bluuebunny/crossref_metadata_2025_split huggingface dataset:
Geomorphic Response of a Coastal Berm to Storm Surge and the Importance of Sheet Flow Dynamics
Authors: Pontiki M. , Puleo J. A. , Bond H. , Wengrove M. , Feagin R. A. , Hsu T.‐J. , Huff T. 
Published: September 2023

AbstractDuring a storm, as the beach profile is impacted by increased wave forcing and rapidly changing water levels, sand berms may help mitigate erosion of the backshore. However, the mechanics of berm morphodynamics have not been fully described. In this study, 26 trials were conducted in a large wave flume to explore the response of a near‐prototype berm to scaled storm conditions. Sensors were used to quantify hydrodynamics, sheet flow dynamics, and berm evolution. Results indicate that berm overtopping and offshore sediment transport were key processes causing berm erosion. During the morphological evolution of the beach profile, two sand bars were formed offshore that attenuated subsequent wave energy. The landward extent of that energy was confined to the seaward foreshore, inhibiting inundation of the backshore. Net offshore‐directed transport was dominant when infragravity motions increased in the swash zone. Conversely, the influence of incident‐band motions on sediment transport was relatively greater in the inner‐surf zone. Near‐bed flow velocities and sheet flow layer thicknesses were larger in the swash zone than in the inner‐surf zone. This paper also provides a valuable analysis between morphology‐estimated total sediment transport rates and rates derived from in situ measurements. Sheet flow dynamics dominated foreshore cross‐shore sediment processes, constituting the largest portion of the total sediment transport load throughout the berm erosion.
A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers
Authors: Noh Hyoseob , Park Yong Sung , Seo Il Won 
Published: September 2023

AbstractSediment transport load monitoring is important in civil and environmental engineering fields. Monitoring the total load is difficult, especially because of the cost of the bed load transport measurement. This study proposes estimation models for the suspended‐to‐total load fraction using dimensionless hydro‐morphological variables. Two prominent variable combinations were identified using the recursive feature elimination for support vector regression (SVR): (a) width‐to‐depth ratio, dimensionless particle size, flow Reynolds number, densimetric Froude number, and falling particle Reynolds number, and (b) flow Reynolds number, Froude number, and densimetric Froude number. The explicit relations between the suspended‐to‐total load fraction and the two combinations were revealed by two modern symbolic regression methods: multi‐gene genetic programming and Operon. The five‐variable SVR model showed the best performance. Clustering analyses using a self‐organizing map and Gaussian mixture model, respectively, identified the underlying relationships between dimensionless variables. Subsequently, the one‐at‐a‐time sensitivity of the input variables of the empirical models was investigated. The suspended‐to‐total load fraction is positively related to the flow Reynolds number and is inversely related to the densimetric Froude number. The models developed in this study are practical and easy to implement in other suspended sediment monitoring methods because they only require basic measurable hydro‐morphological variables, such as velocity, depth, width, and median bed material size.
Shocklets and Short Large Amplitude Magnetic Structures (SLAMS) in the High Mach Foreshock of Venus
Authors: Collinson Glyn A. , Hietala Heli , Plaschke Ferdinand , Karlsson Tomas , Wilson Lynn B. , Archer Martin , Battarbee Markus , Blanco‐Cano Xochitl , Bertucci Cesar , Long David , Opher Merav , Sergis Nick , Gasque Claire , Liu Terry , Raptis Savvas , Burne Sofia , Frahm Rudy , Zhang Tielong , Futaana Yoshifumi 
Published: September 2023

AbstractShocklets and short large‐amplitude magnetic structures (SLAMS) are steepened magnetic fluctuations commonly found in Earth's upstream foreshock. Here we present Venus Express observations from the 26th of February 2009 establishing their existence in the steady‐state foreshock of Venus, building on a past study which found SLAMS during a substantial disturbance of the induced magnetosphere. The Venusian structures were comparable to those reported near Earth. The 2 Shocklets had magnetic compression ratios of 1.23 and 1.34 with linear polarization in the spacecraft frame. The 3 SLAMS had ratios between 3.22 and 4.03, two of which with elliptical polarization in the spacecraft frame. Statistical analysis suggests SLAMS coincide with unusually high solar wind Alfvén mach‐number at Venus (12.5, this event). Thus, while we establish Shocklets and SLAMS can form in the stable Venusian foreshock, they may be rarer than at Earth. We estimate a lower limit of their occurrence rate of ≳14%.
Reply to “Comment on ‘‘Unraveling the Role of IMF Bx in Driving Geomagnetic Activity’’” by Lauri Holappa et al.
Authors: Kubyshkina M. V. , Semenov V. S. , Tsyganenko N. A. , Wang X.‐G. , Kubyshkin I. V. 
Published: September 2023

