bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of the microbial community diversity in various regions of the healthy oral cavity
Authors: Liu Yuchang , Qiao Feng , Meng Ge , Gu Yeqing , Wu Hongmei , Liu Dayong , Niu Kaijun 
Published: April 2023

Abstract
Background: To investigate the differences in microbial colony distribution in different parts of a healthy oral cavity.
Methods: We assessed four sites and two methods for sampling the bacterial community of healthy individuals, and identified the colonization of bacteria on the tooth surface and buccal mucosa. Specifically, we analyzed buccal mucosa (n=10) and tooth surface (n=20) samples from healthy individuals using 16S rDNA gene sequencing. Additionally, we detected bacterial signals in healthy individuals through fluorescence in situ hybridization (FISH), which targeted the bacterial 16S rDNA gene.
Results: The results indicate that there are no significant differences between the buccal mucosa and tooth surfaces in normal individuals. However, when detecting disease-associated pathogens such as Enterococcus faecalis and Porphyromonas gingivalis, it is important to use different methods and specific sampling sites. The statistics suggest that although there were no significant differences in colony composition, there were differences in the abundance and distribution of colonies on the dental and buccal mucosal surfaces. Compared to right tooth sampling with a curette, the swab sampling group had higher levels of Firmicutes, while Fusobacteria and Bacteroidetes were more prevalent in the curette tissues.
Conclusions: These data provide a different perspective for future investigation on healthy people’s bacterial distribution. In normal individuals, there is no difference in the bacterial composition of the oral buccal mucosa and the dental surface, differing only in abundance. Thus, the buccal mucosa can ask as a substitute for the teeth in epidemiological investigations exploring the bacterial composition of the oral cavity.
The Depression Anxiety Stress Scale 8: Investigating its cutoff scores in relevance to loneliness and burnout among dementia family caregivers
Authors: Ali Amira M. , Alkhamees Abdulmajeed A. , Al-Dwaikat Tariq N. , Khatatbeh Haitham , Al-Dossary Saeed A. 
Published: April 2023

Abstract

The Depression Anxiety Stress Scale 8-items (DASS-8) is a brief form of the DASS-21, which demonstrated adequate psychometrics relative to the parent scale in many studies. Aiming to determine an optimal cutoff score of the DASS-8, we recruited a convenient sample of 571 European caregivers (Mean age = 53 ± 12 years, Italian = 74.4%, Swiss = 25.6%). K-means clustering classified the sample into high- and low-distress clusters based on DASS-8 score of 19. Based on score of 48 and 7 of the Zarit Burden Interview (ZBI) and the Three-Item University of California, Los Angeles, Loneliness Scale-version 3 (UCLALS3), receiver operator curve (ROC) analysis revealed an optimal cutoff point of 12.5 and 14.5 (area under the curve (AUC) = 0.85 & 0.92, p values = 0.001, 95% CI: 0.82–0.88 & 0.89 to 0.94, sensitivity = 0.81 & 0.78, specificity = 0.76 & 0.89, Youden index = 0.57 & 0.67, respectively). Decision modeling revealed that DASS-8 scores of 9.5 and 14.5 can predict low and high ZBI and UCLALS3 categories, respectively. Based on the median cutoff of the DASS-8 (14.5), the prevalence of high distress in this sample was 50.8%. In path analysis, the ZBI, UCLALS3, care dependency, receiving help with caregiving, predicted distress, especially in older female spouse caregivers. A DASS-8 score of 14.5 can detect mental distress symptomatology and reflect key mental problems such as burnout and loneliness in this vulnerable group. Further diagnostic workup may confirm psycho-pathogenicity. More investigations in other countries and conditions are needed to confirm the validity of this score.
Art Design and Production Based on Artificial Intelligence and Improved Neural Network
Authors: Liao Zeming 
Published: April 2023

