bluuebunny/crossref_metadata_2025_split huggingface dataset:
Low‐Level Jet and Its Effect on the Onset of Summertime Nocturnal Rainfall in Beijing
Authors: Li Ning , Guo Jianping , Wu Mengxi , Zhang Fan , Guo Xiaoran , Sun Yuping , Zhang Zhen , Liang Hong , Chen Tianmeng 
Published: October 2024

AbstractLow‐level Jets (LLJs) play a critical role in triggering nocturnal rainfall in many areas on our planet. Nevertheless, little is known regarding the vertically resolved evolution of LLJs preceding rainfall. Here, the high‐resolution wind measurements from a radar wind profiler network in Beijing are used to monitor the continuous evolution of boundary‐layer jets (BLJs) and synoptic‐system‐related LLJs (SLLJs) before nocturnal rainfall under the southwesterlies synoptic pattern for the summers of 2020–2023. The results show that inertial oscillations precede LLJ‐induced nocturnal rainfall. The typical northward propagation of LLJ is driven primarily by horizontal advection. Starting 48 min before rainfall, the BLJ nose rapidly strengthens and moves downward from 0.8 km above ground level (AGL), while the SLLJ nose moves upward to 2.4 km AGL. This leads to enhanced moisture transport and convergence, which triggers nocturnal rainfall. The findings are of great significance for improving the forecast skill of nocturnal rainfall.
Trajectories of Terrestrial Vegetation Productivity and Its Driving Factors in China's Drylands
Authors: Gong Haixing , Wang Guoyin , Wang Xiaoyan , Kuang Zexing , Cheng Tiantao 
Published: October 2024

AbstractClimate change and large‐scale ecological restoration programs have profoundly influenced vegetation greening and gross primary productivity (GPP) in China's drylands. However, the specific pathways through which climatic factors and vegetation greening influence GPP remain poorly understood. This study examines the spatiotemporal changes in GPP across China's drylands from 2001 to 2020 and investigates the direct and indirect effects of climatic factors and leaf area index (LAI) on GPP. The results reveal that the overall improvement in vegetation cover has positively increased GPP in these regions. Although the direct effects of climatic factors on GPP are minimal, they exert a substantial indirect effect by regulating vegetation growth, highlighting that LAI is a key intermediary in mediating the effects of climatic factors on GPP. Furthermore, these complex interactions vary significantly along the aridity gradient. This study emphasizes the necessity of comprehensively considering the intricate interactions among multiple climate and vegetation factors.
Reply to Comment by Chang on “Anticyclonic Suppression of the North Pacific Transient Eddy Activity in Midwinter”
Authors: Okajima Satoru , Nakamura Hisashi , Kaspi Yohai 
Published: October 2024

AbstractChang (2024, https://doi.org/10.1029/2024gl110011) challenged the methodology proposed recently by Okajima et al. for evaluating cyclonic and anticyclonic contributions to Eulerian eddy statistics and atmospheric energetics based on the local flow curvature. He argued that using the local wind curvature to separate energetic contributions from cyclones and anticyclones is not physically meaningful. Here we argue that his claims are based on an unrealistic assumption of monopolar relative vorticity in an entire storm‐track domain and a meridionally uniform zonal background flow atypical to midlatitudes. We also demonstrate that the error in attributing eddy statistics to cyclones and anticyclones is significantly smaller than his estimation. Rather, we further demonstrate that the curvature‐based methodology effectively eliminates the shear influence to identify cyclonic and anticyclonic regions, which is dismissed in his argument. We conclude that the curvature‐based methodology is beneficial in evaluating distinct cyclonic and anticyclonic contributions to atmospheric energetics in realistic conditions.
Revealing the Local Time Structure of the Alfvén Radius and Travel Times in Jupiter's Magnetosphere
Authors: Jenkins A. , Ray L. C. , Fell T. , Badman S. V. , Lorch C. T. S. 
Published: October 2024

