Serum Cytokine Alterations Associated with Age of Patients with Nephropathia Epidemica

Nephropathia epidemica (NE) is a zoonotic disease caused by hantaviruses transmitted from rodents, endemic in the Republic of Tatarstan, Russia. The disease presents clinically with mild, moderate, and severe forms, and time-dependent febrile, oliguric, and polyuric stages of the disease are also recognized. The patient’s cytokine responses have been suggested to play a central role in disease pathogenesis; however, little is known about the different patterns of cytokine expression in NE in cohorts of different ages and sexes. Serum samples and clinical records were collected from 139 patients and 57 controls (healthy donors) and were used to analyze 48 analytes with the Bio-Plex multiplex magnetic bead-based antibody detection kits. Principal component analysis of 137 patient and 55 controls (for which there was full data) identified two components that individually accounted for >15% of the total variance in results and together for 38% of the total variance. PC1 represented a proinflammatory TH17/TH2 cell antiviral cytokine profile and PC2 a more antiviral cytokine profile with patients tending to display one or the other of these. Severity of disease and stage of illness did not show any correlation with PC1 profiles; however, significant differences were seen in patients with high PC1 profiles vs. lower for a number of individual clinical parameters: High PC1 patients showed a reduced number of febrile days, but higher maximum urine output, higher creatinine levels, and lower platelet levels. Overall, the results of this study point towards a stronger proinflammatory profile occurring in younger NE patients, this being associated with markers of acute kidney injury and low levels of high-density cholesterol. This is consistent with previous work indicating that the pathology of NE is immune driven, with an inflammatory immune response being associated with disease and that this immune response is more extreme in younger patients.

Extreme precipitating events in satellite and rain-gauge products over the Sahel

Over the recent decades, Extreme Precipitation Events (EPE) have become more frequent over the Sahel. Their properties, however, have so far received little attention. In this study the spatial distribution, intensity, seasonality and interannual variability of EPEs are examined, using both a reference dataset, based on a high-density rain-gauge network over Burkina Faso and 24 precipitation gridded datasets. The gridded datasets are evaluated in depth over Burkina Faso while their commonalities are used to document the EPE properties over the Sahel. EPEs are defined as the occurrence of daily-accumulated precipitation exceeding the all-day 99th percentile over a 1°x1° pixel. Over Burkina Faso, this percentile ranges between 21 and 33 mm day-1. The reference dataset show that EPEs occur in phase with the West African monsoon annual cycle, more frequently during the monsoon core season and during wet years. These results are consistent among the gridded datasets over Burkina Faso but also over the wider Sahel. The gridded datasets exhibit a wide diversity of skills when compared to the Burkinabe reference. The Global Precipitation Climatology Centre Full Data Daily version 1 (GPCC-FDDv1) and the Global Satellite Mapping of Precipitation gauge Reanalysis version 6.0 (GSMaP-gauge-RNL v6.0) are the only products that properly reproduce all of the EPE features examined in this work. The datasets using a combination of microwave and infrared measurements are prone to overestimate the EPE intensity, while infrared-only products generally underestimate it. Their calibrated versions perform than their uncalibrated (near-real-time) versions. This study finally emphasizes that the lack of rain-gauge data availability over the whole Sahel strongly impedes our ability to gain insights in EPE properties.

K2-79b and K2-222b: Mass Measurements of Two Small Exoplanets with Periods beyond 10 days that Overlap with Periodic Magnetic Activity Signals

We present mass and radius measurements of K2-79b and K2-222b, two transiting exoplanets orbiting active G-type stars observed with HARPS-N and K2. Their respective 10.99 day and 15.39 day orbital periods fall near periods of signals induced by stellar magnetic activity. The two signals might therefore interfere and lead to an inaccurate estimate of exoplanet mass. We present a method to mitigate these effects when radial velocity (RV) and activity-indicator observations are available over multiple observing seasons and the orbital period of the exoplanet is known. We perform correlation and periodogram analyses on subsets composed of each target's two observing seasons, in addition to the full data sets. For both targets, these analyses reveal an optimal season with little to no interference at the orbital period of the known exoplanet. We make a confident mass detection of each exoplanet by confirming agreement between fits to the full RV set and the optimal season. For K2-79b, we measure a mass of 11.8 ± 3.6 M ⊕ and a radius of 4.09 ± 0.17 R ⊕. For K2-222b, we measure a mass of 8.0 ± 1.8 M ⊕ and a radius of 2.35 ± 0.08 R ⊕. According to model predictions, K2-79b is a highly irradiated Uranus analog and K2-222b hosts significant amounts of water ice. We also present a RV solution for a candidate second companion orbiting K2-222 at 147.5 days.

