bluuebunny/crossref_metadata_2025_split huggingface dataset:
Starch-dependent sodium accumulation in the leaves of Vigna riukiuensis
Authors: Noda Yusaku , Hirose Atsushi , Wakazaki Mayumi , Sato Mayuko , Toyooka Kiminori , Kawachi Naoki , Furukawa Jun , Tanoi Keitaro , Naito Ken 
Published: May 2023

AbstractThis research provides insight into a unique salt tolerance mechanism of Vigna riukiuensis. V. riukiuensis is one of the salt-tolerant species identified from the genus Vigna. We have previously reported that V. riukiuensis accumulates a higher amount of sodium in the leaves, whereas V. nakashimae, a close relative of V. riukiuensis, suppresses sodium allocation to the leaves. We first suspected that V. riukiuensis would have developed vacuoles for sodium sequestration, but there were no differences compared to a salt-sensitive species V. angularis. However, many starch granules were observed in the chloroplasts of V. riukiuensis. In addition, forced degradation of leaf starch by shading treatment resulted in no radio-Na (22Na) accumulation in the leaves. We performed SEM–EDX to locate Na in leaf sections and detected Na in chloroplasts of V. riukiuensis, especially around the starch granules but not in the middle of. Our results could provide the second evidence of the Na-trapping system by starch granules, following the case of common reed that accumulates starch granule at the shoot base for binding Na.
Improving landslide inventories by combining satellite interferometry and landscape analysis: the case of Sierra Nevada (Southern Spain)
Authors: Reyes-Carmona Cristina , Galve Jorge Pedro , Pérez-Peña José Vicente , Moreno-Sánchez Marcos , Alfonso-Jorde David , Ballesteros Daniel , Torre Davide , Azañón José Miguel , Mateos Rosa María 
Published: May 2023

AbstractAn updated and complete landslide inventory is the starting point for an appropriate hazard assessment. This paper presents an improvement for landslide mapping by integrating data from two well-consolidated techniques: Differential Synthetic Aperture Radar (DInSAR) and Landscape Analysis through the normalised channel steepness index (ksn). The southwestern sector of the Sierra Nevada mountain range (Southern Spain) was selected as the case study. We first propose the double normalised steepness (ksnn) index, derived from theksnindex, to remove the active tectonics signal. The obtainedksnnanomalies (or knickzones) along rivers and the unstable ground areas from the DInSAR analysis rapidly highlighted the slopes of interest. Thus, we provided a new inventory of 28 landslides that implies an increase in the area affected by landslides compared with the previous mapping: 33.5% in the present study vs. 14.5% in the Spanish Land Movements Database. The two main typologies of identified landslides are Deep-Seated Gravitational Slope Deformations (DGSDs) and rockslides, with the prevalence of large DGSDs in Sierra Nevada being first revealed in this work. We also demonstrate that the combination of DInSAR and Landscape Analysis could overcome the limitations of each method for landslide detection. They also supported us in dealing with difficulties in recognising this type of landslides due to their poorly defined boundaries, a homogeneous lithology and the imprint of glacial and periglacial processes. Finally, a preliminary hazard perspective of these landslides was outlined.
Reference isotherms for water vapor sorption on nanoporous carbon: results of an interlaboratory study
Authors: Nguyen Huong Giang T. , Toman Blaza , van Zee Roger D. , Prinz Carsten , Thommes Matthias , Ahmad Riaz , Kiska David , Salinger Jamie , Walton Ian M. , Walton Krista S. , Broom Darren P. , Benham Michael J. , Ansari Humera , Pini Ronny , Petit Camille , Adolphs Jürgen , Schreiber Andreas , Shigeoka Toshihiro , Konishi Yuko , Nakai Kazuyuki , Henninger Matthias , Petrzik Thomas , Kececi Can , Martis Vladimir , Paschke Thomas , Mangano Enzo , Brandani Stefano 
Published: May 2023

