bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO2 Retrievals
Authors: He Wei , Jiang Fei , Ju Weimin , Chevallier Frédéric , Baker David F. , Wang Jun , Wu Mousong , Johnson Matthew S. , Philip Sajeev , Wang Hengmao , Bertolacci Michael , Liu Zhiqiang , Zeng Ning , Chen Jing M. 
Published: July 2023

AbstractThe magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient observational constraints; satellite column‐average dry‐air mole fraction carbon dioxide (XCO2) retrievals may fill some of this gap. Here, we estimate China's carbon sink using atmospheric inversions of the Orbiting Carbon Observatory 2 (OCO‐2) XCO2 retrievals within different platforms, including the Global Carbon Assimilation System (GCAS) v2, the Copernicus Atmosphere Monitoring Service, and the OCO‐2 Model Inter‐comparison Project (MIP). We find that they consistently place the largest net biome production (NBP) in the south on an annual basis compared to the northeast and other main agricultural areas during peak growing season, coinciding well with the distribution of forests and crops, respectively. Moreover, the mean seasonal cycle amplitude of NBP in OCO‐2 inversions is obviously larger than that of biosphere model simulations and slightly greater than surface CO2 inversions. More importantly, the mean seasonal cycle of the OCO‐2 inversions is well constrained in the temperate, tropical, and subtropical monsoon climate zones, with better inter‐model consistency at a sub‐regional scale compared to in situ inversions and biosphere model simulations. In addition, the OCO‐2 inversions estimate the mean annual NBP in China for 2015–2019 to be between 0.34 (GCASv2) and 0.47 ± 0.16 PgC/yr (median ± std; OCO‐2 v10 MIP), and indicate the impacts of climate extremes (e.g., the 2019 drought) on the interannual variations of NBP. Our results suggest that assimilating OCO‐2 XCO2 retrievals is crucial for improving our understanding of China's terrestrial carbon sink regime.
Sampling‐Based Methods for Uncertainty Propagation in Flood Modeling Under Multiple Uncertain Inputs: Finding Out the Most Efficient Choice
Authors: Hajihassanpour Mahya , Kesserwani Georges , Pettersson Per , Bellos Vasilis 
Published: July 2023

AbstractIn probabilistic flood modeling, uncertainty manifests in frequency of occurrence, or histograms, for quantities of interest, including the Flood Extent and hazard rating (HR). Such modeling at the field‐scale requires the identification of a more efficient alternative to the Standard Monte Carlo (SMC) method that can reproduce comparable output probability distributions with a relatively reduced sample size, including detailed histograms of quantities of interest. Latin hypercube sampling (LHS) is the most evaluated alternative for fluvial floods but yields no considerable sample size reduction. Potentially better alternatives include adaptive stratified sampling (ASS), Quasi Monte Carlo (QMC) and Haar‐wavelet expansion (HWE), which are yet unevaluated for probabilistic flood modeling. To fulfill this gap, LHS, ASS, QMC, and HWE are compared to quantify sample size reduction to reproduce output detailed histograms—for Flood Extent, and average and maximum HR—while keeping the difference below 10% to the reference SMC prediction. The comparison is done for two test cases with two (i.e., inflow discharge and Manning's coefficient) and three (i.e., further including the ground elevation) input random variables, and a real case with five input random variables. With two input random variables, all four alternatives yield sample size reductions, with QMC and HWE considerably outperforming the others; with three and more input random variables, HWE becomes inflexible and LHS underperforms. Still, QMC is a better choice than ASS to boost sample size reduction for the real case and shall be preferred in probabilistic flood modeling. Accompanying research codes are openly available online.
Imaging and Modeling the Impact of Multi‐Scale Pore Connectivity on Two‐Phase Flow in Mixed‐Wet Rock
Authors: Wang Shan , Mascini Arjen , Ruspini Leonardo C. , Øren Pål‐Eric , Bultreys Tom 
Published: July 2023

