ICML - On the Number of Linear Regions of Convolutional Neural Networks

One fundamental problem in deep learning is understanding the outstanding performance of deep Neural Networks (NNs) in practice. One explanation for the superiority of NNs is that they can realize a large class of complicated functions, i.e., they have powerful expressivity. The expressivity of a ReLU NN can be quantified by the maximal number of linear regions it can separate its input space into. In this paper, we provide several mathematical results needed for studying the linear regions of CNNs, and use them to derive the maximal and average numbers of linear regions for one-layer ReLU CNNs. Furthermore, we obtain upper and lower bounds for the number of linear regions of multi-layer ReLU CNNs. Our results suggest that deeper CNNs have more powerful expressivity than their shallow counterparts, while CNNs have more expressivity than fully-connected NNs per parameter.Comment: International Conference on Machine Learning (ICML) 202

Codebook Configuration for 1-bit RIS-aided Systems Based on Implicit Neural Representations

Reconfigurable intelligent surfaces (RISs) have become one of the key technologies in 6G wireless communications. By configuring the reflection beamforming codebooks, RIS focuses signals on target receivers. In this paper, we investigate the codebook configuration for 1-bit RIS-aided systems. We propose a novel learning-based method built upon the advanced methodology of implicit neural representations. The proposed model learns a continuous and differentiable coordinate-to-codebook representation from samplings. Our method only requires the information of the user's coordinate and avoids the assumption of channel models. Moreover, we propose an encoding-decoding strategy to reduce the dimension of codebooks, and thus improve the learning efficiency of the proposed method. Experimental results on simulation and measured data demonstrated the remarkable advantages of the proposed method

Two variants on T2DM susceptible gene HHEX are associated with CRC risk in a Chinese population

Increasing amounts of evidence has demonstrated that T2DM (Type 2 Diabetes Mellitus) patients have increased susceptibility to CRC (colorectal cancer). As HHEX is a recognized susceptibility gene in T2DM, this work was focused on two SNPs in HHEX, rs1111875 and rs7923837, to study their association with CRC. T2DM patients without CRC (T2DM-only, n=300), T2DM with CRC (T2DM/CRC, n=135), cancer-free controls (Control, n=570), and CRC without T2DM (CRC-only, n=642) cases were enrolled. DNA samples were extracted from the peripheral blood leukocytes of the patients and sequenced by direct sequencing. The χ(2) test was used to compare categorical data. We found that in T2DM patients, rs1111875 but not the rs7923837 in HHEX gene was associated with the occurrence of CRC (p= 0.006). for rs1111875, TC/CC patients had an increased risk of CRC (p=0.019, OR=1.592, 95%CI=1.046-2.423). Moreover, our results also indicated that the two variants of HEEX gene could be risk factors for CRC in general population, independent on T2DM (p< 0.001 for rs1111875, p=0.001 for rs7923837). For rs1111875, increased risk of CRC was observed in TC or TC/CC than CC individuals (p<0.001, OR= 1.780, 95%CI= 1.385-2.287; p<0.001, OR= 1.695, 95%CI= 1.335-2.152). For rs7923837, increased CRC risk was observed in AG, GG, and AG/GG than AA individuals (p< 0.001, OR= 1.520, 95%CI= 1.200-1.924; p=0.036, OR= 1.739, 95%CI= 0.989-3.058; p< 0.001, OR= 1.540, 95%CI= 1.225-1.936). This finding highlights the potentially functional alteration with HHEX rs1111875 and rs7923837 polymorphisms may increase CRC susceptibility. Risk effects and the functional impact of these polymorphisms need further validation

Effects of tumor metabolic microenvironment on regulatory T cells

Recent studies have shown that on one hand, tumors need to obtain a sufficient energy supply, and on the other hand they must evade the body’s immune surveillance. Because of their metabolic reprogramming characteristics, tumors can modify the physicochemical properties of the microenvironment, which in turn affects the biological characteristics of the cells infiltrating them. Regulatory T cells (Tregs) are a subset of T cells that regulate immune responses in the body. They exist in large quantities in the tumor microenvironment and exert immunosuppressive effects. The main effect of tumor microenvironment on Tregs is to promote their differentiation, proliferation, secretion of immunosuppressive factors, and chemotactic recruitment to play a role in immunosuppression in tumor tissues. This review focuses on cell metabolism reprogramming and the most significant features of the tumor microenvironment relative to the functional effects on Tregs, highlighting our understanding of the mechanisms of tumor immune evasion and providing new directions for tumor immunotherapy

