Disease ecology of bats--the Canadian scene

Bats are hosts to a range of pathogens, which include zoonotic pathogens and pathogens of conservation concern. Brock Fenton's research on bat ecology has always balanced clear communication of potential health risks associated with bats and the need to communicate these risks precisely to avoid unnecessary persecution of bats. Here, we integrate Brock's work in the field of disease ecology with that of his students and collaborators and consider the potential advantages of studying disease ecology of bats within the Canadian context. The broad distribution of a few common species across the vast landscape of present-day Canada provides an opportunity to untangle the impacts of environmental variation on host-pathogen interactions and disease severity, particularly in the context of climate change. The varying migratory strategies and social structure of the bat species found in Canada could also facilitate informative interspecific studies to better understand how bat health is affected by interactions among rapid environmental changes, physiological traits, and the social behaviour of different species. We propose a series of priority research questions and approaches that could further our understanding of bat health and disease ecology in Canada, inspired by the work of Brock, his colleagues, and students.

A Research of Emergency Service Admission Due To Home Accidents Before and During The Covid-19 (Sars-Cov-2) Pandemic In Ankara (Turkey)

Home accidents can cause serious injuries, disabilities, and deaths. Approximately 40 million people were treated in hospitals annually because of injuries occurring in homes, and these injuries were responsible for approximately 76% of preventable deaths. The aims of this study are to compare the home accidents in the one-year period during the pandemic and the home accidents in the one-year period before the pandemic, and to reveal how the home accidents are affected in which part of the home and in which types of injuries. A retrospective study was made of the records of patients injured in home accidents between pre-pandemic and pandemic one-year periods. The patients were classified according to age groups, gender, season, day and time of the home accident, accident type, part of the home, trauma localization and type, and severity of injuries. While 46.5% of the 581 injured patients were before the pandemic, 53.5% were in the pandemic period. The injuries increased as the number of households staying at home increased compared to the pre-pandemic period. Likewise, there was a significant increase in the number of falls from balconies and windows during the pandemic period. It is still not possible to make a definite prediction about the course of the pandemic. In this context, it is of great importance to provide information on prevention from home accidents, especially in television programs and distance education activities.Copyright © 2023, Yuzuncu Yil Universitesi Tip Fakultesi. All rights reserved.

Rising Gold Prices but Lower Incomes for Gold Miners: Evidence on Market Imperfections from Burkina Faso during COVID-19

Although artisanal gold mining is known for human rights violations and environmental degradation, it is an increasingly important economic activity in many African countries, with a high potential to alleviate poverty. Due to increased demand for gold investment during the COVID-19 pandemic, the monthly international gold price has increased by 20% from January to May 2020. To understand how the COVID-19 pandemic has influenced gold miners, we analyse a panel survey of about 170 artisanal gold miners interviewed 2 months before the first case of COVID-19 in Burkina Faso. Follow-up surveys were done early in the pandemic and about 1 year after baseline. Various pre-existing local market failures caused local gold prices to decrease by 20%-30% from January to May 2020, when international gold prices noticeably increased. Market failures include oligopsonistic market conditions on the mines, which worsened due to travel restrictions that disrupted trading routes, reduced local traders' liquidity and made it difficult for traders to reach mines. Moreover, we find that miners have very little knowledge of international gold prices, and due to insecurity and credit constraints, they are unable to wait for local prices to recover. Once travel restrictions were lifted, the local gold price recovered close to the global gold price. To make local markets more competitive and ensure that miners benefit from rising international gold prices, governments could broadcast world gold prices on local radio, increase trading opportunities and provide access to credits for miners.

Few-Shot Semantic Relation Prediction Across Heterogeneous Graphs

Semantic relation prediction aims to mine the implicit relationships between objects in heterogeneous graphs, which consist of different types of objects and different types of links. In real-world scenarios, new semantic relations constantly emerge and they typically appear with only a few labeled data. Since a variety of semantic relations exist in multiple heterogeneous graphs, the transferable knowledge can be mined from some existing semantic relations to help predict the new semantic relations with few labeled data. This inspires a novel problem of few-shot semantic relation prediction across heterogeneous graphs. However, the existing methods cannot solve this problem because they not only require a large number of labeled samples as input, but also focus on a single graph with a fixed heterogeneity. Targeting this novel and challenging problem, in this paper, we propose a Meta-learning based Graph neural network for Semantic relation prediction, named MetaGS. Firstly, MetaGS decomposes the graph structure between objects into multiple normalized subgraphs, then adopts a two-view graph neural network to capture local heterogeneous information and global structure information of these subgraphs. Secondly, MetaGS aggregates the information of these subgraphs with a hyper-prototypical network, which can learn from existing semantic relations and adapt to new semantic relations. Thirdly, using the well-initialized two-view graph neural network and hyper-prototypical network, MetaGS can effectively learn new semantic relations from different graphs while overcoming the limitation of few labeled data. Extensive experiments on three real-world datasets have demonstrated the superior performance of MetaGS over the state-of-the-art methods. IEEE

Global Human Threat: The Potential Synergism between Mercury Intoxication and COVID-19.

