bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models
Authors: Palma Gabriel R. , Thornberry Conor , Commins Seán , Moral Rafael A. 
Published: September 2024

AbstractTheta oscillations, ranging from 4-8 Hz, play a significant role in spatial learning and memory functions during navigation tasks. Frontal theta oscillations are thought to play an important role in spatial navigation and memory. Electroencephalography (EEG) datasets are very complex, making any changes in the neural signal related to behaviour difficult to interpret. However, multiple analytical methods are available to examine complex data structures, especially machine learning-based techniques. These methods have shown high classification performance, and their combination with feature engineering enhances their capability. This paper proposes using hidden Markov and linear mixed effects models to extract features from EEG data. Based on the engineered features obtained from frontal theta EEG data during a spatial navigation task in two key trials (first, last) and between two conditions (learner and non-learner), we analysed the performance of six machine learning methods on classifying learner and non-learner participants. We also analysed how different standardisation methods used to pre-process the EEG data contribute to classification performance. We compared the classification performance of each trial with data gathered from the same subjects, including solely coordinate-based features, such as idle time and average speed. We found that more machine learning methods perform better classification using coordinate-based data. However, only deep neural networks achieved an area under the ROC curve higher than 80% using the theta EEG data alone. Our findings suggest that standardising the theta EEG data and using deep neural networks enhances the classification of learner and non-learner subjects in a spatial learning task.
Normativität und Rekonstruktion
Authors: Franz Julia , Kubisch Sonja , Unterkofler Ursula 
Published: September 2024

Zusammenfassung
In der sozialarbeitswissenschaftlichen Professionalisierungsforschung sind Forschende mit verschiedenen normativen Fragen konfrontiert. Dies betrifft sowohl die Praxis Sozialer Arbeit, die von expliziten Normen und impliziten Wertorientierungen durchdrungen ist, als auch den Diskurs über Profession und Professionalität, der selbst normativ aufgeladen ist, wie auch die Prinzipien rekonstruktiver Sozialforschung, zu denen eine amoralische Analysehaltung zählt. Die Herausforderung besteht darin, normative Aspekte in der Forschungspraxis zu erkennen, zu reflektieren und eine offene Forschungshaltung zu bewahren. In diesem Beitrag werden das Zusammenspiel verschiedener Aspekte von Normativität und die daraus resultierenden Spannungen näher betrachtet, und zwar mit einem Fokus auf rekonstruktiven Forschungsverfahren, mit denen die Strukturlogik professionellen Handelns empirisch untersucht wird. Anknüpfend an Überlegungen zum „sozialpädagogischen Blick“ der Forschung werden Normativität und rekonstruktive Forschung ins Verhältnis gesetzt. Die Autorinnen beziehen sich auf eigene empirisch-rekonstruktive Analysen und zeigen anhand eines empirischen Beispiels auf, welche normativen Fragen sich in der Forschung der Sozialen Arbeit zur Professionalisierung stellen. Sie konkretisieren so den „forschenden Blick Sozialer Arbeit“.
Multi-omics-based Machine Learning for the Subtype Classification of Breast Cancer
Authors: Hassan Asmaa M. , Naeem Safaa M. , Eldosoky Mohamed A. A. , Mabrouk Mai S. 
Published: September 2024

