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Query: UMLS:C0220723 (PCA)
 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Cerebral collaterals are vascular redundancies in the cerebral circulation that can partially maintain blood flow to ischemic tissue when primary conduits are blocked. After occlusion of a cerebral artery, anastomoses connecting the distal segments of the MCA with distal branches of the ACA and PCA (known as leptomeningeal or pial collaterals) allow for partially maintained blood flow in the ischemic penumbra and delay or prevent cell death. However, collateral circulation varies dramatically between individuals, and collateral extent is significant predictor of stroke severity and recanalization rate. Collateral therapeutics attempt to harness these vascular redundancies by enhancing blood flow through pial collaterals to reduce ischemia and brain damage after cerebral arterial occlusion. While therapies to enhance collateral flow remain relatively nascent neuroprotective strategies, experimental therapies including inhaled NO, transient suprarenal aortic occlusion, and electrical stimulation of the parasympathetic sphenopalatine ganglion show promise as collateral therapeutics with the potential to improve treatment of acute ischemic stroke.
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PMID:Cerebral collaterals and collateral therapeutics for acute ischemic stroke. 2535 Nov 2

Stroke in young is a major health problem in developing countries along with CAD, according to various Indian studies its prevalence is 25-34%. Thrombophilic disorders constitute aetiology in 60% cases of stroke of undetermined aetiology. A 20 yrs old young female presented with symptoms of left PCA thrombosis (P2 syndrome), on evaluation--Isolated Protein--S deficiency is noticed. In this case Protein-S deficiency seems to be the only risk factor responsible for stroke.
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PMID:Stroke in young with primary protein--S deficiency. 2590 32

Age and sex selection of prey is an aspect of predator ecology which has been extensively studied in both temperate and African ecosystems. This dimension, along with fecundity, survival rates of prey and mortality factors other than predation are important in laying down the population dynamics of prey and have important implications in the management of species. A carnivore den located in the short-grassland ecological unit of the Serengeti was studied. Sex- and age- class (using five age categories) of the wildebeest remains recovered were analyzed through horn morphology, biometrics of the bones and tooth wear patterns. We compared our results with previous studies from lion and hyaena kills through multivariate analyses. Seasonality of the accumulation was analyzed through tooth histology. PCA and CVA results show that age class selection by predators depends on season, habitat-type, and growth rate of the wildebeest population. Female-biased predation was found to contradict classical hypotheses based on territorial male behaviour. The lion and spotted hyaena showed strong selection on age classes, contrary to previous studies. Migratory wildebeest sex ratio is regulated through differential predation by seasons and female deaths in the wet season are a trade-off for population stability. These data are crucial for an effective management of the species and the new method created may be useful for different carnivore species and their prey.
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PMID:Differential predation by age and sex classes in blue wildebeest in Serengeti: study of a modern carnivore den in Olduvai Gorge (Tanzania). 2601 63

Fetal type PCA may positively affect clinical outcome in patients with ischemic stroke such as protection of a reserve blood flow capacity as well as negative effect on clinical outcome such as certain atypical pathological manifestations. We presented a patient with a posterior cerebral artery territory infarction due to an unexpected etiology as stenosis of internal carotid artery (ICA) with existence of fetal type PCA.
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PMID:Carotid Endarterectomy in a Patient with Posterior Cerebral Artery Infarction: Influence of Fetal Type PCA on Atypical Clinical Course. 2617 Dec 63

There is a great need to develop new approaches for rehabilitation of the upper limb after stroke. Robotic therapy is a promising form of neurorehabilitation that can be delivered in higher doses than conventional therapy. Here we sought to determine whether the reported effects of robotic therapy, which have been based on clinical measures of impairment and function, are accompanied by improved motor control. Patients with chronic hemiparesis were trained for 3 wk, 3 days a week, with titrated assistive robotic therapy in two and three dimensions. Motor control improvements (i.e., skill) in both arms were assessed with a separate untrained visually guided reaching task. We devised a novel PCA-based analysis of arm trajectories that is sensitive to changes in the quality of entire movement trajectories without needing to prespecify particular kinematic features. Robotic therapy led to skill improvements in the contralesional arm. These changes were not accompanied by changes in clinical measures of impairment or function. There are two possible interpretations of these results. One is that robotic therapy only leads to small task-specific improvements in motor control via normal skill-learning mechanisms. The other is that kinematic assays are more sensitive than clinical measures to a small general improvement in motor control.
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PMID:Robotic therapy for chronic stroke: general recovery of impairment or improved task-specific skill? 2618 Jan 20

Meristic, morphometric and distributional patterns of cyprinid fishes of the genus Neolissochilus found in Peninsular Malaysia are presented. Based on the current concept of Neolissochilus, only two species are present: N. soroides and N. hendersoni. Neolissochilus hendersoni differs from N. soroides by having lower scale and gill raker counts. Neolissochilus soroides has three mouth types (normal with a rounded snout, snout with a truncate edge, and lobe with a comparatively thick lower lip). A PCA of log-transformed measurements did not reveal significant differences between N. hendersoni and N. soroides, or between any of the morphotypes of N. soroides; however, a CVA of log-transformed measurements successfully classified 87.1% of all specimens. Removing body size by running a CVA on all of the principal components except PC1 (which was correlated with length) only slightly decreased the successful classification rate to 86.1%. Differences in morphometrics were as great between the three morphotypes of N. soroides as between any of the morphotypes and N. hendersoni suggesting that the morphotypes should be examined in greater detail with genetic tools. The PCA of morphometrics revealed separate clouds for N. hendersoni and N. soroides, but no differences between the N. soroides morphotypes. This study revealed that N. hendersoni is recorded for the first time in the mainland area of Peninsular Malaysia. Other nominal species of Neolissochilus reported to occur in the river systems of Peninsular Malaysia are discussed. Lissochilus tweediei Herre in Herre & Myers 1937 and Tor soro Bishop 1973 are synonyms of Neolissochilus soroides.
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PMID:Cyprinid fishes of the genus Neolissochilus in Peninsular Malaysia. 2625 Feb 5

