New Optimal Binary Sequences with Period 4p4p via Interleaving Ding-Helleseth-Lam Sequences

Binary sequences with optimal autocorrelation play important roles in radar, communication, and cryptography. Finding new binary sequences with optimal autocorrelation has been an interesting research topic in sequence design. Ding-Helleseth-Lam sequences are such a class of binary sequences of period $p$, where $p$ is an odd prime with $p\equiv 1(\bmod~4)$. The objective of this letter is to present a construction of binary sequences of period $4p$ via interleaving four suitable Ding-Helleseth-Lam sequences. This construction generates new binary sequences with optimal autocorrelation which can not be produced by earlier ones

Semantics-Aligned Representation Learning for Person Re-identification

Person re-identification (reID) aims to match person images to retrieve the ones with the same identity. This is a challenging task, as the images to be matched are generally semantically misaligned due to the diversity of human poses and capture viewpoints, incompleteness of the visible bodies (due to occlusion), etc. In this paper, we propose a framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs. Specifically, we build a Semantics Aligning Network (SAN) which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder (SA-Dec) for reconstructing/regressing the densely semantics aligned full texture image. We jointly train the SAN under the supervisions of person re-identification and aligned texture generation. Moreover, at the decoder, besides the reconstruction loss, we add Triplet ReID constraints over the feature maps as the perceptual losses. The decoder is discarded in the inference and thus our scheme is computationally efficient. Ablation studies demonstrate the effectiveness of our design. We achieve the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person reID dataset Partial REID. Code for our proposed method is available at: https://github.com/microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), code has been release

Confounder Identification-free Causal Visual Feature Learning

Confounders in deep learning are in general detrimental to model's generalization where they infiltrate feature representations. Therefore, learning causal features that are free of interference from confounders is important. Most previous causal learning based approaches employ back-door criterion to mitigate the adverse effect of certain specific confounder, which require the explicit identification of confounder. However, in real scenarios, confounders are typically diverse and difficult to be identified. In this paper, we propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders. CICF models the interventions among different samples based on front-door criterion, and then approximates the global-scope intervening effect upon the instance-level interventions from the perspective of optimization. In this way, we aim to find a reliable optimization direction, which avoids the intervening effects of confounders, to learn causal features. Furthermore, we uncover the relation between CICF and the popular meta-learning strategy MAML, and provide an interpretation of why MAML works from the theoretical perspective of causal learning for the first time. Thanks to the effective learning of causal features, our CICF enables models to have superior generalization capability. Extensive experiments on domain generalization benchmark datasets demonstrate the effectiveness of our CICF, which achieves the state-of-the-art performance.Comment: 25 page

CCNF is a potential pancancer biomarker and immunotherapy target

BackgroundCCNF catalyzes the transfer of ubiquitin molecules from E2 ubiquitin-conjugating enzymes to target proteins, thereby regulating the G1/S or G2/M transition of tumor cells. Thus far, CCNF expression and its potential as a pancancer biomarker and immunotherapy target have not been reported.MethodsTCGA datasets and the R language were used to analyze the pancancer gene expression, protein expression, and methylation levels of CCNF; the relationship of CCNF expression with overall survival (OS), recurrence-free survival (RFS), immune matrix scores, sex and race; and the mechanisms for posttranscriptional regulation of CCNF.ResultsCCNF expression analysis showed that CCNF mRNA expression was higher in cancer tissues than in normal tissues in the BRCA, CHOL, COAD, ESCA, HNSC, LUAD, LUSC, READ, STAD, and UCEC; CCNF protein expression was also high in many cancer tissues, indicating that it could be an important predictive factor for OS and RFS. CCNF overexpression may be caused by CCNF hypomethylation. CCNF expression was also found to be significantly different between patients grouped based on sex and race. Overexpression of CCNF reduces immune and stromal cell infiltration in many cancers. Posttranscriptional regulation analysis showed that miR-98-5p negatively regulates the expression of the CCNF gene.ConclusionCCNF is overexpressed across cancers and is an adverse prognostic factor in terms of OS and RFS in many cancers; this phenomenon may be related to hypomethylation of the CCNF gene, which could lead to cancer progression and worsen prognosis. In addition, CCNF expression patterns were significantly different among patients grouped by sex and race. Its overexpression reduces immune and stromal cell infiltration. miR-98-5p negatively regulates CCNF gene expression. Hence, CCNF is a potential pancancer biomarker and immunotherapy target

EST analysis of gene expression in the tentacle of Cyanea capillata

AbstractJellyfish, Cyanea capillata, has an important position in head patterning and ion channel evolution, in addition to containing a rich source of toxins. In the present study, 2153 expressed sequence tags (ESTs) from the tentacle cDNA library of C. capillata were analyzed. The initial ESTs consisted of 198 clusters and 818 singletons, which revealed approximately 1016 unique genes in the data set. Among these sequences, we identified several genes related to head and foot patterning, voltage-dependent anion channel gene and genes related to biological activities of venom. Five kinds of proteinase inhibitor genes were found in jellyfish for the first time, and some of them were highly expressed with unknown functions

Soluble CD83 Alleviates Experimental Autoimmune Uveitis by Inhibiting Filamentous Actin-Dependent Calcium Release in Dendritic Cells

Soluble CD83 (sCD83) is the extracellular domain of the membrane-bound CD83 molecule, and known for its immunoregulatory functions. Whether and how sCD83 participates in the pathogenesis of uveitis, a serious inflammatory disease of the eye that can cause visual disability and blindness, is unknown. By flow cytometry and imaging studies, we show that sCD83 alleviates experimental autoimmune uveitis (EAU) through a novel mechanism. During onset and recovery of EAU, the level of sCD83 rises in the serum and aqueous humor, and CD83+ leukocytes infiltrate the inflamed eye. Systemic or topical application of sCD83 exerts a protective effect by decreasing inflammatory cytokine expression, reducing ocular and splenic leukocyte including CD4+ T cells and dendritic cells (DCs). Mechanistically, sCD83 induces tolerogenic DCs by decreasing the synaptic expression of co-stimulatory molecules and hampering the calcium response in DCs. These changes are caused by a disruption of the cytoskeletal rearrangements at the DC–T cell contact zone, leading to altered localization of calcium microdomains and suppressed T-cell activation. Thus, the ability of sCD83 to modulate DC-mediated inflammation in the eye could be harnessed to develop new immunosuppressive therapeutics for autoimmune uveitis

SIRT2 Maintains Genome Integrity and Suppresses Tumorigenesis Through Regulating APC/C Activity

Members of sirtuin family regulate multiple critical biological processes, yet their role in carcinogenesis remains controversial. To investigate the physiological functions of SIRT2 in development and tumorigenesis, we disrupted Sirt2 in mice. We demonstrated that SIRT2 regulates the anaphase-promoting complex/cyclosome activity through deacetylation of its coactivators, APC(CDH1) and CDC20. SIRT2 deficiency caused increased levels of mitotic regulators, including Aurora-A and -B that direct centrosome amplification, aneuploidy, and mitotic cell death. Sirt2-deficient mice develop gender-specific tumorigenesis, with females primarily developing mammary tumors, and males developing more hepatocellular carcinoma (HCC). Human breast cancers and HCC samples exhibited reduced SIRT2 levels compared with normal tissues. These data demonstrate that SIRT2 is a tumor suppressor through its role in regulating mitosis and genome integrity