bluuebunny/crossref_metadata_2025_split huggingface dataset:
Arctic coastal hazard assessment considering permafrost thaw subsidence, coastal erosion, and flooding
Authors: Wang Ziyi , Xiao Ming , Nicolsky Dmitry , Romanovsky Vladimir , McComb Christopher , Farquharson Louise 
Published: August 2023

Abstract
The thawing of permafrost in the Arctic has led to an increase in coastal land loss, flooding, and ground subsidence, seriously threatening civil infrastructure and coastal communities. However, a lack of tools for synthetic hazard assessment of the Arctic coast has hindered effective response measures. We developed a holistic framework, the Arctic Coastal Hazard Index (ACHI), to assess the vulnerability of Arctic coasts to permafrost thawing, coastal erosion, and coastal flooding. We quantified the coastal permafrost thaw potential (PTP) through regional assessment of thaw subsidence using ground settlement index. The calculations of the ground settlement index involve utilizing projections of permafrost conditions, including future regional mean annual ground temperature, active layer thickness, and talik thickness. The predicted thaw subsidence was validated through a comparison with observed long-term subsidence data. The ACHI incorporates the PTP into seven physical and ecological variables for coastal hazard assessment: shoreline type, habitat, relief, wind exposure, wave exposure, surge potential, and sea-level rise. The coastal hazard assessment was conducted for each 1 km2 coastline of North Slope Borough, Alaska in the 2060s under the Representative Concentration Pathway 4.5 and 8.5 forcing scenarios. The areas that are prone to coastal hazards were identified by mapping the distribution pattern of the ACHI. The calculated coastal hazards potential was subjected to validation by comparing it with the observed and historical long-term coastal erosion mean rates. This framework for Arctic coastal assessment may assist policy and decision-making for adaptation, mitigation strategies, and civil infrastructure planning.
Duality of averaging of quantum states over arbitrary symmetry groups revealing Schur–Weyl duality
Authors: Markiewicz Marcin , Przewocki Janusz 
Published: August 2023

Abstract
It is a well-established fact in quantum information theory, that uniform averaging over the collective action of a unitary group on a multipartite quantum state projects the state to a form equivalent to a permutation operator of the subsystems. Hence states equivalent to permutation operators are untouched by collective unitary noise. A trivial observation shows that uniform averaging over permutation operators projects the state into a form with block-diagonal structure equivalent to the one of the collective action of the unitary group. We introduce a name for this property: duality of averaging. The mathematical reason behind this duality is the fact that the collective action of the unitary group on the tensor product state space of a multipartite quantum system and the action of the permutation operations are mutual commutants when treated as matrix algebras. Such pairs of matrix algebras are known as dual reductive pairs. In this work we show, that in the case of finite dimensional quantum systems such duality of averaging holds for any pairs of symmetry groups being dual reductive pairs, regardless of whether they are compact or not, as long as the averaging operation is defined via iterated integral over the Cartan decomposition of the group action. Although our result is very general, we focus much attention on the concrete example of a dual reductive pair consisting of collective action of special linear matrices and permutation operations, which physically corresponds to averaging multipartite quantum states over non-unitary SLOCC-type (Stochastic Local Operations and Classical Communication) operations. In this context we show, that noiseless subsystems known from collective unitary averaging persist in the case of SLOCC averaging in a conditional way: upon postselection to specific invariant subspaces.
Chimera dynamics of generalized Kuramoto–Sakaguchi oscillators in two-population networks
Authors: Lee Seungjae , Krischer Katharina 
Published: August 2023

