bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrative analysis of toxicometabolomics and toxicoproteomics data: new molecular insights into thiazolidinedione-induced cardiotoxicity
Authors: Al Sultan Abdullah , Rattray Zahra , Rattray Nicholas J. W. 
Published: December 2024

Abstract

Introduction
Despite the well-established efficacy of thiazolidinediones (TZDs), including pioglitazone and rosiglitazone, in type II diabetes management, their potential contribution to heart failure risk remains a significant area of uncertainty. This incomplete understanding, which persists despite decades of clinical use of TZDs, has generated ongoing controversy and unanswered questions regarding their safety profiles, ultimately limiting their broader clinical application.

Objective and methods
This study presented a multi-omics approach, integrating toxicoproteomics and toxicometabolomics data with the goal of uncovering novel mechanistic insights into TZD cardiotoxicity and identifying molecular signatures predictive of side effect progression.

Results
Network analysis of proteo-metabolomic data revealed a distinct fingerprint of disrupted biochemical pathways, which were primarily related to energy metabolism. Downregulation of oxidative phosphorylation and fatty acid synthesis was coupled with increased activity in anaerobic glycolysis, the pentose phosphate pathway, and amino acid and purine metabolism. This suggests a potential metabolic shift in AC16 cells from fatty acid oxidation towards anaerobic glycolysis, potentially contributing to observed cardiotoxicity. Additionally, the study identified a marked disruption in the glutathione system, indicating an imbalanced redox state triggered by TZD exposure. Importantly, our analysis identified key molecular signatures across omics datasets, including prominent signatures of amino acids like L-ornithine, L-tyrosine and glutamine, which are evidently associated with heart failure, supporting their potential use for the early prediction of cardiotoxicity progression.

Conclusion
By uncovering a novel mechanistic explanation for TZD cardiotoxicity, this study simultaneously illuminates potential therapeutic interventions, opening avenues for future research to improve the safety profile of TZD agents. (250 words)

Graphical abstracts
Metallurgical Synthesis Methods for Mg-Al-Ca Scientific Model Materials
Authors: Luo W. , Tanure L. , Felten M. , Nowak J. , Delis W. , Freund M. , Ayeb N. , Zander D. , Thomas C. , Feuerbacher M. , Sandlöbes-Haut S. , Korte-Kerzel S. , Springer H. 
Published: December 2024

AbstractFurther improving the mechanical and corrosion properties of Mg-based alloys requires the investigation of underlying causalities between synthesis, processing, and microstructure, ideally down to the near atomic scale. However, such investigations with high resolution characterisation techniques require model materials of exceptionally controlled chemical composition and microstructure. In this context, the Mg-Al-Ca system, being attractive for offering significant improvement of the inherent brittleness of Mg alloys, is exceptionally challenging from a metallurgical perspective due to the inclusion of components with high reactivity and vapor pressures. Here, we demonstrate the applicability of various synthesis methods from arc-melting over solution growth to diffusion couples, extending to parameters for thermomechanical processing. Suitable pathways to overcome the specific challenges of the Mg-Al-Ca system are demonstrated, as well as the persistent limitations of the current state-of-the-art metallurgical laboratory technology. Solid solution sheet or wrought materials are ideally produced by induction melting in steel crucibles and casting under elevated pressure, with intermittent annealing steps between hot rolling passes to ensure homogenous chemical composition without excessive grain growth. Composite microstructures strongly depend on the solidification rate, and special care needs to be taken with regards to crucible reactions with increasing Al concentrations. Binary intermetallic compounds can be successfully produced in bulk by a variety of techniques, but the interplay between reactivity and evaporation of the melt when both Ca and Al are present required more complex approaches such as the Bridgman method for the synthesis of ternary compounds.
Application of machine learning and deep learning for predicting groundwater levels in the West Coast Aquifer System, South Africa
Authors: Igwebuike Ndubuisi , Ajayi Moyinoluwa , Okolie Chukwuma , Kanyerere Thokozani , Halihan Todd 
Published: December 2024

