bluuebunny/crossref_metadata_2025_split huggingface dataset:
Equol production is associated with bone mass in young women
Authors: Hanano Hiromi , Aoki Takumi , Sasaki Shota , Fuzikawa Hiroyoshi , Oishi Kan , Yamamoto Yuiko , Yamaguchi Hiroki , Mori Takaaki , Ishii Kojiro 
Published: January 2024

Abstract
Background
Osteoporosis, a disease characterized by decreased bone strength and increased risk of fracture, represents a significant health concern in older adults. Primary osteoporosis prevention requires increasing bone mass (BM) to its peak at a young age. The estrogen-like effect of equol has been reported to suppress bone loss in postmenopausal women. This study aimed to investigate the relationship between BM, body mass, skeletal muscle mass, exercise habits, menstrual abnormalities, and equol production in younger women.
Methods
Of a total of 395 female university students recruited, 275 who were not taking any hormonal medications and had no deficiencies in any of the measurement items we evaluated, were included in the analysis. BM was measured in the right calcaneus using an ultrasonic bone densitometer. Body composition was measured via the bioelectrical impedance method, using a multifrequency body composition analyzer. Hormone use, menstrual cycle, current exercise habits, and daily soy intake were assessed using self-administered questionnaires. Equol production was measured using a Soy-Check system. Multiple regression analysis, using the forced-entry method, was performed with BM as the objective variable; and age, body mass index, skeletal muscle mass index, soy intake, exercise habits, menstrual cycle, and equol production as the explanatory variables.
Results
In our multiple regression analysis with osteo-sonoassessment indexas the objective variable, the significantly associated factors were determined to be the amount of equol production (β = 0.11, p < 0.05), skeletal muscle mass index (β = 0.29, p < 0.01), and current exercise habits (β = 0.31, p < 0.01). By contrast, age, body mass index, soy intake, and menstrual cycle were not found to be significantly associated with osteo-sonoassessment index.
Conclusions
Young women with higher equol production, exercise habits, and skeletal muscle mass indexshad higher levels of BM. The acquisition of maximal BM at a young age is protective against osteoporosis; therefore, increased equol production at a young age may protect against osteoporosis.
Pinoresinol targets NF-κB alongside STAT3 pathway to attenuate IL-6-induced inflammation
Authors: Dutta Anupam , Das Dorothy , Chakraborty Rituraj , Baruah Bhargab Jyoti , Sharma Manoj , Sharma Pushpa , Mattaparthi Venkata Satish Kumar , Mukhopadhyay Rupak 
Published: January 2024

Abstract
Objective
Activation of NF-κB alongside STAT3 by IL-6 plays major role in inflammation-induced disease pathophysiology. Here, we report the mechanism of downregulation of NF-κB and JAK-STAT pathways by pinoresinol in IL6-induced macrophages.
Methods
Bioinformatic analysis screened Pinoresinol, among 100 dietary polyphenols, as the most potent to interact with the proteins in NF-κB and JAK-STAT cascades. The effect of pinoresinol on IL-6-activated p65 NF-κB and STAT3 and their regulators was studied by immunoblotting. Localization of the transcription factors were investigated by immunofluorescence and fractionation studies. Effect of pinoresinol on the downstream genes of the NF-κB and JAK-STAT pathways was studied by RT-PCR or immunoblotting. Biological implication this inhibition was shown by attenuation of cellular adhesion and migration.
Results
Pinoresinol repressed IL-6-mediated activation and nuclear translocation of both p65 NF-κB and STAT3. It reduced the phosphorylation of IKK and IκB-α, and degradation of the latter. The expressions of downstream genes e.g. IL-1β, TNF-α, and COX-2 were also attenuated following pinoresinol treatment. The polyphenol reduced the IL-6-mediated macrophage adhesion and migration, which was supported by downregulation of VCAM-1, ICAM-1, MCP-1, MMP-9 and MMP-2 in pinoresinol-treated cells.
Conclusion
Pinoresinol targets NF-κB and JAK-STAT pathways to attenuate IL-6-induced inflammatory condition which highlights its potential as a candidate for anti-inflammatory therapy.
Multi-scenario simulations and analyses of urban ecological sources using PLUS and MSPA models — exemplified by Zhengzhou City.
Authors: Fan Qindong , Wang YuXin , Ping Xiaoying 
Published: January 2024

