bluuebunny/crossref_metadata_2025_split huggingface dataset:
SLPI controls neutrophil migration abilities and impacts neutrophil skin infiltration in experimental psoriasis
Authors: Kwiecinska Patrycja , Santocki Michal , Skrzeczynska-Moncznik Joanna , Sinkevich Ivan , Piwowarczyk Katarzyna , Majewski Pawel , Grygier Beata , Majchrzak-Gorecka Monika , Czyz Jaroslaw , Kolaczkowska Elzbieta , Cichy Joanna 
Published: February 2025

Abstract
Skin infiltration by neutrophils is a hallmark of the chronic inflammatory skin disease psoriasis, yet the mechanisms underlying neutrophil recruitment and positioning in chronically inflamed skin remain poorly understood. In this study, we demonstrate the significant impact of a total genetic deficiency of secretory leukocyte protease inhibitor (SLPI) on neutrophil migration in mouse skin. Without SLPI, neutrophils displayed an unconventional migratory pattern, characterized by altered interactions with vessel walls and reduced efficiency in extravasating from blood vessels into skin tissue during the early stages of experimental psoriasis. This was associated with changes in tissue motility, positioning neutrophils farther from the skin entry vessels and closer to the skin surface. Neutrophil diapedesis was partially dependent on SLPI within the neutrophils themselves. The impact of SLPI on neutrophil movement was further supported by the increased migration of human neutrophils in the presence of neutrophil-penetrant recombinant SLPI. Additionally, our data suggest that neutrophils with varying capacities for vessel wall interaction are released from the bone marrow into circulation in an SLPI-dependent manner. These findings establish a role for SLPI in regulating the spatiotemporal infiltration of neutrophils into the skin in psoriasis, highlighting its relevance to psoriasis pathophysiology.
Value signals guiding choices for cannabis versus non-drug rewards in people who use cannabis near-daily
Authors: Lawn Will , Hao Xuejun , Konova Anna B. , Haney Margaret , Cooper Ziva D. , Van Dam Nicholas , Glimcher Paul , Bedi Gillinder 
Published: February 2025

Abstract

Rationale
Despite the critical role of choice processes in substance use disorders, the neurobehavioral mechanisms guiding human decisions about drugs remain poorly understood.

Objectives
We aimed to characterize the neural encoding of subjective value (SV) for cannabis versus non-drug rewards (snacks) in people who use cannabis on a near-daily/daily frequency (PWUCF) and assessed the impact of cannabis and snack stimuli (‘cues’) on SV encoding.

Methods
Twenty-one non-treatment-seeking PWUCF (≥4 days/week; 1 female) participated in an inpatient, crossover experiment with four counterbalanced conditions: 1. neutral cues/cannabis choices; 2. cannabis cues/cannabis choices; 3. neutral cues/snack choices; and 4. snack cues/snack choices. In each condition, participants were exposed to cues before an fMRI scan during which they repeatedly chose between 0-6 cannabis puffs/snacks and a set monetary amount, with randomly-selected choices implemented. The SV signal was operationalized as the neural correlates of the strength of preference for cannabis/snack choices. fMRI data were analyzed for twenty participants.

Results
Despite equivalent choice behavior, SV signals for cannabis, but not snacks, were observed in regions known to encode SV for various rewards (ventromedial prefrontal cortex, vmPFC; ventral striatum; dorsal posterior cingulate cortex, dPCC). SV encoding in vmPFC was stronger for cannabis than snacks. In the dPCC, the impact of cues on SV signals was moderated by reward type.

Conclusions
PWUCF had expected neural value encoding for cannabis but disrupted non-drug SV encoding, despite equivalent choice behavior. This provides tentative support for theories that highlight dysregulated neural valuation of non-drug rewards as a hallmark of problematic cannabis use.
Discrepancies between physician-assessed and patient-reported complications after cystectomy – a prospective analysis
Authors: Ebner Benedikt , Hirsch Judith , Holz Annkathrin , Volz Yannic , Eismann Lennert , Hermans Julian , Pyrgidis Nikolaos , Kidess Marc , Semmler Marie , Brinkmann Isabel , Aydogdu Can , Chaloupka Michael , Lindner Andrea Katharina , Weinhold Philipp , Stief Christian G. , Schulz Gerald B. 
Published: February 2025

Abstract

Purpose
Despite the high incidence of perioperative complications following cystectomy, there is a lack of evidence regarding patients’ perceptions. Moreover, discrepancies between established complication grading systems and the patient’s perspective remain unexplored.

