bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring supervised machine learning models to estimate blood pressure using non-fiducial features of the photoplethysmogram (PPG) and its derivatives
Authors: Argüello-Prada Erick Javier , Mosquera Carlos David Castaño 
Published: October 2024

Abstract

Machine learning has proven valuable in developing PPG-based approaches for blood pressure (BP) estimation, with many holding some promise for cuff-less BP assessment. Still, they depend on accurate and robust fiducial point detection algorithms to extract several features from collected signals. The present study explores the usefulness of several non-fiducial features of the PPG signal and its derivatives in estimating BP by combining well-known feature selection methods and machine learning techniques. We collected PPG recordings from 56 participants and computed fifty-seven non-fiducial features, including statistical indexes and energy operators. After implementing three feature selection algorithms (i.e., F-test, mRMR, and ReliefF), the most relevant features were employed to train four learning regression model families: linear regressions (LR), regression trees (RT), support vector regressions (SVR), and Gaussian process regressions (GPR). The abovementioned feature selection methods produced different optimal feature subsets for SBP and DBP estimation, with the Matern 5/2 and the rational quadratic GPR models providing the best predictions when combined with ReliefF (MAE = 0.44, MSE = 0.61, and RMSE = 0.78 mmHg for SBP; MAE = 0.31, MSE = 0.40, and RMSE = 0.63 mmHg for DBP). Furthermore, each model utilizes only fifteen features, many of which are relatively easy to compute, thus becoming suitable for computationally constrained hardware. We highlight the need for implementing feature selection algorithms exhaustively, as the most relevant PPG-based features for SBP estimation might not have the same weight for DBP prediction and vice versa.
Prevalence of myopia among primary and secondary school students and its influencing factors in Xinjiang, China: a cross sectional study
Authors: Hu Xiaopeng , Yuan Xianyan , LI HUA , Gong haoxuan , Fu Zhicong , Xie Yuting , Zhu Lin , Chen Haina , Yang Yingli , Rui Dongsheng 
Published: October 2024

Abstract

OBJECTIVE
To understand the prevalence of myopia and its influencing factors among primary and secondary school students in Shihezi City to provide a scientific basis for future interventions for improving, safeguarding and promoting the visual health of primary and secondary school students.
METHODS
The population comprised primary and secondary school students in Shihezi City. A stratified random sampling method was used to determine the research school, which was then stratified by grade. Two classes were randomly selected as sample classes for each grade. All students received a vision examination. Students of grade ≥ 4 completed the investigation of myopia-related influencing factors using questionnaires.
RESULTS
A total of 6732 people were surveyed in this study, and 6092 people were finally included (participation rate: 90.49%), of which boys represented 51.10%. The overall prevalence of myopia was 59.55%. The prevalence of myopia in urban areas was 68.17%, while in rural areas was 42.42%, and the difference was statistically significant. Being female students, having myopic parents, close-distance reading or writing, and receiving education at an older age were risk factors for myopia, while attending school in a rural area was a protective factor against myopia. Additionally, factors influencing myopia varied among subgroups, but parental myopia appeared in almost every subgroup.
CONCLUSION
Myopia rates in Shihezi City are higher than the national average and gradually increase with school age. In addition to inherent factors (gender, increasing school age and genetic factors), myopia prevention should include proper sitting, eye habits and health promotion.
All-cause deaths and North Atlantic accumulated cyclone energy: understanding the relationship based on county-level income and education levels
Authors: Majeed Haris , Zuberi Daniyal 
Published: October 2024