AbstractThis paper reports the results of an international interlaboratory study sponsored by the Versailles Project on Advanced Materials and Standards (VAMAS) and led by the National Institute of Standards and Technology (NIST) on the measurement of water vapor sorption isotherms at 25 °C on a pelletized nanoporous carbon (BAM-P109, a certified reference material). Thirteen laboratories participated in the study and contributed nine pure water vapor isotherms and four relative humidity isotherms, using nitrogen as the carrier gas. From these data, reference isotherms, along with the 95% uncertainty interval (Uk=2), were determined and are reported in a tabular format.
Universum parametric $$\nu $$-support vector regression for binary classification problems with its applications
Authors: Moosaei Hossein , Bazikar Fatemeh , Hladík Milan 
Published: May 2023

AbstractUniversum data sets, a collection of data sets that do not belong to any specific class in a classification problem, give previous information about data in the mathematical problem under consideration to enhance the classifiers’ generalization performance. Recently, some researchers have integrated Universum data into the existing models to propose new models which result in improved classification performance. Inspired by these Universum models, an efficient parametric$$ \nu $$ν-support vector regression with Universum data ($$ {\mathfrak {U}} $$UPar-$$ \nu $$ν-SVR) is proposed in this work. This method, which finds two non-parallel hyperplanes by solving one optimization problem and considers heteroscedastic noise, overcomes some common disadvantages of the previous methods. The$$ {\mathfrak {U}} $$UPar-$$ \nu $$ν-SVR includes unlabeled samples that don’t belong to any class in the training process, which results in a quadratic programming problem. Two approaches are proposed to solve this problem. The first approach derives the dual formulation using the Lagrangian function and KKT conditions. Furthermore, a least squares parametric$$ \nu $$ν-support vector regression with Universum data (named LS-$$ {\mathfrak {U}} $$UPar-$$ \nu $$ν-SVR) is suggested to further increase the generalization performance. The LS-$$ {\mathfrak {U}} $$UPar-$$ \nu $$ν-SVR solves a single system of linear equations, instead of addressing a quadratic programming problem in the dual formulation. Numerical experiments on artificial, UCI, credit card, NDC, and handwritten digit recognition data sets show that the suggested Universum model and its solving methodologies improve prediction accuracy.
Probabilistic Modeling and Uncertainty Quantification of Detailed Combustion Simulation for a Swirl Stabilized Spray Burner
Authors: Enderle Benedict , Rauch Bastian , Grimm Felix , Eckel Georg , Aigner Manfred 
Published: May 2023

AbstractTo enable risk informed decisions in the simulation-based design and development of novel combustors, uncertainties in the simulation results must be considered. However, due to the high computational costs for their quantification, these uncertainties are commonly not taken into account. Therefore, this work aims at applying an efficient methodology for uncertainty quantification based on Polynomial Chaos Expansion to a semi-technical spray burner reflecting characteristics typically found in modern aeroengine combustors. This requires accurate treatment of the multicomponent liquid fuel, a combustion model relying on finite rate chemistry and a scale resolving hybrid turbulence model to account for highly unsteady flow features and combustion. To overcome the need for costly experimental data for the spray boundary conditions, an algebraic primary breakup model is utilized. The resulting reduction in a priori information is compensated through probabilistic modeling and uncertainty quantification. Due to their importance in the design process, temperature distribution in the gas phase as well as the flame position are considered as the primary quantities of interest. For these quantities of interest, moderate uncertainties are found in the probabilistic simulation results. Further, the predictive capability of the simulation model under uncertainties is quantitively assessed by defining accurary metrics for the gas phase temperature prediction. The study further reveals that the imposed input uncertainties affect quantities of interest in both the dispersed and the gas phase through phase coupling effects.
Degradation of MYC by the mutant p53 reactivator drug, COTI-2 in breast cancer cells
Authors: Tang Minhong , Crown John , Duffy Michael J 
Published: May 2023

