syedzayyan/paper_mill_benchmark huggingface dataset:
Construction and Bioinformatics Analysis of the miRNA-mRNA Regulatory Network in Diabetic Nephropathy

Micro rna (mi rna) has been confirmed to be involved in the occurrence, development, and prevention of diabetic nephropathy (dn), but its mechanism of action is still unclear.Objective.With the help of the geo database, bioinformatics methods are used to explore the mi rna-m rna regulatory relationship pairs related to diabetic nephropathy and explain their potential mechanisms of action.Methods.The dn-related mi rna microarray dataset (gse51674) and m rna expression dataset (gse30122) are downloaded through the geo database, online analysis tool geo2r is used for data differential expression analysis, target scan, mi rtar base, and mi rdb databases are used to predict potential downstream target genes regulated by differentially expressed mi rnas, and intersection with differential genes is used to obtain candidate target genes.According to the regulatory relationship between mi rna and m rna, the mi rna-m rna relationship pair is clarified, and the mi rna-m rna regulatory network is constructed using cytoscape.David is used to perform go function enrichment analysis and kegg pathway analysis of candidate target genes.By gene mania prediction of mi rna target genes and coexpressed genes, the protein interaction network is constructed.Results and conclusions.A total of 67 differentially expressed mi rnas were screened in the experiment, of which 42 were upregulated and 25 were downregulated; a total of 448 differentially expressed m rnas were screened, of which 93 were upregulated and 355 were downregulated.Using target scan, mi rtar base, and mi rdb databases to predict downstream targets of differentially expressed mi rnas, 2283 downstream target genes coexisting in 3 databases were predicted to intersect with differentially expressed m rnas to obtain 96 candidate target genes.Finally, 44 mi rna-m rna relationship pairs consisting of 12 differentially expressed mi rnas and 27 differentially expressed m rnas were screened out; further analysis showed that mi rna regulatory network genes may participate in the occurrence and development of diabetic nephropathy through pi3k/akt, ecm-receptor interaction pathway, and ras signaling pathway.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Computed Tomography Imaging Based on Edge Detection Algorithm in Diagnosis and Rehabilitation Nursing of Stroke Patients with Motor Dysfunction

This study was to explore the effect of computed tomography (ct) images processed by image edge detection technology based on the improved canny algorithm in the diagnosis of stroke patients with mobility dysfunction and to evaluate the clinical application value of early rehabilitation nursing (ern).114 patients who were diagnosed and treated in hospital and were suspected of having stroke movement dysfunction were selected as the research objects, and they were randomly divided into two groups, each with 57 patients.Patients in the control group were diagnosed with conventional ct examination, and the patients in observation group were diagnosed based on the ct images processed by the image edge detection technology based on the improved canny algorithm.Patients in the observation group were divided into a group c and a group o.Patients (27 cases) in group o received rehabilitation training within 3 days after their vital signs were stabilized, and patients (30 cases) in group c received rehabilitation training within 3∼7 days after their condition was stabilized.The ct image diagnosis effects on patients of the control group and the observation group were analyzed, and the ern effect on patients of the c group and the o group was compared.The results showed that the mean square error (mse) of the improved canny algorithm (233.78) was smaller than that of the traditional canny algorithm and sobel and prewitt algorithm, and the peak signal-to-noise ratio (psnr) (27.89) was greater than that of the traditional canny algorithm and sobel and prewitt algorithm ( p &lt; 0.05 ).Compared with the control group, the sensitivity (85.00% vs.62.12%), specificity (70.59% vs.36.36%), and accuracy (80.70% vs.54.39%) of the examination method of the observation group were much higher ( p &lt; 0.05 ).In addition, the total effective rate of patients in group o was 89.47%, which was greatly higher than that of group c (70.18%), and the scores of meyer index and barthel index were also higher in contrast to those of group c ( p &lt; 0.05 ).In conclusion, the improved canny algorithm showed a clearer display on the edge detection of ct images and good application effect.It showed the effect of making conventional ct more accurate in the examination and diagnosis of stroke patients, and it was worthy of clinical application and promotion.The research showed that the timelier rehabilitation training, the better the treatment effect of patients.

