108 research outputs found
Infection ostéo articulaire à pneumocoque chez un sujet apparemment sain
Les infections ostéo-articulaires (IOA) à Streptococcus pneumoniae sont rares chez les adultessains et elles surviennent, souvent, sur un terraind’immunodépression ou de pathologie articulaire.Nous rapportons ici un cas d’arthrite septiquesubaiguë de l’épaule à Streptococcus pneumoniaeobjectivée chez un sujet âgé, sain et chez qui on n’apas objectivé de pathologies sous jacentes qui enpourraient être responsable. Ainsi, le Streptococcuspneumoniae doit être évoqué devant toute infectionostéo articulaire, essentiellement chez les vieillards,même en l’absence des facteurs favorisantsclassiquement associés
Did giraffe cardiovascular evolution solve the problem of heart failure with preserved ejection fraction?
The evolved adaptations of other species can be a source of insight for novel biomedical innovation. Limitations of traditional animal models for the study of some pathologies are fueling efforts to find new approaches to biomedical investigation. One emerging approach recognizes the evolved adaptations in other species as possible solutions to human pathology. The giraffe heart, for example, appears resistant to pathology related to heart failure with preserved ejection fraction (HFpEF)-a leading form of hypertension-associated cardiovascular disease in humans. Here, we postulate that the physiological pressure-induced left ventricular thickening in giraffes does not result in the pathological cardiovascular changes observed in humans with hypertension. The mechanisms underlying this cardiovascular adaptation to high blood pressure in the giraffe may be a bioinspired roadmap for preventive and therapeutic strategies for human HFpEF
Models of organometallic complexes for optoelectronic applications
Organometallic complexes have potential applications as the optically active
components of organic light emitting diodes (OLEDs) and organic photovoltaics
(OPV). Development of more effective complexes may be aided by understanding
their excited state properties. Here we discuss two key theoretical approaches
to investigate these complexes: first principles atomistic models and effective
Hamiltonian models. We review applications of these methods, such as,
determining the nature of the emitting state, predicting the fraction of
injected charges that form triplet excitations, and explaining the sensitivity
of device performance to small changes in the molecular structure of the
organometallic complexes.Comment: To appear in themed issue of J. Mat. Chem. on the modelling of
material
Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition
Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for Image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on
massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted
spatio-temporal features classified by supervised deep networks. As we show in our experiments on five popular benchmarks for action recognition, our hybrid
model combines the best of both worlds: it is data efficient (trained on 150 to 10000 short clips) and yet improves significantly on the state of the art, including
recent deep models trained on millions of manually labelled images and videos
Threshold effects in excited charmed baryon decays
Motivated by recent results on charmed baryons from CLEO and FOCUS, we
reexamine the couplings of the orbitally excited charmed baryons. Due to its
proximity to the [Sigma_c pi] threshold, the strong decays of the
Lambda_c(2593) are sensitive to finite width effects. This distorts the shape
of the invariant mass spectrum in Lambda_{c1}-> Lambda_c pi^+pi^- from a simple
Breit-Wigner resonance, which has implications for the experimental extraction
of the Lambda_c(2593) mass and couplings. We perform a fit to unpublished CLEO
data which gives M(Lambda_c(2593)) - M(Lambda_c) = 305.6 +- 0.3 MeV and h2^2 =
0.24^{+0.23}_{-0.11}, with h2 the Lambda_{c1}-> Sigma_c pi strong coupling in
the chiral Lagrangian. We also comment on the new orbitally excited states
recently observed by CLEO.Comment: 9 pages, 3 figure
Negative Parity 70-plet Baryon Masses in the 1/Nc Expansion
The masses of the negative parity SU(6) 70-plet baryons are analyzed in the
1/Nc expansion to order 1/Nc and to first order in SU(3) breaking. At this
level of precision there are twenty predictions. Among them there are the well
known Gell-Mann Okubo and equal spacing relations, and four new relations
involving SU(3) breaking splittings in different SU(3) multiplets. Although the
breaking of SU(6) symmetry occurs at zeroth order in 1/Nc, it turns out to be
small. The dominant source of the breaking is the hyperfine interaction which
is of order 1/Nc. The spin-orbit interaction, of zeroth order in 1/Nc, is
entirely fixed by the splitting between the singlet states Lambda(1405) and
Lambda(1520), and the spin-orbit puzzle is solved by the presence of other
zeroth order operators involving flavor exchange.Comment: 31 pages, 3 figure
Preliminary clinical study of left ventricular myocardial strain in patients with non-ischemic dilated cardiomyopathy by three-dimensional speckle tracking imaging
<p>Abstract</p> <p>Background</p> <p>Non-ischemic dilated cardiomyopathy (DCM) is the most common cardiomyopathy worldwide, with significant mortality. Correct evaluation of the patient's myocardial function has important clinical significance in the diagnosis, therapeutic effect assessment and prognosis in non-ischemic DCM patients. This study evaluated the feasibility of three-dimensional speckle tracking imaging (3D-STE) for assessment of the left ventricular myocardial strain in patients with non-ischemic dilated cardiomyopathy (DCM).</p> <p>Methods</p> <p>Apical full-volume images were acquired from 65 patients with non-ischemic DCM (DCM group) and 59 age-matched normal controls (NC group), respectively. The following parameters were measured by 3D-STE: the peak systolic radial strain (RS), circumferential strain (CS), longitudinal strain (LS) of each segment. Then all the parameters were compared between the two groups.</p> <p>Results</p> <p>The peak systolic strain in different planes had certain regularities in normal groups, radial strain (RS) was the largest in the mid region, the smallest in the apical region, while circumferential strain (CS) and longitudinal strain (LS) increased from the basal to the apical region. In contrast, the regularity could not be applied to the DCM group. RS, CS, LS were significantly decreased in DCM group as compared with NC group (<it>P </it>< 0.001 for all). The interobserver, intraobserver and test-retest reliability were acceptable.</p> <p>Conclusions</p> <p>3D-STE is a reliable tool for evaluation of left ventricular myocardial strain in patients with non-ischemic DCM, with huge advantage in clinical application.</p
An Efficient Human Activity Recognition Technique Based on Deep Learning
In this paper, we present a new deep learning-based human activity recognition technique. First, we track and extract human body from each frame of the video stream. Next, we abstract human silhouettes and use them to create binary space-time maps (BSTMs) which summarize human activity within a defined time interval. Finally, we use convolutional neural network (CNN) to extract features from BSTMs and classify the activities. To evaluate our approach, we carried out several tests using three public datasets: Weizmann, Keck Gesture and KTH Database. Experimental results show that our technique outperforms conventional state-of-the-art methods in term of recognition accuracy and provides comparable performance against recent deep learning techniques. It’s simple to implement, requires less computing power, and can be used for multi-subject activity recognition
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