950 research outputs found
Supplemental_Material - Thermoplastic and soluble co-polyimide resins fabricated via the incorporation of 2,3,3′,4′-biphenyltetracarboxylic dianhydride
No full textSupplemental_Material for Thermoplastic and soluble co-polyimide resins fabricated via the incorporation of 2,3,3′,4′-biphenyltetracarboxylic dianhydride by Jinfeng Hu, Jianhua Wang, Shengli Qi, Guofeng Tian and Dezhen Wu in High Performance Polymers</p
TVEG: Model Selection of the Time-Varying Exponential Family Distributions Graphical Models
No full textThe undirected graphical model, a popular class of statistical model, offers a way to describe and explain the relationships among a set of variables. However, it remains a challenge to choose a certain graphical model to explain the relationships of variables adequately, especially when the relationships of variables are rewiring over time. This paper proposes the Time-Varying Exponential Family Distributions Graphical (TVEG) models, with time-varying structures and exponential family node-wise conditional distributions. TVEG models extend the scope of available graph models and can be applied to time-varying and exponential family distribution observation data in reality. We propose the Temporally Smoothed L1-regularized exponential family graphical estimator (TSLEG), an estimator to infer the structure of TVEG from observations. We derive sufficient conditions for the TSLEG to recover the block partition and sparse pattern with high probability. We derive a message-passing optimization method to solve the TSLEG for time-varying Ising, Gaussian, exponential, and Poisson graphs based on the ADMM. The synthetic network simulations corroborate the theoretical analysis. Analysing of real data of stocks and the US Senate by the time-varying exponential model and Poisson model indicates the effectiveness and practicality of TVEG models
sj-docx-1-jet-10.1177_15266028231163733 – Supplemental material for Conservative Versus Endovascular Treatment for Spontaneous Isolated Superior Mesenteric Artery Dissection: A Clinical and Imaging Follow-up Study
No full textSupplemental material, sj-docx-1-jet-10.1177_15266028231163733 for Conservative Versus Endovascular Treatment for Spontaneous Isolated Superior Mesenteric Artery Dissection: A Clinical and Imaging Follow-up Study by Mengmeng Ye, Qingyun Zhou, Jiacheng Wu, Zheng Zhang, Bo Li, Tao Zheng and Guofeng Shao in Journal of Endovascular Therapy</p
Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding
No full textUnsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods. However, data augmentation is not ideal as it requires a careful selection of the type of augmentations to perform, which in turn can affect the geometric and semantic information learned by the network during self-training. To overcome this issue, we propose an augmentation-free unsupervised approach for point clouds to learn transferable point-level features via soft clustering, named SoftClu.
SoftClu assumes that the points belonging to a cluster should be close to each other in both geometric and feature spaces. This differs from typical contrastive learning, which builds similar representations for a whole point cloud and its augmented versions. We exploit the affiliation of points to their clusters as a proxy to enable self-training through a pseudo-label prediction task. Under the constraint that these pseudo-labels induce the equipartition of the point cloud, we cast SoftClu as an optimal transport problem. We formulate an unsupervised loss to minimize the standard cross-entropy between pseudo-labels and predicted labels. Experiments on downstream applications, such as 3D object classification, part segmentation, and semantic segmentation, show the effectiveness of our framework in outperforming state-of-the-art techniques
Unsupervised Point Cloud Representation Learning by Clustering and Neural Rendering
No full textData augmentation has contributed to the rapid advancement of unsupervised learning on 3D point clouds. However, we argue that data augmentation is not ideal, as it requires a careful application-dependent selection of the types of augmentations to be performed, thus potentially biasing the information learned by the network during self-training. Moreover, several unsupervised methods only focus on uni-modal information, thus potentially introducing challenges in the case of sparse and textureless point clouds. To address these issues, we propose an augmentation-free unsupervised approach for point clouds, named CluRender, to learn transferable point-level features by leveraging uni-modal information for soft clustering and cross-modal information for neural rendering. Soft clustering enables self-training through a pseudo-label prediction task, where the affiliation of points to their clusters is used as a proxy under the constraint that these pseudo-labels divide the point cloud into approximate equal partitions. This allows us to formulate a clustering loss to minimize the standard cross-entropy between pseudo and predicted labels. Neural rendering generates photorealistic renderings from various viewpoints to transfer photometric cues from 2D images to the features. The consistency between rendered and real images is then measured to form a fitting loss, combined with the cross-entropy loss to self-train networks. Experiments on downstream applications, including 3D object detection, semantic segmentation, classification, part segmentation, and few-shot learning, demonstrate the effectiveness of our framework in outperforming state-of-the-art techniques
Structure, function and regulation of drug/xenobiotic transporter
No full textOrganic anion transporters (OATs), as one group of the important Drug/xenobitic transporters, play vital roles in the body disposition of environmental toxins and clinically anionic drugs. Numerous works have forged an extensive framework on OATs. However, more works are required to explore the functionally critical amino acid residues and motifs to glean comprehensive information on relationship between structure and function. In the first part of the thesis, we investigated the role of dileucine (L6L7) at the amino terminus of hOAT1 and GXXXG motifs in its transmembrane domains 2 (G144XXXG148) and 5 (G223XXXG227) of hOAT1 in the expression and function of the transporter by using mutants made by site-directed mutagenesis approach. Mutant transporter L6A/L7A, G144A and G148A showed no transport activity due to its complete loss of surface expression. Proteasomal inhibitor and lysosomal inhibitor treatment suggested the mutant L6A/L7A- G144A- and G148A transporters were degraded through proteasomal pathway. Treatment of L6A/L7A- expressing cells with two chemical chaperones could not repair the misfolding of the mutant transporter in Endoplasmic Reticulum. For mutant transporters G223A and G227A, only G227 showed dramatic reduced transport activity due to dramatic loss in expression. Proteasomal or lysosomal inhibitors resulted in partial recovery of total cell expression of the mutant G227A hOAT1, but not recovery of surface expression and function. Our data suggest that the L6L7 and GXXXG motifs in transmembrane domains 2 and 5 play critical roles in the stability of hOAT1. Our lab has reported that activation of protein kinase C (PKC) leads to accelerated internalization of hOAT1. However the underlying mechanism is still unclear. In the second part of the thesis, we indentified that ubiquitination of hOAT1 was significantly increased after PKC activation by phorbol 12-myristate 13-acetate (PMA) and Angiotensin II (AngII). And the PKC-induced ubiquitination of a mutant N5KR carrying multiple lysine substitutions was abolished. Importantly, cell surface biotinylation experiments showed that the N5KR mutant which has the minimal ubiquitination counteracted against the enhanced retrieval and accelerated degradation of surface hOAT1 proteins by PKC activation, which established a strong correlation of PKC-dependent endocytosis and PKC-dependent ubiquitination of hOAT1.M.S.Includes bibliographical referencesby Jinwei W
Identification of Immune Gene Signature Associated with T Cells and Natural Killer Cells in Type 1 Diabetes [Corrigendum]
No full textWang N, Wang G, Feng X, Yang T. Diabetes Metab Syndr Obes. 2024;17:2983—2996.
The authors have advised the affiliation callouts in the author list on page 2983 are incorrect. The correct author callouts should read as follows:
Na Wang1, Guofeng Wang1,2, Xiuli Feng1, Teng Yang
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