bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterization of Porous In‐Stream Structures to Assess Their Implications on Flow Dynamics and Sediment Transport
Authors: You Hojung , Tinoco Rafael O. 
Published: February 2025

AbstractIn aquatic environments, the presence of porous obstacles induces intricate flow dynamics as the flow passes through and around them. These flows exhibit large local vertical and lateral gradients, influencing the evolution of downstream flow structures across various scales. In this study, we investigated flow around five idealized porous obstacles with varying porosity and pore arrangements using Particle Image Velocimetry (PIV). By introducing a two‐layer model and computing turbulent kinetic energy budgets, we quantified jet velocity and length to predict the development of downstream flow structures. Recirculation zones were observed downstream of obstacles with small pore sizes, while forward flow motions prevailed downstream with larger pore sizes due to increased jet velocity and length. To study the effect of multiple porous obstacles, we installed a second obstacle at various downstream distances, which showed minimal influence on jet length and velocity once the distance between obstacles exceeded the jet length determined from single obstacle analysis, particularly with obstacles featuring large pore sizes. Our study identifies the need to properly characterize in‐stream obstacles based on both their porosity and their representative pore sizes, as the jets created through the obstacles significantly alter the expected flow structures from solid‐obstacle predictions. Based on the insights from the hydrodynamic study and using the balance between resistance and driving force of sediment motions, we discuss ecological and geomorphic applications in the vicinity of porous obstacles, highlighting the potential locations for sediment erosion and deposition.
Controls of Morphometric and Climatic Catchment Characteristics on Debris Flow and Flood Hazard on Alluvial Fans in High Mountain Asia: A Machine Learning Approach
Authors: Bazilova Varvara O. , de Haas Tjalling , Immerzeel Walter W. 
Published: February 2025

AbstractDebris flows and floods pose considerable hazards to populated areas of High Mountain Asia (HMA). Debris flows are generally more hazardous than floods, and therefore identification of process type is important for hazard assessment and mitigation. Prior statistical assessments, though informative, typically considered a limited number of parameters, excluded climatic variables, and failed to address classification probability and uncertainty. Here we developed a machine learning model to determine process type and its likelihood for a diverse set of 1,793 catchments in HMA using a wide range of morphometric and climatic parameters. We classified the alluvial fans of these catchments as either debris flow or flood dominated based on surface morphology. A data set of morphometric (e.g., catchment area, slope, relief, Melton ratio) and climatic features (e.g., temperature and precipitation regime, freeze–thaw cycles, glacier and permafrost presence) per catchment was subsequently built, and a CatBoost machine learning model to quantify debris flow and flood probabilities was employed. The CatBoost model has a high classification accuracy compared to traditional approaches, and offers the advantage of providing classification uncertainty. Results show that catchment slope, area, and perimeter are the main morphometric controls on process type across HMA, in line with previous work, and further show that including climate information leads to a minor improvement of model performance. These findings shed light on controls on debris flow and flood occurrence in mountainous area, showcase the potential of machine learning models in mountain hazard research, and provide insights for assessing risks.
Ultrasensitive photoelectric detection with room temperature extremum
Authors: Wu Tuntan , Li Yongzhen , Zhou Qiangguo , Qiu Qinxi , Gao Yanqing , Zhou Wei , Yao Niangjuan , Chu Junhao , Huang Zhiming 
Published: February 2025

