bluuebunny/crossref_metadata_2025_split huggingface dataset:
PICNIC accurately predicts condensate-forming proteins regardless of their structural disorder across organisms
Authors: Hadarovich Anna , Singh Hari Raj , Ghosh Soumyadeep , Scheremetjew Maxim , Rostam Nadia , Hyman Anthony A. , Toth-Petroczy Agnes 
Published: December 2024

AbstractBiomolecular condensates are membraneless organelles that can concentrate hundreds of different proteins in cells to operate essential biological functions. However, accurate identification of their components remains challenging and biased towards proteins with high structural disorder content with focus on self-phase separating (driver) proteins. Here, we present a machine learning algorithm, PICNIC (Proteins Involved in CoNdensates In Cells) to classify proteins that localize to biomolecular condensates regardless of their role in condensate formation. PICNIC successfully predicts condensate members by learning amino acid patterns in the protein sequence and structure in addition to the intrinsic disorder. Extensive experimental validation of 24 positive predictions in cellulo shows an overall ~82% accuracy regardless of the structural disorder content of the tested proteins. While increasing disorder content is associated with organismal complexity, our analysis of 26 species reveals no correlation between predicted condensate proteome content and disorder content across organisms. Overall, we present a machine learning classifier to interrogate condensate components at whole-proteome levels across the tree of life.
Thermodynamically inconsistent extreme precipitation sensitivities across continents driven by cloud-radiative effects
Authors: Ghausi Sarosh Alam , Zehe Erwin , Ghosh Subimal , Tian Yinglin , Kleidon Axel 
Published: December 2024

AbstractExtreme precipitation events are projected to intensify with global warming, threatening ecosystems and amplifying flood risks. However, observation-based estimates of extreme precipitation-temperature (EP-T) sensitivities show systematic spatio-temporal variability, with predominantly negative sensitivities across warmer regions. Here, we attribute this variability to confounding cloud radiative effects, which cool surfaces during rainfall, introducing covariation between rainfall and temperature beyond temperature’s effect on atmospheric moisture-holding capacity. We remove this effect using a thermodynamically constrained surface-energy balance, and find positive EP-T sensitivities across continents, consistent with theoretical arguments. Median EP-T sensitivities across observations shift from −4.9%/°C to 6.1%/°C in the tropics and −0.5%/°C to 2.8%/°C in mid-latitudes. Regional variability in estimated sensitivities is reduced by more than 40% in tropics and about 30% in mid and high latitudes. Our findings imply that projected intensification of extreme rainfall with temperature is consistent with observations across continents, after confounding radiative effect of clouds is accounted for.
Strong-field quantum control in the extreme ultraviolet domain using pulse shaping
Authors: Richter Fabian , Saalmann Ulf , Allaria Enrico , Wollenhaupt Matthias , Ardini Benedetto , Brynes Alexander , Callegari Carlo , Cerullo Giulio , Danailov Miltcho , Demidovich Alexander , Dulitz Katrin , Feifel Raimund , Fraia Michele Di , Ganeshamandiram Sarang Dev , Giannessi Luca , Gölz Nicolai , Hartweg Sebastian , von Issendorff Bernd , Laarmann Tim , Landmesser Friedemann , Li Yilin , Manfredda Michele , Manzoni Cristian , Michelbach Moritz , Morlok Arne , Mudrich Marcel , Ngai Aaron , Nikolov Ivaylo , Pal Nitish , Pannek Fabian , Penco Giuseppe , Plekan Oksana , Prince Kevin C. , Sansone Giuseppe , Simoncig Alberto , Stienkemeier Frank , Squibb Richard James , Susnjar Peter , Trovo Mauro , Uhl Daniel , Wouterlood Brendan , Zangrando Marco , Bruder Lukas 
Published: December 2024

