Gender recognition from unconstrained selfie images: a convolutional neural network approach

<p>Human gender recognition is an essential demographic tool. This is reflected in forensic science, surveillance systems and targeted marketing applications. This research was always driven using standard face images and hand-crafted features. Such way has achieved good results, however, the reliability of the facial images had a great effect on the robustness of extracted features, where any small change in the query facial image could change the results. Nevertheless, the performance of current techniques in unconstrained environments is still inefficient, especially when contrasted against recent breakthroughs in different computer vision research. This paper introduces a novel technique for human gender recognition from non-standard selfie images using deep learning approaches. Selfie photos are uncontrolled partial or full-frontal body images that are usually taken by people themselves in real-life environment. As far as we know this is the first paper of its kind to identify gender from selfie photos, using deep learning approach. The experimental results on the selfie dataset emphasizes the proposed technique effectiveness in recognizing gender from such images with 89% accuracy. The performance is further consolidated by testing on numerous benchmark datasets that are widely used in the field, namely: Adience, LFW, FERET, NIVE, Caltech WebFaces and<br />CAS-PEAL-R1.</p>

Nosocomial RSV-related in-hospital mortality in children <5 years: a global case series

Background According to the World Health Organization the global burden of nosocomial infections is poorly characterized as surveillance systems for nosocomial infection are lacking. Nosocomial infections occur at higher rates in low- and lower-middle-income countries (LMICs) than in high-income countries (HICs). Current global RSV burden estimates are largely based on community-acquired disease. We aimed to characterize children with nosocomial RSV-related mortality and to understand the potential impact of RSV immunization strategies. Methods RSV GOLD is a global registry of children younger than 5 years who died with laboratory-confirmed RSV infection. We compared clinical and demographic characteristics of children with nosocomial and community-acquired RSV in-hospital mortality. Results We included 231 nosocomial and 931 community-acquired RSV-related in-hospital deaths from 65 countries. Median age at death was similar for both groups (5.4 vs 6 months). As expected, a higher proportion of children with nosocomial infection had comorbidities (87% vs 57%; p<0.001) or was born preterm (46% vs 24%; p<0.001) than children with community-acquired infection. The proportion of nosocomial deaths among all RSV deaths was lower in LMICs than in upper-middle-income countries (UMICs) and HICs (12% vs 18% and 26%, respectively). Conclusions This is the first global case series of children dying with nosocomial RSV infection. Future infant-targeted immunization strategies can prevent the majority of nosocomial RSV-related deaths. Although nosocomial RSV deaths are expected to occur at highest rates in LMICs, the number of reported nosocomial RSV deaths was low in these countries. Hospital-based surveillance is needed to capture the full burden of nosocomial RSV mortality in LMICs.

The role of veterinarians in the One Health approach to antimicrobial resistance perspectives in Jordan (Preprint)

UNSTRUCTURED This study aims to evaluate the role of Jordanian veterinarians in terms of their knowledge, attitudes and common practices in combating antimicrobial resistance (AMR) and summarize the registered veterinary drugs between 2017 2020. Descriptive study data were collected using a standardized questionnaire that focused on the knowledge, attitudes, and practices of Jordanian veterinarians. The findings were analyzed descriptively; 84% of the participants agreed with the statement on the definition of AMR. The majority (95.65%) of participants agreed that AMR is a challenge for the veterinary sector in Jordan and that it should be prioritized over other zoonotic diseases. Approximately 69% of the participants believed that the misuse and overuse of antimicrobials by unqualified, fraudulent, or unauthorized practitioners is the primary reason for the rise of cases associated with AMR and the challenges that accompany these. The most common practice among the respondents in this study was to recommend clients (e.g., farmers and owners) to practice good animal husbandry (80.00%). The study also revealed that there was a significant difference (p = 0.015) between attendance at AMR training sessions and the professional sector (private, public, and academic) of the veterinarians. This study underscores the importance of implementing a continuous education program on AMR so as to enhance the all-round knowledge of veterinarians and improve their advisory skills. In addition, laws should be enacted to ensure that veterinarians prescribe the correct antimicrobials and to improve surveillance systems for monitoring the use of antimicrobials in veterinary medicine. INTERNATIONAL REGISTERED REPORT RR2-https://doi.org/10.1186/s44149-021-00033-5

In-Competition Severe Injury Events in Elite Alpine Ski Racing from 1997 to 2020: The Case of the Austrian Ski Team

