bluuebunny/crossref_metadata_2025_split huggingface dataset:
Uncertainty Quantification in Reduced‐Order Gas‐Phase Atmospheric Chemistry Modeling Using Ensemble SINDy
Authors: Guo Lin , Yang Xiaokai , Zheng Zhonghua , Riemer Nicole , Tessum Christopher W. 
Published: December 2024

AbstractUncertainty quantification during atmospheric chemistry modeling is computationally expensive as it typically requires a large number of simulations using complex models. As such, large‐scale modeling is typically performed with simplified chemical mechanisms for computational tractability. Here, we describe a probabilistic surrogate modeling method using principal components analysis (PCA) and Ensemble Sparse Identification of Nonlinear Dynamics (E‐SINDy) to both automatically simplify a gas‐phase chemistry mechanism and to quantify the uncertainty introduced when doing so. We demonstrate the application of this method on a small photochemical box model for ozone formation. With 100 ensemble members, the calibration ‐squared value is 0.86 among the three latent species on average and 0.98 for ozone, demonstrating that predicted model uncertainty aligns well with actual model error. In addition to uncertainty quantification, this probabilistic method also improves accuracy as compared to an equivalent deterministic version, by 64% for the ensemble prediction mean or 45% for deterministic prediction by the best‐performing single ensemble member. Overall, the ozone testing root mean square error (RMSE) is 20% of its root mean square (RMS) concentration. Although our probabilistic ensemble simulation ends up being slower than the reference model it emulates, we hypothesize that use of a more complex reference model in future work will result in additional opportunities for acceleration. Versions of this approach applied to full‐scale chemical mechanisms may result in improved uncertainty quantification in models of atmospheric composition, leading to enhanced atmospheric understanding and improved support for air quality control and regulation.
Importance of Monitoring Frequency for Representation of Dissolved Organic Matter Dynamics in Urban Rivers
Authors: Zhu Hongzheng , Khamis Kieran , Hannah David M. , Krause Stefan 
Published: December 2024

AbstractIn‐situ dissolved organic matter (DOM) monitoring frequencies have often been chosen for convenience or based on perceived wisdom, without fully assessing their impact on representation of DOM dynamics. To address this gap, we collected 5‐min fluorescence data in an urban headwater and resampled it at coarser intervals to investigate the impact of monitoring frequencies on the detectability of DOM dynamics during storms. Expecting hydrometeorological conditions to modify the impact of monitoring frequency, we categorized 85 storm events into groups: Group A (low intensity, short duration), Group B (high intensity, short duration), and Group C (low intensity, long duration). Surprisingly, our analysis indicated that monitoring frequency has minimal influence on commonly used biogeochemical indexes (e.g., maximum, hysteresis and flushing index), which are employed to characterize solute behavior, regardless of storm type. To facilitate a direct comparison between monitoring frequencies, we back‐interpolated coarser data into 5‐min intervals and calculated mean squared errors by comparing them with original high‐resolution data. Our findings indicated that in colder periods with predominately Type A and C storms, a coarser monitoring frequency (>30 min) can capture DOM dynamics. Conversely, in warmer periods when Type B storms dominate, a finer frequency (≤15 min) is necessary to capture key solute chemograph processes (e.g., first flush and dilution). Generally, we suggest a 15‐min monitoring frequency as optimal for similar urban headwater systems, and advocate an adaptive approach based on seasonal variations to improve efficiency, especially when power, data transfer, and storage are constraints.
Remote Sensing Estimation of Shallow and Deep Aquifer Response to Precipitation‐Based Recharge Through Downscaling
Authors: Kalu Ikechukwu , Ndehedehe Christopher E. , Ferreira Vagner G. , Janardhanan Sreekanth , Currell Matthew , Crosbie Russell S. , Kennard Mark J. 
Published: December 2024

