bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dual Vacancy-Regulated Pathway of Photoreduction of CO2 on Bi19S27Cl3 Nanorods into Multi-Carbon Solar Fuels
Authors: Zhou Yong , Cheng Tingting , Lin Wangqiang , Chen Guangyu , Yang Yong , Zhang Yongcai , Qu Junnan , Feng Yichen , Li Qiang , Long Ran , Wang Jinlan , Xiong Yujie , Zou Zhigang 
Published: January 2025

Abstract

Modulation of dual bismuth (Bi) and sulfur (S) vacancies on Bi19S27Cl3 nanorods was successfully utilized for efficient photoconversion of CO₂ into ethane (C₂H₆) as the dominant product with electron-based selectivity as high as 88.6% in the presence of water vapor. The introduction of Bi and S dual vacancies typically promote the adsorption and activation of CO₂ molecules, while simultaneously lowering the energy barrier for the hydrogenation of *C1 intermediates and subsequent C1-C1 coupling. A synergistic catalysis mechanism is proposed for the promising photoconversion of CO2. Sulfur vacancies enhance CO₂ adsorption and *CO generation, while bismuth vacancies make
the adjacent S atoms more electronically accessible than the Bi atoms, resulting in highly reactive and promoting C-C coupling of *CO therein. This work elucidates the synergistic effects of cationic and anionic vacancies in CO₂ photoreduction, offering valuable insights for the development of advanced CO₂ photoreduction catalysts.
Is ChatGPT's Knowledge on Rhinology Accurate? Can It Be Utilized in Medical Education and Patient Information?
Authors: ÖZDOĞAN Aykut , TELLİOĞLU Burçay , KATAR Oğuzhan , ÖZER Serdar 
Published: January 2025

Abstract

Background: ChatGPT is a new artificial intelligence model designed to create human-like chat. As a result of advancing knowledge and technological improvements, it is promising in the field of medicine, especially as a resource that patients and clinicians can apply.Objective: The aim of our study is to measure the accuracy and consistency of ChatGPT's answers to questions in the field of rhinology.Methods: In March 2024, the ChatGPT (ChatGPT version 4) was presented with 130 questions in rhinology. Each question was asked to ChatGPT twice and the consistency/reproducibility of the answers was investigated. The answers were evaluated by three ENT physicians. The physicians followed a standardised 4-point format (1:Completely correct, 2:Partially correct, 3:A mix of accurate and inaccurate/misleading, 4:Completely incorrect/ irrelevant).Results: The answers given by ChatGPT were consistent at a rate of 91.5%(119/130). Among the inconsistent answers, the second answer was found to be more correct in 10/11. Statistically, the second answer was found to be more correct (p: 0011). In 130 questions, as a result of the controllers' evaluation, the number of answers evaluated as completely correct was 99/81/80(76.2%/62.3%/61.5%) respectively. However, completely incorrect answers were 7/6/7(5.4%/4.6%/5.4%), respectively. Accordingly, it is seen that there is no statistical difference between the controllers(p:0.270).Conclusion: The inaccuracy of ChatGPT in patient information and education process is considered to be at an acceptable level and reliable. However, it is also seen that ChatGPT answers are not completely correct and can give misleading answers to some questions. We believe that it would be safer and more accurate to use ChatGPT as an informative and educational material for patients with the control of experts.
Enhancing sensorimotor and executive functioning in autistic children with a rhythmic videogame: A pilot study
Authors: Jamey Kevin , Laflamme Hugo , Foster Nicholas E. V. , Rigoulot Simon , Hyde Krista L. , Bella Simone Dalla 
Published: January 2025