AbstractWe thank Lauri Holappa, Jone Peter Reistad and Anders Ohma for their interest in our research and for having published their comments, referred hereafter as HEA2023. Here we present our counterarguments to the points presented in HEA2023. We explain the observed excess of AL values for different polarities of IMF By over different IMF Bx polarities by an additional negative IMF Bz, which exist due to the 7.25‐degree tilt of the solar equatorial plane to the ecliptic plane. We emphasize again that the AL index strongly depends on the dipole tilt, so that under the same driving conditions <AL> is more than twice larger during non‐tilted dipole periods than at solstices. The IMF Bx component, depending on its polarity, moves the magnetotail plasma sheet away from or toward the equatorial plane, and thus changes the effective dipole tilt angle. That change may be a cause of the asymmetric response of the AL‐index to positive or negative values of the IMF Bx.
Mechanisms Controlling Interannual Variability of Seasonal Hypoxia Off the Changjiang River Estuary
Authors: Zhang Wenxia , Zhou Feng , Huang Daji , Chen Jianfang , Zhu Jianrong 
Published: September 2023

AbstractHypoxia has long been a symptom of deteriorating ecosystem that threatens the health of estuarine and coastal waters. Episodic hypoxia events and intraseasonal variation of coastal hypoxia have been amply investigated. However, interannual variability of coastal hypoxia has only been assessed in few regions. Bottom hypoxia forms seasonally off the Changjiang River Estuary in the East China Sea mainly due to the large riverine inputs. Large river discharge and its interactions with ambient water combine to contribute to the development of episodic hypoxia events and the intraseasonal migration of bottom hypoxia. However, little is known about the interannual variation of bottom hypoxia in this region. This study used a well‐evaluated, coupled physical–biogeochemical model to explore the long‐term feature of hypoxia in the East China Sea. The bottom water in the hypoxic zone lost oxygen with a rate of −1.2 mmol/m3/year. Bottom hypoxia showed large interannual variations of geographical location, severity, volume expansion, and sustainment. Large Changjiang River discharge was a prerequisite for hypoxia formation and the associated interannual variation. The interannual variations in the direction and strength of shelf wind controlled long‐term distribution of Changjiang diluted water. The delivery of freshwater fundamentally determined the strength of vertical stratification and the rates of biogeochemical cycles, contributing 73% of the interannual variation of bottom hypoxia.
Intercomparison of Deep Learning Architectures for the Prediction of Precipitation Fields With a Focus on Extremes
Authors: Otero Noelia , Horton Pascal 
Published: September 2023

AbstractIn recent years, the use of deep learning methods has rapidly increased in many research fields. Similarly, they have become a powerful tool within the climate scientific community. Deep learning methods have been successfully applied for different tasks, such as the identification of atmospheric patterns, weather extreme classification, or weather forecasting. However, due to the inherent complexity of atmospheric processes, the ability of deep learning models to simulate natural processes, particularly in the case of weather extremes, is still challenging. Therefore, a thorough evaluation of their performance and robustness in predicting precipitation fields is still needed, especially for extreme precipitation events, which can have devastating consequences in terms of infrastructure damage, economic losses, and even loss of life. In this study, we present a comprehensive evaluation of a set of deep learning architectures to simulate precipitation, including heavy precipitation events (>95th percentile) and extreme events (>99th percentile) over the European domain. Among the architectures analyzed here, the U‐Net network was found to be superior and outperformed the other networks in simulating precipitation events. In particular, we found that a simplified version of the original U‐Net with two encoder‐decoder levels generally achieved similar skill scores than deeper versions for predicting precipitation extremes, while significantly reducing the overall complexity and computing resources. We further assess how the model predicts through the attribution heatmaps from a layer‐wise relevance propagation explainability method.
Mechanical stiffness promotes skin fibrosis through Piezo1-mediated arginine and proline metabolism
Authors: He Jiahao , Fang Bin , Shan Shengzhou , Li Qingfeng 
Published: September 2023

