RESULT: The localization error is validated on the two datasets with superior performance over the state-of-the-art methods and variation in the expression is visualized using Principal Components (PCs). The deformations show various expression regions in the faces. The results indicate that Sad expression has the lowest recognition accuracy on both datasets. The classifier achieved a recognition accuracy of 99.58 and 99.32% on Stirling/ESRC and Bosphorus, respectively.
CONCLUSION: The results demonstrate that the method is robust and in agreement with the state-of-the-art results.
METHOD: This study investigates three-dimensional (3D) soft-tissue craniofacial variation, with relation to ethnicity, sex and age variables in British and Irish white Europeans. This utilizes a geometric morphometric approach on a subsampled dataset comprising 292 scans, taken from a Liverpool-York Head Model database. Shape variation and analysis of each variable are tested using 20 anchor anatomical landmarks and 480 sliding semi-landmarks.
RESULTS: Significant ethnicity, sex, and age differences are observed for measurement covering major aspects of the craniofacial shape. The ethnicity shows subtle significant differences compared to sex and age; even though it presents the lowest classification accuracy. The magnitude of dimorphism in sex is revealed in the facial, nasal and crania measurement. Significant shape differences are also seen at each age group, with some distinct dimorphic features present in the age groups.
CONCLUSIONS: The patterns of shape variation show that white British individuals have a more rounded head shape, whereas white Irish individuals have a narrower head shape. White British persons also demonstrate higher classification accuracy. Regarding sex patterns, males are relatively larger than females, especially in the mouth and nasal regions. Females presented with higher classification accuracy than males. The differences in the chin, mouth, nose, crania, and forehead emerge from different growth rates between the groups. Classification accuracy is best for children and senior adult age groups.
MATERIALS AND METHODS: Plasma samples were collected prevaccination, 2 weeks and 6 months post-vaccination and tested for total immunoglobulin levels using ELISA method.
RESULTS: A small percentage of HCW (2.2%, 15/677) had elevated anti-S antibody levels in their pre-vaccination plasma samples (median 20.4, IQR 5.8), indicating that they were exposed to SARS-CoV-2 infection prior to vaccination. The mRNA vaccine significantly increased anti-S levels of both previously infected and uninfected individuals to saturation levels (median 21.88, IQR.0.88) at 2 weeks postsecond dose of the vaccine. At 6 months post-vaccination, the antibody levels appeared to be maintained among the recipients of the mRNA vaccine. However, at this time point, anti-S antibody levels were lower in individuals given inactivated vaccine (median 20.39, IQR 7.31, n=28), and interestingly, their antibody levels were similar to anti-S levels in pre-vaccination exposed individuals. Antibody levels were not different between the sexes.
CONCLUSION: Anti-S levels differ in individuals given the different vaccines. While further study is required to determine the threshold level for protection against SARSCoV- 2, individuals with low antibody levels may be considered for boosters.
METHODS: A nationwide data set was examined for this secondary data analysis. The dependent variable was the degree of risk, which was measured based on the number of high-risk behaviours in which adolescents participated. Age, gender, ethnicity, self-rated academic performance, family size, parental marital status and parental academic attainment were included as independent variables. Analyses stratified by educational level were conducted. Odds ratios (ORs) were calculated using ordered logit.
RESULTS: The most common high-risk behaviour among Malaysian adolescents was physical inactivity (35.97%), followed by smoking (13.27%) and alcohol consumption (4.45%). The majority of adolescents had low risks (52.93%), while only a small proportion had high risks (6.08%). Older age was associated with increased odds of having high risks (OR: 1.26). Male adolescents had higher odds of being in a high-risk category compared to female adolescents (OR: 1.28). Compared to Malays, Chinese adolescents had higher odds of being in a high-risk category (OR: 1.71), whereas Indian adolescents had lower odds (OR: 0.65). Excellent academic performance was associated with reduced odds of participating in high-risk behaviours (OR: 0.41).
CONCLUSION: Personal factors are important determinants of high-risk behaviours. This study provides a better understanding of those adolescent groups that are at greater risk.
PRACTICAL IMPLICATIONS: An intervention directed towards reducing participation in high-risk behaviours among adolescents who have both poor academic performance and less-educated parents may yield promising outcomes.
Patients and methods: A nationally representative data of adolescents that consists of 25399 respondents is used. The demographic (age, gender, education) and lifestyle (fruits and vegetables consumption, carbonated soft drink consumption, cigarette smoking, alcohol drinking, sex behaviour, participation in physical education class, obesity) determinants of physical activity are assessed using binomial regression.
Results: The results show that age is negatively associated with time spent in physical activity. However, being male and education levels are positively related to time spent in physical activity. Having unhealthy lifestyle and being obese are associated with low levels of physical activity. Physical education seems to promote participation in physical activity.
Conclusion: In conclusion, demographic and lifestyle factors play an important role in determining levels of physical activity among adolescents. In order to reduce the prevalence of physically inactive adolescents, policy makers should focus primarily on late adolescents, females, adolescents who engage in unhealthy lifestyle and seldom attend physical education classes, as well as obese adolescents.
OBJECTIVE: To examine ethnic differences in participation in medical check-ups among the elderly.
DESIGN: A nationally representative data set was employed. Multiple logistic regressions were utilised to examine the relationship between ethnicity and the likelihood of undergoing medical check-ups. The regressions were stratified by age, income, marital status, gender, household location, insurance access and health status. These variables were also controlled for in the regressions (including stratified regressions).
PARTICIPANTS: The respondents were required to be residents of Malaysia and not be institutionalised. Overall, 30,806 individuals were selected to be interviewed, but only 28,650 were actually interviewed, equivalent to a 93% response rate. Of those, only 2248 were used in the analyses, because 26,402 were others or below aged 60.
MAIN MEASURES: The dependent variable was participation in a medical check-up. The main independent variables were the three major ethnic groups in Malaysia (Malay, Chinese, Indian).
KEY RESULTS: Among the elderly aged 70-79 years, Chinese (aOR 1.89; 95% CI 1.28, 2.81) and Indians (aOR 2.39; 95% CI 1.20, 4.74) were more likely to undergo medical check-ups than Malays. Among the elderly with monthly incomes of ≤ RM999, Chinese (aOR 1.44; 95% CI 1.12, 1.85) and Indians (aOR 1.50; 95% CI 0.99, 2.28) were more likely to undergo medical check-ups than Malays. Indian males were more likely to undergo medical check-ups than Malay males (aOR 2.32; 95% CI 1.15, 4.67). Chinese with hypercholesterolaemia (aOR 1.45; 95% CI 1.07, 1.98) and hypertension (aOR 1.32; 95% CI 1.02, 1.72) were more likely to undergo medical check-ups than Malays.
CONCLUSIONS: There were ethnic differences in participation in medical check-ups among the elderly. These ethnic differences varied across age, income, marital status, gender, household location, insurance access and health status.