bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform
Authors: Maleki Lonbar Sajjad , Beigi Akram , Bagheri Nasour , Peris-Lopez Pedro , Camara Carmen 
Published: December 2024

IntroductionIdentity verification plays a crucial role in modern society, with applications spanning from online services to security systems. As the need for robust automatic authentication systems increases, various methodologies—software, hardware, and biometric—have been developed. Among these, biometric modalities have gained significant attention due to their high accuracy and resistance to falsification. This paper focuses on utilizing electrocardiogram (ECG) signals for identity verification, capitalizing on their unique, individualized characteristics.MethodsIn this study, we propose a novel identity verification framework based on ECG signals. Notable datasets, such as the NSRDB and MITDB, are employed to evaluate the performance of the system. These datasets, however, contain inherent noise, which necessitates preprocessing. The proposed framework involves two main steps: (1) signal cleansing to remove noise and (2) transforming the signals into the frequency domain for feature extraction. This is achieved by applying the Wigner-Ville distribution, which converts ECG signals into image data. Each image captures unique cardiac signal information of the individual, ensuring distinction in a noise-free environment. For recognition, deep learning techniques, particularly convolutional neural networks (CNNs), are applied. The GoogleNet architecture is selected for its effectiveness in processing complex image data, and is used for both training and testing the system.ResultsThe identity verification model achieved impressive results across two benchmark datasets. For the NSRDB dataset, the model achieved an accuracy of 99.3% and an Equal Error Rate (EER) of 0.8%. Similarly, for the MITDB dataset, the model demonstrated an accuracy of 99.004% and an EER of 0.8%. These results indicate that the proposed framework offers superior performance in comparison to alternative biometric authentication methods.DiscussionThe outcomes of this study highlight the effectiveness of using ECG signals for identity verification, particularly in terms of accuracy and robustness against noise. The proposed framework, leveraging the Wigner-Ville distribution and GoogleNet architecture, demonstrates the potential of deep learning techniques in biometric authentication. The results from the NSRDB and MITDB datasets reflect the high reliability of the model, with exceptionally low error rates. This approach could be extended to other biometric modalities or combined with additional layers of security to enhance its practical applications. Furthermore, future research could explore additional preprocessing techniques or alternative deep learning architectures to further improve the performance of ECG-based identity verification systems.
How does co-regulation with Adaptive Learning Technologies affect primary school students' goal-setting, regulation of practice behavior and learning outcomes?
Authors: Horvers Anne , Kooi Rianne , Knoop-van Campen Carolien A. N. , Dijkstra Rick , Baars Martine , Molenaar Inge 
Published: December 2024

IntroductionMonitoring and controlling learning is often difficult for primary school students. This issue is partially resolved when Adaptive Learning Technologies (ALTs) take over part of these self-regulated learning (SRL) processes. Trace data in ALTs provides elaborate information on students' learning process, which can be translated into monitoring support. However, this data does not provide insight into students' goal-setting behavior, which is a crucial part of the monitoring loop. Therefore, we developed a form of co-regulation between the student and the ALT with goal-setting prompts and monitoring support.MethodThis experimental study compared an experimental condition in which primary school students worked with the co-regulation intervention while practicing mathematics problems in the ALT to a control condition where they only practiced with the ALT. Firstly, we examined the effects of co-regulation with ALTs on regulation of practice behavior and learning outcomes by comparing the experimental and control conditions. Secondly, to gain further insight into students' goal-setting behavior in co-regulation, we examined how the experimental condition set their goals and attained them.ResultsResults showed that students in the experimental condition were more effective in regulating their practice behavior, shown by more problems solved and higher accuracy than the control condition. Similar learning gain was found in both conditions on the easy and intermediate learning topics. For the hard learning topic, the control condition showed a higher learning gain. Higher week goals than day goals on all three learning topics and similar goals were set between the learning topics. Students' goal attainment was less for the easy compared to the hard learning topic.DiscussionCombined, these results indicate that co-regulation with an ALT positively affects students' regulation of practice behavior and goal- setting behavior but does not yet increase learning gain. A practical implication is that teachers could support students by explicitly providing students with goal-setting opportunities.
Adjuvant immunotherapy after neoadjuvant immunochemotherapy and esophagectomy for esophageal squamous cell carcinoma: a real-world study
Authors: Feng Jifeng , Wang Liang , Yang Xun , Chen Qixun 
Published: December 2024

