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

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

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

In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 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.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches

Identifying Multiple Influential Spreaders in Complex Networks by Considering the Dispersion of Nodes

Identifying multiple influential spreaders, which relates to finding k (k > 1) nodes with the most significant influence, is of great importance both in theoretical and practical applications. It is usually formulated as a node-ranking problem and addressed by sorting spreaders’ influence as measured based on the topological structure of interactions or propagation process of spreaders. However, ranking-based algorithms may not guarantee that the selected spreaders have the maximum influence, as these nodes may be adjacent, and thus play redundant roles in the propagation process. We propose three new algorithms to select multiple spreaders by taking into account the dispersion of nodes in the following ways: (1) improving a well-performed local index rank (LIR) algorithm by extending its key concept of the local index (an index measures how many of a node’s neighbors have a higher degree) from first-to second-order neighbors; (2) combining the LIR and independent set (IS) methods, which is a generalization of the coloring problem for complex networks and can ensure the selected nodes are non-adjacent if they have the same color; (3) combining the improved second-order LIR method and IS method so as to make the selected spreaders more disperse. We evaluate the proposed methods against six baseline methods on 10 synthetic networks and five real networks based on the classic susceptible-infected-recovered (SIR) model. The experimental results show that our proposed methods can identify nodes that are more influential. This suggests that taking into account the distances between nodes may aid in the identification of multiple influential spreaders

Comparative non-targeted metabolomic analysis reveals insights into the mechanism of rice yellowing

Yellowing of rice during storage is a highly concerned issue for managing rice quality whereas the yellowing mechanism is not clearly elucidated so far. Thus, the comparative untargeted metabolomic analysis was performed in this study. The results revealed that glycolysis pathway and tricarboxylic acid cycle (TCA) were significantly enhanced in yellowed rice, indicating the activated energy metabolism was trigged during the yellowing process. In addition, the increased aromatic compounds (4-hydroxycinnamic acid and benzoic acid) and their precursors (phenylalanine, tyrosine) suggested the activation of shikimate-phenylpropanoid biosynthesis in yellowed rice, which is an antioxidant defense related pathway. In particular, the pathways involved in the metabolism of glutamate and arginine also significantly altered in yellowed rice. Therefore, the enriched pathways of increased amino acids, sugars, sugar alcohols, and intermediates of the TCA cycle during yellowing process are proposed to be associated with the response of heat and dry induced by the yellowing process. © 2019 Elsevier Lt

Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images

In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which requires no nonlinear registration or tissue segmentation in the application stage and is robust to the inconsistency among longitudinal scans. Specifically, (1) the discriminative landmarks are first automatically discovered from the whole brain, which can be efficiently localized using a fast landmark detection method for the testing images; (2) Highlevel statistical spatial features and contextual longitudinal features are then extracted based on those detected landmarks. Using the spatial and longitudinal features, a linear support vector machine (SVM) is adopted for distinguishing AD subjects from healthy controls (HCs) and also mild cognitive impairment (MCI) subjects from HCs, respectively. Experimental results demonstrate the competitive classification accuracies, as well as a promising computational efficiency

Iron ethylene polymerization catalysts incorporating trifluoromethoxy functionality: Effects on PE molecular weight and productivity

The capacity to broaden the range of molecular weights displayed by polyolefinic materials is an important factor to be considered in the design of polymerization catalysts. Herein, the 2,6-dibenzhydryl-4-trifluoromethoxy modified bis(imino)pyridyl-ferrous chlorides, [2-[CMeN{2,6-{(C6H5)2CH}2-4-(F3CO)C6H2}]-6-(CMeNAr)C5H3N]FeCl2 [Ar = 2,6-Me2C6H3 Fe1, 2,6-Et2C6H3 Fe2, 2,6-i-Pr2C6H3 Fe3, 2,4,6-Me3C6H2 Fe4, 2,6-Et2-4-MeC6H2 Fe5], are used as precatalysts in the solution polymerization of ethylene. On the activation with either MAO or MMAO, all complexes displayed high productivity [up to 18.4 × 106 g (PE) mol-1 (Fe) h-1 for Fe5/MAO], generating highly linear polyethylenes with a wide range of molecular weights (Mw range: 0.85 × 103 to 8.80 × 105 g mol-1). Notably, higher activity was achieved in hexane than in toluene under MAO activation, while the opposite trend was seen with MMAO, highlighting the key role played by solvent in the polymerization process. By comparison with structurally related iron catalysts, the presence of the electron withdrawing para-trifluoromethoxy group has the effect of increasing the molecular weight of the polyethylene. In addition to the polymerization studies, full synthetic and characterization details are presented for Fe1-Fe5 including the X-ray structures of Fe1 and Fe2

Attenuation of metabolic syndrome in the ob/ob mouse model by resistant starch intervention is dose dependent

The current study applied an ob/ob mouse model of obesity for investigating the impact of different RS doses in a high-fat (HF) diet on the attenuation of metabolic syndrome. Although a significant reduction of body weight was not achieved, RS intervention significantly decreased liver weight with suppressed lipid accumulation in the liver tissue and reduced adipocyte size in the fat tissue. All levels of RS intervention were associated with significantly enriched pathways for PPAR, NAFLD and cGMP-PKG signaling. In contrast, either a medium or a higher RS intake (MRS and HRS, respectively) led the AMPK signaling pathway to be significantly enriched but not a diet with lower RS intake. More importantly, sphingolipid biosynthesis activity was noted with MRS and HRS intervention, which is highly associated with the improvement in insulin resistance, and the pathway of type II diabetes mellitus was correspondingly significantly enriched in the HRS group, demonstrating a dose-dependent manner. Similarly, there was no significant difference in the ratio of Bacteroidetes and Firmicutes between high-fat diet and RS groups until RS reached a certain level (i.e. in the HRS group). Furthermore, increased profiles of both Prevotellaceae and Coriobacteriaceae in the HF group were noted for the first time with a revised function from RS intervention, which is consistent with the content of lipopolysaccharides in their corresponding serum. Gut microbiota functional analysis showed that primary and secondary bile acid biosynthesis was also noted to be enriched following the RS intervention, benefiting cholesterol homeostasis. This study further highlights the association of RS consumption with the attenuation of metabolic syndrome in an obesity model, and its functionality is characterized by dose-dependence. © 2019 The Royal Society of Chemistry