Methods
We prospectively evaluated perioperative complications after cystectomy using the Clavien-Dindo Classification (CDC) and the Comprehensive Complication Index (CCI). The CDC and CCI were compared to patient-reported complication grades through Spearman correlation analysis. Discrepancies between physician-assessed and patient-reported complication grades were then evaluated. The study was registered at ClinicalTrials.gov (NCT05153694).

Results
Between December 2021 and March 2024, 172 patients underwent open cystectomy at our department. Of those, 154 provided written consent to participate in the study, and 111 completed the post-discharge questionnaire. We found a moderate correlation between physician-assessed and patient-reported complication grades (CDC: r = 0.34, CCI: r = 0.39; p < 0.001). Patients with matching grades were defined as realists (50%). Those who reported lower complication grades than assessed by physicians were defined as optimists (38%), while those who reported higher grades were defined as pessimists (12%). Optimists rated the preoperative medical information better than pessimists (“very good”: 79% vs. 38%, p = 0.006). We found no significant differences between optimists and pessimists regarding age, gender, tumor characteristics or educational level.

Conclusion
In our prospective study, the correlation between physician-assessed and patient-reported complication grades was only moderate. Only half of the cystectomy patients accurately perceived the severity of their complications. Our findings represent the first study to investigate patients’ perspectives on complications in urology and underscore the importance of thorough preoperative medical information.
Modeling and simulation of multiphase flow in highly fractured porous media with a data-driven multiscale approach
Authors: Gimenez Juan M. , Idelsohn Sergio R. , Oñate Eugenio 
Published: February 2025

Abstract
The pseudo-direct numerical simulation (P-DNS) method is a recently developed multiscale strategy designed for high-fidelity computational simulation of complex flow physics. This physics-based data-driven approach involves numerically solving both the fine and global scales. The former is precomputed into representative volume elements (RVEs), whose homogenized responses serve to train machine learning-based surrogate models. This upscaling model feeds the global scale, which is then effectively solved in coarse meshes. In this work, the multiscale P-DNS method is applied to the study of multiphase flow in highly fractured porous media. The aim is overcoming the current limitations of simulation techniques for oil reservoirs due to the complex geological heterogeneities. A novel characterization of the geometry of the fracture networks is proposed. The local intrinsic permeability tensor is homogenized via RVE simulations accounting for embedded fractures, thus allowing efficient computation of reservoir-scale transport. The multiscale method is applied to two-dimensional single-phase and two-phase flow problems on different reservoir scenarios. The accuracy of the predictions is assessed relative to detailed simulations with embedded fractures on very fine meshes. For the cases considered, it is shown that the P-DNS homogenization technique is capable of compute accurate flow rates and pressure fields on coarser meshes than the high-fidelity approach, while achieving speedups in the solution time of about a factor of 500.
Structural transformation in Pd nanoclusters induced by Cu doping: an ADFT study
Authors: Santiago-Silva L. , Cruz-Martínez H. , Rojas-Chávez H. , López-Sosa L. , Calaminici P. 
Published: February 2025

Abstract

Context
Transition metal nanoparticles have gained great importance due to their promising applications in various fields such as energy, electronics, medicine, and agriculture. For these applications, materials with outstanding properties are currently required. Therefore, different strategies have been established to improve the properties of pure nanoparticles such as alloying, doping, and formation of composites. Among these strategies, doping is gaining great importance because it has been demonstrated that doped nanoparticles have better properties than pure nanoparticles. Therefore, it is essential to know the role of doping on the structures and properties of clusters with more than 16 atoms. Consequently, in this study, we propose a theoretical study of structures and properties focusing on pure Pd19, Cu-doped Pd18 (Pd18Cu), and Cu2-doped Pd17 (Pd17Cu2) nanoclusters and thus elucidate the role of Cu atoms on the structures and properties of larger doped Pd nanoclusters than those already presented in the literature. We have selected a nanocluster with 19 atoms since the most stable structure of this system is characterized by defined shapes such as octahedron or double-icosahedron.