Abstract
In cities, ecological sources are patches that exert the strongest ecological effects. The landscape pattern and ecological functions of these ecological sources play a critical role in addressing climate change, conserving biodiversity, and fostering sustainable development. Utilizing the 2010 and 2020 land use data, the landscape pattern changes of green spaces in Zhengzhou's central urban area under the three scenarios (natural evolution scenario, cropland protection scenario, and ecological protection scenario) in 2030 were simulated by the PLUS model. Subsequently, morphological spatial pattern analysis (MSPA) was employed to identify ecological sources across three scenarios, while their landscape patterns were analyzed by landscape pattern indices. Our results indicate significant changes in Zhengzhou's main urban area's landscape patterns between 2010 and 2020, with notable expansion in ecological source areas. In all of the 2030 multi-scenario simulations, the ecological source areas show a westward expansion. Meanwhile, the Largest Patch Index (LPI) of ecological sources also exhibited an upward trend under the 2030 multi-scenario simulations, suggesting that the large, contiguous patches will be the dominant form of ecological source expansion in the future. Our research aids in identifying and safeguarding ecological sources, offering a foundation for future urban ecological planning decisions.
Reliability analysis of PVD-improved soft soil settlement prediction using Bayesian back analysis framework and simplified Hypothesis B method
Authors: Zhai Shijie , Du Guangyin , Peng Tao , Wang Yuxiao , Shang Zhiheng 
Published: January 2024

Abstract
The time-dependent settlement of soft soils is one of the key problems in geotechnical engineering. Using Bayesian back analysis, this study examined the reliability of settlement of the Ballina embankment in Australia. As random variables, the primary compression index (Cc), swelling index (Ce), and secondary compression index (Cαe) were examined for their influence on the settlement probability distribution. To generate compression index samples, Monte Carlo Markov chain simulations （MCMCS） were used, and predicted settlement samples were derived from the compression index samples. Consequently, the predicted settlement samples can be used for the reliability analysis. Based on a comparison between the field settlement data and the predicted settlement, the results indicate that the 90% confidence interval of the predicted settlement is in reasonable agreement with the field settlement monitoring data. With the incorporation of more monitored settlement data into the Bayesian framework, the distribution of predicted settlement shifts from the Weibull distribution to the normal distribution. In addition, the degree of uncertainty in the prediction of settlement decreases with the amount of data incorporated into the model. Additionally, the small amount of data used in the Bayesian framework can also lead to underestimations of failure probability.
m 6 A demethylase ALKBH5 regulates TP53 to promote malignant transformation of lung cells induced by radon exposure via BTG2-mediated mitochondrial pathway
Authors: Wang Jin , Xu Qian , Tan Lirong , Liu Kaifan , Chen Bin , Fang Lijun , Tong Jian , Tian Hailin , Li Jianxiang , Li Jiawen 
Published: January 2024

Abstract

Lung cancer is a multi-etiological disease and radon is known as the second most common risk factor for the disease. The tumor suppressor gene TP53 is commonly mutated in human lung cancer, thus contributing to the alteration of mitochondrial outer membrane permeability (MOMP). Thus, this study aimed to investigate the role of the P53-mediated mitochondrial pathway in radon-induced lung cancer. Long-term radon exposure downregulated P53, enhanced mitochondrial membrane potential (MMP), and increased mitochondrial DNA copy number in BEAS-2B cells, as well as in the lung tissues of mice exposed to radon. The above effects significantly contributed to the radon-induced malignant transformation of BEAS-2B cells. In addition, high-throughput sequencing analysis revealed that BTG2 downregulation was involved in the P53 mitochondrial pathway-mediated radon-induced malignant transformation. BTG2 overexpression significantly restored the TP53-mediated alterations in MOMP, apoptosis, and MMP. Mechanically, TP53 downregulation was regulated by m6A demethylase ALKBH5, which was validated by treatment of ALKBH5 inhibitor IOX1 and mRNA stability assay in BEAS-2B and H1299 cells. Our research shows that the ALKBH5-mediated downregulation of TP53 affects the carcinogenesis of radon, and the BTG2 suppression in the P53-mediated mitochondrial pathway plays an important role in radon-induced lung cancer.
An Immunogenic Cell Death-Related Genes Predicts Prognosis And Tumor Microenvironment Features Of Early-stage Primary Hepatic Cell Carcinoma Patients
Authors: Liu Jun , Wang Ling , Jiang Yuhuan , Wang Jingzhi , Li Hunming 
Published: January 2024