Journal: Scientific Programming (2021)
Publication types: Research Article;
Computational Technologies on Modeling of Museum Interactive Virtual Display Based on User Experience

In order to solve the problems of the traditional museum interactive virtual display modeling methods such as poor clarity, low user satisfaction, and long interactive response time, a museum interactive virtual display modeling method based on user experience was proposed.The museum information collection system is set up to collect 3d point cloud data of exhibits by using the system, and the 3d point cloud data are denoised by the moving least squares method.The museum building data and denoised 3d point cloud data are integrated, and all point cloud data are registered.According to the registration results, the interactive virtual display model of the museum is preliminarily constructed, and the user experience model is used to adjust the parameters of the display model to realize the interactive virtual display modeling of the museum.The simulation results show that the proposed method has a high definition of the interactive virtual display of a museum, the average user satisfaction is 9.45, and the interactive response time is always below 0.4s, which fully verifies the superiority of the proposed method.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Clinical Data and Biocalculation Methods of GABRD Determine the Clinical Characteristics and Immune Relevance of Colorectal Cancer

The aim of this study was to clarify the expression of gamma-aminobutyric acid type a receptor delta subunit (gabrd) gene in pan-cancer and its correlation with patient prognosis, and to investigate the function and possible mechanism of gabrd in colorectal cancer (crc).Methods.The cancer genome atlas (tcga) data were used to analyze the expression differences of gabrd in pan-cancer, and the correlation between gabrd and clinical prognosis of various tumors was analyzed by cox regression method.According to the expression level of gabrd, gene function annotation (go) and kyoto encyclopedia of genomes (kegg) functional enrichment analysis were performed on the differentially expressed genes.Expression of gabrd gene and 44 marker genes of three types of rna modification (m1a (10), m5c (13), m6a (21)) genes in different tumors was observed.Pearson correlation of gabrd gene and marker genes of five immune pathways was measured.Results: tcga data analysis showed that gabrd was significantly upregulated in various tumor tissues, especially coad and readcoad.Survival analysis showed that gabrd was a prognostic protective factor in crc ( p &lt; 0.001 ).The results of survival nomogram showed that gabrd, age, and tumor (t) lymph node (n) distant metastasis (m) stage were independent prognostic factors, and the survival model c-index was 0.724 (0.644-1).Gene enrichment and functional analysis showed that gabrd may be related to protein digestion and absorption, ecm-receptor interaction, extracellular structure organization, extracellular matrix organization, pancreatic secretion, and antimicrobial humoral response.The expression of gabrd was positively correlated in m1a-, m5c-, and m6a-related genes.The gabrd gene was found in b cell, t cell cd4, t cell cd8, neutrophil, macrophage in tcga-coad (n = 282), and tcga-coadread (n = 373).The infiltration level and dc was significantly positively correlated ( p &lt; 0.05 ).Also, the pearson correlation coefficient is the largest.Conclusion.The involvement of gabrd in the occurrence and development of crc may be related to protein digestion and absorption, ecm-receptor interaction, extracellular structure organization, extracellular matrix organization, pancreatic secretion, and antimicrobial humoral response.Gabrd can be used as a molecular marker for the prognosis of crc.

Journal: Evidence-Based Complementary and Alternative Medicine (2022)
Publication types: Research Article;
circRNA LDLRAD3 Enhances the Malignant Behaviors of NSCLC Cells via the miR-20a-5p-SLC7A5 Axis Activating the mTORC1 Signaling Pathway