AbstractCancer is a complicated disease that produces deregulatory changes in cellular activities (such as proteins). Data from these levels must be integrated into multi-omics analyses to better understand cancer and its progression. Deep learning approaches have recently helped with multi-omics analysis of cancer data. Breast cancer is a prevalent form of cancer among women, resulting from a multitude of clinical, lifestyle, social, and economic factors. The goal of this study was to predict breast cancer using several machine learning methods. We applied the architecture for mono-omics data analysis of the Cancer Genome Atlas Breast Cancer datasets in our analytical investigation. The following classifiers were used: random forest, partial least squares, Naive Bayes, decision trees, neural networks, and Lasso regularization. They were used and evaluated using the area under the curve metric. The random forest classifier and the Lasso regularization classifier achieved the highest area under the curve values of 0.99 each. These areas under the curve values were obtained using the mono-omics data employed in this investigation. The random forest and Lasso regularization classifiers achieved the maximum prediction accuracy, showing that they are appropriate for this problem. For all mono-omics classification models used in this paper, random forest and Lasso regression offer the best results for all metrics (precision, recall, and F1 score). The integration of various risk factors in breast cancer prediction modeling can aid in early diagnosis and treatment, utilizing data collection, storage, and intelligent systems for disease management. The integration of diverse risk factors in breast cancer prediction modeling holds promise for early diagnosis and treatment. Leveraging data collection, storage, and intelligent systems can further enhance disease management strategies, ultimately contributing to improved patient outcomes.
Simulation methods for local microstructure evolution-cooling and time–temperature transformation behavior in heat treatment of tool steels
Authors: Schuppener Jannik , Benito Santiago , Weber Sebastian 
Published: September 2024

AbstractThis paper presents the development of a simulative workflow capable of predicting microstructural evolution during heat treatment processes. It represents a meaningful advance in this field by extending existing simulation models previously published by the authors. In this previous work, the software solutions MatCalc®, MATLAB®, and Abaqus FEA® were coupled to calculate several local microstructural properties: the carbide content, the type, the distribution, and the chemical composition of the matrix. In addition, the model could determine the proportions of microstructural components such as martensite and retained austenite within the matrix. The hardening treatment was simplified by assuming a fast quenching, leading to complete martensitic phase transformation. However, this assumption may not be valid for larger components, leaving room for optimization. Therefore, the simulation model in this publication has been successfully extended to include local solution-state dependent time–temperature transformation behavior. In addition, an automated microstructure simulation of the entire component is now possible. As an application example, two tool geometries of different sizes were simulated with an identical furnace heat treatment. The same furnace temperature (T = 1050 °C) and the same holding time (t = 60 min) were simulated with a slow air cooling (Tair = 25 °C). The austenitizing temperature and holding time were chosen to dissolve a sufficient amount of carbides during austenitization, and the slow cooling rates were chosen to form diffusion controlled phases such as bainite or pearlite. To validate the simulation model, the simulated time–temperature sequences were reproduced experimentally in a quenching dilatometer. The resulting real microstructures were compared with the simulated ones.
First Red List of Ecosystems assessment of a tropical glacier ecosystem to diagnose the pathways towards imminent collapse
Authors: Ferrer-Paris José R. , Llambí Luis D. , Melfo Alejandra , Keith David A. 
Published: September 2024

Abstract
Tropical glaciers are rapidly disappearing, particularly in isolated mountain peaks below 5,000 m elevation. These glaciers are fundamental substrates for unique cryogenic ecosystems in high tropical environments where the ice, melting water and rocky substrate sustain microbiological communities and other meso- and macro-biota. This study uses the Red List of Ecosystems guidelines to diagnose the collapse of the tropical glacier ecosystem of the Cordillera de Mérida, Venezuela. We undertook the assessment with existing estimates of glacier ice extent, indirect historical estimates of ice mass balance and global mechanistic models of future ice mass balance. We complemented these with additional statistical analysis of trends and bioclimatic suitability modelling to calculate and predict rates of decline and relative severity of degradation in selected ecosystem indicators. The evidence suggests an extreme risk of collapse (Critically Endangered) because of a prolonged and acute reduction in ice extent and changes in climatic conditions that are leading to the complete loss of ice mass. The ice substrate has declined 90% in the last 20 years, and observed acceleration of the rate of decline suggests it will probably disappear within the next 5 years. Loss of ice substrate will trigger an immediate loss of supraglacial, englacial and subglacial biotic compartments and initiate a decades-long succession of forefield vegetation. However, ongoing inventories of native biota and monitoring of ecosystem transitions can provide valuable insights and lessons for other ecosystems facing similar risks. The Red List of Ecosystems assessment protocol provides a useful framework for comparative analysis of cryogenic ecosystems.
Using Satellite Observations of Lightning and Precipitation to Diagnose the Behavior of Deep Convection in Tropical Cyclones Traversing the Midlatitudes
Authors: Heuscher Lena , Gatlin Patrick , Petersen Walter A. 
Published: September 2024

