Online Person Identification based on Multitask Learning

In the digital world, everything is digitized and data are generated consecutively over the times. To deal with this situation, incremental learning plays an important role. One of the important applications that needs an incremental learning is person identification. On the other hand, password and code are no longer the only way to prevent the unauthorized person to access the information and it tends to be forgotten.  Therefore, biometric characteristics system is introduced to solve the problems. However, recognition based on single biometric may not be effective, thus, multitask learning is needed. To solve the problems, incremental learning is applied for person identification based on multitask learning. Considering that the complete data is not possible to be collected at one time, online learning is adopted to update the system accordingly. Linear Discriminant Analysis (LDA) is used to create a feature space while Incremental LDA (ILDA) is adopted to update LDA. Through multitask learning, not only human faces are trained, but fingerprint images are trained in order to improve the performance. The performance of the system is evaluated by using 50 datasets which includes both male and female datasets. Experimental results demonstrate that the learning time of ILDA is faster than LDA. Apart from that, the learning accuracies are evaluated by using K-Nearest Neighbor (KNN) and achieve more than 80% for most of the simulation results. In the future, the system is suggested to be improved by using better sensor for all the biometrics. Other than that, incremental feature extraction is improved to deal with some other online learning problems

Integrated Mapping of Yaws and Trachoma in the Five Northern-Most Provinces of Vanuatu.

Yaws and trachoma are targeted for eradication and elimination as public health problems. In trachoma-endemic populations mass administration of azithromycin can simultaneously treat yaws. We conducted a population-based prevalence survey in the five northernmost provinces of Vanuatu, where trachoma and yaws are suspected to be co-endemic. Clinical signs of trachoma were evaluated using the WHO simplified grading system, and skin examination with a serological rapid diagnostic test used to identify yaws. We enrolled 1004 households in 59 villages over 16 islands, and examined 3650 individuals of all ages for trachoma. The overall adjusted prevalence of trachomatous inflammation-follicular (TF) in 1-9 year-olds was 12.0% (95% Confidence Interval: 8.1-16.7%), and the overall adjusted prevalence of TT in those aged 15 years and greater was 0.04% (95% CI 0-0.14%). In multivariate analysis, the odds of children having TF was 2.6 (95% CI = 1.5-4.4) times higher in households with unimproved latrines, and independently associated with the number of children in the household (OR 1.3, 95% CI = 1.0-1.6 for each additional child). We examined the skin of 821 children aged 5-14 years. Two children had yaws, giving an estimated prevalence of active yaws in those aged 5-14 years of 0.2% (95% CI = 0.03-0.9%). Mass treatment with azithromycin is recommended in these provinces. Given the apparent low burden of yaws, integration of yaws and trachoma control programmes is likely to be useful and cost-effective to national programmes

Online Person Identification based on Multitask Learning

In the digital world, everything is digitized and data are generated consecutively over the times. To deal with this situation, incremental learning plays an important role. One of the important applications that needs an incremental learning is person identification. On the other hand, password and code are no longer the only way to prevent the unauthorized person to access the information and it tends to be forgotten. Therefore, biometric characteristics system is introduced to solve the problems. However, recognition based on single biometric may not be effective, thus, multitask learning is needed. To solve the problems, incremental learning is applied for person identification based on multitask learning. Considering that the complete data is not possible to be collected at one time, online learning is adopted to update the system accordingly. Linear Discriminant Analysis (LDA) is used to create a feature space while Incremental LDA (ILDA) is adopted to update LDA. Through multitask learning, not only human faces are trained, but fingerprint images are trained in order to improve the performance. The performance of the system is evaluated by using 50 datasets which includes both male and female datasets. Experimental results demonstrate that the learning time of ILDA is faster than LDA. Apart from that, the learning accuracies are evaluated by using K-Nearest Neighbor (KNN) and achieve more than 80% for most of the simulation results. In the future, the system is suggested to be improved by using better sensor for all the biometrics. Other than that, incremental feature extraction is improved to deal with some other online learning problems