AbstractTailored light–matter interactions in the strong coupling regime enable the manipulation and control of quantum systems with up to unit efficiency1,2, with applications ranging from quantum information to photochemistry3–7. Although strong light–matter interactions are readily induced at the valence electron level using long-wavelength radiation8, comparable phenomena have been only recently observed with short wavelengths, accessing highly excited multi-electron and inner-shell electron states9,10. However, the quantum control of strong-field processes at short wavelengths has not been possible, so far, because of the lack of pulse-shaping technologies in the extreme ultraviolet (XUV) and X-ray domain. Here, exploiting pulse shaping of the seeded free-electron laser (FEL) FERMI, we demonstrate the strong-field quantum control of ultrafast Rabi dynamics in helium atoms with high fidelity. Our approach reveals a strong dressing of the ionization continuum, otherwise elusive to experimental observables. The latter is exploited to achieve control of the total ionization rate, with prospective applications in many XUV and soft X-ray experiments. Leveraging recent advances in intense few-femtosecond to attosecond XUV to soft X-ray light sources, our results open an avenue to the efficient manipulation and selective control of core electron processes and electron correlation phenomena in real time.
Formation of a low-mass galaxy from star clusters in a 600-million-year-old Universe
Authors: Mowla Lamiya , Iyer Kartheik , Asada Yoshihisa , Desprez Guillaume , Tan Vivian Yun Yan , Martis Nicholas , Sarrouh Ghassan , Strait Victoria , Abraham Roberto , Bradač Maruša , Brammer Gabriel , Muzzin Adam , Pacifici Camilla , Ravindranath Swara , Sawicki Marcin , Willott Chris , Estrada-Carpenter Vince , Jahan Nusrath , Noirot Gaël , Matharu Jasleen , Rihtaršič Gregor , Zabl Johannes 
Published: December 2024

AbstractThe most distant galaxies detected were seen when the Universe was a scant 5% of its current age. At these times, progenitors of galaxies such as the Milky Way were about 10,000 times less massive. Using the James Webb Space Telescope (JWST) combined with magnification from gravitational lensing, these low-mass galaxies can not only be detected but also be studied in detail. Here we present JWST observations of a strongly lensed galaxy at zspec = 8.296 ± 0.001, showing massive star clusters (the Firefly Sparkle) cocooned in a diffuse arc in the Canadian Unbiased Cluster Survey (CANUCS)1. The Firefly Sparkle exhibits traits of a young, gas-rich galaxy in its early formation stage. The mass of the galaxy is concentrated in 10 star clusters (49–57% of total mass), with individual masses ranging from 105M⊙ to 106M⊙. These unresolved clusters have high surface densities (>103M⊙ pc−2), exceeding those of Milky Way globular clusters and young star clusters in nearby galaxies. The central cluster shows a nebular-dominated spectrum, low metallicity, high gas density and high electron temperature, hinting at a top-heavy initial mass function. These observations provide our first spectrophotometric view of a typical galaxy in its early stages, in a 600-million-year-old Universe.
Towards autonomous device protection using behavioural profiling and generative artificial intelligence
Authors: Gupta Sandeep , Crispo Bruno 
Published: December 2024

AbstractDemand for autonomous protection in computing devices cannot go unnoticed, considering the rapid proliferation of deployed devices and escalating cyberattacks. Consequently, cybersecurity measures with an improved generalisation that can proactively determine the indicators of compromises to predict 0‐day threats or previously unseen malware together with known malware are highly desirable. In this article, the authors present a novel concept of autonomous device protection based on behavioural profiling by continuously monitoring internal resource usage and leveraging generative artificial intelligence (genAI) to distinguish between benign and malicious behaviour. The authors design a proof‐of‐concept for Windows‐based computing devices relying on a built‐in event tracing mechanism for log collection that is converted into structured data using a graph data structure. The authors extract graph‐level features, that is, graph depth, nodes count, number of leaf nodes, node degree statistics, and events count and node‐level features (NLF), that is, process start, file create and registry events details for each graph. Further, the authors investigate the use of genAI exploiting a pre‐trained large language network—a simple contrastive sentence embedding framework to extract strong features, that is, dense vectors from event graphs. Finally, the authors train a random forest classifier using both the graph‐level features and NLF to obtain classification models that are evaluated on a collected dataset containing one thousand benign and malicious samples achieving accuracy up to 99.25%.
High precision light field image depth estimation via multi‐region attention enhanced network
Authors: Li Jie , Yang Wenxuan , Zhang Chuanlun , Li Heng , Li Xinjia , Wang Lin , Wang Yanling , Wang Xiaoyan 
Published: December 2024

