bluuebunny/crossref_metadata_2025_split huggingface dataset:
Subduction Zone Geometry Modulates the Megathrust Earthquake Cycle: Magnitude, Recurrence, and Variability
Authors: Biemiller J. , Gabriel A.‐A. , May D. A. , Staisch L. 
Published: August 2024

AbstractMegathrust geometric properties exhibit some of the strongest correlations with maximum earthquake magnitude in global surveys of large subduction zone earthquakes, but the mechanisms through which fault geometry influences subduction earthquake cycle dynamics remain unresolved. Here, we develop 39 models of sequences of earthquakes and aseismic slip (SEAS) on variably‐dipping planar and variably‐curved nonplanar megathrusts using the volumetric, high‐order accurate code tandem to account for fault curvature. We vary the dip, downdip curvature and width of the seismogenic zone to examine how slab geometry mechanically influences megathrust seismic cycles, including the size, variability, and interevent timing of earthquakes. Dip and curvature control characteristic slip styles primarily through their influence on seismogenic zone width: wider seismogenic zones allow shallowly‐dipping megathrusts to host larger earthquakes than steeply‐dipping ones. Under elevated pore pressure and less strongly velocity‐weakening friction, all modeled fault geometries host uniform periodic ruptures. In contrast, shallowly‐dipping and sharply‐curved megathrusts host multi‐period supercycles of slow‐to‐fast, small‐to‐large slip events under higher effective stresses and more strongly velocity‐weakening friction. We discuss how subduction zones' maximum earthquake magnitudes may be primarily controlled by the dip and dimensions of the seismogenic zone, while second‐order effects from structurally‐derived mechanical heterogeneity modulate the recurrence frequency and timing of these events. Our results suggest that enhanced co‐ and interseismic strength and stress variability along the megathrust, such as induced near areas of high or heterogeneous fault curvature, limits how frequently large ruptures occur and may explain curved faults' tendency to host more frequent, smaller earthquakes than flat faults.
Deep Learning Forecasts Caldera Collapse Events at Kı̄lauea Volcano
Authors: McBrearty Ian W. , Segall Paul 
Published: August 2024

AbstractDuring the 3 month long eruption of Kı̄lauea volcano, Hawaii in 2018, the pre‐existing summit caldera collapsed in over 60 quasi‐periodic failure events. The last 40 of these events, which generated Mw > 5 very long period (VLP) earthquakes, had inter‐event times between 0.8 and 2.2 days. These failure events offer a unique data set for testing methods for predicting earthquake recurrence based on locally recorded GPS, tilt, and seismicity data. In this work, we train a deep learning graph neural network (GNN) to predict the time‐to‐failure of the caldera collapse events using only a fraction of the data recorded at the start of each cycle. We find that the GNN generalizes to unseen data and can predict the time‐to‐failure to within a few hours using only 0.5 days of data, substantially improving upon a null model based only on inter‐event statistics. Predictions improve with increasing input data length, and are most accurate when using high‐SNR tilt‐meter data. Applying the trained GNN to synthetic data with different magma‐chamber pressure decay times predicts failure at a nearly constant stress threshold, revealing that the GNN is sensing the underling physics of caldera collapse. These findings demonstrate the predictability of caldera collapse sequences under well monitored conditions, and highlight the potential of machine learning methods for forecasting real world catastrophic events with limited training data.
Waveguide-integrated metasurfaces for multi-channel crosstalk-free holography
Authors: Heo Hyeonsu , Rho Junsuk 
Published: August 2024

AbstractLimited information capacity and inter-channel crosstalk in metaholograms hinder their practical use in display applications. Leveraging waveguide-based metasurfaces, the integration of spin and angle-of-incidence multiplexing facilitates the generation of broadband six-channel metaholograms free from crosstalk.
Does strontium coated titanium implants enhance the osseointegration in animal models under osteoporotic condition? A systematic review and meta-analysis
Authors: Sayed Osama , Abdalla Mohamed Mahmoud , Elsayed Ayman , El-Mahallawy Yehia , Al-Mahalawy Haytham 
Published: August 2024

Abstract
Purpose
The aim of this study was to systematically review the literature to address the effect of strontium modified titanium implants on the osseointegration in the presence of osteoporotic conditions through animal models.

