bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risk and Realism: Using a Board Game Mobile App to Illustrate an International Relations Theory
Authors: Hendrickson Petra 
Published: February 2025

ABSTRACT
When introducing students to broad theoretical frameworks, simulations and games can help to make theoretical concepts clearer. One example is the use of the mobile app for the board game Risk to illustrate many of the concepts associated with realism. The mobile app has advantages over the physical board game in that the length of the games often is much shorter and allows students to experiment with the number of opponents to determine how that changes the dynamics of the game. Survey results from students in an introduction to international relations course who played Risk in proximity to lessons on realism report that playing iterations of the game clarified realist topics including the balance of power, the security dilemma, and the temporary nature of alliances.
Cultural Influences on Transgender Rights: Examining the Role of Religion
Authors: Williamson Myles 
Published: February 2025

Abstract
Transgender rights are receiving increasingly greater attention in policymaking arenas around the globe. However, few studies empirically investigate the factors that shape the protection of these types of legal rights from a global perspective. Therefore, this article aims to contribute to the literature by examining how one potential correlate – religion – influences countries’ recognition of transgender rights. More specifically, this article distinguishes between the presence of religion in state institutions versus the society, allowing for a more nuanced investigation of the different avenues through which religion may influence a country’s trans-specific laws. Analyzing data from 173 countries between 2000 and 2021, this study finds that while religious influence over society more consistently explains variation in transgender rights, religion in government also plays a significant role. Overall, the findings shed light on an often-understudied population and offer valuable insights into the areas where transgender individuals may be most vulnerable.
The YTHDC1 reader protein recognizes and regulates the lncRNA MEG3 following its METTL3-mediated m6A methylation: a novel mechanism early during radiation-induced liver injury
Authors: Song Gui-yuan , Yu Qing-hua , Xing Xue-kun , Fan Xin-ming , Xu Si-guang , Zhang Wen-bo , Wu Yao-yao , Zhang Nan , Chao Tian-zhu , Wang Fei , Ding Cheng-shi , Guo Cun-yang , Ma Li , Sun Chang-ye , Duan Shu-yan , Xu Ping 
Published: February 2025

Abstract
While apoptotic cell death is known to be central to the pathogenesis of radiation-induced liver injury (RILI), the mechanistic basis for this apoptotic activity remains poorly understood. N6-methyladenosine (m6A) modifications are the most common form of reversible methylation observed on lncRNAs in eukaryotic cells, with their presence leading to pronounced changes in the activity of a range of biological processes. The degree to which m6A modification plays a role in the induction of apoptotic cell death in response to ionizing radiation (IR) in the context of RILI remains to be established. Here, IR-induced apoptosis was found to significantly decrease the levels of m6A present, with a pronounced decrease in the expression of methyltransferase-like 3 (METTL3) at 2 d post radiation in vitro. From a mechanistic perspective, a methylated RNA immunoprecipitation assay found that lncRNA MEG3 was a major METTL3 target. The expression of MEG3 was upregulated via METTL3-mediated m6A in a process that was dependent on YTHDC1, ultimately reversing the miR-20b-mediated inhibition of BNIP2 expression. Together, these findings demonstrate that the responsivity of METTL3 activity to IR plays a role in IR-induced apoptotic cell death, leading to the reverse of miR-20b-mediated BNIP2 inhibition through the YTHDC1-dependent m6A modification of MEG3, suggesting that this process may play a central role in RILI incidence.
Regional autozygosity association with albumin-to-creatinine ratio reveals a novel FTO region in an Indigenous Australian population
Authors: Arunachalam Vignesh , Tran Kim N. , Hoy Wendy , Lea Rodney A. , Nagaraj Shivashankar H. 
Published: February 2025