AbstractThis study uses a unique combination of geostationary and low‐Earth orbiting satellite‐based lightning and precipitation observations, respectively, to examine the evolution of deep convection during the tropical cyclone (TC) lifecycle. The study spans the 2018–2021 Atlantic Basin hurricane seasons and is unique as it provides the first known analysis of total lightning (intra‐cloud and cloud‐to‐ground) observed in TCs through their extratropical transition and post‐tropical cyclone (PTC) phases. We consider the TC lifecycle stage, geographic location (e.g., land, coast, and ocean), shear strength, and quadrant relative to the storm motion and environmental shear vectors. Total lightning maxima are found in the forward right quadrant relative to storm motion and downshear of the TC center, consistent with previous studies using mainly cloud‐to‐ground lightning. Increasing environmental shear focuses the lightning maxima to the downshear right quadrant with respect to the shear vector in tropical storm phases. Vertical profiles of radar reflectivity from the Global Precipitation Measurement mission show that super‐electrically active convective precipitation features (>75 flashes) within the PTC phase of TCs have deeper mixed phase depths and higher reflectivity at −10°C than other phases, indicating the presence of more intense convection. Differences in the net convective behavior observed throughout TC evolution manifest in both the TC‐scale frequency of lightning‐producing cells and the intensity variations amongst individual convective cells. The combination of continuous lightning observations and precipitation snapshots improves our understanding of convective‐scale processes in TCs, especially in PTC phases, as they traverse the tropics and mid‐latitudes.
2D Magnetotelluric Inversion Using Linear Finite Element Methods and a Discretize‐Last Strategy With First and Second–Order Anisotropic Regularization
Authors: Codd Andrea , Gross Lutz , Kerr Janelle 
Published: September 2024

AbstractWe present a new inversion scheme for 2D magnetotelluric data. In contrast to established approaches, it is based on a mesh‐free formulation of the Quasi‐Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) iteration which uses the cost function gradient to implicitly construct approximations of the Hessian inverse to update the unknown conductivity. We introduce conventional first–order regularization as well as second–order regularization where inversions based on the latter are more appropriate for sparse data and can be read as maximum likelihood estimation of the unknown conductivity. We apply first–order finite element method (FEM) discretizations of the inversion scheme, forward and adjoint problems, where the latter is required for the construction of the cost function gradients. We allow for unstructured first–order triangular meshes supporting an enhanced ground level resolution including topographical features and coarsening at the far field leading to significant reduction in computational costs from using structured mesh. Formulating the inversion iteration in continuous form prior to discretization eliminates bias due to local refinements in the mesh and gives way for computationally efficient sparse matrix techniques in the implementation. A keystone in the new scheme is the multi‐grid approximation of the Hessian of the regularizations to construct efficient preconditioning for the inversion iteration. The method is applied to the Commeni4 benchmark and two field data sets. Tests show that for both first and second–order regularization an anisotropic approach is important to address the vast differences in horizontal and vertical spatial scale which in conventional approaches is implicitly introduced through the elongated shape of grid cells.
A Systematic Review of Meta‐Surface Based Antennas for Thz Applications
Authors: Sharma Nipun , Kaur Amrit 
Published: September 2024

AbstractThe growing demand for advanced wireless communication, high‐resolution imaging, and innovative medical applications in the Terahertz (THz) frequency range has driven remarkable developments in meta‐surface‐based antennas. This comprehensive review delves into the cutting‐edge advancements, novel designs, and practical applications of meta‐surfaces in the THz spectrum. The review begins by exploring the materials employed in meta‐surfaces and their crucial role in achieving efficient THz operation. It delves into the realm of polarization diversity, revealing innovative approaches to harnessing the potential of meta‐surfaces for polarization control and conversion. A key area of focus is beam‐steering technology, with a thorough investigation into beam‐steering techniques that have significant implications for enhancing wireless communication, high‐resolution imaging, and the internet of things. The paper highlights the potential of these techniques in addressing real‐world challenges and advancing THz technology. Furthermore, this review provides an in‐depth examination of the innovative antenna designs tailored for THz applications, shedding light on their characteristics and benefits. It also explores the exciting possibilities of THz technology within the medical field, including precise bio sensing and cancer cell detection.
Extracellular vesicles containing GAS6 protect the liver from ischemia-reperfusion injury by enhancing macrophage efferocytosis via MerTK-ERK-COX2 signaling
Authors: Miao Longyu , Yu Chaoqun , Guan Ge , Luan Xiaoyu , Jin Xiaoshuang , Pan Meiqi , Yang Yuzhen , Yan Jiaoyang , Chen Peng , Di Guohu 
Published: September 2024

