IL-10-differentiated dendritic cells treatment for Experimental Autoimmune Encephalomyelitis (EAE), a model of human Multiple Sclerosis

Multiple sclerosis is a chronic autoimmune neurological disease characterized by inflammatory cell infiltration and demyelination in the central nervous system (CNS). It is considered to be mediated by Th1 and Th17 immune responses. Experimental autoimmune encephalomyelitis (EAE) is widely used as a mouse model to study MS as it has features and histopathology similar to that of MS. Tolerogenic dendritic cells (DC) are reported to efficiently prevent sensitization for EAE. In this research, we induced tolerogenic DC (DC10) by differentiating them with IL-10. Compared to immature DC, DC10 did not show increased expression of MHC II or the co-stimulatory molecules CD40, CD80 and CD86, and produced low levels of pro-inflammatory cytokines IL-1â, IL-6, and IL-12 but higher levels of IL-10. This is consistent with their possessing a tolerogenic phenotype. We found that three intraperitoneal (i.p.) injections of DC10 successfully inhibited the signs of established, ongoing EAE: DC10 significantly reduced the clinical scores, demyelination and cell infiltration in the spinal cord, as well as the production of IL-4, IL-6, IL-10, IL-17 and IFN-ã by spleen and lymph node (LN) lymphocytes. DC10 treatments did not significantly affect inflammatory cytokine mRNA levels in the CNS. We found that there was higher FoxP3 expression in the CNS in response to DC10 treatments relative to PBS-treated animals. We also found that DC10 treatments significantly enhanced IgG1, IgG2a and IgG2b production and total spleen and LN lymphocyte proliferation following challenge with myelin oligodendrocyte glycoprotein (MOG) antigen. As far as we know, this is the first report showing the successful therapeutic treatment with tolerogenic DC10 of established EAE in mice

Probability-Dependent Gradient Decay in Large Margin Softmax

In the past few years, Softmax has become a common component in neural network frameworks. In this paper, a gradient decay hyperparameter is introduced in Softmax to control the probability-dependent gradient decay rate during training. By following the theoretical analysis and empirical results of a variety of model architectures trained on MNIST, CIFAR-10/100 and SVHN, we find that the generalization performance depends significantly on the gradient decay rate as the confidence probability rises, i.e., the gradient decreases convexly or concavely as the sample probability increases. Moreover, optimization with the small gradient decay shows a similar curriculum learning sequence where hard samples are in the spotlight only after easy samples are convinced sufficiently, and well-separated samples gain a higher gradient to reduce intra-class distance. Based on the analysis results, we can provide evidence that the large margin Softmax will affect the local Lipschitz constraint of the loss function by regulating the probability-dependent gradient decay rate. This paper provides a new perspective and understanding of the relationship among concepts of large margin Softmax, local Lipschitz constraint and curriculum learning by analyzing the gradient decay rate. Besides, we propose a warm-up strategy to dynamically adjust Softmax loss in training, where the gradient decay rate increases from over-small to speed up the convergence rate

Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives

Name ambiguity is common in academic digital libraries, such as multiple authors having the same name. This creates challenges for academic data management and analysis, thus name disambiguation becomes necessary. The procedure of name disambiguation is to divide publications with the same name into different groups, each group belonging to a unique author. A large amount of attribute information in publications makes traditional methods fall into the quagmire of feature selection. These methods always select attributes artificially and equally, which usually causes a negative impact on accuracy. The proposed method is mainly based on representation learning for heterogeneous networks and clustering and exploits the self-attention technology to solve the problem. The presentation of publications is a synthesis of structural and semantic representations. The structural representation is obtained by meta-path-based sampling and a skip-gram-based embedding method, and meta-path level attention is introduced to automatically learn the weight of each feature. The semantic representation is generated using NLP tools. Our proposal performs better in terms of name disambiguation accuracy compared with baselines and the ablation experiments demonstrate the improvement by feature selection and the meta-path level attention in our method. The experimental results show the superiority of our new method for capturing the most attributes from publications and reducing the impact of redundant information

Visualization study on operating performance of a dual compensation chamber loop heat pipe under acceleration condition

© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. https://creativecommons.org/licenses/by/4.0/In this article, a novel visual dual compensation chamber loop heat pipe (DCCLHP) under acceleration conditions was experimentally investigated. The working fluid was deionized water and the wick material was sintered nickel powder. Visual windows were installed on both compensation chambers (CCs) and condenser in order to observe the vapor and liquid distribution. The operating performance and physical mechanism of the proposed DCCLHP under both acceleration direction A and B at different heat loads and acceleration magnitudes were analysed in a systematic manner. Direction A refers the acceleration direction which was parallel to the axis of the evaporator and the CC without a bayonet placed at the outer edge of the rotating arm. While direction B is defined as the acceleration direction was perpendicular to the axis of the evaporator and the evaporator was placed at the outer edge of the rotating arm. In the current study, the heat load varies from 30 W to 130 W and the acceleration magnitude ranges from 1 g to 15 g. Experimental results revealed that: (i) The larger the heat load, the higher the operating temperature. Obviously waving of the vapor-liquid interface in the CC is observed at direction A. Bubbles generated in the CCs and the vapor-liquid interface moves back and forth in the condenser during temperature oscillation at both 70 W and 90 W for the case of 13 g and direction B. (ii) Under direction B, the DCCLHP presents lower operating temperature and higher thermal conductance. The maximum temperature is 143.2 °C at 5 g and 90 W under direction A. The maximum thermal conductance is 1.70 W/K at 13 g and 130 W under direction B. (iii) In general, the operating temperature shows a trend of decreasing first and then increasing with the increase of acceleration. Whereas the thermal conductance shows an opposite behavior. The transition acceleration, namely the acceleration magnitude at the minimum temperature, is 13 g for the case of direction A. However, under direction B, the large heat load can result in a large transition acceleration. (iv) Intermittent spattering of liquid drops is observed in the CCs at 70 W and 15 g under direction A. The flow pattern under direction A is different with that under direction B at each heat load. Multiple segments of the liquid and vapor phase alternately distribute and stratified flow forms in the condenser.Peer reviewe

Prediction of steady-state two-phase flow of nitrogen + extinguishant in the pipeline and a correlation for mass flow rate

© 2021 Springer Nature. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/s10973-021-10798-zNitrogen used for pressurization of a fire extinguisher could be partially dissolved in the fire extinguishing agent, forming a binary mixture accompanied by a phase change while flowing inside the pipeline. Notwithstanding the widespread use of fire extinguishing system, an effective method has never been considered to predict two-phase flow performance of nitrogen + extinguishant in the pipeline. This paper presents investigation of the steady-state two-phase flow of extinguishant in the pipeline, including C3HF7 (HFC227ea), CF3I, and C2HF5 (HFC125). The average viscosity of mixture was calculated using six quoted methods (VM-1 to VM-6). Subsequently, inspired by one-dimensional adiabatic isenthalpic flow of refrigerant in a capillary tube, the corresponding prediction models (STFM-1 to STFM-6) for large mass flux nitrogen + extinguishant in a fire extinguishing pipeline were developed based on the VM-1 to VM-6. In comparison with previous experimental and theoretical data, the applicability and accuracy of the proposed mathematical models was examined from two different aspects, mass flow rate and pressure drop. The results indicated that both models, STFM-2 and STFM-3, predicted accurately for mass flow rate, and STFM-2 model predicted accurately for pressure drop. Finally, new correlations for mass flow rate and pressure drop have been established accurately based on summarizing the relevant predicted data, respectively. This work contributes to a good theoretical approach on the analysis of two-phase flow of nitrogen + extinguishant.Peer reviewe

Single-Shot Object Detection with Enriched Semantics

We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module. The segmentation branch is supervised by weak segmentation ground-truth, i.e., no extra annotation is required. In conjunction with that, we employ a global activation module which learns relationship between channels and object classes in a self-supervised manner. Comprehensive experimental results on both PASCAL VOC and MS COCO detection datasets demonstrate the effectiveness of the proposed method. In particular, with a VGG16 based DES, we achieve an mAP of 81.7 on VOC2007 test and an mAP of 32.8 on COCO test-dev with an inference speed of 31.5 milliseconds per image on a Titan Xp GPU. With a lower resolution version, we achieve an mAP of 79.7 on VOC2007 with an inference speed of 13.0 milliseconds per image.This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216