Methods
Ground state structures and properties of Pd19, Pd18Cu, and Pd17Cu2 nanoclusters were studied using the auxiliary density functional theory (ADFT), as implemented in the deMon2k code. For obtaining the ground state structures of Pd19, Pd18Cu, and Pd17Cu2 nanoclusters, several dozen initial structures were taken along Born–Oppenheimer molecular dynamics (BOMD) trajectories and subsequently optimized without symmetry restrictions. The optimizations were performed with the revised PBE functional in combination with TZVP-GGA for the Cu atoms and using an 18-electron QECP|SD basis set for the Pd atoms. Different energetic and electronic properties were calculated for the most stable structures of Pd19, Pd18Cu, and Pd17Cu2 nanoclusters. Interestingly, when the Pd nanocluster is doped with two Cu atoms (Pd17Cu2), there is a structural transition, because the most stable structures for Pd19 and Pd18Cu are icosahedral. While the Pd17Cu nanocluster is characterized for a double-icosahedral-base structure. The binding energy per atom increases when the Cu concentration in the nanoclusters increases. According to the HOMO–LUMO gap, the chemical reactivity of the nanoclusters tends to increase as the Cu content in the nanoclusters increases.
Supporting adaptive pathways planning using archetypes for climate adaptation
Authors: Di Fant Valeria , Middelkoop Hans , Dunn Frances E. , Haasnoot Marjolijn 
Published: February 2025

Abstract
Adaptive pathways planning supports adaptation under deep uncertainty. The approach has been broadly applied, resulting in the development of multiple methods and tools. As a result, practitioners are not only challenged with the urgent and complex task of adaptation planning but may also have difficulties in selecting adequate methods. With this research, we introduce three adaptive pathways planning archetypes that represent typologies of contexts (e.g. a city or a region) based on Adaptation Awareness and Capacity. We validate them with local coastal adaptation data. The archetype is determined at a specific moment in time, and a context can evolve from one archetype to another over time by further developing Adaptation Awareness and/or Capacity. The archetypes aim to guide practitioners in selecting methods and tools for adaptive pathways planning. For this purpose, a questionnaire is developed for practitioners to position their case within the archetypes. Based on literature and interviews, we provide recommendations of adaptive pathways planning methods and tools for each archetype. We illustrate the approach for Venice, Italy. Our results confirm how each archetype can develop adaptive pathways with fit-for-purpose methods and tools. Considering the recently started 7th IPCC assessment cycle and implementation of the work program on the global goal on adaptation, it is worthwhile to consider how the archetypes presented here may provide adequate tools for meta-assessments of the status of adaptation worldwide.
Why is the Earth System Oscillating at a 6-Year Period?
Authors: Cazenave Anny , Pfeffer Julia , Mandea Mioara , Dehant Véronique , Gillet Nicolas 
Published: February 2025

Abstract
A 6-year cycle has long been recognized to influence the Earth’s rotation, the internal magnetic field and motions in the fluid Earth’s core. Recent observations have revealed that a 6-year cycle also affects the angular momentum of the atmosphere and several climatic parameters, including global mean sea level rise, precipitation, land hydrology, Arctic surface temperature, ocean heat content and natural climate modes. In this review, we first present observational evidences supporting the existence of a 6-year cycle in the Earth system, from its deep interior to the climate system. We then explore potential links between the Earth’s core, mantle and atmosphere that might explain the observations, and investigate various mechanisms that could drive the observed 6-year oscillation throughout the whole Earth system.
Adopting Maker Education: A Comparative Analysis of Intentions Between In-Service and Pre-Service STEM Teachers
Authors: Cheng Ya-Wen , Chen Cheng-Huan , Chen Nian-Shing 
Published: February 2025

