A simulation framework to determine optimal strength training and musculoskeletal geometry for sprinting and distance running.

Musculoskeletal geometry and muscle volumes vary widely in the population and are intricately linked to the performance of tasks ranging from walking and running to jumping and sprinting. As an alternative to experimental approaches, where it is difficult to isolate factors and establish causal relationships, simulations can be used to independently vary musculoskeletal geometry and muscle volumes, and develop a fundamental understanding. However, our ability to understand how these parameters affect task performance has been limited due to the high computational cost of modelling the necessary complexity of the musculoskeletal system and solving the requisite multi-dimensional optimization problem. For example, sprinting and running are fundamental to many forms of sport, but past research on the relationships between musculoskeletal geometry, muscle volumes, and running performance has been limited to observational studies, which have not established cause-effect relationships, and simulation studies with simplified representations of musculoskeletal geometry. In this study, we developed a novel musculoskeletal simulator that is differentiable with respect to musculoskeletal geometry and muscle volumes. This simulator enabled us to find the optimal body segment dimensions and optimal distribution of added muscle volume for sprinting and marathon running. Our simulation results replicate experimental observations, such as increased muscle mass in sprinters, as well as a mass in the lower end of the healthy BMI range and a higher leg-length-to-height ratio in marathon runners. The simulations also reveal new relationships, for example showing that hip musculature is vital to both sprinting and marathon running. We found hip flexor and extensor moment arms were maximized to optimize sprint and marathon running performance, and hip muscles the main target when we simulated strength training for sprinters. Our simulation results provide insight to inspire future studies to examine optimal strength training. Our simulator can be extended to other athletic tasks, such as jumping, or to non-athletic applications, such as designing interventions to improve mobility in older adults or individuals with movement disorders.

OpenCap: Human movement dynamics from smartphone videos.

Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in large-scale research studies or clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing both the kinematics (i.e., motion) and dynamics (i.e., forces) of human movement using videos captured from two or more smartphones. OpenCap leverages pose estimation algorithms to identify body landmarks from videos; deep learning and biomechanical models to estimate three-dimensional kinematics; and physics-based simulations to estimate muscle activations and musculoskeletal dynamics. OpenCap's web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap's practical utility through a 100-subject field study, where a clinician using OpenCap estimated musculoskeletal dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.

An ecosystem service perspective on urban nature, physical activity, and health.

Nature underpins human well-being in critical ways, especially in health. Nature provides pollination of nutritious crops, purification of drinking water, protection from floods, and climate security, among other well-studied health benefits. A crucial, yet challenging, research frontier is clarifying how nature promotes physical activity for its many mental and physical health benefits, particularly in densely populated cities with scarce and dwindling access to nature. Here we frame this frontier by conceptually developing a spatial decision-support tool that shows where, how, and for whom urban nature promotes physical activity, to inform urban greening efforts and broader health assessments. We synthesize what is known, present a model framework, and detail the model steps and data needs that can yield generalizable spatial models and an effective tool for assessing the urban nature-physical activity relationship. Current knowledge supports an initial model that can distinguish broad trends and enrich urban planning, spatial policy, and public health decisions. New, iterative research and application will reveal the importance of different types of urban nature, the different subpopulations who will benefit from it, and nature's potential contribution to creating more equitable, green, livable cities with active inhabitants.

NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress.

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective.

Health behaviors are inextricably linked to health and well-being, yet issues such as physical inactivity and insufficient sleep remain significant global public health problems. Mobile technology-and the unprecedented scope and quantity of data it generates-has a promising but largely untapped potential to promote health behaviors at the individual and population levels. This perspective article provides multidisciplinary recommendations on the design and use of mobile technology, and the concomitant wealth of data, to promote behaviors that support overall health. Using physical activity as anexemplar health behavior, we review emerging strategies for health behavior change interventions. We describe progress on personalizing interventions to an individual and their social, cultural, and built environments, as well as on evaluating relationships between mobile technology data and health to establish evidence-based guidelines. In reviewing these strategies and highlighting directions for future research, we advance the use of theory-based, personalized, and human-centered approaches in promoting health behaviors.

Large-scale physical activity data reveal worldwide activity inequality.