Abstract
The times are developing and society is progressing. In recent years, the level of random science and technology has been continuously improved, and the application of artificial intelligence technology has become more and more common in people's lives. The further combination of artificial intelligence and art design has also largely optimized the methods and expressions of art design. This article takes the decentralization of art design in the era of artificial intelligence as the main research object, and conducts an in-depth discussion and analysis of the performance and causes of art design, aiming to study the influence of artificial intelligence technology on the development of human art design in the future. This article first takes the causes of the decentralization phenomenon produced by art design as the research object, and conducts a specific analysis of the development of art design, including the extension of art design related to artificial intelligence technology, and the aesthetics of neural network technology to the public And how popular culture promotes the decentralized change of art design. Secondly, we analyzed the function and role of neural network technology in art design, and found that it not only provides a basis for the decentralization of art design, but also plays an important role in many different aspects of art design. The influence of centralization and spatial decentralization. Then, we also analyzed the specific impact of neural network intelligent technology on customer groups and customer thinking, as well as its specific embodiment in the content of art design. In the end, we came to the conclusion that although the communication method of artificial intelligence is conducive to the personalized development of art design in many aspects, it is influenced by the mass media and the specific mode of communication in the process of use, resulting in artificial intelligence technology in art. There is great innovation ability in design. Therefore, for professional art designers, they should not only complete their own commercial art design tasks, but also make contributions and efforts for the prosperity of art on this basis.
Application of Q-learning Virtual Network and Embedded Processor in Chinese English Translation Sentence Accuracy Analysis
Authors: CHEN Chen 
Published: April 2023

Abstract
The widely available data is exploding with the continuous development of the Internet, leading people to the era of big data. Nowadays, massive amounts of English information can generate information redundancy, which to some extent affects the user's information browsing and filtering process, and Chinese-English translation needs to be further improved in accuracy. Therefore, it is very important to summarize the information in English source text and accurately translate it into Chinese to help people effectively and quickly grasp the key points and basic knowledge of English articles, among which satisfying sentence accuracy is the most important. Based on this point, this article proposes a new type of Chinese-English translation system, which is equipped with a Q-learning virtual network and built-in processor. With the development of recent years, language recognition technology has become increasingly mature. In a sense, this technology can be said to be the "cornerstone" of fully realizing artificial intelligence technology. Nowadays, the background of embedded processor based language recognition systems is microprocessor platforms (such as chips), which can create language training models in language databases, and can match the models with speech fragments to be tested to complete recognition. In particular, recognizing non specific language has evolved from a laboratory environment to become increasingly practical. This paper designs and implements a class of Chinese-English translation systems based on embedded processors and Q-learning algorithms to optimize system speed and achieve efficient and convenient system processing.
Developing a Simple Conceptual Model for Predicting Early Recurrence and Mortality after Curative Surgery for Colorectal Cancer Patients
Authors: mahmoudi leila , Alinia Shayeste , Ahmadi Samira , Mohammadi Zahra , shirvandeh Farzaneh Rastkar , Asghari-Jafarabadi Mohammad , Safari Malihe , Roshanaei Ghodratollah 
Published: April 2023

Abstract
Introduction: A generalized structural equation model with accelerated failure time for survival analysis in predicting early recurrence and mortality following curative surgery in colorectal cancer patients.
Methods: This retrospective cohort study included 284 patients with colorectal cancer who underwent surgery at Imam Khomeini Clinic in Hamadan between 2001 and 2017. Demographic and other relevant data were extracted from patient records, including age at diagnosis, sex, clinical and pathological variables, first treatment type and date, tumor differentiation degree, disease stage, and treatment types (surgery, chemotherapy, and radiotherapy). Predictors were included in a generalized structural equation model for survival analysis with an accelerated failure time (AFT) model, and both unadjusted and adjusted time ratios (TRs) were calculated.
Results: The study findings indicate that patients who did not receive chemotherapy had a lower survival time ratio [TR=0.443, 0.307 - 0.637], and those who underwent multiple chemotherapy sessions had an increased survival time [TR=2.007, 1.690 - 2.383]. Female patients had longer survival times than male patients [TR=1.137, 0.981- 1.318]. Additionally, disease progression to higher stages was associated with a decrease in survival times [TR=0.433, 0.348- 0.539].
Conclusion: According to the optimal model, age, number of chemotherapy sessions, and disease stage were found to have a significant relationship. Therefore, regular screening and preventive measures are recommended for target groups to help identify and manage colorectal cancer early and potentially improve patient outcomes.
An application of stereo matching algorithm based on transfer learning on robots in multiple scenarios
Authors: Bi Yuanwei , Li Chuanbiao , Wang Guohui 
Published: April 2023

