Quantitative analysis with machine learning models for multi-parametric brain imaging data

Gliomas are considered to be the most common primary adult malignant brain tumor. With the dramatic increases in computational power and improvements in image analysis algorithms, computer-aided medical image analysis has been introduced into clinical applications. Precision tumor grading and genotyping play an indispensable role in clinical diagnosis, treatment and prognosis. Gliomas diagnostic procedures include histopathological imaging tests, molecular imaging scans and tumor grading. Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human study has limitations that can result in low reproducibility and inter-observer agreement. Compared with histopathological images, Magnetic resonance (MR) imaging present the different structure and functional features, which might serve as noninvasive surrogates for tumor genotypes. Therefore, computer-aided image analysis has been adopted in clinical application, which might partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure multilevel features on multi-parametric medical information. Imaging features obtained from a single modal image do not fully represent the disease, so quantitative imaging features, including morphological, structural, cellular and molecular level features, derived from multi-modality medical images should be integrated into computer-aided medical image analysis. The image quality differentiation between multi-modality images is a challenge in the field of computer-aided medical image analysis. In this thesis, we aim to integrate the quantitative imaging data obtained from multiple modalities into mathematical models of tumor prediction response to achieve additional insights into practical predictive value. Our major contributions in this thesis are: 1. Firstly, to resolve the imaging quality difference and observer-dependent in histological image diagnosis, we proposed an automated machine-learning brain tumor-grading platform to investigate contributions of multi-parameters from multimodal data including imaging parameters or features from Whole Slide Images (WSI) and the proliferation marker KI-67. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. A quantitative interpretable machine learning approach (Local Interpretable Model-Agnostic Explanations) was followed to measure the contribution of features for single case. Most grading systems based on machine learning models are considered “black boxes,” whereas with this system the clinically trusted reasoning could be revealed. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. 2. Based on the automated brain tumor-grading platform we propose, multimodal Magnetic Resonance Images (MRIs) have been introduced in our research. A new imaging–tissue correlation based approach called RA-PA-Thomics was proposed to predict the IDH genotype. Inspired by the concept of image fusion, we integrate multimodal MRIs and the scans of histopathological images for indirect, fast, and cost saving IDH genotyping. The proposed model has been verified by multiple evaluation criteria for the integrated data set and compared to the results in the prior art. The experimental data set includes public data sets and image information from two hospitals. Experimental results indicate that the model provided improves the accuracy of glioma grading and genotyping

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning models. In this paper, we investigate contributions of multi-parameters from multimodal data including imaging parameters or features from the Whole Slide images (WSI) and the proliferation marker Ki-67 for automated brain tumor grading. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. On the basis of machine learning models, our platform classifies gliomas into grades II, III, and IV. Furthermore, we quantitatively interpret and reveal the important parameters contributing to grading with the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. The performance of our grading model was evaluated with cross-validation, which randomly divided the patients into non-overlapping training and testing sets and repeatedly validated the model on the different testing sets. The primary results indicated that this modular platform approach achieved the highest grading accuracy of 0.90 ± 0.04 with support vector machine (SVM) algorithm, with grading accuracies of 0.91 ± 0.08, 0.90 ± 0.08, and 0.90 ± 0.07 for grade II, III, and IV gliomas, respectively

Analysis and treatment of current unbalance abnormal situation of 750 kV double-circuit parallel transmission line

At present, the biggest problem faced by short-distance double-circuit parallel lines is the three-phase parameter asymmetry problem caused by non-transposition. This leads to three-phase current asymmetry, especially during heavy load performance is more prominent. Take a 750 kV double circuit parallel erection line in actual operation as an example, use EMTPE electromagnetic transient simulation software to simulate and analyze the current imbalance phenomenon. This paper studies the influence of phase sequence arrangement, distance between loops, distance between wires, line length, equivalent impedance, ground wire conditions and line power flow on current imbalance, and puts forward reasonable restraining measures. The simulation results show that when the line power flow is heavy, the double circuit lines are arranged in the same phase sequence and the distance between circuits is relatively short, there will be a large amplitude of zero sequence current on the line, mainly circulation current. If it is not suitable to adjust the protection measures, it can be considered to adjust the double-circuit lines to reverse phase sequence operation mode. The research results can provide reference for the engineering design of double-circuit parallel transmission lines

Data-Driven Optimization Control for Dynamic Reconfiguration of Distribution Network