AbstractJovian magnetospheric plasma is coupled to the ionosphere through Alfvén waves. Alfvén waves enable the transport of angular momentum and energy between the planet and magnetospheric plasma, a process that ultimately generates Jupiter's bright auroral emissions. However, past the Alfvén radius, the location where the radial velocity is greater than the Alfvén velocity, magnetospheric plasma is decoupled from the planet. Alfvén waves launched by magnetospheric plasma do not reach the ionosphere, angular momentum cannot be transported from the planet, and auroral emissions should diminish. Knowledge of Jupiter's Alfvén radius location is critical for interpreting drivers of auroral emissions, in situ data, and applications of numerical models. Previous studies that determined the location of the Alfvén radius assumed an azimuthally symmetric magnetosphere and local‐time independent magnetic field. Here, we employ a statistical description of the magnetic field that includes local time effects. We find a minimum Alfvén radius of 30 (Jupiter radii) at 6 LT, with plasma decoupled from the planet in the post‐dusk through dawn sector. Furthermore, no Alfvén radius exists within 60 between 8 and 20 LT. At distances greater than 50 , the Alfvén travel time is such that magnetospheric plasma moves substantially in the magnetosphere before angular momentum can be efficiently transferred from the atmosphere. Therefore, the angular momentum supplied may no longer be sufficient for the local conditions. Our results highlight the importance of local time considerations and offer new interpretations for local time dependent auroral features, such as the polar collar.
Functional activity and connectivity signatures of ketamine and lamotrigine during negative emotional processing: a double-blind randomized controlled fMRI study
Authors: Meiering Marvin S. , Weigner David , Gärtner Matti , Carstens Luisa , Keicher Christian , Hertrampf Rita , Beckmann Christian F. , Mennes Maarten , Wunder Andreas , Weigand Anne , Grimm Simone 
Published: October 2024

AbstractKetamine is a highly effective antidepressant (AD) that targets the glutamatergic system and exerts profound effects on brain circuits during negative emotional processing. Interestingly, the effects of ketamine on brain measures are sensitive to modulation by pretreatment with lamotrigine, which inhibits glutamate release. Examining the antagonistic effects of ketamine and lamotrigine on glutamate transmission holds promise to identify effects of ketamine that are mediated through changes in the glutamatergic system. Investigating this modulation in relation to both the acute and sustained effects of ketamine on functional activity and connectivity during negative emotional processing should therefore provide novel insights. 75 healthy subjects were investigated in a double-blind, single-dose, randomized, placebo-controlled, parallel-group study with three treatment conditions (ketamine, lamotrigine pre-treatment, placebo). Participants completed an emotional face viewing task during ketamine infusion and 24 h later. Acute ketamine administration decreased hippocampal and Default Mode Network (DMN) activity and increased fronto-limbic coupling during negative emotional processing. Furthermore, while lamotrigine abolished the ketamine-induced increase in functional connectivity, it had no acute effect on activity. Sustained (24 h later) effects of ketamine were only found for functional activity, with a significant reduction in the posterior DMN. This effect was blocked by pretreatment with lamotrigine. Our results suggest that both the acute increases in fronto-limbic coupling and the delayed decrease in posterior DMN activity, but not the attenuated limbic and DMN recruitment after ketamine, are mediated by altered glutamatergic transmission.
Myosteatosis and sarcopenia are linked to autonomous cortisol secretion in patients with aldosterone-producing adenomas
Authors: Lee Bo-Ching , Chang Yu-Ling , Chen Po-Ting , Liu Li-Wen , Liu Kao-Lang , Chang Chin-Chen , Wu Vin-Cent , Lin Yen-Hung ,   On behalf of the TAIPAI Study Group, Wu Vin-Cent , Lai Tai-Shuan , Chueh Shih-Chieh Jeff , Yang Shao-Yu , Liu Kao-Lang , Chang Chin-Chen , Lee Bo-Ching , Wang Shuo-Meng , Huang Kuo-How , Lin Po-Chih , Lin Yen-Hung , Hung Chi-Sheng , Lin Lian-Yu , Liao Shih-Cheng , Lu Ching-Chu , Chan Chieh-Kai , Er Leay-Kiaw , Hu Ya-Hui , Wu Che-Hsiung , Tsai Yao-Chou , Chen Zheng-Wei , Pan Chien-Ting , Liao Che-Wei , Tsai Cheng-Hsuan , Chang Yi-Yao , Ho Chen-Hsun , Huang Wei-Chieh , Chen Ying-Ying 
Published: October 2024