Abstract
Robot vision technology based on binocular vision has significant potential for development in various fields, including 3D scenes and reconstruction, target detection, autonomous driving, and other fields. To date, current binocular vision methods used in robotics engineering suffer from high costs, complex algorithms, and low reliability of the generated disparity map in multiple scenarios. Robots require a cost-effective algorithm with cross-domain generalization capabilities for multiple scenarios. To address these issues, a cross-domain stereo matching algorithm for binocular vision based on transfer learning was proposed in this paper, named Cross-Domain Adaptation and Transfer Learning Network (Ct-Net), which has shown valuable results in multiple robot scenes. First, this paper introduces a General Feature Extractor (GFE) to extract rich general feature information for domain adaptive stereo matching tasks. Then, a feature adapter is used to adapt the general features to the stereo matching network. Furthermore, a Domain Adaptive Cost Optimization Module (DACOM) was designed to optimize the matching cost. A disparity score prediction module was also embedded to adaptively adjust the search range of disparity and optimize the cost distribution. The overall framework was trained using a phased strategy, and ablation experiments were conducted to verify the effectiveness of the training strategy. On KITTI 2015 benchmark, compared with the prototype PSMNet, the 3PE − fg of Ct-Net in all regions and non-occluded regions decreased by 19.3% and 21.1% respectively. On the Middlebury dataset, the 2PE of Ct-Net achieved comparable results on all samples. The quantitative and qualitative results obtained from Middlebury, Apollo, and other datasets demonstrate that Ct-Net significantly improves the cross-domain performance of stereo matching. Stereo matching experiments in real-world scenarios have shown that it can effectively address visual tasks in multiple robot scenes.
Improvement of Image Quality in Low-Count Bone Scintigraphy Using Deep Learning
Authors: Murata Taisuke , Onoguchi Masahisa , Hashimoto Takuma , Shibutani Takayuki , Iimori Takashi , Sawada Koichi , Umezawa Tetsuro , Masuda Yoshitada , Uno Takashi 
Published: April 2023

Abstract
Objective To improve image quality for low-count bone scintigraphy whole-body images using deep learning and evaluate their applicability in clinical practice.Methods Five hundred fifty patients were included in the study. Low-count Original images (75%, 50%, 25%, 10%, and 5% counts) were generated from Reference images (100% counts) using Poisson resampling. Patients were randomly divided into training (500) and evaluation (50) groups. Output (DL-filtered) images were obtained after training with U-Net using Reference images as teacher data. Gaussian-filtered images were generated for comparison. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) to the Reference image were calculated to determine image quality. Artificial neural network (ANN) value, bone scan index (BSI), and number of hotspots (Hs) were computed using BONENAVI analysis for patients with and without bone metastases, to assess diagnostic performance. Accuracy of bone metastasis detection and area under the curve (AUC) were calculated. Original, Gaussian-filtered, and DL-filtered images were compared with Reference images.Results PSNR and SSIM for DL-filtered images were highest in all count percentages. BONENAVI analysis values for DL-filtered images did not differ significantly regardless of the presence or absence of bone metastases. BONENAVI analysis values for Original and Gaussian-filtered images differed significantly at < 25% counts in patients without bone metastases. In patients with bone metastases, BSI and Hs for Original and Gaussian-filtered images differed significantly at < 10% counts, whereas ANN values did not. Accuracy of bone metastasis detection was highest for DL-filtered images in all count percentages; AUC did not differ significantly. Accuracy of Original and Gaussian-filtered images worsened with decreasing count percentage; AUC differed significantly for Original images at < 50% counts and for Gaussian-filtered images at < 25% counts.Conclusions Our deep learning model improved image quality and bone metastasis detection accuracy for low-count bone scintigraphy whole-body images, suggesting its applicability in clinical practice.
PI-RADS-based segmented threshold of PSMA-PET SUVmax is better than traditional fixed threshold for diagnosing clinically significant prostate cancer especially for PI-RADS 3 lesions
Authors: Meng Xiaoli , Ma Wenhui , Zhang Jingliang , Quan Zhiyong , Zhang Mingru , Ye Jiajun , Shu Jun , Ren Jing , Qin Weijun , Kang Fei , Wang Jing 
Published: April 2023