AbstractGroundwater models are valuable tools to quantify the response of groundwater level to hydrological stresses induced by climate variability and groundwater extraction. These models strive for sustainable groundwater management by balancing recharge, discharge, and natural processes, with groundwater level serving as a critical response variable. While traditional numerical models are labour-intensive, machine learning and deep learning offer a data-driven alternative, learning from historical data to predict groundwater level variations. The groundwater level in wells is typically recorded as continuous groundwater level time series data and is essential for implementing managed aquifer recharge within a particular region. Machine learning and deep learning are essential tools to generate a data-driven approach to modeling an area, and there is a need to understand if they are the most suitable tools to improve model prediction. To address this objective, the study evaluates two machine learning algorithms - Random Forest (RF) and Support Vector Machine (SVM); and two deep learning algorithms - Simple Recurrent Neural Network (SimpleRNN) and Long Short-Term Memory (LSTM) for modeling groundwater level changes in the West Coast Aquifer System of South Africa. Analysis of regression error metrics on the test dataset revealed that SVM outperformed the other models in terms of the root mean square error, whereas random forest had the best performance in terms of the MAE. In the accuracy analysis of predicted groundwater levels, SVM achieved the highest accuracy with an MAE of 0.356 m and an RMSE of 0.372 m. The study concludes that machine learning and deep learning are effective tools for improved modeling and prediction of groundwater level. Further research can incorporate more detailed geologic information of the study area for enhanced interpretation.
Insights of Cardiovascular Involvement in Women by Non-Invasive Imaging – Focus on Polycystic Ovaries Syndrome
Authors: Różewicz-Juraszek Monika , Ganbat Mijidsuren , Nagel Eike , Puntmann Valentina O. 
Published: December 2024

Abstract
Purpose of Review
The purpose of this paper is to review the results of studies from the last 3 years, presenting the current state of knowledge on the pathophysiological effect of polycystic ovary syndrome (PCOS) on the cardiovascular system, with cardiac involvement using non-invasive cardiovascular investigations.

Recent Findings
PCOS is a complex endocrine disorder that has a significant impact on the cardiovascular system. Recent studies confirm that PCOS increases the risk of cardiovascular disease (CVD) as well as the occurrence of cardiovascular events (CVE), including myocardial infarction (MI), coronary artery disease, and revascularization. These findings support the need for early screening and implementation of CVD prevention in women with PCOS.

Summary
Current evidence documents important and previously unrecognized mechanisms by which PCOS may adversely affect the cardiovascular system in affected women. Further studies are needed to in-depth characterisation of the cardiovascular phenotype of patients with PCOS. There is still a lack of studies using advanced cardiac imaging methods, including cardiac magnetic resonance imaging (CMR).

Graphical Abstract
Less Frequent but Equally Useful: Social and Temporal Comparisons in Light of Mindfulness and Self-Compassion
Authors: Borgdorf Kira Skirwitt Ann , Kuechler Gabriela , Wrzus Cornelia , Aguilar-Raab Corina 
Published: December 2024

Abstract
Objectives
Social and temporal comparisons are ubiquitous and considered important sources of an individual’s self-knowledge. Yet, comparisons are inherently evaluative and may result in negative affective consequences. In contrast, mindfulness and self-compassion are characterized by a present-moment, non-evaluative, and accepting stance toward the self and one’s experiences and are associated with numerous positive psychological health indicators. This study innovatively examined the associations of mindfulness and self-compassion with frequency and perceived utility of social (i.e., with others) and past-temporal (i.e., with the past self) comparisons across different life domains.

Method
In a binational online study, we examined the associations between mindfulness, self-compassion, and comparisons across five different domains (i.e., extraversion, emotional stability, appearance, professional success, and private life) in a gender- and age-diverse sample at two measurement points 6 months apart (NT1 = 615, NT2 = 310, 18–84 years, 51.5% female).

Results
Results of multiple regression analyses indicated that the more mindful or self-compassionate individuals were, the less they compared themselves with others or their past self (across domains β =  − 0.25 to − 0.51; all p-values < 0.001). Contrary to our expectations, the overall pattern of results suggests that mindfulness and self-compassion were generally not significantly associated with perceiving comparisons as less useful (after family-wise error correction; β =  − 0.04 to − 0.14; all p-values ≥ 0.008).