Abstract
Room-temperature photodetection holds pivotal significance in diverse applications such as sensing, imaging, telecommunications, and environmental remote sensing due to its simplicity, versatility, and indispensability. Although different kinds of photon and thermal detectors have been realized, high sensitivity of photodetection with room temperature extremum is not reported until now. Herein, we find evident peaks in the photoelectric response originated from the anomalous excitonic insulator phase transition in tantalum nickel selenide (Ta2NiSe5) for room-temperature optimized photodetection from visible light to terahertz ranges. Extreme sensitivity of photoconductive detector with specific detectivity (D*) of 5.3 × 1011 cm·Hz1/2·W−
1 and electrical bandwidth of 360 kHz is reached in the terahertz range, which is one to two orders of magnitude improvement compared to that of the state-of-the-art room-temperature terahertz detectors. The van der Waals heterostructure of Ta2NiSe5/WS2 is further constructed to suppress the dark current at room temperature with much improved ambient D* of 4.1 × 1012 cm·Hz1/2·W−1 in the visible wavelength, rivaling that of the typical photodetectors, and superior photoelectric performance in the terahertz range compared to the photoconductor device. Our results open a new avenue for optoelectronics via excitonic insulator phase transition in broad wavelength bands and pave the way for applications in sensitive environmental and remote sensing at room temperature.
Costunolide normalizes neuroinflammation and improves neurogenesis deficits in a mouse model of depression through inhibiting microglial Akt/mTOR/NF-κB pathway
Authors: Zhang Shao-qi , Deng Qiao , Tian Cheng , Zhao Huan-huan , Yang Li-ying , Cheng Xin-wei , Wang Guo-ping , Liu Dong 
Published: February 2025

Abstract
Neuroinflammation is crucial for the pathogenesis of major depression. Preclinical studies have shown the potential of anti-inflammatory agents, specifically costunolide (COS), correlate with antidepressant effects. In this study, we investigated the molecular mechanisms underlying the antidepressant actions of COS. Chronic restraint stress (CRS) was induced in male mice. The mice were treated with either intra-DG injection of COS (5 μM, 1 μL per side) or COS (20 mg/kg, i.p.) for 1 week. We showed that administration of COS through the both routes significantly ameliorated the depressive-like behavior in CRS-exposed mice. Furthermore, administration of COS significantly improved chronic stress-induced adult hippocampal neurogenesis deficits in the mice through attenuating microglia-derived neuroinflammation. We demonstrated that COS (5 μM) exerted anti-neuroinflammatory effects in LPS-treated BV2 cells via inhibiting microglial Akt/mTOR/NF-κB pathway; inactivation of mTOR/NF-κB/IL-1β pathway was required for the pro-neurogenic action of COS in CRS-exposed mice. Our results reveal the antidepressant mechanism of COS that is normalizing neuroinflammation to improve neurogenesis deficits, supporting anti-inflammatory agents as a potential therapeutic strategy for depression.
Interpreting mammalian synonymous site conservation in light of the unwanted transcript hypothesis
Authors: Christmas Matthew J. , Dong Michael X. , Meadows Jennifer R. S. , Kozyrev Sergey V. , Lindblad-Toh Kerstin 
Published: February 2025

Abstract
Mammalian genomes are biased towards GC bases at third codon positions, likely due to a GC-biased ancestral genome and the selectively neutral recombination-related process of GC-biased gene conversion. The unwanted transcript hypothesis posits that this high GC content at synonymous sites may be beneficial for protecting against spurious transcripts, particularly in species with low effective population sizes. Utilising a 240 placental mammal genome alignment and single-base resolution conservation scores, we interpret sequence conservation at mammalian four-fold degenerate sites in this context and find evidence in support of the unwanted transcript hypothesis, including a strong GC bias, high conservation at sites relating to exon splicing, less human genetic variation at conserved four-fold degenerate sites, and conservation of sites important for epigenetic regulation of developmental genes. Additionally, we show that high conservation of four-fold degenerate sites in essential developmental genes, including homeobox genes, likely relates to the low mutation rates experienced by these genes.
Calibrating quantum gates up to 52 qubits in a superconducting processor
Authors: Fan Daojin , Liu Guoding , Li Shaowei , Gong Ming , Wu Dachao , Zhang Yiming , Zha Chen , Chen Fusheng , Cao Sirui , Ye Yangsen , Zhu Qingling , Ying Chong , Guo Shaojun , Qian Haoran , Wu Yulin , Deng Hui , Wu Gang , Peng Cheng-Zhi , Ma Xiongfeng , Zhu Xiaobo , Pan Jian-Wei 
Published: February 2025

