bluuebunny/crossref_metadata_2025_split huggingface dataset:
Maximizing the consistency between regional and global reference frames utilizing inheritance of seasonal displacement parameters
Authors: Gómez Demián D. , Bevis Michael G. , Caccamise Dana J. 
Published: January 2022

AbstractAlthough modern global geometric reference frames (GRFs) such as the International Terrestrial Reference Frame (ITRF) can be used anywhere on Earth, regional reference frames (RRFs) are still used to densify geodetic control and optimize solutions for continental-scale areas and national purposes. Such RRFs can be formed by densifying the ITRF, utilizing GPS / GNSS stations common to both the ITRF and the RRF. It is possible to attach a RRF to a GRF by ensuring that some or all of the coefficients of the trajectory models in the RRF are ‘inherited’ from the trajectory models that define the GRF. This can be done on an epoch-by-epoch basis, or (our preference) via transformations that operate simultaneously in space and time. This paper documents inconsistencies in the densification of ITRF that arise when the common stations’ trajectory models ignore periodic displacements. This results in periodic coordinate biases in the RRF. We describe a generalized procedure to minimize this inconsistency when realizing any RRF aligned to the ITRF or any other ‘primary’ frame. We show the method used to realize the Argentine national frame Posiciones Geodésicas Argentinas (POSGAR) and discuss our results. Discrepancies in the periodic motion amplitudes in the vertical were reduced from 4 mm to less than 1 mm for multiple stations after applying our technique. We also propose adopting object-oriented programming terminology to describe the relationship between different reference frames, such as a regional and a global frame. This terminology assists in describing and understanding the hierarchy in geodetic reference frames.
Increased elasticity of sucrose demand during hyperdopaminergic states in rats
Authors: Minnaard A. Maryse , Luijendijk Mieneke C. M. , Baars Annemarie M. , Drost Lisa , Ramakers Geert M. J. , Adan Roger A. H. , Lesscher Heidi M. B. , Vanderschuren Louk J. M. J. 
Published: January 2022

AbstractRationaleDeficits in cost–benefit decision-making are a core feature of several psychiatric disorders, including substance addiction, eating disorders and bipolar disorder. Mesocorticolimbic dopamine signalling has been implicated in various processes related to cognition and reward, but its precise role in reward valuation and cost–benefit trade-off decisions remains incompletely understood.ObjectivesWe assessed the role of mesocorticolimbic dopamine signalling in the relationship between price and consumption of sucrose, to better understand its role in cost–benefit decisions.MethodsDopamine neurons in the ventral tegmental area (VTA) were chemogenetically activated in rats, and a behavioural economics approach was used to quantify the relationship between price and consumption of sucrose. Motivation for sucrose was also assessed under a progressive ratio (PR) schedule of reinforcement. To further gauge the role of dopamine in cost–benefit trade-offs for sucrose, the effects of treatment with D-amphetamine and the dopamine receptor antagonist alpha-flupentixol were assessed.ResultsChemogenetic activation of VTA dopamine neurons increased demand elasticity, while responding for sucrose under a PR schedule of reinforcement was augmented upon stimulation of VTA dopamine neurons. Treatment with amphetamine partially replicated the effects of chemogenetic dopamine neuron activation, whereas treatment with alpha-flupentixol reduced free consumption of sucrose and had mixed effects on demand elasticity.ConclusionsStimulation of mesocorticolimbic dopaminergic neurotransmission altered cost–benefit trade-offs in a complex manner. It reduced the essential value of palatable food, increased incentive motivation and left free consumption unaltered. Together, these findings imply that mesocorticolimbic dopamine signalling differentially influences distinct components of cost expenditure processes aimed at obtaining rewards.
Performance of free-flow field-step electrophoresis as cleanup step for the non-target analysis of environmental water samples
Authors: Rösch Tobias , Weber Gerhard , Bader Tobias , Wicht Anna-Jorina , Huhn Carolin 
Published: January 2022