To be able to curb the global pandemic of physical inactivity and the associated 5.3 million deaths per year, we need to understand the basic principles that govern physical activity. However, there is a lack of large-scale measurements of physical activity patterns across free-living populations worldwide. Here we leverage the wide usage of smartphones with built-in accelerometry to measure physical activity at the global scale. We study a dataset consisting of 68 million days of physical activity for 717,527 people, giving us a window into activity in 111 countries across the globe. We find inequality in how activity is distributed within countries and that this inequality is a better predictor of obesity prevalence in the population than average activity volume. Reduced activity in females contributes to a large portion of the observed activity inequality. Aspects of the built environment, such as the walkability of a city, are associated with a smaller gender gap in activity and lower activity inequality. In more walkable cities, activity is greater throughout the day and throughout the week, across age, gender, and body mass index (BMI) groups, with the greatest increases in activity found for females. Our findings have implications for global public health policy and urban planning and highlight the role of activity inequality and the built environment in improving physical activity and health.

OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations.

BACKGROUND: The ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate and capable of assessing and mitigating drift. METHODS: We computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-min trials: walking and a repeated sequence of varied lower-extremity movements. To validate the approach, we compared the joint angles computed with IMU orientations to the joint angles computed from optical motion capture using root mean square (RMS) difference and Pearson correlations, and estimated drift using a linear regression on each subject's RMS differences over time. RESULTS: IMU-based kinematic estimates agreed with optical motion capture; median RMS differences over all subjects and all minutes were between 3 and 6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60-0.87). We observed minimal drift in the RMS differences over 10 min; the average slopes of the linear fits to these data were near zero (- 0.14-0.17 deg/min). CONCLUSIONS: Our workflow produced joint kinematics consistent with those estimated by optical motion capture, and could mitigate kinematic drift even in the trials of continuous walking without rest, which may obviate the need for explicit sensor recalibration (e.g. sitting or standing still for a few seconds or zero-velocity updates) used in current drift-mitigation approaches when studying similar activities. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments.

Effect of D614G Spike Variant on Immunoglobulin G, M, or A Spike Seroassay Performance.

Emergence of a new spike protein variant (D614G) with increased infectivity has prompted many to analyze its role in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. There is concern regarding whether an individual exposed to one variant of a virus will have cross-reactive memory to the second variant. Accordingly, we analyzed the serologic reactivity of both variants, and we found that antibodies from 88 donors from a high-incidence population reacted toward both the original spike and the D614 spike variant. These data suggest that patients who are exposed to either variant have cross-responsive humoral immunity. This represents an important finding both for SARS-CoV-2 disease biology and for therapeutics.

Serologic Cross-Reactivity of SARS-CoV-2 with Endemic and Seasonal Betacoronaviruses.

In order to properly understand the spread of SARS-CoV-2 infection and development of humoral immunity, researchers have evaluated the presence of serum antibodies of people worldwide experiencing the pandemic. These studies rely on the use of recombinant proteins from the viral genome in order to identify serum antibodies that recognize SARS-CoV-2 epitopes. Here, we discuss the cross-reactivity potential of SARS-CoV-2 antibodies with the full spike proteins of four other betacoronaviruses that cause disease in humans, MERS-CoV, SARS-CoV, HCoV-OC43, and HCoV-HKU1. Using enzyme-linked immunosorbent assays (ELISAs), we detected the potential cross-reactivity of antibodies against SARS-CoV-2 towards the four other coronaviruses, with the strongest cross-recognition between SARS-CoV-2 and SARS /MERS-CoV antibodies, as expected based on sequence homology of their respective spike proteins. Further analysis of cross-reactivity could provide informative data that could lead to intelligently designed pan-coronavirus therapeutics or vaccines.

OpenSim Moco: Musculoskeletal optimal control.

Musculoskeletal simulations are used in many different applications, ranging from the design of wearable robots that interact with humans to the analysis of patients with impaired movement. Here, we introduce OpenSim Moco, a software toolkit for optimizing the motion and control of musculoskeletal models built in the OpenSim modeling and simulation package. OpenSim Moco uses the direct collocation method, which is often faster and can handle more diverse problems than other methods for musculoskeletal simulation. Moco frees researchers from implementing direct collocation themselves-which typically requires extensive technical expertise-and allows them to focus on their scientific questions. The software can handle a wide range of problems that interest biomechanists, including motion tracking, motion prediction, parameter optimization, model fitting, electromyography-driven simulation, and device design. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which enable modeling of kinematic loops (e.g., cycling models) and complex anatomy (e.g., patellar motion). To show the abilities of Moco, we first solved for muscle activity that produced an observed walking motion while minimizing squared muscle excitations and knee joint loading. Next, we predicted how muscle weakness may cause deviations from a normal walking motion. Lastly, we predicted a squat-to-stand motion and optimized the stiffness of an assistive device placed at the knee. We designed Moco to be easy to use, customizable, and extensible, thereby accelerating the use of simulations to understand the movement of humans and other animals.

