Providing local interpolation, tension and normal control in the manipulation of loop subdivision surfaces

Summary1. The following coagulation changes were found in rats with experimental hypersplenism: a mild prolongation of the recalcification time, shortened times in Quick’s test, a lowered activity in plasma thrombin time and shortened times in the partial thromboplastin test. Concentrations of factor II, V, VII (+X), VIII and X did not differ from those of normal control rats.2. The administration of adrenaline to hypersplenic rats induced the correction of the partial thromboplastin test, Quick’s test and plasma thrombin time to normal values. Concentrations of coagulation factors were not significantly changed. An increase was found in factor V.3. Splenectomy performed in hypersplenic rats was followed by a shortened recalcification time, a prolongation of the partial thromboplastin test and of the test with partial thromboplastin and kaolin. A prolongation was also observed in Quick’s test. Complete correction of plasma thrombin time was not observed. The concentration of factor VII increased.4. The administration of adrenaline to splenectomized rats with experimental hypersplenism did not induce any significant changes with the exception of a corrected plasma thrombin time and a decreased concentration of factor VIII.5. A different reaction of factor VIII to adrenaline in normal and hypersplenic rats is pointed out.

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ANALYSIS OF N200-P300 COMPLEX IN VISUAL ODDBALL PARADIGM: AMPLITUDE DIFFERENCE BETWEEN ADHD AND NORMAL CONTROL

Neuropediatrics ◽

10.1055/s-2006-945692 ◽

2006 ◽

Vol 37 (S 1) ◽

Author(s):

M Kaga ◽

Y Inoue ◽

N Kokubo ◽

A Ishiguro ◽

A Gunji ◽

...

Keyword(s):

Normal Control ◽

Oddball Paradigm ◽

Amplitude Difference ◽

Visual Oddball Paradigm ◽

Visual Oddball

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A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽

10.3390/healthcare8030234 ◽

2020 ◽

Vol 8 (3) ◽

pp. 234 ◽

Cited By ~ 3

Author(s):

Hyun Yoo ◽

Soyoung Han ◽

Kyungyong Chung

Keyword(s):

Neural Network ◽

Big Data ◽

Fourier Transform ◽

Fast Fourier Transform ◽

Real Time ◽

Normal Control ◽

Input Data ◽

Deep Neural Network ◽

Pattern Mining ◽

F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

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Construction and analysis of unified 4-point interpolating nonstationary subdivision surfaces

Advances in Difference Equations ◽

10.1186/s13662-021-03234-x ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Mehwish Bari ◽

Ghulam Mustafa ◽

Abdul Ghaffar ◽

Kottakkaran Sooppy Nisar ◽

Dumitru Baleanu

Keyword(s):

Computer Graphics ◽

Geometric Modeling ◽

Tension Control ◽

Free Form ◽

Subdivision Surfaces ◽

Smooth Curves ◽

Practical Applications ◽

Tension Parameter ◽

Exact Reproduction ◽

Free Form Surfaces

AbstractSubdivision schemes (SSs) have been the heart of computer-aided geometric design almost from its origin, and several unifications of SSs have been established. SSs are commonly used in computer graphics, and several ways were discovered to connect smooth curves/surfaces generated by SSs to applied geometry. To construct the link between nonstationary SSs and applied geometry, in this paper, we unify the interpolating nonstationary subdivision scheme (INSS) with a tension control parameter, which is considered as a generalization of 4-point binary nonstationary SSs. The proposed scheme produces a limit surface having $C^{1}$ C 1 smoothness. It generates circular images, spirals, or parts of conics, which are important requirements for practical applications in computer graphics and geometric modeling. We also establish the rules for arbitrary topology for extraordinary vertices (valence ≥3). The well-known subdivision Kobbelt scheme (Kobbelt in Comput. Graph. Forum 15(3):409–420, 1996) is a particular case. We can visualize the performance of the unified scheme by taking different values of the tension parameter. It provides an exact reproduction of parametric surfaces and is used in the processing of free-form surfaces in engineering.