Circular rna ldlrad3 behaved as an oncogene in several malignancies, but its effects in nsclc and the involvement of downstream molecules and activation of signaling pathways had not been fully reported.We planned to explore how ldlrad3 facilitated the malignancy of nsclc.Qrt-pcr was performed to evaluate the expression levels of ldlrad3, mi r-20a-5p, and slc7a5 in nsclc tissues and cells.Si-ldlrad3 was transfected to a549 and h1299 cells to knock down intrinsic ldlrad3 to determine its oncogenic roles.Cck-8 assay and transwell assay were executed to assess cell proliferative, migrative, and invasive abilities.Dual-luciferase reporter (dlr) assay was manipulated to verify the encori-predicted relationships between ldlrad3 and mi r-20a-5p and between mi r-20a-5p and slc7a5.Western blot, immunofluorescent assay, and immunohistochemistry were applied to explore the expression levels of slc7a5, and the levels of m torc1 pathway-related proteins were evaluated using western blot.Rescue experiments were conducted by transfecting si-ldlrad3, mi r-20a-5p inhibitor, and si-slc7a5 to explore the influence of the ldlrad3-mi r-20a-5p-slc7a5 axis on the malignant behaviors of nsclc cells.The expression levels of ldlrad3 and slc7a5 were boosted, whereas mi r-20a-5p was impeded in nsclc tissues and cell lines.Knockdown of ldlrad3 weakened the proliferation, migration, and invasion of a549 and h1299 cells.Ldlrad3 was verified to sponge mi r-20a-5p and mi r-20a-5p targeted slc7a5.Ldlrad3 activated the m torc1 singling pathway via the mi r-20a-5p-slc7a5 axis to strengthen the malignant properties of a549 and h1299 cells.We concluded that ldlrad3 exerted oncogenic effects via the mi r-20a-5p-slc7a5 axis to activate the m torc1 signaling pathway in nsclc.Our findings enlightened that ldlrad3 could become a potential therapeutic target in the treatment and management of nsclc.

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;
circFAT1(e2) Promotes Papillary Thyroid Cancer Proliferation, Migration, and Invasion via the miRNA-873/ZEB1 Axis

Circular rnas (circ rnas) play an extremely important regulatory role in the occurrence and development of various malignant tumors including papillary thyroid cancer (ptc).Circ fat1(e2) is a new type of circ rna derived from exon 2 of the fat1 gene, which is distributed in the cytoplasm and nucleus of ptc cells.However, so far, the role of circ fat1(e2) in ptc is still unclear.In this study, circ fat1(e2) was found to be highly expressed in ptc cell lines and tissues.Circ fat1(e2) knockdown suppressed ptc cell growth, migration, and invasion.Also, circ fat1(e2) acted as a sponge for potential micro rnas (mi rnas) to modulate cancer progression.A potential mi rna target was discovered to be mi r-873 which was targeted by circ fat1(e2) in ptc.The dual-luciferase assay conducted later also confirmed that there was indeed a direct interaction between circ fat1(e2) and mi r-873.This study also confirmed that circ fat1(e2) inhibited the mi r-873 expression and thus promoted the zeb1 expression, thus affecting the proliferation, metastasis, and invasion of ptc cells.In conclusion, the results of this study indicated that circ fat1(e2) played a carcinogenic role by targeting the mi r-873/zeb1 axis to promote ptc invasion and metastasis, which might become a potential novel target for therapy of ptc.

Journal: Computational and Mathematical Methods in Medicine (2020)
Publication types: Research Article;
B-Ultrasound Image Analysis of Intrauterine Pregnancy Residues after Mid-Term Pregnancy Based on Smart Medical Big Data

Abdominal b-ultrasound images of intrauterine pregnancy tissue residues were analyzed to discuss their diagnostic value.With the rapid development of computer technology and medical imaging technology, doctors are also faced with more and more medical image diagnosis tasks, and computer-aided diagnosis systems are especially important in order to reduce the work pressure of doctors.In recent years, deep learning has made rapid development and achieved great breakthroughs in various fields.In medical-aided diagnostic systems, deep learning has greatly improved the diagnostic efficiency, but there are no mature research results for abdominal b-ultrasound image recognition of intrauterine pregnancy tissue residues.Therefore, the study of liver ultrasound image classification based on deep learning has important practical application value.In this paper, we propose to give a cnn model optimization method based on grid search.Compared with the conventional cnn model design, this method saves time and effort by eliminating the need to manually adjust parameters based on experience and has an accuracy of more than 92% in classifying abdominal b-ultrasound images of intrauterine pregnancy tissue residues.The diagnosis of intrauterine pregnancy tissue residues by abdominal b-ultrasound can effectively improve the diagnosis and provide important reference for patients to receive treatment, which has high diagnostic value.