Abstract
Chimera dynamics, an intriguing phenomenon of coupled oscillators, is characterized by the coexistence of coherence and incoherence, arising from a symmetry-breaking mechanism. Extensive research has been performed in various systems, focusing on a system of Kuramoto–Sakaguchi (KS) phase oscillators. In recent developments, the system has been extended to the so-called generalized Kuramoto model, wherein an oscillator is situated on the surface of an M-dimensional unit sphere, rather than being confined to a unit circle. In this paper, we exploit the model introduced in Tanaka (2014 New. J. Phys.
16 023016) where the macroscopic dynamics of the system was studied using the extended Watanabe–Strogatz transformation both for real and complex spaces. Considering two-population networks of the generalized KS oscillators in 2D complex spaces, we demonstrate the existence of chimera states and elucidate different motions of the order parameter vectors depending on the strength of intra-population coupling. Similar to the KS model on the unit circle, stationary and breathing chimeras are observed for comparatively strong intra-population coupling. Here, the breathing chimera changes their motion upon decreasing intra-population coupling strength via a global bifurcation involving the completely incoherent state. Beyond that, the system exhibits periodic alternation of the two order parameters with weaker coupling strength. Moreover, we observe that the chimera state transitions into a componentwise aperiodic dynamics when the coupling strength weakens even further. The aperiodic chimera dynamics emerges due to the breaking of conserved quantities that are preserved in the stationary, breathing and alternating chimera states. We provide a detailed explanation of this scenario in both the thermodynamic limit and for finite-sized ensembles. Furthermore, we note that an ensemble in 4D real spaces demonstrates similar behavior.
Current-climate sea ice amount and seasonality as constraints for future Arctic amplification
Authors: Linke Olivia , Feldl Nicole , Quaas Johannes 
Published: August 2023

Abstract
The recent Arctic sea ice loss is a key driver of the amplified surface warming in the northern high latitudes, and simultaneously a major source of uncertainty in model projections of Arctic climate change. Previous work has shown that the spread in model predictions of future Arctic amplification (AA) can be traced back to the inter-model spread in simulated long-term sea ice loss. We demonstrate that the strength of future AA is further linked to the current climate’s, observable sea ice state across the multi-model ensemble of the 6th Coupled Model Intercomparison Project (CMIP6). The implication is that the sea-ice climatology sets the stage for long-term changes through the 21st century, which mediate the degree by which Arctic warming is amplified with respect to global warming. We determine that a lower base-climate sea ice extent and sea ice concentration (SIC) in CMIP6 models enable stronger ice melt in both future climate and during the seasonal cycle. In particular, models with lower Arctic-mean SIC project stronger future ice loss and a more intense seasonal cycle in ice melt and growth. Both processes systemically link to a larger future AA across climate models. These results are manifested by the role of climate feedbacks that have been widely identified as major drivers of AA. We show in particular that models with low base-climate SIC predict a systematically stronger warming contribution through both sea-ice albedo feedback and temperature feedbacks in the future, as compared to models with high SIC. From our derived linear regressions in conjunction with observations, we estimate a 21st-century AA over sea ice of 2.47–3.34 with respect to global warming. Lastly, from the tight relationship between base-climate SIC and the projected timing of an ice-free September, we predict a seasonally ice-free Arctic by mid-century under a high-emission scenario.
Evolutionary history of Castanea sativa in the Caucasus driven by Middle and Late Pleistocene paleoenvironmental changes
Authors: Beridze Berika , Sękiewicz Katarzyna , Walas Łukasz , Thomas Peter A , Danelia Irina , Kvartskhava Giorgi , Farzaliyev Vahid , Bruch Angela A , Dering Monika 
Published: August 2023

