LEDCNet: A Lightweight and Efficient Semantic Segmentation Algorithm Using Dual Context Module for Extracting Ground Objects from UAV Aerial Remote Sensing Images

Semantic segmentation for extracting ground objects, such as road and house, from UAV remote sensing images by deep learning becomes a more efficient and convenient method than traditional manual segmentation in surveying and mapping field. In recent years, with the deepening of layers and boosting of complexity, the number of parameters in convolution-based semantic segmentation neural networks considerably increases, which is obviously not conducive to the wide application especially in the industry. In order to make the model lightweight and improve the model accuracy, a new lightweight and efficient network for the extraction of ground objects from UAV remote sensing images, named LEDCNet, is proposed. The proposed network adopts an encoder-decoder architecture in which a powerful lightweight backbone network called LDCNet is developed as the encoder. We would extend the LDCNet become a new generation backbone network of lightweight semantic segmentation algorithms. In the decoder part, the dual multi-scale context modules which consist of the ASPP module and the OCR module are designed to capture more context information from feature maps of UAV remote sensing images. Between ASPP and OCR, a FPN module is used to and fuse multi-scale features extracting from ASPP. A private dataset of remote sensing images taken by UAV which contains 2431 training sets, 945 validation sets, and 475 test sets is constructed. The proposed model performs well on this dataset, with only 1.4M parameters and 5.48G FLOPs, achieving an mIoU of 71.12%. The more extensive experiments on the public LoveDA dataset and CITY-OSM dataset to further verify the effectiveness of the proposed model with excellent results on mIoU of 65.27% and 74.39%, respectively. All the experimental results show the proposed model can not only lighten the network with few parameters but also improve the segmentation performance.Comment: 11 page

Reconstruction of Cardiac Cine MRI under Free-breathing using Motion-guided Deformable Alignment and Multi-resolution Fusion

Objective: Cardiac cine magnetic resonance imaging (MRI) is one of the important means to assess cardiac functions and vascular abnormalities. However, due to cardiac beat, blood flow, or the patient's involuntary movement during the long acquisition, the reconstructed images are prone to motion artifacts that affect the clinical diagnosis. Therefore, accelerated cardiac cine MRI acquisition to achieve high-quality images is necessary for clinical practice. Approach: A novel end-to-end deep learning network is developed to improve cardiac cine MRI reconstruction under free breathing conditions. First, a U-Net is adopted to obtain the initial reconstructed images in k-space. Further to remove the motion artifacts, the Motion-Guided Deformable Alignment (MGDA) method with second-order bidirectional propagation is introduced to align the adjacent cine MRI frames by maximizing spatial-temporal information to alleviate motion artifacts. Finally, the Multi-Resolution Fusion (MRF) module is designed to correct the blur and artifacts generated from alignment operation and obtain the last high-quality reconstructed cardiac images. Main results: At an 8$\times$ acceleration rate, the numerical measurements on the ACDC dataset are SSIM of 78.40%$\pm$4.57%, PSNR of 30.46$\pm$1.22 dB, and NMSE of 0.0468$\pm$0.0075. On the ACMRI dataset, the results are SSIM of 87.65%$\pm$4.20%, PSNR of 30.04$\pm$1.18 dB, and NMSE of 0.0473$\pm$0.0072. Significance: The proposed method exhibits high-quality results with richer details and fewer artifacts for cardiac cine MRI reconstruction on different accelerations under free breathing conditions.Comment: 28 pages, 5 tables, 11 figure

Estimating Causal Effects using a Multi-task Deep Ensemble

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks.Comment: 18 pages, 7 figures, 3 tables, published at the 40th International Conference on Machine Learning (ICML 2023

Unraveling the role of VLDL in the relationship between type 2 diabetes and coronary atherosclerosis: a Mendelian randomization analysis

BackgroundThe causal link between Type 2 diabetes (T2D) and coronary atherosclerosis has been established through wet lab experiments; however, its analysis with Genome-wide association studies (GWAS) data remains unexplored. This study aims to validate this relationship using Mendelian randomization analysis and explore the potential mediation of VLDL in this mechanism.MethodsEmploying Mendelian randomization analysis, we investigated the causal connection between T2D and coronary atherosclerosis. We utilized GWAS summary statistics from European ancestry cohorts, comprising 23,363 coronary atherosclerosis patients and 195,429 controls, along with 32,469 T2D patients and 183,185 controls. VLDL levels, linked to SNPs, were considered as a potential mediating causal factor that might contribute to coronary atherosclerosis in the presence of T2D. We employed the inverse variance weighted (IVW), Egger regression (MR-Egger), weighted median, and weighted model methods for causal effect estimation. A leave-one-out sensitivity analysis was conducted to ensure robustness.ResultsOur Mendelian randomization analysis demonstrated a genetic association between T2D and an increased coronary atherosclerosis risk, with the IVW estimate at 1.13 [95% confidence interval (CI): 1.07–1.20]. Additionally, we observed a suggestive causal link between T2D and VLDL levels, as evidenced by the IVW estimate of 1.02 (95% CI: 0.98–1.07). Further supporting lipid involvement in coronary atherosclerosis pathogenesis, the IVW-Egger estimate was 1.30 (95% CI: 1.06–1.58).ConclusionIn conclusion, this study highlights the autonomous contributions of T2D and VLDL levels to coronary atherosclerosis development. T2D is linked to a 13.35% elevated risk of coronary atherosclerosis, and within T2D patients, VLDL concentration rises by 2.49%. Notably, each standard deviation increase in VLDL raises the likelihood of heart disease by 29.6%. This underscores the significant role of lipid regulation, particularly VLDL, as a mediating pathway in coronary atherosclerosis progression

EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography

This paper introduces the Efficient Decoupled Masked Autoencoder (EDMAE), a novel self-supervised method for recognizing standard views in pediatric echocardiography. EDMAE introduces a new proxy task based on the encoder-decoder structure. The EDMAE encoder is composed of a teacher and a student encoder. The teacher encoder extracts the potential representation of the masked image blocks, while the student encoder extracts the potential representation of the visible image blocks. The loss is calculated between the feature maps output by the two encoders to ensure consistency in the latent representations they extract. EDMAE uses pure convolution operations instead of the ViT structure in the MAE encoder. This improves training efficiency and convergence speed. EDMAE is pre-trained on a large-scale private dataset of pediatric echocardiography using self-supervised learning, and then fine-tuned for standard view recognition. The proposed method achieves high classification accuracy in 27 standard views of pediatric echocardiography. To further verify the effectiveness of the proposed method, the authors perform another downstream task of cardiac ultrasound segmentation on the public dataset CAMUS. The experimental results demonstrate that the proposed method outperforms some popular supervised and recent self-supervised methods, and is more competitive on different downstream tasks.Comment: 15 pages, 5 figures, 8 tables, Published in Biomedical Signal Processing and Contro

The role and characteristics of low-level jet during a persistent rainstorm in Guangxi

In 2022, Guangxi experienced the strongest Dragon Boat Precipitation since the founding of the People's Republic of China. The persistent rainstorm process from 17 June to 22 June 2022 had large accumulations and overlapping falling areas, leading to floods, torrential floods, landslides, and other disasters. The impacts of the low-level jet and its characteristics during this process were analyzed based on multi-source observation data and ERA5 reanalysis data. The results show that: (1) The low-level jet enhanced significantly during the night, and the convective system developed on the north side of the jet core. During the daytime, the low-level jet weakened and the convective system gradually disappeared, resulting in the heavy precipitation being mainly concentrated during the night. (2) During the night, the positive vorticity zone on the left of the 850 hPa low-level jet coincided with the exit zone of the 925 hPa boundary-level jet in the northeast of Guangxi, which combined with the mountain terrain barrier, and caused deep low-level convergence. The low-level convergence was conducive to the enhancement of the upward movement in northeast Guangxi and favored the continuous development of the convective system. At the same time, the convective instability in the lower atmosphere also increased rapidly, which provided a favorable unstable stratification environment for heavy precipitation. (3) The variation of the low-level jet can be well explained by the inertial oscillation mechanism. During the day, the surface heating in the central and southern regions of Guangxi led to the gradual enhancement of turbulent friction, resulting in jet deceleration with the characteristics of sub-geostrophic. During the night, the turbulent friction was weakened, and the jet accelerated, which gradually presented the characteristics of super-geostrophic. The Coriolis force acting on ageostrophic wind was the main contributor to the momentum of the jet, while friction dissipation and vertical transport were the momentum sinks

Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning

Purpose: Congenital heart defect (CHD) is the most common birth defect. Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. Materials and methods: We select two standard views of the atrial septum (subAS) and low parasternal four-compartment view (LPS4C) as the two views to identify ASD. We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). We propose an echocardiography video-based atrial septal defect diagnosis system. In our model, we present a block random selection, maximal agreement decision and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. Results: We validate our model using our private dataset by five-cross validation. For ASD detection, we achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and 81.99 F1 score. Conclusion: The proposed model is multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors

p38β MAPK mediates ULK1-dependent induction of autophagy in skeletal muscle of tumor-bearing mice

Muscle wasting is the key manifestation of cancer-associated cachexia, a lethal metabolic disorder seen in over 50% of cancer patients. Autophagy is activated in cachectic muscle of cancer hosts along with the ubiquitin-proteasome pathway (UPP), contributing to accelerated protein degradation and muscle wasting. However, established signaling mechanism that activates autophagy in response to fasting or denervation does not seem to mediate cancer-provoked autophagy in skeletal myocytes. Here, we show that p38β MAPK mediates autophagy activation in cachectic muscle of tumor-bearing mice via novel mechanisms. Complementary genetic and pharmacological manipulations reveal that activation of p38β MAPK, but not p38α MAPK, is necessary and sufficient for Lewis lung carcinoma (LLC)-induced autophagy activation in skeletal muscle cells. Particularly, muscle-specific knockout of p38β MAPK abrogates LLC tumor-induced activation of autophagy and UPP, sparing tumor-bearing mice from muscle wasting. Mechanistically, p38β MAPK-mediated activation of transcription factor C/EBPβ is required for LLC-induced autophagy activation, and upregulation of autophagy-related genes LC3b and Gabarapl1. Surprisingly, ULK1 activation (phosphorylation at S555) by cancer requires p38β MAPK, rather than AMPK. Activated ULK1 forms a complex with p38β MAPK in myocytes, which is markedly increased by a tumor burden. Overexpression of a constitutively active p38β MAPK in HEK293 cells increases phosphorylation at S555 and other amino acid residues of ULK1, but not several of AMPK-mediated sites. Finally, ULK1 activation is abrogated in tumor-bearing mice with muscle-specific knockout of p38β MAPK. Thus, p38β MAPK appears a key mediator of cancer-provoked autophagy activation, and a therapeutic target of cancer-induced muscle wasting