AbstractFor the analysis of low concentrations of micropollutants in environmental water samples, efficient sample enrichment and cleanup are necessary to reduce matrix effects and to reach low detection limits. For analytes of low and medium polarity, solid-phase extraction is used, but robust methods for the preconcentration of highly polar or ionizable analytes are scarce. In this work, field-step electrophoresis (FSE) was developed as an environmental sample cleanup technique for ionizable micropollutants and ionic transformation products. The FSE electrolyte system preconcentrated 15 acidic model analytes (pKa from −2.2 to 9.1) present in aqueous samples in two fractions by factors of 5–10. Simultaneously, highly mobile matrix compounds were removed including inorganic ions such as sulfate and chloride. The fractions were either directly injected for downstream analysis by reversed-phase liquid chromatography (RPLC) or further processed by evaporative preconcentration with subsequent reconstitution in an organic solvent suitable for separation methods like hydrophilic interaction chromatography. The FSE/RPLC-MS method exhibited high quantitative precision with RSDs of 3–6%. The method was successfully applied to a spiked river water sample and its performance compared with common solid-phase extraction and evaporative concentration, demonstrating a high analyte coverage. FSE combined with non-target screening by RPLC-MS revealed a strong reduction in matrix load especially at low retention times. Seventeen compounds were identified in the FSE fractions sampled at the field step boundary by retention time, accurate mass, and mass fragments. Suspect screening by FSE/RPLC-MS was facilitated by FSE’s selectivity for anionic compounds.
Growth, crystal structure, Hirshfeld surface and vibrational properties of a new supramolecular hybrid material: (C4H7N2)2TeBr6
Authors: Mhiri Marwa , Kammoun Omar , Lhoste Jerome , Auguste Sandy , Boujelbene Mohamed 
Published: January 2022

AbstractA novel tellurium (IV) hybrid compound with 2-methylimidazole was prepared. The crystal was grown by slow evaporation method from aqueous solutions at room temperature giving birth to a new compound with formula (C4H7N2)2TeBr6. The structure was determined by single-crystal X-ray diffraction. It crystallizes in the monoclinic system, space group C 2/c, with the following parameters: a = 18.577(2)Å, b = 9.1497(10)Å, c = 13.5355(17)Å, α = 90°, β = 123.835(5)°, γ = 90° and Z = 8. The structure was solved with a final R = 0.044 for 2783 independent reflections. The crystal arrangement consists of [TeBr6]2− anions surrounded by [C4H7N2]2+ cations. The stability of the structure was ensured by hydrogen bonding contacts (N–H…Br) lengths that are in the range of 3.490–3.682 Å. The infrared spectra (FTIR) were recorded in the 4000–550 cm−1, confirming the existence of vibrational modes that correspond to the organic and inorganic groups. The crystal packing was stabilized mainly by Br…H (73.6%) interactions. Hirshfeld surface calculations were conducted to further investigate intermolecular interactions, associated 2D fingerprint plots and enrichment ratio, revealing the quantitatively relative contribution of these interactions in the crystal packing.
Seasonal forecast skill of upper-ocean heat content in coupled high-resolution systems
Authors: McAdam Ronan , Masina Simona , Balmaseda Magdalena , Gualdi Silvio , Senan Retish , Mayer Michael 
Published: January 2022

AbstractOcean heat content (OHC) anomalies typically persist for several months, making this variable a vital component of seasonal predictability in both the ocean and the atmosphere. However, the ability of seasonal forecasting systems to predict OHC remains largely untested. Here, we present a global assessment of OHC predictability in two state-of-the-art and fully-coupled seasonal forecasting systems. Overall, we find that dynamical systems make skilful seasonal predictions of OHC in the upper 300 m across a range of forecast start times, seasons and dynamical environments. Predictions of OHC are typically as skilful as predictions of sea surface temperature (SST), providing further proof that accurate representation of subsurface heat contributes to accurate surface predictions. We also compare dynamical systems to a simple anomaly persistence model to identify where dynamical systems provide added value over cheaper forecasts; this largely occurs in the equatorial regions and the tropics, and to a greater extent in the latter part of the forecast period. Regions where system performance is inadequate include the sub-polar regions and areas dominated by sharp fronts, which should be the focus of future improvements of climate forecasting systems.
Potential vorticity perspective of the genesis of a Tibetan Plateau vortex in June 2016
Authors: Wu Guoxiong , Tang Yiqiong , He Bian , Liu Yimin , Mao Jiangyu , Ma Tingting , Ma Ting 
Published: January 2022

