bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spatial Structure of the Radio‐Frequency Noise Field in a Large City
Authors: Meyer Aaron C. , Breton Daniel J. , Kamrath Matthew J. , Vecherin Sergey N. 
Published: July 2024

AbstractThe urban radio‐frequency (RF) noise generated by our cities continues to change with time. Although models exist to describe the RF noise as functions of frequency and urban land use types, very few models describe the spatial character or structure of the noise on the scales of city blocks (50–150 m). The goal of this work is to investigate the connection between urban morphology and the higher‐order spatial statistics of the noise field. To achieve this goal, a large measurement campaign was conducted in Boston, Massachusetts. Many spatial measurements allowed for calculation of spatial correlation functions of noise power in three different neighborhoods, which were used to quantify the spatial structure of the fields. A statistical point source model is then developed, with adjustable parameters relating to urban morphology. Good agreement between the model and the experimental correlation functions suggests the 25 MHz urban noise field is well described by a random network of fixed point sources, radiating with a 1/r power law behavior.
Quantifying Compound and Nonlinear Effects of Hurricane‐Induced Flooding Using a Dynamically Coupled Hydrological‐Ocean Model
Authors: Bao Daoyang , Xue Z. George , Warner John C. 
Published: July 2024

AbstractWe recently developed a dynamically coupled hydrological‐ocean modeling system that provides seamless coverage across the land‐ocean continuum during hurricane‐induced compound flooding. This study introduced a local inertial equation and a diagonal flow algorithm to the overland routing of the coupled system’s hydrology model (WRF‐Hydro). Using Hurricane Florence (2018) as a test case, the performance of the coupled model was significantly improved, evidenced by its enhanced capability of capturing backwater and increased water level simulation accuracy and stability. With four model experiments, we present a framework to detangle, define, and quantify compound and nonlinear effects. The results revealed that the flood peaks in the lower Cape Fear River Basin and the coastal waters were contributed by inland flooding and storm surge, respectively. These two processes had comparable contributions to the flooding in the Cape Fear River Estuary. The compound effect was identified when the flood levels resulting from the combination of land and ocean processes surpassed those caused by an individual process alone. The compound effect during Hurricane Florence exhibited limited impact on flood peaks, primarily due to the time lag between the peaks of the storm surge and the inland flooding. In the period between the two peaks, the compound effect was salient and significantly impacted the magnitude and variation of the flood level. The nonlinear effect, defined as the difference between the compound flood level and the superposition of storm surge and inland flooding water levels, reduced flood levels in the river channels while increasing flood levels on the floodplain.
Quantifying the Role of EMIC Wave Scattering During the 27 February 2014 Storm by RAM‐SCB Simulations
Authors: Lyu Xingzhi , Jordanova Vania K. , Engel Miles , Tu Weichao , Ma Qianli 
Published: July 2024

AbstractElectromagnetic Ion Cyclotron (EMIC) wave scattering has been proved to be responsible for the fast loss of both radiation belt (RB) electrons and ring current (RC) protons. However, its role in the concurrent dropout of these two co‐located populations remains to be quantified. In this work, we study the effect of EMIC wave scattering on both populations during the 27 February 2014 storm by employing the global physics‐based RAM‐SCB model. Throughout this storm event, MeV RB electrons and 100s keV RC protons experienced simultaneous dropout following the occurrence of intense EMIC waves. By implementing data‐driven initial and boundary conditions, we perform simulations for both populations through the interplay with EMIC waves and compare them against Van Allen Probes observations. The results indicate that by including EMIC wave scattering loss, especially by the He‐band EMIC waves, the model aligns closely with data for both populations. Additionally, we investigate the simulated pitch angle distributions (PADs) for both populations. Including EMIC wave scattering in our model predicts a 90° peaked PAD for electrons with stronger losses at lower pitch angles, while protons exhibit an isotropic PAD with enhanced losses at pitch angles above 40°. Furthermore, our model predicts considerable precipitation of both particle populations, predominantly confined to the afternoon to midnight sector (12 hr < MLT < 24 hr) during the storm's main phase, corresponding closely with the presence of EMIC waves.
A Diffusion‐Based Uncertainty Quantification Method to Advance E3SM Land Model Calibration
Authors: Lu Dan , Liu Yanfang , Zhang Zezhong , Bao Feng , Zhang Guannan 
Published: July 2024

