The role of the peripheral and central nervous systems in rotator cuff disease

Rotator cuff (RC) disease is an extremely common condition associated with shoulder pain, reduced functional capacities and impaired quality of life. It primarily involves alterations in tendon health and mechanical properties that can ultimately lead to tendon failure. RC tendon tears induce progressive muscular changes that negatively impact surgical reparability of the RC tendons and clinical outcomes. At the same time, a significant base of clinical data suggests a relatively weak relationship between RC integrity and clinical presentation, emphasizing the multifactorial aspects of RC disease. This review aims to summarize the potential contribution of peripheral, spinal and supraspinal neural factors that may: (i) exacerbate structural and functional muscle changes induced by tendon tear, (ii) compromise the reversal of these changes during surgery and rehabilitation, (iii) contribute to pain generation and persistence of pain, iv) impair shoulder function through reduced proprioception, kinematics and muscle recruitment, and iv) help to explain interindividual differences and response to treatment. Given the current clinical and scientific interest in peripheral nerve injury in the context of RC disease and surgery, we carefully reviewed this body of literature with a particular emphasis for suprascapular neuropathy that has generated a large number of studies in the past decade. Within this process, we highlight the gaps in current knowledge and suggest research avenues for scientists and clinicians

Abstraction-Guided Truncations for Stationary Distributions of Markov Population Models

To understand the long-run behavior of Markov population models, the computation of the stationary distribution is often a crucial part. We propose a truncation-based approximation that employs a state-space lumping scheme, aggregating states in a grid structure. The resulting approximate stationary distribution is used to iteratively refine relevant and truncate irrelevant parts of the state-space. This way, the algorithm learns a well-justified finite-state projection tailored to the stationary behavior. We demonstrate the method’s applicability to a wide range of non-linear problems with complex stationary behaviors

Moment-Based Parameter Estimation for Stochastic Reaction Networks in Equilibrium

Calibrating parameters is a crucial problem within quantitative modeling approaches to reaction networks. Existing methods for stochastic models rely either on statistical sampling or can only be applied to small systems. Here we present an inference procedure for stochastic models in equilibrium that is based on a moment matching scheme with optimal weighting and that can be used with high-throughput data like the one collected by flow cytometry. Our method does not require an approximation of the underlying equilibrium probability distribution and, if reaction rate constants have to be learned, the optimal values can be computed by solving a linear system of equations. We discuss important practical issues such as the selection of the moments and evaluate the effectiveness of the proposed approach on three case studies

Ligand-functionalized Pt nanoparticles as asymmetric heterogeneous catalysts: Molecular reaction control by ligand-reactant interactions

The asymmetric hydrogenation of β-keto esters over platinum nanoparticles (NPs) functionalized with different α-amino acids was explored. By systematic variation of the structural complexity of the functionalizing ligands, we determined structure-selectivity relationships and further improved our understanding of ligand-reactant interactions. We identified attractive interactions between the lipophilic substituents of ligands and reactants as one of the major contributing factors governing the mutual orientation of ligands and reactants and, thus, the stereoselectivity of the catalytic reaction. For the first time, an enantiomeric excess (ee) above 80% is reported for supported ligand-functionalized NPs, which demonstrates the potential of these materials as a novel type of asymmetric heterogeneous catalyst. Our results reveal that the molecular principles employed in homogeneous catalysis can be utilized to achieve stereoselective catalytic reactions even on rather non-uniform surfaces like small NPs. This opens up yet unexplored possibilities for manipulating reactions on catalytic surfaces to control selectivity