AbstractThe Gnangara groundwater system is a highly productive water resource in southwestern Australia. However, it is considered one of the most vulnerable groundwater systems to climate change, due to consistent declines in precipitation and recharge, and regional climate models project further declines into the future. This study introduces a new framework underpinned by machine learning techniques to provide reliable estimates of precipitation‐based recharge over the whole Perth Basin (including the Gnangara system). By combining estimates of baseflow, groundwater evaporation, and extraction, groundwater recharge was estimated over the Perth (testing site) and Gnangara (calibration site) systems using downscaled Groundwater Storage Anomalies (GWSA) from the Gravity Recovery and Climate Experiment (GRACE) mission. The random forest regression (RFR) model was used to downscale the spatial resolution of GRACE to 0.05° (approx. 5 km), providing estimable signals over the relatively small calibration site (∼2,200 km2) in order to discern any meaningful signals from the original GRACE resolution. Our study reveals that downscaled signals from GRACE can be used to provide precipitation‐based recharge estimates for groundwater systems accurately. However, the growing impacts of climate change, which has led to sporadic precipitation patterns over Western Australia, can limit the efficiency of satellite remote sensing methods in estimating recharge, especially in deep and complex aquifers.
Inhibition of furin in CAR macrophages directs them toward a proinflammatory phenotype and enhances their antitumor activities
Authors: Ziane-Chaouche Lydia , Raffo-Romero Antonella , Hajjaji Nawale , Kobeissy Firas , Pinheiro Donna , Aboulouard Soulaimane , Cozzani Adeline , Mitra Suman , Fournier Isabelle , Cizkova Dasa , Salzet Michel , Duhamel Marie 
Published: December 2024

AbstractChimeric antigen receptor (CAR)-T-cell therapy has revolutionized cellular immunotherapy, demonstrating remarkable efficacy in hematological cancers. However, its application in solid tumors faces significant challenges, including limited T-cell infiltration and tumor-induced immunosuppression. Given the prominent role of macrophages in the tumor microenvironment, their phenotypic plasticity and inherent antitumor properties, such as phagocytosis, offer a promising avenue for therapeutic intervention. This study focuses on the development of a second generation of CAR macrophages (CAR-Ms). We elucidated the role of the proprotein convertase furin in macrophages, demonstrating its overexpression in the presence of tumor cells. Importantly, furin inhibition maintains a proinflammatory macrophage phenotype, potentially redirecting them towards an antitumor state. Compared to furin-expressing counterparts, furin-inhibited CAR-Ms exhibited heightened antitumor phagocytic activity against breast cancer cells and ex vivo patient-derived tumoroids. Notably, they sustained a persistent proinflammatory profile, indicative of enhanced tumoricidal potential. Additionally, furin-inhibited CAR-Ms secreted factors that promote T-cell activation, offering a means to modulate the tumor microenvironment. In summary, our work highlights the translational potential of furin-inhibited CAR-Ms as a potent cellular therapy to mitigate macrophage exhaustion within the tumor environment. By capitalizing on macrophage-mediated antitumor responses, these findings pave the way for the development of second-generation CAR-M therapeutic strategies tailored for solid tumors.
New particle formation from isoprene under upper-tropospheric conditions
Authors: Shen Jiali , Russell Douglas M. , DeVivo Jenna , Kunkler Felix , Baalbaki Rima , Mentler Bernhard , Scholz Wiebke , Yu Wenjuan , Caudillo-Plath Lucía , Sommer Eva , Ahongshangbam Emelda , Alfaouri Dina , Almeida João , Amorim Antonio , Beck Lisa J. , Beckmann Hannah , Berntheusel Moritz , Bhattacharyya Nirvan , Canagaratna Manjula R. , Chassaing Anouck , Cruz-Simbron Romulo , Dada Lubna , Duplissy Jonathan , Gordon Hamish , Granzin Manuel , Große Schute Lena , Heinritzi Martin , Iyer Siddharth , Klebach Hannah , Krüger Timm , Kürten Andreas , Lampimäki Markus , Liu Lu , Lopez Brandon , Martinez Monica , Morawiec Aleksandra , Onnela Antti , Peltola Maija , Rato Pedro , Reza Mago , Richter Sarah , Rörup Birte , Sebastian Milin Kaniyodical , Simon Mario , Surdu Mihnea , Tamme Kalju , Thakur Roseline C. , Tomé António , Tong Yandong , Top Jens , Umo Nsikanabasi Silas , Unfer Gabriela , Vettikkat Lejish , Weissbacher Jakob , Xenofontos Christos , Yang Boxing , Zauner-Wieczorek Marcel , Zhang Jiangyi , Zheng Zhensen , Baltensperger Urs , Christoudias Theodoros , Flagan Richard C. , El Haddad Imad , Junninen Heikki , Möhler Ottmar , Riipinen Ilona , Rohner Urs , Schobesberger Siegfried , Volkamer Rainer , Winkler Paul M. , Hansel Armin , Lehtipalo Katrianne , Donahue Neil M. , Lelieveld Jos , Harder Hartwig , Kulmala Markku , Worsnop Doug R. , Kirkby Jasper , Curtius Joachim , He Xu-Cheng 
Published: December 2024