Abstract
Benchmarking large-scale quantum gates, typically involving multiple native two-qubit and single-qubit gates, is crucial in quantum computing. Global fidelity, encompassing information about inter-gate correlations, offers a comprehensive metric for evaluating and optimizing gate performance, unlike the fidelities of individual local native gates. In this work, utilizing the character-average benchmarking protocol implementable in a shallow circuit, we successfully benchmark gate fidelities up to 52 qubits. Notably, we achieved a fidelity of 63.09% ± 0.23% for a 44-qubit parallel CZ gate. Utilizing the global fidelity of the parallel CZ gate, we explore the correlations among local CZ gates by introducing an inter-gate correlation metric, enabling one to simultaneously quantify crosstalk error when benchmarking gate fidelity. Finally, we apply our methods in gate optimization. By leveraging global fidelity for optimization, we enhance the fidelity of a 6-qubit parallel CZ gate from 87.65% to 92.04% and decrease the gate correlation from 3.53% to 3.22%, compared to local gate fidelity-based optimization. The experimental results align well with our established composite noise model, incorporating depolarizing and Z
Z-coupling noises, and provide valuable insight into further study and mitigation of correlated noise.
CRISPRi-ART enables functional genomics of diverse bacteriophages using RNA-binding dCas13d
Authors: Adler Benjamin A. , Al-Shimary Muntathar J. , Patel Jaymin R. , Armbruster Emily G. , Colognori David , Charles Emeric J. , Miller Kate V. , Lahiri Arushi , Cui Michael L. , Oromí-Bosch Agnès , Voelker Angela , Trinidad Marena , Lee Jina , Beurnier Sebastien , Boger Ron , Nomburg Jason , Barrangou Rodolphe , Mutalik Vivek K. , Schoeniger Joseph S. , Pogliano Joseph A. , Savage David F. , Doudna Jennifer A. , Cress Brady F. 
Published: February 2025

Abstract
Bacteriophages constitute one of the largest reservoirs of genes of unknown function in the biosphere. Even in well-characterized phages, the functions of most genes remain unknown. Experimental approaches to study phage gene fitness and function at genome scale are lacking, partly because phages subvert many modern functional genomics tools. Here we leverage RNA-targeting dCas13d to selectively interfere with protein translation and to measure phage gene fitness at a transcriptome-wide scale. We find CRISPR Interference through Antisense RNA-Targeting (CRISPRi-ART) to be effective across phage phylogeny, from model ssRNA, ssDNA and dsDNA phages to nucleus-forming jumbo phages. Using CRISPRi-ART, we determine a conserved role of diverse rII homologues in subverting phage Lambda RexAB-mediated immunity to superinfection and identify genes critical for phage fitness. CRISPRi-ART establishes a broad-spectrum phage functional genomics platform, revealing more than 90 previously unknown genes important for phage fitness.
A dataset of laymen olfactory perception for 74 mono-molecular odors
Authors: Bierling Antonie Louise , Croy Alexander , Jesgarzewsky Tim , Rommel Maria , Cuniberti Gianaurelio , Hummel Thomas , Croy Ilona 
Published: February 2025

Abstract
The molecular structure of an odor determines whether and how it is perceived by humans. However, the principles of how odorant chemistry links to perceptual patterns remain largely unknown and are primarily studied using odor rating datasets from highly trained olfactory experts, such as perfumers. This limits our knowledge of typical odor perception and its variability over individuals. We provide a dataset featuring free descriptions, evaluative ratings, and qualitative labels for 74 chemically diverse mono-molecular odorants, rated by a large sample of young adults. A total of 1,227 participants described and rated the odors, and completed questionnaires covering their demographic background, personality traits, and the role of olfaction in their daily lives. The dataset offers a valuable foundation for research aimed at understanding the fundamentals of olfactory perception.
Effects of startle on cognitive performance and physiological activity revealed by fNIRS and thermal imaging
Authors: Schwartz Flora , Deniel Jonathan , Causse Mickaël 
Published: February 2025