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation.

Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically correct motions of a musculoskeletal model. Typically, researchers have developed control models that encode physiologically plausible motor control hypotheses and compared the resulting simulation behaviors to measurable human motion data. While such plausible control models were able to simulate and explain many basic locomotion behaviors (e.g. walking, running, and climbing stairs), modeling higher layer controls (e.g. processing environment cues, planning long-term motion strategies, and coordinating basic motor skills to navigate in dynamic and complex environments) remains a challenge. Recent advances in deep reinforcement learning lay a foundation for modeling these complex control processes and controlling a diverse repertoire of human movement; however, reinforcement learning has been rarely applied in neuromechanical simulation to model human control. In this paper, we review the current state of neuromechanical simulations, along with the fundamentals of reinforcement learning, as it applies to human locomotion. We also present a scientific competition and accompanying software platform, which we have organized to accelerate the use of reinforcement learning in neuromechanical simulations. This "Learn to Move" competition was an official competition at the NeurIPS conference from 2017 to 2019 and attracted over 1300 teams from around the world. Top teams adapted state-of-the-art deep reinforcement learning techniques and produced motions, such as quick turning and walk-to-stand transitions, that have not been demonstrated before in neuromechanical simulations without utilizing reference motion data. We close with a discussion of future opportunities at the intersection of human movement simulation and reinforcement learning and our plans to extend the Learn to Move competition to further facilitate interdisciplinary collaboration in modeling human motor control for biomechanics and rehabilitation research.

Can you hear me now?: Improving palliative care access through telehealth.

Telehealth has been increasingly used to expand healthcare access over the last two decades. However, this had not been the case for palliative care (PC), because telehealth was considered nontraditional and impractical due to the sensitive nature of conversations and a "high touch" philosophy. Motivated by limited PC access to rural and underserved populations and positive PC telehealth studies, clinical PC telehealth models have been developing. However, nearly overnight, the COVID-19 pandemic accelerated the use and uptake of telehealth across health care and especially in PC. As a result, clinicians, administrators, and others agree that telehealth is "here to stay," and will likely maintain widespread use and refinement beyond rural areas. The purpose of this review is to describe exemplar PC telehealth programs in research and clinical practice, including pros and cons, lessons learned, and future directions for the ongoing development and expansion of PC via telehealth across diseases and the lifespan.

Enrichment of ovine gonadotropes via adenovirus gene targeting enhances assessment of transcriptional changes in response to estradiol-17 beta†.

Gonadotropes represent approximately 5-15% of the total endocrine cell population in the mammalian anterior pituitary. Therefore, assessing the effects of experimental manipulation on virtually any parameter of gonadotrope biology is difficult to detect and parse from background noise. In non-rodent species, applying techniques such as high-throughput ribonucleic acid (RNA) sequencing is problematic due to difficulty in isolating and analyzing individual endocrine cell populations. Herein, we exploited cell-specific properties inherent to the proximal promoter of the human glycoprotein hormone alpha subunit gene (CGA) to genetically target the expression of a fluorescent reporter (green fluorescent protein [GFP]) selectively to ovine gonadotropes. Dissociated ovine pituitary cells were cultured and infected with an adenoviral reporter vector (Ad-hαCGA-eGFP). We established efficient gene targeting by successfully enriching dispersed GFP-positive cells with flow cytometry. Confirming enrichment of gonadotropes specifically, we detected elevated levels of luteinizing hormone (LH) but not thyrotropin-stimulating hormone (TSH) in GFP-positive cell populations compared to GFP-negative populations. Subsequently, we used next-generation sequencing to obtain the transcriptional profile of GFP-positive ovine gonadotropes in the presence or absence of estradiol 17-beta (E2), a key modulator of gonadotrope function. Compared to non-sorted cells, enriched GFP-positive cells revealed a distinct transcriptional profile consistent with established patterns of gonadotrope gene expression. Importantly, we also detected nearly 200 E2-responsive genes in enriched gonadotropes, which were not apparent in parallel experiments on non-enriched cell populations. From these data, we conclude that CGA-targeted adenoviral gene transfer is an effective means for selectively labeling and enriching ovine gonadotropes suitable for investigation by numerous experimental approaches.

