Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive cha...
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With the recent development of sequence-to-sequence framework, generation approach for short text conversation becomes attractive. Traditional sequence-to-sequence method for short text conversation often suffers from...
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Accumulating evidences have shown gut microbial dysbiosis in patients with bipolar disorder(BD),but little is known how the microbiota-gut-brain axis operates in *** this study,we aimed to characterize the gut microbi...
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Accumulating evidences have shown gut microbial dysbiosis in patients with bipolar disorder(BD),but little is known how the microbiota-gut-brain axis operates in *** this study,we aimed to characterize the gut microbiota with metagenomic sequencing and explored its linkage to tetratricopeptide repeat and ankyrin repeat containing 1(TRANK1),a risk gene of *** samples were obtained from 60 healthy individuals and 62 patients in acute depressive episodes,who received over one-month quetiapine treatment *** analysis of BD microbiota revealed specific alterations in microbial diversity and compositions and the impacts of quetiapine treatment on microbial *** neural oscillation in hippocampus was positively associated with the abundance of Clostridium *** transplanted with feces from depressed patients presented depression-like *** mRNA expressions of inflammatory cytokines,brain-derived neurotrophic factor,kynurenine aminotransferase Ⅱ,and TRANK1 were detected in mice hippocampus and prefrontal ***3.1(+) vector encoding human TRANK 1 was constructed to explore the impact of TRANK1 overexpression on neuronal morphology,which manifested as decreased dendritic spine density in primary cortical *** findings add new evidences to microbiota-gut-brain regulation in BD,indicating that microbiota is possibly involved in the neuropathogenesis of BD by modulating the expression of TRANK1.
Currently, reliable, robust and ready-to-use CT-based tools for prediction of COVID-19 progression are still lacking. To address this problem, we present DABC-Net, a novel deep learning (DL) tool that combines a 2D U-...
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This study investigated the positive effect of surface modification with ozone on the photocatalytic performance of anatase TiO2 with dominated（001） facets for toluene *** performance of photocatalyst was tested on ...
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This study investigated the positive effect of surface modification with ozone on the photocatalytic performance of anatase TiO2 with dominated（001） facets for toluene *** performance of photocatalyst was tested on a home-made volatile organic compounds degradation system. The ozone modification, toluene adsorption and degradation mechanism were established by a combination of various characterization methods, in situ diffuse reflectance infrared fourier transform spectroscopy, and density functional theory *** surface modification with ozone can significantly enhance the photocatalytic degradation performance for toluene. The abundant unsaturated coordinated 5 c-Ti sites on（001）facets act as the adsorption sites for ozone. The formed Ti–O bonds reacted with H2O to generate a large amount of isolated Ti5 c-OH which act as the adsorption sites for toluene,and thus significantly increase the adsorption capacity for toluene. The outstanding photocatalytic performance of ozone-modified TiO2 is due to its high adsorption ability for toluene and the abundant surface hydroxyl groups, which produce very reactive OH·radicals under irradiation. Furthermore, the O2 generated via ozone dissociation could combine with the photogenerated electrons to form superoxide radicals which are also conductive to the toluene degradation.
Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow i...
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Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn
In recent years, scene parsing has captured increasing attention in computer vision. Previous works have demonstrated promising performance in this task. However, they mainly utilize holistic features, whilst neglecti...
In recent years, scene parsing has captured increasing attention in computer vision. Previous works have demonstrated promising performance in this task. However, they mainly utilize holistic features, whilst neglecting the rich semantic knowledge and inter-object relationships in the scene. In addition, these methods usually require a large number of pixel-level annotations, which is too expensive in practice. In this paper, we propose a novel Knowledge Embedded Generative Adversarial Networks, dubbed as KE-GAN, to tackle the challenging problem in a semi-supervised fashion. KE-GAN captures semantic consistencies of different categories by devising a Knowledge Graph from the large-scale text corpus. In addition to readily-available unlabeled data, we generate synthetic images to unveil rich structural information underlying the images. Moreover, a pyramid architecture is incorporated into the discriminator to acquire multi-scale contextual information for better parsing results. Extensive experimental results on four standard benchmarks demonstrate that KE-GAN is capable of improving semantic consistencies and learning better representations for scene parsing, resulting in the state-of-the-art performance.
The BRAF gene is an important signaling molecule in human cells that is involved in the regulation of cell growth,differentiation,and *** the BRAF gene mutates,it can lead to abnormal activation of the signaling pathw...
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The BRAF gene is an important signaling molecule in human cells that is involved in the regulation of cell growth,differentiation,and *** the BRAF gene mutates,it can lead to abnormal activation of the signaling pathway,which promotes cell proliferation,inhibits cell apoptosis,and ultimately contributes to the occurrence and development of *** mutations are widely present in various cancers,including malignant melanoma,thyroid cancer,colorectal cancer,non-small cell lung cancer,and hairy cell leukemia,among *** is an important target for the treatment of various solid tumors,and targeted combination therapies,represented by BRAF inhibitors,have become one of the main treatment modalities for a variety of BRAF-mutation-positive solid tumors.
Despite the great success achieved by supervised fully convolutional models in semantic segmentation, training the models requires a large amount of labor-intensive work to generate pixel-level annotations. Recent wor...
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ISBN:
(数字)9781728148038
ISBN:
(纸本)9781728148045
Despite the great success achieved by supervised fully convolutional models in semantic segmentation, training the models requires a large amount of labor-intensive work to generate pixel-level annotations. Recent works exploit synthetic data to train the model for semantic segmentation, but the domain adaptation between real and synthetic images remains a challenging problem. In this work, we propose a Separated Semantic Feature based domain adaptation network, named SSF-DAN, for semantic segmentation. First, a Semantic-wise Separable Discriminator (SS-D) is designed to independently adapt semantic features across the target and source domains, which addresses the inconsistent adaptation issue in the class-wise adversarial learning. In SS-D, a progressive confidence strategy is included to achieve a more reliable separation. Then, an efficient Class-wise Adversarial loss Reweighting module (CA-R) is introduced to balance the class-wise adversarial learning process, which leads the generator to focus more on poorly adapted classes. The presented framework demonstrates robust performance, superior to state-of-the-art methods on benchmark datasets.
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