bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of Spectral Wave Models Physics as Applied to a 100‐Year Southern Hemisphere Extra‐Tropical Cyclone Sea State
Authors: Meucci Alberto , Young Ian R. , Pepler Acacia , Rudeva Irina , Ribal Agustinus , Bidlot Jean‐Raymond , Babanin Alexander V. 
Published: August 2023

AbstractExtreme significant wave height estimates, and their probability of exceedance, are fundamental offshore and coastal engineering design parameters. These estimates are characterized by uncertainty due to an incomplete understanding of the atmosphere‐ocean energy and momentum exchanges during intense storms. This particularly affects extreme wave statistics of ocean regions exposed to large and frequent synoptic disturbances such as Extra‐Tropical Cyclones (ETCs). In this work, we assessed the performance of global phase‐averaged spectral wave models in representing the 1 in 100‐year sea state generated by a Southern Ocean ETC in April 2021. We collected in situ and remote sensing observations, from the storm generation region to its decaying phase and the impact on South‐East Australian coastlines. We compared the observations with a suite of reanalysis and hindcast global wave model data sets. While comparing well for wind speed up to 20 m/s, the models presented differences in solving the air‐sea momentum exchange between the atmosphere and the ocean for wind speed velocities between 20 and 35 m/s, which are a distinctive characteristic of ETCs. Despite marked differences in the storm generation region, the models converged to a similar representation of the swell systems impacting the South‐East Australian coastlines, as demonstrated by a comparison with deep‐water buoy observations close to the coastlines. Furthermore, we found that the energy of the ERA5 reanalysis, which assimilates satellite wave height measurements is quickly dispersed and, as such, of little advantage in representing the 9.9 m significant wave heights that impacted the South‐East Australian coastlines on 10 April 2021.
Estimating the Maximum Safe Pumping Rate in Sloping Unconfined Coastal Aquifers
Authors: Sun Jiazhi , Wu Huiqiang , Yin Jina , Lu Chunhui 
Published: August 2023

AbstractUnconfined coastal aquifers with a sloping aquifer bed are ubiquitous. However, most analytical solutions previously developed to estimate pumping‐induced seawater intrusion considered a horizontal aquifer bed. In this study, we first developed a steady‐state analytical solution to quantify the maximum safe pumping rate in sloping unconfined coastal aquifers with a fixed‐flux inland boundary condition, using the single potential approach. The analytical solution corrected by an empirical factor is validated against the numerical simulation, which reproduces the corresponding numerical results well. It is found that coastal aquifers with a higher aquifer bed toward inland (i.e., a positive sloping angle) yield a higher maximum safe pumping rate than that of coastal aquifers with a horizontal aquifer base, since the elevated freshwater head prevents seawater intrusion and enhances groundwater pumping. Specifically, for the aquifer bases with the sloping angle of 0.01 and −0.01, the maximum safe pumping rate is increased by 42.6% and decreased by 48.4%, respectively, in comparison to the case of a horizontal aquifer base, suggesting that neglecting the aquifer base slop can result in a significant error in estimating the maximum safe pumping rate. Moreover, the sensitivity analysis reveals that the maximum safe pumping rate increases with a lower hydraulic conductivity and a larger dispersivity. Our results provide valuable insights into the effect of the aquifer base slope on the maximum safe pumping in coastal aquifers, and analytical solutions developed can serve as a powerful tool for quick assessment of pumping‐induced seawater intrusion in sloping unconfined coastal aquifers.
Hydraulic Tomography Estimates Improved by Zonal Information From the Clustering of Geophysical Survey Data
Authors: Wang Chenxi , Illman Walter A. 
Published: August 2023

