Individual Aesthetic Differences Evaluation of Yan’an Urban Forests in the Loess Plateau China

Mingxia Zhang,Dexiang Wang,Zhitao Wang,Xinping Zhang,Ganggang Zhang,Yong Xu 보안공학연구지원센터 2014 International Journal of Multimedia and Ubiquitous Vol.9 No.10

Magnetite-based Biochar Coupled with Binary Oxidants for the Effective Removal of Mixed Dye from Wastewater

Mingxia Yu,Huosheng Li,Keke Li,Yuting Li,Fengli Liu,Gaosheng Zhang,Tangfu Xiao,Ping Zhang,Hongguo Zhang,Jianyou Long 한국섬유공학회 2022 Fibers and polymers Vol.23 No.2

Decolorization and organic degradation of wastewater containing multiple dyes are still challenging inwastewater treatment. Magnetic biochar coupled with advanced oxidation is a potential solution to this issue. In this study,a series of magnetite-based biochar composites (Fe3O4@C) was prepared and compared for the removal of mixed dyes,including methyl orange (MO), rhodamine B (RhB), methylene blue (MB), and an organic macromolecule, humic acid(HA). The pyrolysis of watermelon rinds followed by precipitation of Fe3O4 onto the biochar was selected as the optimummethod to prepare an adsorbent and catalyst to couple binary oxidants (hypochlorite and persulfate) for color and totalorganic carbon removal. Persulfate was prone to degrade HA and MB, while hypochlorite was inclined to oxidize MO andRhB. Fe3O4@C exhibited better dye removal performance in coupling with binary oxidants than with a single oxidant. Formixed dye solutions with an initial concentration of 50 mg/l for each dye, the highest TOC (57.24±3.17 %) and the colorremoval efficiencies (94.13±1.68 %) for the mixed dye solution were achieved at a sorbent dosage of 1 g/l and an oxidantdosage of 5 mmol/l for both hypochlorite and persulfate. Multiple free radicals, including hydroxyl radicals, sulfateradicals, and hypochlorite-induced radicals, play critical roles in the degradation of mixed dyes and color removal. Theregeneratibility and reutilization of the magnetic Fe3O4@C composite were effective and stable. The results obtained inthis study show that the combined Fe3O4@C and binary oxidants technique is promising for the treatment of multi-dyewastewater.

A Composite Index-based Approach for Hierarchical Assessment of Forest Ecosystem Health : An Example of Pinus tabulaeformis

Mingxia Zhang,Dexiang Wang,Zhao Xu,Qiuju Guo 보안공학연구지원센터 2014 International Journal of Multimedia and Ubiquitous Vol.9 No.7

Small-conductance Ca2+-activated K+ channels: insights into their roles in cardiovascular disease

Mingxia Gu,Yanrong Zhu,Xiaorong Yin,Dai-Min Zhang 생화학분자생물학회 2018 Experimental and molecular medicine Vol.50 No.-

Life-threatening malignant arrhythmias in pathophysiological conditions can increase the mortality and morbidity of patients with cardiovascular diseases. Cardiac electrical activity depends on the coordinated propagation of excitatory stimuli and the generation of action potentials in cardiomyocytes. Action potential formation results from the opening and closing of ion channels. Recent studies have indicated that small-conductance calcium-activated potassium (SK) channels play a critical role in cardiac repolarization in pathophysiological but not normal physiological conditions. The aim of this review is to describe the role of SK channels in healthy and diseased hearts, to suggest cardiovascular pathophysiologic targets for intervention, and to discuss studies of agents that target SK channels for the treatment of cardiovascular diseases.