Abstract

North Atlantic hurricanes have intensified in the past decades, resulting in excess deaths especially among lower-income populations who tend to have elevated mortality rates in coastal regions. Yet the effects of hurricane intensity on mortality rates by income and education levels has not been understood. This population-based study used the North Atlantic accumulated cyclone energy (ACE) index to examine the effects on all-cause mortality (20–84 years) by county-level income and college education level stratification (high and low) in the southeastern United States from 2014–2022. We found that an extreme (≥ 160×104 kt2) ACE period (in comparison to normal ACE period) was associated with all-cause mortality by a rate ratio (RR) of 1.056 (P < 0.001) times. The relationship between ACE index and all-cause mortality was found to be linear. Furthermore, when stratified by income and education levels, only those counties with high-income and high-education were found to have significant associations between above (≥ 126×104 kt2) ACE (in comparison to normal ACE period) and mortality (RR 1.056, P = 0.0008). All other combinations of income and education levels were found to have insignificant associations with mortality during above normal intensity hurricane years. This appears to be explained by the geographic concentration of high income and high education counties along the coasts as compared to the more inland location of low income and low education countries, thus increasing the exposure to impacts of hurricane events. This is the first study to denote that hurricane effects vary based on a combination of income and education levels. Further investigations are needed to identify the effects of hurricanes on cause-specific events based on income and education levels in the United States.
Improving the hydrophilic microenvironment surrounding the catalytic site of fructosyltransferase enhances its catalytic ability
Authors: Wang Fanzhi , Permaul Kugen , Singh Suren 
Published: October 2024

Abstract

The hydrophilic microenvironment surrounding the active site of an enzyme may affect its catalytic activity. In the present study, the impact of improving the hydrophilic microenvironment on the catalytic ability of Aspergillus niger fructosyltransferase, SucC, was investigated. By comprehensive and comparative bioinformatic analysis, it was predicted that a cysteine residue (C66) close to the catalytic triad (D64, D194 and E271) in SucC could play an important role in maintaining the spatial structure of the active site and facilitate the transport of substrates through the substrate tunnel. By simulating saturated mutagenesis, tertiary structure alignment, and molecular docking, it was predicted that a mutation from cysteine to serine (C66S) could increase the hydrophilic environment surrounding the active site without major changes in its structure. Two more amino acid residues (E296 and H310), in addition to D64, D122, R193, D194, and E271 in mutant C66S were predicted to be interacting with sucrose with the binding energy changing from -3.65 to -4.14 kcal/mol. Subsequently, mutant C66S was constructed by site-directed mutagenesis and expressed in Pichia pastoris GS115. The purified C66S mutant showed improved enzymatic activity with a 61.3% increase in its specific activity. Its Km value was decreased by 13.5% while the kcat value increased by 21.6%. When the mutant enzyme was used for FOS production, transfructosylation activity increased in the initial stages. The increase of hydrophilicity surrounding the active site enhanced the transfructosylating activity of the enzyme. Therefore, modification of the hydrophilic microenvironment surrounding the active site could be an alternative way to artificially evolve an enzyme’s catalytic efficiency.
An Improved Sparrow Search Algorithm Based on Complementary Inertia Weight and LSTM Optimization for Timer Serial Forecasting
Authors: Ma Jiaqi 
Published: October 2024

Abstract

When dealing with optimization problems that require to achieve optimal result within fewer iterations, the basic Sparrow Search Algorithm (SSA) shows insufficient solution accuracy and poor stability. In view of the above shortcomings, an improved adaptive Sparrow Search Algorithm based on complementary inertia weight (CIW-SSA) is proposed. Firstly, considering the lack of population diversity, chaos mapping is used to generate all individuals. Secondly, in order to obtain high-precision optimization results within short-term iterations, this paper created and implemented a pair of complementary inertia weights to improve search efficiency. The cooperation of this pair of complementary inertia weights not only adjusts the inertia of each individual, but also adjusts the step size of movement, which greatly improves the speed and accuracy of convergence. Thirdly, to response to the alert value of basic SSA and improve the capacity of exploration, Gaussian mutation and Cauchy mutation are organically added together to process of position update of producer. Simulation experiments were carried out through 18 classical benchmark functions with different characteristic, and the result show that the improved algorithm has faster convergence speed, more stable convergence properties and higher convergence accuracy. In addition, to prove the superiority of CIW-SSA within a few iterations, CIW-SSA is employed to optimize the hyperparameter of Long Short-Term Memory (LSTM) model for wind speed forecasting, and the test results prove the effectiveness of the improvement strategies.
Clinical Validation of a Deep Learning Model forSegmenting and Quantifying Intracranial andVentricular Volumes on Computed Tomography
Authors: Pinto Bruna Garbes Gonçalves , Olegário Tayran Milá Mendes , Silva Pedro Vinicius Alves , Ferracioli Gabriel Monteiro , Paulo Artur José Marques , Schumacher Klaus , Cunha Mateus Trinconi , Lee Henrique Min Ho , Rodrigues Mariana Athaniel Silva , Kitamura Felipe Campos , de Paiva Joselisa Peres Queiroz , Loureiro Rafael Maffei 
Published: October 2024

