bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identifying influential factors using machine learning techniques on the intention to receive a COVID-19 booster dose and vaccine fatigue among partially vaccinated individuals
Authors: Bikaki Athina , Luningham Justin M. , Thompson Erika L. , Krenek Brittany , Vishwanatha Jamboor K. , Kakadiaris Ioannis A. 
Published: November 2024

AbstractThis study assesses COVID-19 booster intentions and hesitancy in Texas, a state known for its diversity and libertarian values. A survey was conducted with 274 participants residing in Texas between June and July 2022. The analysis examined sociodemographic and health-related factors, trusted information sources, and preventive behaviors. The survey focused on vaccinated participants and their intention to receive the booster dose, which was categorized into three outcomes: yes, no, and not sure. Machine learning techniques were employed to analyze the survey responses of vaccinated participants to identify the most critical factors. Among the participants, 113 expressed their intention to get the booster (41.2%), 107 did not plan to receive the booster (39.1%), and 54 remained undecided (19.7%). Our findings indicate that the perception of vaccine safety significantly influenced the decision to receive the booster dose. Those who reported trust in social media contacts as reliable information sources were more likely to intend to boost. Additionally, among those hospitalized when diagnosed with COVID-19, the largest proportion were unwilling to receive the booster (47.0%) compared to those who intended to receive the booster (33.3%). In contrast, most of those who believed they would be hospitalized if infected with COVID-19 intended to get the booster. Other factors did not demonstrate a significant association. Our findings are highly transferable and can offer valuable insights, particularly for countries where COVID-19 remains prevalent and are pivotal both presently and in the future for developing strategies to improve booster uptake and shape public health initiatives in epidemic and pandemic outbreaks.
Sport participation levels among New York City’s kids remain low months after the COVID-19 pandemic
Authors: Jeong Yealeen , Salunga Arianne , Karamitopoulos Mara , Carter Cordelia 
Published: November 2024

AbstractThe COVID-19 pandemic resulted in decreased sports participation due to social distancing policies and strict return-to-play protocols. This effect has been felt profoundly in New York City, one of the earliest COVID-19 hotspots, even as restrictions have eased. This study aimed to describe sport participation rates in New York City before and after the COVID-19 pandemic. Using a cross-sectional, online retrospective survey, this study assessed sports participation among children aged 6–17 with musculoskeletal complaints. The survey included parents and guardians of 242 children (123 males, 119 females) who had an average age of 12.5 years. Statistical analyses were performed using McNemar’s test to assess changes in sports participation. The results revealed significant decreases in participation in gym class, pick-up games, basketball, and soccer after the pandemic. Notably, female adolescents were more affected, particularly in gym class. This study underscores a substantial decline in specific sports participation among New York City’s kids post-pandemic. The findings highlight the need for targeted public health strategies to encourage physical activity.
Chronic corticosterone exposure causes anxiety- and depression-related behaviors with altered gut microbial and brain metabolomic profiles in adult male C57BL/6J mice
Authors: Shoji Hirotaka , Maeda Yasuhiro , Miyakawa Tsuyoshi 
Published: November 2024