Abstract
Objective
To elucidate the potential relationship between ICD and early-stage HCC prognosis, we sought to identify ICD-related genes and construct a model predicting patient response and survival outcomes to immunotherapy.
Methods
A cohort of 252 early-stage HCC patients was obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). Immunogenic Cell Death-associated Gene Signatures (ICD-AGS) were curated from 57 genes implicated in ICD. Comparative analysis identified differentially expressed ICD-associated genes (DE-IRGs), which were subsequently functionally annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping.The relationship between ICD expression and the immune microenvironment was assessed using the ESTIMATE and CIBERSORT algorithms. A risk model was constructed through univariate Cox regression, LASSO technique, and gene expression profiling, validated with ROC curve analysis. Results Consensus clustering identified two distinct ICD expression groups, with the high-expression group associated with a favorable prognosis. Differential gene expression analysis revealed enrichment in immune-related pathways in the high ICD expression group. Somatic mutation analysis indicated variations in TP53, CTNNB1, and MUC16 between the two groups. The ICD high-expression group exhibited increased immune cell infiltration and higher expression of immune checkpoints. The ICD risk model containing 8 genes showed significant prognostic value and was externally validated.
Conclusion
This study unveils a novel relationship between ICD subtypes and the immune tumor microenvironment in early-stage HCC. The established prognostic risk model related to ICD provides valuable insights for predicting patient outcomes and guiding immunotherapeutic interventions in HCC.
Food-washing monkeys recognize the law of diminishing returns
Authors: Dominy Nathaniel , Rosien Jessica , Fannin Luke , Yeakel Justin , Malaivijitnond Suchinda , Tan Amanda 
Published: January 2024

Abstract

Few animal species have the cognitive faculties or prehensile abilities needed to eliminate costly tooth-damaging grit from food surfaces. Some populations of monkeys wash sandy foods when standing water is readily accessible, but this propensity varies within groups for reasons unknown. Spontaneous food-washing emerged recently in a group of long-tailed macaques (Macaca fascicularis) on Koram Island, Thailand, motivating us to explore the factors that drive individual variation. We measured the mineral and physical properties of contaminant sands and conducted a field experiment, eliciting 1,282 food-handling bouts by 42 monkeys. Our results verify two long-standing presumptions, that monkeys have a strong aversion to sand and that removing it is intentional. Reinforcing this point, monkeys clean foods beyond the point of diminishing returns, a suboptimal behaviour that varies with rank. Dominant monkeys abstain from washing, balancing the long-term benefits of mitigating tooth wear against immediate energetic requirements, an essential predictor of reproductive fitness.
The Effect of Mindfulness on Well-Being in Education: A Systematic Review
Authors: Moreno-Pérez Raquel , Delgado-Ríos Myriam 
Published: January 2024

Abstract
Objectives
This article is a response to the growing need to prioritise well-being and balance cognitive and socioemotional aspects in the educational environment. Its objective is to conduct a systematic review of studies that have analysed the influence of mindfulness practice on well-being in educational settings, in order to investigate whether the effects of its practice can be extrapolated to the educational environment as a whole.
Methods
: Following the PRISMA guidelines, a comprehensive search of major databases was carried out using the keywords ‘mindfulness’, ‘education’ and ‘well-being’. After peer review, the final sample consisted of 15 articles that met the inclusion and exclusion criteria.
Results
The findings show that mindfulness practice has positive effects on the well-being of the educational community; a positive relationship has also been found between mindfulness practice and performance.
Conclusions
Despite the limitations, mindfulness programmes showed a positive association with well-being among both students and teaching staff. This highlights the need for future research to overcome the limitations identified, with a view to addressing the paucity of studies and existing methodological heterogeneity. It is suggested that future research endeavours should be extended to other educational stages and to continue exploring the appropriateness and effectiveness of mindfulness practices in the educational context.
Utilizing Publicly Accessible Machine Learning for Training Student Formulators in Personal Care Product Formulation: Specification-Driven and Cost-Conscious Experiments
Authors: Bilbao McKinnley , Carmona Tomas , Covarrubias Morgan , Goslin Alex , Judge Katherine , Munn Garland , Ticas Hazel , Tonioli Abe , Tuttle Collin , West Caitlin , Scott Daniel 
Published: January 2024

Abstract
This work explores the application of the questionable use of machine learning (ML), specifically the ChatGPT 3.5 system, in the training of student formulators. Here, an experiment is undertaken to explore the ability of ML to aid in training students in the role of formulators of a personal care product. The focus is on whether or not the students can successfully rely on ML to guide them through the formulation process of a 10-minute hydrating face mask recipe. While exploring the iterative process of recipe adjustments with ML, it was found that the language model demonstrates the ability to help formulators in training due to its above-average knowledge in chemistry, but when given clear prompts, it performs much better at providing helpful suggestions for ingredient substitutions. However, ML lacks a reliable memory, even within a single extended conversation and struggles with mathematical calculations. ML is not found to be proficient in accurately calculating cost adjustments. Additionally, the contribution of ML may only be marginally helpful in the training of more seasoned formulator. Despite its limitations, ML can quickly and effectively, in the hands of student formulators in-training, provide direction and support to produce and improve upon a base formula resulting in a quality product.
NRS2002 combined with nutritional, immune and inflammatory indicators for the nomogram to predict Sarcopenia
Authors: Liu Jie , Liu Jingjin , Xian Xuejiao , Hu Tao , Bi Zhengfeng , Yang Hongjun 
Published: January 2024