Conclusions
The findings suggest that comparisons, when done mindfully and self-compassionately, can be a valuable source of information for self-knowledge. At the same time, mindfulness and self-compassion may buffer against negative affective outcomes of comparison processes.

Preregistration
This study is preregistered on the Open Science Framework: https://osf.io/6hfb2.
Historicizing the liberal antiracism of Cultural Evolution
Authors: Brinitzer Cameron 
Published: December 2024

AbstractThe Cultural Evolution Society was established in 2015 to “catalyze a theoretical synthesis” in the scientific study of human culture. As a field of research, cultural evolution took shape in the 1970s and 1980s around the aim of incorporating culture into biology’s modern evolutionary synthesis. Cultural evolution grew around the turn of the twenty-first century at the interface of population genetics and cognitive psychology. This article locates the origins of research on cultural evolution in projects of postwar scientific antiracism and U.S.-based debates about race and intelligence in the 1960s. Charting the development of prominent approaches to studying cultural evolution, I show how population geneticists and cognitive psychologists worked to redefine culture in statistical, populational, and geographic terms to politically neutralize the study of human difference. I situate the forms of genetic and cognitive culturalism that emerged as a result in a longer history of twentieth-century scientific antiracism.
Comparative analysis of biopolymer films derived from corn and potato starch with insights into morphological, structural and thermal properties
Authors: Pooja N. , Banik Soumyabrata , Chakraborty Ishita , Sudeeksha H. C. , Mal Sib Sankar , Srisungsitthisunti Pornsak , Patil Ajeetkumar , Mahato Krishna Kishore , Mazumder Nirmal 
Published: December 2024

AbstractStarch biopolymer films were prepared using the solvent casting method involving acetic acid hydrolysis and glycerol plasticization. This process facilitated a more uniform distribution of plasticizers within the starch matrix, enhancing the films' flexibility. Fourier-transform infrared (FTIR) and Raman spectroscopy confirmed the formation of ester linkages and structural changes in the biopolymer films, attributed to glycerol integration. The optimal formulation comprised 6% starch, 6.8% acetic acid, and 6.8% glycerol. X-ray diffraction (XRD) analysis revealed a reduction in crystallinity of the starch during film formation, enhancing flexibility. Second harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) microscopy indicated that potato starch films had higher crystallinity compared to corn starch films. Thermal analysis via differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) showed that potato starch films exhibited lower gelatinization temperatures and higher thermal stability compared to corn starch films. Functional characterization demonstrated that higher starch content decreased water solubility and water vapor transmission rate, while increasing starch content improved the film's structural integrity. The films were hydrophilic, with static water contact angles indicating moderate wettability. Degradation studies showed that the films were stable in neutral and basic conditions but degraded under acidic conditions over time. The results suggest that potato starch films, with optimized glycerol and acetic acid content, offer improved flexibility, thermal stability, and structural integrity compared to corn starch films. Their performance in various conditions highlights their potential for specific applications, particularly where moisture and environmental stability are critical.
The spatio-temporal evolution and influencing factors of the coupling coordination degree between tourism supply and demand in the Yangtze river economic belt
Authors: Liu Li , Luo Jin 
Published: December 2024

AbstractThe imbalance between supply and demand is a pressing issue in the development of the tourism industry. Understanding the coupling coordination relationship and impact mechanism of supply-demand in the tourism system can help achieve high-quality tourism development. This study focuses on the Yangtze River Economic Belt (YREB) as the research area, quantifies the tourism supply index (TSI) and tourism demand index (TDI) from 2011 to 2020 using the tourism development index model, calculates the coupling coordination degree (CCD) of TSI and TDI based on the coupling coordination model, and explores the factors influencing the CCD using the geographic detector. The findings indicate that: (1) The TSI and TDI in various provinces show fluctuation but exhibit an overall upward trend. (2) There were apparent spatial disparities of the CCD, with a distribution characteristic of high in the east and low in other regions. The CCD gradually improved, with its gravity center slowly shifting toward the southwest. All provinces entered the intermediate stage of tourism development since 2014. (3) Basic service guarantee, consumption drive, and innovation drive are identified as the dominant factors influencing the CCD. The study can provide valuable insights for tourism coordination and sustainable development.
Product architecture strategies and effects matrices for early evaluation and selection of product architectures
Authors: Rice Scott E. , Sannar Benjamin C. , McKinnon Samuel A. , Humphrey Tyson M. , Mattson Christopher A. , Sorensen Carl D. , Anderson Michael L. 
Published: December 2024

