Plasmodium malariae Prevalence and csp Gene Diversity, Kenya, 2014 and 2015.

In Africa, control programs that target primarily Plasmodium falciparum are inadequate for eliminating malaria. To learn more about prevalence and genetic variability of P. malariae in Africa, we examined blood samples from 663 asymptomatic and 245 symptomatic persons from western Kenya during June-August of 2014 and 2015. P. malariae accounted for 5.3% (35/663) of asymptomatic infections and 3.3% (8/245) of clinical cases. Among asymptomatic persons, 71% (32/45) of P. malariae infections detected by PCR were undetected by microscopy. The low sensitivity of microscopy probably results from the significantly lower parasitemia of P. malariae. Analyses of P. malariae circumsporozoite protein gene sequences revealed high genetic diversity among P. malariae in Africa, but no clear differentiation among geographic populations was observed. Our findings suggest that P. malariae should be included in the malaria elimination strategy in Africa and highlight the need for sensitive and field-applicable methods to identify P. malariae in malaria-endemic areas.

Spartan Face Mask Detection and Facial Recognition System.

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices.

Transitioning into an online dental hygiene degree-completion program.

PURPOSE: The University of British Columbia's (UBC) Dental Hygiene Degree-Completion (DC) program has offered an academic pathway for dental hygienists to earn a degree exclusively through an online delivery format since 2006. The students' experiences transitioning to online learning and related student support needs have not been previously studied. Thus, this study investigated students' experiences as they entered an online dental hygiene DC program METHODS: An online survey with open-ended and closed-ended questions was distributed to all 53 currently enrolled dental hygiene DC students in September 2019 RESULTS: Thirty-two students completed the survey for a 60% response rate. Most student respondents (78%) had no prior experience with online education. Only 59% of respondents were confident in their abilities to navigate a Web-based learning environment. Three-quarters (75%) of respondents felt like a valued member of the Faculty of Dentistry community but only 47% felt they belonged to the larger university community. Most (72%) were familiar with the support resources within the Faculty but fewer than half (41%) were aware of additional resources outside of the Faculty. Students aged 30 years and older felt more comfortable reaching out to faculty and staff members (90% vs. 55%; P = 0.05). Students with prior online learning experience were more aware of student support resources outside of the Faculty (57% vs. 36%; P = 0.05) CONCLUSION: Lessons learned from this study have informed several recommendations to better support student transition to online learning that can be applicable to this program and in other institutions.