The COVID-19 pandemic affected billions of people worldwide, and exposure to toxic metals has emerged as an important risk factor for COVID-19 severity. Mercury is currently ranked as the third toxic substance of global concern for human health, and its emissions to the atmosphere have increased globally. Both COVID-19 and mercury exposure present a high prevalence in similar regions: East and Southeast Asia, South America and Sub-Saharan Africa. Since both factors represent a multiorgan threat, a possible synergism could be exacerbating health injuries. Here, we discuss key aspects in mercury intoxication and SARS-CoV-2 infection, describing the similarities shared in clinical manifestations (especially neurological and cardiovascular outcomes), molecular mechanisms (with a hypothesis in the renin-angiotensin system) and genetic susceptibility (mainly by apolipoprotein E, paraoxonase 1 and glutathione family genes). Literature gaps on epidemiological data are also highlighted, considering the coincident prevalence. Furthermore, based on the most recent evidence, we justify and propose a case study of the vulnerable populations of the Brazilian Amazon. An understanding of the possible adverse synergism between these two factors is crucial and urgent for developing future strategies for reducing disparities between developed and underdeveloped/developing countries and the proper management of their vulnerable populations, particularly considering the long-term sequelae of COVID-19.

Camera-Based Blood Pressure Estimation via Windkessel Model and Waveform Features

Blood pressure (BP) is generally regarded as the vital sign most strongly correlated with human health. However, for decades, BP measurement has involved a cuff, which causes discomfort and even carries a risk of infection, given the current prevalence of COVID-19. Some studies address these problems using remote photoplethysmography (rPPG), which has shown great success in heart rate detection. Nevertheless, these approaches are not robust, and few have been evaluated with a sufficiently large dataset. We propose an rPPG-based BP estimation algorithm that predicts BP by leveraging the Windkessel model and hand-crafted waveform characteristics. A waveform processing procedure is presented for the rPPG signals to obtain a robust waveform template and thus extract BP-related features. Redundant and unstable features are eliminated via Monte Carlo simulation and according to their relationship with latent parameters (LSs) in the Windkessel model. For a comprehensive evaluation, the Chiao Tung BP (CTBP) dataset was constructed. The experiment was conducted over a four-week period of time to evaluate the validity period of the personalization in our system. On all the data, the proposed method outperforms the benchmark algorithms and yields mean absolute errors (MAEs) of 6.48 and 5.06 mmHg for systolic BP (SBP) and diastolic BP (DBP), respectively. The performance achieves a 'B' grade according to the validation protocol from the British Hypertension Society (BHS) for both SBP and DBP. © 1963-2012 IEEE.

Enhanced natural releases of mercury in response to the reduction in anthropogenic emissions during the COVID-19 lockdown by explainable machine learning

The wide spread of the coronavirus (COVID-19) has significantly impacted the global human activities. Compared to numerous studies on conventional air pollutants, atmospheric mercury that has matched sources from both anthropogenic and natural emissions is rarely investigated. At a regional site in eastern China, an intensive measurement was performed, showing obvious decreases in gaseous elemental mercury (GEM) during the COVID-19 lockdown, while it was not as significant as most of the other measured air pollutants. Before the lockdown, when anthropogenic emissions dominated, GEM showed no correlation with temperature and negative correlations with wind speed and the height of the boundary layer. In contrast, GEM showed significant correlation with temperature, while the relationship between GEM and the wind speed/boundary layer disappeared during the lockdown, suggesting the enhanced natural emissions of mercury. By applying a machine learning model and the SHAP (SHapley Additive exPlanations) approach, it was found that the mercury pollution episodes before the lockdown were driven by anthropogenic sources, while they were mainly driven by natural sources during and after the lockdown. Source apportionment results showed that the absolute contribution of natural surface emissions to GEM unexpectedly increased (44 %) during the lockdown. Throughout the whole study period, a significant negative correlation was observed between the absolute contribution of natural and anthropogenic sources to GEM. We conclude that the natural release of mercury could be stimulated to compensate for the significantly reduced anthropogenic GEM via the surface-air exchange in the balance of mercury.