AbstractThe wetting properties of pore walls have a strong effect on multiphase flow through porous media. However, the fluid flow behavior in porous materials with both complex pore structures and non‐uniform wettability are still unclear. Here, we performed unsteady‐state quasi‐static oil‐ and water‐flooding experiments to study multiphase flow in two sister heterogeneous sandstones with variable wettability conditions (i.e., one natively water‐wet and one chemically treated to be mixed‐wet). The pore‐scale fluid distributions during this process were imaged by laboratory‐based X‐ray micro‐computed tomography (micro‐CT). In the mixed‐wet case, we observed pore filling events where the fluid interface appeared to be at quasi‐equilibrium at every position along the pore body (13% by volume), in contrast to capillary instabilities typically associated with slow drainage or imbibition. These events corresponded to slow displacements previously observed in unsteady‐state experiments, explaining the wide range of displacement time scales in mixed‐wet samples. Our new data allowed us to quantify the fluid saturations below the image resolution, indicating that slow events were caused by the presence of microporosity and the wetting heterogeneity. Finally, we investigated the sensitivity of the multi‐phase flow properties to the slow filling events using a state‐of‐the‐art multi‐scale pore network model. This indicated that pores where such events took place contributed up to 19% of the sample's total absolute permeability, but that the impact on the relative permeability may be smaller. Our study sheds new light on poorly understood multiphase fluid dynamics in complex rocks, of interest to for example, groundwater remediation and subsurface CO2 storage.
Impact of Stochastic Ocean Density Corrections on Air‐Sea Flux Variability
Authors: Agarwal Niraj , Small R. Justin , Bryan Frank O. , Grooms Ian , Pegion Philip J. 
Published: July 2023

AbstractAir‐sea flux variability has contributions from both ocean and atmosphere at different spatio‐temporal scales. Atmospheric synoptic scales and the air‐sea turbulent heat flux that they drive are well represented in climate models, but ocean mesoscales and their associated variability are often not well resolved due to non‐eddy‐resolving spatial resolutions of current climate models. We deploy a physics‐based stochastic subgrid‐scale parameterization for ocean density, that reinforces the lateral density variations due to oceanic eddies, and examine its effect on air‐sea heat flux variability in a comprehensive coupled climate model. The stochastic parameterization substantially modifies sea surface temperature (SST) and latent heat flux (LHF) variability and their co‐variability, primarily at scales near the resolution of the ocean model grid. Enhancement in the SST‐LHF anomaly covariance, and correlations, indicate that the ocean‐intrinsic component of the air‐sea heat flux variability is more consistent with high‐resolution satellite observations, especially in Gulf Stream region.
Knife’s edge: Balancing immunogenicity and reactogenicity in mRNA vaccines
Authors: Lee Jisun , Woodruff Matthew C. , Kim Eui Ho , Nam Jae-Hwan 
Published: July 2023

AbstractSince the discovery of messenger RNA (mRNA), there have been tremendous efforts to wield them in the development of therapeutics and vaccines. During the COVID-19 pandemic, two mRNA vaccines were developed and approved in record-breaking time, revolutionizing the vaccine development landscape. Although first-generation COVID-19 mRNA vaccines have demonstrated over 90% efficacy, alongside strong immunogenicity in humoral and cell-mediated immune responses, their durability has lagged compared to long-lived vaccines, such as the yellow fever vaccine. Although worldwide vaccination campaigns have saved lives estimated in the tens of millions, side effects, ranging from mild reactogenicity to rare severe diseases, have been reported. This review provides an overview and mechanistic insights into immune responses and adverse effects documented primarily for COVID-19 mRNA vaccines. Furthermore, we discuss the perspectives of this promising vaccine platform and the challenges in balancing immunogenicity and adverse effects.
MicroRNA: trends in clinical trials of cancer diagnosis and therapy strategies
Authors: Kim Taewan , Croce Carlo M. 
Published: July 2023

AbstractAs a type of short noncoding RNAs, microRNA (miRNA) undoubtedly plays a crucial role in cancer development. Since the discovery of the identity and clinical functions of miRNAs, over the past few decades, the roles of miRNAs in cancer have been actively investigated. Numerous pieces of evidence indicate that miRNAs are pivotal factors in most types of cancer. Recent cancer research focused on miRNAs has identified and characterized a large cohort of miRNAs commonly dysregulated in cancer or exclusively dysregulated in specific types of cancer. These studies have suggested the potential of miRNAs as biomarkers in the diagnosis and prognostication of cancer. Moreover, many of these miRNAs have oncogenic or tumor-suppressive functions. MiRNAs have been the focus of research given their potential clinical applications as therapeutic targets. Currently, various oncology clinical trials using miRNAs in screening, diagnosis, and drug testing are underway. Although clinical trials studying miRNAs in various diseases have been reviewed before, there have been fewer clinical trials related to miRNAs in cancer. Furthermore, updated results of recent preclinical studies and clinical trials of miRNA biomarkers and drugs in cancer are needed. Therefore, this review aims to provide up-to-date information on miRNAs as biomarkers and cancer drugs in clinical trials.
Care access and utilization among medicare beneficiaries living with Parkinson’s disease
Authors: Pearson Caroline , Hartzman Alex , Munevar Dianne , Feeney Megan , Dolhun Rachel , Todaro Veronica , Rosenfeld Sheera , Willis Allison , Beck James C. 
Published: July 2023