AbstractTP53 (p53) and MYC are amongst the most frequently altered genes in cancer. Both are thus attractive targets for new anticancer therapies. Historically, however, both genes have proved challenging to target and currently there is no approved therapy against either. The aim of this study was to investigate the effect of the mutant p53 reactivating drug, COTI-2 on MYC. Total MYC, pSer62 MYC and pThr58 MYC were detected using Western blotting. Proteasome-mediated degradation was determined using the proteasome, inhibitor MG-132, while MYC half-life was measured using pulse chase experiments in the presence of cycloheximide. Cell proliferation was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method. Treatment of 5 mutant p53 breast cancer cell lines with COTI-2 resulted in dose-dependent MYC degradation. Addition of the proteasome inhibitor, MG132, rescued the degradation, suggesting that this proteolytic system was at least partly responsible for the inactivation of MYC. Using cycloheximide in pulse chase experiments, COTI-2 was found to reduce the half-life of MYC in 2 different mutant p53 breast cancer cell lines, i.e., from 34.8 to 18.6 min in MDA-MB-232 cells and from 29.6 to 20.3 min in MDA-MB-468 cells. Co-treatment with COTI-2 and the MYC inhibitor, MYCi975 resulted in synergistic growth inhibition in all 4 mutant p53 cell lines investigated. The dual ability of COTI-2 to reactivate mutant p53 and degrade MYC should enable this compound to have broad application as an anticancer drug.
Novel nontarget LC-HRMS-based approaches for evaluation of drinking water treatment
Authors: Nováková Petra , Švecová Helena , Bořík Adam , Grabic Roman 
Published: May 2023

AbstractA conventional evaluation methodology for drinking water pollution focuses on analysing hundreds of compounds, usually by liquid chromatography–tandem mass spectrometry. High-resolution mass spectrometry allows comprehensive evaluation of all detected signals (compounds) based on their elemental composition, intensity, and numbers. We combined target analysis of 192 emerging micropollutants with nontarget (NT) full-scan/MS/MS methods to describe the impact of treatment steps in detail and assess drinking water treatment efficiency without compound identification. The removal efficiency based on target analytes ranged from − 143 to 97%, depending on the treatment section, technologies, and season. The same effect calculated for all signals detected in raw water by the NT method ranged between 19 and 65%. Ozonation increased the removal of micropollutants from the raw water but simultaneously caused the formation of new compounds. Moreover, ozonation byproducts showed higher persistence than products formed during other types of treatment. We evaluated chlorinated and brominated organics detected by specific isotopic patterns within the developed workflow. These compounds indicated anthropogenic raw water pollution but also potential treatment byproducts. We could match some of these compounds with libraries available in the software. We can conclude that passive sampling combined with nontargeted analysis shows to be a promising approach for water treatment control, especially for long-term monitoring of changes in technology lines because passive sampling dramatically reduces the number of samples and provides time-weighted average information for 2 to 4 weeks.
Explainability, Public Reason, and Medical Artificial Intelligence
Authors: Da Silva Michael 
Published: May 2023

Abstract
The contention that medical artificial intelligence (AI) should be ‘explainable’ is widespread in contemporary philosophy and in legal and best practice documents. Yet critics argue that ‘explainability’ is not a stable concept; non-explainable AI is often more accurate; mechanisms intended to improve explainability do not improve understanding and introduce new epistemic concerns; and explainability requirements are ad hoc where human medical decision-making is often opaque. A recent ‘political response’ to these issues contends that AI used in high-stakes scenarios, including medical AI, must be explainable to meet basic standards of legitimacy: People are owed reasons for decisions that impact their vital interests, and this requires explainable AI. This article demonstrates why the political response fails. Attending to systemic considerations, as its proponents desire, suggests that the political response is subject to the same criticisms as other arguments for explainable AI and presents new issues. It also suggests that decision-making about non-explainable medical AI can meet public reason standards. The most plausible version of the response amounts to a simple claim that public reason demands reasons why AI is permitted. But that does not actually support explainable AI or respond to criticisms of strong requirements for explainable medical AI.
University students’ opinion on gamete donor identification regimes
Authors: Nogueira Ana , Ammar Omar , Bilir Enes , Iftene Lavinia , Torrero Ignácio , Ceschin Nathan , Nogueira-Silva Cristina , Brandão Pedro 
Published: May 2023