AbstractLight field (LF) depth estimation is a key task with numerous practical applications. However, achieving high‐precision depth estimation in challenging scenarios, such as occlusions and detailed regions (e.g. fine structures and edges), remains a significant challenge. To address this problem, the authors propose a LF depth estimation network based on multi‐region selection and guided optimisation. Firstly, we construct a multi‐region disparity selection module based on angular patch, which selects specific regions for generating angular patch, achieving representative sub‐angular patch by balancing different regions. Secondly, different from traditional guided deformable convolution, the guided optimisation leverages colour prior information to learn the aggregation of sampling points, which enhances the deformable convolution ability by learning deformation parameters and fitting irregular windows. Finally, to achieve high‐precision LF depth estimation, the authors have developed a network architecture based on the proposed multi‐region disparity selection and guided optimisation module. Experiments demonstrate the effectiveness of network on the HCInew dataset, especially in handling occlusions and detailed regions.
Gallbladder cancer detection via ultrasound image analysis: An end‐to‐end hierarchical feature‐fused model
Authors: Dadjouy Sara , Sajedi Hedieh 
Published: December 2024

AbstractGallbladder cancer is a fatal disease, and its early diagnosis can significantly impact patient treatment. Ultrasound imaging is often the initial diagnostic test for gallbladder cancer, making the enhancement of cancer detection accuracy from these images crucial. Despite the promising results of artificial intelligence techniques in disease diagnosis, their black‐box nature hinders the reliability of their results and their practical application. Therefore, it is essential not to rely solely on a single model's output and to further investigate for more reliable outcomes. This study presents a step‐by‐step structural investigation of forming an end‐to‐end model, a conjunction of two convolutional neural network based methods, for detecting gallbladder conditions. The final model, leveraging feature fusions and hierarchical classification, achieved a high accuracy of 92.62% for detecting normal, benign, and malignant gallbladders. It also achieved a remarkable accuracy of 98.36% for classifying normal and non‐normal instances and 92.22% for classifying benign and malignant cases. Finally, comprehensive post‐processing investigations, including cross‐validation, temperature scaling, and uncertainty estimation, along with error analysis, are conducted to gain more insights into the model's output. Among these insights, the model demonstrated resilience of its results to active dropout and augmentation at the inference phase. Furthermore, when applied with test‐time data augmentation, uncertainty estimation methods have better distinguishability between the uncertainties of correctly and incorrectly classified instances, which provides additional information about the model's output. The source code of experiments conducted in this study is available at https://github.com/SaraDadjouy/GBCRet.
UCFA‐Net: A U‐shaped cross‐fusion network with attention mechanism for enhanced polyp segmentation
Authors: Wang Shuai , Zhao Tiejun , Wang Guocun , Han Ye , Wu Fan 
Published: December 2024

AbstractEnhancing the precision of computer‐assisted polyp segmentation and delineation during colonoscopies assists in the removal of potentially precancerous tissue, thus reducing the risk of malignant transformation. Most of the current medical segmentation models use the traditional U‐shaped network structure, but they suffer from the problem of information loss during the encoding and decoding of images. To advance towards an autonomous model for detailed polyp segmentation, the authors propose a new framework for polyp segmentation called U‐shaped cross‐fusion network with attention mechanism (UCFA‐Net), which employs a pyramid vision transformer as encoder to extract image features at multiple scales. Furthermore, the multi‐scale cross‐fusion module cross‐fuses the different scale features and then goes through the multi‐scale convolutional parallel feedforward transformer module for modelling the global and local information. Finally, progressive attentional up‐sampling module acts as a decoder for up‐sampling with progressive attention to get the final polyp segmentation result. The authors comprehensive testing demonstrates that their network achieves superior average scores across the five datasets and exhibits greater robustness in the face of diverse and demanding scenarios, when compared to current state‐of‐the‐art approaches.
Time‐domain design for misalignment‐tolerant dynamic wireless charging
Authors: Quirós Juan Carlos , Calvo Álvaro Llamas , Triviño Alicia , Guerrrero Eliseo Villagrasa 
Published: December 2024