Alzheimer's Disease Diagnosis Using Landmark-Based Features From Longitudinal Structural MR Images

Zhang, Jun,Liu, Mingxia,Le An,Gao, Yaozong,Shen, Dinggang IEEE 2017 IEEE Journal of Biomedical and Health Informatics Vol.21 No.6

<P>Structural magnetic resonance imaging (MRI) has been proven to be an effective tool for Alzheimer's disease (AD) diagnosis. While conventional MRI-based AD diagnosis typically uses images acquired at a single time point, a longitudinal study is more sensitive in detecting early pathological changes of AD, making it more favorable for accurate diagnosis. In general, there are two challenges faced in MRI-based diagnosis. First, extracting features from structural MR images requires time-consuming nonlinear registration and tissue segmentation, whereas the longitudinal study with involvement of more scans further exacerbates the computational costs. Moreover, the inconsistent longitudinal scans (i.e., different scanning time points and also the total number of scans) hinder extraction of unified feature representations in longitudinal studies. In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which does not require nonlinear registration or tissue segmentation in the application stage and is also robust to inconsistencies among longitudinal scans. Specifically, first, the discriminative landmarks are automatically discovered from the whole brain using training images, and then efficiently localized using a fast landmark detection method for testing images, without the involvement of any nonlinear registration and tissue segmentation; and second, high-level statistical spatial features and contextual longitudinal features are further extracted based on those detected landmarks, which can characterize spatial structural abnormalities and longitudinal landmark variations. Using these spatial and longitudinal features, a linear support vector machine is finally adopted to distinguish AD subjects or mild cognitive impairment (MCI) subjects from healthy controls (HCs). Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate the superior performance and efficiency of the proposed method, with classification accuracies of 88.30% for AD versus HC and 79.02% for MCI versus HC, respectively.</P>

Relationship Induced Multi-Template Learning for Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment

Liu, Mingxia,Zhang, Daoqiang,Shen, Dinggang Institute of Electrical and Electronics Engineers 2016 IEEE transactions on medical imaging Vol.35 No.6

<P>As shown in the literature, methods based on multiple templates usually achieve better performance, compared with those using only a single template for processing medical images. However, most existing multi-template based methods simply average or concatenate multiple sets of features extracted from different templates, which potentially ignores important structural information contained in the multi-template data. Accordingly, in this paper, we propose a novel relationship induced multi-template learning method for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI), by explicitly modeling structural information in the multi-template data. Specifically, we first nonlinearly register each brain's magnetic resonance (MR) image separately onto multiple pre-selected templates, and then extract multiple sets of features for this MR image. Next, we develop a novel feature selection algorithm by introducing two regularization terms to model the relationships among templates and among individual subjects. Using these selected features corresponding to multiple templates, we then construct multiple support vector machine (SVM) classifiers. Finally, an ensemble classification is used to combine outputs of all SVM classifiers, for achieving the final result. We evaluate our proposed method on 459 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 97 AD patients, 128 normal controls (NC), 117 progressive MCI (pMCI) patients, and 117 stable MCI (sMCI) patients. The experimental results demonstrate promising classification performance, compared with several state-of-the-art methods for multi-template based AD/MCI classification.</P>

Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks

Jun Zhang,Mingxia Liu,Dinggang Shen IEEE 2017 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.26 No.10

<P>One of the major challenges in anatomical landmark detection, based on deep neural networks, is the limited availability of medical imaging data for network learning. To address this problem, we present a two-stage task-oriented deep learning method to detect large-scale anatomical landmarks simultaneously in real time, using limited training data. Specifically, our method consists of two deep convolutional neural networks (CNN), with each focusing on one specific task. Specifically, to alleviate the problem of limited training data, in the first stage, we propose a CNN based regression model using millions of image patches as input, aiming to learn inherent associations between local image patches and target anatomical landmarks. To further model the correlations among image patches, in the second stage, we develop another CNN model, which includes a) a fully convolutional network that shares the same architecture and network weights as the CNN used in the first stage and also b) several extra layers to jointly predict coordinates of multiple anatomical landmarks. Importantly, our method can jointly detect large-scale (e.g., thousands of) landmarks in real time. We have conducted various experiments for detecting 1200 brain landmarks from the 3D T1-weighted magnetic resonance images of 700 subjects, and also 7 prostate landmarks from the 3D computed tomography images of 73 subjects. The experimental results show the effectiveness of our method regarding both accuracy and efficiency in the anatomical landmark detection.</P>

Significance of Preoperative Nerve Reconstruction Using Diffusion Tensor Imaging Tractography for Facial Nerve Protection in Vestibular Schwannoma

Yuanlong Zhang,Hongliang Ge,Mingxia Xu,Wenzhong Mei 대한신경외과학회 2023 Journal of Korean neurosurgical society Vol.66 No.2