AbstractHepatic ischemia-reperfusion injury (HIRI) is a significant issue during liver transplantation and surgery, contributing to the liver failure or even mortality. Although extracellular vesicles derived from mesenchymal stem cells (MSC-EVs) have shown substantial potentials in cell replacement therapy of various organ ischemia reperfusion injuries (IRIs), the precise mechanisms remain unclear. In this study, we demonstrate that systemic MSC-EVs administration is predominantly absorbed by macrophages, and verified that it could significantly reduce the liver injury and inflammatory response in mice suffering from HIRI. Furthermore, treatment with MSC-EVs induces macrophage polarization toward an anti-inflammatory phenotype. Mechanistically, proteomic profiling reveals an enrichment of growth arrest-specific 6 (GAS6) in MSC-EVs, significantly promoting the activation of myeloid-epithelial-reproductive tyrosine kinase/extracellular regulated protein kinases/cyclooxygenase 2 (MerTK/ERK/COX2) signaling pathway in macrophages and further enhancing their efferocytosis efficiency. Knockdown of GAS6 via lentiviral transfection or inhibition of MerTK using UNC2025 (a MerTK small molecule inhibitor) partially eliminates the protective effects of MSC-EVs on macrophage efferocytosis and liver injury. Overall, our findings support that MSC-EVs enriched GAS6 execute an anti-inflammation effect, highlighting that treatment based on the modulation of macrophage function by MSC-EVs as a promising approach in IRI.
Post-functionalization of alternating π-conjugated copolymers containing fluorene moieties via anodic chlorination using AlCl3
Authors: Kurioka Tomoyuki , Tomita Ikuyoshi , Inagi Shinsuke 
Published: September 2024

AbstractFluorene (Fl) derivatives are representative emitting motifs; thus, they are often installed into alternating π-conjugated copolymers (P(Fl-Ar)) as soluble polymeric emitters. Many researchers have focused on modifying the combined arylene units in P(Fl-Ar) derivatives to tune their optoelectronic properties; however, P(Fl-Ar) derivatives that contain fluorene units with functional groups at their sp2 carbons remain limited. Here, we synthesize P(Fl-Ar) derivatives comprising sp2-chlorinated fluorene units via anodic chlorination using aluminum chloride (AlCl3). The introduced chlorine atoms affect the optoelectronic properties of the pristine P(Fl-Ar) derivatives. Compared with the precursor P(Fl-Ar) derivatives, chlorinated P(Fl-Ar) derivatives exhibit longer maximum emission wavelengths.
Subclinical impairment of the left atrium is associated with MRI-based lung volume but not with parameters from pulmonary function testing
Authors: Wintergerst Charlotte , Lorbeer Roberto , Mujaj Blerim , Bulwer Bernard E. , Rospleszcz Susanne , Askani Esther , Schulz Holger , Karrasch Stefan , Peters Annette , Schlett Christopher L. , Bamberg Fabian , von Krüchten Ricarda 
Published: September 2024