Abstract
Objectives
Sarcopenia is a geriatric syndrome characterized by age-related loss of muscle mass and strength, with or without physical function decline. In clinical work, it is complicated to consider it as a geriatric syndrome, and the diagnostic criteria are often ignored by clinical workers. This study aims to construct a predictive model for sarcopenia using commonly used clinical indicators.
Design:
By collecting the basic clinical data, NRS2002 score scale, nutrition, immunity, inflammation, and other blood indicators of the subjects, the diagnosis and prediction model of sarcopenia was established. The LASSO regression method was used to screen the variables and select predictors. logistic regression analysis was used to construct the modal map, and the discriminant ability of the model was determined by calculating the area under the curve (AUC). Finally, the training set and validation set were randomly split for internal verification, and the AUC was used to judge the verification effect.
Participants:
The study was conducted from June 2023 to September 2022 in the First Affiliated Hospital of Kunming Medical University; Elderly inpatients over 60 years old were included, and sarcopenia was diagnosed using the Asian Working Group for Sarcopenia (AWGS2019) diagnostic criteria. NRS2002 score, nutrition, immunity, and inflammation indexes were collected to construct the model.
Results
Four variables were selected and screened by the LASSO regression method, and a diagnostic and prediction model was established based on these variables. The AUC of the prediction model was 0.80. In the internal validation, the total number of samples was randomly divided into training set and validation set according to a 0.85 split ratio, and the ROC curve was used to verify the results, and the AUC was 0.8047 and 0.9065 respectively. Finally, the model was used to correct the curve, and the curve fit was good, the mean absolute error (MAE) was 0.014, and the prediction effect was good. The model can be used to diagnose and predict sarcopenia in clinical patients.
Conclusion
In this study, NRS2002 combined with BMI, lymphocyte count, and BNP were used to construct a diagnosis and prediction model for sarcopenia, which has important value for the prediction of sarcopenia.
A Euclidean Distance Diagnostic of North Atlantic Basin Activity
Authors: Senter Jason , Lupo Anthony 
Published: January 2024

Abstract

An analysis of North Atlantic basin activity was conducted by comparing the similarity of accumulated cyclone energy (ACE) to tropical storms and hurricanes (named storms) from 1851 to 2022. These time series data sets share common in-phase dynamics, spatially and temporally. The data sets also characterize a stochastic system where the shared underlying signals are comprised of annual, El Niño-Southern Oscillation (ENSO), and Pacific Decadal teleconnections [1]. Using the magnitude of ACE as a measure of basin activity, and the Euclidean distance as a measure of similarity between ACE and named storms; it can be observed that the distances measured fluctuate predictably overtime. These predictable fluctuations of similarities illustrate modality of basin activity oscillating between different primary behavioral analogues of ACE during both pre-satellite and post-satellite introduction time periods. Using the Euclidean distance, it is possible to characterize ACE based upon intrinsic in-phase and magnitude similarities with basin measures over a defined period of time.
Applying Deep Learning Techniques for Network Traffic Classification: A Comparison Study on the NSL-KDD Dataset
Authors: Trrad Issam 
Published: January 2024

Abstract

The escalating intricacy and refinement of network attacks require the implementation of advanced methodologies in network security and intrusion detection. This study centers on the utilization of machine learning techniques for the categorization of network traffic, specifically employing the NSL-KDD dataset. This study investigates the efficacy of various classifiers, namely Linear Support Vector Machine (SVM), Quadratic SVM, K-Nearest Neighbor (kNN), and Long Short-Term Memory (LSTM), for the precise detection of anomalous network activity. The data is preprocessed through various techniques, including one-hot encoding and normalization, in order to enhance the performance of the model. Feature selection is utilized as a means to improve the outcomes of classification. By conducting a thorough evaluation and analysis, we present an assessment of the classifiers' performance in terms of precision, recall, and F1-score. The findings demonstrate the potential application of machine learning techniques in the field of network security, underscoring the significance of carefully choosing suitable algorithms and preprocessing approaches to achieve efficient intrusion detection. The results of our study make a significant contribution to the advancement of intrusion detection systems based on machine learning. These findings offer valuable insights for both network security practitioners and researchers in the field.
A lattice model with a progressive damage applied to fracture problems of wood
Authors: Braun Matías , Aranda-Ruiz Josué , Sal-Anglada Gastón , Ariza M. Pilar 
Published: January 2024

Abstract

In this study, we have implemented the first lattice model that incorporates progressive material damage, taking into account ductile failure under compression and brittle failure under tension. In addition, the lattice fracture criterion includes a typical failure criterion for wood and assumes a coefficient of variation in elastic constants, and strengths to account for the heterogeneity of wood. The lattice model relies on axial springs, with their mechanical properties explicitly calculated based on the wood's macroscopic mechanical properties. The model's capability is evidenced by simulating two fracture tests and comparing the results with previously presented numerical and experimental data. The observed results align well with experimental observations.