Bookerkrogh6045

Since late 2019, COVID-19 has devastated the global economy, with indirect implications for the environment. As governments' prioritized health and implemented measures such as the closure of non-essential businesses and social distancing, many workers have lost their jobs, been furloughed, or started working from home. Consequently, the world of work has drastically transformed and this period is likely to have major implications for mobility, transportation and the environment. This paper estimates the potential for people to engage in remote work and social distancing using O*NET data and Irish Census data and calculates the potential emission savings, by commuter type from a switch to remote working and occupational social distancing. The results show that while those who commute by car have a relatively high potential for remote work, they are less likely to be able to engage in social distancing in their workplace. While this may be negative for employment prospects in the short run, our analysis indicates that this pattern has the potential for positive environmental implications in the short and long run.Measurement errors are present in many data collection procedures and can harm analyses by biasing estimates. To correct for measurement error, researchers often validate a subsample of records and then incorporate the information learned from this validation sample into estimation. In practice, the validation sample is often selected using simple random sampling (SRS). However, SRS leads to inefficient estimates because it ignores information on the error-prone variables, which can be highly correlated to the unknown truth. Applying and extending ideas from the two-phase sampling literature, we propose optimal and nearly-optimal designs for selecting the validation sample in the classical measurement-error framework. We target designs to improve the efficiency of model-based and design-based estimators, and show how the resulting designs compare to each other. Our results suggest that sampling schemes that extract more information from the error-prone data are substantially more efficient than SRS, for both design- and model-based estimators. The optimal procedure, however, depends on the analysis method, and can differ substantially. This is supported by theory and simulations. We illustrate the various designs using data from an HIV cohort study.The pattern of coronavirus spread at different geographical scales verifies that travel or shipment by air, sea or road are potential to transmit viruses from one location to somewhere far away in a very short time. Simulation and analysis of such a situation requires the development of models that support long distance transmission of viruses. Cellular Automata (CA) are a family of spatiotemporal computational models frequently employed in analysis of biomedical systems. A CA consists of a topological combination of units called cells as well as a transition function that propagates the configuration of cells locally and step by step. In this paper, we first present some patterns that show the local interaction between CA cells is not sufficient for virus spread modeling, especially at large spatial scales. Then, we generalize the concept of CA by providing a symbiosis between the neighborhood relationship of cells and the transmission channels represented by a dynamic weighted multigraph. Furthermore, we characterize the capabilities of the proposed modeling tool in simulation of the virus spread, and estimating the risk control during the movement restrictions and related health protocols. Finally, we simulate the coronavirus outbreak in the five study areas including three states and two countries. Our experiments using the proposed model verify that the proposed model is capable of formulating different ways of virus transmission, including long-distance transmission, and supports high-precision simulation of the pandemic.Highly urbanized and industrialized Asansol Durgapur industrial belt of Eastern India is characterized by severe heat island effect and high pollution level leading to human discomfort and even health problems. However, COVID-19 persuaded lockdown emergency in India led to shut-down of the industries, traffic system, and day-to-day normal work and expectedly caused changes in air quality and weather. The present work intended to examine the impact of lockdown on air quality, land surface temperature (LST), and anthropogenic heat flux (AHF) of Asansol Durgapur industrial belt. Satellite images and daily data of the Central Pollution Control Board (CPCB) were used for analyzing the spatial scale and numerical change of air quality from pre to amid lockdown conditions in the study region. Results exhibited that, in consequence of lockdown, LST reduced by 4.02 °C, PM10 level decreased from 102 to 18 μg/m3 and AHF declined from 116 to 40W/m2 during lockdown period. Qualitative upgradation of air quality index (AQI) from poor to very poor state to moderate to satisfactory state was observed during lockdown period. To regulate air quality and climate change, many steps were taken at global and regional scales, but no fruitful outcome was received yet. Such lockdown (temporarily) is against economic growth, but it showed some healing effect of air quality standard.In this paper, we propose and analyze a nonsmoothly two-dimensional map arising in a seasonal influenza model. Such map consists of both linear and nonlinear dynamics depending on where the map acts on its domain. The map exhibits a complicated and unpredictable dynamics such as fixed points, period points, chaotic attractors, or multistability depending on the ranges of a certain parameters. Surprisingly, bistable states include not only the coexistence of a stable fixed point and stable period three points but also that of stable period three points and a chaotic attractor. Among other things, we are able to prove rigorously the coexistence of the stable equilibrium and stable period three points for a certain range of the parameters. Our results also indicate that heterogeneity of the population drives the complication and unpredictability of the dynamics. Specifically, the most complex dynamics occur when the underlying basic reproduction number with respect to our model is an intermediate value and the large portion of the population in the same compartment changes in states the following season.Vegetated buffers and filter strips are a widely used Best Management Practice (BMP) for enhancing streamside ecosystem quality and water quality improvement through nonpoint source pollutant removal. Most existing studies are either site-specific, rely on limited data points, or evaluate buffer width and slope as the only design variables for predicting sediment reduction, not considering other parameters such as soil texture, vegetation types, and runoff loads that can significantly influence the buffer efficiency. In this paper, we carry out a meta-analysis of published studies and fit regression models to explore the sediment removal capacity of riparian buffers. We compiled 905 data points from over 90 studies (including data from an online BMP database) documenting sediment trapping by vegetated buffers and recorded data regarding buffer characteristics such as buffer width, slope, area, vegetation type, sediment loading, water flow rates, and sediment removal efficiency. We found that an exponential regression model describing the relationship between sediment removal efficiency by the buffer and water inflow/outflow volume ratio explained 44% of the variance. Adding the square root of roughness increased the R 2 to 0.50. The model performance was compared with other sediment reduction regression models reported in the literature. The results point towards the importance of considering flow parameters in vegetative buffer design. The improved empirical relationships derived here can be used at local scales to understand sediment trapping potential by vegetated buffers for water quality mitigation purposes and can be built into extant hydrologic models for improved watershed-scale assessments.Live cells acquire different fates including apoptosis, necrosis, and senescence in response to stress and stimuli. Rapid and label-free enrichment of live cells from a mixture of cells adopting various cell fates remains a challenge. We developed a ViaChip for high-throughput enrichment of Viable cells via size-based separation on a multi-stage microfluidic Chip. Our chip takes advantage of the characteristic increase in cell size during cellular senescence and decreases during apoptosis and necrosis, in comparison to their viable and healthy counterparts. The core component of our ViaChip is a slanted and tunable 3D filter array in the vertical direction (z-gap) for rapid and continuous cell sieving. The shape of the 3D filter array is optimized for target cells to prevent clogging during continuous separation. We demonstrated enrichment of live human and mouse mesenchymal stem cells in culture and from live animals, as well as the removal of senescent and necrotic MSCs, respectively, achieving an enrichment efficiency of ~67% with the continuous flow at 1.5 mL/hour. With further improvements in throughput and separation efficiency, our ViaChip could find applications in cell-based drug screening for anti-cancer and anti-aging cell therapies.