AbstractAn estimated 90% of people living with Parkinson’s disease (PD) in the US are covered by Medicare health insurance. How these beneficiaries use and engage the health care system is important to understand in the face of a rapidly growing PD population. Here, we analyzed health care utilization patterns of those with a PD diagnosis enrolled in Medicare in 2019. By our estimates, PD beneficiaries number 685,116 or 1.2% of the total Medicare population. Compared to the overall Medicare population, 56.3% are male (vs 45.6%), 77.9% over age 70 (vs 57.1%), 14.7% people of color (vs 20.7%), and 16.0% are rural residents (vs 17.5%). Our analysis identified significant disparities in care. Surprisingly, 40% of PD beneficiaries (n = 274,046) did not see a neurologist at all during the calendar year and only 9.1% visited a movement disorder specialist (MDS). Few Medicare beneficiaries diagnosed with PD use recommended services such as physical, occupational, or speech therapy. People of color and rural residents were least likely to access a neurologist or therapy services. Despite 52.9% of beneficiaries being diagnosed with depression, only 1.8% had a clinical psychology visit. Our findings emphasize the need for further research on population-specific barriers to accessing PD-related health care.
Visual field prediction using a deep bidirectional gated recurrent unit network model
Authors: Kim Hwayeong , Lee Jiwoong , Moon Sangwoo , Kim Sangil , Kim Taehyeong , Jin Sang Wook , Kim Jung Lim , Shin Jonghoon , Lee Seung Uk , Jang Geunsoo , Hu Yuanmeng , Park Jeong Rye 
Published: July 2023

AbstractAlthough deep learning architecture has been used to process sequential data, only a few studies have explored the usefulness of deep learning algorithms to detect glaucoma progression. Here, we proposed a bidirectional gated recurrent unit (Bi-GRU) algorithm to predict visual field loss. In total, 5413 eyes from 3321 patients were included in the training set, whereas 1272 eyes from 1272 patients were included in the test set. Data from five consecutive visual field examinations were used as input; the sixth visual field examinations were compared with predictions by the Bi-GRU. The performance of Bi-GRU was compared with the performances of conventional linear regression (LR) and long short-term memory (LSTM) algorithms. Overall prediction error was significantly lower for Bi-GRU than for LR and LSTM algorithms. In pointwise prediction, Bi-GRU showed the lowest prediction error among the three models in most test locations. Furthermore, Bi-GRU was the least affected model in terms of worsening reliability indices and glaucoma severity. Accurate prediction of visual field loss using the Bi-GRU algorithm may facilitate decision-making regarding the treatment of patients with glaucoma.
Machine learning dislocation density correlations and solute effects in Mg-based alloys
Authors: Salmenjoki H. , Papanikolaou S. , Shi D. , Tourret D. , Cepeda-Jiménez C. M. , Pérez-Prado M. T. , Laurson L. , Alava M. J. 
Published: July 2023

AbstractMagnesium alloys, among the lightest structural materials, represent excellent candidates for lightweight applications. However, industrial applications remain limited due to relatively low strength and ductility. Solid solution alloying has been shown to enhance Mg ductility and formability at relatively low concentrations. Zn solutes are significantly cost effective and common. However, the intrinsic mechanisms by which the addition of solutes leads to ductility improvement remain controversial. Here, by using a high throughput analysis of intragranular characteristics through data science approaches, we study the evolution of dislocation density in polycrystalline Mg and also, Mg–Zn alloys. We apply machine learning techniques in comparing electron back-scatter diffraction (EBSD) images of the samples before/after alloying and before/after deformation to extract the strain history of individual grains, and to predict the dislocation density level after alloying and after deformation. Our results are promising given that moderate predictions (coefficient of determination $$R^2$$

