Notes

Id associated with Center Genes For this Progression of Severe Kidney Harm simply by Weighted Gene Co-Expression Network Analysis.
3D symmetry detection is a fundamental problem in computer vision and graphics. Most prior works detect symmetry when the object model is fully known, few studies symmetry detection on objects with partial observation, such as single RGB-D images. Recent work addresses the problem of detecting symmetries from incomplete data with a deep neural network by leveraging the dense and accurate symmetry annotations. However, due to the tedious labeling process, full symmetry annotations are not always practically available. In this work, we present a 3D symmetry detection approach to detect symmetry from single-view RGB-D images without using symmetry supervision. The key idea is to train the network in a weakly-supervised learning manner to complete the shape based on the predicted symmetry such that the completed shape be similar to existing plausible shapes. To achieve this, we first propose a discriminative variational autoencoder to learn the shape prior in order to determine whether a 3D shape is plausible or not. Based on the learned shape prior, a symmetry detection network is present to predict symmetries that produce shapes with high shape plausibility when completed based on those symmetries. Moreover, to facilitate end-to-end network training and multiple symmetry detection, we introduce a new symmetry parametrization for the learning-based symmetry estimation of both reflectional and rotational symmetry. The proposed approach, coupled symmetry detection with shape completion, essentially learns the symmetry-aware shape prior, facilitating more accurate and robust symmetry detection. Experiments demonstrate that the proposed method is capable of detecting reflectional and rotational symmetries accurately, and shows good generality in challenging scenarios, such as objects with heavy occlusion and scanning noise. Moreover, it achieves state-of-the-art performance, improving the F1-score over the existing supervised learning method by 2%-11% on the ShapeNet and ScanNet datasets.Generating photo-realistic images from labels (e.g., semantic labels or sketch labels) is much more challenging than the general image-to-image translation task, mainly due to the large differences between extremely sparse labels and detail rich images. We propose a general framework Lab2Pix to tackle this issue from two aspects 1) how to extract useful information from the input; and 2) how to efficiently bridge the gap between the labels and images. Specifically, we propose a Double-Guided Normalization (DG-Norm) to use the input label for semantically guiding activations in normalization layers, and use global features with large receptive fields for differentiating the activations within the same semantic region. To efficiently generate the images, we further propose Label Guided Spatial Co-Attention (LSCA) to encourage the learning of incremental visual information using limited model parameters while storing the well-synthesized part in lower-level features. Accordingly, Hierarchical Perceptual Discriminators with Foreground Enhancement Masks are proposed to toughly work against the generator thus encouraging realistic image generation and a sharp enhancement loss is further introduced for high-quality sharp image generation. We instantiate our Lab2Pix for the task of label-to-image in both unpaired (Lab2Pix-V1) and paired settings (Lab2Pix-V2). Extensive experiments conducted on various datasets demonstrate that our method significantly outperforms state-of-the-art methods quantitatively and qualitatively in both settings.This work has aimed to synthesize less cytotoxic but antibacterial effective materials. Here we synthesized zinc, titanium nanoparticles based multishell hollow spheres (ZnO@TiO2 MSHS) via sequential template approach (STA) and studied their comparative antimicrobial activity with pure zinc and titanium nanoparticles (NPs). Various techniques have been used to explore the physico-chemical properties of the hybrid shells (ZnO@TiO2 MSHS). FTIR, XRD measurements approved the enhanced crystallinity of synthesized hybrid MSHS via STA technique constructed by ZnO, TiO2 NPs. The optical transmittance was enhanced 67.08% for ZnO@TiO2 MSHS where 50.59%, and 53.32% of pure ZnO, TiO2 NPs respectively. TEM images showed MSHS made up of zinc and titanium nanoparticles distributed evenly in the structure. The antibacterial activity has been studied and measured via MIZ confirmed that the ZnO@TiO2 multishell hollow spheres exhibit the antibacterial performance. On the other hand the cytotoxicity studies show the cell toxicity was decreased for ZnO@TiO2 MSHS than pure ZnO and TiO2 NPs. So it is recommended that ZnO@TiO2 multishell hollow spheres may be used as a safe and potential antibacterial agent in the field of food packaging, painting, drug delivery and other antibacterial applications.The Internet of Medical Things (IoMT) has risen to prominence as a possible backbone in the health sector, with the ability to improve quality of life by broadening user experience while enabling crucial solutions such as near real-time remote di- agnostics. However, privacy and security problems remain largely unresolved in the safety area. Various rule-based methods have been considered to recognize aberrant behaviors in IoMT and have demonstrated high accuracy of misbehavior detection appropriate for lightweight IoT devices. However, most of these solutions have privacy concerns, especially when giving context during misbe- havior analysis. Moreover, falsified or modified context generates a high percentage of false positives and, in some cases, causes a by-pass in misbehavior detection. Relying on the recent pow- erful consolidation of Blockchain and federated learning (FL), we propose an efficient privacy-preserving framework for secure mis- behavior detection in lightweight IoMT devices, particularly