Analysis of the positive forces exhibiting on the mooring line of composite-type sea cage

According to the commonly used arrangements of gravity sea cages in deep sea farming, 5 composite type working models are designed with modelbased testing by which the forces acting on the mooring line are measured under pure current, pure wave and the combination of both. Meanwhile, the forces acting on the normal mooring line are also analyzed. Based on the test data, a conclusion is made about the characteristics of the mooring line force. In the end, some feasible suggestions are given, which should be adopted for arrangement of sea cages in real farming

Identification and characterization of genes associated with tapping panel dryness from Hevea brasiliensis latex using suppression subtractive hybridization

<p>Abstract</p> <p>Background</p> <p>Tapping panel dryness (TPD) is one of the most serious threats to natural rubber production. Although a great deal of effort has been made to study TPD in rubber tree, the molecular mechanisms underlying TPD remain poorly understood. Identification and systematical analyses of the genes associated with TPD are the prerequisites for elucidating the molecular mechanisms involved in TPD. The present study is undertaken to generate information about the genes related to TPD in rubber tree.</p> <p>Results</p> <p>To identify the genes related to TPD in rubber tree, forward and reverse cDNA libraries from the latex of healthy and TPD trees were constructed using suppression subtractive hybridization (SSH) method. Among the 1106 clones obtained from the two cDNA libraries, 822 clones showed differential expression in two libraries by reverse Northern blot analyses. Sequence analyses indicated that the 822 clones represented 237 unique genes; and most of them have not been reported to be associated with TPD in rubber tree. The expression patterns of 20 differentially expressed genes were further investigated to validate the SSH data by reverse transcription PCR (RT-PCR) and real-time PCR analysis. According to the Gene Ontology convention, 237 unique genes were classified into 10 functional groups, such as stress/defense response, protein metabolism, transcription and post-transcription, rubber biosynthesis, etc. Among the genes with known function, the genes preferentially expressed were associated with stress/defense response in the reverse library, whereas metabolism and energy in the forward one.</p> <p>Conclusions</p> <p>The genes associated with TPD were identified by SSH method in this research. Systematic analyses of the genes related to TPD suggest that the production and scavenging of reactive oxygen species (ROS), ubiquitin proteasome pathway, programmed cell death and rubber biosynthesis might play important roles in TPD. Therefore, our results not only enrich information about the genes related to TPD, but also provide new insights into understanding the TPD process in rubber tree.</p

Coupled modeling of lipid deposition, inflammatory response and intraplaque angiogenesis in atherosclerotic plaque

We propose a multiphysical mathematical model by fully coupling lipid deposition, monocytes/macrophages recruitment and angiogenesis to investigate the pathophysiological responses of an atherosclerotic plaque to the dynamic changes in the microenvironment. The time evolutions of cellular (endothelial cells, macrophages, smooth muscle cells, etc.) and acellular components (low density lipoprotein, proinflammatory cytokines, extravascular plasma concentration, etc.) within the plaque microenvironment are assessed quantitatively. The thickening of the intima, the distributions of the lipid and inflammatory factors, and the intraplaque hemorrhage show a qualitative consistency with the MRI and histology data. Models with and without angiogenesis are compared to demonstrate the important role of neovasculature in the accumulation of blood-borne components in the atherosclerotic lesion by extravasation from the leaky vessel wall, leading to the formation of a lipid core and an inflammatory microenvironment, which eventually promotes plaque destabilization. This model can serve as a theoretical platform for the investigation of the pathological mechanisms of plaque progression and may contribute to the optimal design of atherosclerosis treatment strategies, such as lipid-lowering or anti-angiogenetic therapies

Coronary Plaque Characterization From Optical Coherence Tomography Imaging With a Two-Pathway Cascade Convolutional Neural Network Architecture

Background: The morphological structure and tissue composition of a coronary atherosclerotic plaque determine its stability, which can be assessed by intravascular optical coherence tomography (OCT) imaging. However, plaque characterization relies on the interpretation of large datasets by well-trained observers. This study aims to develop a convolutional neural network (CNN) method to automatically extract tissue features from OCT images to characterize the main components of a coronary atherosclerotic plaque (fibrous, lipid, and calcification). The method is based on a novel CNN architecture called TwopathCNN, which is utilized in a cascaded structure. According to the evaluation, this proposed method is effective and robust in the characterization of coronary plaque composition from in vivo OCT imaging. On average, the method achieves 0.86 in F1-score and 0.88 in accuracy. The TwopathCNN architecture and cascaded structure show significant improvement in performance (p < 0.05). CNN with cascaded structure can greatly improve the performance of characterization compared to the conventional CNN methods and machine learning methods. This method has a higher efficiency, which may be proven to be a promising diagnostic tool in the detection of coronary plaques

