Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks

Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical information and is often necessary for accurate quantitative analysis. However, high spatial resolution typically comes at the expense of longer scan time, less spatial coverage, and lower signal to noise ratio (SNR). Single Image Super-Resolution (SISR), a technique aimed to restore high-resolution (HR) details from one single low-resolution (LR) input image, has been improved dramatically by recent breakthroughs in deep learning. In this paper, we introduce a new neural network architecture, 3D Densely Connected Super-Resolution Networks (DCSRN) to restore HR features of structural brain MR images. Through experiments on a dataset with 1,113 subjects, we demonstrate that our network outperforms bicubic interpolation as well as other deep learning methods in restoring 4x resolution-reduced images.Comment: Accepted by ISBI'1

Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network

High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical information important for clinical application and quantitative image analysis. However, HR MRI conventionally comes at the cost of longer scan time, smaller spatial coverage, and lower signal-to-noise ratio (SNR). Recent studies have shown that single image super-resolution (SISR), a technique to recover HR details from one single low-resolution (LR) input image, could provide high-quality image details with the help of advanced deep convolutional neural networks (CNN). However, deep neural networks consume memory heavily and run slowly, especially in 3D settings. In this paper, we propose a novel 3D neural network design, namely a multi-level densely connected super-resolution network (mDCSRN) with generative adversarial network (GAN)-guided training. The mDCSRN quickly trains and inferences and the GAN promotes realistic output hardly distinguishable from original HR images. Our results from experiments on a dataset with 1,113 subjects show that our new architecture beats other popular deep learning methods in recovering 4x resolution-downgraded im-ages and runs 6x faster.Comment: 10 pages, 2 figures, 2 tables. MICCAI 201

Robust prognostic model based on immune infiltration-related genes and clinical information in ovarian cancer

Immune infiltration of ovarian cancer (OV) is a critical factor in determining patient's prognosis. Using data from TCGA and GTEx database combined with WGCNA and ESTIMATE methods, 46 genes related to OV occurrence and immune infiltration were identified. Lasso and multivariate Cox regression were applied to define a prognostic score (IGCI score) based on 3 immune genes and 3 types of clinical information. The IGCI score has been verified by K-M curves, ROC curves and C-index on test set. In test set, IGCI score (C-index = 0.630) is significantly better than AJCC stage (C-index = 0.541, p < 0.05) and CIN25 (C-index = 0.571, p < 0.05). In addition, we identified key mutations to analyse prognosis of patients and the process related to immunity. Chi-squared tests revealed that 6 mutations are significantly (p < 0.05) related to immune infiltration: BRCA1, ZNF462, VWF, RBAK, RB1 and ADGRV1. According to mutation survival analysis, we found 5 key mutations significantly related to patient prognosis (p < 0.05): CSMD3, FLG2, HMCN1, TOP2A and TRRAP. RB1 and CSMD3 mutations had small p-value (p < 0.1) in both chi-squared tests and survival analysis. The drug sensitivity analysis of key mutation showed when RB1 mutation occurs, the efficacy of six anti-tumour drugs has changed significantly (p < 0.05).Peer reviewe

Effect of APOE ɛ4 Status on Brain Amyloid-β and Cognitive Function in Amnestic and Nonamnestic Mild Cognitive Impairment: A 18F Florbetapir PET-CT Study

Mild cognitive impairment (MCI) is recognized as a predementia syndrome caused by multiple etiologies and nonmemory symptoms in MCI have recently gained increasing attention. However, the pattern of Aβ deposition and the effect of APOE (apolipoprotein E, APOE) ε4 on cognitive impairment in amnestic MCI (aMCI) and nonamnestic MCI (naMCI) patients has not been demonstrated. In this work, the amyloid-β (Aβ) load by [$^{18}$F]florbetapir PET imaging and cognitive performance is compared by comprehensive neuropsychological scales in participants with different MCI types or different APOE ε4 carriage status. According to the Aβ positivity and results of voxel-wise analysis, higher Aβ loads are observed in aMCI patients than naMCI patients, especially aMCI patients with APOE ε4. Additionally, it is observed that memory domain Z scores show a strong negative correlation with global florbetapir SUVR in the aMCI group (r = – 0.352, p < 0.001) but not in the naMCI group (r = –0.016, p = 0.924). Moreover, this correlation is independent of APOE e4 carriage status. This study aims to identify high-risk groups at an early stage of AD(Alzheimer's Disease, AD) through cognitive performance and APOE ε4 carrier status, which can be important for guiding clinical intervention trials

