Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering

The estimation of vascular network topology in complex networks is important in understanding the relationship between vascular changes and a wide spectrum of diseases. Automatic classification of the retinal vascular trees into arteries and veins is of direct assistance to the ophthalmologist in terms of diagnosis and treatment of eye disease. However, it is challenging due to their projective ambiguity and subtle changes in appearance, contrast and geometry in the imaging process. In this paper, we propose a novel method that is capable of making the artery/vein (A/V) distinction in retinal color fundus images based on vascular network topological properties. To this end, we adapt the concept of dominant set clustering and formalize the retinal blood vessel topology estimation and the A/V classification as a pairwise clustering problem. The graph is constructed through image segmentation, skeletonization and identification of significant nodes. The edge weight is defined as the inverse Euclidean distance between its two end points in the feature space of intensity, orientation, curvature, diameter, and entropy. The reconstructed vascular network is classified into arteries and veins based on their intensity and morphology. The proposed approach has been applied to five public databases, INSPIRE, IOSTAR, VICAVR, DRIVE and WIDE, and achieved high accuracies of 95.1%, 94.2%, 93.8%, 91.1%, and 91.0%, respectively. Furthermore, we have made manual annotations of the blood vessel topologies for INSPIRE, IOSTAR, VICAVR, and DRIVE datasets, and these annotations are released for public access so as to facilitate researchers in the community

Active Cancellation Stealth Analysis Based on Cancellaty

Active cancellation stealth is a significant developing direction in modern stealth technology field. In this paper, according to characteristics of linear frequency modulated (LFM) signal and nonlinear frequency modulated (NLFM) signal, the cancellation signal was designed. An important parameter called cancellaty which is used to measure the effect of cancellation is proposed. The basic theory of active cancellation stealth is introduced. Based on radar target fluctuation models, the formulas of the radar detection probability are given. Combining the definition of cancellaty with radar detection probability, the effective scope of the cancellaty is ensured. Simulation results show the effectiveness and the practicability of active cancellation

Bone-Induced Expression of Integrin β3 Enables Targeted Nanotherapy of Breast Cancer Metastases

Bone metastases occur in approximately 70% of metastatic breast cancer patients, often leading to skeletal injuries. Current treatments are mainly palliative and underscore the unmet clinical need for improved therapies. In this study, we provide preclinical evidence for an antimetastatic therapy based on targeting integrin β3 (β3), which is selectively induced on breast cancer cells in bone by the local bone microenvironment. In a preclinical model of breast cancer, β3 was strongly expressed on bone metastatic cancer cells, but not primary mammary tumors or visceral metastases. In tumor tissue from breast cancer patients, β3 was significantly elevated on bone metastases relative to primary tumors from the same patient (n = 42). Mechanistic investigations revealed that TGFβ signaling through SMAD2/SMAD3 was necessary for breast cancer induction of β3 within the bone. Using a micelle-based nanoparticle therapy that recognizes integrin αvβ3 (αvβ3-MPs of ∼12.5 nm), we demonstrated specific localization to breast cancer bone metastases in mice. Using this system for targeted delivery of the chemotherapeutic docetaxel, we showed that bone tumor burden could be reduced significantly with less bone destruction and less hepatotoxicity compared with equimolar doses of free docetaxel. Furthermore, mice treated with αvβ3-MP-docetaxel exhibited a significant decrease in bone-residing tumor cell proliferation compared with free docetaxel. Taken together, our results offer preclinical proof of concept for a method to enhance delivery of chemotherapeutics to breast cancer cells within the bone by exploiting their selective expression of integrin αvβ3 at that metastatic site

A compactness based saliency approach for leakages detection in fluorescein angiogram