AbstractAircraft observations have revealed ubiquitous new particle formation in the tropical upper troposphere over the Amazon1,2 and the Atlantic and Pacific oceans3,4. Although the vapours involved remain unknown, recent satellite observations have revealed surprisingly high night-time isoprene mixing ratios of up to 1 part per billion by volume (ppbv) in the tropical upper troposphere5. Here, in experiments performed with the CERN CLOUD (Cosmics Leaving Outdoor Droplets) chamber, we report new particle formation initiated by the reaction of hydroxyl radicals with isoprene at upper-tropospheric temperatures of −30 °C and −50 °C. We find that isoprene-oxygenated organic molecules (IP-OOM) nucleate at concentrations found in the upper troposphere, without requiring any more vapours. Moreover, the nucleation rates are enhanced 100-fold by extremely low concentrations of sulfuric acid or iodine oxoacids above 105 cm−3, reaching rates around 30 cm−3 s−1 at acid concentrations of 106 cm−3. Our measurements show that nucleation involves sequential addition of IP-OOM, together with zero or one acid molecule in the embryonic molecular clusters. IP-OOM also drive rapid particle growth at 3–60 nm h−1. We find that rapid nucleation and growth rates persist in the presence of NOx at upper-tropospheric concentrations from lightning. Our laboratory measurements show that isoprene emitted by rainforests may drive rapid new particle formation in extensive regions of the tropical upper troposphere1,2, resulting in tens of thousands of particles per cubic centimetre.
Evolution of myeloid-mediated immunotherapy resistance in prostate cancer
Authors: Lyu Aram , Fan Zenghua , Clark Matthew , Lea Averey , Luong Diamond , Setayesh Ali , Starzinski Alec , Wolters Rachel , Arias-Badia Marcel , Allaire Kate , Wu Kai , Gurunathan Vibha , Valderrábano Laura , Wei Xiao X. , Miller Richard A. , Van Allen Eliezer M. , Fong Lawrence 
Published: December 2024

Abstract
Patients with advanced metastatic castration-resistant prostate cancer (mCRPC) are refractory to immune checkpoint inhibitors (ICIs)1,2, partly because there are immunosuppressive myeloid cells in tumours3,4. However, the heterogeneity of myeloid cells has made them difficult to target, making blockade of the colony stimulating factor-1 receptor (CSF1R) clinically ineffective. Here we use single-cell profiling on patient biopsies across the disease continuum and find that a distinct population of tumour-associated macrophages with elevated levels of SPP1 transcripts (SPP1
hi-TAMs) becomes enriched with the progression of prostate cancer to mCRPC. In syngeneic mouse modelling, an analogous macrophage population suppresses CD8+ T cell activity in vitro and promotes ICI resistance in vivo. Furthermore, Spp1
hi-TAMs are not responsive to anti-CSF1R antibody treatment. Pathway analysis identifies adenosine signalling as a potential mechanism for SPP1
hi-TAM-mediated immunotherapeutic resistance. Indeed, pharmacological inhibition of adenosine A2A receptors (A2ARs) significantly reverses Spp1
hi-TAM-mediated immunosuppression in CD8+ T cells in vitro and enhances CRPC responsiveness to programmed cell death protein 1 (PD-1) blockade in vivo. Consistent with preclinical results, inhibition of A2ARs using ciforadenant in combination with programmed death 1 ligand 1 (PD-L1) blockade using atezolizumab induces clinical responses in patients with mCRPC. Moreover, inhibiting A2ARs results in a significant decrease in SPP1
hi-TAM abundance in CRPC, indicating that this pathway is involved in both induction and downstream immunosuppression. Collectively, these findings establish SPP1
hi-TAMs as key mediators of ICI resistance in mCRPC through adenosine signalling, emphasizing their importance as both a therapeutic target and a potential biomarker for predicting treatment efficacy.
Multiscale experimental study of H$$_2$$/brine multiphase flow in porous rock characterizing relative permeability hysteresis, hydrogen dissolution, and Ostwald ripening
Authors: Boon Maartje , Rademaker Tim , Winardhi Chandra Widyananda , Hajibeygi Hadi 
Published: December 2024