Abstract
The genetic distinctiveness of Indigenous Australian populations is well established, yet the Tiwi population remains underrepresented in genetic research. Due to their prolonged geographic isolation, these populations are prone to increased runs of homozygosity (ROH). We investigated the genetic diversity of the Tiwi population, isolated from mainland Australia for decades, based on ROH and their associations with clinical traits. We analyzed 455 whole genome sequences to identify population structure via PCA and performed a comparison with UK Biobank, Melanesian, and Polynesian cohorts. ROH assessment and genome-wide and regional measures of homozygosity were used to explore associations between clinical traits and autozygosity. Our analysis revealed distinct genetic characteristics of the Tiwi population that aligned closely with those of the Melanesian cohort. Tiwi individuals exhibited an increased burden of ROH, particularly in LINC0109, FMLN1, and RPL17P45 genes on chromosomes 2, 17, and 18, respectively, indicating prolonged isolation and genetic drift. A positive correlation was observed between genomic F
ROH
and albumin-to-creatinine ratio (ACR) levels, suggesting a potential link between autozygosity and renal health markers. Furthermore, regional autozygosity association analysis revealed an association between elevated ACR and a region in FTO, implicating its role in obesity, kidney disease, and cardiovascular conditions. Importantly, we found that this association is strong under the recessive model. This research lays a robust foundation for further exploration of ROH mapping and its implications for disease susceptibility within Indigenous communities worldwide.
Astrocyte heterogeneity reveals region-specific astrogenesis in the white matter
Authors: Bocchi Riccardo , Thorwirth Manja , Simon-Ebert Tatiana , Koupourtidou Christina , Clavreul Solène , Kolf Keegan , Della Vecchia Patrizia , Bottes Sara , Jessberger Sebastian , Zhou Jiafeng , Wani Gulzar , Pilz Gregor-Alexander , Ninkovic Jovica , Buffo Annalisa , Sirko Swetlana , Götz Magdalena , Fischer-Sternjak Judith 
Published: February 2025

Abstract
Astrocyte heterogeneity has been well explored, but our understanding of white matter (WM) astrocytes and their distinctions from gray matter (GM) astrocytes remains limited. Here, we compared astrocytes from cortical GM and WM/corpus callosum (WM/CC) using single-cell RNA sequencing and spatial transcriptomics of the murine forebrain. The comparison revealed similarities but also significant differences between WM and GM astrocytes, including cytoskeletal and metabolic hallmarks specific to WM astrocytes with molecular properties also shared with human WM astrocytes. When we compared murine astrocytes from two different WM regions, the cortex and cerebellum, we found that they exhibited distinct, region-specific molecular properties, with the cerebellum lacking, for example, a specific cluster of WM astrocytes expressing progenitor and proliferation genes. Functional experiments confirmed astrocyte proliferation in the WM/CC, but not in the cerebellar WM, suggesting that the WM/CC may be a source of continued astrogenesis.
Correlation between gut microbiota dysbiosis, metabolic syndrome and breast cancer
Authors: Abdelqader Eslam M. , Mahmoud Walaa S. , Gebreel Hassan M. , Kamel Mahmoud M. , Abu-Elghait Mohammed 
Published: February 2025

Abstract
Breast cancer is a widespread cancer with a high death rate globally. The incidence of breast cancer is expected to increase, particularly in low and middle-income countries due to environmental factors and lifestyle changes. Several risk factors, such as age, family history, hormonal and reproductive factors, have been identified to influence breast cancer development. Metabolic syndrome, is a metabolic disorder that has also been linked to breast cancer risk. The gut microbiome has been suggested as one of the environmental factors leading to breast cancer. The human microbiome is mainly colonized in the intestine by various bacterial species, including Lactobacillus, Bifidobacterium, and Streptococcus and protect the host against pathogenic microorganisms and regulate the immune system. This study included 50 female breast cancer patients and 50 healthy controls with matched ages. Stool fresh samples were taken from test and control groups and stored at − 20 °C until further investigations. DNA of the bacteria in stool samples was extracted using reverse transcription-quantitative polymerase chain reaction to check for the bacterial 16s rRNA gene. The exclusion criteria included other malignancies, recent intestinal surgery, infectious diarrhea, prolonged use of antibiotics, substance addiction, and pregnancy or lactation. Our findings exhibited that breast cancer patients had a higher incidence of metabolic syndrome (60%) compared to cancer-free controls (40%). Furthermore, breast cancer patients had significantly lower Bifidobacterium and Lactobacillus counts than the controls. No significant difference was found in Streptococcus counts between groups. These findings support the relationship between breast cancer and metabolic syndrome and suggest the potential involvement of Lactobacillus and Bifidobacterium in breast cancer pathophysiology. Our study supports the relation between breast cancer and disorder of metabolic syndrome and suggests the potential involvement of Lactobacillus and Bifidobacterium in breast cancer pathophysiology. Further research is necessary to investigate the complex interactions between genes, the environment, and the gut microbiome in breast cancer development. Understanding these interactions could lead to the progress of novel strategies for breast cancer prevention and treatment.
Research on optimal control design of displaced left turn signal at one two way and three one way traffic intersections
Authors: Han Ning , Cheng Guozhu , Lin Jiadong , Tang Zhiyun , Xie Fei 
Published: February 2025

