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Diya Jhuti,1,2 Gohar Zakaryan,1 Hussein El-Kechen,3 Nadia Rehman,3 Mark Youssef,4 Cristian Garcia,4 Vaibhav Arora,1 Babalwa Zani,5 Alvin Leenus,6 Michael Wu,7 Oluwatoni Makanjuola,8 Lawrence Mbuagbaw3,9– 11 1Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada; 2Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 3Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada; 4Faculty of Medicine, University of Toronto, Toronto, ON, Canada; 5Public Health Research Unit, AB Consulting, Cape Town, South Africa; 6Faculty of Law, University of Ottawa, Ottawa, ON, Canada; 7Michael DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada; 8Faculty of Science, McMaster University, Hamilton, ON, Canada; 9Biostatistics Unit, Father Sean O’Sullivan Research Centre, Hamilton, ON, Canada; 10Centre for Development of Best Practices in Health, Yaoundé Central Hospital, Yaoundé, Cameroon; 11Department of Global Health, Stellenbosch University, Cape Town, South AfricaCorrespondence: Lawrence Mbuagbaw, Biostatistics Unit/FSORC, 50 Charlton Avenue East, St Joseph’s Healthcare—Hamilton, 3rd Floor Martha Wing, Room H321, Hamilton, ON, L8N 4A6, Canada, Tel +1-905-522-1155 ext 35929, Fax +1-905-528-7386, Email mbuagblc@mcmaster.caIntroduction: Engagement in the HIV care cascade is required for people living with HIV (PLWH) to achieve an undetectable viral load. However, varying definitions of engagement exist, contributing to heterogeneity in research regarding how many individuals are actively participating and benefitting from care. A standardized definition is needed to enhance comparability and pooling of data from engagement studies.Objectives: The objective of this paper was to describe the various definitions for engagement used in HIV clinical trials.Methods: Articles were retrieved from CASCADE, a database of 298 clinical trials conducted to improve the HIV care cascade (https://hivcarecascade.com/), curated by income level, vulnerable population, who delivered the intervention, the setting in which it was delivered, the intervention type, and the level of pragmatism of the intervention. Studies with engagement listed as an outcome were selected from this database.Results: 13 studies were eligible, of which five did not provide an explicit definition for engagement. The remaining studies used one or more of the following: appointment adherence (n=6), laboratory testing (n=2), adherence to antiretroviral therapy (n=2), time specification (n=5), intervention adherence (n=5), and quality of interaction (n=1).Conclusion: This paper highlights the existing diversity in definitions for engagement in the HIV care cascade and categorize these definitions into appointment adherence, laboratory testing, adherence to antiretroviral therapy, time specification, intervention adherence, and quality of interaction. We recommend consensus on how to describe and measure engagement.Keywords: HIV, engagement, antiretroviral therapy, adherence, retention, cascade

Results from the fully and biogeochemically coupled simulations in which CO2 increases at a rate of 1%yr-1 (1pctCO2) from its preindustrial value are analyzed to quantify the magnitude of carbon-concentration and carbon-climate feedback parameters which measure the response of ocean and terrestrial carbon pools to changes in atmospheric CO2 concentration and the resulting change in global climate, respectively. The results are based on 11 comprehensive Earth system models from the most recent uncertain over land than over ocean as has been seen in existing studies. These values and their spread from 11 CMIP6 models have not changed significantly compared to CMIP5 models. The absolute values of feedback parameters are lower for land with models that include a representation of nitrogen cycle. The transient climate response to cumulative emissions (TCRE) from the 11 CMIP6 models considered here is 1.77±0.37 ° C EgC-1 and is similar to that found in CMIP5 models (1.63±0.48 °C EgC-1) but with somewhat reduced model spread. The expressions for feedback parameters based on the fully and biogeochemically coupled configurations of the 1pctCO2 simulation are simplified when the small temperature change in the biogeochemically coupled simulation is ignored. Decomposition of the terms of these simplified expressions for the feedback parameters is used to gain insight into the reasons for differing responses among ocean and land carbon cycle models.