AbstractHydraulic tomography (HT) has been demonstrated as a robust approach to characterize subsurface heterogeneity through the inverse modeling of multiple pumping data. However, smooth or even erroneous tomograms can result when insufficient observations are involved in the inversion. In this study, the feasibility of integrating geophysical survey data into HT analysis is investigated. First, k‐means clustering is utilized to extract zonal information from borehole geophysical logs, and a new type of spatial constraints containing geological knowledge is proposed to obtain improved hydrostratigraphic boundaries along boreholes. Next, zonation models are constructed by applying clustering‐based zone geometry and populating zonal estimates of hydraulic conductivity (K) from analyzing pumping data. Afterwards, zonation models are treated as the initial guess of spatial variability in the geostatistical inversion of HT analysis. Additionally, local K measurements can be utilized to further improve HT estimates. Comparative cases of HT analyses are designed for a numerical sandbox experiment to highlight the HT performance integrated with geophysical surveys, in which the geostatistical inversion is initialized with: (a) a homogeneous K field; (b) zonation models built by the clustering of disparate geophysical surveys with/without spatial constraints; and (c) zonation improved by incorporating local K measurements. Based on ln K field comparisons and validation through predictions of drawdowns and tracer plume migration from independent tests not used in the calibration effort, we find that integration of geophysical surveys into HT analysis by clustering with spatial constraints is demonstrated as an effective approach, and local K measurements can further improve HT estimates.
Predicting 3D Physical Properties From a Single 2D Slice Based on Convolutional Neural Networks: 2D‐Slice‐To‐3D‐Properties for Porous Rocks
Authors: Chen Xiaojun , Yang Junwei , Ma Lin , Rabbani Arash , Babaei Masoud 
Published: August 2023

AbstractThe 3D physical properties of porous rocks directly determine the subsurface flow and modeling. However, predicting a wide range of 3D physical rocks remains a formidable challenge and requires a large amount of input. Reliable microstructure‐property correlations can accurately predict 3D physical properties, avoiding time‐consuming experimental testing. Here, we propose a new dimensionless convolutional neural network (CNN)‐based method to find inherent correlations for accurately predicting 3D properties of porous rocks using only a single 2D slice or a series of 2D slices. Training and testing were conducted on 9 properties of 16,409 semi‐realistic 3D porous rocks. Eight properties, except for the formation factor, were predicted with an acceptable correlation coefficient of 0.92. Furthermore, the modeling results indicate that the network architecture of the model has a significant impact on its performance. The classical CNN Model 4 achieved an average validating loss of 1.0 × 10−3. Interval 2D slice sampling can significantly reduce the computational cost in training model. The developed model was verified and tested by five independent porous samples. Additionally, the proposed method accurately predicted permeability in different directions using only 2D slices trained in one direction, for porous rocks where anisotropy is not significant. This work demonstrates the ability to predict various 3D properties rapidly and accurately using only a single 2D slice from semi‐realistic 3D volumes. It provides insights into the conversion of 2D slices to 3D properties for porous media in a wide range of applications, including property estimation from 2D images without structural construction and simulation in 3D.
Exploiting the fibroblast growth factor receptor-1 vulnerability to therapeutically restrict the MYC-EZH2-CDKN1C axis-driven proliferation in Mantle cell lymphoma
Authors: Sircar Anuvrat , Singh Satishkumar , Xu-Monette Zijun Y. , Coyle Krysta Mila , Hilton Laura K. , Chavdoula Evangelia , Ranganathan Parvathi , Jain Neeraj , Hanel Walter , Tsichlis Philip , Alinari Lapo , Peterson Blake R. , Tao Jianguo , Muthusamy Natarajan , Baiocchi Robert , Epperla Narendranath , Young Ken H. , Morin Ryan , Sehgal Lalit 
Published: August 2023

AbstractMantle cell lymphoma (MCL) is a lethal hematological malignancy with a median survival of 4 years. Its lethality is mainly attributed to a limited understanding of clinical tumor progression and resistance to current therapeutic regimes. Intrinsic, prolonged drug treatment and tumor-microenvironment (TME) facilitated factors impart pro-tumorigenic and drug-insensitivity properties to MCL cells. Hence, elucidating neoteric pharmacotherapeutic molecular targets involved in MCL progression utilizing a global “unified” analysis for improved disease prevention is an earnest need. Using integrated transcriptomic analyses in MCL patients, we identified a Fibroblast Growth Factor Receptor-1 (FGFR1), and analyses of MCL patient samples showed that high FGFR1 expression was associated with shorter overall survival in MCL patient cohorts. Functional studies using pharmacological intervention and loss of function identify a novel MYC-EZH2-CDKN1C axis-driven proliferation in MCL. Further, pharmacological targeting with erdafitinib, a selective small molecule targeting FGFRs, induced cell-cycle arrest and cell death in-vitro, inhibited tumor progression, and improved overall survival in-vivo. We performed extensive pre-clinical assessments in multiple in-vivo model systems to confirm the therapeutic potential of erdafitinib in MCL and demonstrated FGFR1 as a viable therapeutic target in MCL.
Brain-cognition relationships in late-life depression: a systematic review of structural magnetic resonance imaging studies
Authors: Marawi Tulip , Ainsworth Nicholas J. , Zhukovsky Peter , Rashidi-Ranjbar Neda , Rajji Tarek K. , Tartaglia Maria Carmela , Voineskos Aristotle N. , Mulsant Benoit H. 
Published: August 2023