AbstractPurposeThis study aims to evaluate the opinion of university students about the identification or nonidentification of gamete donation and the probability of donation according to the different regimes.MethodsThis was a cross-sectional observational study based on an online anonymous survey including questions about sociodemographic data, reasons for considering donations, information about the donation process and legislation, and their opinions about the different regimes and how they would influence donations.ResultsIn total, 1393 valid responses were obtained, with a mean age of 24.0 years (SD = 4.8), most of the respondents being female (68.5%), living in a relationship (56.7%), and without children (88.4%). The main reasons for considering donation would be altruism and monetary compensation. Overall, it was found that participants were poorly informed about the donation procedure and legislation. Students revealed preference for nonidentified donation, and they were less likely to donate in an open identity regime.ConclusionMost university students consider themselves poorly informed about gamete donation, express a preference for nonidentified gamete donation, and would less likely donate on an open identity basis. Thus, an identified regime may be less attractive to potential donors and lead to a decrease in the availability of gamete donors.
A multi-task learning-based optimization approach for finding diverse sets of microstructures with desired properties
Authors: Iraki Tarek , Morand Lukas , Dornheim Johannes , Link Norbert , Helm Dirk 
Published: May 2023

AbstractOptimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed to manufacture workpieces with targeted material microstructures. These microstructures are defined by the material properties of interest and identifying them is a question of materials design. In the present paper, we addresse this issue and introduce a generic multi-task learning-based optimization approach. The approach enables the identification of sets of highly diverse microstructures for given desired properties and corresponding tolerances. Basically, the approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task learning with siamese neural networks. The resulting model (1) relates microstructures and properties, (2) estimates the likelihood of a microstructure of being producible, and (3) performs a distance preserving microstructure feature extraction in order to generate a lower dimensional latent feature space to enable efficient optimization. The proposed approach is applied on a crystallographic texture optimization problem for rolled steel sheets given desired properties.
Early Warning Software for Emergency Department Crowding
Authors: Tuominen Jalmari , Koivistoinen Teemu , Kanniainen Juho , Oksala Niku , Palomäki Ari , Roine Antti 
Published: May 2023

AbstractEmergency department (ED) crowding is a well-recognized threat to patient safety and it has been repeatedly associated with increased mortality. Accurate forecasts of future service demand could lead to better resource management and has the potential to improve treatment outcomes. This logic has motivated an increasing number of research articles but there has been little to no effort to move these findings from theory to practice. In this article, we present first results of a prospective crowding early warning software, that was integrated to hospital databases to create real-time predictions every hour over the course of 5 months in a Nordic combined ED using Holt-Winters’ seasonal methods. We show that the software could predict next hour crowding with an AUC of 0.94 (95% CI: 0.91-0.97) and 24 hour crowding with an AUC of 0.79 (95% CI: 0.74-0.84) using simple statistical models. Moreover, we suggest that afternoon crowding can be predicted at 1 p.m. with an AUC of 0.84 (95% CI: 0.74-0.91).
Facile synthesis of thermally stable anatase titania with a high-surface area and tailored pore sizes
Authors: Sui Ruohong , Jacobs John H. , Chou Nancy , Deering Connor E. , Lavery Christopher B. , Marriott Robert A. 
Published: May 2023

