bluuebunny/crossref_metadata_2025_split huggingface dataset:
An experimental study of multi-sensor tool wear monitoring and its application to predictive maintenance
Authors: Granados German Herrera , Misaka Takashi , Herwan Jonny , Komoto Hitoshi , Furukawa Yoshiyuki 
Published: January 2024

Abstract
Wear in cutting tools is a critical issue that can lead to reduced product quality, increased production costs, and unexpected downtime. To mitigate these challenges, the implementation of tool wear monitoring systems and predictive maintenance strategies has gained significant attention in recent years. Early detection or prediction of tool wear is vital to optimize tool life and maintain the manufacturing processes efficiently. This paper presents a method to determine the tool wear progression based on the collaboration of direct and indirect monitoring techniques. By analyzing the monitoring of data from force, vibration, sound, and current sensors to estimate the tool wear state, and correlating this information with a photographic database of the tool wear progression used to create an image recognition system that can classify the tool wear at any moment into three states: Good, Moderate and Worn. A case study was conducted to test the advantages and limitations of the proposed method. The case study also shows that the improvement of the prediction of the tool wear state might be useful in the decision-making of whether the tool life can be extended, or the tool must be replaced, as well as in the detection of anomalies during the machining process, aiming its improvement and the reduction of operational costs.
miR-30b-5p targets CD73 and inhibits gastric cancer migration and invasion via PTEN/AKT/GSK3β/mTOR pathway
Authors: Liu Hongli , Guo Shuang , Li Ya , Lei Ting , Chen Qian 
Published: January 2024

Abstract

Background and purpose: Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer-related deaths worldwide. CD73 has been found to be overexpressed in a variety of cancers including GC and is associated with poor cancer prognosis. However, its specific mechanisms regulating the progression of GC are not sufficiently clear. In this study, we aimed to investigate the function of CD73 in GC and to explore its upstream and downstream molecular mechanisms.
Methods: Immunohistochemistry (IHC) and western blotting were used to detect the protein levels of CD73 and other proteins. Quantitative real-time PCR (RT-qPCR) was used to detect the RNA levels of CD73, miR-30b-5p, and other genes. CCK-8 and clonogenic assays were used to test cell proliferation. Scracth and Transwell were used to analyze the migration and invasion of GC cells. In addition, CD73 stable knockdown and overexpression cell lines were established to detect the expression of PTEN/AKT/GSK3β/mTOR pathway-related molecules. Dual luciferase reporter assay was used to detect the binding of CD73 and miR-30b-5p.
Results: We found that miR-30b-5p targeted binding and inhibited CD73 overexpression, and suppressed GC cell proliferation, migration and invasion in GC cells. We further revealed that these effects were mediated through the PTEN/AKT/GSK3β/mTOR signalling pathway.
Conclusions: In summary, our results reveal the relevance of the miR-30b-5p/CD73/PTEN/AKT/GSK3β /mTOR regulatory axis to migration and invasion in gastric cancer.
HIF-1α is Associated with Improved Survival in ARDS due to COVID-19: A Prospective Study
Authors: Sipahioglu Hilal , Koyuncu Sumeyra , Akalın Hilal , Karasu Nilgun , Sipahioğlu Murat , Eren Esma Eryılmaz , Onuk Sevda , Elay Gulseren , Saatci Cetin , Yüksel Recep Civan , Gundogan Kursat , Dundar Munis 
Published: January 2024