AbstractThe increased mechanics of fibrotic skin tissue continuously regulate fibroblast functions such as survival and differentiation. Although all these processes consume metabolites, it is unclear whether and how cells adapt their metabolic activity to increased matrix stiffness. Here, we show that transferring mouse dermal fibroblasts from soft to stiff substrates causes an up-regulation of arginine and proline metabolism. Increased matrix stiffness stimulates the expression and activity of key metabolic enzymes, leading to the synthesis of L-proline, a major source of collagen. In addition, the novel mechanosensitive channel Piezo1 was identified as a key regulator of arginine and proline metabolism in fibroblasts under increased stiffness. Consistently, targeting Piezo1 to dermal fibroblasts in vivo effectively reduces fibrosis and arginine-proline metabolism in mouse skin. Therefore, mechanical stiffness is a critical environmental cue for fibroblast metabolism and skin fibrosis progression.
The complexity of NISQ
Authors: Chen Sitan , Cotler Jordan , Huang Hsin-Yuan , Li Jerry 
Published: September 2023

AbstractThe recent proliferation of NISQ devices has made it imperative to understand their power. In this work, we define and study the complexity class , which encapsulates problems that can be efficiently solved by a classical computer with access to noisy quantum circuits. We establish super-polynomial separations in the complexity among classical computation, , and fault-tolerant quantum computation to solve some problems based on modifications of Simon’s problems. We then consider the power of for three well-studied problems. For unstructured search, we prove that cannot achieve a Grover-like quadratic speedup over classical computers. For the Bernstein-Vazirani problem, we show that only needs a number of queries logarithmic in what is required for classical computers. Finally, for a quantum state learning problem, we prove that is exponentially weaker than classical computers with access to noiseless constant-depth quantum circuits.
Social memory deficit caused by dysregulation of the cerebellar vermis
Authors: Chao Owen Y. , Pathak Salil Saurav , Zhang Hao , Augustine George J. , Christie Jason M. , Kikuchi Chikako , Taniguchi Hiroki , Yang Yi-Mei 
Published: September 2023

AbstractSocial recognition memory (SRM) is a key determinant of social interactions. While the cerebellum emerges as an important region for social behavior, how cerebellar activity affects social functions remains unclear. We selectively increased the excitability of molecular layer interneurons (MLIs) to suppress Purkinje cell firing in the mouse cerebellar vermis. Chemogenetic perturbation of MLIs impaired SRM without affecting sociability, anxiety levels, motor coordination or object recognition. Optogenetic interference of MLIs during distinct phases of a social recognition test revealed the cerebellar engagement in the retrieval, but not encoding, of social information. c-Fos mapping after the social recognition test showed that cerebellar manipulation decreased brain-wide interregional correlations and altered network structure from medial prefrontal cortex and hippocampus-centered to amygdala-centered modules. Anatomical tracing demonstrated hierarchical projections from the central cerebellum to the social brain network integrating amygdalar connections. Our findings suggest that the cerebellum organizes the neural matrix necessary for SRM.
A chromosome-level reference genome of the Antarctic blackfin icefish Chaenocephalus aceratus
Authors: Lee Seung Jae , Kim Jinmu , Choi Eun Kyung , Jo Euna , Cho Minjoo , Kim Jeong-Hoon , Park Hyun 
Published: September 2023