R
2

ranging from 0.25 to 0.32) are achieved already with a relatively small dataset ($$\sim$$
∼
5000 sub-millimeter grains).
Performance investigation of state-of-the-art metaheuristic techniques for parameter extraction of solar cells/module
Authors: Sharma Abhishek , Sharma Abhinav , Averbukh Moshe , Jately Vibhu , Rajput Shailendra , Azzopardi Brian , Lim Wei Hong 
Published: July 2023

AbstractOne of the greatest challenges for widespread utilization of solar energy is the low conversion efficiency, motivating the needs of developing more innovative approaches to improve the design of solar energy conversion equipment. Solar cell is the fundamental component of a photovoltaic (PV) system. Solar cell’s precise modelling and estimation of its parameters are of paramount importance for the simulation, design, and control of PV system to achieve optimal performances. It is nontrivial to estimate the unknown parameters of solar cell due to the nonlinearity and multimodality of search space. Conventional optimization methods tend to suffer from numerous drawbacks such as a tendency to be trapped in some local optima when solving this challenging problem. This paper aims to investigate the performance of eight state-of-the-art metaheuristic algorithms (MAs) to solve the solar cell parameter estimation problem on four case studies constituting of four different types of PV systems: R.T.C. France solar cell, LSM20 PV module, Solarex MSX-60 PV module, and SS2018P PV module. These four cell/modules are built using different technologies. The simulation results clearly indicate that the Coot-Bird Optimization technique obtains the minimum RMSE values of 1.0264E-05 and 1.8694E−03 for the R.T.C. France solar cell and the LSM20 PV module, respectively, while the wild horse optimizer outperforms in the case of the Solarex MSX-60 and SS2018 PV modules and gives the lowest value of RMSE as 2.6961E−03 and 4.7571E−05, respectively. Furthermore, the performances of all eight selected MAs are assessed by employing two non-parametric tests known as Friedman ranking and Wilcoxon rank-sum test. A full description is also provided, enabling the readers to understand the capability of each selected MA in improving the solar cell modelling that can enhance its energy conversion efficiency. Referring to the results obtained, some thoughts and suggestions for further improvements are provided in the conclusion section.
Prediction of hub genes and key pathways associated with the radiation response of human hematopoietic stem/progenitor cells using integrated bioinformatics methods
Authors: Sato Yoshiaki , Yoshino Hironori , Ishikawa Junya , Monzen Satoru , Yamaguchi Masaru , Kashiwakura Ikuo 
Published: July 2023

AbstractHematopoietic stem cells (HSCs) are indispensable for the maintenance of the entire blood program through cytokine response. However, HSCs have high radiosensitivity, which is often a problem during radiation therapy and nuclear accidents. Although our previous study has reported that the combination cytokine treatment (interleukin-3, stem cell factor, and thrombopoietin) improves the survival of human hematopoietic stem/progenitor cells (HSPCs) after radiation, the mechanism by which cytokines contribute to the survival of HSPCs is largely unclear. To address this issue, the present study characterized the effect of cytokines on the radiation-induced gene expression profile of human CD34+ HSPCs and explored the hub genes that play key pathways associated with the radiation response using a cDNA microarray, a protein–protein interaction-MCODE module analysis and Cytohubba plugin tool in Cytoscape. This study identified 2,733 differentially expressed genes (DEGs) and five hub genes (TOP2A, EZH2, HSPA8, GART, HDAC1) in response to radiation in only the presence of cytokines. Furthermore, functional enrichment analysis found that hub genes and top DEGs based on fold change were enriched in the chromosome organization and organelle organization. The present findings may help predict the radiation response and improve our understanding of this response of human HSPCs.
Role of face masks and ventilation rates in mitigating respiratory disease transmission in ICU
Authors: Arumuru Venugopal , Kusuluri Rajendra , Mirikar Dnyanesh 
Published: July 2023