AbstractBoth affordability and stability are important for commercial-scale production and industrial applications of TiO2. In addition, the ability to tailor nanostructure and physicochemical properties can provide advantages for future applications. Herein a facile sol‒gel process was investigated by using titanyl sulfate as an inexpensive feedstock reacting with water in the media of acetic acid and isopropanol. An anatase phase was readily produced at 65 °C, followed by drying at 80 °C. The anatase was stable up to 800 °C due to the residual sulfate and nitrogen, where sulfate and ammonium slowly decomposed when heating beyond 400 °C. The monolithic TiO2 xerogels were composed of agglomerated TiO2 spherical particles with diameters of ca. 50 or 100 nm. The TiO2 spherical particles were built by anatase crystallites with a diameter of ca. 5 nm. As a result, the TiO2 exhibited both bimodal mesopores and macropores: Large mesopores (10‒30 nm) were present due to the void spaces between the TiO2 spherical particles, while the smaller mesopores (ca. 3 nm) were due to the void spaces between the anatase crystallites within each TiO2 particle. There were also larger macropores (a few micrometers), which were caused by gas bubbles generated during the sol‒gel reactions. From a mass transfer viewpoint, these large pores within TiO2 xerogels could have advantages in their potential applications for catalysis and/or filtration processes.
Graphical Abstract
Where is the brand growth potential? An examination of buyer groups
Authors: Trinh Giang Tue , Dawes John , Sharp Byron 
Published: May 2023

Abstract
Practitioners and academics have long discussed strategies for brand sales growth. A recent example is an industry debate in which different brand growth strategies were argued: https://www.mmaglobal.com/thegreatdebate (MMA Global & Neustarr, 2021). A central question in this arena is whether a brand should focus on its heavy, light, or non-buyers in its efforts to grow its sales. This study contributes to our knowledge about how sales growth can occur by investigating the potential contribution these three buyer groups can make to any sales gain. Using both, a simulation study and an empirical study of purchases of approximately 12,400 households in the UK, across different brands and categories, we show that almost any brand’s headroom growth potential lies mostly in light or non-buyers of that brand. Even for large brands with high penetration the growth potential of light brand buyers eclipses heavy brand buyers.
Nonlinear energy localisation in a model of plane metamaterial
Authors: Coclite Giuseppe Maria , De Tommasi Domenico , Maddalena Francesco , Romano Marzia , Trentadue Francesco 
Published: May 2023

AbstractApplying the concepts of nonlinear normal modes and limiting phase trajectories introduced by Manevitch in Manevitch (Arch Appl Mech 77:301–312, 2007) to a two-dimensional mass–spring system, the authors propose a generalised method to tune a plane metamaterial and get the desirable resonant behaviour at short wavelengths. Indeed, the account of nonlinear coupling between the oscillators enables the localisation of energy leading the origin of a bandgap at short wavelengths regardless the existence of external disturbances. Moreover, further restrictions on the modes amplitude allow the observation of Fermi–Pasta–Ulam–Tsingou recurrence and super-recurrence in the two-dimensional metamaterial. These findings can open the way to further research in order to improve efficiency and performance of resonant metamaterials.
Effects of GLP-1 receptor agonists on neurological complications of diabetes
Authors: García-Casares Natalia , González-González Guillermo , de la Cruz-Cosme Carlos , Garzón-Maldonado Francisco J , de Rojas-Leal Carmen , Ariza María J , Narváez Manuel , Barbancho Miguel Ángel , García-Arnés Juan Antonio , Tinahones Francisco J. 
Published: May 2023

AbstractEmerging evidence suggests that treatment with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) could be an interesting treatment strategy to reduce neurological complications such as stroke, cognitive impairment, and peripheral neuropathy. We performed a systematic review to examine the evidence concerning the effects of GLP-1 RAs on neurological complications of diabetes. The databases used were Pubmed, Scopus and Cochrane. We selected clinical trials which analysed the effect of GLP-1 RAs on stroke, cognitive impairment, and peripheral neuropathy. We found a total of 19 studies: 8 studies include stroke or major cardiovascular events, 7 involve cognitive impairment and 4 include peripheral neuropathy. Semaglutide subcutaneous and dulaglutide reduced stroke cases. Liraglutide, albiglutide, oral semaglutide and efpeglenatide, were not shown to reduce the number of strokes but did reduce major cardiovascular events. Exenatide, dulaglutide and liraglutide improved general cognition but no significant effect on diabetic peripheral neuropathy has been reported with GLP-1 RAs. GLP-1 RAs are promising drugs that seem to be useful in the reduction of some neurological complications of diabetes. However, more studies are needed.