Abstract
Patients with adrenal aldosterone-producing adenomas (APA) face elevated cardiovascular risks, especially when cortisol is co-secreted, yet the impact on muscle health remains unclear. Myosteatosis, characterized by fatty infiltration into muscles, is linked to cardiometabolic diseases and decreased survival. We aimed to investigate the association between autonomous cortisol secretion (ACS) in APA and muscle quantity and quality. In this study, we analyzed data from 228 APA patients undergoing laparoscopic adrenalectomy between 2009 and 2024, assessing muscle composition via computed tomography. Intermuscular adipose tissue (IMAT), skeletal muscle area and density, visceral and subcutaneous adipose tissue area at L3 were measured. Comparisons were made between ACS and non-ACS groups. We found that among 228 patients, 76 (33.3%) had ACS. Those with ACS exhibited significantly higher IMAT area (P = 0.042) and lower skeletal muscle area (P = 0.002) and density (P < 0.001). Multivariable regression confirmed ACS positively associated with IMAT area and negatively associated with skeletal muscle area and density. At 1-year follow-up, ACS patients (n = 15) experienced decreased IMAT area (P = 0.001) and increased skeletal muscle area (P = 0.031) post-adrenalectomy, while those without ACS (n = 29) showed no IMAT change but increased visceral (P < 0.001) and subcutaneous (P = 0.008) adipose tissue area. In summary, myosteatosis and sarcopenia are linked to ACS in APA patients, and these parameters improve following adrenalectomy.
Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP
Authors: Liu Shuang , Qu Enxiang , LV Chun , Zhang Xueyuan 
Published: October 2024

AbstractThe blasting block size of open-pit mines is influenced by many factors, and the influencing factors have a very complex nonlinear relationship. Traditional empirical formulas and a single neural network model cannot meet the requirements of modern blasting safety. To improve the prediction accuracy of blasting block size, the measured data of Beskuduk open-pit coal mine is used as training and testing samples. Seven factors including rock tensile strength, rock compressive strength, and blast hole spacing are selected as input variables of the prediction model. The average size of blasting fragmentation X50 is used as the output variable of the prediction model. The kernel principal component analysis (KPCA) is adopted to reduce the dimensionality of the input variables. The beetle antennae search algorithm (BAS) is selected to optimize the parameters of the initial weights and thresholds of the back propagation (BP) neural network. Finally, prediction model of blasting fragmentation in open-pit coal mine based on KPCA-BAS-BP is established. The results show that the average relative error of the model is 1.77%, and the root mean square error is 1.52%. Compared with the unoptimized BP neural network and the BP neural network optimized by the artificial bee colony algorithm (ABC) model, this model has higher prediction accuracy and is more suitable for predicting the blasting block size of open-pit coal mines, it provides a new method for predicting the fragmentation of blasting under the influence of multiple factors, filling the gap in related theoretical research, and has certain practical application value.
Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation
Authors: Pramudito Muhammad Adnan , Fuadah Yunendah Nur , Qauli Ali Ikhsanul , Marcellinus Aroli , Lim Ki Moo 
Published: October 2024

AbstractThe Comprehensive In-vitro Proarrhythmia Assay (CiPA) initiative aims to refine the assessment of drug-induced torsades de pointes (TdP) risk, utilizing computational models to predict cardiac drug toxicity. Despite advancements in machine learning applications for this purpose, the specific contribution of in-silico biomarkers to toxicity risk levels has yet to be thoroughly elucidated. This study addresses this gap by implementing explainable artificial intelligence (XAI) to illuminate the impact of individual biomarkers in drug toxicity prediction. We employed the Markov chain Monte Carlo method to generate a detailed dataset for 28 drugs, from which twelve in-silico biomarkers of 12 drugs were computed to train various machine learning models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests (RF), XGBoost, K-Nearest Neighbors (KNN), and Radial Basis Function (RBF) networks. Our study’s innovation is leveraging XAI, mainly through the SHAP (SHapley Additive exPlanations) method, to dissect and quantify the contributions of biomarkers across these models. Furthermore, the model performance was evaluated using the test set from 16 drugs. We found that the ANN model coupled with the eleven most influential in-silico biomarkers namely $$\frac{dVm}{dt}_{repol}, \frac{dVm}{dt}_{max}, {APD}_{90}, {APD}_{50}, {APD}_{tri}, {CaD}_{90}, {CaD}_{50}, {Ca}_{tri}, {Ca}_{Diastole}, qInward, and qNet$$