Automated classification of coronary plaque calcification in OCT pullbacks with 3D deep neural networks

Significance: Detection and characterization of coronary atherosclerotic plaques often need reviews of a large number of optical coherence tomography (OCT) imaging slices to make a clinical decision. However, it is a challenge to manually review all the slices and consider the interrelationship between adjacent slices. Approach: Inspired by the recent success of deep convolutional network on the classification of medical images, we proposed a ResNet-3D network for classification of coronary plaque calcification in OCT pullbacks. The ResNet-3D network was initialized with a trained ResNet-50 network and a three-dimensional convolution filter filled with zeros padding and non-zeros padding with a convolutional filter. To retrain ResNet-50, we used a dataset of a1/44860 OCT images, derived by 18 entire pullbacks from different patients. In addition, we investigated a two-phase training method to address the data imbalance. For an improved performance, we evaluated different input sizes for the ResNet-3D network, such as 3, 5, and 7 OCT slices. Furthermore, we integrated all ResNet-3D results by majority voting. Results: A comparative analysis proved the effectiveness of the proposed ResNet-3D networks against ResNet-2D network in the OCT dataset. The classification performance (F1-scores = 94 % for non-zeros padding and F1-score = 96 % for zeros padding) demonstrated the potential of convolutional neural networks (CNNs) in classifying plaque calcification. Conclusions: This work may provide a foundation for further work in extending the CNN to voxel segmentation, which may lead to a supportive diagnostic tool for assessment of coronary plaque vulnerability. </p

Characterization of coronary atherosclerotic plaque composition based on Convolutional Neural Network (CNN)

The tissue composition and morphological structure of atherosclerotic plaques determine its stability or vulnerability. Intravascular optical coherence tomography ( IV OCT) has rapidly become the method of choice for assessing the pathology of the coronary arterial wall in vivo due to its superior resolution. However, in clinical practice, the analysis of plaque composition of OCT images mainly relies on the interpretation of images by well - trained experts, which is a time - consuming, labor - intensive procedure and it is also subjective . The purpose of this study is to use the Convolutional neural network ( CNN) method to automatically extract the best feature information from the OCT images to characterize the three basic components of atherosclerotic plaque (fibrous, lipid, and calcification). This study select ed the OCT images of 20 patients from Nanjing Drum Tower Hospital from 2015.12 to 2016.12. The OCT - reading expert first excluded the image s containing the brackets, and then divide d all the remaining images, resulting in 1500 plaque OCT images. The expert label ed plaque composition in each image, cut ting it into 11*11 image patches and obtained 87390 patches. 75000 of them were set as training examples and the others were set for testing. The classification accuracy of the test set serve d as the evaluation criterion. The experimental results show that the average classification accuracy of the fibrous, calcification, and lipid patches by the CNN classifier as over 75 %, especially to characterize the fibrous patches, whose accuracy could reach more than 80% . The proposed method is effective and robust in the analysis of atherosclerotic plaque composition in coronary OCT images, providing a base for further segmentation study

Automatic classification of atherosclerotic tissue in intravascular optical coherence tomography images

Intravascular optical coherence tomography (IVOCT) has been successfully utilized for in vivo diagnostics of coronary plaques. However, classification of atherosclerotic tissues is mainly performed manually by experienced experts, which is time-consuming and subjective. To overcome these limitations, an automatic method of segmentation and classification of IVOCT images is developed in this paper. The method is capable of detecting the plaque contour between the fibrous tissues and other components. Subsequently, the method classifies the tissues based on their texture features described by Fourier transform and discrete wavelet transform. The experimental results of 103 images show that an overall classification accuracy of over 80% in the indicator of depth and span angle is achieved in comparison to manual results. The validation suggests that this method is objective, accurate, and automatic without any manual intervention. The proposed method is able to demonstrate the artery wall morphology successfully, which is valuable for the research of atherosclerotic disease

The correlation between texture features and fibrous cap thickness of lipid-rich atheroma based on optical coherence tomography imaging

