Histological characterization of the early‐stage infection events of Setosphaeria turcica in maize

2021 ◽  
Author(s):  
Ya‐Li Fang ◽  
Yao‐Yao Zhou ◽  
Xin Li ◽  
Yue Gao ◽  
De‐Long Wang ◽  
...  
Keyword(s):  
Early Stage ◽  
Setosphaeria Turcica ◽  
Stage Infection

Molecular cloning and characterization of calcineurin A in Setosphaeria turcica

2009 ◽  
Vol 31 (10) ◽  
pp. 1059-1064
Author(s):  
Zhi-Yong LI ◽  
Zhi-Min HAO ◽  
Zhi-Ping DONG ◽  
He-Long SI ◽  
Jin-Gao DONG
Keyword(s):  
Molecular Cloning ◽  
Setosphaeria Turcica ◽  
Calcineurin A

Exploring wall shear stress spatiotemporal heterogeneity in coronary arteries combining correlation-based analysis and complex networks with computational hemodynamics

2020 ◽  
Vol 234 (11) ◽  
pp. 1209-1222 ◽  
Author(s):  
Karol Calò ◽  
Giuseppe De Nisco ◽  
Diego Gallo ◽  
Claudio Chiastra ◽  
Ayla Hoogendoorn ◽  
...  
Keyword(s):  
Shear Stress ◽  
Wall Shear Stress ◽  
Complex Networks ◽  
Coronary Arteries ◽  
Early Stage ◽  
Flow Direction ◽  
Computational Hemodynamics ◽  
Time Histories ◽  
Wall Shear

Atherosclerosis at the early stage in coronary arteries has been associated with low cycle-average wall shear stress magnitude. However, parallel to the identification of an established active role for low wall shear stress in the onset/progression of the atherosclerotic disease, a weak association between lesions localization and low/oscillatory wall shear stress has been observed. In the attempt to fully identify the wall shear stress phenotype triggering early atherosclerosis in coronary arteries, this exploratory study aims at enriching the characterization of wall shear stress emerging features combining correlation-based analysis and complex networks theory with computational hemodynamics. The final goal is the characterization of the spatiotemporal and topological heterogeneity of wall shear stress waveforms along the cardiac cycle. In detail, here time-histories of wall shear stress magnitude and wall shear stress projection along the main flow direction and orthogonal to it (a measure of wall shear stress multidirectionality) are analyzed in a representative dataset of 10 left anterior descending pig coronary artery computational hemodynamics models. Among the main findings, we report that the proposed analysis quantitatively demonstrates that the model-specific inlet flow-rate shapes wall shear stress time-histories. Moreover, it emerges that a combined effect of low wall shear stress magnitude and of the shape of the wall shear stress–based descriptors time-histories could trigger atherosclerosis at its earliest stage. The findings of this work suggest for new experiments to provide a clearer determination of the wall shear stress phenotype which is at the basis of the so-called arterial hemodynamic risk hypothesis in coronary arteries.

scholarly journals Characterization of long non-coding RNA expression profiles in lymph node metastasis of early-stage cervical cancer

2016 ◽  
Vol 35 (6) ◽  
pp. 3185-3197 ◽  
Author(s):  
CHUNLIANG SHANG ◽  
WENHUI ZHU ◽  
TIANYU LIU ◽  
WEI WANG ◽  
GUANGXIN HUANG ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Lymph Node ◽  
Early Stage ◽  
Expression Profiles ◽  
Rna Expression ◽  
Node Metastasis ◽  
Non Coding Rna ◽  
Early Stage Cervical Cancer ◽  
Long Non Coding Rna

Characterization of Metabolic Activity Induced by Kainic Acid in Adult Rat Whole Brain at the Early Stage: A 18FDG-PET Study

2021 ◽  
pp. 147621
Author(s):  
Arturo Avendaño-Estrada ◽  
Camilo Rios ◽  
Iñigo Aguirre-Aranda ◽  
Miguel Ángel Ávila-Rodríguez ◽  
Joaquín Manjarrez-Marmolejo ◽  
...  
Keyword(s):  
Kainic Acid ◽  
Metabolic Activity ◽  
Early Stage ◽  
Whole Brain ◽  
Adult Rat ◽  
18Fdg Pet

scholarly journals Genomic and functional characterization of a mucosal symbiont involved in early-stage colorectal cancer

2021 ◽  
Author(s):  
Melissa C. Kordahi ◽  
Ian B. Stanaway ◽  
Marion Avril ◽  
Denise Chac ◽  
Marie-Pierre Blanc ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Early Stage ◽  
Functional Characterization

scholarly journals Fabrication and Characterization of Ta–GelMA–BG Scaffolds by Chemical Crosslinking Processing for Promotion Osteointegration

2021 ◽  
Vol 8 ◽  
Author(s):  
Fujian Zhao ◽  
Xiongfa Ji ◽  
Yang Yan ◽  
Zhen Yang ◽  
Xiaofeng Chen ◽  
...  
Keyword(s):  
Early Stage ◽  
Bone Defects ◽  
Chemical Crosslinking ◽  
Composite Scaffolds ◽  
Load Bearing ◽  
3D Printed ◽  
Fabrication And Characterization ◽  
Cell Adhesion And Proliferation

The repair of bone defects in load-bearing positions still faces great challenges. Tantalum (Ta) has attempted to repair bone defects based on the excellent mechanical properties. However, the osseointegration of Ta needs to be improved due to the lack of osteoinduction. Herein, tantalum–gelatin–methacryloyl–bioactive glass (Ta–GelMA–BG) scaffolds were successfully fabricated by loading BG in 3D-printed Ta scaffolds through a chemical crosslinking method. The results showed that the composite scaffolds have the ability to promote cell adhesion and proliferation. The incorporation of BG resulted in a significant increase in apatite-forming and osteogenesis differentiation abilities. In vivo results indicated that the Ta–GelMA–BG scaffolds significantly enhanced the osteointegration at the early stage after implantation. Overall, the Ta–GelMA–BG scaffolds are a promising platform for the load bearing bone regeneration field.

scholarly journals Text Data Augmentation for Deep Learning

2021 ◽  
Author(s):  
Connor Shorten ◽  
Taghi M. Khoshgoftaar ◽  
Borko Furht
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Data Augmentation ◽  
Early Stage ◽  
Practical Implementation ◽  
Text Data ◽  
Training Strategy ◽  
Local Decision ◽  
Decision Boundaries

Abstract Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, and the distinction between meaning and form. We follow these motifs with a concrete list of augmentation frameworks that have been developed for text data. Deep Learning generally struggles with the measurement of generalization and characterization of overfitting. We highlight studies that cover how augmentations can construct test sets for generalization. NLP is at an early stage in applying Data Augmentation compared to Computer Vision. We highlight the key differences and promising ideas that have yet to be tested in NLP. For the sake of practical implementation, we describe tools that facilitate Data Augmentation such as the use of consistency regularization, controllers, and offline and online augmentation pipelines, to preview a few. Finally, we discuss interesting topics around Data Augmentation in NLP such as task-specific augmentations, the use of prior knowledge in self-supervised learning versus Data Augmentation, intersections with transfer and multi-task learning, and ideas for AI-GAs (AI-Generating Algorithms). We hope this paper inspires further research interest in Text Data Augmentation.

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