AbstractChronic exposure to glucocorticoids in response to long-term stress is thought to be a risk factor for major depression. Depression is associated with disturbances in the gut microbiota composition and peripheral and central energy metabolism. However, the relationship between chronic glucocorticoid exposure, the gut microbiota, and brain metabolism remains largely unknown. In this study, we first investigated the effects of chronic corticosterone exposure on various domains of behavior in adult male C57BL/6J mice treated with the glucocorticoid corticosterone to evaluate them as an animal model of depression. We then examined the gut microbial composition and brain and plasma metabolome in corticosterone-treated mice. Chronic corticosterone treatment resulted in reduced locomotor activity, increased anxiety-like and depression-related behaviors, decreased rotarod latency, reduced acoustic startle response, decreased social behavior, working memory deficits, impaired contextual fear memory, and enhanced cued fear memory. Chronic corticosterone treatment also altered the composition of gut microbiota, which has been reported to be associated with depression, such as increased abundance of Bifidobacterium, Turicibacter, and Corynebacterium and decreased abundance of Barnesiella. Metabolomic data revealed that long-term exposure to corticosterone led to a decrease in brain neurotransmitter metabolites, such as serotonin, 5-hydroxyindoleacetic acid, acetylcholine, and gamma-aminobutyric acid, as well as changes in betaine and methionine metabolism, as indicated by decreased levels of adenosine, dimethylglycine, choline, and methionine in the brain. These results indicate that mice treated with corticosterone have good face and construct validity as an animal model for studying anxiety and depression with altered gut microbial composition and brain metabolism, offering new insights into the neurobiological basis of depression arising from gut-brain axis dysfunction caused by prolonged exposure to excessive glucocorticoids.
Combining microscale ATR-FTIR and chemometrics to interpret degradation characteristics of earlywood, latewood, and compression wood in waterlogged archaeological pine wood
Authors: Li Ren , Zhang Zhiguo , Jiao Lichao , Yin Yafang , Tie Fude , Sun Manli 
Published: November 2024

Abstract
Interpreting the degradation characteristics of waterlogged archaeological wood (WAW) is crucial for the conservation of wooden cultural heritage. Generally, multidisciplinary diagnostic methods, including physical, micromorphological, and chemical approaches, are employed to evaluate the preservation state of WAW. However, primarily focused on the sample level, this methodology limits the understanding of the variability in degradation from a detailed perspective. In this paper, we adopted the in-situ microscale attenuated total reflectance Fourier transform infrared (ATR-FTIR) method to investigate the degradation variability in waterlogged archaeological Masson pine (Pinus massoniana) wood excavated from the ancient Chinese shipwreck Nanhai No. 1. Specifically, spectra of earlywood (EW), latewood (LW), and compression wood (CW) were extracted and combined with chemometrics to achieve rapid classification of their degradation levels. The micromorphological features of wood cell walls in conjunction with the ratios of lignin (A1509) and carbohydrate (A1370) peak areas were used to estimate the degradation levels. Unlike recent wood, moderate degradation in CW and severe degradation in EW and LW were classified in archaeological samples. The degradation levels were effectively determined through principal component analysis (PCA) and sparse partial least squares discriminant analysis (sPLSDA). The results suggest that chemometric analysis is a promising method to discern the variable degradation levels of archaeological wood at the tissue level. The methodologies developed in this study provide detailed insights into the degradation characteristics in WAW and improve the accuracy of evaluating the preservation state.
Accurate acoustic classification research of visually similar monochrome porcelain fragments
Authors: Jin Xiaoxue , Wang Xiufeng , Zhang Xiaoyu , Xue Chaohua 
Published: November 2024

Abstract
Exploring a non-destructive and rapid evaluation method for precious ancient ceramic relics is of significant importance. Currently, there are countless monochrome porcelain fragments awaiting measurement and categorization. Various instruments such as XRF, XRD, SEM, OM, and thermoluminescence dating have been extensively utilized by numerous researchers to study ancient ceramics. However, these techniques pose challenges in reliably identifying monochrome porcelain fragments from the same kiln with similar appearance, content, and microstructure due to their limitations. To address this issue, this study presents an acoustic measurement system that utilizes audible frequencies to non-destructively evaluate monochrome porcelain fragments. The proposed method enables the extraction of parameters related to time domain analysis (e.g., group delay), frequency domain analysis (e.g., resonance), and sound loss characteristics of these fragments. This non-destructive and efficient technology for detecting acoustic characteristics of monochrome porcelain fragments presented in this work clarifies the fundamental principles governing the interaction between sound waves and ancient ceramic fragments while providing a completely non-destructive and highly efficient method for classifying and restoring valuable solid cultural heritages like stone, jade, bronze etc. Moreover, this approach can also be applied for non-destructive testing of elastic modulus in advanced ceramic devices including detecting small cracks, deterioration effects due to aging as well as other defects.
Study on the prehistoric decorated spindle whorls in Haidai region of China
Authors: Tao Yuan , Wang Qicai 
Published: November 2024