Abstract
Due to global climate cooling and aridification since the Paleogene, members of the Neogene flora were extirpated from the Northern Hemisphere or were confined to a few refugial areas. For some species, the final reduction/extinction came in the Pleistocene, but some others have survived climatic transformations up to the present. This has occurred in Castanea sativa, a species of high commercial value in Europe and a significant component of the Caucasian forests’ biodiversity. In contrast to the European range, neither the historical biogeography nor the population genetic structure of the species in its isolated Caucasian range has been clarified. Here, based on a survey of 21 natural populations from the Caucasus and a single one from Europe, we provide a likely biogeographic reconstruction and genetic diversity details. By applying Bayesian inference, species distribution modelling and fossil pollen data, we estimated (i) the time of the Caucasian—European divergence during the Middle Pleistocene, (ii) the time of divergence among Caucasian lineages and (iii) outlined the glacial refugia for species. The climate changes related to the Early–Middle Pleistocene Transition are proposed as the major drivers of the intraspecific divergence and European–Caucasian disjunction for the species, while the impact of the last glacial cycle was of marginal importance.
Prediction of drug–protein interaction based on dual channel neural networks with attention mechanism
Authors: Tan Dayu , Jiang Haijun , Li Haitao , Xie Ying , Su Yansen 
Published: August 2023

Abstract
The precise identification of drug–protein inter action (DPI) can significantly speed up the drug discovery process. Bioassay methods are time-consuming and expensive to screen for each pair of drug proteins. Machine-learning-based methods cannot accurately predict a large number of DPIs. Compared with traditional computing methods, deep learning methods need less domain knowledge and have strong data learning ability. In this study, we construct a DPI prediction model based on dual channel neural networks with an efficient path attention mechanism, called DCA-DPI. The drug molecular graph and protein sequence are used as the data input of the model, and the residual graph neural network and the residual convolution network are used to learn the feature representation of the drug and protein, respectively, to obtain the feature vector of the drug and the hidden vector of protein. To get a more accurate protein feature vector, the weighted sum of the hidden vector of protein is applied using the neural attention mechanism. In the end, drug and protein vectors are concatenated and input into the full connection layer for classification. In order to evaluate the performance of DCA-DPI, three widely used public data, Human, C.elegans and DUD-E, are used in the experiment. The evaluation metrics values in the experiment are superior to other relevant methods. Experiments show that our model is efficient for DPI prediction.
A comparison of behavioral paradigms assessing spatial memory in tree shrews
Authors: Li Cheng-Ji , Hui Yi-Qing , Zhang Rong , Zhou Hai-Yang , Cai Xing , Lu Li 
Published: August 2023

Abstract
Impairments in spatial navigation in humans can be preclinical signs of Alzheimer's disease. Therefore, cognitive tests that monitor deficits in spatial memory play a crucial role in evaluating animal models with early stage Alzheimer's disease. While Chinese tree shrews (Tupaia belangeri) possess many features suitable for Alzheimer's disease modeling, behavioral tests for assessing spatial cognition in this species are lacking. Here, we established reward-based paradigms using the radial-arm maze and cheeseboard maze for tree shrews, and tested spatial memory in a group of 12 adult males in both tasks, along with a control water maze test, before and after bilateral lesions to the hippocampus, the brain region essential for spatial navigation. Tree shrews memorized target positions during training, and task performance improved gradually until reaching a plateau in all 3 mazes. However, spatial learning was compromised post-lesion in the 2 newly developed tasks, whereas memory retrieval was impaired in the water maze task. These results indicate that the cheeseboard task effectively detects impairments in spatial memory and holds potential for monitoring progressive cognitive decline in aged or genetically modified tree shrews that develop Alzheimer's disease-like symptoms. This study may facilitate the utilization of tree shrew models in Alzheimer's disease research.
Effects of long-term feeding by spotted lanternfly (Hemiptera: Fulgoridae) on ecophysiology of common hardwood host trees
Authors: Hoover Kelli , Iavorivska Lidiia , Lavely Emily K , Uyi Osariyekemwen , Walsh Brian , Swackhamer Emelie , Johnson Anne , Eissenstat David M 
Published: August 2023

