940 resultados para HINDLIMB KINEMATICS


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We present a catalogue of galaxies in Abell 3653 from observations made with the 2-degree field (2dF) spectrograph at the Anglo-Australian Telescope. Of the 391 objects observed, we find 111 are bona fide members of Abell 3653. We show that the cluster has a velocity of cz= 32 214 +/- 83 km s(-1) (z= 0.10 738 +/- 0.00 027), with a velocity dispersion typical of rich, massive clusters of sigma(cz)= 880(-54)(+66). We find that the cD galaxy has a peculiar velocity of 683 +/- 96 km s(-1) in the cluster rest frame - some 7 sigma away from the mean cluster velocity, making it one of the largest and most significant peculiar velocities found for a cD galaxy to date. We investigate the cluster for signs of substructure, but do not find any significant groupings on any length scale. We consider the implications of our findings on cD formation theories.

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Patellofemoral pain (PFP) may be related to unfavorable knee joint loading. Delayed and/or reduced activity of vastus medialis obliquus (VMO) and different movement patterns have been identified in individuals with PFP in some studies, whereas other studies have failed to show a difference compared to non-affected controls. The discrepancy between study results may depend on the different tasks that have been investigated. No previous study has investigated these variables in postural responses to unpredictable perturbations in PFP. Whole body three dimensional kinematics and surface EMG of quadriceps muscles activation was studied in postural responses to unpredictable support surface translations in 17 women with PFP who were pain free at the time of testing, and 17 matched healthy controls. The results of the present study showed earlier onset of VMO activity and associated changes in kinematics to anterior platform translation in the PFP subjects. We suggest that the relative timing between the portions quadriceps muscles may be task specific and part of an adapted response in attempt to reduce knee joint loading. This learned response appears to remain even when the pain is no longer present.

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Sliding and rolling are two outstanding deformation modes in granular media. The first one induces frictional dissipation whereas the latter one involves deformation with negligible resistance. Using numerical simulations on two-dimensional shear cells, we investigate the effect of the grain rotation on the energy dissipation and the strength of granular materials under quasistatic shear deformation. Rolling and sliding are quantified in terms of the so-called Cosserat rotations. The observed spontaneous formation of vorticity cells and clusters of rotating bearings may provide an explanation for the long standing heat flow paradox of earthquake dynamics.

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Minimization of a sum-of-squares or cross-entropy error function leads to network outputs which approximate the conditional averages of the target data, conditioned on the input vector. For classifications problems, with a suitably chosen target coding scheme, these averages represent the posterior probabilities of class membership, and so can be regarded as optimal. For problems involving the prediction of continuous variables, however, the conditional averages provide only a very limited description of the properties of the target variables. This is particularly true for problems in which the mapping to be learned is multi-valued, as often arises in the solution of inverse problems, since the average of several correct target values is not necessarily itself a correct value. In order to obtain a complete description of the data, for the purposes of predicting the outputs corresponding to new input vectors, we must model the conditional probability distribution of the target data, again conditioned on the input vector. In this paper we introduce a new class of network models obtained by combining a conventional neural network with a mixture density model. The complete system is called a Mixture Density Network, and can in principle represent arbitrary conditional probability distributions in the same way that a conventional neural network can represent arbitrary functions. We demonstrate the effectiveness of Mixture Density Networks using both a toy problem and a problem involving robot inverse kinematics.

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Relationships between clustering, description length, and regularisation are pointed out, motivating the introduction of a cost function with a description length interpretation and the unusual and useful property of having its minimum approximated by the densest mode of a distribution. A simple inverse kinematics example is used to demonstrate that this property can be used to select and learn one branch of a multi-valued mapping. This property is also used to develop a method for setting regularisation parameters according to the scale on which structure is exhibited in the training data. The regularisation technique is demonstrated on two real data sets, a classification problem and a regression problem.

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Conventional feed forward Neural Networks have used the sum-of-squares cost function for training. A new cost function is presented here with a description length interpretation based on Rissanen's Minimum Description Length principle. It is a heuristic that has a rough interpretation as the number of data points fit by the model. Not concerned with finding optimal descriptions, the cost function prefers to form minimum descriptions in a naive way for computational convenience. The cost function is called the Naive Description Length cost function. Finding minimum description models will be shown to be closely related to the identification of clusters in the data. As a consequence the minimum of this cost function approximates the most probable mode of the data rather than the sum-of-squares cost function that approximates the mean. The new cost function is shown to provide information about the structure of the data. This is done by inspecting the dependence of the error to the amount of regularisation. This structure provides a method of selecting regularisation parameters as an alternative or supplement to Bayesian methods. The new cost function is tested on a number of multi-valued problems such as a simple inverse kinematics problem. It is also tested on a number of classification and regression problems. The mode-seeking property of this cost function is shown to improve prediction in time series problems. Description length principles are used in a similar fashion to derive a regulariser to control network complexity.