Camera-based Blood Pressure Estimation via Windkessel Model and Waveform Features

Blood pressure (BP) is generally regarded as the vital sign most strongly correlated with human health. However, for decades, BP measurement has involved a cuff, which causes discomfort and even carries a risk of infection, given the current prevalence of COVID-19. Some studies address these problems using remote photoplethysmography (rPPG), which has shown great success in heart rate detection. Nevertheless, these approaches are not robust, and few have been evaluated with a sufficiently large dataset. We propose an rPPG-based BP estimation algorithm that predicts BP by leveraging the Windkessel model and hand-crafted waveform characteristics. A waveform processing procedure is presented for the rPPG signals to obtain a robust waveform template and thus extract BP-related features. Redundant and unstable features are eliminated via Monte Carlo simulation and according to their relationship with latent parameters in the Windkessel model. For a comprehensive evaluation, the Chiao Tung Blood Pressure (CTBP) dataset was constructed. The experiment was conducted over a four week period of time to evaluate the validity period of the personalization in our system. On all the data, the proposed method outperforms the benchmark algorithms and yields mean absolute errors of 6.48 mmHg and 5.06 mmHg for SBP and DBP, respectively. The performance achieves a “B”grade according to the validation protocol from the British Hypertension Society for both SBP and DBP. IEEE

Covid-19 Image Segmentation based on Masi Entropy and HGS Optimization Algorithm

The covid-19 epidemic has killed a lot of people over the past three years. A lot of solutions have been proposed to prevent the outbreak of the disease, including the fast diagnosis which helps to control the number of people infected across the world by providing the best treatment to the patient in a timely manner. A chest X-ray is a quick way to detect the infected area in the lungs. However, it is difficult to determine whether the lungs are infected or not? Hence, this paper proposes a powerful computer vision technology that is an efficient approach of multi-level thresholding based on the hunger game search (HGS) optimization algorithm. This algorithm used Masi's entropy as an objective function until the best solution was arrived at and, in particular, to determine the infection areas of lungs. Thus there are two kinds of images sets used to demonstrate the efficiency of the proposed approach. The first one is Non-COVID-19 chest X-ray images used as a reference and the second one COVID-19 chest X-ray images to test and detect affected areas in the lungs. © 2022 IEEE.

2021 The 6th International Conference of Indonesia Forestry Researchers - Stream 1 Emerging Environmental Quality for Better Living, Tangerang, Indonesia (Virtual), 8 September 2021

These proceedings contain 18 papers presenting and discussing the current environmental issues in Indonesia including monitoring of environmental pollution, role of environmental laboratory and set up national standard of environmental monitoring;circular economy and environmental quality management by businesses and relevant activities;mercury pollution, progress of National Action Plan for mercury reduction and elimination as a part of the ratification of Minamata Convention on Mercury;medical wastes and disposal concerning Covid-19 pandemic and antibiotically resistance;domestic waste, hazardous and poisonous materials and wastes;restoration and remediation of contaminated lands;and freshwater litters.

Use of Hand Creams during the Period of Frequent Disinfection in COVID-19 Pandemic-Preference Survey and Evaluation of Mercury Contamination.

The skin is one of the ways the human body is exposed to toxic elements, including mercury (Hg). Hand creams are cosmetics that should be of high quality due to the fact that they can be used on irritated skin, which can facilitate the absorption of many ingredients. The study consisted of two stages: a consumer questionnaire was conducted regarding the preferences of the respondents, and then the Hg content in hand creams was determined. The survey covered 184 people with an age of 26.9 ± 7.8 years. Based on their preferences, 140 hand creams were selected for the study. The Hg content in the creams was determined by atomic absorption spectrometry with the amalgamation technique. The median content of this toxic element was 4.067 µg/kg. No influence of the country of origin, price, package size, main ingredients, and direction of action on the Hg content was shown. Noncarcinogenic risk assessment using the hazard quotient (HQ) indicated that the tested creams are safe. However, it should be emphasized that Hg has been shown in over 99% of the samples, which indicates that the content of this element in hand creams should be monitored.