This study has developed a novel saliency detection method based on compactness feature for detecting three common types of leakage in retinal fluorescein angiogram: large focal, punctate focal, and vessel segment leakage. Leakage from retinal vessels occurs in a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. The proposed framework consists of three major steps: saliency detection, saliency refinement and leakage detection. First, the Retinex theory is adapted to address the illumination inhomogeneity problem. Then two saliency cues, intensity and compactness, are proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. Finally, the leaking sites can be detected by masking the vessel and optic disc regions. The effectiveness of this framework has been evaluated by applying it to different types of leakage images with cerebral malaria. The sensitivity in detecting large focal, punctate focal and vessel segment leakage is 98.1, 88.2 and 82.7 %, respectively, when compared to a reference standard of manual annotations by expert human observers. The developed framework will become a new powerful tool for studying retinal conditions involving retinal leakage

Research on Sustainable Development of Resource-Based Cities Based on the DEA Approach: A Case Study of Jiaozuo, China

Jiaozuo is a typical resource-based city, and its economic transformation has been an example of success in China. However, quantitative evaluation of the city’s development has scarcely been performed, and future development is not clear. Because of this, using the relevant data from 1999 to 2013, this paper uses the data envelopment analysis (DEA) model to evaluate development after the transformation of Jiaozuo with the aim of providing a basis for its future developing plan. The results show that DEA was effective in 2000, 2004, 2006, 2010, and 2012, was weakly effective in 1999, 2001, 2002, 2003, and 2013, and was ineffective in 2005, 2007, 2008, 2009, and 2011. By evaluating the development of Jiaozuo, this paper provides policy implications for Jiaozuo’s sustainable development, and it may serve as a reference for the sustainable development of China’s other resources-based cities

Economic evaluation of the air pollution effect on public health in China’s 74 cities

Air deterioration caused by pollution has harmed public health. The existing studies on the economic loss caused by a variety of air pollutants in multiple cities are lacking. To understand the effect of different pollutants on public health and to provide the basis of the environmental governance for governments, based on the dose–response relation and the willingness to pay, this paper used the latest available data of the inhalable particulate matter (PM(10)) and sulphur dioxide (SO(2)) from January 2015 to June 2015 in 74 cities by establishing the lowest and the highest limit scenarios. The results show that (1) in the lowest and highest limit scenario, the health-related economic loss caused by PM(10) and SO(2) represented 1.63 and 2.32 % of the GDP, respectively; (2) For a single city, in the lowest and the highest limit scenarios, the highest economic loss of the public health effect caused by PM(10) and SO(2) was observed in Chongqing; the highest economic loss of the public health effect per capita occurred in Hebei Baoding. The highest proportion of the health-related economic loss accounting for GDP was found in Hebei Xingtai. The main reason is that the terrain conditions are not conducive to the spread of air pollutants in Chongqing, Baoding and Xingtai, and the three cities are typical heavy industrial cities that are based on coal resources. Therefore, this paper proposes to improve the energy structure, use the advanced production process, reasonably control the urban population growth, and adopt the emissions trading system in order to reduce the economic loss caused by the effects of air pollution on public health

DeepGrading: Deep Learning Grading of Corneal Nerve Tortuosity

Accurate estimation and quantification of the corneal nerve fiber tortuosity in corneal confocal microscopy (CCM) is of great importance for disease understanding and clinical decision-making. However, the grading of corneal nerve tortuosity remains a great challenge due to the lack of agreements on the definition and quantification of tortuosity. In this paper, we propose a fully automated deep learning method that performs image-level tortuosity grading of corneal nerves, which is based on CCM images and segmented corneal nerves to further improve the grading accuracy with interpretability principles. The proposed method consists of two stages: 1) A pre-trained feature extraction backbone over ImageNet is fine-tuned with a proposed novel bilinear attention (BA) module for the prediction of the regions of interest (ROIs) and coarse grading of the image. The BA module enhances the ability of the network to model long-range dependencies and global contexts of nerve fibers by capturing second-order statistics of high-level features. 2) An auxiliary tortuosity grading network (AuxNet) is proposed to obtain an auxiliary grading over the identified ROIs, enabling the coarse and additional gradings to be finally fused together for more accurate final results. The experimental results show that our method surpasses existing methods in tortuosity grading, and achieves an overall accuracy of 85.64% in four-level classification. We also validate it over a clinical dataset, and the statistical analysis demonstrates a significant difference of tortuosity levels between healthy control and diabetes group. We have released a dataset with 1500 CCM images and their manual annotations of four tortuosity levels for public access. The code is available at: https://github.com/iMED-Lab/TortuosityGrading