A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model

IntroductionThe identification and localization of tea picking points is a prerequisite for achieving automatic picking of famous tea. However, due to the similarity in color between tea buds and young leaves and old leaves, it is difficult for the human eye to accurately identify them.MethodsTo address the problem of segmentation, detection, and localization of tea picking points in the complex environment of mechanical picking of famous tea, this paper proposes a new model called the MDY7-3PTB model, which combines the high-precision segmentation capability of DeepLabv3+ and the rapid detection capability of YOLOv7. This model achieves the process of segmentation first, followed by detection and finally localization of tea buds, resulting in accurate identification of the tea bud picking point. This model replaced the DeepLabv3+ feature extraction network with the more lightweight MobileNetV2 network to improve the model computation speed. In addition, multiple attention mechanisms (CBAM) were fused into the feature extraction and ASPP modules to further optimize model performance. Moreover, to address the problem of class imbalance in the dataset, the Focal Loss function was used to correct data imbalance and improve segmentation, detection, and positioning accuracy.Results and discussionThe MDY7-3PTB model achieved a mean intersection over union (mIoU) of 86.61%, a mean pixel accuracy (mPA) of 93.01%, and a mean recall (mRecall) of 91.78% on the tea bud segmentation dataset, which performed better than usual segmentation models such as PSPNet, Unet, and DeeplabV3+. In terms of tea bud picking point recognition and positioning, the model achieved a mean average precision (mAP) of 93.52%, a weighted average of precision and recall (F1 score) of 93.17%, a precision of 97.27%, and a recall of 89.41%. This model showed significant improvements in all aspects compared to existing mainstream YOLO series detection models, with strong versatility and robustness. This method eliminates the influence of the background and directly detects the tea bud picking points with almost no missed detections, providing accurate two-dimensional coordinates for the tea bud picking points, with a positioning precision of 96.41%. This provides a strong theoretical basis for future tea bud picking

Ganoderma Lucidum Polysaccharide Peptide Alleviates Hepatoteatosis via Modulating Bile Acid Metabolism Dependent on FXR-SHP/FGF

Background/Aims: Non-alcoholic fatty liver disease (NAFLD) encompasses a series of pathologic changes ranging from steatosis to steatohepatitis, which may progress to cirrhosis and hepatocellular carcinoma. The purpose of this study was to determine whether ganoderma lucidum polysaccharide peptide (GLPP) has therapeutic effect on NAFLD. Methods: Ob/ ob mouse model and ApoC3 transgenic mouse model were used for exploring the effect of GLPP on NAFLD. Key metabolic pathways and enzymes were identified by metabolomics combining with KEGG and PIUmet analyses and key enzymes were detected by Western blot. Hepatosteatosis models of HepG2 cells and primary hepatocytes were used to further confirm the therapeutic effect of GLPP on NAFLD. Results: GLPP administrated for a month alleviated hepatosteatosis, dyslipidemia, liver dysfunction and liver insulin resistance. Pathways of glycerophospholipid metabolism, fatty acid metabolism and primary bile acid biosynthesis were involved in the therapeutic effect of GLPP on NAFLD. Detection of key enzymes revealed that GLPP reversed low expression of CYP7A1, CYP8B1, FXR, SHP and high expression of FGFR4 in ob/ob mice and ApoC3 mice. Besides, GLPP inhibited fatty acid synthesis by reducing the expression of SREBP1c, FAS and ACC via a FXR-SHP dependent mechanism. Additionally, GLPP reduced the accumulation of lipid droplets and the content of TG in HepG2 cells and primary hepatocytes induced by oleic acid and palmitic acid. Conclusion: GLPP significantly improves NAFLD via regulating bile acid synthesis dependent on FXR-SHP/FGF pathway, which finally inhibits fatty acid synthesis, indicating that GLPP might be developed as a therapeutic drug for NAFLD

Effect of carbon fiber crystallite size on the formation of hafnium carbide coating and the mechanism of the reaction of hafnium with carbon fibers

The effect of carbon source crystallite size on the formation of hafnium carbide (HfC) coating was investigated via direct reaction of hafnium powders with mesophase pitch-based carbon fibers (CFs) heat-treated at various temperatures. X-ray diffraction, scanning electron microscopy and energy dispersive X-ray spectroscopy analyses reveal that uniform and dense HfC coatings are preferentially formed on CFs containing larger and more ordered graphite crystallites. The carbide synthesis temperature and the sizes of crystallites in the CFs have a remarkable influence on the integrity and thickness of the coatings. The formation the HfC coatings can be attributed to the surface diffusion of hafnium and the bi-directional diffusion of hafnium and carbon sources inside the HfC coating. The reaction of HfC coated carbon fibers with zirconium powders leads to the growth of ZrC on the HfC coating and this has been shown to occur by the diffusion of carbon from the carbon fiber core through the carbide coating to its surface

Complex 3D microfluidic architectures formed by mechanically guided compressive buckling.

Microfluidic technologies have wide-ranging applications in chemical analysis systems, drug delivery platforms, and artificial vascular networks. This latter area is particularly relevant to 3D cell cultures, engineered tissues, and artificial organs, where volumetric capabilities in fluid distribution are essential. Existing schemes for fabricating 3D microfluidic structures are constrained in realizing desired layout designs, producing physiologically relevant microvascular structures, and/or integrating active electronic/optoelectronic/microelectromechanical components for sensing and actuation. This paper presents a guided assembly approach that bypasses these limitations to yield complex 3D microvascular structures from 2D precursors that exploit the full sophistication of 2D fabrication methods. The capabilities extend to feature sizes <5 μm, in extended arrays and with various embedded sensors and actuators, across wide ranges of overall dimensions, in a parallel, high-throughput process. Examples include 3D microvascular networks with sophisticated layouts, deterministically designed and constructed to expand the geometries and operating features of artificial vascular networks