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The scaling problems which afflict attempts to optimise neural networks (NNs) with genetic algorithms (GAs) are disclosed. A novel GA-NN hybrid is introduced, based on the bumptree, a little-used connectionist model. As well as being computationally efficient, the bumptree is shown to be more amenable to genetic coding lthan other NN models. A hierarchical genetic coding scheme is developed for the bumptree and shown to have low redundancy, as well as being complete and closed with respect to the search space. When applied to optimising bumptree architectures for classification problems the GA discovers bumptrees which significantly out-perform those constructed using a standard algorithm. The fields of artificial life, control and robotics are identified as likely application areas for the evolutionary optimisation of NNs. An artificial life case-study is presented and discussed. Experiments are reported which show that the GA-bumptree is able to learn simulated pole balancing and car parking tasks using only limited environmental feedback. A simple modification of the fitness function allows the GA-bumptree to learn mappings which are multi-modal, such as robot arm inverse kinematics. The dynamics of the 'geographic speciation' selection model used by the GA-bumptree are investigated empirically and the convergence profile is introduced as an analytical tool. The relationships between the rate of genetic convergence and the phenomena of speciation, genetic drift and punctuated equilibrium arc discussed. The importance of genetic linkage to GA design is discussed and two new recombination operators arc introduced. The first, linkage mapped crossover (LMX) is shown to be a generalisation of existing crossover operators. LMX provides a new framework for incorporating prior knowledge into GAs.Its adaptive form, ALMX, is shown to be able to infer linkage relationships automatically during genetic search.

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In this thesis the relationship between visual attention, affordance and action was investigated using a combination of neuroimaging and behavioural studies. Neuronal activity and movement construction were assessed when individuals passively viewed or produced action towards stimuli varying in their affordance and/or attentional attributes. The main findings were: (i) the passive perception of both object and abstract visual patterns was associated with decreased alpha and/or beta activity in sensori-motor cortex, occipito-temporal cortex and cerebellum. These are brain regions associated with the planning and production of visually guided action; (ii) for object patterns, decreased alpha and beta activity was also observed in regions of superior parietal and premotor cortex. These regions contain neurons argued to be essential for matching hand kinematics with manipulate objects; and (iii) in both control participants and a deafferented individual, studies of planned and unplanned pointing manoeuvres revealed that the attentional bias of a stimulus was critical for fast, efficient action production whereas the affordance bias was critical in determining end-point accuracy. Taken together, these findings demonstrate that affordance is not a necessary prerequisite for the potential of motor codes. Rather, affordance enables the construction of motor responses that reflect object functionality and/or manipulability. They further demonstrate that visual attention is associated with the potentiation of motor codes. Indeed, directed visual attention would appear critical for speeded responses. These findings provide new insights into the roles of directed visual attention and affordance upon action.

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Novel molecular complexity measures are designed based on the quantum molecular kinematics. The Hamiltonian matrix constructed in a quasi-topological approximation describes the temporal evolution of the modelled electronic system and determined the time derivatives for the dynamic quantities. This allows to define the average quantum kinematic characteristics closely related to the curvatures of the electron paths, particularly, the torsion reflecting the chirality of the dynamic system. A special attention has been given to the computational scheme for this chirality measure. The calculations on realistic molecular systems demonstrate reasonable behaviour of the proposed molecular complexity indices.

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S-glutathionylation occurs when reactive oxygen or nitrogen species react with protein-cysteine thiols. Glutaredoxin-1 (Glrx) is a cytosolic enzyme which enzymatically catalyses the reduction in S-glutathionylation, conferring reversible signalling function to proteins with redox-sensitive thiols. Glrx can regulate vascular hypertrophy and inflammation by regulating the activity of nuclear factor κB (NF-κB) and actin polymerization. Vascular endothelial growth factor (VEGF)-induced endothelial cell (EC) migration is inhibited by Glrx overexpression. In mice overexpressing Glrx, blood flow recovery, exercise function and capillary density were significantly attenuated after hindlimb ischaemia (HLI). Wnt5a and soluble Fms-like tyrosine kinase-1 (sFlt-1) were enhanced in the ischaemic-limb muscle and plasma respectively from Glrx transgenic (TG) mice. A Wnt5a/sFlt-1 pathway had been described in myeloid cells controlling retinal blood vessel development. Interestingly, a Wnt5a/sFlt-1 pathway was found also to play a role in EC to inhibit network formation. S-glutathionylation of NF-κB components inhibits its activation. Up-regulated Glrx stimulated the Wnt5a/sFlt-1 pathway through enhancing NF-κB signalling. These studies show a novel role for Glrx in post-ischaemic neovascularization, which could define a potential target for therapy of impaired angiogenesis in pathological conditions including diabetes.