Abstract
Purpose Our purpose was to compare the performance of prostate-specific membrane antigen (PSMA)-positron emission tomography (PET) traditional fixed threshold (FT) and newly-established Prostate Imaging Reporting and Data System (PI-RADS)-based segmented threshold (ST) for diagnosing clinically significant prostate cancer (csPCa).Methods The study retrospectively included 218 patients who underwent multiparametric magnetic resonance imaging (mpMRI) and PSMA-PET examination for suspected prostate cancer (PCa) from January 2018 to November 2021. Lesions with Gleason score (GS) ≥ 3 + 4 were diagnosed as csPCa. In PSMA-PET maximum standardized uptake value (SUVmax), the FT for all the lesions and ST for lesions with different PI-RADS score for diagnosing csPCa were determined by receiver operating characteristic (ROC) curves analysis and compared with Z test. The McNemar test was used to compare sensitivity and specificity.Results Among the 218 patients, there were 113 csPCa and 105 non-csPCa. The PSMA-PET FT was SUVmax > 5.3 (area under the curve, AUC = 0.842) and STs for PI-RADS 3/4/5 were SUVmax > 4.2/5.7/6.0 (AUCs = 0.870/0.867/0.882), respectively. The AUC of PSMA-PET ST was higher than that of PSMA-PET FT (0.872 vs. 0.842), especially for PI-RADS 3 (0.870 vs. 0.653). Multimodality diagnostic criteria combining PSMA-PET ST and PI-RADS scores of mpMRI was established and its AUC was higher than that of PSMA-PET ST (0.893 vs. 0.872), and significantly higher than that of PSMA-PET FT (0.893 vs. 0.842) with an improvement in sensitivity (93% vs. 78%, p < 0.05) without significantly sacrificing specificity (86% vs. 91%, p > 0.05).Conclusions For diagnosing csPCa, PI-RADS-based PSMA-PET ST achieved better performance than traditional FT, especially for PI-RADS 3 lesions.
Pre-deformation effect on the hot deformation behavior and microstructure of AA6011 Tube: experimentation and modelling
Authors: Bao Zhennan , Qu Shuguang , Liang Jiayu , Yang Song , Zheng Kailun , Fu Kunning , He Zhubin 
Published: April 2023

Abstract
Hot gas forming (HGF) is an advanced technique for fabricating complex-shaped hollow tubular parts. Practically, multi-step pre-forming involving pre-deformation is often necessary prior to HGF. This paper performs an experimental investigation to simulate the pre-forming operation evaluating the effect of pre-strain on the subsequent HGF. Firstly, the dislocation density was accumulated with uniaxial pre-stretching with different strains (5%, 10% and 15%) at room temperature, simulating the pre-forming operations. Secondly, the sub-sized specimen from the pre-stretched sample was characterized at different temperatures (350, 400 and 450 ℃) to evaluate the effect of pre-strain on successive hot deformation for simulating practical HGF. The experimental results proves the coupled influence of pre-strain and temperature on the flow stress and stress-strain variations. To thoroughly understand the micro-mechanisms, EBSD analysis of grains and grain boundary angles was carried out under different pre-deformation levels and temperatures, which shows the recrystallization phenomenon at 15% pre-strain and 450 ℃ temperature. Finally, a physical-mechanism constitutive model is established based on the determined macro and micro results where the pre-strain effect shows the accurate modeling of stress flow behavior of the material.
Noninvasive prediction for intracranial pressure related parameters in traumatic brain injury patients using computed tomography radiomic features.
Authors: Li Yihua , Shan Yingchi , Zhu Jun , Wu Xiang , Gao Guoyi 
Published: April 2023