AbstractTo safely and efficiently utilize porous reservoirs for underground hydrogen storage (UHS), it is essential to characterize hydrogen transport properties at multiple scales. In this study, hydrogen/brine multiphase flow at 50 bar and 25 °C in a 17 cm Berea sandstone rock core was characterized and visualized at the pore and core scales using micro X-ray CT. The experiment included a single drainage and imbibition cycle during which relative permeability hysteresis was measured, and two no-flow periods to study the redistribution of hydrogen in the pore space during storage periods. An end-point relative permeability of 0.043 was found at $$S_w=0.56$$, and the residual gas saturation was measured to be 0.32. Despite extensive pre-equilibration, significant dissolution of hydrogen into brine occurred near the core inlet due to elevated pressures and the corresponding increase in hydrogen solubility. During drainage, many disconnected hydrogen ganglia were observed further down the core which could be explained by the exsolution of the dissolved hydrogen. During imbibition, the dissolution of hydrogen led to the formation of preferential flow paths near the inlet, and eventually removed most of the trapped hydrogen in the final stage of the experiment. The two no-flow periods were characterized by the fragmentation of medium-sized hydrogen ganglia and the growth of a few larger ganglia, providing evidence for hydrogen re-connection through the dissolution-driven process of Ostwald ripening. These results demonstrate that despite the low solubility of hydrogen in brine, hydrogen dissolution can significantly influence the observed multiphase flow and trapping behavior in the reservoir and should be considered in UHS modeling.
Beauty growth-mindset promotes prosocial and altruistic behavior
Authors: Hung Iris W. , Faust Natalie T. 
Published: December 2024

AbstractThe effects of beauty on judgment and behavior are well-established and somewhat “unidirectional” (i.e., it seems that only beautiful people reap social benefits). In particular, a person’s beauty does not seem to have any effect on his/her own prosocial behavior. In the current research, we focus on how people relate themselves to beauty, namely beauty mindset, and how it may shape prosocial and altruistic behavior. We present 10 experiments (N = 4,449). Participants who hold a beauty growth-mindset (i.e., believing that beauty is improvable) donated more money to charity (vs. fixed-mindset), were more likely to get vaccinated against COVID-19 after exposure to an intervention focusing on herd-protection (vs. self-protection), and showed increased intention to engage in other forms of prosocial and altruistic behavior. Empirically, these effects are mediated by a self-perception of the ability to exert impact on others. The present research serves as a starting point for investigating how beauty growth-mindset brings profound societal effects such as promoting prosociality.
Transformers significantly improve splice site prediction
Authors: Jónsson Benedikt A. , Halldórsson Gísli H. , Árdal Steinþór , Rögnvaldsson Sölvi , Einarsson Eyþór , Sulem Patrick , Guðbjartsson Daníel F. , Melsted Páll , Stefánsson Kári , Úlfarsson Magnús Ö. 
Published: December 2024

AbstractMutations that affect RNA splicing significantly impact human diversity and disease. Here we present a method using transformers, a type of machine learning model, to detect splicing from raw 45,000-nucleotide sequences. We generate embeddings with residual neural networks and apply hard attention to select splice site candidates, enabling efficient training on long sequences. Our method surpasses the leading tool, SpliceAI, in detecting splice sites in GENCODE and ENSEMBL annotations. Using extensive RNA sequencing data from an Icelandic cohort of 17,848 individuals and the Genotype-Tissue Expression (GTEx) project, our method demonstrates superior performance in detecting splice junctions compared to SpliceAI-10k (PR-AUC = 0.834 vs. PR-AUC = 0.820) and is more effective at identifying disease-related splice variants in ClinVar (PR-AUC = 0.997 vs. PR-AUC = 0.996). These advancements hold promise for improving genetic research and clinical diagnostics, potentially leading to better understanding and treatment of splicing-related diseases.
Super-resolution imaging of the neuronal cytoskeleton
Authors: Butler-Hallissey Ciarán , Leterrier Christophe 
Published: December 2024

AbstractThe complexity of the brain organization and the unique architecture of neurons have motivated neuroscientists to stay at the forefront of cellular microscopy and rapidly take advantage of technical developments in this field. Among these developments, super-resolution microscopy has transformed our understanding of neurobiology by allowing us to image identified macromolecular scaffolds and complexes directly in cells. Super-resolution microscopy approaches have thus provided key insights into the organization and functions of the neuronal cytoskeleton and its unique nanostructures. These insights are the focus of our review, where we attempt to provide a panorama of super-resolution microscopy applications to the study of the neuronal cytoskeleton, delineating the progress they have made possible and the current challenges they meet.
Recent advances in the oxidative activation of the C2–C3 π bond of indoles and its applications
Authors: Yu Lemao , Cai Ruonan , Lv Lujing , Dong Huaping , Xu Yingjie , Zhang Qiang , Shen Runpu 
Published: December 2024

