bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterizing the Impacts of Turbulence Closures on Real Hurricane Forecasts: A Comprehensive Joint Assessment of Grid Resolution, Horizontal Turbulence Models, and Horizontal Mixing Length
Authors: Romdhani Oussama , Zhang Jun A. , Momen Mostafa 
Published: August 2022

AbstractHurricanes are highly complex geophysical flows that have caused billions of dollars in damage in recent years. Despite the significance of these extreme weather events, the turbulence mechanisms that derive the dynamics of hurricane flow systems are poorly understood and ineffectively parameterized in numerical weather prediction (NWP) models. The objective of this study is to bridge these knowledge gaps by assessing the accuracy and deficiencies of existing horizontal turbulence models in NWPs for hurricane forecasts. In particular, the Weather and Research Forecasting (WRF) Model is employed to conduct 135 simulations of five real hurricanes by varying the grid resolution, turbulence models, and horizontal mixing length values. Decreasing the default horizontal mixing length values both in low and high resolution WRF simulations significantly improves the wind intensity forecasts. This result indicates that the existing horizontal diffusion parameterizations are overly dissipative for hurricane flows, and thus, generate a weaker vortex compared to observations. These deficiencies are shown to stem from the horizontal mixing‐length parameterization in WRF that is prescribed as a function of grid size without considering the physics of the flows (e.g., rotation). The paper provides notable insights into the role of turbulent fluxes in simulated hurricane evolutions that can be useful to advance the turbulence parameterizations of NWP models for hurricane forecasts.
A Modeling Framework for Assessing the Value of Learning in Dynamic Adaptive Planning: Application to Green Infrastructure Investment Evaluation
Authors: Hung F. , Hobbs B. F. , McGarity A. , Chen X. 
Published: August 2022

AbstractTo cope with the uncertainty of green infrastructure planning, many cities take an adaptive approach and use learning‐by‐doing to improve estimates of the cost and efficacy of stormwater management practices (SMPs) and use that information to improve stormwater plans. However, deciding whether that learning is worth its expense has been a challenge for practitioners. We propose a modeling framework to assess the economic value of learning. Methodologically, we present a generalized adaptive planning method that includes learning from direct and indirect investments and multiple degrees of learning. The formulation enables users to specify possible knowledge gains from near‐term actions and quantify its value by assessing its impacts on subsequent decisions and their performance. Further, we quantify the values of both learning and adaptability by calculating differences in expected system performance between three types of decision making: non‐adaptive (no learning between decisions), passive adaptive (adaptive planning that passively accepts incidental learning), and active adaptive (adaptive planning that considers potential learning opportunities when choosing investments). For illustration, we apply the framework to an example inspired by a real stormwater management setting in Philadelphia, PA. In the example, the ability of SMPs to reduce runoff is evaluated by hydrological simulation. The literature and expert opinions inform estimates of costs, SMP performance deterioration over time, and predictions of possible knowledge gains. The results show that active adaptive planning supported by stochastic optimization can achieve substantial cost savings.
Errors in Simple Climate Model Emulations of Past and Future Global Temperature Change
Authors: Jackson L. S. , Maycock A. C. , Andrews T. , Fredriksen H.‐B. , Smith C. J. , Forster P. M. 
Published: August 2022

AbstractClimate model emulators are widely used to generate temperature projections for climate scenarios, including in the recent Intergovernmental Panel on Climate Change Sixth Assessment Report. Here we evaluate the performance of a two‐layer energy balance model in emulating historical and future temperature projections from Coupled Model Intercomparison Project Phase 6 models. We find that emulation errors can be large (>0.5°C for SSP2‐4.5) and differ markedly between climate models, forcing scenarios and time periods. Errors arise in emulating the near‐surface temperature response to both greenhouse gas and aerosol forcing; in some periods the errors due to these forcings oppose one another, giving the spurious impression of better emulator performance. Climate feedbacks are assumed constant in the emulator, introducing time‐varying or state dependent feedbacks may reduce prediction errors. Close emulations can be produced for a given period but, crucially, this does not guarantee reliable emulations of other scenarios and periods. Therefore, rigorous out‐of‐sample evaluation is necessary to characterize emulator performance.
The Internal Structure and Dynamics of Jupiter Unveiled by a High‐Resolution Magnetic Field and Secular Variation Model
Authors: Sharan S. , Langlais B. , Amit H. , Thébault E. , Pinceloup M. , Verhoeven O. 
Published: August 2022