AbstractIndoor environments are major contributing locations where the respiratory virus transmission occurs. Higher air change rate (ACH) values (up to 12) have been recommended in hospital environments to reduce virus transmission. In the present study, the Large Eddy Simulation (LES) data of particle transport in a typical intensive care unit (ICU) is used to calculate the infection risk in close proximity interaction. Three different ACH (6, 9, 12) rates with face masks and one case with a healthy person wearing a face shield are considered. The average resident time of the droplets in the ICU is calculated to find the optimal ACH rate. Of the different types of masks analyzed in the present study, the triple-layer mask has shown the most resistance ($$0 \%$$

0
%

probability of infection) to the penetration of virus-laden droplets, while the single-layer mask has shown the highest risk of infection (up to $$97 \%)$$

97
%
)

. The results show that the ACH rate has little effect on close proximity transmission. The ACH 9 case provided optimal value for the particle removal, while the ACH 12 has inferior performance to that of ACH 9. From an energy consumption view, our results recommend not using higher ACH in similar indoor environments. Inside indoor environments, it is advised to wear a three-layer face mask and face shield to reduce the risk of infection.
Design of a novel dual-polarized microwave sensor for human bone fracture detection using reactive impedance surfaces
Authors: Nouri Moqadam Aslan , Kazemi Robab 
Published: July 2023

AbstractThis paper presents a novel miniaturized dual-polarized transceiver sensor system for detecting fractures in human bone tissues. The system features a patch antenna and a Reactive Impedance Surface (RIS) layer that reduces its size by 30% compared to conventional designs, resulting in enhanced fracture detection accuracy. Additionally, the system includes a dielectric plano-concave lens that adapts to the human body and improves impedance matching for optimal performance. The lens contains via holes filled with a lossy dielectric material similar to human fat tissue, which concentrates electromagnetic (EM) power and increases penetration depth for more effective crack detection. To detect fractures, two identical sensors are placed opposite each other on the tissue and moved simultaneously. The amount of EM power collected by the receiver sensor is measured using S-parameters; the transmission coefficient (S21) phases and contrast between the crack and surrounding tissue are used to construct images of fractured bones. Full-wave simulations and experimental measurements on a semi-solid human arm mimicking phantom demonstrate the proposed dual-polarized sensor's ability to detect the location and orientation of narrow cracks in the millimeter range. The system exhibits reliable performance across different human bodies.
HGF/c-Met signaling promotes the migration and proliferation of deer antler MSCs
Authors: Wang Miao , Lin Chuan , Jia Xiaodong , Fang Di , Gao Qinhua , Han Chunmei 
Published: July 2023

AbstractThe complete regeneration of deer antlers is based on the proliferation and differentiation of stem cells. Mesenchymal stem cells (MSCs) of antlers have an important role in antler regeneration and rapid growth and development. HGF is mainly synthesized and secreted by mesenchymal cells. After binding to its receptor c-Met, which transduces signals into cells to stimulate cell proliferation and migration in various organs to promote tissue morphogenesis and angiogenesis. However, the role and mechanism of the HGF/c-Met signaling pathway on antler MSCs are still unclear. In this study, we established antler MSCs with overexpression and interference of HGF gene by lentivirus and small interference RNA, observed the effect of HGF/c-Met signal pathway on the proliferation and migration of antler MSCs, and detected the expression of downstream related signal pathway genes, to explore the mechanism of HGF/c-MET signal pathway on the proliferation and migration of antler MSCs. The results showed that the HGF/c-Met signal affects the expression of RAS, ERK and MEK genes, regulates the proliferation of pilose antler MSCs through Ras/Raf, MEK/ERK pathway, affects the expression of Gab1, Grb2, AKT and PI3K genes, and regulates the migration of MSCs of pilose antler through Gab1/Grb2 and PI3K/AKT pathway.
Ein Gespräch werden und voneinander hören: Wie Therapie das Liebe(n) lernen unterstützen kann
Authors: Fischer Alexander 
Published: July 2023

ZusammenfassungDieser Beitrag nähert sich aus philosophisch-therapeutischer Perspektive der Frage nach der Möglichkeit des Liebe(n)lernens. Hierzu wird in philosophischer Reflexion ein pragmatischer Liebe(n)sbegriff erarbeitet, der wiederum Ansatzpunkte für die Arbeit mit Klienten hervorbringt. Auf diese Ansatzpunkte wird sodann fokussiert, um eine individuell gestaltete Therapie anzuregen, die Klienten beim Liebe(n) lernen hilfreich sein soll.