CS ²-Net:Deep learning segmentation of curvilinear structures in medical imaging

Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the morphological changes of these curvilinear organ structures informs clinicians for understanding the mechanism, diagnosis, and treatment of e.g. cardiovascular, kidney, eye, lung, and neurological conditions. In this work, we propose a generic and unified convolution neural network for the segmentation of curvilinear structures and illustrate in several 2D/3D medical imaging modalities. We introduce a new curvilinear structure segmentation network (CS 2-Net), which includes a self-attention mechanism in the encoder and decoder to learn rich hierarchical representations of curvilinear structures. Two types of attention modules - spatial attention and channel attention - are utilized to enhance the inter-class discrimination and intra-class responsiveness, to further integrate local features with their global dependencies and normalization, adaptively. Furthermore, to facilitate the segmentation of curvilinear structures in medical images, we employ a 1×3 and a 3×1 convolutional kernel to capture boundary features. Besides, we extend the 2D attention mechanism to 3D to enhance the network's ability to aggregate depth information across different layers/slices. The proposed curvilinear structure segmentation network is thoroughly validated using both 2D and 3D images across six different imaging modalities. Experimental results across nine datasets show the proposed method generally outperforms other state-of-the-art algorithms in various metrics. </p

A combination of phospholipids and long chain polyunsaturated fatty acids supports neurodevelopmental outcomes in infants: a randomized, double-blind, controlled clinical trial

Phospholipids (PLs) and long-chain polyunsaturated fatty acids (LCPUFAs) are naturally present in breast milk and play important roles in promoting the growth of the infant. Several studies have investigated the effects of the combination of PLs and LCPUFAs on neurodevelopment. However, data on the effectiveness of infant formula containing both PLs and LCPUFAs on the neurodevelopment of infants is still scarce. This randomized, double-blind, controlled clinical study was designed to evaluate the effect of an infant formula enriched with PLs and LCPUFAs on growth parameters and neurodevelopmental outcomes in term infants up to 365 days of age. Infants were enrolled within 30 days of birth who were then randomly assigned to either a control group (n = 150) or an investigational group (n = 150). Both groups consist of cow’s milk-based formula which were generally identical in terms of composition, except that the investigational formula was additionally supplemented with PLs and LCPUFAs. The infants were followed for the first year of life. Breastfed infants were the reference (n = 150). Bayley Scales of Infant Development [3rd edition (Bayley-III)], Carey Toddler Temperament Scales (TTS), MacArthur-Bates Communicative Development Inventories (CDI), Single Object Attention and Free Play Tasks were used to evaluate neurodevelopmental outcomes of infant at 365 days of age. In addition, Ages and Stages Questionnaires (ASQ) were also conducted at 120, 180, and 275 days of age. Compared to breastfeeding, both infant formulas were well-tolerated and provided adequate growth, with no adverse events being reported throughout the study. Infants of the investigational group showed higher mean scores in Bayley-III cognitive performance (104.3 vs. 99.0, p &lt; 0.05), language (106.9 vs. 104.5, p &lt; 0.05), and motor skills (109.2 vs. 103.9, p &lt; 0.05) compared the control group. Similar results were being reported for other developmental scales including TTS and ASQ. Notably, the test scores of infants fed the investigational formula were similar to those who were breastfed. Our results indicate that PL and LCPUFA supplementation may be beneficial for neurodevelopment of infants throughout the first year of life. Further studies are needed to investigation long-term effects PL and LCPUFA on neurodevelopment in early life