Abstract
Spindle whorls are a kind of spinning tools of crucially important in prehistoric period. Haidai region is one of the main historical and cultural areas of prehistoric China, and had a relatively developed textile handicraft production, where a large number of spindle whorls were unearthed, including many decorated ones. In order to make clear the uses of the decorated spindle whorls and the meanings of the patterns on them, the data of prehistoric decorated spindle whorls unearthed in archaeological sites of the period from Beixin Culture to Yueshi Culture in Haidai region of China were collected and analyzed, and the morphological data (dimension and shapes) of them were compared with that of undecorated ones. The patterns on the decorated spindle whorls were analyzed and classified, and the characteristics of the patterns in different cultural periods were studied, and based on this, the purpose of making decorated spindle whorls and the function and meanings of the patterns were discussed. The results showed that the range of morphological data of the decorated spindle whorls in Haidai region is consistent with that of undecorated ones belonging to the same period and unearthed in the same site, while compared with undecorated spindle whorls, decorated ones are more affected by regional culture and thus show different features from the ones unearthed in other archaeological sites belonging to the same cultural period. It is concluded that the most basic function of the decorated spindle whorls should be as practical tools for normal spinning production, and the patterns on them should have multiple meanings such as practical, decorative, distinctive and symbolic. The study of decorated spindle whorls can help us understand the prehistoric regional culture in Haidai region.
Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods
Authors: Ameen Asmaa , Fattoh Ibrahim Eldesouky , Abd El-Hafeez Tarek , Ahmed Kareem 
Published: November 2024

AbstractCardiovascular diseases (CVD) have been found to be prevalent in society, frequently ending in death. According to the findings of a recent survey, the mortality rate is increasing due to the prevalence of adult cigarette consumption, elevated blood pressure, high cholesterol levels, and obesity. The previously mentioned causes are exacerbating the severity of the condition. A pressing necessity exists for a study on the variability of these factors and their impact on cardiovascular disease (CVD). This involves the use of advanced tools to detect the disease early on and aid in the reduction of fatality rates. With their extensive methodologies that would help in the early CVD prediction and recognition of behavioral patterns in large amounts of data, artificial intelligence, and data mining disciplines offer a broad study potential. The results of these predictions will help physicians make decisions and early diagnoses, decreasing the risk of patient death. This work compares and reports the classification, machine learning, and deep learning algorithms that predict cardiovascular illnesses. For this study, articles from 2012 to 2023 were considered; after filtering, 82 articles were chosen for primary research. Future researchers will benefit from this review on cardiovascular disorders by better understanding the Deep Learning and Machine Learning models now in the healthcare sector. The review encompasses commonly employed methodologies such as support vector machine, decision tree, random forest, and convolutional neural networks (CNNs). Additionally, this survey aggregates and presents information on the performance metrics used to report accuracy. It also goes over the most popular datasets used by various diagnostic models (ECG and PCG signals datasets). In addition, it emphasizes prominent publishers, journals, and conferences that serve as platforms for the evaluation of scholarly works. Additionally, it will facilitate their understanding of the unresolved challenges or hurdles experienced by past researchers. A lack of more extensive and consistent datasets was the most common issue, followed by the need to improve existing models.
Low-level turbulence risk assessment and visualization using temporal rate of change of headwind of an aircraft
Authors: Ito Koji , Ohba Haruka , Mizuno Shinya 
Published: November 2024