Abstract

Children on the autism spectrum often experience sensorimotor and executive functioning difficulties, which are linked to rhythm disorders and symptom severity. To evaluate the feasibility and effects of training rhythmic skills as a way to improve cognitive functioning, we tested Rhythm Workers (RW), a finger-tapping serious game, in autistic children (ages 7–13; n = 26). Over two weeks, participants played either RW or an active control game with similar auditory-motor demands. Feasibility results showed high compliance (>88% of target training duration) and similar enjoyment for both games. Children who played RW improved in sensorimotor synchronization compared to the control group. Gains in executive functioning were also detected following RW training but not in the control group. Importantly, improvements in sensorimotor synchronization were linked to training duration and symptom severity (social awareness and repetitive behaviors). These findings demonstrate RW’s feasibility and potential as an accessible digital intervention for improving motor and cognitive skills in autistic children.
Design and Implementation of an Open-Source Security Operations Center for Effective Cyber Threat Detection and Response
Authors: Bhavsar Rakesh , Thakar Vishvjit 
Published: January 2025

Abstract

Given the increasing number of various cyberattacks targeting companies of all categories, it is crucial to make sure that organizational assets and infrastructure are correctly monitored for security threats in order to facilitate early detection and response. It is possible to provide sufficient security monitoring and incident response by utilizing a security information and event management (SIEM) tool in conjunction with additional security technologies, such as an extended detection and response (XDR) tool, all of which are located within an organizational unit. This study creates a OCS architecture with multiple elements to guarantee full security visibility across digital assets and endpoints. After that, it suggests open-source, inexpensive tools for putting this architecture into practice. In order to verify that this architecture is performing correctly, Utilizing the proposed methods, the architecture was put into practice. These included Suricata for network intrusion detection, The Hive for case management, and the Wazuh platform for XDR and SIEM tools. This deployed architecture was then used to monitor endpoints and run a variety of cybersecurity scenarios, including malware downloads, brute force attacks, and denial-of-service attacks. According to the results, the tools used carried out the proper exposure assessment and were able to identify and react to the different circumstances. Using open-source tools, this article presented a operations Center for Security design and successfully applied it to identify common cybersecurity threats.
Quantitative and Qualitative Differences of Common Microplastic Detection Procedures: Nile Red- assisted Fluorescence Microscopy and Confocal Micro-Raman Spectroscopy
Authors: Utecht Steve , Krause Stefan , Schuetz Tobias 
Published: January 2025

Abstract

Microplastics (MPs) are pervasive and widespread pollutants penetrating ecosystems worldwide, including aquatic environments and sediments. The lack of standardised evaluation procedures and limited sample throughput hampers accurate assessment of global MP pollution. High-throughput analytical methods are crucial for advancing our understanding of MP cycling in the environment. This study compares MP observations by confocal micro-Raman spectroscopy and Nile Red-assisted fluorescence microscopy to evaluate their effectiveness for high-throughput MP analysis using the percentage differences between the results of the two methods. The results show the influence of particle size on the detected percentage differences and demonstrate that both methods deliver better matching results at smaller particle sizes. The overall percentage difference between the two methods is 421%, with variations ranging over three orders of magnitude depending on morphological characteristics (particles and fibres) and particle size, whereas plastic type did not significantly affect results. The combination of the Fenton reagent's limited organic matter removal and the resulting increased risk of false-positive MP detection, along with Raman spectroscopy's ability to reliably distinguish MPs from organic components, offers opportunities for data validation and correction to enhance accuracy and reliability of the results. This study contributes to the development of robust methods for high-throughput MP analysis, enabling improved spatial and temporal monitoring of its fate and transport in natural fluxes.
EXPLORE: learning interpretable rules for patient-level prediction
Authors: Markus Aniek F. , Kors Jan A. , Fridgeirsson Egill A. , Verhamme Katia M.C. , Rijnbeek Peter R. 
Published: January 2025