PurposeFibrous cap thickness (FCT) is seen as critical to plaque vulnerability. Therefore, the development of automatic algorithms for the quantification of FCT is for estimating cardiovascular risk of patients. Intravascular optical coherence tomography (IVOCT) is currently the only in vivo imaging modality with which FCT, the critical component of plaque vulnerability, can be assessed accurately. This study was aimed to discussion the correlation between the texture features of OCT images and the FCT in lipid-rich atheroma. MethodsFirstly, a full automatic segmentation algorithm based on unsupervised fuzzy c means (FCM) clustering with geometric constrains was developed to segment the ROIs of IVOCT images. Then, 32 features, which are associated with the structural and biochemical changes of tissue, were carried out to describe the properties of ROIs. The FCT in grayscale IVOCT images were manually measured by two independent observers. In order to analysis the correlation between IVOCT image features and manual FCT measurements, linear regression approach was performed. ResultsInter-observer agreement of the twice manual FCT measurements was excellent with an intraclass correlation coefficient (ICC) of 0.99. The correlation coefficient between each individual feature set and mean FCT of OCT images were 0.68 for FOS, 0.80 for GLCM, 0.74 for NGTDM, 0.72 for FD, 0.62 for IM and 0.58 for SP. The fusion image features of automatic segmented ROIs and FCT measurements improved the results significantly with a high correlation coefficient (r= 0.91, p<0.001). Conclusion The OCT images features demonstrated the perfect performances and could be used for automatic qualitative analysis and the identification of high-risk plaques instead manual FCT measurements

Atherosclerotic Plaque Tissue Characterization:An OCT-Based Machine Learning Algorithm With ex vivo Validation

There is a need to develop a validated algorithm for plaque characterization which can help to facilitate the standardization of optical coherence tomography (OCT) image interpretation of plaque morphology, and improve the efficiency and accuracy in the application of OCT imaging for the quantitative assessment of plaque vulnerability. In this study, a machine learning algorithm was implemented for characterization of atherosclerotic plaque components by intravascular OCT using ex vivo carotid plaque tissue samples. A total of 31 patients underwent carotid endarterectomy and the ex vivo carotid plaques were imaged with OCT. Optical parameter, texture features and relative position of pixels were extracted within the region of interest and then used to quantify the tissue characterization of plaque components. The potential of individual and combined feature set to discriminate tissue components was quantified using sensitivity, specificity, accuracy. The results show there was a lower classification accuracy in the calcified tissue than the fibrous tissue and lipid tissue. The pixel-wise classification accuracy obtained by the developed method, to characterize the fibrous, calcified and lipid tissue by comparing with histology, were 80.0, 62.0, and 83.1, respectively. The developed algorithm was capable of characterizing plaque components with an excellent accuracy using the combined feature set.</p

The correlation between texture features and fibrous cap thickness of lipid-rich atheroma based on optical coherence tomography imaging

Free to read\ud \ud <b>Purpose</b>\ud \ud Fibrous cap thickness (FCT) is seen as critical to plaque vulnerability. Therefore, the development of automatic algorithms for the quantification of FCT is for estimating cardiovascular risk of patients. Intravascular optical coherence tomography (IVOCT) is currently the only in vivo imaging modality with which FCT, the critical component of plaque vulnerability, can be assessed accurately. This study was aimed to discussion the correlation between the texture features of OCT images and the FCT in lipid-rich atheroma. \ud \ud <b>Methods</b>\ud \ud Firstly, a full automatic segmentation algorithm based on unsupervised fuzzy c means (FCM) clustering with geometric constrains was developed to segment the ROIs of IVOCT images. Then, 32 features, which are associated with the structural and biochemical changes of tissue, were carried out to describe the properties of ROIs. The FCT in grayscale IVOCT images were manually measured by two independent observers. In order to analysis the correlation between IVOCT image features and manual FCT measurements, linear regression approach was performed. \ud \ud <b>Results</b>\ud \ud Inter-observer agreement of the twice manual FCT measurements was excellent with an intraclass correlation coefficient (ICC) of 0.99. The correlation coefficient between each individual feature set and mean FCT of OCT images were 0.68 for FOS, 0.80 for GLCM, 0.74 for NGTDM, 0.72 for FD, 0.62 for IM and 0.58 for SP. The fusion image features of automatic segmented ROIs and FCT measurements improved the results significantly with a high correlation coefficient (r= 0.91, p<0.001). Conclusion The OCT images features demonstrated the perfect performances and could be used for automatic qualitative analysis and the identification of high-risk plaques instead manual FCT measurements