AbstractIn this study, we focused on the temporal rate of change of headwind, which is one of the recorded parameters in the aircraft’s in-flight quick access recorder data. We selected the Laplace distribution and utilized the scaling parameter to construct a low-level turbulence risk assessment model. Using this model, we calculated the risk of low-level turbulence occurrence at five airports in Japan based on the month, time of day, and wind speed. We visualized how the geographical conditions at each airport influenced risk in relation to airport wind speeds. We developed a low-level turbulence visualization site linked to weather conditions using these results to enable pilots to easily verify low-level turbulence risk and incorporate this information into their flight routines. These findings are anticipated to significantly enhance aircraft safety.
Exploring the application of ChatGPT in ESL/EFL education and related research issues: a systematic review of empirical studies
Authors: Lo Chung Kwan , Yu Philip Leung Ho , Xu Simin , Ng Davy Tsz Kit , Jong Morris Siu-yung 
Published: November 2024

AbstractChatGPT, a sophisticated artificial intelligence (AI) chatbot capable of providing personalised responses to users’ inquiries, recently has had a substantial impact on education. Many studies have explored the use of ChatGPT in English as a second language (ESL) and English as a foreign language (EFL) education since its release on 30 November 2022. However, there has been a lack of systematic reviews summarising both the current knowledge and the gaps in this research area. This systematic review analyses 70 empirical studies related to the use of ChatGPT in ESL/EFL education within a 1.5-year period following its release. Using the Technology-based Learning Model, we provide a comprehensive overview of the domains in which ChatGPT has been applied, the methodological approaches, and associated research issues. The included studies collectively provide solid evidence regarding the affordances (e.g., increased learning opportunities, personalised learning, and teacher support) and potential drawbacks (e.g., incorrect information, privacy leakage, and academic dishonesty) of ChatGPT use in ESL/EFL education. However, our findings indicate that the majority of studies have focused on students’ use of this AI tool in writing, while few studies have quantitatively examined its effects on students’ performance and motivation. In addition, the impact of ChatGPT on other language skills, such as reading, speaking, and listening, remains under-researched. Therefore, we recommend that longer-term studies with rigorous research designs (e.g., quasi-experimental designs) and objective data sources (e.g., standardised tests) be conducted to provide more robust evidence regarding the influence of ChatGPT on students’ English language acquisition.
Organ-specific SPECT activity calibration using 3D printed phantoms for molecular radiotherapy dosimetry
Authors: Robinson Andrew P. , Tipping Jill , Cullen David M. , Hamilton David , Brown Richard , Flynn Alex , Oldfield Christopher , Page Emma , Price Emlyn , Smith Andrew , Snee Richard 
Published: November 2024

Abstract
Background
Patient-specific absorbed dose calculations for molecular radiotherapy require accurate activity quantification. This is commonly derived from Single-Photon Emission Computed Tomography (SPECT) imaging using a calibration factor relating detected counts to known activity in a phantom insert.

Methods
A series of phantom inserts, based on the mathematical models underlying many clinical dosimetry calculations, have been produced using 3D printing techniques. SPECT/CT data for the phantom inserts has been used to calculate new organ-specific calibration factors for 99m
Tc and 177Lu. The measured calibration factors are compared to predicted values from calculations using a Gaussian kernel.

Results
Measured SPECT calibration factors for 3D printed organs display a clear dependence on organ shape for 99m
Tc and 177Lu. The observed variation in calibration factor is reproduced using Gaussian kernel-based calculation over two orders of magnitude change in insert volume for 99m
Tc and 177Lu. These new organ-specific calibration factors show a 24, 11 and 8 % reduction in absorbed dose for the liver, spleen and kidneys, respectively.

Conclusions
Non-spherical calibration factors from 3D printed phantom inserts can significantly improve the accuracy of whole organ activity quantification for molecular radiotherapy, providing a crucial step towards individualised activity quantification and patient-specific dosimetry. 3D printed inserts are found to provide a cost effective and efficient way for clinical centres to access more realistic phantom data.
Advances in genomic tools for plant breeding: harnessing DNA molecular markers, genomic selection, and genome editing
Authors: Kumar Rahul , Das Sankar Prasad , Choudhury Burhan Uddin , Kumar Amit , Prakash Nitish Ranjan , Verma Ramlakhan , Chakraborti Mridul , Devi Ayam Gangarani , Bhattacharjee Bijoya , Das Rekha , Das Bapi , Devi H. Lembisana , Das Biswajit , Rawat Santoshi , Mishra Vinay Kumar 
Published: November 2024