To improve the reliability and reduce power loss of distribution network, the dynamic reconfiguration is widely used. It is employed to find an optimal topology for each time interval while satisfying all the physical constraints. Dynamic reconfiguration is a non-deterministic polynomial problem, which is difficult to find the optimal control strategy in a short time. The conventional methods solved complex model of dynamic reconfiguration in different ways, but only local optimal solutions can be found. In this paper, a data-driven optimization control for dynamic reconfiguration of distribution network is proposed. Through two stages that include rough matching and fine matching, the historical cases which are similar to current case are chosen as candidate cases. The optimal control strategy suitable for the current case is selected according to dynamic time warping (DTW) distances which evaluate the similarity between the candidate cases and the current case. The advantage of the proposed approach is that it does not need to solve complex model of dynamic reconfiguration, and only uses historical data to obtain the optimal control strategy for the current case. The cases study shows that the optimization results and the computation time of the proposed approach are superior to conventional methods

LINC02015 modulates the cell proliferation and apoptosis of aortic vascular smooth muscle cells by transcriptional regulation and protein interaction network

Abstract Long intergenic nonprotein coding RNA 2015 (LINC02015) is a long non-coding RNA that has been found elevated in various cell proliferation-related diseases. However, the functions and interactive mechanism of LINC02015 remain unknown. This study aimed to explore the role of LINC02015 in the cell proliferation and apoptosis of vascular smooth muscle cells (VSMCs) to explain the pathogenesis of aortic diseases. Ascending aorta samples and angiotensin-II (AT-II) treated primary human aortic VSMCs (HAVSMCs) were used to evaluate the LINC02015 expression. RNA sequencing, chromatin isolation by RNA purification sequencing, RNA pull-down, and mass spectrometry (MS) were applied to explore the potential interacting mechanisms. LINC02015 expression was found elevated in aortic dissection and AT-II-treated HAVSMCs. Cell proliferation and cell cycle were activated in HAVSMCs with LINC02015 knockdown. The cyclins family and caspase family were found to participate in regulating the cell cycle and apoptosis via the NF-κB signaling pathway. RXRA was discovered as a possible hub gene for LINC02015 transcriptional regulating networks. Besides, the protein interaction network of LINC02015 was revealed with candidate regulating molecules. It was concluded that the knockdown of LINC02015 could promote cell proliferation and inhibit the apoptosis of HAVSMCs through an RXRA-related transcriptional regulation network, which could provide a potential therapeutic target for aortic diseases

Generation of an induced pluripotent stem cell line from a Loeys-Dietz syndrome patient with transforming growth factor-beta receptor-2 gene mutation

Loeys-Dietz syndrome (LDS) is an autosomal-dominant connective tissue disorder, commonly caused by genetic mutation of transforming growth factor-beta receptor (TGFBR)-1 or TGFBR2. This study describes the generation of human induced pluripotent stem cells (hiPSCs) from peripheral blood mononuclear cells obtained from an LDS patient with TGFBR2 mutation (R193W). Analysis confirmed the cells had a normal karyotype, expressed typical pluripotency markers, had the ability to differentiate into all three germ layers in vivo, and retained the TGFBR2 mutation from the derived hiPSCs. This iPSC line represents a potentially useful tool for investigating LDS disease mechanisms

Correlations between organic matrix and eggshell properties of 3 kinds of eggshells in Muscovy duck (Cairina moschata)

ABSTRACT: In order to analyze the relationship between organic matrix and eggshell properties in Muscovy duck eggshells with different qualities, the organic matrices in the eggshells of normal, pimpled, and striped eggs of white-feathered Muscovy ducks were extracted and separated into acid-insoluble, water-insoluble, and facultative-soluble matrix (both acid- and water-soluble). There was no significant difference in calcified shell thickness between normal and pimpled eggs. However, the percentages of acid-insoluble matrix and total matrix were significantly higher, and the breaking strength was significantly lower in pimpled eggs than those in normal eggs. In striped eggs, the percentages of acid-insoluble matrix, facultative-soluble matrix, and total matrix, calcified shell weight, calcified shell thickness, and breaking strength were significantly lower than those in normal eggs. The amount and percentage of 3 organic matrices (water-insoluble matrix, facultative-soluble matrix, and total matrix) were significantly positively correlated with calcified shell thickness in normal eggs rather than striped and pimpled eggs. Our results also demonstrated that there was no linear correlation between the organic components in the 3 Muscovy duck eggshells and the mechanical properties of the eggshells. The lower breaking strength of pimpled eggshells might be due to the unbalanced enrichment of certain proteins, whereas the striped eggs might mainly result from thinner calcified shells and poor balance between different sedimentary layers

International representations of inclusive education : How is inclusive practice reflected in the Professional Teaching Standards of China and Australia?

Inclusive education focuses on addressing marginalisation, segregation and exclusion within policy and practice. The purpose of this article is to use critical discourse analysis to examine how inclusion is represented in the education policy and professional documents of two countries, Australia and China. In particular, teacher professional standards from each country are examined to determine how an expectation of inclusive educational practice is promoted to teachers. The strengthening of international partnerships to further support the implementation of inclusive practices within both countries is also justified