Probabilistic Musculoskeletal Simulation Methods to Address Intersegmental Dependencies of the Knee, Hip, and Spine

Orthropaedic clinical practice in the area of the knee, hip, and spine has benefited from the concept of regional interdependence, where interventions applied to one region can influence the outcome and function of other regions of the body that may be seemingly unrelated to the applied intervention. An understanding of the biomechanical mechanisms that describe clinical practice involving knee, hip, and spine regional interdependence can improve treatment of a wide range of pathological conditions. Improvement in this area can be particularly impactful on the outcomes of patients with total joint replacement, where pathology and compensatory strategies develop during multi-joint interactions. Additionally, probabilistic methods are well suited to address knee, hip, and spine regional interdependence by using input distributions to quantify the impact of variability on the range of possible output variables. Outputs from probabilistic methods include variable interaction effects and provides sensitivity information, resulting in a more comprehensive evaluation of a system The main objectives of the work presented in this dissertation were to further our understanding of the interdependencies of the knee, hip, and spine with probabilistic musculoskeletal modeling. These objectives were achieved by developing a probabilistic plugin for use in OpenSim and performing investigations of the regional interdependence of the knee, hip, and spine involving patients with total joint replacement. An initial study identified how uncertainty in musculoskeletal simulation inputs can propagate through the stages of analysis and impact interpretation of outputs from a simulation of gait. Second, improvements to current modeling methodology for patients with total hip arthroplasty were made through the implementation of patient-specific strength scaling and input uncertainty assessment. The third study then applied these methods in an investigation of knee, hip, and spine regional interdependence in rehabilitation of patients with total hip arthroplasty to quantify the influence of simulated strengthening of hip musculature on the dynamic and mechanical interdependencies of the knee, hip and spine. A final study demonstrated how population-based musculoskeletal modeling can further impact the study of knee, hip, and spine regional interdependence by presenting the feasibility study of performing population-based musculoskeletal modeling. These studies include several novel methods for investigating the regional interdependencies of the knee, hip, and spine that have been used to translate outputs from musculoskeletal simulations into rehabilitation practice

Instantaneous Generation of Subject-Specific Finite Element Models of the Hip Capsule

Subject-specific hip capsule models could offer insights into impingement and dislocation risk when coupled with computer-aided surgery, but model calibration is time-consuming using traditional techniques. This study developed a framework for instantaneously generating subject-specific finite element (FE) capsule representations from regression models trained with a probabilistic approach. A validated FE model of the implanted hip capsule was evaluated probabilistically to generate a training dataset relating capsule geometry and material properties to hip laxity. Multivariate regression models were trained using 90% of trials to predict capsule properties based on hip laxity and attachment site information. The regression models were validated using the remaining 10% of the training set by comparing differences in hip laxity between the original trials and the regression-derived capsules. Root mean square errors (RMSEs) in laxity predictions ranged from 1.8° to 2.3°, depending on the type of laxity used in the training set. The RMSE, when predicting the laxity measured from five cadaveric specimens with total hip arthroplasty, was 4.5°. Model generation time was reduced from days to milliseconds. The results demonstrated the potential of regression-based training to instantaneously generate subject-specific FE models and have implications for integrating subject-specific capsule models into surgical planning software

Experimental simulation of closed timelike curves

Closed timelike curves are among the most controversial features of modern physics. As legitimate solutions to Einstein's field equations, they allow for time travel, which instinctively seems paradoxical. However, in the quantum regime these paradoxes can be resolved, leaving closed timelike curves consistent with relativity. The study of these systems therefore provides valuable insight into nonlinearities and the emergence of causal structures in quantum mechanics-essential for any formulation of a quantum theory of gravity. Here we experimentally simulate the nonlinear behaviour of a qubit interacting unitarily with an older version of itself, addressing some of the fascinating effects that arise in systems traversing a closed timelike curve. These include perfect discrimination of non-orthogonal states and, most intriguingly, the ability to distinguish nominally equivalent ways of preparing pure quantum states. Finally, we examine the dependence of these effects on the initial qubit state, the form of the unitary interaction and the influence of decoherence