Materials and methods
The databases (PubMed, Scopus, Web of Science, and EBSCO) were searched electronically, and manual searches were performed till December 2022 to identify preclinical studies on the osseointegration of strontium coated titanium implants in animals with induced osteoporotic conditions. The primary outcomes were the bone-implant contact percentage (BIC%), bone area (BA) from the histomorphometric analysis, and the osseointegration parameters from biomechanical tests; the secondary outcomes were the osseointegration parameters from the micro computed tomography.

Results
Nineteen articles were included for the quantitative analysis on basis of the inclusion criteria. The results revealed that Sr-modified implants showed a significant 19.05% increase in BIC, and 15.01% increase in BA. The results of biomechanical tests indicated a significant effect in favor of Sr-coated implants. Furthermore, Results of the secondary outcomes supported the significant advantages of Sr-coated implants over the un-coated implants. The overall, systematic analysis of implants osteointegration parameters proved a significant increase in favor of Sr-coated titanium implants (P < 0.01).

Conclusion
The present results provide evidence that strontium-coated titanium implants enhanced the osseointegration in animal models under osteoporotic condition as this surface modification techniques have improved the mechanical and biological properties of the titanium implants.
Natural Killer cells at the frontline in the fight against cancer
Authors: Coënon Loïs , Geindreau Mannon , Ghiringhelli François , Villalba Martin , Bruchard Mélanie 
Published: August 2024

AbstractNatural Killer (NK) cells are innate immune cells that play a pivotal role as first line defenders in the anti-tumor response. To prevent tumor development, NK cells are searching for abnormal cells within the body and appear to be key players in immunosurveillance. Upon recognition of abnormal cells, NK cells will become activated to destroy them. In order to fulfill their anti-tumoral function, they rely on the secretion of lytic granules, expression of death receptors and production of cytokines. Additionally, NK cells interact with other cells in the tumor microenvironment. In this review, we will first focus on NK cells’ activation and cytotoxicity mechanisms as well as NK cells behavior during serial killing. Lastly, we will review NK cells’ crosstalk with the other immune cells present in the tumor microenvironment.
Quasi-one-dimensional hydrogen bonding in nanoconfined ice
Authors: Ravindra Pavan , Advincula Xavier R. , Schran Christoph , Michaelides Angelos , Kapil Venkat 
Published: August 2024

AbstractThe Bernal-Fowler ice rules stipulate that each water molecule in an ice crystal should form four hydrogen bonds. However, in extreme or constrained conditions, the arrangement of water molecules deviates from conventional ice rules, resulting in properties significantly different from bulk water. In this study, we employ machine learning-driven first-principles simulations to identify a new stabilization mechanism in nanoconfined ice phases. Instead of forming four hydrogen bonds, nanoconfined crystalline ice can form a quasi-one-dimensional hydrogen-bonded structure that exhibits only two hydrogen bonds per water molecule. These structures consist of strongly hydrogen-bonded linear chains of water molecules that zig-zag along one dimension, stabilized by van der Waals interactions that stack these chains along the other dimension. The unusual interplay of hydrogen bonding and van der Waals interactions in nanoconfined ice results in atypical proton behavior such as potential ferroelectric behavior, low dielectric response, and long-range proton dynamics.
Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution
Authors: An K. , Xu M. , Mucchietto A. , Kim C. , Moon K.-W. , Hwang C. , Grundler D. 
Published: August 2024