Predicting gait adaptations due to ankle plantarflexor muscle weakness and contracture using physics-based musculoskeletal simulations.

Deficits in the ankle plantarflexor muscles, such as weakness and contracture, occur commonly in conditions such as cerebral palsy, stroke, muscular dystrophy, Charcot-Marie-Tooth disease, and sarcopenia. While these deficits likely contribute to observed gait pathologies, determining cause-effect relationships is difficult due to the often co-occurring biomechanical and neural deficits. To elucidate the effects of weakness and contracture, we systematically introduced isolated deficits into a musculoskeletal model and generated simulations of walking to predict gait adaptations due to these deficits. We trained a planar model containing 9 degrees of freedom and 18 musculotendon actuators to walk using a custom optimization framework through which we imposed simple objectives, such as minimizing cost of transport while avoiding falling and injury, and maintaining head stability. We first generated gaits at prescribed speeds between 0.50 m/s and 2.00 m/s that reproduced experimentally observed kinematic, kinetic, and metabolic trends for walking. We then generated a gait at self-selected walking speed; quantitative comparisons between our simulation and experimental data for joint angles, joint moments, and ground reaction forces showed root-mean-squared errors of less than 1.6 standard deviations and normalized cross-correlations above 0.8 except for knee joint moment trajectories. Finally, we applied mild, moderate, and severe levels of muscle weakness or contracture to either the soleus (SOL) or gastrocnemius (GAS) or both of these major plantarflexors (PF) and retrained the model to walk at a self-selected speed. The model was robust to all deficits, finding a stable gait in all cases. Severe PF weakness caused the model to adopt a slower, "heel-walking" gait. Severe contracture of only SOL or both PF yielded similar results: the model adopted a "toe-walking" gait with excessive hip and knee flexion during stance. These results highlight how plantarflexor weakness and contracture may contribute to observed gait patterns.

Recruiting and Retaining Youth and Young Adults in the Policy and Communication Evaluation (PACE) Vermont Study: Randomized Controlled Trial of Participant Compensation.

BACKGROUND: The standard approach for evaluating the effects of population-level substance use prevention efforts on youth and young adult perceptions and behaviors has been to compare outcomes across states using national surveillance data. Novel surveillance methods that follow individuals over shorter time intervals and capture awareness of substance use prevention policy and communication efforts may provide a stronger basis for their evaluation than annual cross-sectional studies. OBJECTIVE: This study aimed to identify a combination of strategies to recruit a sample of youth and young adults sufficiently representative of the Vermont population and determine how best to retain a web-based panel of youth and young adults over a 6-month period. METHODS: Eligible participants were Vermont residents aged 12 to 25 years who were willing to complete three 10 to 15-minute web-based surveys over a 6-month period. Recruitment was conducted via the following three main mechanisms: (1) web-based recruitment (paid and unpaid), (2) community-based recruitment through partners, and (3) participant referrals via a personalized link. Upon completion of the baseline survey, participants were randomly assigned to one of the following three retention incentive conditions: (1) guaranteed incentive (US $10), (2) lottery incentive (US $50 weekly lottery drawing), and (3) preferred method (guaranteed or lottery). Analyses examined cost per survey start by recruitment source, distribution of demographic characteristics across incentive conditions, and retention by study condition at 3-month and 6-month follow-ups. RESULTS: Over a 10-week period in 2019, we recruited 480 eligible youth (aged 12-17 years) and 1037 eligible young adults (aged 18-25 years) to the Policy and Communication Evaluation (PACE) Vermont Study. Facebook and Instagram advertising produced the greatest number of survey starts (n=2013), followed by posts to a state-wide web-based neighborhood forum (n=822) and Google advertisements (n=749). Retention was 78.11% (1185/1517) at 3 months and 72.18% (1095/1517) at 6 months. Retention was equivalent across all incentive study conditions at both waves, despite a strong stated preference among study participants for the guaranteed payment at baseline. Youth had greater retention than young adults at both waves (wave 2: 395/480, 82.3% vs 790/1037, 76.18%; wave 3: 366/480, 76.3% vs 729/1037, 70.30%). Substance use prevalence in this cohort was similar to national and state-level surveillance estimates for young adults, but was lower than state-level surveillance estimates for youth. Most participants retained at wave 3 provided positive qualitative feedback on their experience. CONCLUSIONS: Our study supports the feasibility of recruiting a web-based cohort of youth and young adults with representation across an entire state to evaluate substance use prevention efforts. Findings suggest that a guaranteed payment immediately upon survey completion coupled with a bonus for completing all survey waves and weekly survey reminders may facilitate retention in a cohort of youth and young adults.

OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement.

Movement is fundamental to human and animal life, emerging through interaction of complex neural, muscular, and skeletal systems. Study of movement draws from and contributes to diverse fields, including biology, neuroscience, mechanics, and robotics. OpenSim unites methods from these fields to create fast and accurate simulations of movement, enabling two fundamental tasks. First, the software can calculate variables that are difficult to measure experimentally, such as the forces generated by muscles and the stretch and recoil of tendons during movement. Second, OpenSim can predict novel movements from models of motor control, such as kinematic adaptations of human gait during loaded or inclined walking. Changes in musculoskeletal dynamics following surgery or due to human-device interaction can also be simulated; these simulations have played a vital role in several applications, including the design of implantable mechanical devices to improve human grasping in individuals with paralysis. OpenSim is an extensible and user-friendly software package built on decades of knowledge about computational modeling and simulation of biomechanical systems. OpenSim's design enables computational scientists to create new state-of-the-art software tools and empowers others to use these tools in research and clinical applications. OpenSim supports a large and growing community of biomechanics and rehabilitation researchers, facilitating exchange of models and simulations for reproducing and extending discoveries. Examples, tutorials, documentation, and an active user forum support this community. The OpenSim software is covered by the Apache License 2.0, which permits its use for any purpose including both nonprofit and commercial applications. The source code is freely and anonymously accessible on GitHub, where the community is welcomed to make contributions. Platform-specific installers of OpenSim include a GUI and are available on simtk.org.

Effect of exercise on metabolic syndrome in black women by family history and predicted risk of breast cancer: The FIERCE Study.

BACKGROUND: This study examined the effects of supervised and home-based exercise interventions on changes in metabolic syndrome (MetS) according to breast cancer risk (high vs low) in black women enrolled in the Focused Intervention on Exercise to Reduce Cancer (FIERCE) trial. METHODS: Postmenopausal, obese, metabolically unhealthy black women, 45 to 65 years old, were randomized to supervised aerobic exercise (73 women), home-based walking-based exercise (69 women), or a control arm (71 women). Participants in the exercise arms underwent a 6-month intervention with study assessments conducted at the baseline and 6 months. The primary outcome measure was MetS (fasting glucose, waist circumference, blood pressure, serum triglycerides, and high-density lipoprotein [HDL]). The intervention effects on MetS, stratified by breast cancer risk as measured by the family history of breast cancer and model-based projected breast cancer risk, were examined with intent-to-treat analyses using generalized estimating equation models. RESULTS: Among women with a family history of breast cancer, the exercise arms had lower mean MetS z scores, which suggested an improvement in the metabolic profile, than controls at 6 months (controls, + 0.55; home-based arm, -0.97, P < .01; supervised arm, -0.89, P < .01). Stratified analyses by projected breast cancer risk suggested similar but statistically nonsignificant findings, with those at high risk having more favorable changes in the MetS z score in the exercise arms versus the control arm. These changes were primarily attributable to changes in blood pressure, triglycerides, and HDL. CONCLUSIONS: Short-term aerobic activity regimens may improve the metabolic profile and thereby reduce breast cancer risk in obese, metabolically unhealthy black women at high risk for cancer. © 2018 American Cancer Society.

Perspectives on Sharing Models and Related Resources in Computational Biomechanics Research.