Abstract
As the nodes of urban road traffic network, intersections serve as an effective means to improve traffic flow efficiency and alleviate traffic congestion through the optimization of reasonable traffic organization and signal schemes for intersections. In recent years, domestic research on unconventional intersections has attracted increasing attention. This paper first summarized the types of single-double traffic intersections and preliminarily discussed the applicability of displaced left-turn (DLT) signal organization. We then constructed a calculation method for determining the important design parameters in the DLT signal traffic organization, based on the design of the channelization form of the intersection of Haihe Road and Xuanqing Street in Harbin City, China. Based on the average delay of vehicles, we established a signal optimization control model with two components: the main signal delay and the pre-signal delay, aiming to minimize the delay of vehicles. The signal optimization control model was established considering both the main signal delay and the pre-signal delay. Finally, the selected examples were compared and verified using VISSIM simulation. The simulation results show that through the design of one two-way and three one-way traffic intersections with DLT signals and signal optimization control, the average vehicle delay at the intersection is significantly reduced, and traffic efficiency is enhanced.
An assessment of machine learning methods to quantify blood lactate from neutrophils phagocytic activity
Authors: Tahir Muhammad Nabeel , Wagner Kurt , Hassan Umer 
Published: February 2025

Abstract
Phagocytosis is a critical component of innate immunity that helps the body defend itself against infection, foreign particles, and cellular debris. Investigating and quantifying phagocytosis can help understand how the immune system identifies foreign particles and how phagocytosis relates to other biomarkers, e.g., cytokines, cell surface receptors, or blood lactate levels. In particular, increased blood lactate levels can be a potential biomarker to study diseases, e.g., septic shock. Establishing a relationship between phagocytosis and lactate levels can serve as an effective tool to monitor the immune response and may help stratify patients. In this study, we use phagocytosis activity data to classify the patients into two groups of blood lactate levels (High and Low) with machine learning models. The neutrophils extracted from the whole blood samples of 19 patients were used to collect data on phagocytosis, where the neutrophils were allowed to internalize IgG coated fluorescent bioparticles. The data collection process involved collecting whole blood samples, neutrophil isolation, adding fluorescent beads, incubating, and imaging the sample using a fluorescence microscope. The phagocytosis assay images were used to generate a numerical dataset by manually counting the number of particles engulfed by each cell. The study first presents an improved understanding by employing hierarchical clustering and heatmaps to generate the graphical representation of phagocytosis data. By comparing the results of heat maps and clustering techniques, it can be observed that the phagocytosis activity data can be used to differentiate blood lactate levels in two groups (control and high-risk). Later, three machine learning models (Decision Tree, k-nearest Neighbor, and Naïve Bayes) were trained on the original and pruned datasets after the outliers were removed. The AI models classified the data into high-risk and low-risk groups of blood lactate levels. A maximum classification accuracy of 78% and an area under the curve of 0.78 was achieved using the trained models.
Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning
Authors: Ordinola Alfredo , Abramian David , Herberthson Magnus , Eklund Anders , Özarslan Evren 
Published: February 2025