AbstractNonlinearity of dynamic systems plays a key role in neuromorphic computing, which is expected to reduce the ever-increasing power consumption of machine learning and artificial intelligence applications. For spin waves (magnons), nonlinearity combined with phase coherence is the basis of phenomena like Bose–Einstein condensation, frequency combs, and pattern recognition in neuromorphic computing. Yet, the broadband electrical detection of these phenomena with high-frequency resolution remains a challenge. Here, we demonstrate the generation and detection of phase-coherent nonlinear magnons in an all-electrical GHz probe station based on coplanar waveguides connected to a vector network analyzer which we operate in a frequency-offset mode. Making use of an unprecedented frequency resolution, we resolve the nonlocal emergence of a fine structure of propagating nonlinear magnons, which sensitively depends on both power and a magnetic field. These magnons are shown to maintain coherency with the microwave source while propagating over macroscopic distances. We propose a multi-band four-magnon scattering scheme that is in agreement with the field-dependent characteristics of coherent nonlocal signals in the nonlinear excitation regime. Our findings are key to enable the seamless integration of nonlinear magnon processes into high-speed microwave electronics and to advance phase-encoded information processing in magnonic neuronal networks.
Selective hydrogenation of nitro compounds to amines by coupled redox reactions over a heterogeneous biocatalyst
Authors: Sokolova Daria , Lurshay Tara C. , Rowbotham Jack S. , Stonadge Georgia , Reeve Holly A. , Cleary Sarah E. , Sudmeier Tim , Vincent Kylie A. 
Published: August 2024

AbstractCleaner synthesis of amines remains a key challenge in organic chemistry because of their prevalence in pharmaceuticals, agrochemicals and synthetic building blocks. Here, we report a different paradigm for chemoselective hydrogenation of nitro compounds to amines, under mild, aqueous conditions. The hydrogenase enzyme releases electrons from H2 to a carbon black support which facilitates nitro-group reduction. For 30 nitroarenes we demonstrate full conversion (isolated yields 78 – 96%), with products including pharmaceuticals benzocaine, procainamide and mesalazine, and 4-aminophenol – precursor to paracetamol (acetaminophen). We also showcase gram-scale synthesis of procainamide with 90% isolated yield. We demonstrate potential for extension to aliphatic substrates. The catalyst is highly selective for reduction of the nitro group over other unsaturated bonds, tolerant to a wide range of functional groups, and exhibits excellent stability in reactions lasting up to 72 hours and full reusability over 5 cycles with a total turnover number over 1 million, indicating scope for direct translation to fine chemical manufacturing.
GTalign: spatial index-driven protein structure alignment, superposition, and search
Authors: Margelevičius Mindaugas 
Published: August 2024

AbstractWith protein databases growing rapidly due to advances in structural and computational biology, the ability to accurately align and rapidly search protein structures has become essential for biological research. In response to the challenge posed by vast protein structure repositories, GTalign offers an innovative solution to protein structure alignment and search—an algorithm that achieves optimal superposition at high speeds. Through the design and implementation of spatial structure indexing, GTalign parallelizes all stages of superposition search across residues and protein structure pairs, yielding rapid identification of optimal superpositions. Rigorous evaluation across diverse datasets reveals GTalign as the most accurate among structure aligners while presenting orders of magnitude in speedup at state-of-the-art accuracy. GTalign’s high speed and accuracy make it useful for numerous applications, including functional inference, evolutionary analyses, protein design, and drug discovery, contributing to advancing understanding of protein structure and function.
AlphaFold predictions of fold-switched conformations are driven by structure memorization
Authors: Chakravarty Devlina , Schafer Joseph W. , Chen Ethan A. , Thole Joseph F. , Ronish Leslie A. , Lee Myeongsang , Porter Lauren L. 
Published: August 2024

