1,658 research outputs found
Osteocytes as a record of bone formation dynamics: A mathematical model of osteocyte generation in bone matrix
The formation of new bone involves both the deposition of bone matrix, and
the formation of a network of cells embedded within the bone matrix, called
osteocytes. Osteocytes derive from bone-synthesising cells (osteoblasts) that
become buried in bone matrix during bone deposition. The generation of
osteocytes is a complex process that remains incompletely understood. Whilst
osteoblast burial determines the density of osteocytes, the expanding network
of osteocytes regulates in turn osteoblast activity and osteoblast burial. In
this paper, a spatiotemporal continuous model is proposed to investigate the
osteoblast-to-osteocyte transition. The aims of the model are (i) to link
dynamic properties of osteocyte generation with properties of the osteocyte
network imprinted in bone, and (ii) to investigate Marotti's hypothesis that
osteocytes prompt the burial of osteoblasts when they become covered with
sufficient bone matrix. Osteocyte density is assumed in the model to be
generated at the moving bone surface by a combination of osteoblast density,
matrix secretory rate, rate of entrapment, and curvature of the bone substrate,
but is found to be determined solely by the ratio of the instantaneous burial
rate and matrix secretory rate. Osteocyte density does not explicitly depend on
osteoblast density nor curvature. Osteocyte apoptosis is also included to
distinguish between the density of osteocyte lacuna and the density of live
osteocytes. Experimental measurements of osteocyte lacuna densities are used to
estimate the rate of burial of osteoblasts in bone matrix. These results
suggest that: (i) burial rate decreases during osteonal infilling, and (ii) the
control of osteoblast burial by osteocytes is likely to emanate as a collective
signal from a large group of osteocytes, rather than from the osteocytes
closest to the bone deposition front.Comment: 11 pages, 6 figures. V2: substantially augmented version. Addition of
Section 4 (osteocyte apoptosis
Analytical study of STOL Aircraft in ground effect. Part 2: Nonplanar, nonlinear method applicable to three dimensional jets of finite thickness
The ability of the potential flow analysis (POTFAN) to predict the influence of ground proximity on lift systems is examined. A two dimensional study employing vortex lattice methodology provides confidence that ground effect phenomenon can be predicted using discrete singularity representation. Two dimensional quasi-steady ascent and descent behavior determined provides guidance in interpreting three dimensional results. Steady and quasi-steady ground effect aerodynamic characteristics predicted by POTFAN are presented for several basic unpowered configurations. POTFAN results are compared with experimental data and results of other analytical methods. Modification of POTFAN to incorporate multienergy flow analysis is discussed. General aspects of thick jet models are examined to provide a basic for extending POTFAN's scope to include analysis of propulsive lift interactions
Principles of calculating the dynamical response of misaligned complex resonant optical interferometers
In the long-baseline laser interferometers for measuring gravitational waves that are now under construction, understanding the dynamical response to small distortions such as angular alignment fluctuations presents a unique challenge. These interferometers comprise multiple coupled optical resonators with light storage times approaching 100 m. We present a basic formalism to calculate the frequency dependence of periodic variations in angular alignment and longitudinal displacement of the resonator mirrors. The electromagnetic field is decomposed into a superposition of higher-order spatial modes, Fourier frequency components, and polarization states. Alignment fluctuations and length variations of free-space propagation are represented by matrix operators that act on the multicomponent state vectors of the field
A fuzzy clustering algorithm to detect planar and quadric shapes
In this paper, we introduce a new fuzzy clustering algorithm to detect an unknown number of planar and quadric shapes in noisy data. The proposed algorithm is computationally and implementationally simple, and it overcomes many of the drawbacks of the existing algorithms that have been proposed for similar tasks. Since the clustering is performed in the original image space, and since no features need to be computed, this approach is particularly suited for sparse data. The algorithm may also be used in pattern recognition applications
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Practicable assessment of cochlear size and shape from clinical CT images
There is considerable interpersonal variation in the size and shape of the human cochlea, with evident consequences for cochlear implantation. The ability to characterize a specific cochlea, from preoperative computed tomography (CT) images, would allow the clinician to personalize the choice of electrode, surgical approach and postoperative programming. In this study, we present a fast, practicable and freely available method for estimating cochlear size and shape from clinical CT. The approach taken is to fit a template surface to the CT data, using either a statistical shape model or a locally affine deformation (LAD). After fitting, we measure cochlear size, duct length and a novel measure of basal turn non-planarity, which we suggest might correlate with the risk of insertion trauma. Gold-standard measurements from a convenience sample of 18 micro-CT scans are compared with the same quantities estimated from low-resolution, noisy, pseudo-clinical data synthesized from the same micro-CT scans. The best results were obtained using the LAD method, with an expected error of 8-17% of the gold-standard sample range for non-planarity, cochlear size and duct length.Evelyn Trust,
MRC Confidence in Concept Fund
Cambridge Hearing Trust
A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds
This paper proposes a segmentation-free, automatic and efficient procedure to
detect general geometric quadric forms in point clouds, where clutter and
occlusions are inevitable. Our everyday world is dominated by man-made objects
which are designed using 3D primitives (such as planes, cones, spheres,
cylinders, etc.). These objects are also omnipresent in industrial
environments. This gives rise to the possibility of abstracting 3D scenes
through primitives, thereby positions these geometric forms as an integral part
of perception and high level 3D scene understanding.
As opposed to state-of-the-art, where a tailored algorithm treats each
primitive type separately, we propose to encapsulate all types in a single
robust detection procedure. At the center of our approach lies a closed form 3D
quadric fit, operating in both primal & dual spaces and requiring as low as 4
oriented-points. Around this fit, we design a novel, local null-space voting
strategy to reduce the 4-point case to 3. Voting is coupled with the famous
RANSAC and makes our algorithm orders of magnitude faster than its conventional
counterparts. This is the first method capable of performing a generic
cross-type multi-object primitive detection in difficult scenes. Results on
synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201
Geometric phase shift for detection of gravitational radiation
An effect of geometrical phase shift is predicted for a light beam
propagating in the field of a gravitational wave. Gravitational radiation
detection experiments are proposed using this new effect, the corresponding
estimates being given.Comment: LaTeX2e, 12 p
Possibilistic clustering for shape recognition
Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, we cast the clustering problem into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We constructed an appropriate objective function whose minimum will characterize a good possibilistic partition of the data, and we derived the membership and prototype update equations from necessary conditions for minimization of our criterion function. In this paper, we show the ability of this approach to detect linear and quartic curves in the presence of considerable noise
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