Evaluating the effects of agricultural wastes on concrete and composite mechanical properties: a review

Wastes generation and emission of greenhouse gases are the major concerns of the contemporary world. Concrete’s cements companies in the globe are producing up to 2.8 billion tons of cements annually. This contributed to the emission of anthropogenic substances into the atmosphere which destroys the ozone layers. The incessant disposal of these agricultural wastes has detrimental effect on the environmental and human health. Thus, utilizing these wastes as secondary resources in concrete is a reasonable consideration in sustainable waste management in the circular economy. The use of agricultural wastes in concrete production has been gaining attraction in recent years, however, their effectiveness and performance in concrete need evaluation. This study presents an overview of the effects of some agricultural wastes: Bagasse, Coconut shell, Cotton, Oil palm and Hemp fibers on concrete and composite’s mechanical properties. As reviewed, Sugar-Cane Bagasse Ash (SCBA) and Coconut Shell Ash (CSA) are rich in cementitious (pozzolanic) properties (SiO2, Fe2O3 and Al2O3) for cement production up to 70%. Sugar-cane bagasse and oil palm-fiber ashes improved concrete workability. SCBA and CSA highly increased the concrete compressive strengths. The concrete tensile strengths were increased up to 97% with the inclusion of cotton and bagasse ashes. The SCBA, hemp-fiber and treated oil palm - fiber ash increased the concrete and composite’s flexural strengths up to 11.3%, 26.2% and 50.7% respectively. In conclusion, the output of this review will supply full data of the research gaps yet to cover on the use of agro-wastes in concrete for future investigations

Hybrid Data Driven Approach for Reservoir Production Forecast

Analytical models, in particular Decline Curve Analysis (DCA) are widely used in the oil and gas industry. However, they are often solely based on production data from the declining wells and do not leverage the other data available in the field e.g. petrophysics at well, completion length, distance to contacts... This paper describes a workflow to quickly build hybrid models for reservoir production forecast based on a mix of classic reservoir methods and machine learning algorithms. This workflow is composed of three main steps applied on a well by well basis. First, we build an object called forecaster which contains the subject matter knowledge. This forecaster can represent parametric functions trained on the well itself or more complex models that learn from a larger data set (production and petrophysics data, synthesis properties). Secondly this forecaster is tested on a subset of production history to qualify it. Finally, the full data set is used to forecast the production profile. It has been applied to all fluids (oil, water, gas, liquid) and revealed particularly useful for fields with large number of wells and long history, as an alternative to classical simulations when grid models are too complex or difficult to history match. Two use cases from conventional and unconventional fields will be presented in which this workflow helped quickly generate robust forecast for existing wells (declining or non-declining) and new wells. This workflow brings the technology, structure and measurability of Data Science to Reservoir Engineering. It enables the application of the state of the art data science methods to solve concrete reservoir engineering problems. In addition, forecast results can be confronted to historical data using what we call "Blind Testing" which allows a quantification of the forecast uncertainty and avoid biases. Finally, the automated workflow has been used to generate a range of possible realizations and allows the quantification the uncertainty associated with the models.

Unlocking Opportunities for Gas Lift Well Surveillance - Building the Framework for Consolidated Data Capture and Processing

Gas lift operations are highly dependent on data quality and team competence to operate the asset efficiently. Traditional methods for gas lift well surveillance and diagnostics rely on wireline services, a method with growing constraints to adapt to constantly evolving well and operational challenges. The Well Intervention-less Tracer Surveillance System (WITSS) provides a cost effective, comprehensive approach to well surveillance without the reliance on tools entering the well. This results in reduced HSE risks and no associated deferred production. This paper describes a pilot implementation to evaluate the adequacy and accuracy of this technology in the context of ADNOC Onshore gas lift producers. The objective is to evaluate its performance against conventional method data sets and assess the reproducibility of data where no reference existed. The 10 well pilot included both accessible and obstructed wells. Data from the custom designed modular portable kit used for executing the surveillance activities, was analyzed and compared against conventional flowing gradient surveys with full data consumption in well models for comprehensive nodal analysis and opportunity identification. For this pilot, ten wells were surveyed twice using the WITSS method. Results were compared to traditional methods acquired through wireline surveys for accessible wells, and against established multi-phase flow correlations for obstructed wells. The pilot confirmed the WITSS method is as accurate as conventional gauge measurements in mapping pressure and temperature profiles in gas lifted wells. The WITSS method provided additional insight on accurate gas consumption based on the assessment of total gas lift utilization per well and allowed comprehensive model calibration and well performance definition. It also identified potential integrity issues via identification of primary injection at designed stations and secondary unwanted injection sites. Continuous compositional gas analysis of both injected and produced gas streams provided additional verification for analyzing gas lift injection performance. It also highlighted a change in fluid compositional analysis opening discussions for material selection review of the assets. Production uplift identified from 50% of wells was compliant with the reservoir management strategy. The value proposals of flow stabilization through gas lift valve re-calibrations and replacements, adjustment of injection flow rate and further controls on injection pressure management are under process for implementation. Full field scale up scenario is under preparation.

