bluuebunny/crossref_metadata_2025_split huggingface dataset:
Principles of nociceptive coding in the anterior cingulate cortex
Authors: Acuña Mario A. , Kasanetz Fernando , De Luna Paolo , Falkowska Marta , Nevian Thomas 
Published: May 2023

The perception of pain is a multidimensional sensory and emotional/affective experience arising from distributed brain activity. However, the involved brain regions are not specific for pain. Thus, how the cortex distinguishes nociception from other aversive and salient sensory stimuli remains elusive. Additionally, the resulting consequences of chronic neuropathic pain on sensory processing have not been characterized. Using in vivo miniscope calcium imaging with cellular resolution in freely moving mice, we elucidated the principles of nociceptive and sensory coding in the anterior cingulate cortex, a region essential for pain processing. We found that population activity, not single-cell responses, allowed discriminating noxious from other sensory stimuli, ruling out the existence of nociception-specific neurons. Additionally, single-cell stimulus selectivity was highly dynamic over time, but stimulus representation at the population level remained stable. Peripheral nerve injury-induced chronic neuropathic pain led to dysfunctional encoding of sensory events by exacerbation of responses to innocuous stimuli and impairment of pattern separation and stimulus classification, which were restored by analgesic treatment. These findings provide a novel interpretation for altered cortical sensory processing in chronic neuropathic pain and give insights into the effects of systemic analgesic treatment in the cortex.
PRC2 direct transfer from G-quadruplex RNA to dsDNA has implications for RNA-binding chromatin modifiers
Authors: Hemphill Wayne O. , Fenske Regan , Gooding Anne R. , Cech Thomas R. 
Published: May 2023

The chromatin-modifying enzyme, Polycomb Repressive Complex 2 (PRC2), deposits the H3K27me3 epigenetic mark to negatively regulate expression at numerous target genes, and this activity has been implicated in embryonic development, cell differentiation, and various cancers. A biological role for RNA binding in regulating PRC2 histone methyltransferase activity is generally accepted, but the nature and mechanism of this relationship remains an area of active investigation. Notably, many in vitro studies demonstrate that RNA inhibits PRC2 activity on nucleosomes through mutually antagonistic binding, while some in vivo studies indicate that PRC2’s RNA-binding activity is critical for facilitating its biological function(s). Here we use biochemical, biophysical, and computational approaches to interrogate PRC2’s RNA and DNA-binding kinetics. Our findings demonstrate that PRC2-polynucleotide dissociation rates are dependent on the concentration of free ligand, indicating the potential for direct transfer between nucleic acid ligands without a free-enzyme intermediate. Direct transfer explains the variation in previously reported dissociation kinetics, allows reconciliation of prior in vitro and in vivo studies, and expands the potential mechanisms of RNA-mediated PRC2 regulation. Moreover, simulations indicate that such a direct transfer mechanism could be obligatory for RNA to recruit proteins to chromatin.
Rise ofRuppiain Chesapeake Bay: Climate change–driven turnover of foundation species creates new threats and management opportunities
Authors: Hensel Marc J. S. , Patrick Christopher J. , Orth Robert J. , Wilcox David J. , Dennison William C. , Gurbisz Cassie , Hannam Michael P. , Landry J. Brooke , Moore Kenneth A. , Murphy Rebecca R. , Testa Jeremy M. , Weller Donald E. , Lefcheck Jonathan S. 
Published: May 2023

