Cartilage canals in newborn dogs : histochemical and immunohistochemical findings

Cartilage canals (CCs) are microscopic structures involved in secondary ossification centers (SOCs) development. The features of CCs were investigated in the humeral and femoral proximal epiphyses of small-sized newborn dogs (from premature to 28 days after birth) with histochemical and immunohistochemical approaches. Masson's Trichrome revealed a ring-shaped area around CCs, which changes in colour from green (immature collagen) to red (mature collagen) as ossification progresses; perichondrium staining always matched the ring colour. Safranin-O was always negative. Immunohistochemical analysis revealed immunopositivity for both collagen type I and V around the CCs; collagen type II was negative. CCs count showed a tendency to be higher in the humerus than in the femur. This work enlightened for the first time changes in composition of CCs surrounding matrix during SOCs development in dogs, paving the way to further investigations

Thermal spatio-temporal data for stress recognition

Stress is a serious concern facing our world today, motivating the development of a better objective understanding through the use of non-intrusive means for stress recognition by reducing restrictions to natural human behavior. As an initial step in computer vision-based stress detection, this paper proposes a temporal thermal spectrum (TS) and visible spectrum (VS) video database ANUStressDB - a major contribution to stress research. The database contains videos of 35 subjects watching stressed and not-stressed film clips validated by the subjects. We present the experiment and the process conducted to acquire videos of subjects' faces while they watched the films for the ANUStressDB. Further, a baseline model based on computing local binary patterns on three orthogonal planes (LBP-TOP) descriptor on VS and TS videos for stress detection is presented. A LBP-TOP-inspired descriptor was used to capture dynamic thermal patterns in histograms (HDTP) which exploited spatio-temporal characteristics in TS videos. Support vector machines were used for our stress detection model. A genetic algorithm was used to select salient facial block divisions for stress classification and to determine whether certain regions of the face of subjects showed better stress patterns. Results showed that a fusion of facial patterns from VS and TS videos produced statistically significantly better stress recognition rates than patterns from VS or TS videos used in isolation. Moreover, the genetic algorithm selection method led to statistically significantly better stress detection rates than classifiers that used all the facial block divisions. In addition, the best stress recognition rate was obtained from HDTP features fused with LBP-TOP features for TS and VS videos using a hybrid of a genetic algorithm and a support vector machine stress detection model. The model produced an accuracy of 86%