We experimentally demonstrate the transition of fast electron generation mechanism from J × B heating to stochastic heating by varying preplasma scale length in the interaction of ultrashort (∼25 fs) high intensity (∼3–4 × 1019 W/cm2) laser with thin foil. At sharp plasma density laser interaction (contrast ∼2 × 10−10 at 1 ns, L/λ ≪ 1), fast electrons were observed along the laser propagation direction demonstrating J × B heating. Interestingly, fast electron temperature in this case was less than ponderomotive scaling. The reasons were identified to be the small excursion length of electron compared to laser wavelength in sharp density interaction along with energy loss while escaping through the rear surface. A simplistic model has been proposed to understand the energy loss mechanism from the rear surface. Next, preplasma was introduced gradually by varying the amplified spontaneous emission contrast and additional picosecond prepulse at different delays. It resulted in an increase in the energy and temperature of fast electrons. Most importantly, at larger scale length (L/λ ≫ 1), fast electron temperature beyond the ponderomotive limit was observed. The temperature scales with scale length as T ∝ L 0.59 and shows a saturation effect at longer scale length. The results indicate a gradual change in the fast electron generation mechanism to stochastic heating producing superponderomotive energy. Particle-in-cell simulation also very well reproduces our experimental findings. [ABSTRACT FROM AUTHOR]

Cardiac arrhythmia is a condition in which the heart beats with an irregular rhythm. There is an abnormality in the site of the origin of impulse, rate, or conduction. Arrhythmia affects millions of people around the world. While they may occur at any age, they are most commonly seen in older patients. Sudden cardiac death (SCD) often occurs as a result of arrhythmias. SCD accounts for nearly 50% of deaths due to cardiovascular disease and about 15% of all deaths globally. About 80% of SCD occur as a result of ventricular arrhythmias [1]. Atrial fibrillation (AF) and atrial flutter (AFl) resulted in 112,000 deaths in 2013 — a stark increase from 29,000 in 1990. A patient may present with symptoms of palpitation, giddiness, uneasiness, syncope, SCD, or with non-specific complaints. A lot of patients may remain asymptomatic for variable periods till the first episode and in-between episodes.

Thermal non-destructive testing and evaluation (NDT&E) is crucial in ensuring the quality and safety of industrial materials, components and structures. It serves as a key tool for assessing their operational reliability, thus enhancing safety in a wide range of industries. There is a growing demand for dependable, swift, remote and secure inspection and assessment techniques to detect hidden flaws, especially for sustainable solutions, which prompts adjustments in design and manufacturing standards. Hidden defects often emerge during the service life of these materials and structures due to various stress factors, potentially resulting in catastrophic failures. This study delves into an optimal and dependable experimental method for conducting fast, remote and secure inspections and assessments of carbon fibre-reinforced polymer (CFRP) materials using infrared imaging (IRI) as part of thermal non-destructive testing and evaluation (TNDT&E). Additionally, it examines the post-processing approach associated with this technique. This perspective also sheds light on the current state-of-the-art of infrared imaging methods employed in TNDT&E, emphasising the strengths and weaknesses in their ability to detect subsurface defects present within the material. Most of the methods discussed in previous research primarily focus on the thermal differences in specific areas of a sample using processed thermal images, even though these images come from analysing a series of images captured over time. This study highlights the latest research in thermal/infrared non-destructive testing and evaluation, along with related post-processing techniques. It not only aims to show hidden subsurface defects through thermal differences but also provides information about how these defects change over time. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19), a spectrum of disease spanning from a barely symptomatic infection to critical illness, is caused by the new coronavirus - severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although lung injury and acute respiratory distress syndrome have been at a centre stage as the most dreadful complications of COVID-19, the heart damage has recently emerged as yet another grim outcome in the virus’s repertoire of possible complications. The cardiac involvement occurs as a direct invasion of cardiomyocytes by the virus via the angiotensin‐converting enzyme 2 (ACE2) receptors and also indirectly by the inflammatory reaction due to cytokine storm. COVID-19 increases the severity of an already pre-existing heart disease and also unmasks the cardiac symptoms in a previously undiagnosed heart disease as well. Apart from this the drugs that are repurposed and are supposed to have a preventive or a therapeutic potential are also supposedly causing cardiac side effects. In the absence of substantial database all these seem to be a hypothetical correlation and one needs to rely on clinical skills and acumen.