Abstract
Background
Most patients with late-life depression (LLD) have cognitive impairment, and at least one-third meet diagnostic criteria for mild cognitive impairment (MCI), a prodrome to Alzheimer’s dementia (AD) and other neurodegenerative diseases. However, the mechanisms linking LLD and MCI, and brain alterations underlying impaired cognition in LLD and LLD + MCI remain poorly understood.

Methods
To address this knowledge gap, we conducted a systematic review of studies of brain-cognition relationships in LLD or LLD + MCI to identify circuits underlying impaired cognition in LLD or LLD + MCI. We searched MEDLINE, PsycINFO, EMBASE, and Web of Science databases from inception through February 13, 2023. We included studies that assessed cognition in patients with LLD or LLD + MCI and acquired: (1) T1-weighted imaging (T1) measuring gray matter volumes or thickness; or (2) diffusion-weighted imaging (DWI) assessing white matter integrity. Due to the heterogeneity in studies, we only conducted a descriptive synthesis.

Results
Our search identified 51 articles, resulting in 33 T1 studies, 17 DWI studies, and 1 study analyzing both T1 and DWI. Despite limitations, reviewed studies suggest that lower thickness or volume in the frontal and temporal regions and widespread lower white matter integrity are associated with impaired cognition in LLD. Lower white matter integrity in the posterior cingulate region (precuneus and corpus callosum sub-regions) was more associated with impairment executive function and processing speed than with memory.

Conclusion
Future studies should analyze larger samples of participants with various degrees of cognitive impairment and go beyond univariate statistical models to assess reliable brain-cognition relationships in LLD.
Challenges in developing Geroscience trials
Authors: Rolland Yves , Sierra Felipe , Ferrucci Luigi , Barzilai Nir , De Cabo Rafael , Mannick Joan , Oliva Anthony , Evans William , Angioni Davide , De Souto Barreto Philipe , Raffin Jeremy , Vellas Bruno , Kirkland James L. ,   the G.C.T-TF group, Andrieu Sandrine , Bacqueville Daniel , Bischoff-Ferrari Heike , Blivet Guillaume , Cash Timothy , Cuervo Ana-Maria , Davezac Noelie , Dimet-Wiley Andrea , Fleming Alexander , Gérard Friedlander , Gonzales Mitzi , Guyonnet Sophie , Hare Joshua , Hendrix Suzanne , Jorgensen Christian , Kaeberlein Matt , Khan Mehmood , Kritchevsky Stephen , Labokha Aksana , Lairez Olivier , Lederman Stephanie , Mariani Jean , Middleton Lefkos , Newman John , Parini Angelo , Peschin Susan , Prunotto Marco , Sagol Sami , Tomlinson Suzanne , Terstappen Georg , Touchon Jacques , Tourette Cendrine , Van Maanen Rob , Verdin Eric , Watowich Stan , Zhang Lei , Zhavoronkov Alex 
Published: August 2023

AbstractGeroscience is becoming a major hope for preventing age-related diseases and loss of function by targeting biological mechanisms of aging. This unprecedented paradigm shift requires optimizing the design of future clinical studies related to aging in humans. Researchers will face a number of challenges, including ideal populations to study, which lifestyle and Gerotherapeutic interventions to test initially, selecting key primary and secondary outcomes of such clinical trials, and which age-related biomarkers are most valuable for both selecting interventions and predicting or monitoring clinical responses (“Gerodiagnostics”). This article reports the main results of a Task Force of experts in Geroscience.
APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants
Authors: Bianco Salvatore Daniele , Parca Luca , Petrizzelli Francesco , Biagini Tommaso , Giovannetti Agnese , Liorni Niccolò , Napoli Alessandro , Carella Massimo , Procaccio Vincent , Lott Marie T. , Zhang Shiping , Vescovi Angelo Luigi , Wallace Douglas C. , Caputo Viviana , Mazza Tommaso 
Published: August 2023