BackgroundThe role of immunotherapy in the adjuvant setting seems promising in recent years. As per the findings of the CheckMate 577 trial, patients with esophageal cancer (EC) who had neoadjuvant chemoradiation with residual pathologic disease should be considered adjuvant immunotherapy (AIT). However, it is unknown if individuals with esophageal squamous cell carcinoma (ESCC) who have received neoadjuvant immunochemotherapy (NICT) followed by radical surgery also require AIT.MethodsA retrospective analysis was performed on the data from patients who underwent NICT and radical surgery for ESCC between 2019 and 2020. To compare disease-free survival (DFS) and overall survival (OS), Kaplan-Meier survival curves were produced. To determine the parameters linked to DFS and OS, a Cox model using hazard ratios (HRs) was completed.ResultsAmong the 292 eligible patients, 215 cases with a mean age of 63.3 ± 6.8 years, including 190 (88.4%) men and 25 (11.6%) women, were finally recruited. The percentage of R0 resection was 98.3%. After NICT, 65 (30.2%) patients achieved pathological complete response. AIT was given to 78 (36.3%) patients following radical resection. For all patients, the 3-year DFS and OS were 62.3% and 74.0%, respectively. In terms of 3-year DFS (61.5% vs. 62.8%, P=0.984) or OS (76.9% vs. 72.3%, P=0.384), no statistically significant difference was found between patients with and without AIT. AIT significantly improved survival in patients with ypT+N+ (DFS: 23.9% vs. 38.5%, P=0.036; OS: 37.0% vs. 61.5%, P=0.010), but not in those with ypT0N0 or ypT+N0. It was found that AIT was related to both DFS (HR: 0.297; P&lt;0.001) and OS (HR: 0.321; P=0.001) in patients with ypT+N+.ConclusionIn ypT+N+ ESCC patients, AIT after NICT followed by radical surgery reduces the recurrence and death, thereby improving the DFS and OS. Randomized controlled trials ought to be conducted to further assess the results of this retrospective investigation.
The clinical value of local consolidative therapy for oligo-residual disease in PD-1/PD-L1 inhibitors-treated non-small cell lung cancer
Authors: Su Yuqi , Luo Pan , Ni Ling , Hu Jianbin , Weng Jie , Shen Erdong , Zhou Qiang , Chen Tao , Xiao Jiwen , Xiao Jia , Xie Wangti , Shan Rong , Yao Xiang , Wen Fang 
Published: December 2024

