The value of the glenohumeral joint cross-sectional area as a morphological parameter of glenohumeral osteoarthritis.

Glenohumeral joint (GHJ) space narrowing has been demonstrated to be an important morphologic parameter of glenohumeral osteoarthritis (GHO). However, the morphology of GHJ space is irregular because of degeneration of subchondral bone and articular cartilage. Thus, we devised GHJ cartilage cross-sectional area (GHJCCSA) as a new diagnostic morphological parameter to assess the irregular morphologic change of GHJ. GHJ samples were acquired from 33 patients with GHO and from 33 normal controls without evidence of GHO based on shoulder magnetic resonance imaging. T2-weighted coronal MRIs were collected at the GHJ level for all individuals. GHJCCSA and GHJ cartilage thickness (GHJCT) at the GHJ were measured on MRIs using a graphic measuring system. The GHJCCSA was measured as the whole cartilage cross-sectional area of the GHJ. The average GHJCCSA was 115.28 ±â€…17.36 mm2 in normal individuals and 61.77 ±â€…13.74 mm2 in the GHO group. The mean GHJCT was 2.06 ±â€…0.35 mm in normal individuals and 1.50 ±â€…0.28 mm in the GHO group. GHO patients had significantly lower GHJCCSA (P < .001) and GHJCT (P < .001) than normal individuals. Receiver operator characteristics curve analysis revealed that the optimal cutoff score of the GHJCCSA was 82.21 mm2, with a sensitivity of 97.0%, a specificity of 97.0%, and an area under the curve of 0.99 (95% CI: 0.97-1.00). Although GHJCCSA and GHJCT were both significantly associated with GHO, the GHJCCSA was a more sensitive measurement parameter.

Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering.

Emotion information represents a user's current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The "genetic algorithms as a feature selection method" (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.

Pressure-induced red shift and broadening of the Qy absorption of main light-harvesting antennae chlorosomes from green photosynthetic bacteria and their dependency upon alkyl substituents of the composite bacteriochlorophylls.

When pressure was applied to the main light-harvesting apparatus (chlorosomes) isolated from several green photosynthetic bacteria (up to 128 MPa), the Qy-absorption band in an aqueous solution was shifted to longer wavelengths. The shift, deltav, was completely reversible for (de)compression and also showed a linear relation as a function of the applied pressure. The pressure-sensitivity in the deltav was dependent upon the bacterial species. The pressure coefficient, deltav/deltaP, was -565 to -535 cm(-1) GPa(-1) for the chlorosomes from several green sulfur bacteria (Chlorobium species), which have several bacteriochlorophyll(BChl) homologues at the 8- and 12-positions as the antenna pigments. In contrast, a smaller value (-445 cm(-1) GPa(-1)) was estimated for the chlorosomes from the green nonsulfur bacterium (Chloroflexus aurantiacus), which has a single homologue with 8-ethyl and 12-methyl groups. These results were confirmed by the similar pressure dependency of in vitro self-aggregates of isolated BChls-c having various alkyl substituents at the 8- and 12-positions. The present pressurization study enables us to discuss a physiological meaning of a variety of antenna pigments in green photosynthetic bacteria.

Predictive risk factors for coronary artery abnormalities in Kawasaki disease.

Clinical characteristics to predict the development of coronary artery abnormalities (CAA) in Kawasaki disease (KD) were assessed by reviewing medical records of patients diagnosed with KD at Korea University Medical Center from March 2001 to February 2005. Of the 285 patients diagnosed with KD, 19 developed CAA (6.7%). Compared with the CAA(-) group, the CAA(+) group had a longer duration of fever after intravenous gamma-globulin (IVGG) injection (2.4+/-2.9 vs. 1.5+/-1.2 days, p=0.008) and higher C-reactive protein (CRP)(12.3+/-7.8 vs. 8.7+/-7.1 mg/dL, p=0.038). In particular, the CAA(+) group tended to have more than 7 days of fever before IVGG and more than 3 days of fever after IVGG (26.3 vs. 5.3%, p<0.001; 26.3 vs. 6.4%, p=0.002). When the IVGG responsiveness was defined by the presence of defervescence within 3 days after IVGG, IVGG-non-responders showed a higher incidence of CAA (22.7 vs. 5.3%, p=0.002). Non-responders had a longer duration of fever after IVGG (5.5+/-1.9 vs. 1.2+/-0.6 days, p<0.001) and a significantly increased CRP, AST, ALT and total bilirubin. Multivariate regression analysis for CAA showed that the only factor significantly associated with the development of CAA was total fever that lasted for longer than 8 days (OR=4.052, 95% CI=1.151-14.263, p=0.0293). Conclusively, the most important predictor of CAA in KD is total duration of fever longer than 8 days. Early identification of IVGG non-responders and active therapeutic intervention for fever in KD cases might decrease the incidence of CAA.