AbstractLeft atrial (LA) physiology and hemodynamics are intimately connected to cardiac and lung function in health and disease. This study examined the relationship between MRI-based left atrial (LA) size and function with MRI-based lung volume and pulmonary function testing (PFT) parameters in the population-based KORA study cohort of 400 participants without overt cardiovascular disease. MRI quantification assessed LA size/function in sequences with and without ECG synchronization, alongside lung volume. Regression analysis explored the relationship of LA with MRI lung volume and PFT parameters. Among 378 participants (average age 56.3 ± 9.2 years; 42.3% women), non-gated LA size averaged 16.8 cm2, while maximal and minimal LA size from gated measurements were 19.6 cm2 and 11.9 cm2 respectively. The average MRI-derived lung volume was 4.0 L, with PFT showing a total lung capacity of 6.2 L, residual lung volume of 2.1 L, and forced vital capacity of 4.1 L. Multivariate regression analysis, adjusted for age, gender, and cardiovascular risk factors, revealed an inverse association between maximum LA size, non-gated LA, and LA area fraction with lung volume (ß = − 0.03, p = 0.006; ß = − 0.03, p = 0.021; ß = − 0.01, p = 0.012), with no significant association with PFT parameters. This suggests that MRI-based assessment may offer greater sensitivity in detecting subclinical LA impairment than PFT.
Adherence to non-pharmaceutical interventions following COVID-19 vaccination: a federated cohort study
Authors: Rader Benjamin , Sehgal Neil K. R. , Michelman Julie , Mellem Stefan , Schultheiss Marinanicole D. , Hoddes Tom , MacFarlane Jamie , Clark Geoff , O’Banion Shawn , Eastham Paul , Tuli Gaurav , Taylor James A. , Brownstein John S. 
Published: September 2024

AbstractIn pandemic mitigation, strategies such as social distancing and mask-wearing are vital to prevent disease resurgence. Yet, monitoring adherence is challenging, as individuals might be reluctant to share behavioral data with public health authorities. To address this challenge and demonstrate a framework for conducting observational research with sensitive data in a privacy-conscious manner, we employ a privacy-centric epidemiological study design: the federated cohort. This approach leverages recent computational advances to allow for distributed participants to contribute to a prospective, observational research study while maintaining full control of their data. We apply this strategy here to explore pandemic intervention adherence patterns. Participants (n = 3808) were enrolled in our federated cohort via the “Google Health Studies” mobile application. Participants completed weekly surveys and contributed empirically measured mobility data from their Android devices between November 2020 to August 2021. Using federated analytics, differential privacy, and secure aggregation, we analyzed data in five 6-week periods, encompassing the pre- and post-vaccination phases. Our results showed that participants largely utilized non-pharmaceutical intervention strategies until they were fully vaccinated against COVID-19, except for individuals without plans to become vaccinated. Furthermore, this project offers a blueprint for conducting a federated cohort study and engaging in privacy-preserving research during a public health emergency.
O-GlcNAcylation mediates Wnt-stimulated bone formation by rewiring aerobic glycolysis
Authors: You Chengjia , Shen Fangyuan , Yang Puying , Cui Jingyao , Ren Qiaoyue , Liu Moyu , Hu Yujie , Li Boer , Ye Ling , Shi Yu 
Published: September 2024

AbstractWnt signaling is an important target for anabolic therapies in osteoporosis. A sclerostin-neutralizing antibody (Scl-Ab), that blocks the Wnt signaling inhibitor (sclerostin), has been shown to promote bone mass in animal models and clinical studies. However, the cellular mechanisms by which Wnt signaling promotes osteogenesis remain to be further investigated. O-GlcNAcylation, a dynamic post-translational modification of proteins, controls multiple critical biological processes including transcription, translation, and cell fate determination. Here, we report that Wnt3a either induces O-GlcNAcylation rapidly via the Ca2+-PKA-Gfat1 axis, or increases it in a Wnt-β-catenin-dependent manner following prolonged stimulation. Importantly, we find O-GlcNAcylation indispensable for osteoblastogenesis both in vivo and in vitro. Genetic ablation of O-GlcNAcylation in the osteoblast-lineage diminishes bone formation and delays bone fracture healing in response to Wnt stimulation in vivo. Mechanistically, Wnt3a induces O-GlcNAcylation at Serine 174 of PDK1 to stabilize the protein, resulting in increased glycolysis and osteogenesis. These findings highlight O-GlcNAcylation as an important mechanism regulating Wnt-induced glucose metabolism and bone anabolism.