AbstractRecent work suggests that AlphaFold (AF)–a deep learning-based model that can accurately infer protein structure from sequence–may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. We find that (1) AF is a weak predictor of fold switching and (2) some of its successes result from memorization of training-set structures rather than learned protein energetics. Combining >280,000 models from several implementations of AF2 and AF3, a 35% success rate was achieved for fold switchers likely in AF’s training sets. AF2’s confidence metrics selected against models consistent with experimentally determined fold-switching structures and failed to discriminate between low and high energy conformations. Further, AF captured only one out of seven experimentally confirmed fold switchers outside of its training sets despite extensive sampling of an additional ~280,000 models. Several observations indicate that AF2 has memorized structural information during training, and AF3 misassigns coevolutionary restraints. These limitations constrain the scope of successful predictions, highlighting the need for physically based methods that readily predict multiple protein conformations.
CASCADE: Dataset of extant coccolithophore size, carbon content and global distribution
Authors: de Vries Joost , Poulton Alex J. , Young Jeremy R. , Monteiro Fanny M. , Sheward Rosie M. , Johnson Roberta , Hagino Kyoko , Ziveri Patrizia , Wolf Levi J. 
Published: August 2024

AbstractCoccolithophores are marine calcifying phytoplankton important to the carbon cycle and a model organism for studying diversity. Here, we present (Coccolithophore Abundance, Size, Carbon And Distribution Estimates), a new global dataset for 139 extant coccolithophore taxonomic units. includes a trait database (size and cellular organic and inorganic carbon contents) and taxonomic-unit-specific global spatiotemporal distributions (Latitude/Longitude/Depth/Month/Year) of coccolithophore abundance and organic and inorganic carbon stocks. covers all ocean basins over the upper 275 meters, spans the years 1964-2019 and includes 33,119 gridded taxonomic-unit-specific abundance observations. Within , we characterise the underlying uncertainties due to measurement errors by propagating error estimates between the different studies. This error propagation pipeline is statistically robust and could be applied to other plankton groups. can contribute to (observational or modelling) studies that focus on coccolithophore distribution and diversity and the impacts of anthropogenic pressures on historical populations. Additionally, our new taxonomic-unit-specific cellular carbon content estimates provide essential conversions to quantify the role of coccolithophores on ecosystem functioning and global biogeochemistry.
Variations in CTAC batches from different suppliers highly affect the shape yield in seed-mediated synthesis of gold nanotriangles
Authors: Podlesnaia Ekaterina , Hoxha Amarildo , Achikkulathu Sreevalsan , Kandathikudiyil Antony Athulesh , Antony Jerestine Philomina , Spörl Kathrin , Csáki Andrea , Leiterer Matthias , Fritzsche Wolfgang 
Published: August 2024

AbstractThe rapidly developing miniaturization in numerous fields require low-demanding but robust methods of nanomaterial production. Colloidal synthesis provides great flexibility in product material, size, and shape. Gold nanoparticle synthesis has been thoroughly studied, however, recent reports on mechanistic insights of crystal formation have been hindered by the numerous procedures and parameter optimization works. With every new study, scientists fill another blank space on the map of understanding anisotropic growth and find out the critical parameters. In the current work, we highlight the choice importance for surfactant supplier in achieving the gold nanotriangle formation. We systematically study the variation in the shape yield when utilizing five batches of cetyltrimethylammonium chloride (CTAC) from varied suppliers. Using analytical techniques, we search for deviations causing such variation, e.g. different impurity content. We found only a marginal effect of iodine contamination on the studied system, excluding this factor as decisive in contrast to what was proposed earlier in the literature, and leaving the high dependency of the yield to originate from yet unknown reagent characteristics. A deeper understanding of these factors would provide highly effective protocols lowering the reagent consumption and increasing the accessibility of nanomaterials manufactured in a sustainable manner.
Predicting social experience from dyadic interaction dynamics: the BallGame, a novel paradigm to study social engagement
Authors: Lübbert Annika , Sengelmann Malte , Heimann Katrin , Schneider Till R. , Engel Andreas K. , Göschl Florian 
Published: August 2024