The role of computational modeling for biomechanics research and related clinical care will be increasingly prominent. The biomechanics community has been developing computational models routinely for exploration of the mechanics and mechanobiology of diverse biological structures. As a result, a large array of models, data, and discipline-specific simulation software has emerged to support endeavors in computational biomechanics. Sharing computational models and related data and simulation software has first become a utilitarian interest, and now, it is a necessity. Exchange of models, in support of knowledge exchange provided by scholarly publishing, has important implications. Specifically, model sharing can facilitate assessment of reproducibility in computational biomechanics and can provide an opportunity for repurposing and reuse, and a venue for medical training. The community's desire to investigate biological and biomechanical phenomena crossing multiple systems, scales, and physical domains, also motivates sharing of modeling resources as blending of models developed by domain experts will be a required step for comprehensive simulation studies as well as the enhancement of their rigor and reproducibility. The goal of this paper is to understand current perspectives in the biomechanics community for the sharing of computational models and related resources. Opinions on opportunities, challenges, and pathways to model sharing, particularly as part of the scholarly publishing workflow, were sought. A group of journal editors and a handful of investigators active in computational biomechanics were approached to collect short opinion pieces as a part of a larger effort of the IEEE EMBS Computational Biology and the Physiome Technical Committee to address model reproducibility through publications. A synthesis of these opinion pieces indicates that the community recognizes the necessity and usefulness of model sharing. There is a strong will to facilitate model sharing, and there are corresponding initiatives by the scientific journals. Outside the publishing enterprise, infrastructure to facilitate model sharing in biomechanics exists, and simulation software developers are interested in accommodating the community's needs for sharing of modeling resources. Encouragement for the use of standardized markups, concerns related to quality assurance, acknowledgement of increased burden, and importance of stewardship of resources are noted. In the short-term, it is advisable that the community builds upon recent strategies and experiments with new pathways for continued demonstration of model sharing, its promotion, and its utility. Nonetheless, the need for a long-term strategy to unify approaches in sharing computational models and related resources is acknowledged. Development of a sustainable platform supported by a culture of open model sharing will likely evolve through continued and inclusive discussions bringing all stakeholders at the table, e.g., by possibly establishing a consortium.

Drug discovery at the single molecule level: inhibition-in-solution assay of membrane-reconstituted ß-secretase using single-molecule imaging.

Inhibition-in-solution assays (ISA) employing surface-based biosensors such as surface plasmon resonance (SPR) are an effective screening approach in drug discovery. However, analysis of potent binders remains a significant hurdle due to limited sensitivity and accompanied depletion of the inhibiting compounds due to high protein concentrations needed for detectable binding signals. To overcome this limitation, we explored a microscopy-based single-molecule ISA compatible with liposome-reconstituted membrane proteins. Using a set of validated small molecule inhibitors against ß-secretase 1 (BACE1), the assay was benchmarked with respect to sensitivity and dynamic range against SPR. We demonstrate that the dynamic range of measurable affinities is greatly extended by more than 2 orders of magnitude as compared to SPR, thus facilitating measurements of highly potent (Kd < nM) compounds.

Preliminary outcomes of a pilot physical therapy program for HIV-infected patients with chronic pain.

Chronic pain in HIV-infected individuals is common and often undertreated. Physical therapy (PT) is an evidence-based nonpharmacologic treatment for chronic pain. Our objective is to present the results of a pilot PT program in an HIV pain/palliative care clinic, which is embedded within a Ryan White-funded multidisciplinary HIV primary care clinic. Medical records of HIV-infected patients participating in a PT program between November 2012 and July 2013 were retrospectively reviewed. Pain scores on a 0-10 scale and cost data were collected and analyzed. Among 43 patients referred, 27 collectively attended 86 sessions. Median age of enrolled patients was 54 (IQR 49-58). Sixteen (59%) were African-American and 20 (77%) had an undetectable HIV viral load. Mean pain score at initial visit was 6.5 (SD = 1.1). The average session-level decrease was 2.6 (SD = 1.7) and patient-level decrease was 2.5 (SD = 1.2). The largest payors were Medicare managed care (28%), Medicaid (21%), and Ryan White grant-related funds (18%). When the first four months of the program are excluded to account for slow start-up, the program's monthly net revenue during the remaining five months was $163. We present preliminary data from a low-cost pilot PT program integrated into an HIV clinic in a primary care setting associated with clinically significant improvements in pain. Further investigation into the implementation of such programs is essential.