bluuebunny/crossref_metadata_2025_split huggingface dataset:
An assessment of Sentinel‐1 synthetic aperture radar, geophysical and topographical covariates for estimating topsoil particle‐size fractions
Authors: Deodoro Sandra Cristina , Moral Rafael Andrade , Fealy Reamonn , McCarthy Tim , Fealy Rowan 
Published: August 2023

AbstractData derived from synthetic aperture radar (SAR) are widely employed to predict soil properties, particularly soil moisture and soil carbon content. However, few studies address the use of microwave sensors for soil texture retrieval and those that do are typically constrained to bare soil conditions. Here, we test two statistical modelling approaches—linear (with and without interaction terms) and tree‐based models, namely compositional linear regression model (LRM) and random forest (RF)—and both nongeophysical (e.g., surface soil moisture, topographic, etc) and geophysical‐based (electromagnetic, magnetic and radiometric) covariates to estimate soil texture (sand %, silt % and clay %), using microwave remote sensing data (ESA Sentinel‐1). The statistical models evaluated explicitly consider the compositional nature of soil texture and were evaluated with leave‐one‐out cross‐validation (LOOCV). Our findings indicate that both modelling approaches yielded better estimates when fitted without the geophysical covariates. Based on the Nash–Sutcliffe efficiency coefficient (NSE), LRM slightly outperformed RF, with NSE values for sand, silt and clay of 0.94, 0.62 and 0.46, respectively; for RF, the NSE values were 0.93, 0.59 and 0.44. When interaction terms were included, RF was found to outperform LRM. The inclusion of interactions in the LRM resulted in a decrease in NSE value and an increase in the size of the residuals. Findings also indicate that the use of radar‐derived variables (e.g., VV, VH, RVI) alone was not able to predict soil particle size without the aid of other covariates. Our findings highlight the importance of explicitly considering the compositional nature of soil texture information in statistical analysis and regression modelling. As part of the continued assessment of microwave remote sensing data (e.g., ESA Sentinel‐1) for predicting topsoil particle size, we intend to test surface scattering information derived from the dual‐polarimetric decomposition technique and integrate that predictor into the models in order to deal with the effects of vegetation cover on topsoil backscattering.
Fragrance in dermocosmetic emulsions: From microstructure to skin application
Authors: Dallay Charlotte , Malhiac Catherine , Picard Céline , Savary Géraldine 
Published: August 2023

AbstractFragrance is added to almost all dermocosmetic emulsions, as it has been found to be a key driver in consumer choice and contributes to the perception of product performance. Fragrance is a complex mixture of odorant chemicals at different concentrations. When incorporated into a formulation, the individual fragrance chemicals partition between the emulsion phases depending on their physicochemical properties, which can impact the structure, stability, texture and odour of the final product. On the other hand, it is well known in the food industry how the composition and structure of food emulsion matrices influence the release of aroma chemicals. Fragranced dermocosmetic emulsions have been studied to a lesser extent but it is interesting to apply findings from the food domain since emulsion structure, composition and aroma compounds share common features. This review aims to give an overview of the literature dealing with the interactions between fragrance and dermocosmetic emulsions. The effects of fragrance on emulsion microstructure, stability and texture are highlighted and discussed. The effects of composition and structure of emulsion on the release of fragrance molecules are also presented. Finally, the interactions between skin and fragranced emulsions are addressed.
Evaluation of a new technique using artificial intelligence for quantification of plasma cells on CD138 immunohistochemistry
Authors: Gantana Ethan James , Nell Erica‐Mari , Musekwa Ernest , Lohlun Robert Kingsley , Chetty Carissa , Moodley Keshanya , Chabunya Sylvester , Ras Jacqui , Chapanduka Zivanai Cuthbert 
Published: August 2023