AbstractUnique information about the dynamo process acting at Jupiter can be inferred by modeling and interpreting its magnetic field. Using the fluxgate magnetometer measurements acquired during the 4 years of the Juno mission, we derive a magnetic field model which describes simultaneously the main field and the secular variation (SV) up to spherical harmonic degrees 16 and 8, respectively. Apart from the Earth's, this is the first time another planetary magnetic field along with its time variation is described to such a high degree. We use properties of the power spectrum of the static field to infer the upper boundary of the dynamo convective region at 0.830 ± 0.022 Jupiter radius. The SV and correlation times are relatively comparable to the Earth's and indicate that the field is dominated by advection. The field and SV morphologies suggest zonal as well as non‐zonal deep fluid motions.
Numerical Modeling of Tsunami‐Generated Acoustic‐Gravity Waves in Mesopause Airglow
Authors: Inchin P. A. , Heale C. J. , Snively J. B. , Zettergren M. D. 
Published: August 2022

AbstractNumerical simulations of mesopause airglow (MA) fluctuations induced by tsunami‐generated acoustic and gravity waves (TAGWs) are performed. Simulated tsunamis over realistic bathymetry are used to excite atmospheric waves at the surface level of a three‐dimensional nonlinear and compressible neutral atmospheric model. The model incorporates the dynamics and chemistry of hydroxyl OH(3,1) MA under nighttime assumptions. We report case study results of eight recent large tsunami events and demonstrate that TAGW‐induced MA fluctuations are readily detectable with modern ground‐ and space‐based imagers, and may provide quantitative insight. The amplitudes of MA fluctuations reflect the evolution of ocean surface displacements, enhancing or decreasing accordingly, and revealing the tsunami's lobes and local wave focusing. The results suggest that MA observations have potential to supplement early‐warning systems, providing information on spatial and temporal evolution of tsunami waves of ∼10 cm and higher for the cases shown. They may find applications in tsunami tracking over large open ocean areas, as well as in the investigation or reconstruction of tsunami source characteristics.
Rotational Extension Promotes Coeval Upper Crustal Brittle Faulting and Deep‐Seated Rift‐Axis Parallel Flow: Dynamic Coupling Processes Inferred From Analog Model Experiments
Authors: Schmid Timothy Chris , Schreurs Guido , Adam Jürgen 
Published: August 2022

AbstractThe lower parts of warm, thick continental crust can flow in a ductile fashion to accommodate thinning of the upper brittle crust during extension. Naturally occurring continental rifts with a rift‐axis parallel deformation gradient imply an underlying rotational component. In such settings, rift‐parallel crustal flow transports material perpendicular to the direction of rifting. We use analog experiments to investigate rotational rifting and coeval crustal flow. To test the effect of rift‐axis parallel flow on rift evolution, we use different gravitational loads resulting in a range of horizontal pressure gradient magnitudes which drive horizontal lower‐crustal flow. The use of (three dimensional) 3D Digital Volume Correlation techniques on X‐ray CT data combined with 3D Digital Image Correlation techniques applied to topographic stereo images provides detailed insights on the contemporaneous evolution of ductile flow patterns and brittle rift structures, respectively. Our results depict a complex flow field in the ductile lower crust during rotational rifting with: (a) extension‐parallel horizontal inward flow and vertical upward flow that compensates thinning of the brittle upper crustal layer; (b) rift‐axis parallel lateral flow, that compensates greater amounts of thinning further away from the rotation axis; and (c) different degrees of mechanical coupling between the brittle and viscous layers that change during rift propagation. Our analog experiments provide insights into ductile lower crustal flow patterns during rift evolution. The results emphasize the three dimensionality of rifting, which is an important effect that should be considered when estimating the amount of crustal extension from two dimensional (2D) cross sections.
Application of Machine Learning to Characterizing Magma Fertility in Porphyry Cu Deposits
Authors: Zou Shaohao , Chen Xilian , Brzozowski Matthew J. , Leng Cheng‐Biao , Xu Deru 
Published: August 2022