Abstract

Objective
We investigate whether a trade-off occurs between predictive performance and model interpretability in real-world health care data and illustrate how to develop clinically optimal decision rules by learning under constraints with the Exhaustive Procedure for Logic-Rule Extraction (EXPLORE) algorithm.
Methods
We enhanced EXPLORE’s scalability to enable its use with real-world datasets and developed an R package that generates simple decision rules. We compared EXPLORE’s performance to 7 state-of-the-art model algorithms across 5 prediction tasks using data from the Dutch Integrated Primary Care Information (IPCI) database. Additionally, we characterized EXPLORE’s space of near-optimal models (i.e. Rashomon set) and conducted experiments on incorporating domain knowledge and improving existing models.
Results
The prediction models developed using LASSO, RandomForest, and XGBoost consistently performed best in terms of AUROC, followed by DecisionTree and EXPLORE. However, the decision rules generated by EXPLORE are much simpler (at most 5 predictors) than the aforementioned. GOSDT-G, IHT, and RIPPER performed worse. Moreover, we demonstrated that EXPLORE’s Rashomon set is very large (1,381 − 20,320 models) with a large variability in both the generalizability and model diversity. We then showed there is a potential to find more clinically optimal decision rules using EXPLORE by incorporating domain knowledge (age/sex and task-specific features) or improving existing models (the CHADS2 score).
Conclusions
Our study shows that more complex models generally outperform simpler ones, confirming the expected interpretability-performance trade-off, although it varies in strength across prediction tasks. EXPLORE’s ability to learn under constraints is valuable for generating clinically optimal decision rules.
Investigation of the clinical significance of RDW in sepsis
Authors: Cai Wei , Liu Jia , Jin Yaqian , Xu Pan 
Published: January 2025

Abstract

Objective
Our study focused on the clinical relevance of red blood cell distribution width (RDW) in sepsis.
Methods
This retrospective analysis involved 57 sepsis patients from the University-Town Hospital of Chongqing Medical University (observation group) between February 2021 and November 2024, and 46 healthy controls who were examined during the same period. We compared the levels of RDW and other relevant biomarkers between the two groups, analyzed the correlation between RDW and PCT, CRP, and WBC, and evaluated the diagnostic and prognostic utility of RDW in sepsis.
Results
RDW, PCT, and WBC levels were distinctly higher in the observation group relative to the controls (P < 0.05). RDW was positively correlated with PCT (r = 0.255, P < 0.05). Receiver operating characteristic curve analysis revealed that the area under the curve for RDW was 0.870, with a sensitivity of 78%, specificity of 87%, and a 95% confidence interval (CI) of 0.784–0.956. The optimal cut-off value for RDW was 13.35. Logistic regression analysis indicated that an elevated RDW level was linked to higher risk of sepsis (odds ratio = 4.458, 95% CI 2.331–8.524, P < 0.001).
Conclusion
RDW may serve as a valuable indicator for diagnosing sepsis, with high predictive value for its occurrence.
In vitro susceptibility of clinical Clostridioides difficile isolates to ridinilazole and ibezapolstat in Israel, 2020-2022
Authors: Schwartz Orna , Azrad Maya , Peretz Avi 
Published: January 2025

Abstract

Background
Even though they constitute a risk factor, antibiotics are still the current primary treatment of C. difficile infection. Due to C. difficile's rapid development of resistance and high recurrences rates, there is an unmet need for new antimicrobials. In the current study, we assessed the in vitro susceptibility of clinical isolates from Israel to two recently developed antibiotics, ridinilazole and ibezpolstat, and to currently used antibiotics.
Methods
We collected 313 C. difficile isolates from several medical centers across Israel, that were recovered from patients of both community and hospital facilities. Isolates were typed to different strains (strain type-ST) by multi-locus sequencing typing (MLST). Susceptibility to metronidazole and vancomycin was determined by Etest; susceptibility to fidaxomicin, ridinilazole and ibezpolstat was determined by agar dilution.
Results
The most prevalent STs were ST42, with 39 (12.5%) isolates and ST2, with 36 isolates (11.5%). Resistance rate to metronidazole and vancomycin was low (2.2%, 1.6%, respectively). Ridinilazole (RDZ) MIC ranged between 0.06 to 0.5 mg/L, and the MIC50/90 were 0.25/0.5 mg/L. Ibezpolstat (IBZ) had an MIC50/90 of 4 mg/L. No significant differences were noted in MIC of different strains.
Conclusions
We demonstrated a potent in vitro activity of RDZ and IBZ against 313 C. difficile isolates, belonging to different STs. These two antimicrobials may serve as a treatment for C. difficile infection, as they have an excellent activity against C. difficile on one hand, and minimal effect on gut microbiome, on the other hand.
Deep-learning based Vehicle Trajectory Reconstruction for Arterial intersection Under Connected Vehicle Environment
Authors: Xu Mengyun , Zheng Wei 
Published: January 2025