Objective : The facial nerve trace on the ipsilateral side of the vestibular schwannoma was reconstructed by diffusion tensor imaging tractography to identify the adjacent relationship between the facial nerve and the tumor, and to improve the level of intraoperative facial nerve protection. Methods : The clinical data of 30 cases of unilateral vestibular schwannoma who underwent tumor resection via retrosigmoid approach were collected between January 2019 and December 2020. All cases underwent magnetic resonance imaging examination before operation. Diffusion tensor imaging and anatomical images were used to reconstruct the facial nerve track of the affected side, so as to predict the course of the nerve and its adjacent relationship with the tumor, to compare the actual trace of the facial nerve during operation, verify the degree of coincidence, and evaluate the nerve function (House-Brackmann grade) after surgery. Results : The facial nerve of 27 out of 30 cases could be displayed by diffusion tensor imaging tractography, and the tracking rate was 90% (27/30). The intraoperative locations of facial nerve shown in 25 cases were consistent with the preoperative reconstruction results. The coincidence rate was 92.6% (25/27). The facial nerves were located on the anterior middle part of the tumor in 14 cases, anterior upper part in eight cases, anterior lower part in seven cases, and superior polar in one case. Intraoperative facial nerve anatomy was preserved in 30 cases. Among the 30 patients, total resection was performed in 28 cases and subtotal resection in two cases. The facial nerve function was evaluated 2 weeks after operation, and the results showed grade I in 12 cases, grade II in 16 cases and grade III in two cases. Conclusion : Preoperative diffusion tensor imaging tractography can clearly show the trajectory and adjacent position of the facial nerve on the side of vestibular schwannoma, which is beneficial to accurately identify and effectively protect the facial nerve during the operation, and is worthy of clinical application and promotion.

Landmark-based deep multi-instance learning for brain disease diagnosis

Liu, Mingxia,Zhang, Jun,Adeli, Ehsan,Shen, Dinggang Elsevier 2018 Medical image analysis Vol.43 No.-

<P><B>Abstract</B></P> <P>In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, <I>i.e.</I>, 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (<I>i.e.</I>, ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We developed a novel deep multi-instance convolutional neural network to automatically learn both local and global representations for MR images. </LI> <LI> We proposed a landmark-based image patch extraction approach based on a data-driven landmark discovery algorithm. </LI> <LI> We trained the model on ADNI-1 and tested it on two independent datasets (i.e., ADNI-2 and MIRIAD). </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

View-aligned hypergraph learning for Alzheimer’s disease diagnosis with incomplete multi-modality data

Liu, Mingxia,Zhang, Jun,Yap, Pew-Thian,Shen, Dinggang Elsevier 2017 Medical image analysis Vol.36 No.-

<P><B>Abstract</B></P> <P>Effectively utilizing incomplete multi-modality data for the diagnosis of Alzheimer’s disease (AD) and its prodrome (<I>i.e.</I>, mild cognitive impairment, MCI) remains an active area of research. Several multi-view learning methods have been recently developed for AD/MCI diagnosis by using incomplete multi-modality data, with each view corresponding to a specific modality or a combination of several modalities. However, existing methods usually ignore the underlying coherence among views, which may lead to sub-optimal learning performance. In this paper, we propose a view-aligned hypergraph learning (VAHL) method to explicitly model the coherence among views. Specifically, we first divide the original data into several views based on the availability of different modalities and then construct a hypergraph in each view space based on sparse representation. A view-aligned hypergraph classification (VAHC) model is then proposed, by using a view-aligned regularizer to capture coherence among views. We further assemble the class probability scores generated from VAHC, via a multi-view label fusion method for making a final classification decision. We evaluate our method on the baseline ADNI-1 database with 807 subjects and three modalities (<I>i.e.</I>, MRI, PET, and CSF). Experimental results demonstrate that our method outperforms state-of-the-art methods that use incomplete multi-modality data for AD/MCI diagnosis.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We developed a new hypergraph learning model to capture the coherence among views. </LI> <LI> We proposed a sparse representation based hypergraph construction method. </LI> <LI> We designed a multi-view label fusion method for making classification decisions. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>