AbstractLarge and easily accessible porphyry Cu deposits have already been identified, exploited, and gradually exhausted. Novel strategies, therefore, are required to identify new, deeply buried deposits. Previous studies have proposed several lithogeochemical and mineralogical approaches for identifying porphyry Cu systems. Most of these methods, however, require significant a priori knowledge of the exploration region and are, generally, of low effectiveness. In this study, machine learning models using Random Forest and Deep Neural Network algorithms are utilized to characterize magma fertility. The two models have first been trained using a large trace‐element data set of magmatic zircon and then validated on unseen data set during the training process. The performance of both models was evaluated using a fivefold cross‐validation technique, which demonstrates that the models provide consistent results and yield good classification accuracy (∼90% on average) with low false positive rates. Feature importance analysis of the models suggests that Eu/Eu*, Eu/Eu*/Y, Ce/Nd, Ce/Ce*, Dy, Hf, and Ti are the important parameters that distinguish fertile and barren zircons. The real‐world applicability of the validated models was evaluated using two well‐characterized porphyry Cu deposits in subduction and postcollisional settings—the Highland Valley porphyry Cu district (south‐central British Columbia, Canada) and the southern Gangdese belt (Tibet, China), respectively. The results demonstrate that our generalized models can discriminate zircon from igneous rocks associated with porphyry Cu deposits from those in nonmineralized systems with high accuracy and independent of geological setting, suggesting that this new approach can be used effectively in greenfield and brownfield exploration.
Vapor Bubbles and Velocity Control on the Cooling Rates of Lava and Pyroclasts During Submarine Eruptions
Authors: Moitra Pranabendu , Sonder Ingo 
Published: August 2022

AbstractInvestigating the conditions behind the formation of pyroclast textures and lava flow morphologies is important to understand the dynamics of submarine volcanic eruptions, which are hard to observe. The development of clast textures and lava morphologies depends on the competing effects of their eruption rates and the rates of solidification. While eruption rates are governed by subsurface magmatic processes, the solidification timescales depend on the rate of heat loss from lava to the external water. However, the effect of the speed of lava flow or clast on their solidification timescales under two‐phase (liquid water and vapor bubbles) water boiling conditions is poorly constrained. Using laboratory experiments with remelted igneous rocks, we investigate the effect of the relative motion between lava and external water on its cooling timescale. We use a range of water speed (0–12.5 cm s−1) in our experiments while keeping our sample stationary to simulate a range of relative speed between lava and ambient water. Using transient heat transfer modeling, we find that heat flux from the surface of the sample to the external water overall increases with increasing water speed. We find heat transfer coefficients of up to ∼1.72 × 103 W m−2 K−1. The implications of high heat flux on the formation of solid lava crust under submarine conditions are discussed.
Scientists get a closer look at mechanism of Deep Bolivian Quake
Authors: Wakefield Julie 
Published: August 2022

While big earthquakes typically wreack havoc for humankind, the biggest deep earthquake ever recorded—the magnitude 8.3 event that occurred 637 km beneath the surface of Bolivia on June 9, 1994—has brought enlightenment—of sorts.Instead of bringing death and destruction, the recent Bolivian quake, the largest quake of any type in recent decades, has given scientists one of the best probes yet of the Earth's interior. Within several minutes of the deep rupture, the quake benignly shook the ground from Argentina to Canada. But more important, the quake shook the Earth like a bell for months thereafter, giving scientists a raw data set of the planet's free oscillations—like none other.
Macrophages in obesity are characterised by increased IL-1β response to calcium-sensing receptor signals
Authors: Thrum Stephan , Sommer Miriam , Raulien Nora , Gericke Martin , Massier Lucas , Kovacs Peter , Krasselt Marco , Landgraf Kathrin , Körner Antje , Dietrich Arne , Blüher Matthias , Rossol Manuela , Wagner Ulf 
Published: August 2022

Abstract
Objective
Obesity is complicated by inflammatory activation of the innate immune system. Stimulation of the calcium-sensing receptor (CaSR) by extra-cellular calcium ions ([Ca2+]ex) can trigger NLRP3 inflammasome activation and inflammation. We hypothesised, that this mechanism might contribute to the activation of adipose tissue (AT) in obesity, and investigated [Ca2+]ex-induced, CaSR mediated IL-1β release by macrophages in obesity.