Abstract

A complete spatial temporal traffic flow diagram with delicate vehicle trajectories enables traffic managers to totally perceive the fluctuation of traffic status at intersection. Equipped with advanced sensors continuously collecting high-resolution data, Connected Vehicles (CVs) opens up the possibility of reconstructing fully-sampled vehicle trajectories. In this context, this study establishes a novel framework that combines the deep-leaning based method with classic car-following method to reconstruct the trajectories at the micro-perspective level. The cornerstone of framework is the proposed Arrival-GAN model, designed to estimate the distribution of vehicle arrival. This model excels in capturing stochastic arrival patterns even under low-traffic conditions, eliminating the need for the common assumption of uniform arrival between adjacent probe vehicles. Subsequently, an extended car-following model is employed to deduce detailed trajectories based on the driving behavior of CVs. The method evaluation is conducted in the simulated arterial network and compared the results with other classic baselines. Results indicated that the proposed framework successfully reconstructs complete trajectories with significantly improved accuracy, especially in scenarios of low traffic density. This study showcases the potential of utilizing connected vehicles and deep-learning techniques to enhance our understanding of traffic statues, thereby empowering traffic managers with better insights for efficient intersection management.
Outcomes of Severe Covid-19 Among Patients Who Did and Did Not Receive Tocilizumab: A Two-year Prospective Cohort Study
Authors: Beronja Branko , Gazibara Tatjana , Stevanović Olja , Nikolić Nataša , Todorović Nevena , Filipović Ana , Simić Jelena , Lukić Biljana , Hristović Dejan , Karan Aleksandra , Milošević Ivana 
Published: January 2025

Abstract

Evidence about long-term effects of tocilizumab (TCZ) treatment after COVID-19 is sparse. This study explored COVID-19-related outcomes over a two-year period among people who received TCZ and those who did not. Patients were recruited from two healthcare centers in Serbia, from March 2021 to December 2022. Demographic, clinical, and laboratory data were collected from hospital records. Outcomes of COVID-19 over two years of follow-up, such as post-COVID syndrome (PCS) and mortality, were retrieved from hospital records or through telephone calls. Cumulative two-year mortality in the TCZ cohort (150; 32.8% patients) was 20.6%, and in the non-TCZ cohort (307 patients; 67.2%) 24.7%. In TCZ recipients there was a lower cumulative incidence of PCS (3.4% vs. 9.9%, p = 0.018). Predictors of two-year mortality in both cohorts included age, receiving intensive care unit treatment, having insulin-dependent diabetes, and having elevated C-reactive protein and creatinin kinase on hospital admission. In addition, chest pain on admission, being a smoker, having pre-existing respiratory diseases and cancer along with high aspartate aminotranspherase on admission were predictive of a higher two-year mortality in the non-TCZ cohort. Patients who received TCZ had lower cumulative incidence of PCS and cumulative mortality rates after two years of follow-up. This study suggests that TCZ has potential long-term health benefits for people who were hospitalized for COVID-19.
Hydrogen charging and desorption from microstructural viewpoint: A method for deconvoluting hydrogen desorption spectra and unveiling the hydrogen-microstructure interaction
Authors: Cheng Jiahao , Zhong Weicheng , Mayeur Jason , Yamamoto Yukinori , Thapliyal Saket 
Published: January 2025