AbstractThe blackfin Icefish (Chaenocephalus aceratus) belongs to the family Channichthyidae and the suborder Notothenioidei which lives in the Antarctic. We corrected the mis-scaffolds in the previous linkage map results by Hi-C analysis to obtain improved results for chromosome-level genome assembly. The final assembly analysis resulted in a total of 3,135 scaffolds, a genome size of 1,065.72 Mb, and an N50 of 33.46 Mb. 820.24 Mb, representing 88.88% of the total genome, is anchored to 24 chromosomes. The final gene set of 38,024 genes, including AFGPs, was annotated using RNA evidence, proteins, and ab-initio predictions. The complete percentage of BUSCO analysis is 92.7%. In this study, we aim to contribute to the study of polar fishes by improving the genome sequences of the blackfin icefish with the AFGP genes belonging to the Notothenoidei.
Upper limb modeling and motion extraction based on multi-space-fusion
Authors: Wang Honggang , Guo Junlong , Pei Shuo , Wang Jiajia , Yao Yufeng 
Published: September 2023

AbstractModeling and motion extraction of human upper limbs are essential for interpreting the natural behavior of upper limb. Owing to the high degrees of freedom (DOF) and highly dynamic nature, existing upper limb modeling methods have limited applications. This study proposes a generic modeling and motion extraction method, named Primitive-Based triangular body segment method (P-BTBS), which follows the physiology of upper limbs, allows high accuracy of motion angles, and describes upper-limb motions with high accuracy. For utilizing the upper-limb modular motion model, the motion angles and bones can be selected as per the research topics (The generic nature of the study targets). Additionally, P-BTBS is suitable in most scenarios for estimating spatial coordinates (The generic nature of equipment and technology). Experiments in continuous motions with seven DOFs and upper-limb motion description validated the excellent performance and robustness of P-BTBS in extracting motion information and describing upper-limb motions, respectively. P-BTBS provides a new perspective and mathematical tool for human understanding and exploration of upper-limb motions, which theoretically supports upper-limb research.
Similar effects on exercise performance following different respiratory muscle training programs in healthy young men
Authors: Notter Dominic A. , Verges Samuel , Renggli Andrea S. , Beltrami Fernando G. , Spengler Christina M. 
Published: September 2023

AbstractBoth respiratory muscle endurance training (RMET) and inspiratory resistive training (IMT) seem to increase whole-body exercise performance, but direct comparisons between the two are scarce. We hypothesized that the similarity of RMET to exercise-induced ventilation would induce larger improvements compared to IMT. Twenty-six moderately-trained men performed either 4 weeks of RMET, IMT or SHAM training. Before and after the interventions, respiratory muscle endurance, 3-km running time-trial performance and leg muscle fatigue after intense constant-load cycling (assessed with femoral nerve magnetic stimulation) were measured. Both RMET (+ 59%) and IMT (+ 38%) increased respiratory muscle endurance (both p < 0.01 vs. SHAM) but only IMT increased inspiratory strength (+ 32%, p < 0.001 vs. SHAM). 3-km time improved showing a main effect of training (p = 0.026), however with no differences between groups. Leg fatigue after cycling was not attenuated with training (p = 0.088 for group-training interaction). All groups showed a significant (~ 0.3 l) increase in average tidal volume during cycling exercise combined with a concomitant reduction in respiratory exertion. While RMET and IMT improved specific aspects of respiratory muscles performance, no benefits beyond SHAM were seen during whole-body exercise. Changes in respiratory sensations might be a result of altered breathing pattern.
A model-driven approach for fast modeling of three-dimensional laser point cloud in large substation
Authors: Li Ruiheng , Gan Lu , Liu Yang , Di Yi , Wang Chao 
Published: September 2023

AbstractUsing point cloud to reconstruct the 3D model of a substation is crucial for smart grid operation. Its main objective is to swiftly capture equipment point cloud data and align each device’s model within the large and noisy point cloud scene of the substation. However, substation reconstruction needs improvement due to the low efficiency of traditional noise-resistant clustering methods and challenges in accurately classifying similar-looking electrical equipment. This paper proposes an automatic modeling framework for large-scale substation point cloud scenes. Firstly, we reduce the substation scene’s dimensionality to improve clustering efficiency and establish relationships between data dimensions using a re-clustering algorithm. Next, a neural network is developed to identify various device types within clusters, even with limited subdivisions. Finally, a model library is employed to register standard models onto the target device’s point cloud, obtaining device types and orientations. Real substation data processing demonstrates the ability to rapidly extract devices from complex and noisy point cloud scenes, effectively avoiding missegmentation issues. The automatic modeling approach achieves a precise substation calculation rate of 92.86%.
Role of microRNAs and their downstream target transcription factors in zebrafish thrombopoiesis
Authors: Al Qaryoute Ayah , Fallatah Weam , Dhinoja Sanchi , Raman Revathi , Jagadeeswaran Pudur 
Published: September 2023