Methods
[Ca2+]ex-induced IL-1β release was investigated in monocyte-derived macrophages (MDM) generated from peripheral blood of patients with obesity and from normal-weight controls. Visceral and subcutaneous AT biosamples were stimulated with [Ca2+]ex, and IL-1β release, as well as expression of NLRP3 inflammasome and cytokine genes, was determined.

Results
Both MDM and AT readily responded with concentration-dependent IL-1β release already at low, near physiological concentrations to addition of [Ca2+]ex, which was more than 80 fold higher than the LPS-induced effect. IL-1β levels induced by [Ca2+]ex were significantly higher not only in MDM from patients with obesity compared to controls, but also in visceral versus subcutaneous AT. This fat-depot difference was also reflected by mRNA expression levels of inflammasome and cytokine genes.

Conclusions
Obesity renders macrophages more susceptible to [Ca2+]ex-induced IL-1β release and pyroptosis. Increased susceptibility was independent of the response to LPS and circulating CRP arguing against mere pro-inflammatory pre-activation of monocytes. Instead, we propose that CaSR mediated signalling is relevant for the deleterious innate immune activation in obesity.
Liquid crystal-based structural color actuators
Authors: Zhang Pei , de Haan Laurens T. , Debije Michael G. , Schenning Albert P. H. J. 
Published: August 2022

AbstractAnimals can modify their body shape and/or color for protection, camouflage and communication. This adaptability has inspired fabrication of actuators with structural color changes to endow soft robots with additional functionalities. Using liquid crystal-based materials for actuators with structural color changes is a promising approach. In this review, we discuss the current state of liquid crystal-based actuators with structural color changes and the potential applications of these structural color actuators in soft robotic devices.
Association between ADHD and vision problems. A systematic review and meta-analysis
Authors: Bellato Alessio , Perna John , Ganapathy Preethi S. , Solmi Marco , Zampieri Andrea , Cortese Samuele , Faraone Stephen V. 
Published: August 2022

Abstract
Aim
To conduct the first systematic review and meta-analysis assessing whether attention-deficit/hyperactivity disorder (ADHD) is associated with disorders of the eye, and/or altered measures of visual function.

Method
Based on a pre-registered protocol (PROSPERO: CRD42021256352), we searched PubMed, Web of Knowledge/Science, Ovid Medline, Embase and APA PsycINFO up to 16th November 2021, with no language/type of document restrictions. We included observational studies reporting at least one measure of vision in people of any age meeting DSM/ICD criteria for ADHD and in people without ADHD; or the prevalence of ADHD in people with and without vision disorders. Study quality was assessed with the Appraisal tool for Cross-Sectional Studies (AXIS). Random effects meta-analyses were used for data synthesis.

Results
We included 42 studies in the narrative synthesis and 35 studies in the meta-analyses (3,250,905 participants). We found meta-analytic evidence of increased risk of astigmatism (OR = 1.79 [CI: 1.50, 2.14]), hyperopia and hypermetropia (OR = 1.79 [CI: 1.66, 1.94]), strabismus (OR = 1.93 [CI: 1.75, 2.12]), unspecified vision problems (OR = 1.94 [CI: 1.38, 2.73]) and reduced near point of convergence (OR = 5.02 [CI: 1.78, 14.11]); increased lag (Hedge’s g = 0.63 [CI: 0.30, 0.96]) and variability (Hedge’s g = 0.40 [CI: 0.17, 0.64]) of the accommodative response; and increased self-reported vision problems (Hedge’s g = 0.63 [CI: 0.44, 0.82]) in people with ADHD compared to those without ADHD (with no significant heterogeneity). We also found meta-analytic evidence of no differences between people with and without ADHD on retinal nerve fiber layer thickness (Hedge’s g = −0.19 [CI: −0.41, 0.02]) and refractive error (Hedge’s g = 0.08 [CI: −0.26, 0.42]) (with no significant heterogeneity).

