Characterization of the Response of the Cadaveric Human Spine to Loading in a Six-Degree-of-Freedom Spine Testing Apparatus

Chronic back pain has historically been treated through spinal decompression and fusion often accompanied by fixation devices. Concerns regarding the effect of rigid fixation on the surrounding tissue and vertebral levels adjacent to fusion have given rise to a new paradigm based on restoring healthy or natural motion of the operated level. This paradigm revolves around the design and implementation of so-called motion preservation devices. In vitro testing has been and will continue to be an integral step in the design and evaluation process for both rigid fixation and motion preservation devices. However, the metrics commonly used to asses the efficacy of a rigid fixation device are insufficient for the assessment of motion preservation devices. In addition, motion preservation device metrics have not been rigorously defined or characterized in the healthy human spine. The kinematic response of the human cadaveric spine to loading in a six-degree-of-freedom spine testing apparatus can be expressed in terms of Euler angles and the helical axis of motion while the viscoelastic response can be expressed in terms of the energy dissipated by each specimen during a single cycle of testing. Beyond conventional metrics, a new, noninvasive method based on applying test kinematics to a three-dimensional rigid-body model of the spine is developed and used to investigate articulation of the facet joints. Articulation is investigated based on a distance map between adjacent articular surfaces and quantified through the calculation of a parameter describing the proportion of the facet contact area. Statistically significant differences were found between the facet contact area parameter at full extension and full flexion at every level of the lumbar spine during in vitro testing (p<0.037). Additionally, significant differences were found between the mean helical axis locations of some of the levels. A significant difference was found between the anterior/posterior location of the helical axis during flexion and Extension at the L1-L2 level (p=0.003). The sensitivity of these parameters in describing differences in lumbar kinematics between levels and between different portions of the range-of-motion lends credence to their efficacy in evaluating the quality of motion achieved after implantation of a motion preservation device

Advances in semantic representation for multiscale biosimulation: a case study in merging models

As a case-study of biosimulation model integration, we describe our experiences applying the SemSim methodology to integrate independently-developed, multiscale models of cardiac circulation. In particular, we have integrated the CircAdapt model (written by T. Arts for MATLAB) of an adapting vascular segment with a cardiovascular system model (written by M. Neal for JSim). We report on three results from the model integration experience. First, models should be explicit about simulations that occur on different time scales. Second, data structures and naming conventions used to represent model variables may not translate across simulation languages. Finally, identifying the dependencies among model variables is a non-trivial task. We claim that these challenges will appear whenever researchers attempt to integrate models from others, especially when those models are written in a procedural style (using MATLAB, Fortran, etc.) rather than a declarative format (as supported by languages like SBML, CellML or JSim’s MML)

Integration of multi-scale biosimulation models via light-weight semantics

Currently, biosimulation researchers use a variety of computational environments and languages to model biological processes. Ideally, researchers should be able to semi- automatically merge models to more effectively build larger, multi-scale models. How- ever, current modeling methods do not capture the underlying semantics of these models sufficiently to support this type of model construction. In this paper, we both propose a general approach to solve this problem, and we provide a specific example that demon- strates the benefits of our methodology. In particular, we describe three biosimulation models: (1) a cardio-vascular fluid dynamics model, (2) a model of heart rate regulation via baroreceptor control, and (3) a sub-cellular-level model of the arteriolar smooth mus- cle. Within a light-weight ontological framework, we leverage reference ontologies to match concepts across models. The light-weight ontology then helps us combine our three models into a merged model that can answer questions beyond the scope of any single model

Using multiple reference ontologies: Managing composite annotations

There are a growing number of reference ontologies available across a variety of biomedical domains and current research focuses on their construction, organization and use. An important use case for these ontologies is annotation—where users create metadata that access concepts and terms in reference ontologies. We draw on our experience in physiological modeling to present a compelling use case that demonstrates the potential complexity of such annotations. In the domain of physiological biosimulation, we argue that most annotations require the use of multiple reference ontologies. We suggest that these “composite” annotations should be retained as a repository of knowledge about post-coordination that promotes sharing and interoperation across biosimulation models