Walking speed and spatiotemporal step mean measures are reliable during feedback-controlled treadmill walking; however, spatiotemporal step variability is not reliable

The purpose of the study was to compare the effects of a feedback-controlled treadmill (FeedbackTM) to a traditional fixed-speed treadmill (FixedTM) on spatiotemporal gait means, variability, and dynamics. The study also examined inter-session reliability when using the FeedbackTM. Ten young adults walked on the FeedbackTM for a 5-minute familiarization followed by a 16-minute experimental trial. They returned within one week and completed a 5-minute familiarization followed by a 16-minute experimental trial each for FeedbackTM and FixedTM conditions. Mean walking speed and step time, length, width, and speed means and coefficient of variation were calculated from all experimental conditions. Step time, length, width, and speed gait dynamics were analyzed using detrended fluctuation analysis. Mean differences between experimental trials were determined using ANOVAs and reliability between FeedbackTM sessions was determined by intraclass correlation coefficient. No difference was found in mean walking speed nor spatiotemporal variables, with the exception of step width, between the experimental trials. All mean spatiotemporal variables demonstrated good to excellent reliability between sessions, while coefficient of variation was not reliable. Gait dynamics of step time, length, width, and speed were significantly more persistent during the FeedbackTM condition compared to FixedTM, especially step speed. However, gait dynamics demonstrated fair to poor reliability between FeedbackTM sessions. When walking on the FeedbackTM, users maintain a consistent set point, yet the gait dynamics around the mean are different when compared to walking on a FixedTM. In addition, spatiotemporal gait dynamics and variability may not be consistent across separate days when using the FeedbackTM

Reliability of a feedback-controlled treadmill algorithm dependent on the user\u27s behavior

he reliability of the treadmill belt speed using a feedback-controlled treadmill algorithm was analyzed in this study. Using biomechanical factors of the participant\u27s walking behavior, an estimated walking speed was calculated and used to adjust the speed of the treadmill. Our proposed algorithm expands on the current hypotheses of feedback-controlled treadmill algorithms and is presented below. Nine healthy, young adults walked on a treadmill controlled by the algorithm for three trials over two days. Each participant walked on the feedback-controlled treadmill for one 16-minute and one five-minute trial during day one and one 16-minute trial during day two. Mean, standard deviation, interclass correlation coefficient (ICC), and standard error of measurement (SEM) were analyzed on the treadmill belt speed mean, standard deviation, and coefficient of variation. There were significantly high ICC for mean treadmill speed within- and between-days. Treadmill speed standard deviation and coefficient of variation were significantly reliable within-day. These results suggest the algorithm will reliably produce the same treadmill belt speed mean, but may only produce a similar treadmill belt speed standard deviation and coefficient of variation if the trials are performed in the same day. A feedback-controlled treadmill algorithm that accounts for the user\u27s behavior provides a greater level of control and minimizes any possible constraints of walking on a conventional treadmill

Sensitivities of cloud radiative effects to large-scale meteorology and aerosols from global observations