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;
Bioinformatics Analysis Identifies ASCL1 as the Key Transcription Factor in Hepatocellular Carcinoma Progression

Object.To identify and explore the key transcription factors in hepatocellular carcinoma (hcc) progression.Methods.Differentially transcription factors (detfs) were identified from differentially expressed genes (degs) in gse62232 and transcription factors.Then, they were analyzed by regulatory networks, prognostic risk model, and overall survival analyses to identify the key detf.Combined with the regulatory networks and binding site analysis, the target m rna of key detf was determined, and its prognostic value in hcc was evaluated by survival, clinical characteristics analyses, and experiments.Finally, the expressions and functions of the key detf on the dem rnas were investigated in hcc cells.Results.Through multiple bioinformatics analyses, ascl1 was identified as the key detf, and slc6a13 was predicted to be its target m rna with the common binding site of ccagcaactggcc, both downregulated in hcc.In survival analysis, high slc6a13 was related to better hcc prognosis, and slc6a13 was differentially expressed in hcc patients with clinical characteristics.Furthermore, cell experiments showed the m rna expressions of ascl1 and slc6a13 were both reduced in hcc, and their overexpressions suppressed the growth, invasion, and migration of hcc cells.Besides, over-ascl1 could upregulate slc6a13 expression in hcc cells.Conclusion.This study identifies two suppressor genes in hcc progression, ascl1 and slc6a13, and the key transcription factor ascl1 suppresses hcc progression by targeting slc6a13 m rna.They are both potential treatment targets and prognostic biomarkers for hcc patients, which provides new clues for hcc research.

Journal: Disease Markers (2023)
Publication types: Research Article;
Automatic Recognition and Detection System Based on Machine Vision

In order to realize the automation of part identification and detection and the intellectualization of part production, this paper proposes a method of automatic identification and detection of parts based on machine vision (overall method).This paper combines machine vision with motion control theory and uses pulse coupled neural network (pcnn) edge detection and recognition algorithms to preliminarily design a set of machine vision automatic recognition and detection systems and carry out detection and recognition experiments on small parts such as relay covers.The experimental results show that the whole process of part size detection and recognition, including part feeding, image acquisition, size recognition, part screening, and reset of the experimental platform, can be completed within 5 s and can carry out high-precision dynamic recognition and detection when the part moves at a speed of 1 m/s.Through the correction and compensation of dynamic error, the detection accuracy of small rectangular parts with a length of 28.87 mm and a width of 12.36 mm can reach 0.04 mm.The visual inspection and recognition automation system improves the automation degree of parts inspection, improves the dimensional accuracy, optimizes the robustness of the system, and finally realizes the real-time screening and classification of parts and the efficient production of parts.

Journal: Journal of Control Science and Engineering (2022)
Publication types: Research Article;
Assistive Relief Tool for Early Childhood and Special Psychological Symptom Groups during the Pandemic: Clothing Design Based on the Virtual Contact Principle