Abstract
This study aims to investigate whether there are differences in the intentions to adopt maker education between in-service and pre-service teachers. It also seeks to identify the key factors affecting their intentions, providing insights for tailoring professional development (PD) training programs to meet the needs of teachers at different career stages. A hybrid maker-based PD program was designed and offered to both in-service and pre-service teachers. The participants' behavioral intentions to adopt maker education were assessed after the PD program using an extended Theory of Planned Behavior model. The intentions of in-service and pre-service teachers were examined, and the key factors affecting their intentions were identified using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results showed that pre-service teachers had significantly lower intentions to adopt maker education compared to in-service teachers. For pre-service teachers, attitude was the key factor influencing their intentions, while for in-service teachers, subjective norms played a crucial role. These findings highlight the importance of tailored PD for teachers at different career stages.
Butterfly diversity in relation to the CORINE land cover of the Culuccia Peninsula (Sardinia, Italy)
Authors: Annessi Matteo , Marzialetti Flavio , Marconi Marilena , Acosta Alicia Teresa Rosario , Di Giulio Andrea 
Published: February 2025

Abstract
Several studies have highlighted the global decline of pollinator insects, with Lepidoptera being particularly affected in large parts of Europe in recent decades. Given the important biogeographic and conservation implications from faunal studies on the large Mediterranean islands, we focused on assessing butterfly diversity within the Culuccia Peninsula in north-eastern Sardinia (Italy). This area, characterized by an environmental mosaic largely untouched by human activities over the last century, is still unknown in terms of flora and fauna. For this reason, we compiled the first butterfly checklist of the Culuccia Peninsula and produced a detailed land cover map of the area to investigate the influence of the land use classes on Lepidoptera communities across seasons. The butterflies were sampled in five sessions from April to October 2022, along seven fixed transects selected to cover land use classes representative of natural and human-exploited areas for agriculture and grazing. Despite the small size of the study area, 23 of the 56 species present in Sardinia were recorded. The butterfly communities presented high dissimilarity across the different land use classes, which was driven primarily by species turnover. Significant differences in Lepidoptera diversity were observed between land cover classes with sparse vegetation and dense maquis, with higher species richness observed in the former. Shrub-dominated land use classes were associated with cooler butterfly communities compared to open environments, suggesting that they could serve as refugia in response to predicted climate change.
Determining What is Common: A Theoretical Account of Common Factors in Psychotherapy through the Lens of Self-Determination Theory
Authors: Baier-Mosch Franziska , Weiher Gerald Marc , Kananian Schahryar 
Published: February 2025

Abstract
The common factors hypothesis as an explanation for the equivalent therapy outcome paradox has stimulated numerous empirical and theoretical studies. However, the existing common factors models (e.g., the contextual model) have hardly succeeded in convincingly linking the quite different common factors and at the same time considering their different conceptualization as technique (e.g., exposure) or process (e.g., expectations). We attempt to overcome these limitations by showing how common factors can be explained and linked against the background of the well-known self-determination theory (SDT). In particular, we will show how common factors can be integrated in the context of SDT concepts of basic psychological needs satisfaction (autonomy, relatedness and competence) and integration of experiences and extrinsic regulations into a unified sense of self. We will argue that repeatedly proposed common factors (e.g., awareness/insight, emotion/behavior regulation, positive expectations/hope, social belonging) can be understood as processes of either autonomy, competence or relatedness need satisfaction and autonomous regulation that lead to positive patient outcomes. Furthermore, we will show how common factors that are techniques rather than change processes (e.g., alliance building) can trigger the common change processes that reflect basic need satisfaction and autonomous regulation.
A comprehensive benchmark of machine learning-based algorithms for medium-term electric vehicle charging demand prediction
Authors: Tolun Ömer Can , Zor Kasım , Tutsoy Onder 
Published: February 2025

