenorhabdus nematophila) both within and apart from its mutualistic partner (a nematode, Steinernema carpocapsae). In nature, the two species cooperatively infect and kill arthropods. We passaged the bacteria either together with (M+), or isolated from (M−), nematodes under two different selection regimes: random selection (S−) and selection for increased virulence against arthropod hosts (S+). We found that the isolated bacteria evolved greater virulence under selection for greater virulence (M−S+) than under random selection (M−S−). In addition, the response to selection in the isolated bacteria (M−S+) caused a breakdown of the mutualism following reintroduction to the nematode. Finally, selection for greater virulence did not alter the evolutionary trajectories of bacteria passaged within the mutualism (M+S+ = M+S−), indicating that selection for the maintenance of the mutualism was stronger than selection for increased virulence. The results show that selection on isolated mutualists can rapidly breakdown beneficial interactions between species, but that selection within a mutualism can supersede external selection, potentially generating co-dependence over time.

Precis: A comparison of 186 glaucoma patients with mixed diagnoses who underwent nonvalved glaucoma drainage device (GDD) implant surgery showed similar long-term intraocular pressure (IOP), medication, and visual acuity (VA) outcomes between those with prior failed trabeculectomy surgery versus those without. Purpose:The purpose of this study was to evaluate whether prior failed trabeculectomy adversely affects the outcome of glaucoma tube surgery.Patients and Methods: A total of 186 eyes of 186 patients who underwent a nonvalved GDD implant surgery by a single surgeon between 1996 and 2015 at a University practice were included. Patients were of mixed diagnoses and over 18 years old. Before the GDD surgery, 65 had a previous failed glaucoma filtering surgery and 121 had no prior glaucoma surgery. Demographic information, preoperative and postoperative IOP, medication, VA, and complications were collected from chart review.Results: No significant difference was noted in mean IOP and mean medication use (13.0 and 12.6 mm Hg on 2.0 and 1.7 medication classes at 5 y postoperatively, respectively), mean VA and change in VA from baseline, or numbers of complications (P > 0.05), between eyes that had a prior failed filtration surgery and those that had not. Kaplan-Meier plots for failure over 5 years using a lower limit of <5 mm Hg and an upper limit of ≥ 18, ≥ 15, or ≥ 12 mm Hg did not show a significant difference between groups. Subanalyses were performed to examine only primary glaucoma eyes and results were similar. Further group subanalyses comparing those with baseline IOP ≥ 25 or <25 mm Hg, age 65 and above or below 65 years and those specifically with Baerveldt 350 mm 2 implants also did not show significant differences. Conclusion:Prior failed filtration surgery does not appear to affect the outcome of future GDD surgery.

Driven by the increasing channel count of neural probes, there is much effort being directed to creating increasingly scalable electrophysiology data acquisition (DAQ) systems. However, all such systems still rely on personal computers for data storage, and thus are limited by the bandwidth and cost of the computers, especially as the scale of recording increases. Here we present a novel architecture in which a digital processor receives data from an analog-to-digital converter, and writes that data directly to hard drives, without the need for a personal computer to serve as an intermediary in the DAQ process. This minimalist architecture may support exceptionally high data throughput, without incurring costs to support unnecessary hardware and overhead associated with personal computers, thus facilitating scaling of electrophysiological recording in the future.