Abstract

Understanding the interaction of hydrogen with microstructure features in metallic materials is crucial for designing hydrogen-resistant alloys. Although thermal desorption spectroscopy (TDS) is widely used for investigating the hydrogen binding behavior of various microstructural features, its application to low hydrogen diffusivity face-centered cubic (fcc) materials is limited. This study establishes a method to investigate deuterium (hydrogen isotope) adsorption, diffusion, and desorption using a microscale hydrogen transport model, directly reconstructed from electron backscatter diffraction (EBSD) maps. Austenitic steel SS316L, known for its high hydrogen solubility, low diffusivity, and strong resistance to embrittlement, is selected as a demonstration material. The model is validated against experimentally obtained TDS measurements in both, as-heat-treated and pre-strained conditions. Key insights into the interaction of hydrogen with dislocations and grain boundaries as revealed in this work are difficult to obtain from experiments alone and are expected to facilitate design of alloys for hydrogen infrastructure.
The evaluation of different combinations of enzyme set, aligner and caller in GBS sequencing of soybean
Authors: Zamalutdinov Aleksei , Boldyrev Stepan , Ben Cécile , Gentzbittel Laurent 
Published: January 2025

Abstract

Background
Genotype-by-sequencing (GBS) is a cost-effective method for large-scale genotyping, widely used across various species, particularly those with large genomes. A critical aspect of GBS lies in the selection of restriction enzymes for genome digestion and the optimization of data analysis pipelines. However, few studies have comprehensively examined the combined effects of enzyme choice and pipeline configuration.
Results
In this study, we created GBS libraries using three commonly used restriction enzyme combinations (HindIII-NlaIII, PstI-MspI, and ApeKI) and assessed multiple SNP-calling pipelines in 15 soybean varieties. We tested four aligners (BWA-MEM, Bowtie2, BBMap, and Strobealign) and seven SNP callers (Bcftools, Stacks, DeepVariant, FreeBayes, VarScan, BBCallVariants, and GATK). Our finding reveal that enzyme choice significantly influences the number of identified SNP, gene localization preferences, and accuracy. Furthermore, the performance of SNP callers varied markedly in terms of SNP count, precision, recall, and false discovery rate (FDR). DeepVariant exhibited the highest accuracy, with 76.0% of its SNPs intersecting with whole-genome sequencing (WGS)-derived SNPs and an FDR of 0.0095, compared to FreeBayes, which had 47.8% intersection and an FDR of 0.6321.
Conclusions
Our findings underscore the importance of optimizing both enzyme selection for sequencing libraries and data analysis pipelines to ensure robust and reproducible results. This study provides a general framework for designing large-scale genotyping experiments aimed to specific quality and quantity requirements in various plant species.
Post-Traumatic Stress Symptoms and Suicide Risk Among Syrian Refugees: The Mediating Role of Anxiety Sensitivity
Authors: METİN Salih , Şenormancı Güliz , Aygün Hüseyin , Şenormancı Ömer , Çetin Mustafa , Ekinci Ferhat 
Published: January 2025

Abstract

Forced migration, as exemplified by the ongoing Syrian refugee crisis, has been linked to elevated psychiatric issues such as post-traumatic stress disorder (PTSD), depression, anxiety, and increased suicide risk. This study explores the mediating role of anxiety sensitivity (AS) in the relationship between PTSD symptoms and suicide risk among Syrian refugees in Turkey. Data were collected from 574 Syrian refugees aged 18–65 years residing in Bursa province, using validated self-report scales, including the Impact of Events Scale-Revised (IES-R), the Beck Depression Inventory (BDI), the Anxiety Sensitivity Index-3 (ASI-3), and the Suicide Probability Scale (SPS). Pearson correlation, independent samples t-test, and multiple binary logistic regression were employed for statistical analysis, while mediation analyses were performed using PROCESS for SPSS. The prevalence of PTSD among participants was 81.2%. Regression analyses indicated that while IES-R intrusion and hyperarousal subscales increased suicide risk, IES-R avoidance and ASI-3 physical concern subscales decreased it. Mediation analysis revealed a partial mediating effect of ASI-3 cognitive concern in the relationship between PTSD symptoms and suicidal ideation. Findings suggest that addressing cognitive concern may help mitigate suicide risk in PTSD-affected migrants. Additionally, avoidance and physical concern may act as temporary protective factors against suicide. These results highlight the need for tailored, trauma-focused mental health interventions to stabilize suicide risk before commencing PTSD treatment in forcibly displaced populations.