AbstractIntroductionThe diagnosis of plasma cell neoplasms depends on the accurate quantification of plasma cells, traditionally done by immunohistochemical CD138 staining of bone marrow biopsies. Currently, there is no fully satisfactory reference method for this quantification. In our previous study, we compared the commonly used overview estimation method (method A) with a novel method for counting plasma cells in three representative areas (method B). Results showed comparable concordance parameters between the two methods. In this follow‐up study, we compared the previously evaluated methods with a digital analysis method (method C) that uses artificial intelligence in open‐source software, QuPath.MethodsArchived CD138 immunohistochemically stained trephine sections of bone marrow samples used in our previous study were used (n = 33). Reviewers selected three representative areas on each sample by taking images with a light microscope and camera. Digital analysis was performed using the positive cell detection function in QuPath. The entire process was repeated by each reviewer to test intraobserver concordance (concordance correlation coefficient [CCC]) in addition to interobserver concordance (intraclass correlation coefficient [ICC]).ResultsIntraobserver concordance of method C showed strong agreement for all reviewers with the lowest CCC = 0.854. Interobserver concordance for method C using ICC was 0.909 and 0.949. This showed high interobserver agreement with significant differences between method C and previously assessed method A (ICC = 0.793 and 0.713) and method B (ICC = 0.657 and 0.658).ConclusionWe were able to successfully count CD138‐positive plasma cells in bone marrow biopsies using artificial intelligence. This method is superior to both manual counting and overview estimation, regardless of tumour load.
Role of weather in the natural gas market: Insights from the STL‐GARCH‐W method
Authors: Peng Lijuan , Xia Zhenglan , Huang Yisu , Pan Zhigang 
Published: August 2023

AbstractWeather has been shown to affect natural gas markets, but there is limited research on the strength and manner in which weather affects predictions of natural gas volatility. In this study, six weather indicators are used as exogenous variables, and seasonal‐trend decomposition‐generalized autoregressive conditional heteroskedasticity‐Weather (STL‐GARCH‐W) and STL‐GJR‐GARCH‐W models are constructed to explore the effect of weather on global natural gas market. The empirical findings indicate that temperature and precipitation have a notable positive effect on natural gas, while solar radiation has a prominent negative effect. Furthermore, the STL‐GARCH‐W model outperform the STL‐GJR‐GARCH‐W model and the benchmark STL‐GARCH model when temperature, precipitation, and solar radiation are considered. In addition, the January effect has been shown to significantly influence natural gas price volatility. Finally, most parameters in both models are of statistical significance, demonstrating that both models accurately forecast natural gas volatility and emphasizing the importance of weather indicators for modelling natural gas price volatility. Our study provides new insights for energy market investors and policy makers.
Acrochordons as a cutaneous sign of impaired carbohydrate metabolism, hyperlipidemia, liver enzyme abnormalities and hypertension: A case–control study
Authors: Şenel E. , Salmanoğlu M. , Solmazgül E. , İnal B. Berçik 
Published: August 2023

AbstractBackgroundAcrochordons are common and benign skin tumours. A few studies with contradictory results have been reported regarding the abnormalities of carbohydrate and/or lipid metabolisms in patients with acrochordons.ObjectivesWe aimed to determine if the presence of acrochordons could be a marker of diabetes, hyperlipidemia, liver enzyme abnormalities and hypertension by comparing with a control group.MethodsA total of 110 patients having two or more acrochordons and age‐ and gender‐matched 110 controls were included in the study. Localization, size and the total number of acrochordons were recorded in the patient group. Fasting plasma glucose (FPG), serum lipids and liver enzyme levels were tested in patient and controls. All participants underwent a standard 2‐h oral glucose tolerance test with 75 g glucose. Diabetes mellitus and impaired glucose intolerance were diagnosed according to the American Diabetes Association criteria. Arterial blood pressures were measured in two groups.ResultsA total of 56 patients and 10 controls were diagnosed with overt DM. Thirteen per cent of the patients and 9% of controls had an impaired glucose tolerance test. The difference was statistically significant for the diagnosis of DM and not significant for the impaired glucose tolerance. The mean levels of FPG, total cholesterol, LDL cholesterol, triglyceride, aspartate aminotransferase, alanine aminotransferase, gamma‐glutamyltransferase and alkaline phosphatase were significantly higher in patients than those in controls. Furthermore, serum levels of HDL were less in patients. Patients with acrochordons had higher systolic and diastolic blood pressures than controls.ConclusionsThe results of our study suggest that acrochordons may represent a cutaneous sign for impaired carbohydrate or lipid metabolism, liver enzyme abnormalities and hypertension.
Diagnostic validation of vertebral heart score machine learning algorithm for canine lateral chest radiographs
Authors: Solomon J. , Bender S. , Durgempudi P. , Robar C. , Cocchiaro M. , Turner S. , Watson C. , Healy J. , Spake A. , Szlosek D. 
Published: August 2023