Abstract
Product architecture decisions are made early in the product development process and have far-reaching effects. Unless anticipated through experience or intuition, many of these effects may not be apparent until much later in the development process, making changes to the architecture costly in time, effort and resources. Many researchers through the years have studied various elements of product architecture and their effects. By using a repeatable process for aggregating statements on the effects of architecture strategies from a selection of the literature on the topic and storing them in a systematic database, this information can then be recalled and presented in the form of a Product Architecture Strategy and Effect (PASE) matrix. PASE matrices allow for the identification, comparison, evaluation, and then selection of the most desirable product architecture strategies before expending resources along a specific development path. This paper introduces the PASE Database and matrix and describes their construction and use in guiding design decisions. This paper also provides metrics for understanding the robustness of this database.
Unsupervised extraction of local and global keywords from a single text
Authors: Aleksanyan Lida , Allahverdyan Armen 
Published: December 2024

Abstract
We propose an unsupervised, corpus-independent method to extract keywords from a single text. It is based on the spatial distribution of words and the response of this distribution to a random permutation of words. Our method has three advantages over existing unsupervised methods (such as YAKE). First, it is significantly more effective at extracting keywords from long texts in terms of precision and recall. Second, it allows inference of two types of keywords: local and global. Third, it extracts basic topics from texts. Additionally, our method is language-independent and applies to short texts. The results are obtained via human annotators with previous knowledge of texts from our database of classical literary works. The agreement between annotators is moderate to substantial. Our results are supported via human-independent arguments based on the average length of extracted content words and on the average number of nouns in extracted words. We discuss relations of keywords with higher-order textual features and reveal a connection between keywords and chapter divisions.
New time-series photometry of the magnetic white dwarf Star Feige 7
Authors: Koen Chris , Kilkenny David , Koester Detlev 
Published: December 2024

Abstract
Existing photometry of the magnetic helium-rich white dwarf Feige 7 is used to derive the parameters

$T_\mathrm{eff}=18\,480$

K and log

$\;g=8.74$

and a frequency of variability of 10.94192 d

$^{-1}$

(period 2.19340 h). New time-series photometry of Feige 7 is presented, covering full cycles of variability in the UBVRI and ugriz filters, which allows the wavelength dependence of the two amplitudes in the double wave light curve to be determined. Amplitudes are virtually constant for wavelengths longer than 5 000 Å, but increase sharply for shorter wavelengths. A simple model consisting of two large cool spots 180

$^\circ$

apart on the surface of star provides a reasonable description of the data.
Radiocarbon dating of the Church of St. Margaret of Antioch in Kopčany (Slovakia): International consortium results
Authors: Povinec Pavel P , Kontuľ Ivan , Cherkinsky Alexander , Hajdas Irka , Gu Yao , Timothy Jull A J , Lupták Tomáš , Molnár Mihály , Steier Peter , Svetlik Ivo 
Published: December 2024

AbstractAn international consortium of radiocarbon laboratories has established the origin of the Church of St. Margaret of Antioch in Kopčany (Slovakia), because its age was not well known from previous investigations. In total, 13 samples of charcoal, wood, mortar, and plaster were analyzed. The 14C results obtained from the different laboratories, as well as between the different sample types, were in good agreement. Resulting the final 14C calibrated age of the Church, based on dating a single piece of a wooden levelling rod is 774–884 AD (95.4% confidence level), which is in very good agreement with Bayesian modeling result based on dating of wood, charcoal and mortar samples (788–884 AD, 95.4% confidence level). The probability distribution from OxCal calibration shows that 79% of the probability distribution lies in the period before 863 AD, implying that the Church could have been constructed before the arrival of Constantine (St. Cyril) and St. Methodius to Great Moravia. If we take as the terminus post quem the documented date of consecration of the church in Nitrava (828 AD), the Bayesian modeling suggests the age of the Church in the range of 837–884 AD (95.4% confidence level). Although the 14C results have very good precision, the specific plateau shape of the calibration curve in this period caused a wide range of the calibrated age. The Church represents, together with the St. George’s Rotunda in Nitrianska Blatnica, probably the oldest standing purpose-built Christian church in the eastern part of Central Europe.
Bayesian-genome-wide association study and post-GWAS on reproductive traits of Holstein dairy cattle
Authors: Jabbari Tourchi Jeyran , Alijani Sadegh , Rafat Seyed Abbas , Abbasi Mokhtar Ali 
Published: December 2024

