bluuebunny/crossref_metadata_2025_split huggingface dataset:
Capitalist Diversity, Legal Origins, and Non-Independent Pasts: The Effects of Institutional Settings on Economic Performance
Authors: Halili Bernadette Louise , Gonzalez Carlos Rodriguez 
Published: October 2023

Abstract
This study explores the role of institutional settings, in their path dependence, stability and complementarity, in determining aggregate economic performance at the country-level. Cross-country differences in institutional design are operationalised in this study as national institutional settings: firstly as institutional configurations in the Varieties of Capitalism framework and secondly, as the interaction between formal institutions such as a modern capitalist state’s legal origins and informal institutions such as its non-independent past. Empirical findings from a panel data of 24 countries for the period 1995–2019 show that national institutional settings such as family market economies, mixed market economies, and the interaction between a country’s non-independent past and transplanted French legal origin, have significant and negative relationships with economic performance. Policy recommendations focus on addressing adverse macroeconomic consequences brought on by the predominance of entrenched elite groups, traditional patron-client relationships, and detrimental patronage networks.
JEL Classifications: F54 Colonialism; Imperialism; Postcolonialism, O43 Institutions and Growth, P16 Political Economy, P48 Political Economy; Legal Institutions; Property Rights; Natural Resources; Energy; Environment; Regional Studies
Cardiopulmonary Exercise Test to Detect Cardiac Dysfunction from Pulmonary Vascular Disease
Authors: Alotaibi Mona , Yang Jenny Z. , Papamatheakis Demosthenes G. , McGuire W. Cameron , Fernandes Timothy M. , Morris Timothy A. 
Published: October 2023

Abstract
Background
Cardiac dysfunction from pulmonary vascular disease causes characteristic findings on cardiopulmonary exercise testing (CPET). We tested the accuracy of CPET for detecting inadequate stroke volume (SV) augmentation during exercise, a pivotal manifestation of cardiac limitation in patients with pulmonary vascular disease.
Methods
We reviewed patients with suspected pulmonary vascular disease in whom CPET and right heart catheterization (RHC) measurements were taken at rest and at anaerobic threshold (AT). We correlated CPET-determined O2·pulseAT/O2·pulserest with RHC-determined SVAT/SVrest. We evaluated the sensitivity and specificity of O2·pulseAT/O2·pulserest to detect SVAT/SVrest below the lower limit of normal (LLN). For comparison, we performed similar analyses comparing echocardiographically-measured peak tricuspid regurgitant velocity (TRVpeak) with SVAT/SVrest.
Results
From July 2018 through February 2023, 83 simultaneous RHC and CPET were performed. Thirty-six studies measured O2·pulse and SV at rest and at AT. O2·pulseAT/O2·pulserest correlated highly with SVAT/SVrest (r = 0.72, 95% CI 0.52, 0.85; p < 0.0001), whereas TRVpeak did not (r = -0.09, 95% CI -0.47, 0.33; p < 0.69). The AUROC to detect SVAT/SVrest below the LLN was significantly higher for O2·pulseAT/O2·pulserest (0.92, SE 0.04; p = 0.0002) than for TRVpeak (0.69, SE 0.10; p = 0.12). O2·pulseAT/O2·pulserest of less than 2.6 was 92.6% sensitive (95% CI 76.6%, 98.7%) and 66.7% specific (95% CI 35.2%, 87.9%) for deficient SVAT/SVrest.
Conclusions
CPET detected deficient SV augmentation more accurately than echocardiography. CPET-determined O2·pulseAT/O2·pulserest may have a prominent role for noninvasive screening of patients at risk for pulmonary vascular disease, such as patients with persistent dyspnea after pulmonary embolism.
Improving ML Accuracy in SQL Injection Detection using NLP and Feature Engineering.
Authors: Jaradat Majdi , Rattrout Amjad , Jayousi Rashid 
Published: October 2023