Background To increase safety in elite alpine ski racing Injury Surveillance Systems were implemented and preventive measures introduced. However, studies analysing the change in athletes’ injury risk by controlling for their exposure are still scarce. Objectives This study aimed to describe and analyse the risk of in-competition severe injury events (SIEcomp) in elite alpine ski racing. Methods Data recorded in the Austrian Ski Federation’s Injury Surveillance System were used to analyse the SIEcomp incidence. Information on athletes’ competition exposure was obtained from the official website of the International Ski Federation. In 23 seasons, 2333 skier seasons were recorded for the Austrian Ski Team. Within a total of 114,531 runs 169 SIEcomp occurred. Generalised Estimating Equation for Poisson Regressions were applied. Results The SIEcomp incidence per 1000 runs was 1.48 [95% confidence interval (CI) 1.26–1.73] for elite alpine ski racers and 2.21 (95% CI 1.79–2.75) for the subgroup of World Cup racers. A significant sex difference was detected for the subgroup of junior racers with a higher risk for female athletes [risk ratio (RR): 2.97, 95% CI 1.46–6.05]. Between the seasons of 1997 and 2020, the seasonal SIEcomp incidence increased by a factor of 2.67 for elite alpine ski racers and 3.53 for World Cup racers. Downhill (2.75, 95% CI 2.18–3.47) had the highest SIEcomp incidence, followed by super-G (1.94, 95% CI 1.30–2.88), giant slalom (1.40, 95% CI 1.06–1.85), and slalom (0.64, 95% CI 0.43–0.96). Conclusion Although many preventive measures have been implemented in elite alpine ski racing, the risk of SIEcomp has increased over the last two decades.

Canadian Dairy Network for Antimicrobial Stewardship and Resistance (CaDNetASR): An On-Farm Surveillance System

Canada has implemented on-farm antimicrobial resistance (AMR) surveillance systems for food-producing animals under the Canadian Integrated Program for Antimicrobial Resistance (CIPARS); however, dairy cattle have not been included in that program yet. The objective of this manuscript was to describe the development and implementation of the Canadian Dairy Network for Antimicrobial Stewardship and Resistance (CaDNetASR). An Expert Panel (EP) of researchers was created to lead the development of the dairy surveillance system. The EP initiated a draft document outlining the essential elements of the surveillance framework. This document was then circulated to a Steering Committee (SC), which provided recommendations used by the EP to finalize the framework. CaDNetASR has the following components: (1) a herd-level antimicrobial use quantification system; (2) annually administered risk factor questionnaires; and (3) methods for herd-level detection of AMR in three sentinel enteric pathogens (generic Escherichia coli, Campylobacter spp., and Salmonella spp.) recovered from pooled fecal samples collected from calves, heifers, cows, and the manure pit. A total of 144 dairy farms were recruited in five Canadian provinces (British-Columbia, Alberta, Ontario, Québec, and Nova-Scotia), with the help of local herd veterinarians and regional field workers, and in September 2019, the surveillance system was launched. 97.1 and 94.4% of samples were positive for E. coli, 63.8, and 49.1% of samples were positive for Campylobacter spp., and 5.0 and 7.7% of samples were positive for Salmonella spp., in 2019 and 2020, respectively. E. coli was equally distributed among all sample types. However, it was more likely that Campylobacter spp. were recovered from heifer and cow samples. On the other hand, it was more common to isolate Salmonella spp. from the manure pit compared to samples from calves, heifers, or cows. CaDNetASR will continue sampling until 2022 after which time this system will be integrated into CIPARS. CaDNetASR will provide online access to farmers and veterinarians interested in visualizing benchmarking metrics regarding AMU practices and their relationship to AMR and animal health in dairy herds. This will provide an opportunity to enhance antimicrobial stewardship practices on dairy farms in Canada.

Comparison of longitudinal trends in self-reported symptoms and COVID-19 case activity in Ontario, Canada