Bio-monitoring of atmospheric heavy metal pollution of major emergency events using moss bags:a case study in Xichang

In order to trace and monitor the atmospheric heavy metal pollution in Xichang City, an investigation activity was carried out with a sort of moss (Taxiphyllum taxirameum) (packed in moss bags) as a biological indicator for monitoring heavy metal pollution. The investigation was conducted from the period from April 2019 to April 2020, during which two grave emergency events occurred during spring monitoring period from January 15 to April 15, 2020, i.e., COVID-19 and "3.30"severe forest fire in Xichang, which inevitably affected the atmospheric quality. Based on the concentration analysis of 12 kinds of heavy metal, including Al, Cr, Fe, Cu, Ni, Pb, Mn, Hg, Zn, V, As and Ba contained in the moss and the local meteorological data, comparing those informative data before and after the time when the emergency events toke place, the paper made an analysis on the impacts of two enormous emergency events on the air pollution of heavy metal in Xichang. The results showed that total amount of enrichment of above-mentioned 12 heavy metals in spring (January 15 to April 15, 2020) is (12.85 +or- 1.57) mg/g, which was significantly higher than in the other three seasons (p < 0.01), but no significant discrepancies about the total enrichment amount in the other three seasons (p > 0.05). Primarily because of COVID-19 pandemic, the level of motor vehicles emissions cut down, and the decrease of the tourism in the related areas perhaps causing the decline of pollution of Pb. In addition, the decrease of unbalanced emission of pollutants led to a noted increase of atmospheric oxidation in urban area, thus boosting the formation of secondary particulate matter, and the particulate matter from surrounding industrial sources was transported into the urban area;as a result, remarkable increases of Hg concentration of moss within the moss bags were detected downwind the industrial area located in the urban fringe. Consequently, the investigation showed that the moss-bag method is an effective biological tool for monitoring air heavy metal pollution, which could reflect the impacts of major pollution events on air quality.

Rapid SARS-CoV-2 inactivation by mercury and LED UV-C lamps on different surfaces.

SARS-CoV-2 remains infectious for several hours on surfaces. It can be inactivated by UV-C irradiation but optimal conditions for rapid inactivation, especially on non-plastic surfaces remains unclear. A SARS-CoV-2 inoculum was irradiated with a UV-C LED (265 nm) or a UV-C mercury lamp (254 nm). Infectivity titers (TCID50/mL) and inactivation rates were then quantified on plastic, steel, tissue, paper and cardboard surfaces. We demonstrated that efficient SARS-CoV-2 inactivation (> 99.999% on plastic and steel, ≥ 99.8% on tissue, paper and cardboard) can be achieved by both a UV-C mercury lamp and a UV-C LED after 30 s of irradiations at 3 cm, corresponding to UV-C doses of 92.85 and 44.7 mJ/cm2, respectively. Inactivation on a plastic surface was more efficient with the mercury UV-C lamp (p < 0.005). The mercury UV-C lamp could be more relevant than the LED in high-risk settings, such as medical care or research laboratories.

Automated Temperature Scanner Sensor In Comparison With Mercury-In-Glass Thermometer

An automated temperature scanner with contact-tracing capability had previously been developed to screen temperature related diseases such as COVID-19, Ebola or Lassa fever and trace possible infected persons. The device uses a non-contact temperature sensor (MLX-90614) to acquire human temperature while the user's identity is obtained by means of Radio Frequency Identification card. This information is sent for storage in remote database and made available for possible contact-tracing via a secured web interface. Due to the fact that several studies contest the validity of non-contact temperature sensors as replacement for contact ones, the present study therefore compares performance of its non-contact temperature sensor with that of the mercury-in-glass thermometer considered as a standard in this study. This is in an attempt to validate performance of the developed automated temperature scanner and to optimize its usage. Investigations reveal that the developed device performs best when user is within a 16 cm distance from the temperature sensor. Any measurement done outside this 16 cm critical distance might not be valid. Other investigations reveal that the developed device with non-contact temperature sensor is faster than the contact thermometer with an average response time of 0.004 second compared with mercury-in-glass of 179.2 seconds. So non-contact sensor would be very useful when speed is of essence but it was found to exhibit a lower precision compared to the contact thermometer. The critical temperature obtained in this study will guide users in the usage and researchers in further studies on the developed automated temperature scanner with contact-tracing capability. © 2022 IEEE.