ObjectivesThe vertebral heart score is a measurement used to index heart size relative to thoracic vertebra. Vertebral heart score can be a useful tool for identifying and staging heart disease and providing prognostic information. The purpose of this study is to validate the use of a vertebral heart score algorithm compared to manual vertebral heart scoring by three board‐certified veterinary cardiologists.Materials and MethodsA convolutional neural network centred around semantic segmentation of relevant anatomical features was developed to predict heart size and vertebral bodies. These predictions were used to calculate the vertebral heart score. An external validation study consisting of 1200 canine lateral radiographs was randomly selected to match the underlying distribution of vertebral heart scores. Three American College of Veterinary Internal Medicine board‐certified cardiologists were enrolled to manually score 400 images each using the traditional Buchanan method. Post‐scoring, the cardiologists evaluated the algorithm for misaligned anatomic landmarks and overall image quality.ResultsThe 95th percentile absolute difference between the cardiologist vertebral heart score and the algorithm vertebral heart score was 1.05 vertebrae (95% confidence interval: 0.97 to 1.20 vertebrae) with a mean bias of −0.09 vertebrae (95% confidence interval: −0.12 to −0.05 vertebrae). In addition, the model was observed to be well calibrated across the predictive range.Clinical SignificanceWe have found the performance of the vertebral heart score algorithm comparable to three board‐certified cardiologists. While validation of this vertebral heart score algorithm has shown strong performance compared to veterinarians, further external validation in other clinical settings is warranted before use in those settings.
Waking up on the wrong side of the bed: Depression severity moderates daily associations between sleep duration and morning affect
Authors: Wescott D. L. , Taylor M. L. , Klevens A. M. , Franzen P. L. , Roecklein K. A. 
Published: August 2023

SummaryMornings are salient times for disrupted affect that may be impacted by prior sleep. The current study extends work linking sleep disruptions with negative affect by examining how nightly changes in sleep duration, timing, and quality relative to a person's average impact morning affect. We further tested whether depression severity moderated the relationship between nightly variations in sleep and morning affect. This is a secondary analysis of participants ages 18–65 years with varying levels of depression (N = 91) who wore an Actiwatch for 3–17 days (n = 73) while reporting morning affect using a visual analogue scale. Multilevel models tested the previous night's sleep duration, timing, or quality as a predictor of morning affect. Sleep measures were group‐mean centred to account for nightly variation in participants' sleep. A cross‐level interaction between depression severity and nightly sleep was entered. Sleeping longer (b = 0.1; p < 0.001) and later (b = 1.8; p = 0.01) than usual were both associated with better morning mood. There was a significant interaction between nightly actigraphic sleep duration and depression severity on morning affect (b = 0.003; p = 0.003). Participants with higher depression severity reported worse affect upon waking after sleeping less than their usual. In comparison, sleeping less than usual did not affect morning affect ratings for participants with lower depression. A similar interaction was found for sleep quality (b = 0.02; p < 0.001). There was no interaction for midsleep timing. Sleeping less than usual impacted morning affect in individuals with greater depression, potentially suggesting a pathway by which sleep disturbances perpetuate depression.
Introduction: W. E. B. Du Bois: Religion and Social Inequality
Authors: Edwards Korie Little 
Published: August 2023