Abstract
Background
Acute respiratory distress syndrome(ARDS) due to COVID-19 is accompanied by severe hypoxemia and hyperinflammation. Hypoxia-inducible factor(HIF) pathway plays a fundamental role in detecting hypoxia and developing appropriate responses. The epidemiological report claimed a lower rate of disease in the population living at high altitudes and hypothesized that adaptation to hypoxia might be advantageous for SARS-CoV-2 infection. This study was designed to examine the frequency of polymorphisms in the HIF-1α and PHD2(prolyl hydroxylase domain 2) genes, which are involved in the adaptation to hypoxia, and the relationship of existing polymorphisms with survival in the ARDS clinic developed due to COVID-19.
Methods
The study included 297 patients who developed ARDS due to COVID-19 infection and were admitted to the tertiary intensive care unit. Age, gender, hospitalization diagnosis, arterial blood pressure, heart rate, APACHEII score, SOFA laboratory parameters during hospitalization, vasopressor, dialysis and mechanical ventilation need during treatment, length of hospital stay, and 30-day mortality status were recorded. DNA was isolated from the blood samples by spin colon method with the QIAamp DNA MiniKit (Cat.No.51106, QIAGEN, Germany).
Results
Patients were divided into 3 groups according to their Hypoxia Inducible Factor-1α (C/T SNP [11549465]) genotypes. Frequencies were 71.13% for the homozygous CC genotype, 26.4% heterozygous CT genotype, and 2.36% for the homozygous TT genotype. Median age (p=0.631), APACHE II (p=0.205), and SOFA (p=0.077) scores were similar in all three groups. However, the need for dialysis, mechanical ventilation, and vasopressor was less in the homozygous TT-genotype group than in the other groups (p<0.05). The mortality rate was also lower in this group compared to other groups (p<0.05). PND2 (C/T SNP [480902] and [516651]) polymorphism, clinical and laboratory features were similar in all groups. Moreover, 30-day mortality did not differ between the groups.
Conclusion
In conclusion, we revealed polymorphism in HIF-lα and PHD2 genes in ARDS patients due to COVID-19. The rate of HIF-lα polymorphism was 26.4% heterozygous CT-genotype and 2.36% for homozygous TT-genotype. 30-day mortality and adverse outcome (dialysis, vasopressor use, MV need) were significantly lower in TT homozygous. However, none of the polymorphisms in the PHD2 genes affected mortality and adverse outcome.
Activities of the Oral Health Team in Primary Health Care: a time-motion study
Authors: Belotti Lorrayne , Maito Sofia , Vesga-Varela Andrea Liliana , de Almeida Leticia Yamawaka , Praça Maira Tamires da Silva , Haddad Ana Estela , Palacio Danielle da Costa , Bonfim Daiana 
Published: January 2024

Abstract

Background
Efficient planning of the oral health workforce in Primary Health Care (PHC) is paramount to ensure equitable community access to services. This requires a meticulous examination of the population's needs, strategic distribution of oral health professionals, and effective human resource management. In this context, the average time spent on care to meet the needs of users/families/communities is the central variable in healthcare professional sizing methods. However, many time measures are solely based on professional judgment or experience.
Objective
Calculate the average time parameters for the activities carried out by the oral health team in primary health care.
Method
This is a descriptive observational study using the time-motion method carried out in five Primary Health Care Units in the city of São Paulo, SP, Brazil. Direct and continuous observation of oral health team members occurred for 40 hours spread over five days of a typical work week.
Results
A total of 696.05 hours of observation were conducted with 12 Dentists, three Oral Health Assistants, and five Oral Health Technicians. The Dentists’ main activity was consultation with an average duration of 24.39 minutes, which took up 42.36% of their working time, followed by documentation with 12.15%. Oral Health Assistants spent 31.57% of their time on infection control, while Oral Health Technicians spent 22.37% on documentation.
Conclusion
The study establishes time standards for the activities performed by the dental care team and provides support for the application of sizing methods that allow for review and optimization of the work process and public personnel policies.
Causal effects of the gut microbiome on immune-related vasculitis: A two-sample Mendelian randomization study
Authors: Chen Si , Nie Rui , Wang Chao , Luan Haixia , Ma Xu , Gui Yuan , Zeng Xiaoli , Yuan Hui 
Published: January 2024

Abstract

Observational data suggest a link between gut microbiota and immune-related vasculitis, but causality remains unclear. A bidirectional mendelian randomization (MR) study was conducted using public genome-wide data. The inverse-variance-weighted (IVW) method identified associations and addressed heterogeneity. Families Clostridiaceae 1 and Actinomycetaceae correlated positively with granulomatosis with polyangiitis risk, while classes Lentisphaeria and Melainabacteria, and families Lachnospiraceae and Streptococcaceae showed negative associations. Behçet's disease was positively associated with the risk of family Streptococcaceae abundance. And other several gut microbiota constituents were identified as potential risk factors for immune-related vasculitis. Furthermore, combining positive association results from the IVW analysis revealed numerous shared gut microbiota constituents associated with immune-related vasculitis. MR analysis demonstrated a causal association between the gut microbiota and immune-related vasculitis, offering valuable insights for subsequent mechanistic and clinical investigations into microbiota-mediated immune-related vasculitis.
Extending a Pretrained Language Model (BERT) using an Ontological Perspective to Classify Students’ Scientific Expertise Level from Written Responses
Authors: Wang Heqiao , Haudek Kevin C. , Manzanares Amanda D. , Romulo Chelsie L. , Royse Emily A. 
Published: January 2024