AbstractAt midnight on 27–28 June 2016, a Tibetan Plateau (TP) Vortex (TPV) generated over the western TP that subsequently caused a downstream record-breaking rainstorm and extremely severe natural disaster. Based on reanalysis data and satellite imagery, this study investigates the formation of this TPV from a potential vorticity (PV) perspective. Results show that, in late June 2016, a remarkable circulation anomaly occurred over the TP and its peripheral area, with easterly flow in the middle and lower troposphere developing in the subtropical zone, replacing the normal westerly flow there. Its forefront merged with the southwesterly flow from the west and penetrated and converged over the western TP where the surface was warmer than normal, forming a low-level jet and downward slantwise isentropic surfaces in-situ. When the air parcel slid down the slantwise isentropic surface, its vertical relative vorticity developed owing to slantwise vorticity development associated with PV restructuring. At the same time, the penetrating southwesterly flow brought abundant water vapor to the western TP and induced increasing sub-cloud entropy and air ascent there. Low-layer cloud formed and the cloud liquid water content increased. The strong latent heat that was released in association with the formation of cloud produced strong diabatic heating near 400 hPa at night and strong PV generation below. The normal diurnal variation was interrupted and the vortex was generated near the surface. These results demonstrate that, against a favorable circulation background, both adiabatic and diabatic PV processes are crucial for TPV genesis.
Feedback of tropical cyclones on El Niño diversity. Part I: Phenomenon
Authors: Wang Qiuyun , Li Jianping 
Published: January 2022

AbstractUnderstanding of the El Niño phenomenon is improving and several studies have considered the dynamics of El Niño diversity, however, the important role of tropical cyclones has rarely been reported. Here we show a clear feedback of tropical cyclones (TCs) over the western North Pacific (WNP) on the El Niño diversity. Strong (weak) accumulated cyclone energy helps to shift the center of strongest sea surface temperature anomalies 3 months later to the equatorial eastern (central) Pacific, and thus supporting the development of the eastern-Pacific (central-Pacific) El Niño. Local sea surface temperature anomalies in the Niño-3.4/Niño-3/Niño-4 region and tropical western Pacific, zonal wind anomalies over the tropical central-western Pacific and Madden‒Julian Oscillation play small roles in the process that WNP TCs affect El Niño diversity. Moreover, the greater number of central-Pacific El Niño events after 1999/2000 may be associated with weaker accumulated cyclone energy in this period.
Wind kinetic energy climatology and effective resolution for the ERA5 reanalysis
Authors: Bolgiani P. , Calvo-Sancho C. , Díaz-Fernández J. , Quitián-Hernández L. , Sastre M. , Santos-Muñoz D. , Farrán J. I. , González-Alemán J. J. , Valero F. , Martín M. L. 
Published: January 2022

AbstractERA5 represents the state of the art for atmospheric reanalyses and is widely used in meteorological and climatological research. In this work, this dataset is evaluated using the wind kinetic energy spectrum. Seasonal climatologies are generated for 30° latitudinal bands in the Northern Hemisphere (periodic domain) and over the North Atlantic area (limited-area domain). The spectra are also assessed to determine the effective resolution of the reanalysis. The results present notable differences between the latitudinal domains, indicating that ERA5 is properly capturing the synoptic conditions. The seasonal variability is adequate too, being winter the most energetic, and summer the least energetic season. The limited area domain results introduce a larger energy density and range. Despite the good results for the synoptic scales, the reanalysis’ spectra are not able to properly reproduce the dissipation rates at mesoscale. This is a source of uncertainties which needs to be taken into account when using the dataset. Finally, a cyclone tropical transition is presented as a case study. The spectrum generated shows a clear difference in energy density at every wavelength, as expected for a highly-energetic status of the atmosphere.
Prion disease modelled in Drosophila
Authors: Bujdoso Raymond , Smith Andrew , Fleck Oliver , Spiropoulos John , Andréoletti Olivier , Thackray Alana M. 
Published: January 2022