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POS0430 EXPRESSION AND CLINICAL SIGNIFICANCE OF AUTOPHAGY-RELATED GENES IN PERIPHERAL BLOOD MONONUCLEAR CELLS OF SYSTEMIC SCLEROSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.3562 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 443.2-443

Author(s):

Y. F. Qing ◽

J. Zheng ◽

S. B. Wang ◽

F. Dai ◽

Y. Jiang ◽

...

Keyword(s):

Systemic Sclerosis ◽

Clinical Significance ◽

Normal Control ◽

Peripheral Blood Mononuclear Cells ◽

Peripheral Blood ◽

Mononuclear Cells ◽

Beclin 1 ◽

Peripheral Blood Mononuclear ◽

Mesenchymal Transition ◽

Blood Mononuclear Cells

Background:Growing evidences have demonstrated that autophagy is a powerful regulators in the pathogenesis of fibrosis and autoimmune diseases. Autophagy abnormalities in SSc involve abnormal autophagy-related protein and autophagy-related gene polymorphism[1-2], however there is a few reports on the expression and clinical significance of autophagy-related genes.Objectives:To investigate the expression and clinical significance of autophagy-related genes LC-3 mRNA, Becline-1 mRNA, Agt-3 mRNA, Agt-5 mRNA, Agt-12 mRNA and Agt-16L1 mRNA in peripheral blood mononuclear cells (PBMC) of systemic sclerosis (SSc).Methods:51 cases of SSc and 60 cases of normal control were received from the Affiliated Hospital of North Sichuan Medical College, and autophagy-related genes were detected by RT-PCR. SPSS19.0 statistical software was used to compare the expression of autophagy-related genes between groups and analyze the relationship between autophagy-related genes and clinical data, P<0.05 was considered statistically significantResults:LC-3, Becline-1, and Agt-3 were highly expressed in SSc compared with normal control [LC-3: 0.78(0.60) ×10-3 vs. 0.52(0.54) ×10-3; Beclin-1: 6.68(3.56)×10-3 vs. 5.22(3.54)×10-3; Agt-3: 17.58(12.33)×10-3 vs. 11.00(4.56)×10-3, P<0.05], however Agt-5, Agt-12 and Agt-16L1 of autophagy-related genes were not statistically significant [AGT-5: 6.67(3.58) ×10-3 vs. 6.67(2.64) ×10-3; AGT-12: 8.64(5.56)×10-3 vs. 8.57(4.66)×10-3; Agt-16L1: 2.69(2.19)×10-3 vs. 2.52(2.26)×10-3] (Figure 1). Beclin-1 and Agt-5 high expressed in SSc with the positive of anti-SSA/Ro antibody. LC-3 was positively correlated with Age(r=0.662) and ESR(r=0.355) (all P<0.05).Conclusion:Autophagy-related genes were increased in PBMC of SSc, and were correlated with Age, ESR and autoantibody, suggested that autophagy is a key feature in the pathogenesis of systemic sclerosis.Figure 1.The relative expression of autophagy-related genesReferences:[1]LIU C, ZHOU X, LU J, et al. Autophagy mediates 2-methoxyestradiol-inhibited scleroderma collagen synthesis and endothelial-to-mesenchymal transition induced by hypoxia[J]. Rheumatology, 2019;58(11):1966–1975.[2]Mayes M D, Bossini-Castillo L, Gorlova O, et al. Immunochip Analysis Identifies Multiple Susceptibility Loci for Systemic Sclerosis[J]. The American Journal of Human Genetics, 2014,94(1):47-61.DOI:10.1016/j.ajhg.2013.12.002.Disclosure of Interests:None declared

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Study on potential differentially expressed genes in stroke by bioinformatics analysis

Neurological Sciences ◽

10.1007/s10072-021-05470-1 ◽

2021 ◽

Author(s):

Xitong Yang ◽

Pengyu Wang ◽

Shanquan Yan ◽

Guangming Wang

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Normal Control ◽

Enrichment Analysis ◽

Normal Control Group ◽

Control Group ◽

Ppi Network ◽

Hub Genes ◽

Pathway Enrichment ◽

Key Genes

AbstractStroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein–protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine − cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

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Erratum: Non‐uniform subdivision surfaces with sharp features

Computer Graphics Forum ◽

10.1111/cgf.14193 ◽

2020 ◽

Keyword(s):

Subdivision Surfaces

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