AbstractMitochondrial dysfunction has pleiotropic effects and is frequently caused by mitochondrial DNA mutations. However, factors such as significant variability in clinical manifestations make interpreting the pathogenicity of variants in the mitochondrial genome challenging. Here, we present APOGEE 2, a mitochondrially-centered ensemble method designed to improve the accuracy of pathogenicity predictions for interpreting missense mitochondrial variants. Built on the joint consensus recommendations by the American College of Medical Genetics and Genomics/Association for Molecular Pathology, APOGEE 2 features an improved machine learning method and a curated training set for enhanced performance metrics. It offers region-wise assessments of genome fragility and mechanistic analyses of specific amino acids that cause perceptible long-range effects on protein structure. With clinical and research use in mind, APOGEE 2 scores and pathogenicity probabilities are precompiled and available in MitImpact. APOGEE 2’s ability to address challenges in interpreting mitochondrial missense variants makes it an essential tool in the field of mitochondrial genetics.
Numerical analysis for tangent-hyperbolic micropolar nanofluid flow over an extending layer through a permeable medium
Authors: Moatimid Galal M. , Mohamed Mona A. A. , Gaber Ahmed A. , Mostafa Doaa M. 
Published: August 2023

AbstractThe principal purpose of the current investigation is to indicate the behavior of the tangent-hyperbolic micropolar nanofluid border sheet across an extending layer through a permeable medium. The model is influenced by a normal uniform magnetic field. Temperature and nanoparticle mass transmission is considered. Ohmic dissipation, heat resource, thermal radiation, and chemical impacts are also included. The results of the current work have applicable importance regarding boundary layers and stretching sheet issues like rotating metals, rubber sheets, glass fibers, and extruding polymer sheets. The innovation of the current work arises from merging the tangent-hyperbolic and micropolar fluids with nanoparticle dispersal which adds a new trend to those applications. Applying appropriate similarity transformations, the fundamental partial differential equations concerning speed, microrotation, heat, and nanoparticle concentration distributions are converted into ordinary differential equations, depending on several non-dimensional physical parameters. The fundamental equations are analyzed by using the Rung-Kutta with the Shooting technique, where the findings are represented in graphic and tabular forms. It is noticed that heat transmission improves through most parameters that appear in this work, except for the Prandtl number and the stretching parameter which play opposite dual roles in tin heat diffusion. Such an outcome can be useful in many applications that require simultaneous improvement of heat within the flow. A comparison of some values of friction with previous scientific studies is developed to validate the current mathematical model.
A fast imaging method for the interpretation of self-potential data with application to geothermal systems and mineral investigation
Authors: Mehanee Salah A. , Essa Khalid S. , Soliman Khaled S. , Diab Zein E. 
Published: August 2023

AbstractWe describe a rapid imaging approach for the interpretation of self-potential data collected along profile by some geometrically simple model of cylinders and spheres. The approach calculates the correlation coefficient between the analytic signal (AS) of the observed self-potential measurements and the AS of the self-potential signature of the idealized model. The depth, electric dipole moment, polarization angle, and center are the inverse parameters we aim to extract from the imaging approach for the interpretative model, and they pertain to the highest value of the correlation coefficient. The approach is demonstrated on noise-free numerical experiments, and reproduced the true model parameters. The accuracy and stability of the proposed approach are examined on numerical experiments contaminated with realistic noise levels and regional fields prior to the interpretation of real data. Following that, five real field examples from geothermal systems and mineral exploration have been successfully analyzed. The results agree well with the published research.
Assessing the relationship between energy-related methane emissions and the burden of cardiovascular diseases: a cross-sectional study of 73 countries
Authors: Mendoza-Cano Oliver , Trujillo Xóchitl , Huerta Miguel , Ríos-Silva Mónica , Lugo-Radillo Agustin , Bricio-Barrios Jaime Alberto , Rueda-Abad José Clemente , Pérez-Rodríguez Rebeca Yasmín , Quintanilla-Montoya Ana Luz , Uribe-Ramos Juan Manuel , Mendoza-Olivo Valeria Argentina , Murillo-Zamora Efrén 
Published: August 2023