AbstractTheories of embodied cognition suggest that a shared environment and ongoing sensorimotor interaction are central for interpersonal learning and engagement. To investigate the embodied, distributed and hence dynamically unfolding nature of social cognitive capacities, we present a novel laboratory-based coordination task: the BallGame. Our paradigm requires continuous sensing and acting between two players who jointly steer a virtual ball around obstacles towards as many targets as possible. By analysing highly resolved measures of movement coordination and gaming behaviour, game-concurrent experience ratings, semi-structured interviews, and personality questionnaires, we reveal contributions from different levels of observation on social experience. In particular, successful coordination (number of targets collected) and intermittent periods of high versus low movement coordination (variability of relation) emerged as prominent predictors of social experience. Importantly, having the same (but incomplete) view on the game environment strengthened interpersonal coordination, whereas complementary views enhanced engagement and tended to generate more complex interactive behaviour. Overall, we find evidence for a critical balance between similarity and synchrony on the one hand, and variability and difference on the other, for successful engagement in social interactions. Finally, following participant reports, we highlight how interpersonal experience emerges from specific histories of coordination that are closely related to the interaction context in both space and time.
Independent replications reveal anterior and posterior cingulate cortex activation underlying state anxiety-attenuated face encoding
Authors: Buehler Sarah K. , Lowther Millie , Lukow Paulina B. , Kirk Peter A. , Pike Alexandra C. , Yamamori Yumeya , Chavanne Alice V. , Gormley Siobhan , Goble Talya , Tuominen Ella W. , Aylward Jessica , McCloud Tayla , Rodriguez-Sanchez Julia , Robinson Oliver J. 
Published: August 2024

AbstractAnxiety involves the anticipation of aversive outcomes and can impair neurocognitive processes, such as the ability to recall faces encoded during the anxious state. It is important to precisely delineate and determine the replicability of these effects using causal state anxiety inductions in the general population. This study therefore aimed to replicate prior research on the distinct impacts of threat-of-shock-induced anxiety on the encoding and recognition stage of emotional face processing, in a large asymptomatic sample (n = 92). We successfully replicated previous results demonstrating impaired recognition of faces encoded under threat-of-shock. This was supported by a mega-analysis across three independent studies using the same paradigm (n = 211). Underlying this, a whole-brain fMRI analysis revealed enhanced activation in the posterior cingulate cortex (PCC), alongside previously seen activity in the anterior cingulate cortex (ACC) when combined in a mega-analysis with the fMRI findings we aimed to replicate. We further found replications of hippocampus activation when the retrieval and encoding states were congruent. Our results support the notion that state anxiety disrupts face recognition, potentially due to attentional demands of anxious arousal competing with affective stimuli processing during encoding and suggest that regions of the cingulate cortex play pivotal roles in this.
RIHINNet: A robust image hiding method against JPEG compression based on invertible neural network
Authors: Jin Xin , Pan Chengyi , Cheng Zien , Dong Yunyun , Jiang Qian 
Published: August 2024

AbstractImage hiding is a task that embeds secret images in digital images without being detected. The performance of image hiding has been greatly improved by using the invertible neural network. However, current image hiding methods are less robust in the face of Joint Photographic Experts Group (JPEG) compression. The secret image cannot be extracted from the stego image after JPEG compression of the stego image. Some methods show good robustness for some certain JPEG compression quality factors but poor robustness for other common JPEG compression quality factors. An image‐hiding network (RIHINNet) that is robust to all common JPEG compression quality factors is proposed. First of all, the loss function is redesigned; thus, the secret image is hidden as much as possible in the area that is less likely to be changed after JPEG compression. Second, the classifier is designed, which can help the model to select the extractor according to the range of JPEG compression degree. Finally, the interval robustness of the secret image extraction is improved through the design of a denoising module. Experimental results show that this RIHINNet outperforms other state‐of‐the‐art image‐hiding methods in the face of JPEG compressed noise with random compression quality factors, with more than 10 dB peak signal‐to‐noise ratio improvement in secret image recovery on ImageNet, COCO and DIV2K datasets.