Abstract
This research proposes the DSQLIA model, which enhances the accuracy of machine learning (ML) algorithms in detecting SQL injection attacks. Traditional rule-based and signature-based methods face limitations in effectively identifying evolving attack techniques. To address this, DSQLIA employs feature engineering and Natural Language Processing (NLP). Relevant features capturing the unique characteristics of SQL injection attacks are identified and created through feature engineering. NLP techniques are applied to analyze the textual content of SQL queries and extract meaningful information for distinguishing between legitimate and malicious queries. The DSQLIA model evaluates different ML algorithms, including decision trees, support vector machines (SVM), and artificial neural networks (ANN), on a dataset. Various performance metrics such as accuracy, precision, recall, and F1-score are used to assess algorithm effectiveness. Results show that the SVM algorithm achieves the highest accuracy of 0.994, followed by the decision tree with 0.975, and the ANN with 0.966. This highlights the improved performance of SVM in accurately classifying SQL queries. By combining feature engineering and NLP techniques, the DSQLIA model enhances ML accuracy in SQL injection detection, offering a valuable approach to mitigating the risks posed by these vulnerabilities in web applications.
Disulfidptosis-related lncRNA signatures in lung adenocarcinoma: Predicting prognosis and evaluating the tumor immune microenvironment
Authors: Song Zipei , Cao Xincen , Wang Xiaokun , Li Yuting , Zhang Weiran , Wang Yuheng , Chen Liang 
Published: October 2023

Abstract
Background
As a novel form of regulated cell death (RCD), disulfidptosis has been reported recently, which brought the significant probability in better understanding for pathogenesis and therapeutic strategies of tumors. Long non-coding RNAs (LncRNAs) regulate the viability of tumor cells by engaging with a range of targets, including DNA, RNA, and proteins. Nonetheless, the understanding about the prognostic value of disulfidptosis-related LncRNAs (DRlncRNAs) in lung adenocarcinoma (LUAD) remains incomplete. Therefore, our study aimed at establishing a prognostic model for LUAD patients based on DRLncRNAs.
Methods
RNA-seq data and corresponding clinical information were acquired from The Cancer Genome Atlas (TCGA) database, enabling the identification of DRlncRNAs. Subsequently, a prognostic model was formulated for LUAD by utilizing a series of analyses including univariate COX, LASSO, and multivariate COX regression. Patients were then categorized into two groups with distinct level of DRLS score, and subsequently subjected to the consensus clustering analysis for assigning LUAD patients to distinct subtypes by employing the DRlncRNAs. Subsequent studies investigated disparities among groups with distinct risk and molecular subtypes in terms of overall survival (OS), functional enrichment, the tumor immune microenvironment (TIME), somatic mutations, and drug sensitivity. Finally, in vitro experiments were conducted to validate the LUAD cellular proliferation and migratory behavior upon GSEA knockdown.
Results
Using the prognostic model consists of 5 DRlncRNAs (AL365181.2, GSEC, AC093673.1, AC012615.1, AL606834.1), the low-risk group exhibited a markedly superior survival in comparison to the high-risk group. The significant differences were observed among patients from different risk groups in OS, immune cell infiltration, immune checkpoint expression, immunotherapy response, and mutation landscape. Experimental results from cellular studies demonstrate the knockdown of lncRNA GSEC leading to a significant reduction in the proliferation and migration abilities of LUAD cells.
Conclusion
Our prognostic model, constructed using 5 DRlncRNAs, exhibited the capacity to independently predict the survival of LUAD patients, providing the potentially significant assistance in prognosis prediction, and treatment effects optimization. Moreover, our study established a foundation for further research on disulfidptosis in LUAD and proposed new perspectives for the treatment of LUAD.
Hydraulic resistance of three-dimensional pial perivascular spaces in the brain
Authors: Boster Kimberly A. S. , Sun Jiatong , Shang Jessica K. , Kelley Douglas H. , Thomas John H. 
Published: October 2023