Citrate esterification of debranched waxy maize starch: Structural, physicochemical and amylolysis properties

Considering that the content of resistant starch (RS) in original starch is very low, chemical modification is one of choices to increase RS level for enhancing its application. The current study applied debranched waxy maize starch (DBS) to produce a citrate-esterified debranched starch (CADS) and investigated impact of the modification on RS formation. Native starch was also citrate esterified without debranching treatment as the control (CANS). Physicochemical and digestion properties of each sample were characterized, and the results indicated an absorption at 1724 cm −1 in FTIR spectrum was determined in either CADS or CANS, indicating the occurrence of the esterification. Debranching of starch molecules led to a higher degree of substitution (DS) of 0.793 for CADS than CANS. Furthermore, re-association of debranched starch achieved a B type crystalline pattern rather than an A type from its native starch granules. Esterification greatly destroyed crystalline regions until completely disappeared in CADS. The loss of crystalline region was highly consistent with the absence of endothermic peak both in CADS and CANS as revealed by DSC. Hydrolysis rate and digestibility of each sample followed the order: native starch > DBS > CANS > CADS, which may indicate that the introduction of the citric anhydride onto the starch molecules led to an increased space steric hindrance that delayed the enzyme contacting within glucosidic bonds. It could be the first report to prepare a citrate-esterified debranched starch with a higher resistance to amylolysis, and the current investigation may highlight a potent to produce a starch derivative with a higher RS content. © 2020 Elsevier Lt

Investigating the Effects of Para-methoxy Substitution in Sterically Enhanced Unsymmetrical Bis(arylimino)pyridine-cobalt Ethylene Polymerization Catalysts

A group of five bis(arylimino)pyridine-cobalt(II) chloride complexes, [2-{(2,6-(Ph2CH)2-4-MeOC6H2)N = CMe}-6-(ArN = CMe)C5H3N] CoCl2 (Ar = 2,6-Me2C6H3Co1, 2,6-Et2C6H3Co2, 2,6-iPr2C6H3Co3, 2,4,6-Me3C6H2Co4, 2,6-Et2-4-MeC6H2Co5), each containing one N-4-methoxy-2,6-dibenzhydrylphenyl group and one smaller sterically/electronically variable N-aryl group, have been synthesized in good yield (>71%) from the corresponding neutral terdentate nitrogen-donor precursor, L1–L5. All complexes have been characterized by 1H-NMR and FTIR spectroscopy with the former highlighting the paramagnetic nature of these cobaltous species and the unsymmetrical nature of the chelating ligand. The molecular structures of Co3 and Co4 emphasize the steric differences of the two inequivalent N-aryl groups and the distorted square pyramidal geometry about the metal centers. In the presence of MAO or MMAO, Co1–Co5 collectively displayed high activities for ethylene polymerization producing high molecular weight polyethylenes that, in general, exhibited narrow dispersities (Mw/Mn values: 2.12–4.07). Notably, the least sterically hindered Co1 when activated with MAO was the most productive (6.92×106 gPE·mol−1(Co)·h−1) at an operating temperature of 60 °C. Conversely, the most sterically hindered Co3/MMAO produced the highest molecular weight polyethylene (Mw=6.29×105 g·mol−1). All the polymers displayed high linearity as demonstrated by their melting temperatures (>130 °C) and their 1H- and 13C-NMR spectra. By comparison of Co1 with its para-methyl, -chloro and -nitro counterparts, the presence of the para-methoxy substituent showed the most noticeable effect of enhancing the thermal stability of the catalyst

Objective Analysis of Hyperreflective Outer Retinal Bands Imaged by Optical Coherence Tomography in Patients With Stargardt Disease.

PURPOSE: To develop and apply an objective algorithm for analyzing outer retinal layers imaged by spectral-domain optical coherence tomography (SD-OCT) in patients with Stargardt disease (STGD1). METHODS: Horizontal macular B-scans were acquired from 20 visually normal controls and 20 genetically confirmed stage 1 STGD1 patients. The number of outer retinal bands was quantified using a semiautomated algorithm that detected bands using the second derivative of longitudinal reflectivity profiles. The present analysis focused on the three outermost bands, currently associated with the ellipsoid zone (EZ), cone outer segment interdigitation zone (IZ), and retinal pigment epithelium (RPE) complex. RESULTS: The RPE complex and EZ bands were detected throughout the B-scan in all controls. The RPE complex was detected throughout the B-scan in all patients, but was atrophic appearing in some locations. The EZ band was detected only outside the central lesion. Interdigitation zone band detection varied as a function of eccentricity for both groups, with detection for controls being highest in the para- and perifovea and lowest in the fovea and near periphery. In patients, the IZ band was generally not present in the fovea or para- or perifovea due to the central lesion. Outside of the lesion, the IZ band was detected in 26% of patients (mean detection across the near periphery), which was approximately half of the detection in controls. CONCLUSIONS: An objective approach for quantifying the number of outer retinal OCT bands found reduced IZ detection in STGD1 patients. This occurred even outside the central lesion, demonstrating an inability to image the IZ, possibly due to enhanced RPE reflectivity or abnormal outer retinal structure