Discussion
ADHD is associated with some self-reported and objectively ascertained functional vision problems, but not with structural alterations of the eye. Further studies should clarify the causal relationship, if any, between ADHD and problems of vision.

Trial registration
PROSPERO registration: CRD42021256352.
Role of branched-chain amino acid metabolism in the pathogenesis of obesity and type 2 diabetes-related metabolic disturbances BCAA metabolism in type 2 diabetes
Authors: Vanweert Froukje , Schrauwen Patrick , Phielix Esther 
Published: August 2022

AbstractBranched-chain amino acid (BCAA) catabolism has been considered to have an emerging role in the pathogenesis of metabolic disturbances in obesity and type 2 diabetes (T2D). Several studies showed elevated plasma BCAA levels in humans with insulin resistance and patients with T2D, although the underlying reason is unknown. Dysfunctional BCAA catabolism could theoretically be an underlying factor. In vitro and animal work collectively show that modulation of the BCAA catabolic pathway alters key metabolic processes affecting glucose homeostasis, although an integrated understanding of tissue-specific BCAA catabolism remains largely unknown, especially in humans. Proof-of-concept studies in rodents -and to a lesser extent in humans – strongly suggest that enhancing BCAA catabolism improves glucose homeostasis in metabolic disorders, such as obesity and T2D. In this review, we discuss several hypothesized mechanistic links between BCAA catabolism and insulin resistance and overview current available tools to modulate BCAA catabolism in vivo. Furthermore, this review considers whether enhancing BCAA catabolism forms a potential future treatment strategy to promote metabolic health in insulin resistance and T2D.
Interpretable machine learning-derived nomogram model for early detection of diabetic retinopathy in type 2 diabetes mellitus: a widely targeted metabolomics study
Authors: Li Jushuang , Guo Chengnan , Wang Tao , Xu Yixi , Peng Fang , Zhao Shuzhen , Li Huihui , Jin Dongzhen , Xia Zhezheng , Che Mingzhu , Zuo Jingjing , Zheng Chao , Hu Honglin , Mao Guangyun 
Published: August 2022

Abstract
Objective
Early identification of diabetic retinopathy (DR) is key to prioritizing therapy and preventing permanent blindness. This study aims to propose a machine learning model for DR early diagnosis using metabolomics and clinical indicators.

Methods
From 2017 to 2018, 950 participants were enrolled from two affiliated hospitals of Wenzhou Medical University and Anhui Medical University. A total of 69 matched blocks including healthy volunteers, type 2 diabetes, and DR patients were obtained from a propensity score matching-based metabolomics study. UPLC-ESI-MS/MS system was utilized for serum metabolic fingerprint data. CART decision trees (DT) were used to identify the potential biomarkers. Finally, the nomogram model was developed using the multivariable conditional logistic regression models. The calibration curve, Hosmer–Lemeshow test, receiver operating characteristic curve, and decision curve analysis were applied to evaluate the performance of this predictive model.

Results
The mean age of enrolled subjects was 56.7 years with a standard deviation of 9.2, and 61.4% were males. Based on the DT model, 2-pyrrolidone completely separated healthy controls from diabetic patients, and thiamine triphosphate (ThTP) might be a principal metabolite for DR detection. The developed nomogram model (including diabetes duration, systolic blood pressure and ThTP) shows an excellent quality of classification, with AUCs (95% CI) of 0.99 (0.97–1.00) and 0.99 (0.95–1.00) in training and testing sets, respectively. Furthermore, the predictive model also has a reasonable degree of calibration.

Conclusions
The nomogram presents an accurate and favorable prediction for DR detection. Further research with larger study populations is needed to confirm our findings.