Abstract
Sudden and threatening stimuli can trigger a startle reflex, a stereotyped physiological response that may lead to a brief cognitive incapacitation. Better understanding this reaction would be beneficial to safety-critical occupational domains. We characterized some physiological correlates of the startle response while participants completed a difficult task (Toulouse N-back task) tapping executive functions. During the task, loud and threatening sounds were presented unpredictably to trigger a startle reflex. Brain activity and facial skin temperature were measured in 34 participants using functional near-infrared spectroscopy (fNIRS) and functional infrared thermal imaging (fITI), respectively. In the high difficulty condition, participants were generally less efficient, but their performance improved slightly following startle in the high difficulty condition. Brain activity in the right prefrontal cortex was also higher following startle, potentially reflecting a compensatory overactivation to sustain performance. Interestingly, higher trait-anxiety was associated with lower task performance, still following startle in the high difficulty condition. Finally, we found a decrease in temperature of the right eye and right cheek as well as an increase in the nose temperature following startle. These results underscore the complexity of startle-induced cognitive and physiological dynamics, which may have implications for occupational settings where managing sudden stressors is crucial.
Intersect between brain mechanisms of conditioned threat, active avoidance, and reward
Authors: Badarnee Muhammad , Wen Zhenfu , Hammoud Mira Z. , Glimcher Paul , Cain Christopher K. , Milad Mohammed R. 
Published: February 2025

Abstract
Active avoidance is a core behavior for human coping, and its excess is common across psychiatric diseases. The decision to actively avoid a threat is influenced by cost and reward. Yet, threat, avoidance, and reward have been studied in silos. We discuss behavioral and brain circuits of active avoidance and the interactions with fear and threat. In addition, we present a neural toggle switch model enabling fear-to-anxiety transition and approaching reward vs. avoiding harm decision. To fully comprehend how threat, active avoidance, and reward intersect, it is paramount to develop one shared experimental approach across phenomena and behaviors, which will ultimately allow us to better understand human behavior and pathology.
The NLRP1 inflammasome is an essential and selective mediator of axon pruning in neurons
Authors: Romero Selena E , Geden Matthew J , Basundra Richa , Kelly-Rajan Kiran , Miao Edward A , Deshmukh Mohanish 
Published: February 2025

Abstract
Axon pruning is a unique process neurons utilize to selectively degenerate axon branches while keeping the neuronal cell body intact. The mechanisms of axon pruning have much in common with those of apoptosis. Both axon pruning and apoptosis pathways require key apoptotic proteins (Bax, Caspase-9, Caspase-3). Interestingly, axon pruning does not require Apaf-1, a key member of the apoptosome complex. As such, exactly how caspases are activated in an apoptosome-independent manner during axon pruning is unknown. Here we show that neurons utilize the NLRP1 inflammasome, an innate immune sensor of pathogens, specifically for axon pruning. Strikingly, NLRP1b-deficient neurons were unable to prune axons both in vitro and in vivo, but fully capable of degenerating during apoptosis. Our results reveal NLRP1 as an immune molecule engaged by neurons for an unexpected physiological function independent of its pathogen-induced proinflammatory role.
Signal‐quality‐aware multisensor fusion for atrial fibrillation detection
Authors: Malone Shane , Cardiff Barry , John Deepu , John Arlene 
Published: February 2025

AbstractThis letter introduces a novel method to enhance atrial fibrillation detection accuracy in healthcare monitoring. Wearable devices often face inconsistent signal quality due to noise. To address this, a multimodal data fusion technique that improves signal reliability during continuous monitoring is proposed. The method improves the precision of detecting R–R intervals by integrating wavelet coefficients from electrocardiogram, photoplethysmogram, and arterial blood pressure signals, weighted according to the quality of each signal. Furthermore, a bi‐directional long short‐term memory network is developed to accurately detect AF based on the derived heartrate or R–R intervals. Unlike prior studies, this work uniquely evaluates the system’s performance under noisy conditions, demonstrating significant accuracy improvements over single‐channel methods. The system's generalizability is confirmed by evaluating the classifier's performance as the number of sensor inputs increases. At a signal‐to‐noise ratio of −10 dB, the accuracy improves by 4.51% with two sensor inputs and by 10.92% with three inputs, compared to using a single input.
Muss eine diskriminierungssensible Haltung erlernt werden?
Authors: Kahraman Birsen 
Published: February 2025

