Institution
Sichuan University
Education•Chengdu, China•
About: Sichuan University is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Population & Catalysis. The organization has 107623 authors who have published 102844 publications receiving 1612131 citations. The organization is also known as: Sìchuān Dàxué.
Topics: Population, Catalysis, Cancer, Adsorption, Randomized controlled trial
Papers published on a yearly basis
Papers
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TL;DR: It is demonstrated for the first time, to the knowledge, that widespread increased regional synchronous neural activity occurs after antipsychotic therapy, accompanied by decreased integration of function across widely distributed neural networks.
Abstract: Context: Most of what we know about antipsychotic drug effects is at the receptor level, distal from the neural system effects that mediate their clinical efficacy. Studying cerebral function in antipsychotic-naive patients with schizophrenia before and after pharmacotherapy can enhance understanding of the therapeutic mechanisms of these clinically effective treatments.
332 citations
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TL;DR: Hybrid graphene aerogels (HGA) consisting of graphene oxide (GO) and graphene nanoplatelets (GNP) were prepared and introduced into polyethylene glycol (PEG) via vacuum impregnation, aiming at obtaining composite phase change materials (PCMs) with high thermal conductivity, outstanding shape-stabilization, high energy storage density, commendable thermal repeatability and the ability to light-to-heat energy storage.
331 citations
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TL;DR: In this paper, the authors identify two distinct homozygous LSS missense mutations (W581R and G588S) in two families with extensive congenital cataracts.
Abstract: The human lens is comprised largely of crystallin proteins assembled into a highly ordered, interactive macro-structure essential for lens transparency and refractive index. Any disruption of intra- or inter-protein interactions will alter this delicate structure, exposing hydrophobic surfaces, with consequent protein aggregation and cataract formation. Cataracts are the most common cause of blindness worldwide, affecting tens of millions of people1, and currently the only treatment is surgical removal of cataractous lenses. The precise mechanisms by which lens proteins both prevent aggregation and maintain lens transparency are largely unknown. Lanosterol is an amphipathic molecule enriched in the lens. It is synthesized by lanosterol synthase (LSS) in a key cyclization reaction of a cholesterol synthesis pathway. Here we identify two distinct homozygous LSS missense mutations (W581R and G588S) in two families with extensive congenital cataracts. Both of these mutations affect highly conserved amino acid residues and impair key catalytic functions of LSS. Engineered expression of wild-type, but not mutant, LSS prevents intracellular protein aggregation of various cataract-causing mutant crystallins. Treatment by lanosterol, but not cholesterol, significantly decreased preformed protein aggregates both in vitro and in cell-transfection experiments. We further show that lanosterol treatment could reduce cataract severity and increase transparency in dissected rabbit cataractous lenses in vitro and cataract severity in vivo in dogs. Our study identifies lanosterol as a key molecule in the prevention of lens protein aggregation and points to a novel strategy for cataract prevention and treatment.
331 citations
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TL;DR: A noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noise is introduced and combined with an adaptive self-ensembling framework for training.
Abstract: Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noise, then propose a novel COVID-19 Pneumonia Lesion segmentation network (COPLE-Net) to better deal with the lesions with various scales and appearances. The noise-robust Dice loss and COPLE-Net are combined with an adaptive self-ensembling framework for training, where an Exponential Moving Average (EMA) of a student model is used as a teacher model that is adaptively updated by suppressing the contribution of the student to EMA when the student has a large training loss. The student model is also adaptive by learning from the teacher only when the teacher outperforms the student. Experimental results showed that: (1) our noise-robust Dice loss outperforms existing noise-robust loss functions, (2) the proposed COPLE-Net achieves higher performance than state-of-the-art image segmentation networks, and (3) our framework with adaptive self-ensembling significantly outperforms a standard training process and surpasses other noise-robust training approaches in the scenario of learning from noisy labels for COVID-19 pneumonia lesion segmentation.
331 citations
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TL;DR: The research and the outstanding results not only present a feasible access to nanostructured Co3O4 but also remind us of paying more attention to the simple synthetic methods without complex processes and sophisticated instruments.
Abstract: A 3D-nanonet structured cobalt-basic-carbonate precursor has been obtained by a facile, low cost and eco-friendly route under ambient temperature and pressure. After calcination in air, the as-prepared precursor was converted to a 3D-nanonet hollow structured Co3O4 with its original frame structure almost preserved. Encouragingly, by alternating experimental parameters (Table S1 in the Supporting Information), such as concentration of the starting reagents and calcination temperature, we got the optimized condition for the final product with desirable electrochemical performance (Figure S1 in the Supporting Information). The pseudocapacitive properties of the obtained Co3O4 were evaluated by cyclic voltammetry (CV), galvanostatic charge–discharge measurement and electrochemical impedance spectroscopy in 6.0 M KOH solution. At different scan rates of 5, 10, 20, and 30 mV s–1, the corresponding specific capacitances were 820, 755, 693, and 656 F g–1, respectively. The material also exhibited superior charge...
331 citations
Authors
Showing all 108474 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jie Zhang | 178 | 4857 | 221720 |
Robin M. Murray | 171 | 1539 | 116362 |
Xiang Zhang | 154 | 1733 | 117576 |
Rui Zhang | 151 | 2625 | 107917 |
Xiaoyuan Chen | 149 | 994 | 89870 |
Yi Yang | 143 | 2456 | 92268 |
Xinliang Feng | 134 | 721 | 73033 |
Chuan He | 130 | 584 | 66438 |
Lei Zhang | 130 | 2312 | 86950 |
Jian Zhou | 128 | 3007 | 91402 |
Shaobin Wang | 126 | 872 | 52463 |
Yi Xie | 126 | 745 | 62970 |
Pak C. Sham | 124 | 866 | 100601 |
Wei Chen | 122 | 1946 | 89460 |
Bo Wang | 119 | 2905 | 84863 |