BackgroundFew real-world studies exist regarding the clinical value of local consolidative therapy (LCT) for oligo-residual disease (ORD) in NSCLC patients treated with immune checkpoint inhibitors. Therefore, we retrospectively evaluated whether LCT could improve the prognosis of NSCL patients with ORD after effective first-line PD-1/PD-L1 inhibitors treatment.MethodsA total of 132 patients with metastatic NSCLC who had received first-line PD-1/PD-L1inhibitors-based systemic treatment and developed ORD (defined as residual tumors limited to three organs and five lesions) were included. The LCT group consisted of 41 patients received LCTs for oligo-residual lesions before treatment failure, and the remaining 91 patients, who did not receive local therapies, constituted the non-LCT group. The progression-free survival (PFS) and overall survival (OS) of the two groups were analyzed.ResultsWith a median follow-up of 12.0 months, 86 patients developed progressive disease and 42 patients died. Compared with the non-LCT group, LCT group exhibited significant longer progression-free survival (PFS) (median 11.0 vs. 7.0 months, P=0.017) and overall survival (OS) (median 26.0 vs. 17.0 months, P=0.003). Multivariable analysis demonstrated that LCT was an independent predictor of prolonged PFS (HR=0.606, 95% CI=0.370–0.964, P=0.035) and OS (HR=0.467, 95% CI=0.229–0.949, P=0.035). Subgroup analysis revealed that the dominant population considerably benefited from LCT in terms of PFS and OS included patients with 1-2 residual tumor sites (mPFS: 11.0 vs. 7.0 months, P=0.013; mOS: 23.0 vs. 17.0 months, P=0.018) and those with high PD-L1 expression (mPFS: 13.0 vs. 7.0 months, P=0.018; mOS: 34.0 vs. 16.0 months, P=0.030). In addition, the All-LCT group had significantly longer PFS (mPFS 16.0 vs. 7.0, P=0.002) and OS (mOS 28.0 vs. 17.0, P= 0.002) than did the non-LCT group. However, patients who received LCT to only some of their lesions had not experienced improvements in PFS (P=0.546) or OS (P=0.198).ConclusionLCT may provide extra survival benefits among patients with oligo-residual NSCLC after effective first-line PD-1/PD-L1 inhibitors treatment, particularly in those patients with one or two residual lesions, high PD-L1 expression, or who had received LCT for all lesions. LCT may be a novel treatment option for this specific population.
South Atlantic meridional overturning circulation and its respective heat and freshwater transports from sustained observations near 34.5°S
Authors: Pita Ivenis , Goes Marlos , Volkov Denis L. , Dong Shenfu , Schmid Claudia 
Published: December 2024

The Atlantic Meridional Overturning Circulation (AMOC) drives northward Meridional Heat Transport (MHT) and affects climate and weather patterns, regional sea levels, and ecosystems. This study uses a methodology recently applied in 22.5°S to estimate the strength and structure of the AMOC, MHT and freshwater (FWT) transports at 34.5°S since 2005. For this, temperature and salinity profiles from sustained observations were used in conjunction with satellite and reanalysis data under two mapping methodologies: (i) an optimized mapping and (ii) a two-layer feed-forward neural network approach. There is strong agreement between both methods for AMOC, MHT and FWT estimates, thus showing the mapping methodologies are robust. In addition, the AMOC variability estimate is significantly correlated with the monthly SAMBA array data (correlation of 0.41). The mean AMOC transport of 17.0 ± 1.6 Sv, a MHT of 0.6 ± 0.1 PW, and a FWT of -0.02 ± 0.01 Sv are estimated between January 2005 and May 2023 at 34.5°S. The MHT and FWT are analyzed in terms of their horizontal (Hhor and Fhor) and overturning contributions (Hov and Fov), and vertical structures. The MHT is dominated by the overturning contribution (correlation of 0.92), while the FWT is controlled by the overturning contribution driven by the wind via Ekman transport at seasonal timescale, and by horizontal contribution at longer timescales. Both horizontal heat (Hhor) and freshwater (Fhor) components are mostly confined to the upper 500 m, with the geostrophic Fov and Fhor offsetting each other between 50 m and 500 m, and the Ekman Fov in the upper 50 m determining the negative FWT. Finally, the estimated mean Fov of -0.15 Sv agrees with previous estimates that the AMOC exports freshwater in the South Atlantic, and suggests that the AMOC is unstable. Although a long-term trend in the Fov was not detected in the past 20 years, there is a salinification trend (0.05 ± 0.01 PSU/decade) in the upper 300 m near 34.5°S since 2005.
Analyzing lipid profiles and dyslipidemia prevalence in hypertensive patients: a cross-sectional study from primary community health institutions
Authors: Wang Wenxin , Li Xinmin , Lv Deliang , Wu Xiaobing , Xie Fengzhu , Xie Wei , Wang Jinxiao , Zhao Zhiguang 
Published: December 2024