Abstract

The field of radiology is experiencing a surge in demand due to advances in medical imaging, particularly in techniques suchas magnetic resonance imaging and computed tomography (CT). However, the interpretation of these scans relies heavilyon the availability of experts, which is challenging in resource-limited regions. Recent advances in artificial intelligence anddeep learning offer promising solutions by assisting radiologists in image interpretation and diagnosis. This study focuses onvalidating DeepCTE3D, a deep learning–based model for segmenting and quantifying intracranial volume (ICV) and lateralventricular volume (LVV) in CT scans. The model’s performance was evaluated using a real-world dataset comprising diversepatient demographics and various scanner models, including normal and pathological scans. The evaluation process involveddeveloping a streamlined pipeline to generate ground-truth results and comparing them to the model’s outputs. DeepCTE3Dachieved high similarity scores for both ICV and LVV. Secondary analyses revealed differences in LVV and ICV between patientsexes and scanner models, although these differences were probably not clinically significant. This study highlights the potentialof DeepCTE3D in enhancing clinical triage and advancing neuroimaging applications, especially in scenarios where MRI is notfeasible.
The diversity of endophytic fungi in Fagopyrum cymosum and the beneficial strain JQ_R2 enhance the host's drought resistance through folate metabolism
Authors: Chen Meiqi , Ding Ziqi , Li Chenglei , Li Qingfeng , Bu Tongliang , Tang Zizhong , Wu Qi , Wang Junzhen , Zhang Kaixuan , Zhou Meiliang , Chen Hui 
Published: October 2024

Abstract

Background
The emerging field of endophytic fungi studies their capability to enhance host plant growth and resistance through interactions. F. cymosum, a renowned medicinal plant, harbors a diverse yet inadequately understood array of endophytic fungi. Moreover, this plant, already at risk of endangerment, confronts additional threats posed by elevated temperatures and prolonged drought conditions. Hence, we aimed to identify drought-resistant endophytic fungi present in F. cymosum as a potential solution to alleviate this issue.
Results
The diversity of endophytic fungi across different habitats and tissue sections of F. cymosum was assessed using high-throughput sequencing technology in this investigation. Through correlation analysis, isolation, identification, and in vitro assays, we identified three target strains (JQ_R2, JQ_R14, JQ_L5) demonstrating growth-promotion and drought-resistance activities. These capabilities were subsequently confirmed in soil and hydroponic experiments involving F. cymosum. Transcriptomic and metabolomic analyses indicate that JQ_R2 enhances drought resistance in F. cymosum by boosting basal folate metabolism. Follow-up experiments validated the role of folate in aiding F. cymosum's drought resistance and demonstrated that the JQ_R2 strain produces folate consistently under both normal and drought conditions. During drought conditions, the dihydrofolate reductase (DHFR) activity of the JQ_R2 strain was substantially increased, suggesting that the JQ_R2 strain enhances drought resistance in F. cymosum, potentially via modulation of the folate metabolism pathway.
Conclusions
In conclusion, our study illustrates that F. cymosum plants thriving in arid regions host a more diverse array of drought-resistant endophytic fungi, with the JQ_R2 strain augmenting drought resistance in F. cymosum by boosting basal folate metabolism. This finding sheds light on the operational mechanism of drought-resistant fungal strains, bearing considerable importance for forthcoming research on endophytic fungi and mitigating agricultural drought challenges.
WITHDRAWN: An accurate interpolation-based iterative frequency estimator of 2D multi- frequency sinusoids
Authors: Peng Yuqin , Tu Yaqing , Mou Zelong , Yang Huiyue 
Published: October 2024