Abstract

The complex and interdisciplinary nature of scientific concepts presents formidable challenges for students in developing their knowledge-in-use skills. The utilization of computerized analysis for evaluating students’ contextualized constructed responses offers a potential avenue for educators to develop personalized and scalable interventions, thus supporting the teaching and learning of science consistent with contemporary calls. While prior research in artificial intelligence has demonstrated the effectiveness of algorithms, including Bidirectional Encoder Representations from Transformers (BERT), in tasks like automated classifications of constructed responses, these efforts have predominantly leaned towards text-level features, often overlooking the exploration of conceptual ideas embedded in students’ responses from a cognitive perspective. Despite BERT’s performance in downstream tasks, challenges may arise in domain-specific tasks, particularly in establishing knowledge connections between specialized and open domains. These challenges become pronounced in small-scale and imbalanced educational datasets, where the available information for fine-tuning is frequently inadequate to capture task-specific nuances and contextual details. The primary objective of the present study is to investigate the effectiveness of a pretrained language model (BERT), when integrated with an ontological framework aligned with a contextualized science assessment, in classifying students’ expertise levels in scientific explanation. Our findings indicate that while pretrained language models such as BERT contribute to enhanced performance in language-related tasks within educational contexts, the incorporation of identifying domain-specific terms and extracting and substituting with their associated sibling terms in sentences through ontology-based systems can significantly improve classification model performance. Further, we qualitatively examined student responses and found that, as expected, the ontology framework identified and substituted key domain specific terms in student responses that led to more accurate predictive scores. The study explores the practical implementation of ontology in assessment evaluation to facilitate formative assessment and formulate instructional strategies.
Hypernetwork-Driven Centralized Contrastive Learning for Federated Graph Classification
Authors: Zhu Jianian , Li Yichen , Wang Haozhao , Qi Yining , Li Ruixuan 
Published: January 2024

Abstract

In the realm of Graph Federated Learning (GFL), current methodologies predominantly concentrate on local client data, a focus that often results in a constrained understanding of more expansive, global patterns. This approach typically struggles with Non-IID issues in cross-domain datasets, which impedes the identification of overarching patterns. Contrastive Learning (CL) has emerged as a potent method to improve model’s ability to differentiate between variations across diverse views. However, existing CL frameworks, which are primarily client-centric, fail to fully exploit this advantage. Our empirical findings reveal that the direct application of conventional CL techniques tends to induce homog-enization among clients, a phenomenon particularly pronounced in settings where client datasets exhibit high heterogeneity. To tackle this statistical heterogene-ity and harness inherent data variability, we propose a hypernetwork-based approach, termed CCL. This innovative, server-centric strategy, underpinned by a hypernetwork, adeptly navigates the core challenges associated with traditional client-centric models in the face of heterogeneous datasets. CCL excels in assimilating global patterns derived from multiple clients, effectively capturing a more diverse spectrum of patterns and, consequently, substantially boosting the overall performance of GFL. Our comprehensive experimental evaluations, encompassing supervised, unsupervised, and other harsh scenarios, distinctly affirm CCL’s superiority over prevailing models. Its remarkable compatibility with standard backbones, and the resulting significant enhancements in GFL performance across various contexts, further underscore its effectiveness.
Quantum Generative Modeling of Sequential Data with Trainable Token Embedding
Authors: Hou Wanda , Li Miao , You Yi-Zhuang 
Published: January 2024