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Accurate measurement of intervertebral kinematics of the cervical spine can support the diagnosis of widespread diseases related to neck pain, such as chronic whiplash dysfunction, arthritis, and segmental degeneration. The natural inaccessibility of the spine, its complex anatomy, and the small range of motion only permit concise measurement in vivo. Low dose X-ray fluoroscopy allows time-continuous screening of cervical spine during patient's spontaneous motion. To obtain accurate motion measurements, each vertebra was tracked by means of image processing along a sequence of radiographic images. To obtain a time-continuous representation of motion and to reduce noise in the experimental data, smoothing spline interpolation was used. Estimation of intervertebral motion for cervical segments was obtained by processing patient's fluoroscopic sequence; intervertebral angle and displacement and the instantaneous centre of rotation were computed. The RMS value of fitting errors resulted in about 0.2 degree for rotation and 0.2 mm for displacements. © 2013 Paolo Bifulco et al.

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This study extends a previous research concerning intervertebral motion registration by means of 2D dynamic fluoroscopy to obtain a more comprehensive 3D description of vertebral kinematics. The problem of estimating the 3D rigid pose of a CT volume of a vertebra from its 2D X-ray fluoroscopy projection is addressed. 2D-3D registration is obtained maximising a measure of similarity between Digitally Reconstructed Radiographs (obtained from the CT volume) and real fluoroscopic projection. X-ray energy correction was performed. To assess the method a calibration model was realised a sheep dry vertebra was rigidly fixed to a frame of reference including metallic markers. Accurate measurement of 3D orientation was obtained via single-camera calibration of the markers and held as true 3D vertebra position; then, vertebra 3D pose was estimated and results compared. Error analysis revealed accuracy of the order of 0.1 degree for the rotation angles of about 1mm for displacements parallel to the fluoroscopic plane, and of order of 10mm for the orthogonal displacement. © 2010 P. Bifulco et al.

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The endothelium produces and responds to reactive oxygen and nitrogen species (RONS), providing important redox regulation to the cardiovascular system in physiology and disease. In no other situation are RONS more critical than in the response to tissue ischemia. Here, tissue healing requires growth factor-mediated angiogenesis that is in part dependent on low levels of RONS, which paradoxically must overcome the damaging effects of high levels of RONS generated as a result of ischemia. While generation of endothelial cell RONS in hypoxia/reoxygenation is acknowledged, the mechanism for their role in angiogenesis is still poorly understood. During ischemia, the major low molecular weight thiol glutathione (GSH) reacts with RONS and protein cysteines, producing GSH-protein adducts. Recent data indicate that GSH adducts on certain proteins are essential to growth factor responses in endothelial cells. Genetic deletion of the enzyme glutaredoxin-1, which selectively removes GSH protein adducts, improves, while its overexpression impairs, revascularization of the ischemic hindlimb of mice. Ischemia-induced GSH adducts on specific cysteine residues of several proteins, including p65 NFkB and the sarcoplasmic reticulum calcium ATPase-2 (SERCA2), appear to promote ischemic angiogenesis. Identifying the specific proteins in the redox response to ischemia has provided therapeutic opportunities to improve clinical outcomes of ischemia.

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The kaon electroproduction reaction H(e, e ′K+)Λ was studied as a function of the four momentum transfer, Q2, for different values of the virtual photon polarization parameter. Electrons and kaons were detected in coincidence in two High Resolution Spectrometers (HRS) at Jefferson Lab. Data were taken at electron beam energies ranging from 3.4006 to 5.7544 GeV. The kaons were identified using combined time of flight information and two Aerogel Čerenkov detectors used for particle identification. For different values of Q2 ranging from 1.90 to 2.35 GeV/c2 the center of mass cross sections for the Λ hyperon were determined for 20 kinematics and the longitudinal, σ L, and transverse, σT, terms were separated using the Rosenbluth separation technique. ^ Comparisons between available models and data have been studied. The comparison supports the t-channel dominance behavior for kaon electroproduction. All models seem to underpredict the transverse cross section. An estimate of the kaon form factor has been explored by determining the sensitivity of the separated cross sections to variations of the kaon EM form factor. From comparison between models and data we can conclude that interpreting the data using the Regge model is quite sensitive to a particular choice for the EM form factors. The data from the E98-108 experiment extends the range of the available kaon electroproduction cross section data to an unexplored region of Q2 where no separations have ever been performed. ^