AbstractW.E.B. Du Bois understood the critical role religion plays in power inequities in the, world. He was very acquainted with how it is used as a tool to exclude and subordinate human beings and yet, at the same time, serve as a source of refuge. This special issue of the Journal for the Scientific Study of Religion is a collection of articles that examines religion and social inequality from a variety of different angles with a Du Boisian lens. When we focus our lens on religion and social inequality, we are highlighting the ways in which religion plays a part in the unequal distribution of power across social groups in society. This special issue focuses on how religion impacts social life and the way individuals and groups embody or struggle to reclaim their agency within a context of silent oppression at times and not‐so‐silent oppression at others times at the personal‐, group‐, and global levels.
Is alveolar ridge preservation an overtreatment?
Authors: Mardas Nikos , Macbeth Neil , Donos Nikolaos , Jung Ronald Ernst , Zuercher Anina Nives 
Published: August 2023

AbstractThe morphology and dimensions of the postextraction alveolar ridge are important for the surgical and restorative phases of implant treatment. Adequate new bone formation and preservation of alveolar ridge dimensions following extraction will facilitate installation of the implant in a restorative position, while preservation of soft tissue contour and volume is essential for an aesthetic and implant‐supported restoration with healthy peri‐implant tissues. Alveolar ridge preservation (ARP) refers to any procedure that aims to: (i) limit dimensional changes in the alveolar ridge after extraction facilitating implant placement without additional extensive bone and soft tissue augmentation procedures (ii) promote new bone formation in the healing alveolus, and (iii) promote soft tissue healing at the entrance of the alveolus and preserve the alveolar ridge contour. Although ARP is a clinically validated and safe approach, in certain clinical scenarios, the additional clinical benefit of ARP over unassisted socket healing has been debated and it appears that for some clinicians may represent an overtreatment. The aim of this critical review was to discuss the evidence pertaining to the four key objectives of ARP and to determine where ARP can lead to favorable outcomes when compared to unassisted socket healing.
No influence of threat uncertainty on fear generalization
Authors: Aslanidou Asimina , Andreatta Marta , Wong Alex H. K. , Wieser Matthias J. 
Published: August 2023

AbstractFear overgeneralization and perceived uncertainty about future outcomes have been suggested as risk factors for clinical anxiety. However, little is known regarding how they influence each other. In this study, we investigated whether different levels of threat uncertainty influence fear generalization. Three groups of healthy participants underwent a differential fear conditioning protocol followed by a generalization test. All groups learned to associate one female face (conditioned stimulus, CS+) with a female scream (unconditioned stimulus, US), whereas the other face (CS−) was not associated with the scream. In order to manipulate threat uncertainty, one group (low uncertainty, n = 26) received 80%, the second group (moderate uncertainty, n = 32) received 60%, and the third group (high uncertainty, n = 30) 40% CS‐US contingency. In the generalization test, all groups saw CS+ and CS− again along with four morphs resembling the CSs in steps of 20%. Subjective (expectancy, valence, and arousal ratings), psychophysiological (skin conductance response, SCR), and visuocortical (steady‐state visual evoked potentials, ssVEPs) indices of fear were registered. Participants expected the US according to their reinforcement schedules and the discriminative responses to CS+/CS− increased with more uncertainty in skin conductance. However, acquisition of conditioned fear was not evident in ssVEPs. During the generalization test, we found no effect of threat uncertainty in any of the measured variables, but the strength of generalization for threat expectancy ratings was positively correlated with dispositional intolerance of uncertainty. This study suggests that mere threat uncertainty does not modulate fear generalization.
Exploring the Limits of Empirical Correlations for the Design of Energy Systems With Complex Fluids: Liquid Sulfur Thermal Energy Storage as a Case Study
Authors: Oliver Madeleine C. , Shah Munjal , Martinek Janna , Nithyanandam Karthik , Ma Zhiwen , Martin Michael James 
Published: August 2023