Background Limitations in laboratory diagnostic capacity impact population surveillance of COVID-19. It is currently unknown whether participatory surveillance tools for COVID-19 correspond to government-reported case trends longitudinally and if it can be used as an adjunct to laboratory testing. The primary objective of this study was to determine whether self-reported COVID-19-like illness reflected laboratory-confirmed COVID-19 case trends in Ontario Canada. Methods We retrospectively analyzed longitudinal self-reported symptoms data collected using an online tool–Outbreaks Near Me (ONM)–from April 20th, 2020, to March 7th, 2021 in Ontario, Canada. We measured the correlation between COVID-like illness among respondents and the weekly number of PCR-confirmed COVID-19 cases and provincial test positivity. We explored contemporaneous changes in other respiratory viruses, as well as the demographic characteristics of respondents to provide context for our findings. Results Between 3,849–11,185 individuals responded to the symptom survey each week. No correlations were seen been self-reported CLI and either cases or test positivity. Strong positive correlations were seen between CLI and both cases and test positivity before a previously documented rise in rhinovirus/enterovirus in fall 2020. Compared to participatory surveillance respondents, a higher proportion of COVID-19 cases in Ontario consistently came from low-income, racialized and immigrant areas of the province- these groups were less well represented among survey respondents. Interpretation Although digital surveillance systems are low-cost tools that have been useful to signal the onset of viral outbreaks, in this longitudinal comparison of self-reported COVID-like illness to Ontario COVID-19 case data we did not find this to be the case. Seasonal respiratory virus transmission and population coverage may explain this discrepancy.

Evaluation of the Kano State Lassa Fever Surveillance System, 2015-2018 – Nigeria

BackgroundLassa fever (LF) is one of the priority diseases under surveillance through the integrated disease surveillance and response system (IDSR). We evaluated the LF surveillance system against its set objectives and assessed its attributes. MethodsWe used cross-sectional study design. Forty-seven stakeholders involved in the surveillance system were interviewed using the Centers for Disease Control and Prevention’s Updated Guidelines for Evaluating Public Health Surveillance Systems. The LF surveillance data from January 2015 to December 2018 were also analyzed. The attribute and objectives of the system were evaluated. ResultsOut of the 76 suspected cases recorded in kano state during the study period, only 54 samples were laboratory tested, 11 of them were confirmed positive with 9 deaths (case fatality rate of 82%). Confirmed cases were predominantly in Tudun Wada LGA (63.6%), while the age-group 20-39 years constituted 55% of the confirmed cases. There was male preponderance of cases (73%). The predictive value positive (PVP) was 14.5%. The surveillance system was however meeting its objectives of determining LF burden and detecting and characterizing cases and outbreak.ConclusionLF surveillance system in Kano was simple, flexible, stable, acceptable and timely. However, data was not representative. We recommended improved reporting from private and tertiary facilities and more personnel training and support to improve the system.

Development of high-resolution melting (HRM) assay to differentiate the species of Shigella isolates from stool and food samples

Shigella species, a group of intracellular foodborne pathogens, are the main causes of bacillary dysentery and shigellosis in humans worldwide. It is essential to determine the species of Shigella in outbreaks and food safety surveillance systems. The available immunological and molecular methods for identifying Shigella species are relatively complicated, expensive and time-consuming. High resolution melting (HRM) assay is a rapid, cost-effective, and easy to perform PCR-based method that has recently been used for the differentiation of bacterial species. In this study, we designed and developed a PCR-HRM assay targeting rrsA gene to distinguish four species of 49 Shigella isolates from clinical and food samples and evaluated the sensitivity and specificity of the assay. The assay demonstrated a good analytical sensitivity with 0.01–0.1 ng of input DNA template and an analytical specificity of 100% to differentiate the Shigella species. The PCR-HRM assay also was able to identify the species of all 49 Shigella isolates from clinical and food samples correctly. Consequently, this rapid and user-friendly method demonstrated good sensitivity and specificity to differentiate species of the Shigella isolates from naturally contaminated samples and has the potential to be implemented in public health and food safety surveillance systems.

Characterizing the transmission patterns of seasonal influenza in Italy: lessons from the last decade

Background Despite thousands of influenza cases annually recorded by surveillance systems around the globe, estimating the transmission patterns of seasonal influenza is challenging. Methods We develop an age-structured mathematical model to influenza transmission to analyze ten consecutive seasons (from 2010 to 2011 to 2019–2020) of influenza epidemiological and virological data reported to the Italian surveillance system. Results We estimate that 18.4–29.3% of influenza infections are detected by the surveillance system. Influenza infection attack rate varied between 12.7 and 30.5% and is generally larger for seasons characterized by the circulation of A/H3N2 and/or B types/subtypes. Individuals aged 14 years or less are the most affected age-segment of the population, with A viruses especially affecting children aged 0–4 years. For all influenza types/subtypes, the mean effective reproduction number is estimated to be generally in the range 1.09–1.33 (9 out of 10 seasons) and never exceeding 1.41. The age-specific susceptibility to infection appears to be a type/subtype-specific feature. Conclusions The results presented in this study provide insights on type/subtype-specific transmission patterns of seasonal influenza that could be instrumental to fine-tune immunization strategies and non-pharmaceutical interventions aimed at limiting seasonal influenza spread and burden.