Goodbye Copenhagen

A deeper look at the Lorentz force causes quantum superposition to vanish into thin air. Fully understanding this 1895 Lorentz force, will help us all finally say Goodbye to the Copenhagen Interpretation of quantum physics. This powerful Lorentz force fully penetrates all atomic orbitals, all atomic nuclei, and all the inner structures of all the subatomic particles. There is no need for the bewildering 96-year-old Copenhagen Interpretation, because we are now able to look very closely at the Lorentz force of 1895. This Goodbye Copenhagen article of 2023, explains how this mighty Lorentz force, completely destroys the old Copenhagen Interpretation of quantum mechanics. These old Self-Contradictory Interpretations are no longer needed today in 2023; because now we are finally able to inspect the 1895 Lorentz force more deeply.71 pages of size 14 fon

Adapting the FlexiArch for widening a complex arch bridge

The 1840's Teewell Hill arch bridge, in the suburbs of Bristol, UK, was no longer adequate for increasing local traffic levels and needed to be widened. Several widening options were considered and it was concluded that the innovative ‘FlexiArch’ would best accommodate the complex geometry of the existing structure while minimising social and economic impacts. In order to elegantly accommodate the raked spandrel walls of the existing bridge Macrete and WSP|Parsons Brinckerhoff worked collaboratively to produce a custom-designed, high-quality, precast concrete FlexiArch, which matched the contours of the existing bridge. As the FlexiArch system has no corrodible reinforcement, it is highly sustainable and will result in reduced maintenance, as for the existing bridge. The elimination of centring and speed of construction (hours not months) minimised disruption to road traffic and to cyclists on the cycle network below the bridge – a key project criterion required by the client. Thus, in addition to addressing an accident black spot, the FlexiArch solution provided South Gloucestershire Council (the client) with an aesthetically pleasing and fully functional solution at a competitive cost

Physical Properties of Biological Entities: An Introduction to the Ontology of Physics for Biology

As biomedical investigators strive to integrate data and analyses across spatiotemporal scales and biomedical domains, they have recognized the benefits of formalizing languages and terminologies via computational ontologies. Although ontologies for biological entities—molecules, cells, organs—are well-established, there are no principled ontologies of physical properties—energies, volumes, flow rates—of those entities. In this paper, we introduce the Ontology of Physics for Biology (OPB), a reference ontology of classical physics designed for annotating biophysical content of growing repositories of biomedical datasets and analytical models. The OPB's semantic framework, traceable to James Clerk Maxwell, encompasses modern theories of system dynamics and thermodynamics, and is implemented as a computational ontology that references available upper ontologies. In this paper we focus on the OPB classes that are designed for annotating physical properties encoded in biomedical datasets and computational models, and we discuss how the OPB framework will facilitate biomedical knowledge integration

Representing physiological processes and their participants with PhysioMaps

BACKGROUND: As the number and size of biological knowledge resources for physiology grows, researchers need improved tools for searching and integrating knowledge and physiological models. Unfortunately, current resources—databases, simulation models, and knowledge bases, for example—are only occasionally and idiosyncratically explicit about the semantics of the biological entities and processes that they describe. RESULTS: We present a formal approach, based on the semantics of biophysics as represented in the Ontology of Physics for Biology, that divides physiological knowledge into three partitions: structural knowledge, process knowledge and biophysical knowledge. We then computationally integrate these partitions across multiple structural and biophysical domains as computable ontologies by which such knowledge can be archived, reused, and displayed. Our key result is the semi-automatic parsing of biosimulation model code into PhysioMaps that can be displayed and interrogated for qualitative responses to hypothetical perturbations. CONCLUSIONS: Strong, explicit semantics of biophysics can provide a formal, computational basis for integrating physiological knowledge in a manner that supports visualization of the physiological content of biosimulation models across spatial scales and biophysical domains

Bridging Biological Ontologies and Biosimulation: The Ontology of Physics for Biology

We introduce and define the Ontology of Physics for Biology (OPB), a reference ontology of physical principles that bridges the gap between bioinformatics modeling of biological structures and the biosimulation modeling of biological processes. Whereas modeling anatomical entities is relatively wellstudied, representing the physics-based semantics of biosimulation and biological processes remains an open research challenge. The OPB bridges this semantic gap--linking the semantics of biosimulation mathematics to structural bio-ontologies. Our design of the OPB is driven both by theory and pragmatics: we have applied systems dynamics theory to build an ontology with pragmatic use for annotating biosimulation models