Global change has converted many structurally complex and ecologically and economically valuable coastlines to bare substrate. In the structural habitats that remain, climate-tolerant and opportunistic species are increasing in response to environmental extremes and variability. The shifting of dominant foundation species identity with climate change poses a unique conservation challenge because species vary in their responses to environmental stressors and to management. Here, we combine 35 y of watershed modeling and biogeochemical water quality data with species comprehensive aerial surveys to describe causes and consequences of turnover in seagrass foundation species across 26,000 ha of habitat in the Chesapeake Bay. Repeated marine heatwaves have caused 54% retraction of the formerly dominant eelgrass (Zostera marina) since 1991, allowing 171% expansion of the temperature-tolerant widgeongrass (Ruppia maritima) that has likewise benefited from large-scale nutrient reductions. However, this phase shift in dominant seagrass identity now presents two significant shifts for management: Widgeongrass meadows are not only responsible for rapid, extensive recoveries but also for the largest crashes over the last four decades; and, while adapted to high temperatures, are much more susceptible than eelgrass to nutrient pulses driven by springtime runoff. Thus, by selecting for rapid post-disturbance recolonization but low resistance to punctuated freshwater flow disturbance, climate change could threaten the Chesapeake Bay seagrass’ ability to provide consistent fishery habitat and sustain functioning over time. We demonstrate that understanding the dynamics of the next generation of foundation species is a critical management priority, because shifts from relatively stable habitat to high interannual variability can have far-reaching consequences across marine and terrestrial ecosystems.
Evidential meanings in native and learner Japanese and English
Authors: Filipović Luna , Brown Mika , Engelhardt Paul E. 
Published: May 2023

Abstract
Evidentiality is a linguistic category that comprises forms and meanings related to the source of information in utterances, the use of which may impact judgments about the degree of certainty expressed by a speaker. The main dichotomy is first-hand (direct) vs. second-hand (indirect) evidence. This distinction is grammaticalised in Japanese only, though certain related meanings can be expressed in English lexically or constructionally. The relevant forms in both languages also function as indirectness-for-politeness markers. We used a judgments elicitation task and found that statements with Japanese evidentials (both first- and second-hand) and with English markers of uncertainty lead to judgments of lower certainty than the statements without the evidential forms and meanings for the majority, but not for all speakers. In addition, monolingual and bilingual usage in both languages has parallels such that these two typologically distinct languages appear closer and certainty judgments by their speakers similar.
Translation and mountaineering, a first case study
Authors: Saroldi Anna 
Published: May 2023

AbstractThis article argues for a dialogue between translation and mountaineering studies and provides examples of how the two could successfully interact. It contributes to literary translator studies and translation history by bringing to the fore the careers of translators and mountaineers Janet Adam Smith and Nea Morin and by establishing links between their collaborative translation andcordée féminine, arguing that they played an active role as agents of both the literary and the mountaineering fields. Moreover, it provides instances of how the awareness of translation processes can expand and deepen the analysis of gendered dynamics in mountaineering non-fiction. Finally, it shows how mountaineering studies can benefit from translation studies in order to become more aware of its dissemination processes and international dynamics, and how translation studies can benefit from mountaineering studies to more fully comprehend and capture the depth of its involvement, dependence, and interaction with the non-literary world.
Boron-induced magneto-optical Kerr spectra and dielectric tensors in ferrimagnetic (Mn4N)B antiperovskite thin films
Authors: Sakaguchi Hotaka , Isogami Shinji , Niimi Makoto , Ishibashi Takayuki 
Published: May 2023

Abstract
Boron-induced electronic states were investigated via a combination of polar magneto-optical Kerr effect (p-MOKE) spectroscopy and spectroscopic ellipsometry for one of the antiperovskite nitrides, Mn4N. The boron content in the Mn4N film varied from 0 to 4.3 at.%, for which the crystal structure was maintained. The amplitude of p-MOKE spectra and the diagonal and off-diagonal dielectric tensors decreased with increasing boron content, which is in agreement with the magnetic properties such as magnetic anisotropy and saturation magnetization. These results were related to the lattice expansion and displacement of the charge density in the Mn4N by boron doping. However, the peak energy of the Lorentz oscillator in the diagonal elements of dielectric tensors suggests that a dominant inter-band transition was independent of boron content.
RCS reduction using embedded meta-structure absorber in X-band
Authors: Saikia Sanghamitra , Bhattacharyya Nidhi Saxena 
Published: May 2023

Abstract
One approach for lowering radar cross section (RCS) is by reducing reflection, which can be achieved by use of absorbers. Embedded meta-structure absorbers (MSAs) are void of metallic resonating structures, which minimize out-of-band reflections thus reducing the chance of detection by radar systems. Here, an X-band MSA is developed by embedding ionic deep eutectic solvent gel as subwavelength cuboidal resonant structures in flexible silicone rubber. The material properties and structural geometry of the gel combines to enhance the

−
10

dB absorption bandwidth to more than

80
%

. The fabricated Gel-Si MSA is sufficiently flexible with an overall thickness of

∼
0.0775

λ
0

.