Stroke patients should be dispatched at the highest level of care available in the shortest time. In this context, a transportable system in specialized ambulances, able to evaluate the presence of an acute brain lesion in a short time interval (i.e., few minutes), could shorten delay of treatment. UWB radar imaging is an emerging diagnostic branch that has great potential for the implementation of a transportable and low-cost device. Transportability, low cost and short response time pose challenges to the signal processing algorithms of the backscattered signals as they should guarantee good performance with a reasonably low number of antennas and low computational complexity, tightly related to the response time of the device. The paper shows that a PCA-based preprocessing algorithm can: (1) achieve good performance already with a computationally simple beamforming algorithm; (2) outperform state-of-the-art preprocessing algorithms; (3) enable a further improvement in the performance (and/or decrease in the number of antennas) by using a multistatic approach with just a modest increase in computational complexity. This is an important result toward the implementation of such a diagnostic device that could play an important role in emergency scenario.
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PMID:PCA-based artifact removal algorithm for stroke detection using UWB radar imaging. 2763 9

Visual observations of dynamic phenomena, such as human actions, are often represented as sequences of smoothly-varying features. In cases where the feature spaces can be structured as Riemannian manifolds, the corresponding representations become trajectories on manifolds. Analysis of these trajectories is challenging due to non-linearity of underlying spaces and high-dimensionality of trajectories. In vision problems, given the nature of physical systems involved, these phenomena are better characterized on a low-dimensional manifold compared to the space of Riemannian trajectories. For instance, if one does not impose physical constraints of the human body, in data involving human action analysis, the resulting representation space will have highly redundant features. Learning an effective, low-dimensional embedding for action representations will have a huge impact in the areas of search and retrieval, visualization, learning, and recognition. Traditional manifold learning addresses this problem for static points in the euclidean space, but its extension to Riemannian trajectories is non-trivial and remains unexplored. The difficulty lies in inherent non-linearity of the domain and temporal variability of actions that can distort any traditional metric between trajectories. To overcome these issues, we use the framework based on transported square-root velocity fields (TSRVF); this framework has several desirable properties, including a rate-invariant metric and vector space representations. We propose to learn an embedding such that each action trajectory is mapped to a single point in a low-dimensional euclidean space, and the trajectories that differ only in temporal rates map to the same point. We utilize the TSRVF representation, and accompanying statistical summaries of Riemannian trajectories, to extend existing coding methods such as PCA, KSVD and Label Consistent KSVD to Riemannian trajectories or more generally to Riemannian functions. We show that such coding efficiently captures trajectories in applications such as action recognition, stroke rehabilitation, visual speech recognition, clustering and diverse sequence sampling. Using this framework, we obtain state-of-the-art recognition results, while reducing the dimensionality/ complexity by a factor of 100-250x. Since these mappings and codes are invertible, they can also be used to interactively-visualize Riemannian trajectories and synthesize actions.
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PMID:Elastic Functional Coding of Riemannian Trajectories. 2811 99

Nowadays portable devices with more number of sensors are used for gait assessment and monitoring for elderly and disabled. However, the problem with using multiple sensors is that if they are placed on the same platform or base, there could be cross talk between them, which could change the signal amplitude or add noise to the signal. Hence, this study uses wavelet PCA as a signal processing technique to separate the original sensor signal from the signal obtained from the sensors through the integrated unit to compare the two types of walking (with and without an exoskeleton). This comparison using wavelet PCA will enable the researchers to obtain accurate sensor data and compare and analyze the data in order to further improve the design of compact portable devices used to monitor and assess the gait in stroke or paralyzed subjects. The advantage of designing such systems is that they can also be used to assess and monitor the gait of the stroke subjects at home, which will save them time and efforts to visit the laboratory or clinic.
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PMID:Wavelet PCA for automatic identification of walking with and without an exoskeleton on a treadmill using pressure and accelerometer sensors. 2826 22

This study uses machine learning methods to distinguish between healthy and pathological gait. Examples of multi-dimensional pathological and normal gait sequences were collected from post-stroke and healthy individuals in a real clinical setting and with two Kinect sensors. The trajectories of rotational angle and global velocity of selected body joints (hips, spine, shoulders, neck, knees and ankles) over time formed the gait sequences. The combination of k nearest neighbor (kNN) and dynamic time warping (DTW) was used for classification. Leave one subject out cross validation was implemented to evaluate the performance of the binary classifier in terms of F1-score in the original feature space, and also in a reduced dimensional feature space using PCA. The pair of k = 1 in kNN and the warping window size 25% of gait sequences in DTW achieved maximum F1-score. Using PCA, pathological gait sequences were discriminated from healthy sequences with the F1-score = 96%.
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PMID:Automated classification of pathological gait after stroke using ubiquitous sensing technology. 2826 56


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