Abstract
While the invasive spotted lanternfly, Lycorma delicatula (White) [Hemiptera: Fulgoridae], continues to expand its range in the United States, there remains a knowledge gap regarding the economic threat that this pest presents to forest ecosystems and production nurseries. L. delicatula uses several common hardwood trees as hosts and a previous study found that short-term feeding can reduce growth of young maple saplings. Herein, long-term feeding over 4 consecutive seasons significantly reduced diameter growth and below-ground starch storage in roots of young silver maples (Acer saccharinum L.), weeping willows (Salix babylonica L.), river birches (Betula nigra L.), and trees of heaven (Ailanthus altissima [Mill.] Swingle) in response to L. delicatula feeding pressure in a density-dependent manner. In Year 3 when feeding pressure was the lowest, silver maple and willow recovered with greater diameter growth than in Year 2. Nutrients essential for photosynthesis and growth (iron, sulfur, and phosphorus) were reduced in leaves of all tree species compared to controls in the second year. This 4-yr study represents a worst-case scenario in which L. delicatula fed on the same trees for 4 consecutive growing seasons. In the wild, population numbers can vary greatly from year to year on individual trees and they move frequently among hosts (until autumn when they settle on A. altissima or other late-season hosts that have not yet senesced). Thus, we would not expect negative impacts of unconfined L. delicatula in natural settings on forest or ornamental trees to be as marked as reported here.
Periodontal outcomes associated with impacted maxillary central incisor and canine teeth following surgical exposure and orthodontic alignment: a systematic review and meta-analysis
Authors: Seehra Jadbinder , Alshammari Aminah , Wazwaz Fidaa , Papageorgiou Spyridon N , Newton Jonathon T , Cobourne Martyn T 
Published: August 2023

Abstract

Background
Maxillary incisor and canine teeth are commonly impacted and require multidisciplinary treatment to accommodate them in the dental arch.

Objectives
To assess the periodontal outcomes of impacted maxillary central incisor and canine teeth, which have been successfully aligned in the arch following surgical exposure and orthodontic traction with fixed appliance therapy.

Search methods
Systematic literature searches without restrictions were undertaken in eight databases.

Selection criteria
Studies reporting surgical interventions in combination with orthodontic traction with fixed appliance therapy to align impacted maxillary incisors or canines published up to January 2023.

Data collection
Duplicate independent study selection, data extraction, and risk of bias assessment.

Analysis
Random-effects meta-analyses of aggregate data.

Results
Twenty-three studies (21 retrospective and 2 prospective) were included in the final analysis. Three studies reported outcomes for maxillary central incisors and 20 reported outcomes for maxillary canines. For maxillary central incisors, all three studies were rated as being at moderate risk of bias. For maxillary canines, 17 studies and 1 study were rated at moderate and high risk of bias, respectively. Both prospective studies were rated at a low risk of bias. Meta-analyses comparing aligned impacted maxillary canines to their non-impacted contralateral counterparts found the former had increased Plaque Index scores (mean difference [MD] 0.19; 95% confidence interval [CI] 0.03, 0.35﻿; P = 0.03), increased clinical attachment loss (MD 0.40 mm; 95% CI 0.17, 0.63﻿; P = 0.01), increased pocket probing depth (MD 0.18 mm; 95% CI 0.07, 0.28﻿; P = 0.001), increased bone loss (MD 0.51 mm; 95% CI 0.31, 0.72﻿; P &lt; 0.001), and reduced keratinized gingival width (MD −0.31 mm; 95% CI −0.61, −0.01﻿; P = 0.04).

Conclusions
Limited evidence suggests that surgical exposure and orthodontic alignment of impacted maxillary central incisor or canine teeth, results in modest adverse effects in the periodontium. These findings should be viewed with caution as our certainty for these outcomes is very low to low due to the bias and heterogeneity. Further well-conducted studies reporting patient centred outcomes are required.