AbstractPrion diseases are fatal neurodegenerative conditions of humans and various vertebrate species that are transmissible between individuals of the same or different species. A novel infectious moiety referred to as a prion is considered responsible for transmission of these conditions. Prion replication is believed to be the cause of the neurotoxicity that arises during prion disease pathogenesis. The prion hypothesis predicts that the transmissible prion agent consists of PrPSc, which is comprised of aggregated misfolded conformers of the normal host protein PrPC. It is important to understand the biology of transmissible prions and to identify genetic modifiers of prion-induced neurotoxicity. This information will underpin the development of therapeutic and control strategies for human and animal prion diseases. The most reliable method to detect prion infectivity is by in vivo transmission in a suitable experimental host, which to date have been mammalian species. Current prion bioassays are slow, cumbersome and relatively insensitive to low titres of prion infectivity, and do not lend themselves to rapid genetic analysis of prion disease. Here, we provide an overview of our novel studies that have led to the establishment of Drosophila melanogaster, a genetically well-defined invertebrate host, as a sensitive, versatile and economically viable animal model for the detection of mammalian prion infectivity and genetic modifiers of prion-induced toxicity.
Feature extraction-based intelligent algorithm framework with neural network for solving conditional nonlinear optimal perturbation
Authors: Yuan Shijin , Zhang Huazhen , Liu Yaxuan , Mu Bin 
Published: January 2022

AbstractConditional nonlinear optimal perturbation (CNOP) defines an optimization problem to study predictability and sensitivity of the oceanic and climatic events in the nonlinear system. One effective method to solve the corresponding problem is feature extraction-based intelligent algorithm (FEIA) framework. In the previous study, the mapper and the re-constructor of the framework are generally obtained by principal component analysis (PCA), but the solving performance still needs to further improve. Recently, neural network has attracted the attention of lots of researcher, and many structures of neural network can be used to construct the mapping-reconstruction structure of FEIA framework. However, the related studies applying neural network in FEIA framework are lacking. Compared with PCA, neural network might obtain a proper structure for FEIA framework with the well-directed training. Therefore, this paper suggests two ways applying neural network in FEIA framework, and the corresponding frameworks are tested to solve CNOP of double-gyre variation in Regional Ocean Modeling System (ROMS). The results show that FEIA framework with neural network can obtain the solutions with better objective function values, and the corresponding solutions have a larger probability leading to the related physical phenomenon.
Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures
Authors: Eltrass Ahmed S. , Tayel Mazhar B. , Ammar Abeer I. 
Published: January 2022

AbstractElectrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several types of heart disorders. In this study, a novel hybrid approach of deep neural network combined with linear and nonlinear features extracted from ECG and heart rate variability (HRV) is proposed for ECG multi-class classification. The proposed system enhances the ECG diagnosis performance by combining optimized deep learning features with an effective aggregation of ECG features and HRV measures using chaos theory and fragmentation analysis. The constant-Q non-stationary Gabor transform technique is employed to convert the 1-D ECG signal into 2-D image which is sent to a pre-trained convolutional neural network structure, called AlexNet. The pair-wise feature proximity algorithm is employed to select the optimal features from the AlexNet output feature vector to be concatenated with the ECG and HRV measures. The concatenated features are sent to different types of classifiers to distinguish three distinct subjects, namely congestive heart failure, arrhythmia, and normal sinus rhythm (NSR). The results reveal that the linear discriminant analysis classifier has the highest accuracy compared to the other classifiers. The proposed system is investigated with real ECG data taken from well-known databases, and the experimental results show that the proposed diagnosis system outperforms other recent state-of-the-art systems in terms of accuracy 98.75%, specificity 99.00%, sensitivity of 98.18%, and computational time 0.15 s. This demonstrates that the proposed system can be used to assist cardiologists in enhancing the accuracy of ECG diagnosis in real-time clinical setting.
Environmental identification of arbuscular mycorrhizal fungi using the LSU rDNA gene region: an expanded database and improved pipeline
Authors: Delavaux Camille S. , Ramos Robert J. , Sturmer Sidney L. , Bever James D. 
Published: January 2022