Abstract

Generative models are a class of machine learning models that aim to learn the underlying probability distribution of data. Unlike discriminative models, generative models focus on capturing the data's inherent structure, allowing them to generate new samples that resemble the original data. To fully exploit the potential of modeling probability distributions using quantum physics, a quantum-inspired generative model known as the Born machines have shown great advancements in learning classical and quantum data over matrix product state(MPS) framework. The Born machines support tractable log-likelihood, autoregressive and mask sampling, and have shown outstanding performance in various unsupervised learning tasks. However, much of the current research has been centered on improving the expressive power of MPS, predominantly embedding each token directly by a corresponding tensor index. In this study, we generalize the embedding method into trainable quantum measurement operators that can be simultaneously honed with MPS. Our study indicated that combined with trainable embedding, Born machines can exhibit better performance and learn deeper correlations from the dataset.
Association of Serum miR-99a level and Nonalcoholic Fatty Liver Disease, Serum mTOR levels in Patients with Type 2 Diabetes Mellitus
Authors: Zhang Yangyang , Zuo Yuqiong , Chen Qian , Cui Yaqiang , Bao Yanxia , Jiang Panpan , Liu Jing , Quan Jinxing , Liu Juxiang 
Published: January 2024

Abstract
Purpose
This study was designed with the goal of exploring miR-99a expression in T2DM patients suffering from comorbid NAFLD and clarifying the importance of miR-99a in this pathological context.
Methods
A total of 137 subjects were included in this study, including 50 T2DM patients with NAFLD (T2DM + NAFLD group),48 T2DM patients without NAFLD (T2DM group), and 39 healthy subjects (Control group). We measured the levels of IL-6, mTOR and SOD in the serum of the subjects by ELISA. The plasma miR-99a levels was detected by RT-PCR. The correlation between serum miR-99a level and other indicators was analyzed.
Results
Serum miR-99a levels (median 0.79 vs 0.16 vs 0.03, P < 0.001) were significantly lower in the T2DM group than the healthy population and further decreased in the T2DM with NAFLD patients (P < 0.001). After adjusting for age, gender, illness duration and BMI, spearman correlation analysis showed that TG, HBA1c, FPG, HOMA-IR, Hs-CRP, IL-6, HDL-C, mTOR(P < 0.05) remained independently linked with serum miR-99a. And stepwise linear regression analysis showed that HBA1c, IL-6 and mTOR are independent serum miR-99a correlation variables (P < 0.05). Moreover, the ROC results indicated that serum miR-99a has a high diagnostic value for T2DM with NAFLD. In conclusion, serum miR-99a may be utilized as a screening biomarker for T2DM with NAFLD.
Conclusions
These data highlight a potential role for miR-99a as a regulator of the comorbid incidence of T2DM and NAFLD, suggesting that measuring the levels of miR-99a can effectively predict the risk of NAFLD in those with T2DM.
Temperature-Driven Structural Evolution during Preparation of MCM-41 Mesoporous Materials
Authors: Xu Tao , Cui Kuixin , Jin Shengming 
Published: January 2024

Abstract
In this study, we explored the structural evolution of MCM-41 mesoporous silica synthesized using water glass as the silicon source under varying hydrothermal crystallization temperatures, with a particular focus on the changes in pore size and structural order. Our experimental results revealed that as the hydrothermal crystallization temperature escalates from 100℃ to 160℃, there is a notable expansion in the lattice structure of MCM-41, accompanied by an increase in pore size. This phenomenon is attributed to the increase in the diameter of the micelles constituting MCM-41, resulting in an expanded distance between the axes of the micelles, while maintaining a relatively stable structural order. However, a further increase in temperature of 180℃-200℃ leads to a loss of material order. This is primarily due to the excessively high temperatures preventing the micelles from maintaining their two-dimensional hexagonal stacking structure, rendering them ineffective as templates for silica condensation. Additionally, the alkaline high-temperature environment contributes to the disruption of the mesoporous channels, resulting in a disordered material structure. The insights gained from this study are crucial for understanding the structural transformation of MCM-41 materials under different hydrothermal conditions and offer a new perspective for controlling the pore size and structural order of mesoporous materials.
Joint design of timetabling and capacity for mixed transit system with autonomous and human-driven buses considering passengers’ acceptance
Authors: Zhou Xucheng , Liu Yundong , Zhao Xiaomei , Xie Dongfan 
Published: January 2024

