Basis for reclassification of nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) shows broad differences in racial and geographical distribution, radiosensitivity, and a multifactorial etiology. This thesis aims to identify molecular biomarkers with potentially valuable for prognostic implications in NPC. This thesis involved a series of studies which were performed on cohorts of patients with NPC to investigate the genetic alterations, Epstein-Barr virus (EBV) infection, and gene expression profiles, and to assess their correlations with clinicopathological parameters and survival of NPC patients. The results indicated that overexpression of caveolin-1 (Cav-1) and extracellular matrix metalloproteinase inducer (EMMPRIN/CD147) in NPC were significantly associated with TNM stage, metastasis, and poor prognosis (Paper I). Loss of heterozygosity (LOH) on 9p21, 16q and 19q13 may be responsible for tumor aggression behavior and progression of NPC, with a possible interaction between allelic loss and EBV infection in the etiology of NPC (Paper II). EBV latent membrane protein (LMP) 1 overexpression was significantly correlated with p53 accumulation in NPC, CD8+ T cell infiltration, and matrix metalloproteinase (MMP) 9 overexpression in NPC cells. Moreover, plasma EBV DNA was detectable at a high frequency in primary NPC (96%). Higher plasma EBV-DNA levels were positively correlated with advanced TNM stages, lymph node metastasis, and NPC relapses (Papers III-V). Overexpression of LMP1 regulated the mTOR signaling pathway in NPC, possibly through phosphorylation of AKT/mammalian target of rapamycin (mTOR)/phospho-P70S6 kinase (P70S6K)/4EBP1. LMP1 expression was closely correlated with expression of p-mTOR, p-P70S6K and p-4EBP1 in NPC tumors, while expression levels of p-P70S6K, p-4EBP1 and LMP1 were significantly correlated with overall survival in NPC patients (Paper V). Paper VI presents a new molecular NPC-space vector modulation (SVM) classifier, which integrates sex and seven genes, including LMP1, CD147, Cav-1, p-P70S6K, MMP11, survivin, and secreted protein acidic and rich in cysteine (SPARC). This NPC-SVM classifier could refine the classification of NPC patients into high- and low-risk groups, which demonstrated significant differences in 5-year disease-specific survival (DSS) rates in a group of 411 validation patients (86.2% vs. 37.6%, p<0.001). Paper VII presents a new histological classification study, which was developed mainly on the basis of morphological characteristics and tumor cell differentiation. Of 3,839 tumors, 2,057 (53.6%) were histologically classified as undifferentiated epithelial cell carcinoma (UECC), 942 (24.5%) as undifferentiated mixed epithelial-sarcomatoid cell carcinoma (UESCC), 640 (16.7%) as undifferentiated sarcomatoid cell carcinoma (USCC), and 200 (5.2%) as squamous cell carcinoma (SCC). Based on the new histological classification system, the 5-year DSS rates were 76.4% for UECC, 66.0% for UESCC, 56.0% for USCC, and 32.7% for SCC. Stratified according to the new classification, patients with UECC and UESCC who received radiochemotherapy (RCT) showed better 5-year DSS rates than those who received radiotherapy (RT) alone. In summary, the results of these studies indicate that LOH, differentially-expressed genes, and EBV markers can act as prognostic biomarkers in NPC patients. The NPC-SVM classifier and the new proposed histopathological classification provide better discriminative prediction of NPC prognosis than the current WHO classification, as well as a means of monitoring the therapeutic efficacy of RCT and RT in advanced-stage NPC patients

Discriminative Block-Diagonal Representation Learning for Image Recognition

Existing block-diagonal representation studies mainly focuses on casting block-diagonal regularization on training data, while only little attention is dedicated to concurrently learning both block-diagonal representations of training and test data. In this paper, we propose a discriminative block-diagonal low-rank representation (BDLRR) method for recognition. In particular, the elaborate BDLRR is formulated as a joint optimization problem of shrinking the unfavorable representation from off-block-diagonal elements and strengthening the compact block-diagonal representation under the semisupervised framework of LRR. To this end, we first impose penalty constraints on the negative representation to eliminate the correlation between different classes such that the incoherence criterion of the extra-class representation is boosted. Moreover, a constructed subspace model is developed to enhance the self-expressive power of training samples and further build the representation bridge between the training and test samples, such that the coherence of the learned intraclass representation is consistently heightened. Finally, the resulting optimization problem is solved elegantly by employing an alternative optimization strategy, and a simple recognition algorithm on the learned representation is utilized for final prediction. Extensive experimental results demonstrate that the proposed method achieves superb recognition results on four face image data sets, three character data sets, and the 15 scene multicategories data set. It not only shows superior potential on image recognition but also outperforms the state-of-the-art methods