Abstract

There are many domains in which frequency estimation of two-dimensional (2D) multi-frequency sinusoids is of great concern. In this paper, based on 2D spectrum interpolation, an accurate and efficient method is proposed to estimate 2D multi-frequency in noise. The method estimates the order of the signal, adopting a coarse-fine strategy, effectively corrects frequency leakage and suppresses interference between frequency components. To obtain the coarse estimation, 2D spectrum interpolation and 2D discrete Fourier transform (2D-DFT) are adopted after the order of sinusoids has been estimated. The fine estimation, with subtraction strategies converting sinusoids into 2D single-frequency sinusoid, suppresses interference from other frequency components. Next, 2D spectrum interpolation is used to obtain an accurate frequency estimate, which accuracy is further improved through iteration. Simulation results demonstrate that the proposed method is capable of achieving the Cramer-Rao lower bound (CRLB) and outperforms other recently proposed frequency domain methods for 2D multi-frequency sinusoids.
Prediction Model for Identifying a High-Risk Group for Food Insecurity among Elderly South Koreans
Authors: Han Myeunghee 
Published: October 2024

Abstract

This study aimed to identify the highest risk group for food insecurity in elderly South Koreans. A cross-sectional study was conducted using data from the 2022 Korea Community Health Survey. Subjects were divided into four groups; A: Sufficient quantity and variety of food, B: Sufficient quantity but not variety of food, C: Sometimes insufficient quantity and variety of food, and D: Often insufficient quantity and variety of food. Decision tree analysis was conducted to build a prediction model of food insecurity. Individuals who were not receiving basic livelihood assistance, were living with a family member, and had a diploma beyond college level were the highest proportion (75.8%) in Group A. For Group B, those who were not receiving basic livelihood assistance, were living alone, and had depression were the highest proportion (49.4%). In Group C, those who were receiving basic livelihood assistance, had no depression, and were living alone were the highest proportion (44.2%). Finally, those who were basic livelihood assistance, showed depression, and were living alone were the highest proportion of Group D (14.3%). The food insecurity of the elderly in South Korea is associated with basic livelihood assistance, depression, education level, and household type.
Periplaneta americana extract in improving antigenic emulsifier-induced recurrent oral ulcers in rats by inhibiting TLR4/NF-κB and activating Nrf2/HO-1 signaling pathways
Authors: Li Weijun , Chen Yi , Li Kailing , Chen Zhongze , Zhang Jingyu , Zhao Guanhua , Sun Fanfan , Xiao Peiyun , Yang Yongshou 
Published: October 2024

Abstract

Recurrent oral ulcers (ROUs) of oral mucosa disease are difficult to cure and relapse easily, and immune imLbalance or dysfunction is considered an essential factor in their occurrence and recurrence. Periplaneta americana extract (PAD), a raw material used in Kangfuxin Liquid and Yunnan Baiyao toothpaste, contains a variety of growth factors such as polypeptides and sticky sugar amino acids that promote tissue repair; this can encourage the growth of the granulation tissue and reduce inflammation on the wound surface. This study aimed to investigate the interventional potential of PAD on recurrent oral ulcers in rats and to elucidate the underlying mechanism of action involving the TLR4/NF-κB and Nrf2/HO-1 signaling pathways. A rat model of recurrent oral ulcer (ROU) was established using an oral antigen emulsifier. Rats in the ROU group were administered PAD by gavage for 7 days. To observe the effect of PDA on ROU mice. HE staining revealed that PAD restored the structure of the oral mucosal tissue and reduced inflammatory infiltration. FCM revealed that PAD upregulated CD3+ and CD4+ levels and the CD4+/CD8+ ratio in peripheral blood T lymphocytes. ELISA revealed that PAD increased the content of IgA, IgG, IgM, VEGF, IL-2, and IL-10, while decreasing IL-6 and TNF-α content. Microplate analysis revealed that PAD significantly increased CAT content in the serum of ROU rats and reduced GSH, NO, SOD, and MDA levels. IHC staining, RT-qPCR, and Western blotting revealed that PAD downregulated Keap1 and IκBα expression, inhibited the TLR4/NF-κB signaling pathway, upregulated Nrf2 and HO-1 expression, and activated the Nrf2/HO-1 signaling pathway. These fndings suggest that PAD improved immune imbalance and oxidative stress in ROU rats by activating the Nrf2/HO-1 pathway and inhibiting the TLR4/NF-κB signaling pathway, thereby promoting the healing of oral ulcer wounds.
Thermal and Flow Optimization via Generative Design Simulations of the Curing Press Manifold Used in Tire Production
Authors: Çağlayan Ahmet Çağrı , Akyüz Fikret Kemal , Feyzioğlu Ahmet , Ersoy Sezgin 
Published: October 2024