AbstractArbuscular mycorrhizal fungi (AMF; Glomeromycota) are difficult to culture; therefore, establishing a robust amplicon-based approach to taxa identification is imperative to describe AMF diversity. Further, due to low and biased sampling of AMF taxa, molecular databases do not represent the breadth of AMF diversity, making database matching approaches suboptimal. Therefore, a full description of AMF diversity requires a tool to determine sequence-based placement in the Glomeromycota clade. Nonetheless, commonly used gene regions, including the SSU and ITS, do not enable reliable phylogenetic placement. Here, we present an improved database and pipeline for the phylogenetic determination of AMF using amplicons from the large subunit (LSU) rRNA gene. We improve our database and backbone tree by including additional outgroup sequences. We also improve an existing bioinformatics pipeline by aligning forward and reverse reads separately, using a universal alignment for all tree building, and implementing a BLAST screening prior to tree building to remove non-homologous sequences. Finally, we present a script to extract AMF belonging to 11 major families as well as an amplicon sequencing variant (ASV) version of our pipeline. We test the utility of the pipeline by testing the placement of known AMF, known non-AMF, and Acaulospora sp. spore sequences. This work represents the most comprehensive database and pipeline for phylogenetic placement of AMF LSU amplicon sequences within the Glomeromycota clade.
Association between relapses, stress, and depression in people with multiple sclerosis during the COVID-19 pandemic
Authors: Sparaco Maddalena , Miele Giuseppina , Lavorgna Luigi , Abbadessa Gianmarco , Bonavita Simona 
Published: January 2022

Abstract
Background
Stress is a potential trigger for clinical and radiological activity in Multiple Sclerosis (MS). COVID-19 pandemic has been a relevant source of mental distress in people with MS (pwMS) and deeply impacted on disease management.

Objective
To investigate the association between stress, anxiety, depression, and risk of relapse during the COVID-19 pandemic.

Methods
From an electronic database used for clinical practice, we extracted data of relapsing–remitting (RR) or relapsing-progressive (RP) MS patients and calculated the annualized relapse rate (ARR) during 2019 and 2020. From 01/12/2020 to 30/12/2020, enrolled patients were invited to fill in a Google Forms survey to investigate depression, anxiety, stress, and Post-Traumatic Stress Disorder (PTSD).

Results
We selected 216 patients with RR or RP-MS to calculate ARR: compared to 2019, in 2020 there was a significant increase in ARR (p = 0.0142).
Over 216 selected pwMS, 154 completed the survey. Matching the survey responses and incidence of relapses in 2020, there was a significant association between relapses and stress (p = 0.030) and relapses and depression (p = 0.011), but not between relapses and anxiety (p = 0.130) or PTSD (p = 0.279).

Conclusions
Our results support the hypothesis that pandemic-related stress is associated to clinical exacerbations, both as a possible consequence of the COVID-19 impact on MS care.
Arboricity games: the core and the nucleolus
Authors: Xiao Han , Fang Qizhi 
Published: January 2022

AbstractThe arboricity of a graph is the minimum number of forests required to cover all its edges. In this paper, we examine arboricity from a game-theoretic perspective and investigate cost-sharing in the minimum forest cover problem. We introduce the arboricity game as a cooperative cost game defined on a graph. The players are edges, and the cost of each coalition is the arboricity of the subgraph induced by the coalition. We study properties of the core and propose an efficient algorithm for computing the nucleolus when the core is not empty. In order to compute the nucleolus in the core, we introduce the prime partition which is built on the densest subgraph lattice. The prime partition decomposes the edge set of a graph into a partially ordered set defined from minimal densest minors and their invariant precedence relation. Moreover, edges from the same partition always have the same value in a core allocation. Consequently, when the core is not empty, the prime partition significantly reduces the number of variables and constraints required in the linear programs of Maschler’s scheme and allows us to compute the nucleolus in polynomial time. Besides, the prime partition provides a graph decomposition analogous to the celebrated core decomposition and the density-friendly decomposition, which may be of independent interest.
Not kennt kein Gebot? Sondervermögen als Speicher von Notlagenkrediten
Authors: Büttner Thiess 
Published: January 2022

AbstractThe German federal government is facing criticism for using the exemption from the national debt brake due to the coronavirus crisis to expand the fiscal leeway for its future energy and climate policies. The budget dispute is taking place against the backdrop of a massive expansion of government debt in Europe. In the following, the author explain the critique of the latest supplementary budget and argues that the federal government has missed the opportunity to prove that the fiscal policy options within the existing rules are sufficient.