The radiative effects of clouds make a large contribution to the Earth's energy balance, and changes in clouds constitute the dominant source of uncertainty in the global warming response to carbon dioxide forcing. To characterize and constrain this uncertainty, cloud-controlling factor (CCF) analyses have been suggested that estimate sensitivities of clouds to large-scale environmental changes, typically in cloud-regime-specific multiple linear regression frameworks. Here, local sensitivities of cloud radiative effects to a large number of controlling factors are estimated in a regime-independent framework from 20 years (2001–2020) of near-global (60° N–60°S) satellite observations and reanalysis data using statistical learning. A regularized linear regression (ridge regression) is shown to skillfully predict anomalies of shortwave (R2=0.63) and longwave cloud radiative effects (CREs) (R2=0.72) in independent test data on the basis of 28 CCFs, including aerosol proxies. The sensitivity of CREs to selected CCFs is quantified and analyzed. CRE sensitivities to sea surface temperature and estimated inversion strength are particularly pronounced in low-cloud regions and generally in agreement with previous studies. The analysis of CRE sensitivities to three-dimensional wind field anomalies reflects the fact that CREs in tropical ascent regions are mainly driven by variability of large-scale vertical velocity in the upper troposphere. In the subtropics, CRE is sensitive to free-tropospheric zonal and meridional wind anomalies, which are likely to encapsulate information on synoptic variability that influences subtropical cloud systems by modifying wind shear and thus turbulence and dry-air entrainment in stratocumulus clouds, as well as variability related to midlatitude cyclones. Different proxies for aerosols are analyzed as CCFs, with satellite-derived aerosol proxies showing a larger CRE sensitivity than a proxy from an aerosol reanalysis, likely pointing to satellite aerosol retrieval biases close to clouds, leading to overestimated aerosol sensitivities. Sensitivities of shortwave CREs to all aerosol proxies indicate a pronounced cooling effect from aerosols in stratocumulus regions that is counteracted to a varying degree by a longwave warming effect. The analysis may guide the selection of CCFs in future sensitivity analyses aimed at constraining cloud feedback and climate forcings from aerosol–cloud interactions using data from both observations and global climate models

Predator diversity hotspots in the blue ocean

Concentrations of biodiversity, or hotspots, represent conservation priorities in terrestrial ecosystems but remain largely unexplored in marine habitats. In the open ocean, many large predators such as tunas, sharks, billfishes, and sea turtles are of current conservation concern because of their vulnerability to overfishing and ecosystem role. Here we use scientific-observer records from pelagic longline fisheries in the Atlantic and Pacific Oceans to show that oceanic predators concentrate in distinct diversity hotspots. Predator diversity consistently peaks at intermediate latitudes (20–30° N and S), where tropical and temperate species ranges overlap. Individual hotspots are found close to prominent habitat features such as reefs, shelf breaks, or seamounts and often coincide with zooplankton and coral reef hotspots. Closed-area models in the northwest Atlantic predict that protection of hotspots outperforms other area closures in safeguarding threatened pelagic predators from ecological extinction. We conclude that the seemingly monotonous landscape of the open ocean shows rich structure in species diversity and that these features should be used to focus future conservation efforts

Probing the accelerating Universe with radio weak lensing in the JVLA Sky Survey

We outline the prospects for performing pioneering radio weak gravitational lensing analyses using observations from a potential forthcoming JVLA Sky Survey program. A large-scale survey with the JVLA can offer interesting and unique opportunities for performing weak lensing studies in the radio band, a field which has until now been the preserve of optical telescopes. In particular, the JVLA has the capacity for large, deep radio surveys with relatively high angular resolution, which are the key characteristics required for a successful weak lensing study. We highlight the potential advantages and unique aspects of performing weak lensing in the radio band. In particular, the inclusion of continuum polarisation information can greatly reduce noise in weak lensing reconstructions and can also remove the effects of intrinsic galaxy alignments, the key astrophysical systematic effect that limits weak lensing at all wavelengths. We identify a VLASS "deep fields" program (total area ~10-20 square degs), to be conducted at L-band and with high-resolution (A-array configuration), as the optimal survey strategy from the point of view of weak lensing science. Such a survey will build on the unique strengths of the JVLA and will remain unsurpassed in terms of its combination of resolution and sensitivity until the advent of the Square Kilometre Array. We identify the best fields on the JVLA-accessible sky from the point of view of overlapping with existing deep optical and near infra-red data which will provide crucial redshift information and facilitate a host of additional compelling multi-wavelength science.Comment: Submitted in response to NRAO's recent call for community white papers on the VLA Sky Survey (VLASS