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showed the highest classification performance among all classifiers with Area Under the Curve (AUC) scores of 0.92 for predicting high-risk, 0.83 for intermediate-risk, and 0.98 for low-risk drugs. We also found that the optimal in silico biomarkers selected based on SHAP analysis may be different for various classification models. However, we also found that the biomarker selection only sometimes improved the performance; therefore, evaluating various classifiers is still essential to obtain the desired classification performance. Our proposed method could provide a systematic way to assess the best classifier with the optimal in-silico biomarkers for predicting the TdP risk of drugs, thereby advancing the field of cardiac safety evaluations.
Sex-specific proximal tubular cell differentiation pathways identified by single-nucleus RNA sequencing
Authors: Lu Yueh-An , Smith Tanya , Deshpande Sumukh , Liao Chia-Te , Talabani Bnar , Grigorieva Irina , Mason Anna , Andrews Robert , Bowen Timothy , Taylor Philip R. , Fraser Donald 
Published: October 2024

AbstractPostnatal kidney growth is substantial and involves expansion in kidney tubules without growth of new nephrons, which are the functional units of the kidney. Proliferation and differentiation pathways underpinning nephron elongation are not well defined. To address this, we performed sequential characterization of mouse kidney transcriptomics at the single cell level. Single nuclear RNA sequencing (snRNA-seq) was performed on kidney tissue from male and female mice at 1, 2, 4 and 12 weeks of age using the 10x Chromium platform. Unbiased clustering was performed on 68,775 nuclei from 16 animals. 31 discrete cellular clusters were seen, which were identified through comparison of their gene expression profiles to canonical markers of kidney cell populations. High levels of proliferation were evident at early time points in some cell types, especially tubular cells, but not in other cell types, for example podocytes. Proliferation was especially evident in Proximal Tubular Cells (PTCs) which are the most abundant cell type in the adult kidney. Uniquely when compared to other kidney cell types, PTCs demonstrated sex-specific expression profiles at late, but not early, time points. Mapping of PTC differentiation pathways using techniques including trajectory and RNA Velocity analyses delineated increasing PTC specialization and sex-specific phenotype specification. Our single-cell transcriptomics data characterise cellular states observed during kidney growth. We have identified PTC differentiation pathways that lead to sex-specific tubular cell phenotypes. Tubular proliferative responses are of central importance in postnatal kidney growth and have also been linked to kidney recovery versus fibrosis following injury. Our unbiased and comprehensive dataset of tubular cell development can be used to identify candidate pathways for therapeutic targeting.
Associations between elevated uric acid and brain imaging abnormalities in pediatric patients with methylmalonic acidemia under 5 years of age
Authors: Du Mengmeng , Wu Shengnan , Chen Yongxing , Yuan Shuxian , Dong Shijie , Wang Huizhen , Wei Haiyan , Zhu Changlian 
Published: October 2024

AbstractMethylmalonic acidemia (MMA) is the most common inborn organic acidemia, presenting multisystemic complications. Uric acid may have neurotoxic or neuroprotective effects due to its antioxidant or pro-inflammatory properties; however, its role in MMA brain injury remains unclear. We examined the correlation between the serum uric acid levels and brain imaging features of MMA. Data were collected from a cross-sectional study of 216 patients with MMA and 216 healthy matched controls aged 0–5 years in China. Serum uric acid levels were measured, and magnetic resonance imaging and computed tomography findings were retrieved from hospital records. Overall, 74.1% patients had brain abnormalities. Patients in the MMA group with abnormal brain imaging had higher serum uric acid levels than those in the MMA normal brain imaging and control groups. The area under the curve of serum uric acid was 0.74, 0.91, and 0.93 for MMA diagnosis with abnormal brain images, basal ganglia changes, and globus pallidus changes, respectively. Higher serum uric acid levels were independently associated with abnormal brain images. Children aged < 5 years with abnormal brain images in MMA exhibit elevated serum uric acid levels, serving as an effective auxiliary diagnostic indicator and independent risk factor for brain tissue injury.
Treatment of polyethylene microplastics degraded by ultraviolet light irradiation causes lysosome-deregulated cell death
Authors: Manabe Sota , Haga Yuya , Tsujino Hirofumi , Ikuno Yudai , Asahara Haruyasu , Higashisaka Kazuma , Tsutsumi Yasuo 
Published: October 2024

Abstract
Background
Microplastics (MPs), plastic particles < 5 mm in size, are prevalent in the environment, and human exposure to them is inevitable. To assess the potential risk of MPs on human health, it is essential to consider the physicochemical properties of environmental MPs, including polymer types, size, shape, and surface chemical modifications. Notably, environmental MPs undergo degradation due to external factors such as ultraviolet (UV) rays and waves, leading to changes in their surface characteristics. However, limited knowledge exists regarding the health effects of MPs, with a specific focus on their surface degradation. This study concentrates on cytotoxic MPs with surface degradation through UV irradiation, aiming to identify the mechanisms underlying their cell toxicity.