During the covid-19 pandemic, some special populations—groups of early childhood and people with autism, among others—faced more profound challenges than the common people.The lack of real physical contact such as embracing greatly affected the effectiveness of development, psychiatric treatment, and other processes for these populations.This study is aimed at developing clothing with appropriate contact pressure based on the contact comfort principle of psychology and providing a type of pressure clothing that can relieve the wearer’s tension by simulating hugging, alleviating the lack of physical contact for early childhood education and special education groups during the pandemic.First, the elementary requirements of clothing design are attained using a questionnaire survey and test method.The analysis revealed that clothing should fulfill the four requirements of pressure comfort, fabric softness, wearing and taking off comfort, and visual beauty.Second, we realized the performance requirements in the fabric and accessories, style design, structure design, and functional design.Finally, the product experience is proposed through a fitting, and the reasonable opinions were fed back to the product design to enhance the functionality of clothing.The research shows that clothing can simulate hugging and can ease the loneliness of the wearer.This study can be used as a good tool to assist during the pandemic for early childhood education and special psychological symptom groups, as well as a broader group of people living alone at home, to play an adjunctive treatment and loneliness relief functions.

Journal: Occupational Therapy International (2022)
Publication types: Research Article;
Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft

The application of artificial intelligence (ai) technology in the field of clothes can provide a good development mode and system under the social context of ai technology development.Ai provides help for the development of intelligent clothing.Intelligent clothing is a high‐tech product that integrates intelligent technology and clothing.It combines cutting‐edge technologies in electronic information technology, sensor technology, textile science, and material science.In the extraction and analysis of environmental factors in clothing handicraft, ai technology has a considerable application prospect and a certain development potential.In order to improve the accuracy of environmental factors extraction in clothing handicraft, this paper uses convolutional neural network (cnn) to extract and analyze environmental factors in traditional clothing handicraft.We carried out experiments on the extraction of environmental factors in clothing handicrafts with pure color, few patterns, patterns, and complex background.The experimental results show that the cnn has a good effect on the extraction of environmental factors in clothing handicraft under different backgrounds.In addition, the model in this paper has good stability, accuracy, and feature extraction speed, which has high practical value and research significance.

Journal: Journal of Environmental and Public Health (2022)
Publication types: Research Article;
Artificial Intelligence Technology Based on Deep Learning in Building Construction Management System Modeling

In order to explore the application of ai technology in construction management system modeling, the author proposed the application of a deep learning-based ai technology in construction management system modeling.The 3 d reconstruction deep learning model is first introduced, and then the model idea of the construction progress reliability control system is designed based on bim (building information model).Second, the construction process of the 4dbim model is described, and the construction method is introduced.The construction of the model provides data information for the construction schedule reliability control system.Finally, the three functional modules of progress monitoring, progress reliability early warning, and progress prediction are realized by combining the s-curve comparison method, and the work of the system is described through case simulation.The early warning result is from june 7 to june 11, the progress deviation is between (−2%, 2%), and the progress is basically controlled.On june 13, the planned workload was 81.099%, and the actual cumulative workload was 7.099%, which was 4% less than the planned workload.The project progress was out of date, so it needs to be closely tracked.On june 15, the planned workload was 85.511%, and the actual cumulative workload was 80.899%, 4.5% less than the planned workload.The forecast result is the line forecast of the actual cumulative completion percentage on june 17.After calculation, the forecast result on june 17 is 84.311%.The progress deviation on june 17 was 5.21%.If no timely delay is taken on june 15, the delay will get worse.In addition, the system can predict the completion period of the project.When the actual percentage of cumulative completion is greater than or equal to 100%, it indicates that the project has been completed.Therefore, we can calculate the completion period of the three-storey project, and the construction period is about june 29 or 30.When the simulation can be carried out, the simulation number is set as 1000 times, the completion probability of the project is only 40%, and the completion probability is not too high.Artificial intelligence technology can realize progress monitoring, progress reliability early warning, and progress prediction.This system model prepares for software development and is conducive to improving the progress reliability control level of construction enterprises.Starting from the schedule planning subsystem and the schedule control subsystem, this paper studies the application of the artificial intelligence technology based on deep learning in the modeling of a building construction management system.The results show that this technology can effectively improve the efficiency of the building construction schedule management.Compared with the existing management methods, it shows great advantages in terms of operating costs and ease of use.It also promotes the application of artificial intelligence technology in the construction phase.

Journal: Advances in Multimedia (2022)
Publication types: Research Article;