Coordinated experimental design and implementation has become a cornerstone of global climate modelling. Model Intercomparison Projects (MIPs) enable systematic and robust analysis of results across many models, by reducing the influence of ad hoc differences in model set-up or experimental boundary conditions. As it enters its 6th phase, the Coupled Model Intercomparison Project (CMIP6) has grown significantly in scope with the design and documentation of individual simulations delegated to individual climate science communities. The Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP) takes responsibility for design, documentation, and analysis of carbon cycle feedbacks and interactions in climate simulations. These feedbacks are potentially large and play a leading-order contribution in determining the atmospheric composition in response to human emissions of CO2 and in the setting of emissions targets to stabilize climate or avoid dangerous climate change. For over a decade, C4MIP has coordinated coupled climate-carbon cycle simulations, and in this paper we describe the C4MIP simulations that will be formally part of CMIP6. While the climate-carbon cycle community has created this experimental design, the simulations also fit within the wider CMIP activity, conform to some common standards including documentation and diagnostic requests, and are designed to complement the CMIP core experiments known as the Diagnostic, Evaluation and Characterization of Klima (DECK). C4MIP has three key strands of scientific motivation and the requested simulations are designed to satisfy their needs: (1) pre-industrial and historical simulations (formally part of the common set of CMIP6 experiments) to enable model evaluation, (2) idealized coupled and partially coupled simulations with 1% per year increases in CO2 to enable diagnosis of feedback strength and its components, (3) future scenario simulations to project how the Earth system will respond to anthropogenic activity over the 21st century and beyond. This paper documents in detail these simulations, explains their rationale and planned analysis, and describes how to set up and run the simulations. Particular attention is paid to boundary conditions, input data, and requested output diagnostics. It is important that modelling groups participating in C4MIP adhere as closely as possible to this experimental design.

Assessment of 28 days strength from accelerated strength (1 day) can be extremely helpful. Early prediction of 28 days compressive strength is required basically for two purposes. First, to finalize the concrete mix proportions in the laboratory and secondly, for quality control purpose during construction. Through this concept designers can easily identify the uncalculated errors during mix design or variations in materials and exposure conditions etc. and take necessary correction and modification measures to attain the desired strengths at 28 days. As per IS: 9013 methodology to predict the 28 days strength of concrete from accelerated cured strength are indicated only for normal/control concrete. In the past few year focus has shifted from Ordinary Portland Cement (OPC) to Portland Pozzolana Cement (PPC). Fly ash is also being used widely at sites as replacement of OPC. However, there is no such guideline available by which the assessment of 28 days strength of Indian fly ash concrete can be made from accelerated strength tests. In the present study an attempt has been made to predict the expected 28 days compressive strength of concrete having PPC or OPC with fly ash using accelerated temperature regime methods. The experimental study includes the use of 2 brands of PPC, 5 brands of OPC and 2 sources of fly ash for replacement of OPC ranging from 20% to 45 %. Two temperature regimes 900C and 820C were used for accelerated curing. The samples were cured for 7.5 hours and 20hours respectively in each regime for expected 28 day compressive strength. The mathematical equations to estimate the 28 days compressive strength of concrete, cured at 900C for 7.5 hours for mixes having PPC, OPC mixed with fly ash 20% to 35% is fexp28 = 1.223 facs +2.024. The mathematical equations to estimate the 28 days compressive strength of concrete, cured at 900C for 7.5 hours for mixes having PPC, OPC mixed with fly ash more than 35% and up to 45% is fexp28 = 0.993 facs + 6.044.