Abstract

Background: Current intracranial pressure (ICP) related parameters monitoring is invasive and tends to cause complications, which limited their use to predict patients’ intracranial status and prognosis.Objective: To utilize postoperative computed tomography (CT) images radiomic features techniques to predict abnormal ICP related parameters levels consisting of an index of cerebrospinal compensatory reserve(RAP) and a pressure reactivity index (PRx)in traumatic brain injury (TBI) patients noninvasively.Methods: 48 patients were enrolled and randomized to training (n=34) and test (n=14) sets. A total of 107 radiomic features were extracted from each patients’ CT image. Their clinical and imaging data was collected and analyzed to establish prediction models of RAP and PRx respectively. Pearson correlation and univariate regression analysis were used for feature selection, multivariate logistic regression was used to develop the predicting models. The performance of models was assessed with their discrimination, calibration and clinical use.Results: The RAP model showed a good discrimination with the area under receiver operating characteristic curve (AUC) of training and test set were 0.771 and 0.727, and a good calibration; The performance of PRx model was inferior to the RAP model, but still have a significant discrimination with the AUCs of training and test were 0.713 and 0.667. Decision curve analysis indicated the prediction model have the potential clinical utility.Conclusion: The study illustrated that CT radiomic features as a clinical aid may have ability to predict ICP related parameters to reflect the intracranial condition of TBI patients noninvasively, given its potential for clinical treatment guidance and prognosis indication.
Research of dry tribochemical mechanical polishing SiC with an innovation abrasive-catalytic abrasive cluster
Authors: wang zhankui , Ding Yangyang , Wang Pengzhan , Pang Minghua , Zhao Hongyuan , Ma Lijie , Nie Fuquan , Su Jianxiu 
Published: April 2023

Abstract
Abrasive particles have a crucial influence on the material removal rate and surface quality of the workpiece in tribochemical mechanical polishing. Therefore, this article selects a self-made catalytic abrasive cluster to polish the 6H-SiC and explore the removal mechanism of polishin silicon carbide in the new catalytic abrasive cluster. The tribochemical mechanical polishing test and friction and wear test of 6H-SiC were carried out with three different abrasives, and the evaluation parameters such as material removal rate, surface roughness and friction coefficient were obtained. Quanta 200 scanning electron microscope(SEM) and oxfobrdinca 250 energy dispersive spectrometer (EDS) and x-ray diffraction(XRD)diffractometer were used to observe the surface, analyze the elements and determine the composition of silicon carbide workpiece after tribochemical mechanical polishing. The experimental result shows that oxygen is produced in the tribochemical mechanical polishing of silicon carbide by catalytic abrasive cluster, which makes the silicon carbide surface generate SiO2 shear film that is easy to be removed. Comparing with iron-based white corundum mixed abrasive and Al2O3 abrasive, the catalytic abrasive cluster has better processability for 6H-SiC, and the material removal rate can reach to 42.928nm/min.
Correlation of MPO expression with 18F-FDG PET/CT parameters in patients with histiocytic necrotizing lymphadenitis
Authors: Song Yingjian , Tian Ke , Han Qingqing , Wang Ruihua 
Published: April 2023

Abstract
Objective: To investigate the relationship between the metabolic activity of 18F-FDG in histiocytic necrotizing lymphadenitis (HNL) and its immunophenotype.
Methods: Retrospective analysis of 76 patients with pathologically confirmed HNL who underwent 18F-FDG PET/CT prior to treatment. Maximum standardized uptake value (SUVmax)，metabolic tumor volume (MTV)，and total lesion glycolysis (TLG) were measured. Analysis of the correlation of each metabolic parameter with the expression status of myeloperoxidase (MPO). The differences of PET/CT metabolic parameters between different expression states of MPO were compared and the receiver operating characteristic (ROC) curve were plotted to obtain the best cut-off values of PET/CT metabolic parameters to predict MPO expression.
Results: 23 cases were negative for MPO expression, 16 cases were weakly positive for MPO expression, and 37 cases were positive for MPO expression. SUVmax , MTV and TLG were all associated with MPO expression status in HNL (P < 0.05). MPO expression status was positively correlated with SUVmax and TLG (P < 0.05). The ROC curves of 18F-FDG PET/CT metabolic parameters for predicting MPO expression showed that SUVmax and TLG were of predictive value, with the highest diagnostic efficiency, sensitivity of 91.9% and 86.5%, and specificity of 71.8% and 76.9% when the cut-off values were 10.90 and 37.1, respectively. Conclusion：18F-FDG PET/CT metabolic parameters, especially SUVmax and TLG, correlated well with MPO expression status. 18F-FDG PET/CT can be used as an imaging tool for non-invasive assessment of MPO immune expression.