AbstractCalibrating land surface models and accurately quantifying their uncertainty are crucial for improving the reliability of simulations of complex environmental processes. This, in turn, advances our predictive understanding of ecosystems and supports climate‐resilient decision‐making. Traditional calibration methods, however, face challenges of high computational costs and difficulties in accurately quantifying parameter uncertainties. To address these issues, we develop a diffusion‐based uncertainty quantification (DBUQ) method. Unlike conventional generative diffusion methods, which are computationally expensive and memory‐intensive, DBUQ innovates by formulating a parameterized generative model and approximates this model through supervised learning, which enables quick generation of parameter posterior samples to quantify its uncertainty. DBUQ is effective, efficient, and general‐purpose, making it suitable for site‐specific ecosystem model calibration and broadly applicable for parameter uncertainty quantification across various earth system models. In this study, we applied DBUQ to calibrate the Energy Exascale Earth System Model land model at the Missouri Ozark AmeriFlux forest site. Results indicated that DBUQ produced accurate parameter posterior distributions similar to those from Markov Chain Monte Carlo sampling but with 30 times less computing time. This significant improvement in efficiency suggests that DBUQ can enable rapid, site‐level model calibration at a global scale, enhancing our predictive understanding of climate impacts on terrestrial ecosystems.
Molecular relapse after first-line intensive therapy in patients with CBF or NPM1-mutated acute myeloid leukemia – a FILO study
Authors: Orvain Corentin , Bertoli Sarah , Peterlin Pierre , Desbrosses Yohann , Dumas Pierre-Yves , Iat Alexandre , Hospital Marie-Anne , Carre Martin , Tavernier Emmanuelle , Riou Jérémie , Bouvier Anne , Bidet Audrey , Tondeur Sylvie , Renosi Florian , Mozziconacci Marie-Joelle , Flandrin-Gresta Pascale , Dadone-Montaudié Bérengère , Delabesse Eric , Pigneux Arnaud , Hunault-Berger Mathilde , Recher Christian 
Published: July 2024

AbstractPatients with Core-Binding Factor (CBF) and NPM1-mutated acute myeloid leukemia (AML) can be monitored by quantitative PCR after having achieved first complete remission (CR) to detect morphologic relapse and drive preemptive therapy. How to best manage these patients is unknown. We retrospectively analyzed 303 patients with CBF and NPM1-mutated AML, aged 18–60 years, without allogeneic hematopoietic cell transplantation (HCT) in first CR, with molecular monitoring after first-line intensive therapy. Among these patients, 153 (51%) never relapsed, 95 (31%) had molecular relapse (53 received preemptive therapy and 42 progressed to morphologic relapse at salvage therapy), and 55 (18%) had upfront morphologic relapse. Patients who received preemptive therapy had higher OS than those who received salvage therapy after having progressed from molecular to morphologic relapse and those with upfront morphologic relapse (three-year OS: 78% vs. 51% vs. 51%, respectively, P = 0.01). Preemptive therapy included upfront allogeneic HCT (n = 19), intensive chemotherapy (n = 21), and non-intensive therapy (n = 13; three-year OS: 92% vs. 79% vs. 58%, respectively, P = 0.09). Although not definitive due to the non-randomized allocation of patients to different treatment strategies at relapse, our study suggests that molecular monitoring should be considered during follow-up to start preemptive therapy before overt morphologic relapse.
Contributions of cortical neuron firing patterns, synaptic connectivity, and plasticity to task performance
Authors: Insanally Michele N. , Albanna Badr F. , Toth Jade , DePasquale Brian , Fadaei Saba Shokat , Gupta Trisha , Lombardi Olivia , Kuchibhotla Kishore , Rajan Kanaka , Froemke Robert C. 
Published: July 2024

AbstractNeuronal responses during behavior are diverse, ranging from highly reliable ‘classical’ responses to irregular ‘non-classically responsive’ firing. While a continuum of response properties is observed across neural systems, little is known about the synaptic origins and contributions of diverse responses to network function, perception, and behavior. To capture the heterogeneous responses measured from auditory cortex of rodents performing a frequency recognition task, we use a novel task-performing spiking recurrent neural network incorporating spike-timing-dependent plasticity. Reliable and irregular units contribute differentially to task performance via output and recurrent connections, respectively. Excitatory plasticity shifts the response distribution while inhibition constrains its diversity. Together both improve task performance with full network engagement. The same local patterns of synaptic inputs predict spiking response properties of network units and auditory cortical neurons from in vivo whole-cell recordings during behavior. Thus, diverse neural responses contribute to network function and emerge from synaptic plasticity rules.
Common and rare genetic variants predisposing females to unexplained recurrent pregnancy loss
Authors: Sonehara Kyuto , Yano Yoshitaka , Naito Tatsuhiko , Goto Shinobu , Yoshihara Hiroyuki , Otani Takahiro , Ozawa Fumiko , Kitaori Tamao ,   the Biobank Japan Project, Yamanashi Yuji , Furukawa Yoichi , Morisaki Takayuki , Murakami Yoshinori , Kamatani Yoichiro , Muto Kaori , Nagai Akiko , Nakamura Yusuke , Obara Wataru , Yamaji Ken , Takahashi Kazuhisa , Asai Satoshi , Takahashi Yasuo , Higashiue Shinichi , Kobayashi Shuzo , Yamaguchi Hiroki , Nagata Yasunobu , Wakita Satoshi , Nito Chikako , Iwasaki Yu-ki , Murayama Shigeo , Yoshimori Kozo , Miki Yoshio , Obata Daisuke , Higashiyama Masahiko , Masumoto Akihide , Koga Yoshinobu , Koretsune Yukihiro , Matsuda Koichi , Nishiyama Takashi , Okada Yukinori , Sugiura-Ogasawara Mayumi 
Published: July 2024