Abstract

The tire industry involves complex processes that require high temperatures and pressures for tire production and processing. Serious environmental problems can occur at this stage of production. Therefore, in addition to maintaining product quality in this production system, considering environmental factors and at the same time increasing efficiency is an important gain. In this production; rubber compound is placed in molds using curing presses and shaped under high temperatures and pressures. Manifold blocks are used to feed the molds in these presses and to ensure equal temperature distribution between the molds. However, existing manifold blocks tend to deform in a short time under high temperatures and pressures and require constant maintenance. These changes can affect production continuity, reduce production quality and increase maintenance costs. Therefore, a more durable and low-maintenance manifold solution was needed to be investigated. In this study, Thermal Flow Analysis was performed on the Curing Press Manifold using the "Manufacturer Design" method and the appropriate operating level of the system was determined.
Fecal microbiota-based: An interpretable GBM-SHAP machine learning model for the diagnosis of ankylosing spondylitis
Authors: Li Mengpan , Zhang Wei , Ma Yichao , Wu Jiangpeng , Liu Wencai , Qi Chaofan , Long Siping , He Jin , Mo Fengbo , Ma Xiaojun 
Published: October 2024

Abstract

Background: Previous studies had shown a significant correlation between ankylosing spondylitis（AS） and the human gut microbiome, and emerging machine learning (ML) techniques can provide a reliable bridge between them. Achieving early diagnosis of AS through human gut microbiota with the help of ML is the paper’s objective.
Methods: The fecal metagenomic sequencing data derived from NCBI that pertain to AS, which processed by data filtering tools: Trimmomatic (Trimming sequence) and Bowtie2（decontamination）. Then, the processed data were classified by Kraken2 and estimated species abundance by Bracken. And we carried out species diversity analysis and actualized the visualization of species abundance. After that, univariate logistics regression and least absolute shrinkage and selection operator (LASSO) regression were utilized to analyze these fecal microbes. Then, the screened biomarkers were utilized to model construction. During the study, six models (include: LR, AB, HLP, BAG, GBM, XGB) were structured for selection, and the optimum model be applied as the tool of diagnosis AS. During this procedure, the behavior of model was contrasted by ten-fold cross-validation, ROC curve, precision recall curve, calibration curve and radar plot. Besides, confusion matrix and five-fold ROC were utilized to make further efforts to emerge the superiority of model. And Shapley Additive Explanations (SHAP) visualized the contribution of some biomarkers in the model.
Results: Totally 211 samples were subsumed in the study which were randomly separated into two sections (training set and test set) in the proportion 3:1 and two parts are utilized to establish and validate ML models. In the alpha diversity analysis, significant difference between the healthy population and AS (P = 0.0237) was demonstrated. In addition, PCA results in beta diversity also showed a significant difference in community structure between the two groups (P=0.001). From the results of LASSO regression analysis, 82 fecal microbes were selected for model construction. According to the performance matrixs of the models, GBM model performed best. In addition, SHAP showed the top ten fecal microbes that contributed to the model construction, which further explaining the model.
Conclusion: The construction of ensemble machine learning model GBM based on fecal microbes was meaningful for early diagnosis of AS and targeted clinical treatment.