The use of confocal microscopy in quantifying changes in membrane potential

Monitoring the plasma membrane potential and its changes can be a time consuming and challenging task especially when conventional electrophysiological techniques are used. The use of potentiometric fluorophores, namely tetramethylrhodamine methylester (TMRM), and digital imaging devices (laser scanning confocal microscopy) provides reliable and time efficient method. Two scorpion pore-forming peptides, namely PP and OP1, were used as a tool to induce depolarization of the plasma membrane potential of neuroblastoma cell line and cardiac myocytes. Alternative methods for the neuroblastoma cells and cardiac myocytes were used. Depolarization of the neuroblastoma cells was calibrated with 140 mM KCl solution with 1 µM valinomycin, after which intensity readers were substituted in the Nernst equation for quantification. Calibration of the alternative method used of the cardiac myocytes' plasma membrane potential changes was calibrated with the use of 5, 20, 40, and 80 mM KCl solutions with 1 µM valinomycin. A calibration curve was then constructed from which plasma membrane potential could be calculated

Natural convection enhancement in a porous cavity with Al2O3-Ethylene glycol/water nanofluids

The natural convection heat transfer of a differentially heated cavity filled with porous material and saturated with nanofluid is studied. The nanofluid used in the present study contains 60% Ethylene glycol, 40% DI-water and 30 nm size Al2O3 nanoparticles. The volume concentration of nanofluid used is in the range of 0.05% ⩽ ϕ ⩽ 0.4%. The range of Rayleigh number in the present study is 1.2 × 108 ⩽ Ra ⩽ 4 × 108 for clear cavity and 3 × 103 ⩽ Ra ⩽ 1.3 × 104 for the porous cavity. Viscosity of the nanofluid is also measured at volume concentration of 0.05% and found one available model works for the calculations. In order to explain the heat transfer behaviour of the present system, heat transferred by both clear and porous cavity, heat transfer coefficients of both hot and cold wall, as well as Nusselt number variation with concentrations of nanofluids are presented. It is found that the performance of porous cavity filled with a nanofluid volume concentration of 0.05% is enhanced while the other concentrations of nanofluids deteriorate the performance. At a volume concentration of 0.05%, the heat transfer capability of porous cavity is enhanced to a maximum of 10% compared to the base fluids.http://www.elsevier.com/locate/ijhmt2018-05-31hb2017Mechanical and Aeronautical Engineerin

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Multi-objective parallelization of efficient global optimization

Design optimization is a subject field where mathematical algorithms are used to improve designs. Analyses of designs using computational techniques often require significant computing resources, and for these problems, an efficient optimization method is needed. Efficient Global Optimization (EGO), first proposed by Jones et al. [25] is an optimization method which aims to use few function evaluations when optimizing a design problem. In this study, we use a multi-objective strategy to parallelize EGO. EGO is part of a set of algorithms called surrogate optimization methods. A set of initial designs are analyzed and then a response surface is fitted to the evaluated designs. In each iteration, EGO selects the set of design variables for which the next analysis will be performed. It makes this decision based on two opposing criteria. EGO will either decide to sample where the predicted objective function value is low, an exploitation approach, or where there is high uncertainty, an exploration approach. In each iteration, the classical EGO only selects one design per iteration. This selected design vector is either a result of exploitation or exploration based on a measure referred to as maximum Expected Improvement (EI). However, the modern day computing environment is capable of running multiple different analyses in parallel. Thus, it would be advantageous if EGO would be able to select multiple designs to evaluate in each iteration. In this research, we treat EGO?s inherent selection criteria to either exploit or explore as a multi-objective optimization problem, since each criterion can be defined by a separate objective function. In general multi-objective optimization problems don?t only have one solution, but a set of solutions called a Pareto optimal set. In our proposed strategy multiple designs from this Pareto optimal set are selected by EGO to be analyzed in the subsequent iteration. This proposed strategy is referred to as Simple Intuitive Multiobjective ParalLElization of Efficient Global Optimization (SIMPLE-EGO). We start our study by investigating the behaviour of classical EGO. During each iteration of EGO, a new design is selected to be evaluated. This is performed by finding the maximum of the Expected Improvement (EI) function. Maximizing this function initially proved challenging. However, by exploiting information regarding the nature of the EI function, the maximization problem is simplified significantly, and the robustness of finding the maximum is enhanced. More importantly, solving this maximization problem robustly, dramatically improves the convergence behaviour once a local basin has been found. We compare our SIMPLE-EGO method to a multi-objective optimization algorithm (EGO-MO) published by Feng et al. [16]. We first investigate the behaviour of EGO, EGO-MO, and SIMPLE-EGO. Thereafter the convergence performance of these methods is quantified. As expected the parallelization of both SIMPLE-EGO and EGO-MO lead to faster convergence on a range of test functions compared to classical EGO, which only sampled one point per iteration. The convergence characteristics of SIMPLE-EGO and EGOMO are also markedly different. We conclude with a discussion on the advantages and disadvantages of the investigated methods.Dissertation (MSc)--University of Pretoria, 2016.tm2016Mechanical and Aeronautical EngineeringMScUnrestricte