10

dB RCS reduction is evaluated in the range over the X-band. The RCS properties are insensitive to polarization angles up to

±

90
∘

and incident angle up to

±

40
∘

.
Enhanced surface insulation for SiO2/epoxy resin composites through co-modification of nanofiller with silane coupling agent and plasma
Authors: Cheng Xian , He Guangyuan , Sun Zhengwei , Wang Yizhi , Geng Shuo , Lian Haoyu 
Published: May 2023

Abstract
To enhance the surface insulation properties of SiO2/epoxy resin composites, the SiO2 filler is co-modified with a chemical method and dielectric barrier discharge plasma in this work. The effects on the micro-structures, electrical parameters and surface insulation properties of the materials are studied. The results show that chemical modification using the silane coupling agent (KH550) can effectively introduce organo-functional groups into SiO2 filler. On the other hand, plasma modification shows little effect on the organo-functional group but significantly increases the dispersity of the nanoparticles, therefore reducing filler conglobation in epoxy resin composite. The composite samples with SiO2 doping concentration of 1 wt.%, 2 wt.%, 3 wt.%, 5 wt.% and 7 wt.% are prepared and characterized. It is found that the synergy of chemical and plasma methods could significantly improve the surface insulation of composite samples. Through doping 2 wt.% of the co-modified SiO2 filler, the direct current flashover voltage of the composites in dry air at atmospheric pressure can be increased to 1.53 times of the pure epoxy. The enhanced surface insulation properties are explained by the trap effect and the change of electrical parameters through the co-modification process.
Impact fault detection for marine current turbines blade via MEGK-means and PCA under variable marine conditions
Authors: Xie Tao , Zhang Weidong , Li Feng , Ahmed Zahoor 
Published: May 2023

Abstract
The advancement of the marine current turbine (MCT) technology has the potential to aid China in achieving its carbon peak and carbon neutrality goals. The performance of MCTs is influenced by fluctuating water velocities and erratic turbulence. The blades of MCTs are susceptible to cracking due to prolonged exposure to seawater. Unpredictable changes in marine currents contribute to unstable working environments. This study presents multiple envelope geometrical K-means to categorize stator current readings and develop fault detection models. It first constructs an envelope geometric feature matrix by using the extracted modulus signal. Then, the matrix is used to select the initial center for clustering, and a multi-scale principal component analysis is performed under each working condition to decrease data dimensionality. Finally, T
2 and squared prediction error (SPE) serve as the projection of the sample vector on the principal element space and the remaining subspace, respectively, and can be utilized to monitor fault cases. The experimental findings demonstrate that the proposed method has excellent recognition capabilities and detection accuracy for the impact faults of MCTs under variable working conditions.
A performance-enhanced DVL/SINS integrated navigation system based on data-driven approach
Authors: Jin Kaidi , Chai Hongzhou , Su Chuhan , Xiang Minzhi 
Published: May 2023