Abstract
Diffusion magnetic resonance imaging (diffusion MRI) is widely employed to probe the diffusive motion of water molecules within the tissue. Numerous diseases and processes affecting the central nervous system can be detected and monitored via diffusion MRI thanks to its sensitivity to microstructural alterations in tissue. The latter has prompted interest in quantitative mapping of the microstructural parameters, such as the fiber orientation distribution function (fODF), which is instrumental for noninvasively mapping the underlying axonal fiber tracts in white matter through a procedure known as tractography. However, such applications demand repeated acquisitions of MRI volumes with varied experimental parameters demanding long acquisition times and/or limited spatial resolution. In this work, we present a deep-learning-based approach for increasing the spatial resolution of diffusion MRI data in the form of fODFs obtained through constrained spherical deconvolution. The proposed approach is evaluated on high quality data from the Human Connectome Project, and is shown to generate upsampled results with a greater correspondence to ground truth high-resolution data than can be achieved with ordinary spline interpolation methods. Furthermore, we employ a measure based on the earth mover’s distance to assess the accuracy of the upsampled fODFs. At low signal-to-noise ratios, our super-resolution method provides more accurate estimates of the fODF compared to data collected with 8 times smaller voxel volume.
Pertussis before, during and after Covid-19
Authors: Locht Camille 
Published: February 2025

Abstract
After the Covid-19 pandemic, pertussis has made a spectacular comeback in Europe and many other parts of the world, while during the pandemic it had essentially disappeared because of the social distancing requirements. However, even before the Covid-19 pandemic, the disease was on the rise in many countries, especially those that have replaced whole-cell pertussis vaccines by acellular pertussis vaccines. Several reasons may account for this upsurge, including strain adaptation to escape vaccine-induced immunity, rapid waning of immunity after vaccination and the failure of current vaccines to prevent infection by and transmission of the causative agent Bordetella pertussis. Various strategies have been deployed to control the disease, the most effective of which is maternal vaccination during pregnancy to protect the newborn against the most severe and deadly forms of the disease. However, ultimate control of pertussis likely requires novel vaccines, which prevent infection and transmission, not only disease. One of them is the live attenuated BPZE1 vaccine, which has shown promise in pre-clinical and clinical studies and may therefore perhaps become a gamechanger.
Picosecond imaging of dynamics of solvated electrons during femtosecond laser-induced plasma generation in water
Authors: Sakakibara Noritaka , Ito Tsuyohito , Hakuta Yukiya , Shimizu Yoshiki , Terashima Kazuo , Miura Eisuke 
Published: February 2025

The dynamics of solvated electrons were visualized using absorption imaging with sub-picosecond time resolution based on a pump–probe measurement during the early stages of femtosecond laser-induced plasma generation in water. The solvated electrons were generated by the propagation of a femtosecond laser pump pulse. In the area with a pump laser intensity over 2 × 1013 W/cm2, where a high density of free electrons was produced, solvated electrons exhibited an additional rapid increase in optical density (OD) at 800 nm, 7–9 ps after the pump pulse excitation. In contrast, no two-step increase in OD was observed when probed at 400 nm, suggesting that the absorption coefficient of the solvated electrons rapidly changed around 800 nm after femtosecond laser excitation for a few picoseconds. This observation might indicate the structural and electronic modulation of solvated electrons owing to the high density of free electrons in water, accompanied by femtosecond-laser-induced plasma generation.
Predicting reading comprehension in Creole Papiamento and Dutch in a post-colonial context
Authors: Mercelina Gil-Marie , Segers Eliane , Severing Ronald , Verhoeven Ludo 
Published: February 2025

Abstract
In postcolonial contexts, children usually learn to read in a language other than their main socialization
language, usually a creole. To understand how this affects reading acquisition we examined the early Creole Papiamento (L1) and
Dutch (L2) reading skills of 128 Caribbean children. We investigated the impact of instructional order, first Papiamento then
Dutch (Papiamento-first group), and vice versa (Dutch-first group), and cognitive and linguistic predictors on grade 2 reading
comprehension. The study also explored linguistic interdependence. Results showed a significant positive effect of instructional
order on Dutch reading comprehension for the Dutch-first group, but no differences between groups on Papiamento reading
comprehension. Both linguistic and cognitive precursors predicted reading comprehension in Papiamento and Dutch. We found that
reading comprehension in Papiamento and Dutch is interdependent, influenced by instruction language, especially in the Dutch-first
group. This underlines the crucial role of L1 in shaping reading comprehension in both L1 and L2.
Neural Network-Based Frequency Optimization for Superconducting Quantum Chips
Authors: Lu Bin-Han , Li Qing-Song , Wang Peng , Chen Zhao-Yun , Wu Yu-Chun , Guo Guo-Ping 
Published: February 2025