AbstractRecurrent pregnancy loss (RPL) is a major reproductive health issue with multifactorial causes, affecting 2.6% of all pregnancies worldwide. Nearly half of the RPL cases lack clinically identifiable causes (e.g., antiphospholipid syndrome, uterine anomalies, and parental chromosomal abnormalities), referred to as unexplained RPL (uRPL). Here, we perform a genome-wide association study focusing on uRPL in 1,728 cases and 24,315 female controls of Japanese ancestry. We detect significant associations in the major histocompatibility complex (MHC) region at 6p21 (lead variant=rs9263738; P = 1.4 × 10−10; odds ratio [OR] = 1.51 [95% CI: 1.33–1.72]; risk allele frequency = 0.871). The MHC associations are fine-mapped to the classical HLA alleles, HLA-C*12:02, HLA-B*52:01, and HLA-DRB1*15:02 (P = 1.1 × 10−10, 1.5 × 10−10, and 1.2 × 10−9, respectively), which constitute a population-specific common long-range haplotype with a protective effect (P = 2.8 × 10−10; OR = 0.65 [95% CI: 0.57–0.75]; haplotype frequency=0.108). Genome-wide copy-number variation (CNV) calling demonstrates rare predicted loss-of-function (pLoF) variants of the cadherin-11 gene (CDH11) conferring the risk of uRPL (P = 1.3 × 10−4; OR = 3.29 [95% CI: 1.78–5.76]). Our study highlights the importance of reproductive immunology and rare variants in the uRPL etiology.
ARID1A safeguards the canalization of the cell fate decision during osteoclastogenesis
Authors: Du Jiahui , Liu Yili , Sun Jinrui , Yao Enhui , Xu Jingyi , Wu Xiaolin , Xu Ling , Zhou Mingliang , Yang Guangzheng , Jiang Xinquan 
Published: July 2024

AbstractChromatin remodeler ARID1A regulates gene transcription by modulating nucleosome positioning and chromatin accessibility. While ARID1A-mediated stage and lineage-restricted gene regulation during cell fate canalization remains unresolved. Using osteoclastogenesis as a model, we show that ARID1A transcriptionally safeguards the osteoclast (OC) fate canalization during proliferation-differentiation switching at single-cell resolution. Notably, ARID1A is indispensable for the transcriptional apparatus condensates formation with coactivator BRD4/lineage-specifying transcription factor (TF) PU.1 at Nfatc1 super-enhancer during safeguarding the OC fate canalization. Besides, the antagonist function between ARID1A-cBAF and BRD9-ncBAF complex during osteoclastogenesis has been validated with in vitro assay and compound mutant mouse model. Furthermore, the antagonistic function of ARID1A-“accelerator” and BRD9-“brake” both depend on coactivator BRD4-“clutch” during osteoclastogenesis. Overall, these results uncover sophisticated cooperation between chromatin remodeler ARID1A, coactivator, and lineage-specifying TF at super-enhancer of lineage master TF in a condensate manner, and antagonist between distinct BAF complexes in the proper and balanced cell fate canalization.
Bionic e-skin with precise multi-directional droplet sliding sensing for enhanced robotic perception
Authors: Xu Yunlong , Sun Zhongda , Bai Zhiqing , Shen Hua , Wen Run , Wang Fumei , Xu Guangbiao , Lee Chengkuo 
Published: July 2024

AbstractElectronic skins with deep and comprehensive liquid information detection are desired to endow intelligent robotic devices with augmented perception and autonomous regulation in common droplet environments. At present, one technical limitation of electronic skins is the inability to perceive the liquid sliding information as realistically as humans and give feedback in time. To this critical challenge, in this work, a self-powered bionic droplet electronic skin is proposed by constructing an ingenious co-layer interlaced electrode network and using an overpass connection method. The bionic skin is used for droplet environment reconnaissance and converts various dynamic droplet sliding behaviors into electrical signals based on triboelectricity. More importantly, the two-dimensional sliding behavior of liquid droplets is comprehensively perceived by the e-skin and visually fed back in real-time on an indicator. Furthermore, the flow direction warning and intelligent closed-loop control of water leakage are also achieved by this e-skin, achieving the effect of human neuromodulation. This strategy compensates for the limitations of e-skin sensing droplets and greatly narrows the gap between artificial e-skins and human skins in perceiving functions.
Non-technological barriers: the last frontier towards AI-powered intelligent optical networks
Authors: Khan Faisal Nadeem 
Published: July 2024