Abstract
The current difficulties faced by evolutionary smart grids, as well as the widespread electric vehicles (EVs) into the modernised electric power system, highlight the crucial balance between electricity generation and consumption. Focusing on renewable energy sources instead of fossil fuels can provide an enduring environment for future generations by mitigating the impacts of global warming. At this time, the popularity of EVs has been ascending day by day due to the fact that they have several advantages such as being environmentally friendly and having better mileage performance in city driving over conventional vehicles. Despite the merits of the EVs, there are also a few disadvantages consisting of the integration of the EVs into the existing infrastructure and their expensiveness by means of initial investment cost. In addition to those, machine learning (ML)-based techniques are usually employed in the EVs for battery management systems, drive performance, and passenger safety. This paper aims to implement an EV monthly charging demand prediction by using a novel technique based on an ensemble of Pearson correlation (PC) and analysis of variance (ANOVA) along with statistical and ML-based algorithms including seasonal auto-regressive integrated moving average with exogenous variables (SARIMAX), convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) decision trees, gated recurrent unit (GRU) networks, long short-term memory (LSTM) networks, bidirectional LSTM (Bi-LSTM) and GRU (Bi-GRU) networks for the Eastern Mediterranean Region of Türkiye. The performance and error metrics, including determination coefficient (R

$$^2$$

2

), mean absolute percentage error (MAPE), mean absolute error (MAE), and mean absolute scaled error (MASE), are evaluated in a benchmarking manner. According to the obtained results, in Scenario 1, a hybrid of PC and XGBoost decision trees model achieved an R

$$^2$$

2

of 96.21%, MAPE of 5.52%, MAE of 6.5, and MASE of 0.195 with a training time of 2.08 s and a testing time of 0.016 s. In Scenario 2, a combination of ANOVA and XGBoost decision trees model demonstrated an R

$$^2$$

2

of 96.83%, a MAPE of 5.29%, a MAE of 6.0, and a MASE of 0.180 with a training time of 1.62 s and a testing time of 0.012 s. These findings highlight the superior accuracy and computational efficiency of the XGBoost models for both scenarios compared to others and reveal XGBoost's suitability for EV charging demand prediction.
Using the Community Cultural Wealth Model for Success: Students of Color Activating Familial Capital
Authors: Lewis Danielle Vegas , Taughrin Ryan , Iantosca Megan 
Published: February 2025

Abstract
Social mobility programs that put low-income students of Color from urban environments in high-achieving, predominantly white institutions aim to offer educational opportunity; however, they may not always recognize the resources that youth and their families bring to these programs and their schools. Drawing upon data from 37 interviews with program participants, we find that youth and their families utilize familial capital to gain information and resources. This assisted in providing access to the program, transitioning, and succeeding in it. Results of this study provide a more in-depth understanding of how youth make use of familial capital to pursue social mobility.
Comments on: Exploratory functional data analysis
Authors: Hyndman Rob J. 
Published: February 2025

Abstract
A useful approach to exploratory functional data analysis is to work in the lower-dimensional principal component space rather than in the original functional data space. I demonstrate this approach by finding anomalies in age-specific US mortality rates between 1933 and 2022. The same approach can be employed for many other standard data analysis tasks and has the advantage that it allows immediate use of the vast array of multivariate data analysis tools that already exist, rather than having to develop new tools for functional data.
Heuristics for the weighted total domination problem
Authors: Casado Alejandra , Sánchez-Oro Jesús , Martínez-Gavara Anna 
Published: February 2025

Abstract
The weighted total domination problem (WTDP) belongs to the family of dominating set problems. Given an edge- and vertex- weighted graph, the WTDP consists in selecting a total dominating set D, such that the sum of vertices and edges weights of the subgraph induced by D plus, for each vertex not in D, the minimum weight of its edge to a vertex in D is minimized. A total dominating set D is a subset of the graph’s vertices, such that every vertex, including those in D, is at least adjacent to one vertex in D. This problem arises in many real-life applications closely related to covering and independent set problems; however, it remains computationally challenging due to its

$$\mathcal{N}\mathcal{P}$$

N
P

-hardness. This work presents a variable neighborhood search (VNS) procedure to tackle the WTDP, and investigates the advantages and disadvantages of a multi-start strategy within VNS methodology. In addition, we develop a biased greedy randomized adaptive search procedure (Biased GRASP) that keeps adding elements once a feasible solution is found to produce high-quality initial solutions. We perform extensive numerical analysis to look into the influences of the algorithmic components and to disclose the contribution of the elements and strategies of our method. Finally, the empirical analysis shows that our proposal outperforms the state-of-art results, and the statistical analysis confirms the superiority of our proposal to find the best total dominating sets.