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Quality Assessment for Natural and Screen Content Images

Quality assessment (QA) of screen content images (SCIs) has gained more and more popularity. SCIs are very different from natural images (NIs) which have been dealing with by most researchers in the literature. QA methods specifically designed for NIs also can be used to evaluate the quality of SCIs. Yet, their performances are unsatisfactory. This may due to the statistical differences of SCIs and NIs. In this paper, SCIs and NIs QA methods in the literature are being compared and studied for both SCIs and NIs benchmarked databases. It is found out that methods that incorporate gradient features work well for both SCIs and NIs. This points out a possible way to utilize gradient features to come out with a QA method that works for both SCIs and NIs simultaneously. Hence, application related to SCIs and NIs such as deep learning and multitasking for person tracking system can be improved with the QA method

Ocular Chlamydia trachomatis infection, anti-Pgp3 antibodies and conjunctival scarring in Vanuatu and Tarawa, Kiribati before antibiotic treatment for trachoma.

INTRODUCTION: In the peri-elimination setting, the positive predictive value of trachomatous inflammation-follicular (TF), the primary marker used to determine need for antibiotics for trachoma, is suboptimal. Here, three non-TF measures are used to compare two regions where TF prevalence exceeds the threshold for intervention, but where the Chlamydia trachomatis (Ct) prevalence is different. METHODS: Population prevalence of trachoma was measured in Vanuatu (n?=?3470) and Kiribati (n?=?2922). Dried blood spots (DBS) and conjunctival photographs were collected from every survey participant, and conjunctival swabs were collected from those aged 1-9 years. Individuals were tested for blood anti-Pgp3 antibodies, Ct DNA at the conjunctiva and severity of conjunctival scarring. RESULTS: The prevalence of TF in 1-9-year-olds was 16.5% in Vanuatu and 38.2% in Tarawa. 7% of people aged ?1 year in Vanuatu had conjunctival scarring compared to 27% in Tarawa. The prevalence of ocular Ct infection in 1-9-year-olds was 1.5% in Vanuatu and 27.4% in Tarawa. The seroconversion rate amongst 1-9-year-old children in Vanuatu and Tarawa was 0.018 and 0.197 events per child per year, respectively. CONCLUSIONS: Comparing Vanuatu to Tarawa demonstrates several markers that could be used to differentiate the trachoma status of populations in these (and other) locations

Conjunctival Scarring, Corneal Pannus, and Herbert's Pits in Adolescent Children in Trachoma-endemic Populations of the Solomon Islands and Vanuatu.

BACKGROUND: In the Solomon Islands and Vanuatu, the sign trachomatous inflammation-follicular (TF) is common, but ocular infection with Chlamydia trachomatis is not. It is therefore debatable whether azithromycin mass drug administration (MDA), the recommended antibiotic treatment strategy for trachoma's elimination as a public health problem, is necessary in this setting. We set out to estimate what proportion of adolescents were at risk of progression of trachomatous scarring. METHODS: A cross-sectional survey was undertaken of all children aged 10-14 years resident in communities identified as high-TF clusters during previous population-based mapping. Graders examined children for clinical evidence of trachomatous scarring, pannus, and Herbert's pits (HPs) or limbal follicles in both eyes. A dried blood spot was collected from each child and tested for antibodies to C. trachomatis. RESULTS: A total of 492 children in 24 villages of the Solomon Islands and Vanuatu were examined. In total, 35/492 (7%) of children had limbal signs (pannus and/or HPs) plus any conjunctival scarring. And 9/492 (2%) had limbal signs and moderate or severe conjunctival scarring; 22% of children were anti-Pgp3 seropositive. CONCLUSIONS: Few adolescents here are at risk of future complications from trachoma, supporting the conclusion that further antibiotic MDA is not currently required for trachoma elimination purposes in these settings