Abstract
Human-driven buses (HBs) with fixed-capacity are difficult to adapt to time-varying demand, resulting in overcrowding and excessive operating cost. With the introduce of self-driving technology in public transit, autonomous buses (ABs) with flexible capacity are intended to alleviate this problem. In this paper, based on a mixed transit system (MTS) with ABs and HBs, a mixed integer nonlinear programming (MINLP) model is formulated for jointly optimizing timetabling and capacity, by taking into account time-varying demand and passenger acceptance of HBs and ABs. The objective function is to minimize the operating cost and passenger cost. Genetic algorithm is employed to solve the model. The results show that MTS, compared to HBs system, can reducing total cost by 10.57% during peak periods, increase the frequency by 29.73%, and improve full load factor by 7.45% during off-peak periods. The sensitivity analysis of passenger acceptance reveals that the total cost of MTS tends to decrease when the proportion of passengers who accept ABs increases.
Effect of Steamed Stauntonia hexaphylla fruit on RAW 264.7 osteoclast and MC3T3-E1 osteoblast differentiation
Authors: Akter Reshmi , Awais Muhammad , Morshed Md Niaj , Kim Jong Hak , Kong Byoung Man , Lee Dong Wook , Choi Sung Keun , Lee Chang Soon , Ahn Jong Chan , Yang Deok Chun 
Published: January 2024

Abstract
Stauntonia hexaphylla (SH), a medicinal plant in the Lardizabalaceae family, holds traditional significance in East Asian countries for its therapeutic properties, particularly in treating rheumatism. Steam processing is a common method employed to augment the therapeutic properties of SH, prompting the need for a detailed investigation into the chemical and biological changes induced by this process. Comparative analyses revealed significantly higher 5-Hydroxymethylfurfural (5-HMF) content, total flavonoid and phenolic content, and antioxidant activity in steamed SH fruit (SHF) samples compared to unprocessed ones. Furthermore, the study explored the effects of steamed SHF extracts on bone health using an in vitro model simulating osteoclast in Raw 264.7 cells and osteoblasts in MC3T3E1 cells. Notably, steamed extracts demonstrated a dual effect on bone health by promoting alkaline phosphatase activity, calcium nodule, and collagen formation in osteoblasts, while inhibiting tartrate-resistant acid phosphatase (TRAP) activity in osteoclasts. Steamed SHF extracts were able to restore the aberrant gene expression of osteoclastogenesis indicators including TRAP, NFTAc1, RANK, MMP9, c-Fos and TRAF6. Additionally, the expression of the osteoblastogenesis biomarkers ALP, Runx2, BGLAP, Col1a1, and OPG were boosted by steaming to levels greater than those in the untreated control group, suggesting that steaming can be used to improve the properties of proteins. The component 5-HMF actively promotes ALP and inhibits TRAP activities. Hence, these results are expected to aid in confirming the efficacy of herbal medicines and identifying suitable materials for medicinal applications.
Recreational Physical Activity Online Decrease Obesity on School Children: A Pilot Study
Authors: Rodriguez-Triviño Claudia-Yaneth , Cerón Nidia-Cristina , Ramírez María-Camila , Zuluaga Juliana-Sofia , Méndez Miguel 
Published: January 2024

Abstract
Objective
To determine the effects of recreational physical activity mediated by technology on the body composition of six- to nine-year-old children. Childhood obesity is a public health problem associated with the development of cardiovascular disease (CVD) and which was exacerbated during the COVID-19 lockdowns, given the restrictions on going to school and participating in group games, among others.
Methods
A quasi-experimental intervention study included 27 schoolchildren (12 boys and 15 girls) with an average age of 6 from an educational establishment during who received three 60-minute online sessions of recreational physical activity per week for 20 weeks.
Results
Medical tests were carried out prior to the intervention, which found that 13.3% of girls were overweight and 40% obese, while 6.7% of boys were overweight and 41.6% obese. After the interventions, there was an average weight reduction of 1.7 kg (p = 0.16), while there were also reductions in the proportion of the participants suffering from overweight and obesity, body fat percentage (0.9 percentage points; p = 0.4) and abdominal circumference (1.01 cm; p = 0.63).
Conclusion
The interventions mediated by technology had a positive impact on anthropometric measurements, promoting healthy practices and physical exercise during lockdown.