Abstract
With the aid of Doppler velocity logger (DVL), strapdown inertial navigation systems (SINSs) can provide continuous and accurate navigation parameters for unmanned underwater vehicles (UUVs). However, owing to the complex underwater environment, partial DVL beams may fail to be reflected by the seafloor, resulting in DVL measurement outage. In this study, a novel data-driven approach enhancing DVL/SINS integrated navigation system is proposed to improve the robustness and accuracy of UUV navigation with limited DVL beams. First, to make full use of the available beam, the velocities of DVL beams are used to assist the SINS navigation instead of the 3D velocity of DVL. Subsequently, a virtual beam predictor based on multi-output least-squares support vector regression (MLS-SVR) is built to run in parallel with the DVL/SINS integrated system. Specifically, when the DVL operates normally, all four beams can be applied for integrated navigation and stored as the training dataset. Once partial beams are missing, the available beams and the outputs of SINS and pressure sensors can train the MLS-SVR model along with the corresponding missing beams in the training set. Subsequently, the missing beams can be predicted by the trained model for integrated navigation together with the available beams. UUV sea trials indicate that the proposed system can accurately predict unavailable beams and improve the positioning accuracy of the DVL/SINS integrated navigation system.
Direct visualization of nanoparticle morphology in thermally sintered nanoparticle ink traces and the relationship among nanoparticle morphology, incomplete polymer removal, and trace conductivity
Authors: Chandel Ghansham Rajendrasingh , Sun Jiayue , Etha Sai Ankit , Zhao Beihan , Sivasankar Vishal Sankar , Nikfarjam Shakiba , Wang Mei , Hines Daniel R , Dasgupta Abhijit , Woehl Taylor , Das Siddhartha 
Published: May 2023

Abstract
A key challenge encountered by printed electronics is that the conductivity of sintered metal nanoparticle (NP) traces is always several times smaller than the bulk metal conductivity. Identifying the relative roles of the voids and the residual polymers on NP surfaces in sintered NP traces, in determining such reduced conductivity, is essential. In this paper, we employ a combination of electron microscopy imaging and detailed simulations to quantify the relative roles of such voids and residual polymers in the conductivity of sintered traces of a commercial (Novacentrix) silver nanoparticle-based ink. High resolution transmission electron microscopy imaging revealed details of the morphology of the inks before and after being sintered at 150 °C. Prior to sintering, NPs were randomly close packed into aggregates with nanometer thick polymer layers in the interstices. The 2D porosity in the aggregates prior to sintering was near 20%. After heating at 150 °C, NPs sintered together into dense aggregates (nanoaggregates or NAgs) with sizes ranging from 100 to 500 nm and the 2D porosity decreased to near 10%. Within the NAgs, the NPs were mostly connected via sintered metal bridges, while the outer surfaces of the NAgs were coated with a nanometer thick layer of polymer. Motivated by these experimental results, we developed a computational model for calculating the effective conductivity of the ink deposit represented by a prototypical NAg consisting of NPs connected by metallic bonds and having a polymer layer on its outer surface placed in a surrounding medium. The calculations reveal that a NAg that is 35%–40% covered by a nanometer thick polymeric layer has a similar conductivity compared to prior experimental measurements. The findings also demonstrate that the conductivity is less influenced by the polymer layer thickness or the absolute value of the NAg dimensions. Most importantly, we are able to infer that the reduced value of the conductivity of the sintered traces is less dependent on the void fraction and is primarily attributed to the incomplete removal of the polymeric material even after sintering.
Chaos and ergodicity in a partially integrable 3d Bohmian system: a comparison with 2d systems
Authors: Tzemos A C , Contopoulos G 
Published: May 2023

Abstract
In the present paper we study the Bohmian dynamics of a partially integrable 3d Bohmian system whose trajectories evolve on spherical surfaces. By use of spherical coordinates (R, ϕ, θ) we study the behaviour of unstable fixed points that generate chaos on the (ϕ, θ) plane and discuss the differences between them and those of planar 2d systems. Finally, we show for the first time that the chaotic trajectories of this system are ergodic although the number of its nodes is very small (two). Thus we can observe ergodicity not only in multinodal 2d systems but also in partially integrable systems with few nodes due to their curved geometry.
Primordial black hole isocurvature modes from non-Gaussianity
Authors: van Laak Raphaël , Young Sam 
Published: May 2023