Results
Polyethylene (PE) and surface-degraded PE achieved through UV light irradiation were employed as model MPs in this study. We explored the impact of PE and degraded PE on cell death in murine macrophage cell line RAW264.7 cells and human monocyte cell line THP-1 cells. Flow cytometric analysis revealed that degraded PE induced programmed cell death without activating caspase 3, while non-degraded PE did not trigger programmed cell death. These findings suggest that degraded PE might induce programmed cell death through mechanisms other than caspase-driven apoptosis. To understand the mechanisms of cell death, we investigated how cells responded to degraded PE-induced cellular stress. Immunofluorescence and western blotting analyses demonstrated that degraded PE induced autophagosome formation and increased p62 expression, indicating inhibited autophagy flux after exposure to degraded PE. Furthermore, degraded PE exposure led to a decrease in acidic lysosomes, indicating lysosomal dysregulation. These results imply that degraded PE induces lysosomal dysfunction, subsequently causing autophagy dysregulation and cell death.

Conclusions
This study unveils that UV-induced degradation of PE results in cell death attributed to lysosomal dysfunction. The findings presented herein provide novel insights into the effects of surface-degraded MPs on biological responses.
A post-Brexit intergroup contact intervention reduces affective polarization between Leavers and Remainers short-term
Authors: Tausch Nicole , Birtel Michèle D. , Górska Paulina , Bode Sidney , Rocha Carolina 
Published: October 2024

AbstractWith mounting evidence of the harmful societal consequences of affective polarization, it is crucial to find ways of addressing it. Employing a randomized controlled trial, this study tested the effectiveness of an intervention based on theories of intergroup contact and interpersonal communication in reducing affective polarization in the context of Brexit. Participants were 120 UK self-identified Leavers and Remainers. Sixty Leaver-Remainer dyads were randomized to engage in either a facilitated intergroup interaction or a control interaction, which was equivalent in structure and tone but was unrelated to Brexit identities. Different aspects of affective polarization were assessed one month prior, immediately after, and one month after the intervention. Results indicate that the intervention increased warmth toward the outgroup, reduced unfavourable attributions of the sources of outgroup positions, and increased willingness to compromise, but only short-term. There were no statistically significant longer-term effects of the intervention. Evidence of selective attrition further suggests that those with more extreme baseline opinions were more likely to drop out. Our findings highlight the challenges of designing effective interventions that engender enduring attitude change in polarized contexts and of engaging those with extreme political views. This study can provide a useful framework for future research.
Energy optimization and age of information enhancement in multi‐UAV networks using deep reinforcement learning
Authors: Kim Jeena , Park Seunghyun , Park Hyunhee 
Published: October 2024

AbstractThis letter introduces an innovative approach for minimizing energy consumption in multi‐unmanned aerial vehicles (multi‐UAV) networks using deep reinforcement learning, with a focus on optimizing the age of information (AoI) in disaster environments. A hierarchical UAV deployment strategy that facilitates cooperative trajectory planning, ensuring timely data collection and transmission while minimizing energy consumption is proposed. By formulating the inter‐UAV network path planning problem as a Markov decision process, a deep Q‐network (DQN) strategy is applied to enable real‐time decision making that accounts for dynamic environmental changes, obstacles, and UAV battery constraints. The extensive simulation results, conducted in both rural and urban scenarios, demonstrate the effectiveness of employing a memory access approach within the DQN framework, significantly reducing energy consumption up to 33.25% in rural settings and 74.20% in urban environments compared to non‐memory approaches. By integrating AoI considerations with energy‐efficient UAV control, this work offers a robust solution for maintaining fresh data in critical applications, such as disaster response, where ground‐based communication infrastructures are compromised. The use of replay memory approach, particularly the online history approach, proves crucial in adapting to changing conditions and optimizing UAV operations for both data freshness and energy consumption.