Cost and emissions pathways towards net-zero climate impacts in aviation

Aviation emissions are not on a trajectory consistent with Paris Climate Agreement goals. We evaluate the extent to which fuel pathways—synthetic fuels from biomass, synthetic fuels from green hydrogen and atmospheric CO_{2}, and the direct use of green liquid hydrogen—could lead aviation towards net-zero climate impacts. Together with continued efficiency gains and contrail avoidance, but without offsets, such an energy transition could reduce lifecycle aviation CO_{2} emissions by 89–94% compared with year-2019 levels, despite a 2–3-fold growth in demand by 2050. The aviation sector could manage the associated cost increases, with ticket prices rising by no more than 15% compared with a no-intervention baseline leading to demand suppression of less than 14%. These pathways will require discounted investments on the order of US$0.5–2.1 trillion over a 30 yr period. However, our pathways reduce aviation CO_{2}-equivalent emissions by only 46–69%; more action is required to mitigate non-CO_{2} impacts

Using dynamic relative climate impact curves to quantify the climate impact of bioenergy production systems over time

The climate impact of bioenergy is commonly quantified in terms of CO2 equivalents, using a fixed 100‐year global warming potential as an equivalency metric. This method has been criticized for the inability to appropriately address emissions timing and the focus on a single impact metric, which may lead to inaccurate or incomplete quantification of the climate impact of bioenergy production. In this study, we introduce Dynamic Relative Climate Impact (DRCI) curves, a novel approach to visualize and quantify the climate impact of bioenergy systems over time. The DRCI approach offers the flexibility to analyze system performance for different value judgments regarding the impact category (e.g., emissions, radiative forcing, and temperature change), equivalency metric, and analytical time horizon. The DRCI curves constructed for fourteen bioenergy systems illustrate how value judgments affect the merit order of bioenergy systems, because they alter the importance of one‐time (associated with land use change emissions) versus sustained (associated with carbon debt or foregone sequestration) emission fluxes and short‐ versus long‐lived climate forcers. Best practices for bioenergy production (irrespective of value judgments) include high feedstock yields, high conversion efficiencies, and the application of carbon capture and storage. Furthermore, this study provides examples of production contexts in which the risk of land use change emissions, carbon debt, or foregone sequestration can be mitigated. For example, the risk of indirect land use change emissions can be mitigated by accompanying bioenergy production with increasing agricultural yields. Moreover, production contexts in which the counterfactual scenario yields immediate or additional climate impacts can provide significant climate benefits. This paper is accompanied by an Excel‐based calculation tool to reproduce the calculation steps outlined in this paper and construct DRCI curves for bioenergy systems of choice.Dynamic Relative Climate Impact (DRCI) curves are a novel approach to quantify the climate impact of bioenergy systems over time. The DRCI approach offers the flexibility to analyze system performance for different value judgments regarding the impact category, equivalency metric, and analytical time horizon. The DRCI curves for four bioenergy systems illustrated in this figure show how the aforementioned value judgments can lead to alternative conclusions about the (relative) performance of bioenergy systems

Marginal climate and air quality costs of aviation emissions

Aviation emissions have been found to cause 5% of global anthropogenic radiative forcing and ∼16 000 premature deaths annually due to impaired air quality. When aiming to reduce these impacts, decision makers often face trade-offs between different emission species or impacts in different times and locations. To inform rational decision-making, this study computes aviation's marginal climate and air quality impacts per tonne of species emitted and accounts for the altitude, location, and chemical composition of emissions. Climate impacts are calculated using a reduced-order climate model, and air quality-related health impacts are quantified using marginal atmospheric sensitivities to emissions from the adjoint of the global chemistry-transport model GEOS-Chem in combination with concentration response functions and the value of statistical life. The results indicate that 90% of the global impacts per unit of fuel burn are attributable to cruise emissions, and that 64% of all damages are the result of air quality impacts. Furthermore, nitrogen oxides (NOx), carbon dioxide (CO2), and contrails are collectively responsible for 97% of the total impact. Applying our result metrics to an example, we find that a 20% NOx stringency scenario for new aircraft would reduce the net atmospheric impacts by 700 m USD during the first year of operation, even if the NOx emission reductions cause a small increase in CO2 emissions of 2%. In such a way, the damage metrics can be used to rapidly evaluate the atmospheric impacts of market growth as well as emissions trade-offs of aviation-related policies or technology improvements

Marginal climate and air quality costs of aviation emissions

Aviation emissions have been found to cause 5% of global anthropogenic radiative forcing and ∼16 000 premature deaths annually due to impaired air quality. When aiming to reduce these impacts, decision makers often face trade-offs between different emission species or impacts in different times and locations. To inform rational decision-making, this study computes aviation's marginal climate and air quality impacts per tonne of species emitted and accounts for the altitude, location, and chemical composition of emissions. Climate impacts are calculated using a reduced-order climate model, and air quality-related health impacts are quantified using marginal atmospheric sensitivities to emissions from the adjoint of the global chemistry-transport model GEOS-Chem in combination with concentration response functions and the value of statistical life. The results indicate that 90% of the global impacts per unit of fuel burn are attributable to cruise emissions, and that 64% of all damages are the result of air quality impacts. Furthermore, nitrogen oxides (NO x ), carbon dioxide (CO2), and contrails are collectively responsible for 97% of the total impact. Applying our result metrics to an example, we find that a 20% NOx stringency scenario for new aircraft would reduce the net atmospheric impacts by 700 m USD during the first year of operation, even if the NO x emission reductions cause a small increase in CO2 emissions of 2%. In such a way, the damage metrics can be used to rapidly evaluate the atmospheric impacts of market growth as well as emissions trade-offs of aviation-related policies or technology improvements