AbstractMachine learning (ML) has been remarkably successful in transforming numerous scientific and technological fields in recent years including computer vision, natural language processing, speech recognition, bioinformatics, etc. Naturally, it has long been considered as a promising mechanism to fundamentally revolutionize the existing archaic optical networks into next-generation smart and autonomous entities. However, despite its promise and extensive research conducted over the last decade, the ML paradigm has so far not been triumphant in achieving widespread adoption in commercial optical networks. In our perspective, this is primarily due to non-addressal of a number of critical non-technological issues surrounding ML-based solutions’ development and use in real-world optical networks. The vision of intelligent and autonomous fiber-optic networks, powered by ML, will always remain a distant dream until these so far neglected factors are openly confronted by all relevant stakeholders and categorically resolved.
Selective nitrogen insertion into aryl alkanes
Authors: Zhang Zheng , Li Qi , Cheng Zengrui , Jiao Ning , Zhang Chun 
Published: July 2024

AbstractMolecular structure-editing through nitrogen insertion offers more efficient and ingenious pathways for the synthesis of nitrogen-containing compounds, which could benefit the development of synthetic chemistry, pharmaceutical research, and materials science. Substituted amines, especially nitrogen-containing alkyl heterocyclic compounds, are widely found in nature products and drugs. Generally, accessing these compounds requires multiple steps, which could result in low efficiency. In this work, a molecular editing strategy is used to realize the synthesis of nitrogen-containing compounds using aryl alkanes as starting materials. Using derivatives of O-tosylhydroxylamine as the nitrogen source, this method enables precise nitrogen insertion into the Csp2-Csp3 bond of aryl alkanes. Notably, further synthetic applications demonstrate that this method could be used to prepare bioactive molecules with good efficiency and modify the molecular skeleton of drugs. Furthermore, a plausible reaction mechanism involving the transformation of carbocation and imine intermediates has been proposed based on the results of control experiments.
Machine learning aided design of single-atom alloy catalysts for methane cracking
Authors: Sun Jikai , Tu Rui , Xu Yuchun , Yang Hongyan , Yu Tie , Zhai Dong , Ci Xiuqin , Deng Weiqiao 
Published: July 2024

AbstractThe process of CH4 cracking into H2 and carbon has gained wide attention for hydrogen production. However, traditional catalysis methods suffer rapid deactivation due to severe carbon deposition. In this study, we discover that effective CH4 cracking can be achieved at 450 °C over a Re/Ni single-atom alloy via ball milling. To explore single-atom alloy catalysis, we construct a library of 10,950 transition metal single-atom alloy surfaces and screen candidates based on C–H dissociation energy barriers predicted by a machine learning model. Experimental validation identifies Ir/Ni and Re/Ni as top performers. Notably, the non-noble metal Re/Ni achieves a hydrogen yield of 10.7 gH2 gcat–1 h–1 with 99.9% selectivity and 7.75% CH4 conversion at 450 °C, 1 atm. Here, we show the mechanical energy boosts CH4 conversion clearly and sustained CH4 cracking over 240 h is achieved, significantly surpassing other approaches in the literature.
CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection
Authors: Li Hao , Han Zebei , Sun Yu , Wang Fu , Hu Pengzhen , Gao Yuang , Bai Xuemei , Peng Shiyu , Ren Chao , Xu Xiang , Liu Zeyu , Chen Hebing , Yang Yang , Bo Xiaochen 
Published: July 2024

AbstractCancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1, PPM1A and DLG2. We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.
Anomalous flocking in nonpolar granular Brownian vibrators
Authors: Chen Yangrui , Zhang Jie 
Published: July 2024

AbstractUsing Brownian vibrators, we investigated the structures and dynamics of quasi-2d granular materials, with packing fractions (ϕ) ranging from 0.111 to 0.832. Our observations revealed a remarkable large-scale flocking behavior in hard granular disk systems, encompassing four distinct phases: granular fluid, flocking fluid, poly-crystal, and crystal. Anomalous flocking emerges at ϕ = 0.317, coinciding with a peak in local density fluctuations, and ceased at ϕ = 0.713 as the system transitioned into a poly-crystal state. The poly-crystal and crystal phases resembled equilibrium hard disks, while the granular and flocking fluids differed significantly from equilibrium systems and previous experiments involving uniformly driven spheres. This disparity suggests that collective motion arises from a competition controlled by volume fraction, involving an active force and an effective attractive interaction resulting from inelastic particle collisions. Remarkably, these findings align with recent theoretical research on the flocking motion of spherical active particles without alignment mechanisms.