AbstractPrevious studies have shown that human platelets and megakaryocytes carry microRNAs suggesting their role in platelet function and megakaryocyte development, respectively. However, a comprehensive study on the microRNAs and their targets has not been undertaken. Zebrafish thrombocytes could be used as a model to study their role in megakaryocyte maturation and platelet function because thrombocytes have both megakaryocyte features and platelet properties. In our laboratory, we identified 15 microRNAs in thrombocytes using single-cell RNA sequencing. We knocked down each of these 15 microRNAs by the piggyback method and found knockdown of three microRNAs, mir-7148, let-7b, and mir-223 in adult zebrafish led to an increase in the percentage of thrombocytes. Functional thrombocyte analysis using plate tilt assay showed no modulatory effect of the three microRNAs on thrombocyte aggregation/agglutination. We also found enhanced thrombosis using arterial laser thrombosis assay in a group of zebrafish larvae after mir-7148, let-7b, and mir-223 knockdowns. These results suggested mir-7148, let-7b, and mir-223 are repressors for thrombocyte production. We then explored miRWalk database for let-7b downstream targets and then selected those that are expressed in thrombocytes, and from this list based on their role in differentiation selected 14 genes, rorca, tgif1, rfx1a, deaf1, zbtb18, mafba, cebpa, spi1a, spi1b, fhl3b, ikzf1, irf5, irf8, and lbx1b that encode transcriptional regulators. The qRT-PCR analysis of expression levels of the above genes following let-7b knockdown showed changes in the expression of 13 targets. We then studied the effect of the 13 targets on thrombocyte production and identified 5 genes, irf5, tgif1, irf8, cebpa, and rorca that showed thrombocytosis and one gene, ikzf1 that showed thrombocytopenia. Furthermore, we tested whether mir-223 regulates any of the above 13 transcription factors after mir-223 knockdown using qRT-PCR. Six of the 13 genes showed similar gene expression as observed with let-7b knockdown and 7 genes showed opposing results. Thus, our results suggested a possible regulatory network in common with both let-7b and mir-223. We also identified that tgif1, cebpa, ikzf1, irf5, irf8, and ikzf1 play a role in thrombopoiesis. Since the ikzf1 gene showed a differential expression profile in let-7b and mir-223 knockdowns but resulted in thrombocytopenia in ikzf1 knockdown in both adults and larvae we also studied an ikzf1 mutant and showed the mutant had thrombocytopenia. Taken together, these studies showed that thrombopoiesis is controlled by a network of transcription regulators that are regulated by multiple microRNAs in both positive and negative manner resulting in overall inhibition of thrombopoiesis.
Full stage networks with auxiliary focal loss and multi-attention module for submarine garbage object detection
Authors: Zheng Hui , Guo Xinwei , Guo Guihai , Cao Yizhi , Hu Xinglei , Yue Pujie 
Published: September 2023

AbstractSubmarine garbage is constantly destroying the marine ecological environment and polluting the ocean. It is critical to use detection methods to quickly locate and identify submarine garbage. The background of submarine garbage images is much more complex than that of natural scene images, with object deformation and missing contours putting higher demands on the detection network. To solve the problem of low accuracy under complex backgrounds, full stage networks with auxiliary focal loss and multi-attention module are proposed for submarine garbage object detection based on YOLO. To maximize the gradient combination, a hierarchical fusion feature mechanism and a segmentation and merging strategy are used in this paper to optimize the difference in gradient combination to obtain full-stage features. Then the criss-cross attention module is used to precisely extract multi-scale features of small object dense regions while removing noise information from complex backgrounds. Finally, the auxiliary focal loss function addresses the issue of unbalanced positive and negative samples, focusing on the learning of difficult samples while improving overall detection precision. Based on comparative experiments and ablation experiments, the FSA networks achieved state-of-the-art performance, and is applicable to the real-time object detection of submarine garbage in complex backgrounds.