AbstractThe energy industry significantly contributes to anthropogenic methane emissions, which add to global warming and have been linked to an increased risk of cardiovascular diseases (CVD). This study aims to evaluate the relationship between energy-related methane emissions and the burden of CVD, measured in disability-adjusted life years (DALYs), in 2019. We conducted a cross-sectional analysis of datasets from 73 countries across all continents. The analyzed datasets included information from 2019 on environmental energy-related methane emissions, burden of DALYs due to CVD. The age-standardized prevalence of obesity in adults and life expectancy at birth were retrieved. The relationship between the variables of interest was evaluated using multiple linear regression models. In the multiple model, we observed a positive linear association between methane emissions and the log-transformed count of DALYs related to CVD. Specifically, for each unit increase in energy-related methane emissions, the burden of CVD increased by 0.06% (95% CI 0.03–0.09%, p < 0.001). The study suggests that reducing methane emissions from the energy industry could improve public health for those at risk of CVD. Policymakers can use these findings to develop strategies to reduce methane emissions and protect public health.
A comparative study using response surface methodology and artificial neural network towards optimized production of melanin by Aureobasidium pullulans AKW
Authors: Saber WesamEldin I. A. , Ghoniem Abeer A. , Al-Otibi Fatimah O. , El-Hersh Mohammed S. , Eldadamony Noha M. , Menaa Farid , Elattar Khaled M. 
Published: August 2023

AbstractThe effect of three independent variables (i.e., tyrosine, sucrose, and incubation time) on melanin production by Aureobasidium pullulans AKW was unraveled by two distinctive approaches: response surface methodology (i.e. Box Behnken design (BBD)) and artificial neural network (ANN) in this study for the first time ever using a simple medium. Regarding BBD, sucrose and incubation intervals did impose a significant influence on the output (melanin levels), however, tyrosine did not. The validation process exhibited a high consistency of BBD and ANN paradigms with the experimental melanin production. Concerning ANN, the predicted values of melanin were highly comparable to the experimental values, with minor errors competing with BBD. Highly comparable experimental values of melanin were achieved upon using BBD (9.295 ± 0.556 g/L) and ANN (10.192 ± 0.782 g/L). ANN accurately predicted melanin production and showed more improvement in melanin production by about 9.7% higher than BBD. The purified melanin structure was verified by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction pattern (XRD), and thermogravimetric analysis (TGA). The results verified the hierarchical architecture of the particles as small compasses by SEM analysis, inter-layer spacing in the XRD analysis, maximal atomic % for carbon, and oxygen atoms in the EDX analysis, and the great thermal stability in the TGA analysis of the purified melanin. Interestingly, the current novel endophytic strain was tyrosine-independent, and the uniquely applied ANN paradigm was more efficient in modeling the melanin production with appreciate amount on a simple medium in a relatively short time (168 h), suggesting additional optimization studies for further maximization of melanin production.
Spectrofluorometric determination of orphenadrine, dimenhydrinate, and cinnarizine using direct and synchronous techniques with greenness assessment
Authors: Ghonim Rana , Tolba Manar M. , Ibrahim Fawzia , El-Awady Mohamed I. 
Published: August 2023

AbstractOrphenadrine (ORP), dimenhydrinate (DMN), and cinnarizine (CNN) were investigated using green-sensitive spectrofluorometric methods. Method, I used for determination of DMN in 0.1 M hydrochloric acid (HCl) and 1.0% sodium dodecyl sulphate (SDS) at 286 nm after λex 222 nm, while for determination of ORP in 1.0% w/v SDS involves measuring the fluorescence at 285 nm after λex 220 nm. For DMN and ORP, the detection and quantitation limits were 2.99 and 4.71 and 9.08 and 14.29 ng/mL, respectively. The ranges of DMN and ORP were 0.10–1.0 and 0.04–0.5 µg/mL, respectively, in micellar aqueous solution. Method II, the derivative intensities of DMN and CNN were measured at a fixed of different wavelength between the excitation and the emission wavelengths (Δλ) = 60 nm at 282 and 322 nm, at the zero crossing of each other, respectively. The detection and quantitation limits for DMN and CNN were 1.77 and 0.88 ng/mL and 5.36 and 2.65 ng/mL, correspondingly, through the entire range of 0.1–1.0 µg/mL for DMN and CNN. The linearity was perfectly determined through the higher values of the correlation coefficient ranging from 0.9997 to 0.9999 for both direct and synchronous methods. The precision of the proposed methods was also confirmed via the lower values of the standard deviation which ranged from 0.39 to 1.11. The technique was expanded to analyze this mixture in combined tablets and laboratory-prepared mixtures. The method validation was done depending on the international conference on harmonization (ICH) recommendations. An analysis of the statistical data revealed a high agreement between the proposed data and the comparison methodology. Three different assessment methods demonstrated the greenness of the technique.