Abstract
Optimizing frequency configurations for qubits and gates in superconducting quantum chips presents a complex NP-complete challenge, critical for mitigating decoherence and crosstalk. This paper introduces a neural network-based approach, leveraging the network as a surrogate model to predict frequency errors. The method employs a closed-loop Bayesian optimization framework to iteratively refine configurations, guided by the network’s knowledge of nonlinear error mechanisms. By focusing on localized chip windows, the optimization identifies optimal frequency settings that minimize errors. The approach is validated through randomized and cross-entropy benchmarking, showing improved energy calculations when optimizing frequency configurations for a crosstalk-aware hardware-efficient ansatz in variational quantum eigensolvers on superconducting quantum chips.
Bayesian Interpretation of Husimi Function and Wehrl Entropy
Authors: Xu Chen , Yu Yiqi , Zhang Peng 
Published: February 2025

Abstract
Husimi function (Q-function) of a quantum state is the distribution function of the density operator in the coherent state representation. It is widely used in theoretical research, such as in quantum optics. The Wehrl entropy is the Shannon entropy of the Husimi function, and is non-zero even for pure states. This entropy has been extensively studied in mathematical physics. Recent research also suggests a significant connection between the Wehrl entropy and many-body quantum entanglement in spin systems. We investigate the statistical interpretation of the Husimi function and the Wehrl entropy, taking the system of $N$ spin-1/2 particles as an example. Due to the completeness of coherent states, the Husimi function and Wehrl entropy can be explained via the positive operator-valued measurement (POVM) theory, although the coherent states are not a set of orthonormal basis. Here, with the help of the Bayes' theorem, we provide an alternative probabilistic interpretation for the Husimi function and the Wehrl entropy. This interpretation is based on direct measurements of the system, and thus does not require the introduction of an ancillary system as in POVM theory. Moreover, under this interpretation the classical correspondences of the Husimi function and Wehrl entropy are just phase-space probability distribution function of $N$ classical tops, and its associated entropy, respectively. Therefore, this explanation contributes to a better understanding of the relationship between the Husimi function, Wehrl entropy, and classical-quantum correspondence. The generalization of this statistical interpretation to continuous-variable systems is also discussed.
Research on insulator defect detection technology based on improved YOLOv10n
Authors: Niu Zongchao 
Published: February 2025

Abstract
We propose an enhanced YOLOv10n algorithm to boost precision in detecting insulator defects amidst complex backgrounds during UAV-assisted inspections. The study first enhances the model’s attention to a larger range of features by labeling the existing dataset in detail, then introduces the AKConv variable convolution mechanism to the front end of the YOLOv10n backbone network, and integrates the EMA_attention multiscale attention mechanism at the end of its neural network. The enhanced YOLOv10n model achieves a 4.4% increase in mAP0.5, a 1.1% increase in mAP50-90, and a 7.1% improvement in accuracy compared to the original YOLOv10n network.
Super twisting algorithm for power conversion system based on LCL filter
Authors: Li Chao , Gao Ning 
Published: February 2025

Abstract
New energy represented by wind and solar energy is often used in conjunction with energy storage devices due to its changing characteristics. For the three-phase two-level power conversion system (PCS), a super twisting algorithm (STA) control strategy based on the system error model is proposed in this paper. This method can maintain the quality of the grid current when the grid has large harmonics; it can operate in different modes to meet different power factor requirements. The proposed method has high parameter robustness and can resist the parameter drift of components. Theoretical analysis and simulation results prove the correctness of the proposed control strategy.