Disentangled Graph Social Recommendation

Social recommender systems have drawn a lot of attention in many online web services, because of the incorporation of social information between users in improving recommendation results. Despite the significant progress made by existing solutions, we argue that current methods fall short in two limitations: (1) Existing social-aware recommendation models only consider collaborative similarity between items, how to incorporate item-wise semantic relatedness is less explored in current recommendation paradigms. (2) Current social recommender systems neglect the entanglement of the latent factors over heterogeneous relations (e.g., social connections, user-item interactions). Learning the disentangled representations with relation heterogeneity poses great challenge for social recommendation. In this work, we design a Disentangled Graph Neural Network (DGNN) with the integration of latent memory units, which empowers DGNN to maintain factorized representations for heterogeneous types of user and item connections. Additionally, we devise new memory-augmented message propagation and aggregation schemes under the graph neural architecture, allowing us to recursively distill semantic relatedness into the representations of users and items in a fully automatic manner. Extensive experiments on three benchmark datasets verify the effectiveness of our model by achieving great improvement over state-of-the-art recommendation techniques. The source code is publicly available at: https://github.com/HKUDS/DGNN.Comment: Accepted by IEEE ICDE 202

Discriminative Elastic-Net Regularized Linear Regression

In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zeroone matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of theses methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available datasets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html

Programmable DNA-mediated multitasking processor

Because of DNA appealing features as perfect material, including minuscule size, defined structural repeat and rigidity, programmable DNA-mediated processing is a promising computing paradigm, which employs DNAs as information storing and processing substrates to tackle the computational problems. The massive parallelism of DNA hybridization exhibits transcendent potential to improve multitasking capabilities and yield a tremendous speed-up over the conventional electronic processors with stepwise signal cascade. As an example of multitasking capability, we present an in vitro programmable DNA-mediated optimal route planning processor as a functional unit embedded in contemporary navigation systems. The novel programmable DNA-mediated processor has several advantages over the existing silicon-mediated methods, such as conducting massive data storage and simultaneous processing via much fewer materials than conventional silicon devices

Duplex quantum communication through a spin chain

Data multiplexing within a quantum computer can allow for the simultaneous transfer of multiple streams of information over a shared medium thereby minimizing the number of channels needed for requisite data transmission. Here, we investigate a two-way quantum communication protocol using a spin chain placed in an external magnetic field. In our scheme, Alice and Bob each play the role of a sender and a receiver as two states cos({\theta}1/2)|0> + sin({\theta}1/2)exp(i{\phi}1)|1>, cos({\theta}2/2)|0> + sin({\theta}2/2)exp(i{\phi}2)|1> are transferred through one channel simultaneously. We find that the transmission fidelity at each end of a spin chain can usually be enhanced by the presence of a second party. This is an important result for establishing the viability of duplex quantum communication through spin chain networks.Comment: 5 pages, 4 figure

Plasmoid ejection and secondary current sheet generation from magnetic reconnection in laser-plasma interaction

Reconnection of the self-generated magnetic fields in laser-plasma interaction was first investigated experimentally by Nilson {\it et al.} [Phys. Rev. Lett. 97, 255001 (2006)] by shining two laser pulses a distance apart on a solid target layer. An elongated current sheet (CS) was observed in the plasma between the two laser spots. In order to more closely model magnetotail reconnection, here two side-by-side thin target layers, instead of a single one, are used. It is found that at one end of the elongated CS a fan-like electron outflow region including three well-collimated electron jets appears. The ($>1$ MeV) tail of the jet energy distribution exhibits a power-law scaling. The enhanced electron acceleration is attributed to the intense inductive electric field in the narrow electron dominated reconnection region, as well as additional acceleration as they are trapped inside the rapidly moving plasmoid formed in and ejected from the CS. The ejection also induces a secondary CS