Data to understand the mechanics of multi-layer tissue structures of the limbs, particularly of the lower and upper legs and arms. Anatomical and mechanical data were collected on extremities of 100 adult subjects (50 male, 50 female) from the general population. An ultrasound system was instrumented with a load transducer for in vivo characterization of skin, fat, and muscle thicknesses in the extremities at unloaded and loaded (indentation) states. The unloaded state provided anatomic measures of the tissue layers for 48 regions, while the loaded state provided the tissue mechanical response in 8 regions.

This resource is the project website to provide fundamental data, open and freely accessible databases of tissue mechanical and anatomical properties, models of multi-layer tissue structures of musculoskeletal extremities.

This project aims to establish the founding knowledge, data and models for the mechanics of multi-layer tissue structures of the limbs, particularly of the lower and upper legs and arms. The activity is targeted to promote scientific research in layered tissue structures and allow reliable virtual surgery simulations for clinical training and certification.

This research and development project titled “Reference Models for Multi-Layer Tissue Structures" was conducted by the Cleveland Clinic Foundation and was made possible by a contract vehicle which was awarded and administered by the U.S. Army Medical Research & Materiel Command under award number: W81XWH-15-1-0232. The views, opinions and/or findings contained in this website are those of the authors and do not necessarily reflect the views of the Department of Defense and should not be construed as an official DoD/Army position, policy or decision unless so designated by other documentation. No official endorsement should be made.

This resource is a project website to provide free access to three-dimensional finite element representations of the knee joint and open development of knee models for collaborative testing and use.

Open knee(s) is a growing database of specimen specific finite element models of the human knee joint. Open Knee(s) - Generation 1 was disseminated in 2010. For open Knee(s) - Generation 2, There are currently eight models being developed for the database representing a wide range of the adult population based on donor age and joint health. The goal of the project is to utilize specimen specific geometry, joint and tissue level mechanical response to develop the models. All the specifications, raw and derivative data including models are routinely publicly disseminated.

This project was funded by the National Institute of General Medical Sciences, National Institutes of Health (1R01GM104139). Early activities were partially funded by the National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health (1R01EB009643).

This project aims for understanding the influence of modelers’ approaches and decisions (essentially their art) throughout the lifecycle of modeling and simulation. It will demonstrate the uncertainty of delivering consistent simulation predictions when the founding data to feed into models remain the same. The project site also aims to be a hub to provide an overview of resources for modeling & simulation of the knee joint. Funding is provided by the National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health (Grant No. R01EB024573).

Modeling and simulation offers a cost-effective and prompt path to respond to the pressing medical needs for restoration of knee function. However, the reproducibility of simulation results, to inform scientific and clinical decision making, is questionable. Reproducibility is a pressing issue in scientific conduct. For modeling and simulation, there is added scrutiny particularly with the desire to repurpose and reuse virtual specimens for prospective solutions of diverse scientific and clinical problems. A significant portion of the modeling and simulation workflow includes development, evaluation, and simulation. This workflow, while based on objective scientific principles, commonly requires intuition during implementation; therefore relies on the knowledge and expertise of the modeler. This ‘art of modeling’ can be a fundamental source of diminished reproducibility. The goal of this study is to understand how modelers’ choices to build models, even when using the same data, may influence predictions and therefore the reproducibility of simulation results. Five modeling and simulation teams will independently develop, calibrate and benchmark computational models of knees based on the same data sets and reuse these models to simulate the same scientifically and clinically relevant scenarios. Ideally, predicted joint and tissue mechanics will be the same. In practice, the skills and experiences of model developers will reflect upon their modeling choices; and as a result, discrepancies will exist. The proposed activity will document the magnitude and potential sources of such discrepancies through comparisons of model components and simulation results. This project will examine and critique the current state of model development and simulation reproducibility in joint and tissue mechanics. This will translate into reliable models of the knee joint for simulation-based discoveries and in silico design and evaluation of medical devices and interventions. The required exchange of data, model components, and simulation results among the teams and with the public will also impact developers and users of such resources. Specifications, to facilitate data and model exchange and to develop data and modeling standards, and guidance, to inform modeling and simulation workflows, will likely emerge as by-products of the research activity. Subsequently, this project aims to curate various modeling & simulation and data resources for scientific and clinical investigations of knee biomechanics. An additional goal of the project site is to be a discussion platform among investigators who collect data and build models for the knee joint.

The auditory stream segregation model leverages the multiplexed and non-linear representation of sounds along an auditory hierarchy and learns local and global statistical structure naturally emergent in natural and complex sounds. The three key components of the architecture are : (1) A stochastic network RBM layer that encodes two-dimensional input spectrogram into localized specto-temporal bases based on short term feature analysis; (2) A dynamic aRBM that captures the long-term temporal dependencies across spectro-temporal bases characterizing the transformation of sound from fast changing details to slower dynamics. (3) A temporal coherence layer that mimics the Hebbian process of binding local and global details together to mediate the mapping from feature space to formation of auditory objects.

The D-REX model is designed for exploring the computational mechanisms in the brain involved in statistical regularity extraction from dynamic sounds. Utilizing a Bayesian inference framework for performing sequential prediction in the presence of unknown changepoints, this model can be used to test alternative statistics collected by the brain while listening to ongoing sounds. Perceptual parameters can be used to fit the model to individual behavior.