The roles of predictors in cardiovascular risk models - a question of modeling culture?

Background While machine learning (ML) algorithms may predict cardiovascular outcomes more accurately than statistical models, their result is usually not representable by a transparent formula. Hence, it is often unclear how specific values of predictors lead to the predictions. We aimed to demonstrate with graphical tools how predictor-risk relations in cardiovascular risk prediction models fitted by ML algorithms and by statistical approaches may differ, and how sample size affects the stability of the estimated relations. Methods We reanalyzed data from a large registry of 1.5 million participants in a national health screening program. Three data analysts developed analytical strategies to predict cardiovascular events within 1 year from health screening. This was done for the full data set and with gradually reduced sample sizes, and each data analyst followed their favorite modeling approach. Predictor-risk relations were visualized by partial dependence and individual conditional expectation plots. Results When comparing the modeling algorithms, we found some similarities between these visualizations but also occasional divergence. The smaller the sample size, the more the predictor-risk relation depended on the modeling algorithm used, and also sampling variability played an increased role. Predictive performance was similar if the models were derived on the full data set, whereas smaller sample sizes favored simpler models. Conclusion Predictor-risk relations from ML models may differ from those obtained by statistical models, even with large sample sizes. Hence, predictors may assume different roles in risk prediction models. As long as sample size is sufficient, predictive accuracy is not largely affected by the choice of algorithm.

Improved Constraints on the Initial-to-final Mass Relation of White Dwarfs Using Wide Binaries

We present observational constraints for the initial-to-final mass relation (IFMR) derived from 11 white dwarfs (WDs) in wide binaries (WBs) that contain a turnoff/subgiant primary. Because the components of WBs are coeval to a good approximation, the age of the WD progenitor can be determined from the study of its wide companion. However, previous works that used WBs to constrain the IFMR suffered from large uncertainties in the initial masses because their main-sequence primaries are difficult to age-date with good precision. Our selection of WBs with slightly evolved primaries avoids this problem by restricting to a region of parameter space where isochrone ages are significantly easier to determine with precision. The WDs of two of our originally selected binaries were found to be close double degenerates and are not used in the IFMR analysis. We obtained more precise constraints than existing ones in the mass range 1–2 M ⊙, corresponding to a previously poorly constrained region of the IFMR. Having introduced the use of turnoff/subgiant–WD binaries, the study of the IFMR is not limited anymore by the precision in initial mass, but now the pressure is on final mass, i.e., the mass of the WD today. Looking at the full data set, our results would suggest a relatively large dispersion in the IFMR at low initial masses. More precise determinations of the mass of the WD components of our targets are necessary for settling this question.

Assessment of Obesity and Cardiometabolic Status by Integrase Inhibitor Use in REPRIEVE: A Propensity-Weighted Analysis of a Multinational Primary Cardiovascular Prevention Cohort of People With Human Immunodeficiency Virus

Background Emerging data demonstrate that the use of integrase inhibitor (INSTI)-based antiretroviral treatment (ART) is associated with increased weight, but the cardiometabolic health consequences of increased weight remains poorly understood. Methods This analysis examined INSTI use (>6 months) at entry among REPRIEVE participants enrolled in High Income and Latin America/Caribbean Global Burden of Disease regions. Primary analyses used linear and logistic regression; secondary analyses used quantile regression to examine differences across the full data distribution. Characteristics of those with and without INSTI use were balanced using inverse probability of treatment weighting. Results Among 4500 REPRIEVE participants, 1848 were on an INSTI-based regimen at entry for an average of 2.1 ± 1.8 years. Integrase inhibitor use (vs no INSTI use) was associated with higher odds of obesity (odds ratio [OR], 1.63; 95% confidence interval [CI], 1.4–1.9) and higher mean body mass index ([BMI] +1.5kg/m2; 95% CI, 1.0–1.9) and waist circumference (+3.6cm; 95% CI, 2.6–4.6). Differences in weight related to INSTI use were greater in the upper tails of the distribution (+3.1kg/m2 [95% CI, 1.9–4.4] at the 90th centile vs +0.7kg/m2 [95% CI, 0.2–1.2] at the 50th centile) and among women and nonwhite participants, with sex and race having an additive effect on BMI. Conversely, INSTI use was not associated with differences in glucose, low-density lipoprotein cholesterol, or higher odds of metabolic syndrome or hypertension. Conclusions Differences in weight and waist circumference associated with INSTI use are (1) not uniform across people with human immunodeficiency virus, (2) greatest among women and nonwhites, and (3) concentrated at the upper tails of weight distribution. These data identify at-risk subgroups for whom long-term cardiovascular disease outcomes should be carefully assessed.