This review highlights oxidation-induced ring-formation/radical addition/rearrangement strategies via oxidative activation of C2–C3 π bond of indoles and the corresponding asymmetric synthesis applications to indole alkaloids and bioactive compounds.
Modelling icing growth on overhead transmission lines: Current advances and future directions
Authors: Hou Hui , Wang Yan , Bai Xiaolu , Lv Jianshuang , Cui Rongjian , Zhang Lin , Li Shilong , Li Zhengmao 
Published: December 2024

AbstractThe increasing impact of climate change raises concerns regarding the vulnerability of overhead transmission lines to ice disasters. To address this issue, this study reviews icing growth modelling in two categories: physical‐driven models (PDMs) and data‐driven models (DDMs), covering current advances and future directions. First, PDMs are summarised, focusing on the thermodynamic and fluid mechanics mechanisms. Existing PDMs are compared based on principles, analysing their advantages, disadvantages, and challenges faced. Second, the summarisation of DDMs involves four aspects: data preparation, algorithm selection, model training, and model evaluation. In data preparation, techniques such as preprocessing methods are reviewed to handle multisource data. In algorithm selection, various modelling algorithms are compared and analysed, from basic to deep learning approaches. In model training, processes are summarised to enhance practical applicability, including data partitioning, hyperparameter adjustment, generalisation capability, and model interpretability. In model evaluation, the predictive capabilities are analysed, covering both regression and classification tasks. Subsequently, based on the analyses, a comparison of PDMs and DDMs across various aspects is presented. Finally, future directions in icing growth modelling are outlined. The aim is to enhance icing assessment by understanding the underlying mechanism in attempt to reduce vulnerability and ensure reliability against adverse weather conditions.
AMC‐YOLO: Improved YOLOv8‐based defect detection for cigarette packs
Authors: Dong Peng , Wang Yongsheng , Yu Qianyuan , Feng Weihua , Zong Guohao 
Published: December 2024

AbstractDefect detection in cigarette packaging is a crucial process for ensuring product quality meets industry standards within the tobacco manufacturing sector. Defect detection methods based on deep learning have significantly enhanced efficiency. However, challenges remain in addressing issues such as blurred boundary textures of cigarette pack defects and the complex differentiability and similarity among defects. Consequently, this study presents the design of a defect detector for cigarette packs that is sensitive to detail features, named AMC‐YOLO. Initially, an Adaptive Spatial Weight Perception (ASWP) module is designed to emphasize local information from different regions during the downsampling process and integrate effective features. Additionally, a Multidimensional Aggregation Radiative Feature Pyramid Networks (MARFPN) is proposed to aggregate multi‐scale semantic information across dimensions and relay it back to various levels within the network to facilitate the learning of more refined feature. Lastly, a Cross‐Layer Collaborative Detection Head (CLCDH) is introduced to further weight and fuse the contextual information between local and global aspects. Experimental results demonstrate that AMC‐YOLO outperforms state‐of‐the‐art methods on the Cigarette Pack Defect and GC10‐DET datasets, exhibiting the highest detection precision and excellent generalization. These findings highlight the significant potential for the application of AMC‐YOLO in cigarette pack defect detection.
Personalized zero‐watermark algorithm based on non‐uniform weighted reconstruction and WGAN
Authors: Peng Yiran , Deng Chenheng , Li Jiaqi , U KinTak 
Published: December 2024

AbstractThe image protection of non‐fungible tokens based on zero‐watermark methods has received widespread attention. However, on the one hand, existing zero‐watermark methods are often limited to complex texture changes in the host image, and the features for constructing the zero‐watermark are vulnerable to geometric attacks. On the other hand, a single watermark image cannot adapt to the diverse usage scenarios of non‐fungible tokens. This paper proposes a robust personalized zero‐watermark scheme to address the challenges above. Firstly, the image regions suitable for constructing zero‐watermarks are highlighted by the non‐uniform weighted reconstruction, and the U‐Net is introduced for feature extraction against the geometric attacks. At the same time, the watermark images generated by generative models that are suitable for the usage scenario have achieved zero‐watermark addition and protection for non‐fungible token image content. The proposed method has been experimentally verified to have a certain degree of robustness in geometric and non‐geometric attacks.