Registration
PROSPERO (CRD42020225639)
Past or Present; Which Exposures Predict Metabolomic Aging Better? The Doetinchem Cohort Study
Authors: Smit Annelot P , Herber Gerrie-Cor M , Kuiper Lieke M , Loef Bette , Picavet H Susan J , Verschuren W M Monique 
Published: August 2023

Abstract
People age differently. Differences in aging might be reflected by metabolites, also known as metabolomic aging. Predicting metabolomic aging is of interest in public health research. However, the added value of longitudinal over cross-sectional predictors of metabolomic aging is unknown. We studied exposome-related exposures as potential predictors of metabolomic aging, both cross-sectionally and longitudinally in men and women. We used data from 4 459 participants, aged 36–75 of Round 4 (2003–2008) of the long-running Doetinchem Cohort Study (DCS). Metabolomic age was calculated with the MetaboHealth algorithm. Cross-sectional exposures were demographic, biological, lifestyle, and environmental at Round 4. Longitudinal exposures were based on the average exposure over 15 years (Round 1 [1987–1991] to 4), and trend in these exposure over time. Random Forest was performed to identify model performance and important predictors. Prediction performances were similar for cross-sectional and longitudinal exposures in both men (R2 6.8 and 5.8, respectively) and women (R2 14.8 and 14.4, respectively). Biological and diet exposures were most predictive for metabolomic aging in both men and women. Other important predictors were smoking behavior for men and contraceptive use and menopausal status for women. Taking into account history of exposure levels (longitudinal) had no added value over cross-sectionally measured exposures in predicting metabolomic aging in the current study. However, the prediction performances of both models were rather low. The most important predictors for metabolomic aging were from the biological and lifestyle domain and differed slightly between men and women.
Study on the influence of digital financial inclusion on agricultural carbon emission efficiency in the Yangtze River Economic Belt
Authors: Jin Shengtian , Chen Zhuo , Bao Bingfei , Zhang Xinming 
Published: August 2023

Abstract
Improving the efficiency of agricultural carbon emission is an inevitable choice for high-quality sustainable development. As a new financial intermediary, what impact does the growth of digital financial inclusion have on the efficiency of agricultural carbon emissions? This paper uses the SBM-ML model to calculate the agricultural carbon emission efficiency of 110 prefecture-level cities in the Yangtze River Economic Belt from 2012 to 2020. Then, it analyzes its temporal and spatial evolution trend. Based on it, the panel regression model is used to analyze how the growth of digital financial inclusion affects agricultural carbon emission efficiency. Results show the following: (1) from the time dimension, the efficiency generally declined first and then rose with less fluctuations and stability, which is determined by technical efficiency and technological progress change, and from the spatial dimension, the number of cities with agricultural carbon emission efficiency greater than 1.0 is generally increasing, reflecting that the overall level is increasing; and (2) the growth of digital financial inclusion has a negative impact on agricultural carbon emission efficiency. The improvement of agricultural carbon emission efficiency has been hampered by the use depth and digitalization level of digital financial inclusion, but its coverage breadth can support this improvement. This paper aims to advance digital financial inclusive growth and low-carbon transformation of agriculture in China.
Solving forward and inverse problems involving a nonlinear three-dimensional partial differential equation via asymptotic expansions
Authors: Chaikovskii Dmitrii , Zhang Ye 
Published: August 2023

Abstract
This paper concerns the use of asymptotic expansions for the efficient solving of forward and inverse problems involving a nonlinear singularly perturbed time-dependent reaction–diffusion–advection equation. By using an asymptotic expansion with the local coordinates in the transition-layer region, we prove the existence and uniqueness of a smooth solution with a sharp transition layer for a 3D partial differential equation. Moreover, with the help of asymptotic expansion, a simplified model is derived for the corresponding inverse source problem, which is close to the original inverse problem over the entire region except for a narrow transition layer. We show that such simplification does not reduce the accuracy of the inversion results when the measurement data contain noise. Based on this simpler inversion model, an asymptotic-expansion regularization algorithm is proposed for efficiently solving the inverse source problem in the 3D case. A model problem shows the feasibility of the proposed numerical approach.