Both Ligand- and Cell-Specific Parameters Control Ligand Agonism in a Kinetic Model of G Protein–Coupled Receptor Signaling

G protein–coupled receptors (GPCRs) exist in multiple dynamic states (e.g., ligand-bound, inactive, G protein–coupled) that influence G protein activation and ultimately response generation. In quantitative models of GPCR signaling that incorporate these varied states, parameter values are often uncharacterized or varied over large ranges, making identification of important parameters and signaling outcomes difficult to intuit. Here we identify the ligand- and cell-specific parameters that are important determinants of cell-response behavior in a dynamic model of GPCR signaling using parameter variation and sensitivity analysis. The character of response (i.e., positive/neutral/inverse agonism) is, not surprisingly, significantly influenced by a ligand's ability to bias the receptor into an active conformation. We also find that several cell-specific parameters, including the ratio of active to inactive receptor species, the rate constant for G protein activation, and expression levels of receptors and G proteins also dramatically influence agonism. Expressing either receptor or G protein in numbers several fold above or below endogenous levels may result in system behavior inconsistent with that measured in endogenous systems. Finally, small variations in cell-specific parameters identified by sensitivity analysis as significant determinants of response behavior are found to change ligand-induced responses from positive to negative, a phenomenon termed protean agonism. Our findings offer an explanation for protean agonism reported in β2-adrenergic and α2A-adrenergic receptor systems

A biomechanical characterization of the gymnastics round-off back handspring first contact and implications for upper extremity orthopedic injury

INTRODUCTION: Women’s gymnastics has the highest injury incidence rates for NCAA female college athletes. Gymnastics maneuvers may require support and transfer of the entire body weight from the feet to the hands. Such motions cause excessive loading and stress across joint surfaces which on occasion can exceed the mechanical strength of upper limb joints and supportive musculoskeletal structures, resulting in injuries ranging from acute fractures to chronic overuse injuries like osteochondritis dissecans. Recent technological advances have only now made it possible to analyze the complex and simultaneous motions in multiple planes required for evaluation of even the most basic gymnastic maneuvers like the round-off back handspring (ROBHS). OBJECTIVES: There is a paucity of data characterizing upper extremity injury causation and biomechanical risk factors in the small number of gymnastics studies conducted. The first hand contact for any gymnastics skill has never been quantitatively assessed. Therefore, the primary objective of this study is to perform a detailed 3D biomechanical characterization of the round-off back handspring (ROBHS) first hand contact and evaluate any potential correlations to upper extremity injury determinants. METHODS: A 3D motion capture camera and force plate system captured the relative positon of reflective markers affixed to 62 anatomical positions on subjects during performance of an ROBHS. A virtual model of each subject was constructed using Nexus C-motion software. Programming with Visual3D and MATLAB software was used to calculate desired force, kinematic and kinetic variables such as joint torques and angles. Past medical history questionnaires were administered, and clinical range of motion and strength measures were assessed. RESULTS: Compared with other factors analyzed, hand contact order appeared to have the highest degree of influence on upper extremity biomechanics at both the time of initial contact and throughout the entire movement sequence. The second contact limb was correlated with a larger average ground contact force, whereas while the first contact limb was related to a shorter time to peak force development and larger magnitude rotational kinematic variables, especially at the elbow—the primary site of upper extremity injury. For the first hand contact, torque development at the elbow and shoulder appeared to be related, and wrist and shoulder variables were presumably related to ground reaction force (GRF) development. The proposed literature elbow injury mechanism may need some adjustment to reflect the impact of elbow flexion angle on GRF and elbow valgus torque, key variables tied to chronic elbow joint capsule overload injuries. CONCLUSIONS: The novel information provided by this study can be used to guide future recommendations for the prevention of upper extremity injury in gymnastics training and competition. Improved understanding of associated force, kinetic, and kinematic biomechanical variables like joint torque could have implications for movement specific body positioning with the potential for extrapolation to gymnastics moves with similar loading patterns. Possible protective technique interventions based on study findings include increasing second hand elbow flexion during the round-off phase of motion or minimizing the time between hand contacts

Casper ter Kuile, The Power of Ritual: Turning Everyday Activities into Soulful Practices

Reviewed by Emily Linderma

A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation

Rodent hippocampal population codes represent important spatial information about the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and propose a nonparametric Bayesian approach to infer rat hippocampal population codes during spatial navigation. To tackle the model selection problem, we leverage a nonparametric Bayesian model. Specifically, to analyze rat hippocampal ensemble spiking activity, we apply a hierarchical Dirichlet process-hidden Markov model (HDP-HMM) using two Bayesian inference methods, one based on Markov chain Monte Carlo (MCMC) and the other based on variational Bayes (VB). We demonstrate the effectiveness of our Bayesian approaches on recordings from a freely-behaving rat navigating in an open field environment. We find that MCMC-based inference with Hamiltonian Monte Carlo (HMC) hyperparameter sampling is flexible and efficient, and outperforms VB and MCMC approaches with hyperparameters set by empirical Bayes