The strength of feedbacks between a changing climate and future CO2 concentrations is uncertain and difficult to predict using Earth System Models (ESMs). We analyzed emission-driven simulations—in which atmospheric CO2levels were computed prognostically—for historical (1850–2005) and future periods (Representative Concentration Pathway (RCP) 8.5 for 2006–2100) produced by 15 ESMs for the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Comparison of ESM prognostic atmospheric CO2 over the historical period with observations indicated that ESMs, on average, had a small positive bias in predictions of contemporary atmospheric CO2. Weak ocean carbon uptake in many ESMs contributed to this bias, based on comparisons with observations of ocean and atmospheric anthropogenic carbon inventories. We found a significant linear relationship between contemporary atmospheric CO2 biases and future CO2levels for the multimodel ensemble. We used this relationship to create a contemporary CO2 tuned model (CCTM) estimate of the atmospheric CO2 trajectory for the 21st century. The CCTM yielded CO2estimates of 600±14 ppm at 2060 and 947±35 ppm at 2100, which were 21 ppm and 32 ppm below the multimodel mean during these two time periods. Using this emergent constraint approach, the likely ranges of future atmospheric CO2, CO2-induced radiative forcing, and CO2-induced temperature increases for the RCP 8.5 scenario were considerably narrowed compared to estimates from the full ESM ensemble. Our analysis provided evidence that much of the model-to-model variation in projected CO2 during the 21st century was tied to biases that existed during the observational era and that model differences in the representation of concentration-carbon feedbacks and other slowly changing carbon cycle processes appear to be the primary driver of this variability. By improving models to more closely match the long-term time series of CO2from Mauna Loa, our analysis suggests that uncertainties in future climate projections can be reduced.

Soil is currently thought to be a sink for carbon; however, the response of this sink to increasing levels of atmospheric carbon dioxide and climate change is uncertain. In this study, we analyzed soil organic carbon (SOC) changes from 11 Earth system models (ESMs) under the historical and high radiative forcing (RCP 8.5) scenarios between 1850 and 2100. We used a reduced complexity model based on temperature and moisture sensitivities to analyze the drivers of SOC losses. ESM estimates of SOC change over the 21st century (2090–2099 minus 1997–2006) ranged from a loss of 72 Pg C to a gain 253 Pg C with a multi-model mean gain of 63 Pg C. All ESMs showed cumulative increases in both NPP (15% to 59%) and decreases in SOC turnover times (15% to 28%) over the 21st century. Most of the model-to-model variation in SOC change was explained by initial SOC stocks combined with the relative changes in soil inputs and decomposition rates (R2 = 0.88,p

Background: Though diabetes mellitus (DM) is a well-recognised risk factor for onychomycosis (OM), the epidemiology of OM in diabetic patients remains largely unexplored, especially from the Indian subcontinent. Aims and objectives: To estimate the prevalence of OM in diabetic patients, to identify and analyse risk factors, and correlate the severity of nail changes with glycemic control (HBA1c). Methods: This cross-sectional, analytical study involved 300 diabetic patients. Patients with the clinical diagnosis of OM, supplanted by at least two of the four tests (KOH, culture, onychoscopy and nail histopathology) were considered cases of OM. Demographic and haematological profile was analysed using chi-square test/Fischer's exact test. Logistic regression was applied to assess the independent risk factors. Results: The prevalence of OM in DM patients was 34% (102/300) and significant risk factors included; age >60 years, male gender, closed shoes, disease duration >5 years, high BMI (>25) and lack of awareness about nail changes. Distal and lateral subungual OM (78%) was the commonest presentation followed by proximal subungual OM, superficial OM and total dystrophic OM. Correlation between HbA1c and the number of nails involved was found to be significant. Limitations: As cases were recruited from a hospital setting, there could be chances of Berksonian bias. Conclusion: The prevalence of OM in diabetic patients is high and the severity of nail changes correlates with HbA1C levels. It is important to diagnose OM early in order to treat and prevent complications. [ABSTRACT FROM AUTHOR]