in the artificial pancreas system (APS). The proposed approach employs privacy-preserving bidirectional long-short term memory (BiLSTM) and augments the security through the integration of Blockchain technology based on Ethereum smart contract environment. Fur- thermore, the effectiveness of the proposed model is bench- marked empirically in terms of sustainable privacy preservation, commensurate incentive scheme with an untraceability feature, ex- haustiveness, and the compact results of a variant neural network approach. As a result, the proposed model has a 99.93% recall rate, showing that it can detect virtually all possible malicious events in the targeted use case. Furthermore, given an initial ether value of 100, the solution's average gas consumption and Ether spent are 84,456.5 and 0.03157625, respectively.Learning similarity is a key aspect in medical image analysis, particularly in recommendation systems or in uncovering the interpretation of anatomical data in images. Most existing methods learn such similarities in the embedding space over image sets using a single metric learner. Images, however, have a variety of object attributes such as color, shape, or artifacts. Encoding such attributes using a single metric learner is inadequate and may fail to generalize. Instead, multiple learners could focus on separate aspects of these attributes in subspaces of an overarching embedding. This, however, implies the number of learners to be found empirically for each new dataset. This work, Dynamic Subspace Learners, proposes to dynamically exploit multiple learners by removing the need of knowing apriori the number of learners and aggregating new subspace learners during training. Furthermore, the visual interpretability of such subspace learning is enforced by integrating an attention module into our method. This integrated attention mechanism provides a visual insight of discriminative image features that contribute to the clustering of image sets and a visual explanation of the embedding features. The benefits of our attention-based dynamic subspace learners are evaluated in the application of image clustering, image retrieval, and weakly supervised segmentation. Our method achieves competitive results with the performances of multiple learners baselines and significantly outperforms the classification network in terms of clustering and retrieval scores on three different public benchmark datasets. Moreover, our method also provides an attention map generated directly during inference to illustrate the visual interpretability of the embedding features. These attention maps offer a proxy-labels, which improves the segmentation accuracy up to 15% in Dice scores when compared to state-of-the-art interpretation techniques.Convolutional neural networks (CNNs) have been successfully applied in the computer-aided ultrasound diagnosis for breast cancer. Up to now, several CNN-based methods have been proposed. However, most of them consider tumor localization and classification as two separate steps, rather than performing them simultaneously. Besides, they suffer from the limited diagnosis information in the B-mode ultrasound (BUS) images. In this study, we develop a novel network ResNet-GAP that incorporates both localization and classification into a unified procedure. To enhance the performance of ResNet-GAP, we leverage stiffness information in the elastography ultrasound (EUS) modality by collaborative learning in the training stage. Specifically, a dual-channel ResNet-GAP network is developed, one channel for BUS and the other for EUS. In each channel, multiple class activity maps (CAMs) are generated using a series of convolutional kernels of different sizes. The multi-scale consistency of the CAMs in both channels are further considered in network optimization. Experiments on 264 patients in this study show that the newly developed ResNet-GAP achieves an accuracy of 88.6%, a sensitivity of 95.3%, a specificity of 84.6%, and an AUC of 93.6% on the classification task, and a 1.0NLF of 87.9% on the localization task, which is better than some state-of-the-art approaches.The functional magnetic resonance imaging (fMRI) at ultra-high field (UHF, [Formula see text]) is a powerful temporal acquisition method which promises to capture neuronal activities at submillimeter scale. But high-spatial-resolution fMRI still remains difficult, as the nuisance temporal noise which also grows with the main magnetic field strength. For decades, mainstream solutions in reducing motion-induced temporal noise include motion-correction algorithms in image post-processing as well as MR acquisition schemes in RF pulse sequence designs, however hardware-related studies have been rarely reported over the RF receive coil. In this study, we have proposed the intrinsic temporal performance model, which is specifically used for measuring coil-related intrinsic temporal SNR (tSNR*), and the intrinsic sensitivity variability and thermal noise variability have been proposed as model parameters. The intrinsic temporal performance of single-channel loops and array coils were evaluated using numerical electromagnetic simulations, and phantom experiments were designed to investigate the intrinsic thermal noise variability. It was observed that the achievable intrinsic tSNR* can be greatly lowered by ~90% even with 2 mm translational motion in the normal direction, suggesting the effect of RF receive coils in producing temporal noise. The proposed model provides a new perspective in optimizing coil designs and array coil temporal combination methods, which may offer a feasible means in achieving submillimeter resolutions at UHF. Moreover, model parameters from the intrinsic temporal performance model can be directly calculated based on single MRI acquisition, offering a practical performance metric for manufactures and customers in quality and assurance checks of RF receive coil products.
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