AbstractWireless power transfer (WPT) offers a safer and more convenient alternative to traditional charging methods. In the automotive sector, dynamic WPT presents a promising solution by reducing battery size and enhancing vehicle usability. The key components of dynamic WPT systems are the coils and their compensation topologies. This paper provides a detailed analysis of these elements to optimize system performance. First, different coil geometries are systematically evaluated using finite element analysis, aiming to identify designs that minimize electrical parameter variations, which can damage electronics and make the control more complex. Then, the most suitable compensation topology is determined through time‐domain analysis, which is more accurate under dynamic conditions than the commonly used phasor‐based approach. This design process based on time‐domain analysis is validated with a 100 W dynamic charger prototype, demonstrating the system's transient behaviour. Experimental results show an efficiency of up to 80% and high tolerance to misalignment under various load conditions.
Single receiver Long‐Term Evolution passive radar system using signal reconstruction for moving target detection
Authors: de Guenin Lionel , Rosson Patrick , Petrochilos Nicolas , Moreau Eric 
Published: December 2024

AbstractThis paper presents a single‐antenna receiver passive radar system in the context of moving target detection such as trains, car, planes and UAVs, leveraging the long‐term evolution (LTE) network as an illumination source. The proposed system uses signal reconstruction enabled by the telecom structure of the opportune signal in order to forego the use of a reference antenna. This presents the advantage of not relying on a physical signal for reference and its possible defect, potentially yielding better performances. The techniques introduced are validated through simulation and experiments. Moreover, a simplified passive radar system emphasises one of the key advantage of passive radar over other competing technologies for moving target detection: stealthiness and cost‐effectiveness.
Robust design of mismatched filters in echo truncation for inter‐pulse waveform diversity radar
Authors: Xu Xiaojian , Zhao Dehua , Zhang Liang 
Published: December 2024

AbstractRange sidelobe modulation is a primary issue that forbids application of inter‐pulse and intra‐coherent processing interval (coherent processing interval) waveform diversity in moving target indication or moving target detection radars. Previous research has adopted mismatched filters (MMFs) to reconstruct identical compressed outputs based on an ideal model. In practice however, echo truncation is inevitable due to transceiver isolation, significantly degrading the effectiveness of MMFs. In this paper, the design of MMFs under an echo‐truncated model to achieve a robust design is considered. Specifically, the authors extract the dominant scattering profile from the close‐range echo and incorporate it into the MMF design model. To judiciously combine the robust design with the ideal one, a segmented pulse compression scheme is proposed. Numerical results indicate that the proposed robust processing scheme can effectively reduce clutter leakage in both truncated and intact scenarios.
Analysis of type 2 diabetes mellitus‐related genes by constructing the pathway‐based weighted network
Authors: Zhang Xue‐Yan , Xu Chuan‐Yun , Cao Ke‐Fei , Luo Hong , Zhang Xu‐Sheng 
Published: December 2024

AbstractComplex network is an effective approach to studying complex diseases, and provides another perspective for understanding their pathological mechanisms by illustrating the interactions between various factors of diseases. Type 2 diabetes mellitus (T2DM) is a complex polygenic metabolic disease involving genetic and environmental factors. By combining the complex network approach with biological data, this study constructs a pathway‐based weighted network model of T2DM‐related genes to explore the interrelationships between genes, here a weight is assigned to each edge in terms of the number of the same pathways in which the two nodes (genes) connected to the edge are involved. The edge weights can reflect differences in the strength of connections (interactions) between nodes (genes), which intuitively reflect the extent of biological correlations between genes and contribute to the importance of the nodes. Analysis of statistical and topological characteristics shows that the edge weights are correlated to the network topology, and the edge weight distribution decays as a power‐law. The disparity of the weights indicates that the edge weight distribution for the nodes with the same degree is of approximately equal weights; and most edges with the higher weights tend to connect with the higher degree nodes. To determine the key hub genes of the weighted network, an integrated ranking index is used to comprehensively reflect the contribution of the three indices (strength, degree and number of pathways) of nodes; by taking the threshold of integrated ranking index greater than 0.56, 12 key hub genes are identified: MAPK1, PIK3CD, PIK3CA, PIK3R1, AKT2, AKT1, KRAS, TNF, MAPK8, PRKCA, IL6 and MTOR. These genes should play an important role in the occurrence and development of T2DM, and can be regarded as potential therapeutic targets for further biological and medical research on their functions in T2DM. It can be expected that combining complex network approach with other data analysis techniques can provide more clues for exploring the pathogenesis and treatment of T2DM and other complex diseases in the future.
Population synthesis of Thorne-Żytkow objects
Authors: Nathaniel K. , Vigna-Gómez A. , Grichener A. , Farmer R. , Renzo M. , Everson R. W. 
Published: December 2024