Abstract
Primordial black holes (PBHs) are black holes that might have formed in high density
regions in the early universe. The presence of local-type non-Gaussianity can lead to large-scale
fluctuations in the PBH formation rate. If PBHs make up a non-negligible fraction of dark matter,
these fluctuations can appear as isocurvature modes, and be used to constrain the amplitude of
non-Gaussianity. Assuming that the parameters of non-Gaussianity are constant over all scales, we
build upon the results of previous work by extending the calculation to include peaks theory and
making use of the compaction C for the formation criteria, accounting for non-linearities
between C and the curvature perturbation ζ. For quadratic models of non-Gaussianity, our
updated calculation gives constraints that are largely unaltered compared to those previously
found, while for cubic models the constraints worsen significantly. In case all of the DM is made
up of PBHs, the parameters of non-Gaussianity are -2.9 · 10-4 < f < 3.8 · 10-4 and
-1.5 · 10-3 < g < 1.9 · 10-3 for quadratic and cubic models respectively.
Infection diagnosis in hydrocephalus CT images: a domain enriched attention learning approach
Authors: Yu Mingzhao , Peterson Mallory R , Cherukuri Venkateswararao , Hehnly Christine , Mbabazi-Kabachelor Edith , Mulondo Ronnie , Nsubuga Kaaya Brian , Broach James R , Schiff Steven J , Monga Vishal 
Published: May 2023

Abstract

Objective. Hydrocephalus is the leading indication for pediatric neurosurgical care worldwide. Identification of postinfectious hydrocephalus (PIH) verses non-postinfectious hydrocephalus, as well as the pathogen involved in PIH is crucial for developing an appropriate treatment plan. Accurate identification requires clinical diagnosis by neuroscientists and microbiological analysis, which are time-consuming and expensive. In this study, we develop a domain enriched AI method for computerized tomography (CT)-based infection diagnosis in hydrocephalic imagery. State-of-the-art (SOTA) convolutional neural network (CNN) approaches form an attractive neural engineering solution for addressing this problem as pathogen-specific features need discovery. Yet black-box deep networks often need unrealistic abundant training data and are not easily interpreted. Approach. In this paper, a novel brain attention regularizer is proposed, which encourages the CNN to put more focus inside brain regions in its feature extraction and decision making. Our approach is then extended to a hybrid 2D/3D network that mines inter-slice information. A new strategy of regularization is also designed for enabling collaboration between 2D and 3D branches. Main results. Our proposed method achieves SOTA results on a CURE Children’s Hospital of Uganda dataset with an accuracy of 95.8% in hydrocephalus classification and 84% in pathogen classification. Statistical analysis is performed to demonstrate that our proposed methods obtain significant improvements over the existing SOTA alternatives. Significance. Such attention regularized learning has particularly pronounced benefits in regimes where training data may be limited, thereby enhancing generalizability. To the best of our knowledge, our findings are unique among early efforts in interpretable AI-based models for classification of hydrocephalus and underlying pathogen using CT scans.
Processing of offshore measurement data for multi-fidelity wake model performance assessments
Authors: Freitas S , Navarro Diaz G , Koutoulakos S , Mihoubi C , Rowen M , Erbslöh S 
Published: May 2023

Abstract
Numerical wake models used for offshore wind farm developments are regularly evaluated through comparisons with field measurements of atmospheric conditions and turbine performance data. However, measurement data from offshore sites can be plagued by a variety of issues that are often neglected from data processing procedures and can strongly impact the reliability of the metrics used for model evaluation. While some of these issues have been addressed through assumptions, a variety of shortcomings are frequently overlooked. In this work, the issues found with measurement data used to compile an evaluation dataset to be applied for numerical wake model assessments are presented. The dataset contains long-term operational data from two neighboring offshore wind farms in the German Bight and meteorological data from a met mast located in between the wind farms. The methodologies used to overcome these issues with minimal impact on the reliability of the evaluation dataset for modelling are presented and discussed. Furthermore, the repercussions of overlooking such shortcomings are highlighted and future challenges posed by the planned expansions of offshore wind energy capacity are addressed.