AbstractConventional pre-genomics breeding methodologies have significantly improved crop yields since the mid-twentieth century. Genomics provides breeders with advanced tools for whole-genome study, enabling a direct genotype–phenotype analysis. This shift has led to precise and efficient crop development through genomics-based approaches, including molecular markers, genomic selection, and genome editing. Molecular markers, such as SNPs, are crucial for identifying genomic regions linked to important traits, enhancing breeding accuracy and efficiency. Genomic resources viz. genetic markers, reference genomes, sequence and protein databases, transcriptomes, and gene expression profiles, are vital in plant breeding and aid in the identification of key traits, understanding genetic diversity, assist in genomic mapping, support marker-assisted selection and speeding up breeding programs. Advanced techniques like CRISPR/Cas9 allow precise gene modification, accelerating breeding processes. Key techniques like Genome-Wide Association study (GWAS), Marker-Assisted Selection (MAS), and Genomic Selection (GS) enable precise trait selection and prediction of breeding outcomes, improving crop yield, disease resistance, and stress tolerance. These tools are handy for complex traits influenced by multiple genes and environmental factors. This paper explores new genomic technologies like molecular markers, genomic selection, and genome editing for plant breeding showcasing their impact on developing new plant varieties.
Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory study
Authors: Glemser Philip Alexander , Freitag Martin , Kovacs Balint , Netzer Nils , Dimitrakopoulou-Strauss Antonia , Haberkorn Uwe , Maier-Hein Klaus , Schwab Constantin , Duensing Stefan , Beuthien-Baumann Bettina , Schlemmer Heinz-Peter , Bonekamp David , Giesel Frederik , Sachpekidis Christos 
Published: November 2024

Abstract
Background
To investigate the ability of artificial intelligence (AI)-based and semi-quantitative dynamic contrast enhanced (DCE) multiparametric MRI (mpMRI), performed within [18F]-PSMA-1007 PET/MRI, in differentiating benign from malignant prostate tissues in patients with primary prostate cancer (PC).

Results
A total of seven patients underwent whole-body [18F]-PSMA-1007 PET/MRI examinations including a pelvic mpMRI protocol with T2w, diffusion weighted imaging (DWI) and DCE image series. Conventional analysis included visual reading of PET/MRI images and Prostate Imaging Reporting & Data System (PI-RADS) scoring of the prostate. On the prostate level, we performed manual segmentations for time-intensity curve parameter formation and semi-quantitative analysis based on DCE segmentation data of PC-suspicious lesions. Moreover, we applied a recently introduced deep learning (DL) pipeline previously trained on 1010 independent MRI examinations with systematic biopsy-enhanced histopathological targeted biopsy lesion ground truth in order to perform AI-based lesion detection, prostate segmentation and derivation of a deep learning PI-RADS score. DICE coefficients between manual and automatic DL-acquired segmentations were compared. On patient-based analysis, PET/MRI revealed PC-suspicious lesions in the prostate gland in 6/7 patients (Gleason Score-GS ≥ 7b) that were histologically confirmed. Four of these patients also showed lymph node metastases, while two of them had bone metastases. One patient with GS 6 showed no PC-suspicious lesions. Based on DCE segmentations, a distinction between PC-suspicious and normal appearing tissue was feasible with the parameters fitted maximum contrast ratio (FMCR) and wash-in-slope. DICE coefficients (manual vs. deep learning) were comparable with literature values at a mean of 0.44. Further, the DL pipeline could identify the intraprostatic PC-suspicious lesions in all six patients with clinically significant PC.

Conclusion
Firstly, semi-quantitative DCE analysis based on manual segmentations of time-intensity curves was able to distinguish benign from malignant tissues. Moreover, DL analysis of the MRI data could detect clinically significant PC in all cases, demonstrating the feasibility of AI-supported approaches in increasing diagnostic certainty of PSMA-radioligand PET/MRI.