Selenium and mercury concentrations in biological samples from patients with COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a systemic disease affecting multiple organs. Furthermore, viral infection depletes several trace elements and promotes complex biochemical reactions in the body. Smoking has been linked to the incidence of COVID-19 and associated mortality, and it may impact clinical effects, viral and bacterial conversion, and treatment outcomes. OBJECTIVES: To study the relationship between severe acute respiratory syndrome coronavirus type 2 and the elemental concentrations of selenium (Se) and mercury (Hg) in biological samples from smokers and nonsmokers infected with the virus and in healthy individuals. METHOD: We evaluated changes in the concentrations of essential (Se) and toxic (Hg) elements in biological samples (blood, nasal fluid, saliva, sputum, serum, and scalp hair) collected from male smokers and nonsmokers (aged 29-59 years) infected with COVID-19 and from healthy men in the same age group. The patients lived in different cities in Sindh Province, Pakistan. The Se and Hg concentrations were determined using atomic absorption spectrophotometry. RESULTS: Se concentrations in all types of biological samples from smokers and nonsmokers with COVID-19 were lower than those of healthy smokers and nonsmokers. Hg concentrations were elevated in both smokers and nonsmokers with COVID-19. CONCLUSIONS: In the current study, persons infected with COVID-19 had higher concentrations of toxic Hg, which could cause physiological disorders, and low concentrations of essential Se, which can also cause weakness. COVID-19 infection showed positive correlations with levels of mercury and selenium. Thus, additional clinical and experimental investigations are essential.

Bright Future of Deep-Ultraviolet Photonics: Emerging UVC Chip Scale Light-Source Technology Platforms, Benchmarking, Challenges, and Outlook for UV Disinfection

The COVID-19 pandemic has generated great interest in ultraviolet (UV) disinfection, particularly for air disinfection. Although UV disinfection was discovered close to 90 years ago, only very recently has it reached the consumer market and achieved much acceptance from the public, starting in the 2000s. The current UV light source of choice has been almost exclusively a low-pressure mercury vapor discharge lamp. Today, however, with emerging deep-UV (DUV) chip-scale technologies, there has been a significant advancement, along with ever-increasing interest, in the development and deployment of disinfection systems that employ compact devices that emit in the deep-UV spectral band (200- 280 nm), including UV light-emitting diodes (LEDs) and cathodoluminescent (CL) chips. This perspective looks into competing UV technologies (including mercury lamps and excimer lamps as benchmarks) on their optical merits and demerits and discusses the emerging chip-scale technologies of DUV electroluminescent and cathodoluminescent devices, comparing them against the benchmarks and providing an overview of the challenges and prospects. The accelerating progress in chip-scale solutions for deep-UV light sources promises a bright future in UV disinfection.

Evaluating the impact of ultraviolet C exposure conditions on coliphage MS2 inactivation on surfaces.

The COVID-19 pandemic has raised interest in using devices that generate ultraviolet C (UVC) radiation as an alternative approach for reducing or eliminating microorganisms on surfaces. Studies investigating the efficacy of UVC radiation against pathogens use a wide range of laboratory methods and experimental conditions that can make cross-comparison of results and extrapolation of findings to real-world settings difficult. Here, we use three different UVC-generating sources - a broad-spectrum pulsed xenon light, a continuous light-emitting diode (LED), and a low-pressure mercury vapour lamp - to evaluate the impact of different experimental conditions on UVC efficacy against the coliphage MS2 on surfaces. We find that a nonlinear dose-response relationship exists for all three light sources, meaning that linear extrapolation of doses resulting in a 1-log10 (90%) reduction does not accurately predict the dose required for higher (e.g. 3-log10 or 99.9%) log10 reductions. In addition, our results show that the inoculum characteristics and underlying substrate play an important role in determining UVC efficacy. Variations in microscopic surface topography may shield MS2 from UVC radiation to different degrees, which impacts UVC device efficacy. These findings are important to consider in comparing results from different UVC studies and in estimating device performance in field conditions.

Portable Ultraviolet-C Chambers for Inactivation of SARS-CoV-2

The goal of this project was to create and optimize the performance of portable chambers for reliable ultraviolet (UV) disinfection of personal protective equipment (PPE) and enable its safe reuse. During unforeseen times of high demand for PPE, such as during the coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), single-use PPE supply can be quickly depleted. UV radiation has been shown to disinfect materials with high efficacy. This paper reports the design and construction of two 280 nm ultraviolet-C (UV-C) disinfection chambers in the form of portable chambers with 46 cm x 46 cm x 46 cm interior dimensions, one using light-emitting diodes and the other using mercury vapor lamps. This paper summarizes and presents a review of SARS-CoV-2 UV deactivation research during 2020 to 2021. Additionally, this paper discusses efforts to increase the uniformity and overall intensity of the UV-C radiation within the chambers through the installation of a UV-reflective, porous polytetrafluoroethylene (PTFE) material. A calculator prototype was additionally designed to calculate the reduction of SARS-CoV-2 as a result of UV-C disinfection, and the prototype code is presented. The paper describes the selection of UV-C radiation sources for the chambers and the chambers' mechanical and electrical design, PTFE installation, testing, and safety considerations.