Context. Common envelope evolution of a massive star and a neutron star companion has two possible outcomes: the formation of a short-period binary (a potential gravitational wave source progenitor) or the merger of the massive star with the neutron star. If the binary merges, a structure may form, comprised of a neutron star core surrounded by a large diffuse envelope, known as a Thorne-Żytkow object (TŻO). The predicted appearance of this hypothetical class of star is very similar to that of a red supergiant, making it difficult to identify them in observations.
Aims. Our objective is to understand the properties of systems that are potential TŻO progenitors; specifically, binary systems that enter a common envelope phase with a neutron star companion. We also aim to distinguish those that have been through a previous stable mass transfer phase, which can rejuvenate the accretor.
Methods. We used the rapid population synthesis code COMPAS at solar metallicity, with the common envelope efficiency parameter set to unity, to determine the population demographics of TŻOs. We used one-dimensional (1D) evolutionary TŻO models from the literature to determine a fit for the TŻO lifetime to estimate the current number of TŻOs in the Milky Way, as well as to assess core disruption during the merger.
Results. We explored the progenitors in the Hertzsprung-Russell diagram, calculated the formation rates, and investigated the kinematics of the progenitor stars. We find that the vast majority (≈92%) of TŻO progenitors in our population have experienced mass transfer and are rejuvenated prior to their formation event. For the Milky Way, we estimate a TŻO formation rate of ≈4 × 104 yr−1, which results in ≈5 ± 1 TŻOs at present.
Non-linear saturation and energy transport in global simulations of magneto-thermal turbulence in the stratified intracluster medium
Authors: Kempf J. M. , Rincon F. 
Published: December 2024

Context. The magneto-thermal instability (MTI) is one of many possible drivers of stratified turbulence in the intracluster medium (ICM) outskirts of galaxy clusters, where the background temperature gradient is most likely aligned with the gravity. This instability occurs because of the fast anisotropic conduction of heat along magnetic field lines. However, the extent to which it impacts the ICM dynamics, energetics, and overall equilibrium is still a matter of debate.
Aims. This work aims to understand MTI turbulence in an astrophysically stratified ICM atmosphere, its underlying saturation mechanism, and its ability to carry energy and to provide non-thermal pressure support.
Methods. We performed a series of 2D and 3D numerical simulations of the MTI in global spherical models of a stratified ICM thanks to the finite-volume Godunov-type code IDEFIX and using Braginskii magnetohydrodynamics. We used well-controlled volume-averaged, shell-averaged, and spectral diagnostics to study the saturation mechanism of the MTI and its radial transport energy budget.
Results. The MTI is found to saturate through a dominant balance between injection and dissipation of available potential energy, which amounts to marginalising the Braginskii heat flux but not the background temperature gradient itself. Accordingly, the strength and injection length of MTI-driven turbulence exhibit clear dependencies on the thermal diffusivity. With realistic Spitzer conductivity, the MTI drives cluster-size motions with Mach numbers up to ℳ ∼ 0.3, even in the presence of strong stable entropy stratification. We show that such mildly compressible flows can provide about ∼15% of the non-thermal pressure support in the outermost ICM regions close to the cluster accretion shock and that the convective transport itself is much less efficient (a few percent only) than conduction at radially transporting energy. Finally, we show that the MTI saturation can be described by a diffusive mixing-length theory, shedding light on the diffusive buoyant nature, rather than the adiabatic convective nature, of the instability.
Conclusions. The MTI seems relevant to both the dynamics and energetics of the ICM through radially biased magnetic fields that enhance the background Braginskii heat flux. Further work including externally forced turbulence, for instance, mimicking accretion-induced turbulence, is needed to assess its overall relative importance in comparison to other drivers of ICM turbulence.