BackgroundA significant proportion of hypertensive patients also suffer from comorbid dyslipidemia, which critically influences their treatment outcomes and overall prognosis. Given its implications, the lipid profiles of hypertensive individuals warrant increased attention for more effective clinical management.MethodsWe analyzed data from 92,443 hypertensive patients registered at primary community health institutions in 2021. Employing a cross-sectional study design, we assessed the distribution of lipid levels and the prevalence of various dyslipidemia subtypes. Stepwise forward logistic regression was used to identify factors associated with dyslipidemia, adjusting for gender, age, body size, and other relevant characteristics.ResultsAccording to the 2023 Chinese Guidelines for the Management of Lipids, the overall prevalence of dyslipidemia was 37.5%. Subtype analysis revealed prevalence of high total cholesterol (TC) at 11.2%, high triglycerides (TG) at 16.0%, low high-density lipoprotein cholesterol (HDL-C) at 16.0%, and high low-density lipoprotein cholesterol (LDL-C) at 10.2%. TG abnormalities were more common among males (16.8%), whereas TC abnormalities predominated in females (14.4%). Notably, hypertensive patients with diabetes had higher levels of TG compared to non-diabetics (p = 0.009). Those with stroke and liver disease comorbidities exhibited lower TG levels than their counterparts (p = 0.018 and p &lt; 0.001, respectively). Additionally, HDL-C levels were significantly lower in hypertensives with diabetes, coronary artery disease, and central obesity (p &lt; 0.001, p = 0.026, p &lt; 0.001, respectively). Regression analysis indicated that dyslipidemia prevalence correlates significantly with gender, age, diabetes, coronary heart disease, stroke, family history of hypertension, body mass index (BMI), central obesity, frequency of physical activity, smoking status, regular alcohol consumption, and abdominal ultrasound findings.ConclusionOur study underscores the necessity of rigorous lipid monitoring and analysis of dyslipidemia-influencing factors for the development of effective health management strategies within the community. There is a critical need to examine lipid profiles comprehensively and implement targeted therapeutic interventions aimed at managing hyperlipidemia, a modifiable risk factor for cardiovascular disease.
The role of blended learning in improving medical students’ academic performance: evidence from Pakistan
Authors: Ashraf Muhammad Azeem , Tsegay Samson Maekele , Gull Nida , Saeed Muneeba , Dawood Hussain 
Published: December 2024

ObjectiveThe study examines the role of blended learning in improving medical students’ academic performance through self-regulatory learning and technological competence and identifies the moderating role of perceived institutional support in the relationships between self-regulatory learning, perceived teacher credibility, technological competencies, and academic performance.MethodsThe study was based on behavioral learning theory as a theoretical framework, and an adapted questionnaire was used to collect the data. In total, 275 medical students participated in the study, and the data was analyzed using structural equation modeling techniques with SmartPLS.ResultsThe results indicate that self-regulatory learning significantly affects student academic performance and mediates the role of teachers’ credibility and technological competencies. Furthermore, perceived institutional support is a significant moderator in the relationship between self-regulated learning, technological competencies, and teacher credibility.ConclusionThe study highlights the importance of self-regulated learning in students’ academic achievement. Moreover, it suggests that educational institutions should advance teachers’ competence and encourage collaborative learning to enhance students’ learning, motivation, and academic performance.
EEG channel and feature investigation in binary and multiple motor imagery task predictions
Authors: Degirmenci Murside , Yuce Yilmaz Kemal , Perc Matjaž , Isler Yalcin 
Published: December 2024

IntroductionMotor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms. State-of-the-art studies were interested in improving classification performance with complex feature extraction and classification methods by neglecting detailed EEG channel and feature investigation in predicting MI tasks from EEGs. Here, we investigate the effects of the statistically significant feature selection method on four different feature domains (time-domain, frequency-domain, time-frequency domain, and non-linear domain) and their two different combinations to reduce the number of features and classify MI-EEG features by comparing low-dimensional matrices with well-known machine learning algorithms.MethodsOur main goal is not to find the best classifier performance but to perform feature and channel investigation in MI task classification. Therefore, the detailed investigation of the effect of EEG channels and features is implemented using a statistically significant feature distribution on 22 EEG channels for each feature set separately. We used the BCI Competition IV Dataset IIa and 288 samples per person. A total of 1,364 MI-EEG features were analyzed in this study. We tested nine distinct classifiers: Decision tree, Discriminant analysis, Logistic regression, Naive Bayes, Support vector machine, k-Nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.ResultsAmong all feature sets considered, classifications performed with non-linear and combined feature sets resulted in a maximum accuracy of 63.04% and 47.36% for binary and multiple MI task predictions, respectively. The ensemble learning classifier achieved the maximum accuracy in almost all feature sets for binary and multiple MI task classifications.DiscussionOur research thus shows that the statistically significant feature-based feature selection method significantly improves the classification performance with fewer features in almost all feature sets, enabling detailed and effective EEG channel and feature investigation.
Exploring neural mechanisms of gender differences in bodily emotion recognition: a time-frequency analysis and network analysis study
Authors: Feng Tingwei , Mi Mingdi , Li Danyang , Wang Buyao , Liu Xufeng 
Published: December 2024