Abstract
Background Perivascular spaces (PVSs) carry cerebrospinal fluid (CSF) around the brain, facilitating healthy waste clearance. Measuring those flows in vivo is difficult, and often impossible, because PVSs are small, so accurate modeling is essential for understanding brain clearance. The most important parameter for modeling flow in a PVS is its hydraulic resistance, defined as the ratio of pressure drop to volume flow rate, which depends on its size and shape. In particular, the local resistance per unit length varies along a PVS and depends on variations in the local cross section.
Methods Using segmented, three-dimensional images of pial PVSs in mice, we performed fluid dynamical simulations to calculate the resistance per unit length. We applied extended lubrication theory to elucidate the difference between the calculated resistance and the expected resistance assuming a uniform flow. We tested four different approximation methods, and a novel correction factor to determine how to accurately estimate resistance per unit length with low computational cost. To assess the impact of assuming unidirectional flow, we also considered a circular duct whose cross-sectional area varied sinusoidally along its length.
Results We found that modeling a PVS as a series of short ducts with uniform flow, and numerically solving for the flow in each, yields good resistance estimates at low cost. If the second derivative of area with respect to axial location is less than 2, error is typically less than 15%, and can be reduced further with our correction factor. To make estimates with even lower cost, we found that instead of solving for the resistance numerically, the well-known resistance of a circular duct could be scaled by a shape factor. As long as the aspect ratio of the cross section was less than 0.7, the additional error was less than 10%.
Conclusions Neglecting off-axis velocity components underestimates the average resistance, but the error can be reduced with a simple correction factor. These results could increase the accuracy of future models of brain-wide and local CSF flow, enabling better prediction of clearance, for example, as it varies with age, brain state, and pathological conditions.
Microbiome characterization of biopsies from lesional skin in Spanish patients with hidradenitis suppurativa shows an abundance of Mycoplasma and a decrease in Cutibacterium species
Authors: Romaní Jorge , Rosés Carles , Fernández-Vela Javier , Muñoz-Santos Carlos , Barceló Anna , Guilabert Antonio 
Published: October 2023

Abstract
Hidradenitis suppurativa (HS) is a chronic inflammatory skin disorder characterized by nodules, abscesses, and tunnels. Understanding the role of the skin microbiome in HS pathogenesis is crucial for developing targeted therapies. Previous studies have reported dysbiosis in HS-affected skin. This study aimed to investigate the microbiome in lesional skin biopsies of HS patients compared to perilesional healthy skin and healthy controls. Additionally, we sought to identify potential differences in the microbiome based on disease endotype, clinical variables, and lesion type. We collected skin samples from 30 HS patients and 20 healthy controls. Participants were categorized into follicular and inflammatory endotypes. Skin biopsies were obtained from lesional and perilesional areas of patients, and from anatomically matched areas in healthy controls. The microbiome was characterized using 16S rRNA gene sequencing. Clinical variables were recorded, including age, sex, body mass index, smoking habit, age of disease onset, family history, and lesion type. The microbiome composition differed significantly between lesional, perilesional, and healthy skin. Lesional skin showed higher abundance of Mycoplasma (P = 0,013) and Bacillus (p < 0,01), while Propionibacterium was more prevalent in healthy controls (p < 0,01). No significant differences were observed in the microbiome composition based on endotypes or lesion types. However, the severity of HS was associated with decreased Propionibacterium abundance. Our study provides novel insights into the microbiome variations in HS-affected skin in a Mediterranean population. The dysbiosis observed, particularly the higher abundance of Mycoplasma, may play a role in the inflammatory process of HS. Understanding the microbiome's impact on HS pathogenesis could lead to innovative treatments aimed at restoring microbiological balance and controlling inflammation. Propionibacterium's lower abundance in severe HS suggests its potential as a therapeutic target. Further investigations into microbiome manipulation as a potential therapeutic approach are warranted, particularly in early-stage lesions with potential for reversibility.
Predicting Highlights in Badminton Matches: A Knowledge- Aware Model Analysis of Rally-by-Rally Statistics
Authors: Liu Xingyu , Ren Hao , Chen Tao 
Published: October 2023

Abstract
In the realm of sports analytics, predicting highlights in badminton matches plays a crucial role in enhancing fan engagement and broadcasting. This study introduces a Knowledge-Aware Model (KAM) that integrates domain-specific knowledge and data-driven techniques to predict game highlights. Analyzing an extensive dataset from the 2017 World Championships and the 2018 Thomas Cup, comprising over 5,180 individual rallies from 140 singles matches, the KAM considers serving and receiving zones, technical stroke nuances, total strokes, rally time and point outcomes. Comparative evaluations against baseline models and state-of-the-art approaches demonstrate the KAM's superiority, achieving an F1-score of 0.793. By combining comprehensive match statistics with rally-specific data, the KAM offers an innovative approach to predicting highlights, with implications extending beyond badminton to multimedia analysis and recommendation systems. This research presents a pivotal step towards more precise and engaging sports analytics.
Microtubule Affinity-Regulating Kinase family members differently affect tau phosphorylation and toxicity in a Drosophila model
Authors: Sultanakhmetov Grigorii , Kato Iori , Asada Akiko , Saito Taro , Ando Kanae 
Published: October 2023

