CD38 expression level and pattern of expression remains a reliable and robust marker of progressive disease in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) follows a variable clinical course which is difficult to predict at diagnosis. We assessed somatic mutation (SHM) status, CD38 and ZAP-70 expression in 87 patients (49 male, 38 female) with stage A CLL and known cytogenetic profile to compare their role in predicting disease progression, which was assessed by the treatment free interval (TFI) from diagnosis. Sixty (69%) patients were SHM+, 24 (28%) were CD38+ and ten (12%) were ZAP-70+. The median TFI for: (i) SHM + versus SHM- patients was 124 versus 26 months; hazard ratio (HR) = 3.6 [95% confidence interval (CI) = 1.8 - 7.3; P = 0.001]: (ii) CD38- versus CD38+ patients was 120 versus 34 months; HR = 2.4 (95% CI = 1.4 - 5.3; P = 0.02); and (iii) ZAP70- versus ZAP70+ was 120 versus 16 months; HR = 3.4 (95% CI = 1.4 - 8.7; P = 0.01). SHM status and CD38 retained prognostic significance on multivariate analysis whereas ZAP-70 did not. We conclude that ZAP-70 analysis does not provide additional prognostic information in this group of patients.

Privacy-Protected Facial Biometric Verification via Fuzzy Forest Learning

Although visual surveillance has emerged as an effective technolody for public security, privacy has become an issue of great concern in the transmission and distribution of surveillance videos. For example, personal facial images should not be browsed without permission. To cope with this issue, face image scrambling has emerged as a simple solution for privacyrelated applications. Consequently, online facial biometric verification needs to be carried out in the scrambled domain thus bringing a new challenge to face classification. In this paper, we investigate face verification issues in the scrambled domain and propose a novel scheme to handle this challenge. In our proposed method, to make feature extraction from scrambled face images robust, a biased random subspace sampling scheme is applied to construct fuzzy decision trees from randomly selected features, and fuzzy forest decision using fuzzy memberships is then obtained from combining all fuzzy tree decisions. In our experiment, we first estimated the optimal parameters for the construction of the random forest, and then applied the optimized model to the benchmark tests using three publically available face datasets. The experimental results validated that our proposed scheme can robustly cope with the challenging tests in the scrambled domain, and achieved an improved accuracy over all tests, making our method a promising candidate for the emerging privacy-related facial biometric applications.

Flow Pattern in Compound Channel with Floodplain Double Groyne

The results of an experimental study and velocity analysis of the flow characteristics in the vicinityof a floodplain with two rows of permeable/impermeable groynes in compound channels with oneand two floodplains are presented. A 60% permeable groyne model with three different lengthsrelative to the floodplain width was used. The results showed that double groyne could beconsidered as one groyne (one block) for aspect ratio Sr < 2 (Sr = distance between twosuccessive groynes/groyne length). When Sr > 2, each groyne started to act independently.The velocity reduction was more than 45-52% of the floodplain’s approach velocity compared with30-35% in the case of a single groyne. The significant velocity reduction was located at a distance1.5-2 times the groyne length downstream of the single or the double groynes. Generally, themaximum velocities in the main channel ranged from 1.1 to 1.35 times the original approachingvelocity. The effective groyne relative length and aspect ratio should not to be more than 0.5 and 2,respectively.

Facial cleanliness and risk of trachoma in families

Trachoma is the leading infectious cause of blindness worldwide, and epidemiologic studies of factors that may increase the transmission of ocular Chlamydia trachomatis are needed. In two villages in a hyperendemic area of Central Tanzania, 472 (90%) of 527 preschool-aged children were examined for specific signs of unclean faces and presence of trachoma. The odds of trachoma were 70% higher in children with flies and nasal discharge on their faces. Other facial signs were not important. In large families, the odds of trachoma increased 4.8-fold if a sibling had trachoma and 6.8-fold if a sibling had trachoma and an unclean face. Health education strategies aimed at improving face washing need to target cleaning nasal discharge and keeping flies off children's faces.

Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels

With the rapid development of internet-of-things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper we propose a new ensemble approach – Many-Kernel Random Discriminant Analysis (MK-RDA) to discover discriminative patterns from chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle chaotic facial patterns in the scrambled domain, where random selections of features are made on semantic components via salience modelling. In our experiments, the proposed MK-RDA was tested rigorously on three human face datasets: the ORL face dataset, the PIE face dataset and the PUBFIG wild face dataset. The experimental results successfully demonstrate that the proposed scheme can effectively handle chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in emerging IoT applications.