Total and regional body fat status among children and young people with cerebral palsy: A scoping review

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151256/1/cob12327_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151256/2/cob12327.pd

Classification d'expressions vocales passives versus actives

Six expressions sont généralement considérées pour caractériser les états émotifs humains : Sourire, Surprise, Colère, Tristesse, dégoût et Neutre. Différentes mesures peuvent être extraites à partir du signal de parole pour caractériser ces expressions, à savoir la fréquence fondamentale, l'énergie, le SPI (rapport des énergies des HF et des BF dans le signal) et le débit de parole. Une classification automatique des cinq expressions basées sur ces caractéristiques présente des conflits entre la Colère, la Surprise et le Sourire d'une part et le Neutre et la Tristesse d'autre part. Ce conflit entre classes d'expressions est également retrouvé chez le classifieur humain. Nous proposons donc de définir deux classes d'expressions: Active regroupant le Sourire, la Surprise et la Colère et Passive regroupant le Neutre et la Tristesse. Une telle classification est également plus réaliste et plus appropriée pour l'intégration d'information de parole dans un système de classification multimodale combinant la parole et la vidéo, ce qui est à long terme le but de notre travail. Dans ce papier, différentes méthodes de classification sont testées: un classifieur Bayésien, une Analyse Discriminante Linéaire (ADL), le classifieur au K plus proches vosins(KNN) et un classifieur à Machine à Vecteur de Support (SVM) avec une fonction de base gaussienne. Pour les deux classes considérées, les meilleurs taux de classification sont obtenus avec le classificateur SVM avec un taux de reconnaissance de 89.74% pour l'état Actif et de 86.54 % pour l'état Passif

Pyomyositis of tensor fascia lata: a case report

<p>Abstract</p> <p>Introduction</p> <p>Pyomyositis is a disease in which an abscess is formed deep within large striated muscles.</p> <p>Case presentation</p> <p>We report the case of a 10-year-old boy who presented with fever and a painful hip and was subsequently diagnosed with pyomyositis of the tensor fascia lata. In children with clinical and laboratory findings of inflammation in the vicinity of the hip joint, the differential diagnosis includes transient synovitis, an early stage of Legg-Calvé-Perthes disease, infectious arthritis of the hip, rheumatologic diseases and extracapsular infection such as osteomyelitis.</p> <p>Conclusion</p> <p>To the best of the authors' knowledge, this is the first report of pyomyositis of the tensor fascia lata. Although pyomyositis is a rare disease and the differential diagnosis includes a variety of other commonly observed diseases, pyomyositis should be considered in cases where children present with fever, leukocytosis and localized pain.</p

The subchondral bone in articular cartilage repair: current problems in the surgical management

As the understanding of interactions between articular cartilage and subchondral bone continues to evolve, increased attention is being directed at treatment options for the entire osteochondral unit, rather than focusing on the articular surface only. It is becoming apparent that without support from an intact subchondral bed, any treatment of the surface chondral lesion is likely to fail. This article reviews issues affecting the entire osteochondral unit, such as subchondral changes after marrow-stimulation techniques and meniscectomy or large osteochondral defects created by prosthetic resurfacing techniques. Also discussed are surgical techniques designed to address these issues, including the use of osteochondral allografts, autologous bone grafting, next generation cell-based implants, as well as strategies after failed subchondral repair and problems specific to the ankle joint. Lastly, since this area remains in constant evolution, the requirements for prospective studies needed to evaluate these emerging technologies will be reviewed

Automatic Defect Segmentation of ‘Jonagold’ Apples on Multi-Spectral Images: A Comparative Study

In this work, several thresholding and classification-based techniques were employed for pixel-wise segmentation of surface defects of ‘Jonagold ’ apples. Observations showed that segmentation by supervised classifiers was more accurate than the rest. Also, average of class-specific recognition errors was more reliable than error measures based on defect size or global recognition. Segmentation accuracy im-proved when pixels were represented as a neighborhood. Effect of down-sampling on segmentation accuracy and computation times showed that multi-layer percep-trons were the best. Russet was the most difficult defect to segment, whereas flesh damage the least. The proposed method was much more precise on healthy fruit

Stem and calyx recognition on ‘Jonagold’ apples by pattern recognition

In this paper, a novel method to recognize stem or calyx regions of ‘Jonagold’ apples by pattern recognition is proposed. The method starts with background removal and object segmentation by thresholding. Statistical, textural and shape features are extracted from each segmented object and these features are introduced to several supervised classification algorithms. Linear discriminant, nearest neigh-bor, fuzzy nearest neighbor, support vector machines classifiers and adaboost are the ones tested. Relevant features are selected by floating forward feature selection algorithm. Support vector machines, which is found to be the best among all classi-fication algorithms tested, correctly recognized 99 % of the stems and 100 % of the calyxes using selected feature subset. These results exhibit considerable improve-ment relative to the ones introduced in the literature

Apple stem and calyx recognition by decision trees, to appear

In this paper, a decision tree-based approach for recognizing stem and calyx regions of apples by computer vision is proposed. The method starts with background removal and object segmentation by thresholding. Statistical, textural and shape features are extracted from each segmented object and these features are introduced to two decision tree algorithms: CART and C4.5. Feature selection is accomplished by sequential floating forward selection method. Analysis showed that feature selection improves accuracy of both system. Eventhough CART performed slightly better than C4.5 after feature selection, McNemar’s test found them statistically indifferent