Abstract
Accumulation of abnormally phosphorylated tau is thought to cause neuronal loss in Alzheimer's disease (AD) and related neurodegenerative disorders. Tau phosphorylation at Ser262 and Ser356 in the KXGS motifs of microtubule-binding repeats plays a critical role in its physiological function and AD pathogenesis. Members of the Microtubule Affinity-Regulating Kinase family (MARK1-4) phosphorylate these residues and are considered potential therapeutic targets for AD. However, whether and how each member affects tau toxicity in vivo is unclear. By using a Drosophila model of tau toxicity, we compared the effect on tau-induced neurodegeneration among MARKs. We found that MARK4, but not other MARKs, promotes tau accumulation. All MARKs increased tau phosphorylation at Ser262, and MARK1, MARK2, and MARK4 increased tau phosphorylation at Ser356. The levels of AT8-tau were decreased with co-expression of MARK2 and MARK3 but not with MARK1 and MARK4, and tau phosphorylation at Ser396 was elevated only with MARK4. MARK4 enhanced tau-induced neurodegeneration more than other MARKs. Our results suggest that MARK4 promotes tau accumulation via enhancing additional phosphorylation and that inhibition of MARK4 may be an effective treatment for AD and related tauopathies.
Incorporating space in hierarchical capture mark recapture models: can we better capture variance?
Authors: Drift Anne-Merel R. van der , Leirs Herwig , Mariën Joachim , Sabuni Christopher , Mulungu Loth , Kirkpatrick Lucinda 
Published: October 2023

Abstract
1. Capture mark recapture (CMR) models allow the estimation of various components of animal populations, such as survival and recapture probabilities and often assume homogenous detection of individuals. However, individual detection probability is not heterogeneous for a range of different reasons, for example due to the location and environmental context of traps within an individual’s home range or individual characteristics such as age. Spatial CMR models incorporate this heterogeneity by including the spatial coordinates of traps, data which is often already collected in standard CMR approaches. 2. We compared how the inclusion of spatial data changed estimations of survival, detection probability, and the probability of seroconversion to an arenavirus, in the multimammate mouse. We used a Bayesian framework to develop non spatial, partially spatial and fully spatial models alongside multievent CMR models and used simulations to test whether parameters were sensitive to starting parameters. 3. We found that bias and precision were similar for all three different model types, with simulations always returning estimates within the 95% credible intervals. When applied to field data, our models predicted a lower survival of individuals exposed to Morogoro virus (MORV) in non spatial models while survival was similar in spatially explicit models. 4. We suggest that spatial coordinates of traps should always be recorded when carrying out CMR and spatially explicit analysis should be used whenever possible, particularly as incorporating spatial variation may capture ecological processes without the need for additional data collection that can be challenging to acquire with wild animals.
Prognostic and Immunotherapeutic Potential of Regulatory T Cell- Associated Signature in Ovarian Cancer
Authors: Liu Yinglei , Shan Feng , Sun Ying , Kai Haili , Cao Yang , Huang Menghui , Liu Jinhui , Zhang Pengpeng , Zheng Yanli 
Published: October 2023