Abstract
Sustainable energy technologies often use fluids with complex properties. As an example, sulfur is a promising fluid for use in thermal energy storage (TES) systems, with highly nonlinear thermophysical properties. The viscosity of liquid-phase sulfur varies by four orders of magnitude due to polymerization of sulfur rings between 400 K and 500 K, followed by depolymerization of long rigid chains, and a decrease in viscosity, as temperature increases. These properties may compromise the accuracy of long-established empirical correlations in the design of TES systems. This work uses computational fluid dynamics to compute steady-state free convection heat transfer coefficients of sulfur in concentric cylinders at temperatures between 400 K and 600 K. The results show that uneven distributions of high and low-viscosity sulfur in the system cause variations in flow patterns and highly nonlinear heat transfer coefficients as temperature gradients increase. As a result, existing empirical correlations for describing system performance become inaccurate. Comparisons of simulation results to predictions from well-established literature correlations show that deviations may surpass 50%. Nusselt versus Rayleigh number correlations for heat transfer are significantly affected by the loss of self-similarity. The analysis proves that existing correlations are not able to capture the complex properties of sulfur in this temperature range, suggesting that alternative modeling techniques are needed for the design and optimization of sulfur TES systems. These challenges are unlikely to be limited to sulfur as a working fluid or TES but will appear in a range of energy systems.
SARS-CoV-2 ORF8 accessory protein is a virulence factor
Authors: Bello-Perez M. , Hurtado-Tamayo J. , Mykytyn A. Z. , Lamers M. M. , Requena-Platek R. , Schipper D. , Muñoz-Santos D. , Ripoll-Gómez J. , Esteban A. , Sánchez-Cordón P. J. , Enjuanes L. , Haagmans B. L. , Sola I. 
Published: August 2023

ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encodes six accessory proteins (3a, 6, 7a, 7b, 8, and 9b) for which limited information is available on their role in pathogenesis. We showed that the deletion of open reading frames (ORFs) 6, 7a, or 7b individually did not significantly impact viral pathogenicity in humanized K18-hACE2 transgenic mice. In contrast, the deletion of ORF8 partially attenuated SARS-CoV-2, resulting in reduced lung pathology and 40% less mortality, indicating that ORF8 is a critical determinant of SARS-CoV-2 pathogenesis. Attenuation of SARS-CoV-2-∆8 was not associated with a significant decrease in replication either in the lungs of mice or in organoid-derived human airway cells. An increase in the interferon signaling at early times post-infection (1 dpi) in the lungs of mice and a decrease in the pro-inflammatory and interferon response at late times post-infection, both in the lungs of mice (6 dpi) and in organoid-derived human airway cells [72 hours post-infection (hpi)], were observed. The early, but not prolonged, interferon response along with the lower inflammatory response could explain the partial attenuation of SARS-CoV-∆8. The presence of ORF8 in SARS-CoV-2 was associated with an increase in the number of macrophages in the lungs of mice. In addition, the supernatant of SARS-CoV-2-WT (wild-type)-infected organoid-derived cells enhanced the activation of macrophages as compared to SARS-CoV-2-∆8-infected cells. These results show that ORF8 is a virulence factor involved in inflammation that could be targeted in COVID-19 therapies.

IMPORTANCE
The relevance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ORF8 in the pathogenesis of COVID-19 is unclear. Virus natural isolates with deletions in ORF8 were associated with wild milder disease, suggesting that ORF8 might contribute to SARS-CoV-2 virulence. This manuscript shows that ORF8 is involved in inflammation and in the activation of macrophages in two experimental systems: humanized K18-hACE2 transgenic mice and organoid-derived human airway cells. These results identify ORF8 protein as a potential target for COVID-19 therapies.