ZusammenfassungWährend „Kultursensibilität“ in den vergangenen Jahren zunehmend Eingang in Curricula der Aus- und Weiterbildung von Psychotherapeut*innen gefunden hat, werden Diskriminierungsstrukturen wie Rassismus und Antisemitismus immer noch selten adressiert. Dieser Beitrag möchte Kolleg*innen ermutigen, sich antisemitismus- und rassismuskritisch weiterzubilden, um ihre Expertise diskriminierungssensibel und menschenrechtsorientiert einbringen zu können.
Micro-computed tomography beamline of the Australian synchrotron: Density measurements
Authors: Arhatari Benedicta D. , Thompson Darren , Stevenson Andrew W. , Walsh Adam , Fiala Tom , Ruben Gary , Feng Tingting , Mudie Stephen 
Published: February 2025

The Micro-Computed Tomography (MCT) beamline at the Australian Synchrotron (ANSTO) offers superior capabilities in micrometer-scale spatial resolution and three-dimensional x-ray imaging. MCT is the first of the eight new BRIGHT beamlines and has been operating successfully with users for approximately two years. It is a bending magnet beamline capable of delivering a white beam, a pink beam, or a monochromatic beam in the 8–40 keV energy range using a Double Multilayer Monochromator (DMM). Ongoing development continues at the MCT beamline to extend its capabilities. In this article, we present the operation and energy calibration of the DMM, highlighting the unique advantages offered by synchrotron-based micro-CT and its application for quantitative imaging, such as density measurements using monochromatic energy.
Machine learning delta-T noise for temperature bias estimation
Authors: Gerry Matthew , Wang Jonathan J. , Li Joanna , Shein-Lumbroso Ofir , Tal Oren , Segal Dvira 
Published: February 2025

Delta-T shot noise is activated in temperature-biased electronic junctions, down to the atomic scale. It is characterized by a quadratic dependence on the temperature difference and a nonlinear relationship with the transmission coefficients of partially opened conduction channels. In this work, we demonstrate that delta-T noise, measured across an ensemble of atomic-scale junctions, can be utilized to estimate the temperature bias in these systems. Our approach employs a supervised machine learning algorithm to train a neural network, with input features being the scaled electrical conductance, the delta-T noise, and the mean temperature. Due to limited experimental data, we generate synthetic datasets, designed to mimic experiments. The neural network, trained on these synthetic data, was subsequently applied to predict temperature biases from experimental datasets. Using performance metrics, we demonstrate that the mean bias—the deviation of predicted temperature differences from their true value—is less than 1 K for junctions with conductance up to 4G0. Our study highlights that, while a single delta-T noise measurement is insufficient for accurately estimating the applied temperature bias due to noise contributions from other sources, averaging over an ensemble of junctions enables predictions within experimental uncertainties. This suggests that machine learning approaches can be utilized for estimation of temperature biases and similarly other stimuli in electronic junctions.
Ensuring resilience in agri-food supply chains
Authors: Tsoulfas Giannis T. 
Published: February 2025

Abstract
Agri-food supply chains (AFSCs) are critical for global food security, but they have been facing challenging disruptions from climate change, pandemics, and geopolitical events. This review analyses how sustainability practices can enhance AFSC resilience and explore the trade-offs between sustainability initiatives and resilience strategies. The methodology for this review article involved a systematic literature search and content analysis approach to investigate the connections between sustainability and resilience in AFSCs. The systematic analysis of 28 papers reveals several key themes. A holistic and cyclical approach to sustainability and resilience in AFSCs allows practices to be adapted to changing conditions and ensures that sustainability efforts do not harm economic outcomes. Collaboration and stakeholder engagement are crucial for leveraging sustainability for resilience. Joint planning, information sharing, and customer integration can lead to better resource management and operational efficiency. Innovation, diversification, and adaptability are key to maintaining economic viability while pursuing sustainability and resilience. They can increase the value of end products, support local markets, and grow profitability in agriculture. Moreover, proposed strategies, policies, and conceptual frameworks for designing sustainable and resilient supply chains are reviewed. Focus is also drawn to the latest technologies that can help balance sustainability, resilience, and economic viability by enhancing decision making. In addition, shifting product demand from global to local suppliers is discussed as a way to reduce impacts from supply chain disruptions and revitalize local economies. Finally, the review emphasizes the importance of understanding trade-offs between sustainability initiatives and resilience strategies to develop integrated approaches.