Abstract

Background: Immunosuppression is a key feature of cancer, promoting tumor growth and progression. Regulatory T cells (Tregs) are a specialized subset of T lymphocytes that mediate immunosuppression in cancer. However, the role of Treg-related genes in ovarian cancer (OC) remains largely unexplored.
Methods: To identify marker genes related to Tregs in OC, we utilized scRNA-seq analysis. Treg scores were then constructed using single-sample gene set enrichment analysis (ssGSEA) based on these markers. To identify gene modules with the strongest correlation with Treg scores in bulk RNA-seq data, we employed the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. Multiple machine learning algorithms were then used to construct risk models with superior predictive performance, which were validated using external independent datasets. A risk score was developed for each OC sample based on the optimal model to evaluate differences in prognosis, immune infiltration, pathway activity, and immunotherapy between high and low-risk groups.
Results: We identified 365 genes governing Treg functionality utilizing the WGCNA algorithm, and determined that 70 of these genes were linked to the prognosis of OC based on univariate Cox analysis. By employing a fusion of the Random Survival Forest (RSF) and Lasso algorithms, we developed a risk model showcasing the most elevated c-index derived from the allocated risk scores. The model's efficacy was substantiated through the utilization of four external datasets. Our analysis unveiled that the low-risk cohort exhibited a more favorable prognosis, augmented infiltration of immune cells, elevated expression of immune checkpoints, as well as noteworthy disparities in pathway enrichment and immunotherapy efficacy among the distinct risk groups.
Conclusion: Our findings provide new insights into the role of Treg cells in the development and progression of OC and highlight the potential for developing novel Treg-targeted therapies for the treatment of this disease.
Virtual Reality, Face-To-Face, and 2D Video Conferencing in Group Work: A Comparative Study on Fatigue, Creativity, Flow, and Interactions
Authors: Macchi Gregorio , De Pisapia Nicola 
Published: October 2023

Abstract

This study investigates the impact of different interaction settings— two-dimensional Video Conferencing (VC), Face-To-Face (FTF), and Virtual Reality (VR)—on group dynamics, cognitive performance, and aspects of well-being in a professional context. Utilizing a sample of 40 participants from a large Italian electricity transmission company, the study employs a within-subjects design to explore various metrics, including flow, creativity, fatigue and aspects of interaction. The results indicate that FTF interactions are optimal for idea generation and task absorption. VR, although initially more fatiguing for first-time users, fosters a more collaborative and peaceful environment, encouraging participants to engage more openly with each other. VC was found to be the least fatiguing, but also the least engaging in terms of task absorption and idea generation. Additionally, age-related differences were observed, particularly in the perception of motivational and emotional fatigue in the VR setting. The study provides empirical evidence supporting the integration of VR in professional settings for specific types of meetings, while also highlighting the limitations and areas for future research. These findings have implications for organizational well-being, cognitive ergonomics, and the evolving landscape of remote work technologies.
Genomic and phenotypic analysis of invasive Streptococcus suis isolated in Spain reveals genetic diversification and associated virulence traits
Authors: uruén Cristina , Fernandez Ana , Arnal Jose Luis , Pozo Mateo del , Amoriieta Maria Casas , de Blas Ignacio , Jurado Paula , calvo Jorge Hugo , Gottschalk Marcelo , González-Vázquez Luis Daniel , Arenas Miguel , Marín Clara M , Arenas Jesús 
Published: October 2023

Abstract
The authors have requested that this preprint be removed from Research Square.
Insights into the DHQ-BN: Mechanical, Electronic, and Optical Properties
Authors: Lima Kleuton Antunes Lopes , de Mendonça Fábio Lúcio Lopes , Giozza William Ferreira , Júnior Rafael Timóteo de Sousa , Junior Luiz Antonio Ribeiro 
Published: October 2023

Abstract
Computational materials research is vital in advancing our understanding of diverse material classes and their properties, contributing valuable insights that aid in forecasting innovative structures and complementing empirical investigations. In this context, DHQ-graphene recently emerged as a stable two-dimensional carbon allotrope composed of decagonal, hexagonal, and quadrilateral carbon rings. Here, we employ density functional theory calculations to investigate its boron nitride counterpart's mechanical, electronic, and optical features (DHQ-BN). Our findings reveal an insulating band gap of 5.11 eV at the HSE06 level and exceptional structural stability supported by phonon calculations and ab initio molecular dynamics simulations. Moreover, DHQ-BN exhibits strong ultraviolet (UV) activity, suggesting its potential as a highly efficient UV light absorber. Its mechanical properties, including Young's modulus (230 GPa) and Poisson's ratio (0.7), provide valuable insights into its mechanical resilience and structural stability. This study underscores DHQ-BN's unique properties and promising role in the BN-based 2D materials landscape.