AbstractBayesian multiple regression models are often used for genomic selection, where all markers are adjusted simultaneously as random effects to reduce the false-discovery rate. The purpose of this research was to identify individual candidate genes for some reproductive traits in Holstein cattle through genome wide association studies (GWAS) using a Bayesian method in combination with post-GWAS analysis. Reproductive traits included: days open (DO), pregnancy rate (PR), calving interval (CI) and age at first calving (AFC). The animals were genotyped using single-nucleotide polymorphism (SNP) panels of different densities imputed to a 50 K SNP density. After quality control, we included 2400 genotyped animals. According to the Bayesian analysis, there were 19 windows with an explained additive genetic variance of >0.1 percent for CI, DO, AFC and PR in Holstein cattle, which were 3, 3, 6 and 7, respectively. Using Bayesian analysis, 79 genes were located within or nearby (250-kb) 19 significant SNPs/windows in the Bos taurus autosomes. Among these genes, we identified 25 candidate genes for reproductive traits, namely CHD7, CLVS1, EVX2, MAT2B, NUDCD2, GPR39, NCKAP5, LYPD1, HOXD13, SEMA5B, CCNG1, SEMA5A, BRF1, PSEN2, CACHD1, SUGTA, ELF1, SNORA70, AKT1, TM2D1, SLF1, MCTPA, PAB2A, MTRF1 and ADCY2. Additionally, another nine candidate genes (CLVS1, GPR39, CENPF, AMOT, ARF1, CCDC186, ADCY2, BDP1 and AMOTL1) were identified in the network cluster analysis as hub genes for reproductive traits. The results of gene set enrichment analysis (GSEA) and pathway analysis, suggest that the most important gene ontology term involving cellular metabolic process was related to the AFC trait. To summarize, Bayesian methods were used to identify SNPs and candidate genes that could be useful in genomic selection to improve reproductive traits of Holstein dairy cattle.
The predictive values of C-reactive protein-neutrophil to lymphocyte ratio for the risk of refractory Mycoplasma pneumoniae pneumonia in children: a retrospective cohort study
Authors: Chen Xiaoli , Wang Shanhu , Cai Hailun , Xia Xiaojiao 
Published: December 2024

Abstract
This cohort study evaluated the associations of C-reactive protein-neutrophil to lymphocyte ratio (C-NLR) and lymphocyte-CRP ratio (LCR) with refractory Mycoplasma pneumoniae pneumonia (RMPP), and the predictive values of C-NLR and LCR for RMPP and prolonged fever in children based on 389 children with MPP. The associations of NLR, C-NLR, and LCR with RMPP and prolonged fever were evaluated by logistic regression analysis. C-NLR was correlated with an increased risk of RMPP in children [odds ratio (OR) = 3.459, 95% confidence interval (CI): 1.598–7.491]. A higher risk of RMPP was identified in the C-NLR > 29.9 group (OR = 2.885, 95% CI: 1.599–5.203). LCR > 1584.2 was associated with a decreased risk of RMPP (OR = 0.500, 95% CI: 0.282–0.887). Increased risk of prolonged fever in children was identified with the increase of C-NLR (OR = 5.913, 95% CI: 2.335–14.972) or NLR (OR = 2.413, 95% CI: 1.689–3.446). The AUCs of C-NLR, LCR, and NLR for predicting RMPP were 0.630, 0.623, and 0.608, respectively. In conclusion, C-NLR was associated with increased RMPP risk in children and had good value for predicting RMPP and prolonged fever in children.