To assess the impact of the COVID-19 pandemic on admissions of patients with acute coronary syndromes (ACS) and primary percutaneous coronary intervention (PPCI) in countries participating in the Stent-Save a Life (SSL) global initiative.

We conducted a multicenter observational survey to collect data on patient admissions for ACS, ST-elevation myocardial infarction (STEMI) and PPCI in participating SSL member countries through a period during the COVID-19 outbreak (March and April 2020) compared with the equivalent period in 2019. Of the 32 member countries of the SSL global initiative, 17 agreed to participate in the survey (three in Africa, five in Asia, six in Europe and three in Latin America). Overall reductions of 27.5% and 20.0% were observed in admissions for ACS and STEMI, respectively. The decrease in PPCI was 26.7%. This trend was observed in all except two countries. In these two, the pandemic peaked later than in the other countries.

This survey shows that the COVID-19 outbreak was associated with a significant reduction in hospital admissions for ACS and STEMI as well as a reduction in PPCI, which can be explained by both patient- and system-related factors.

This survey shows that the COVID-19 outbreak was associated with a significant reduction in hospital admissions for ACS and STEMI as well as a reduction in PPCI, which can be explained by both patient- and system-related factors.Streptococcus suis is one of the most important swine bacterial pathogens causing economic losses. This report presents the serotype distribution of S. suis recovered from diseased pigs in Québec from January 2015 to June 2020. Orforglipron Serotypes 1/2 and 2 predominated, followed by serotypes 7, 3, 5, 4, 9, 1, and 14. Compared to previously reported data, very few changes could be observed concerning the serotype distribution, indicating a relative stability. Half of the untypable isolates did not belong to the species S. suis sensu stricto, as determined by recN polymerase chain reaction. Less than 10% of "real S. suis" isolates were untypable. The genetic diversity of S. suis serotypes 1, 2, and 14, as analyzed by multilocus sequence typing, was mainly represented by sequence type (ST)1, ST28, ST25, and ST94. All ST1 isolates (considered highly virulent) belonged to either serotype 1 or 14.