BackgroundThis study aimed to explore the neural mechanisms underlying gender differences in recognizing emotional expressions conveyed through body language. Utilizing electroencephalogram (EEG) recordings, we examined the impact of gender on neural responses through time-frequency analysis and network analysis to uncover gender disparities in bodily emotion recognition.MethodsThe study included 34 participants, consisting of 18 males and 16 females. A 2 × 2 mixed design was employed, with gender (male and female) and bodily emotion (happy and sad) as the independent variables. Both behavioral and EEG data were collected simultaneously.ResultsMales demonstrated more stable brain activity patterns when recognizing different bodily emotions, while females showed more intricate and highly interconnected brain activity networks, especially when identifying negative emotions like sadness. Differences based on gender were also observed in the significance of brain regions; males had greater importance in central brain areas, whereas females exhibited higher significance in the parietal lobe.ConclusionGender differences do influence the recognition of bodily emotions to some extent. The primary aim of this study was to explore the neural mechanisms underlying gender differences in bodily emotion recognition, with a particular focus on time-frequency analysis and network analysis based on electroencephalogram (EEG) recordings. By elucidating the role of gender in cognitive development, this study contributes to early detection and intervention.
Prmt1-mediated histone H4R3me2a methylation regulates the proliferation, migration and invasion of laryngeal cancer cells by affecting the expression level of NCOA5
Authors: Li Linlu , Cui Jiao , Li Xiaoyu , Zhu Yuting , Wu Hongfei , Zhou Lanzhu 
Published: December 2024

BackgroundLaryngeal cancer is a common head and neck cancer, and its occurrence and development are closely related to a variety of epigenetic modifications. protein arginine methyltransferase 1 (PRMT1) is an important type I protein arginine methyltransferase, which catalyzes the monomethylation and asymmetric dimethylation of arginine and participates in the occurrence and development of a variety of cancers. Current research has found that the expression of PRMT1 is increased in laryngeal carcinoma tissues. Histone modifications play a key role in regulating gene expression and maintaining cellular function. In particular, histone H4 arginine 3 dimethylation (H4R3me2a) has been shown to be associated with the development of a variety of cancers. Nuclear Receptor Coactivator 5 (NCOA5) is an important nuclear receptor coactivator, which regulates gene expression through the interaction between various nuclear receptors and other transcription factors. The present study aimed to investigate how PRMT1-mediated H4R3me2a methylation affects the proliferation, migration and invasion of laryngeal cancer cells and to verify the role of NCOA5 in this process.MethodsThe expression of PRMT1 and NCOA5 was inhibited by siRNA mediated gene knockdown in laryngeal cancer cells. The changes of H4R3me2a protein levels were detected by Western Blotting, and cell proliferation, migration and invasion abilities were evaluated by CCK-8, cell scratch assay, Transwell migration and invasion assay. RT-qPCR was used to detect the mRNA expression levels of related genes. The overexpression experiment of NCOA5 was carried out by constructing overexpression vector to verify its effect.ResultsAfter PRMT1 knockdown, the expression of H4R3me2a in laryngeal cancer cells was significantly decreased, and the cell proliferation, migration and invasion abilities were weakened. Similarly, knockdown of NCOA5 expression also resulted in decreased H4R3me2a levels and attenuated cell proliferation, migration and invasion. Overexpression of NCOA5 partially restored H4R3me2a levels and cell proliferation, migration and invasion abilities.