Enhancement of Canonical Wnt/β-Catenin Signaling Activity by HCV Core Protein Promotes Cell Growth of Hepatocellular Carcinoma Cells

BACKGROUND: The Hepatitis C virus (HCV) core protein has been implicated as a potential oncogene or a cofactor in HCV-related hepatocellular carcinoma (HCC), but the underlying mechanisms are unknown. Overactivation of the Wnt/β-catenin signaling is a major factor in oncogenesis of HCC. However, the pathogenesis of HCV core-associated Wnt/β-catenin activation remains to be further characterized. Therefore, we attempted to determine whether HCV core protein plays an important role in regulating Wnt/β-catenin signaling in HCC cells. METHODOLOGY: Wnt/β-catenin signaling activity was investigated in core-expressing hepatoma cells. Protein and gene expression were examined by Western blot, immunofluorescence staining, RT-qPCR, and reporter assay. PRINCIPAL FINDINGS: HCV core protein significantly enhances Tcf-dependent transcriptional activity induced by Wnt3A in HCC cell lines. Additionally, core protein increases and stabilizes β-catenin levels in hepatoma cell line Huh7 through inactivation of GSK-3β, which contributes to the up-regulation of downstream target genes, such as c-Myc, cyclin D1, WISP2 and CTGF. Also, core protein increases cell proliferation rate and promotes Wnt3A-induced tumor growth in the xenograft tumor model of human HCC. CONCLUSIONS/SIGNIFICANCE: HCV core protein enhances Wnt/β-catenin signaling activity, hence playing an important role in HCV-associated carcinogenesis

Multi-omics analysis reveals a molecular landscape of the early recurrence and early metastasis in pan-cancer

Cancer remains a formidable challenge in medicine due to its propensity for recurrence and metastasis, which can result in unfavorable treatment outcomes. This challenge is particularly acute for early-stage patients, who may experience recurrence and metastasis without timely detection. Here, we first analyzed the differences in clinical characteristics among the primary tumor, recurrent tumor, and metastatic tumor in different stages of cancer, which may be caused by the molecular level. Moreover, the importance of predicting early cancer recurrence and metastasis is emphasized by survival analyses. Next, we used a multi-omics approach to identify key molecular changes associated with early cancer recurrence and metastasis and discovered that early metastasis in cancer demonstrated a high degree of genomic and cellular heterogeneity. We performed statistical comparisons for each level of omics data including gene expression, mutation, copy number variation, immune cell infiltration, and cell status. Then, various analytical techniques, such as proportional hazard model and Fisher’s exact test, were used to identify specific genes or immune characteristics associated with early cancer recurrence and metastasis. For example, we observed that the overexpression of BPIFB1 and high initial B-cell infiltration levels are linked to early cancer recurrence, while the overexpression or amplification of ANKRD22 and LIPM, mutation of IGHA1 and MUC16, high fibroblast infiltration level, M1 polarization of macrophages, cellular status of DNA repair are all linked to early cancer metastasis. These findings have led us to construct classifiers, and the average area under the curve (AUC) of these classifiers was greater than 0.75 in The Cancer Genome Atlas (TCGA) cancer patients, confirming that the features we identified could be biomarkers for predicting recurrence and metastasis of early cancer. Finally, we identified specific early sensitive targets for targeted therapy and immune checkpoint inhibitor therapy. Once the biomarkers we identified changed, treatment-sensitive targets can be treated accordingly. Our study has comprehensively characterized the multi-omics characteristics and identified a panel of biomarkers of early cancer recurrence and metastasis. Overall, it provides a valuable resource for cancer recurrence and metastasis research and improves our understanding of the underlying mechanisms driving early cancer recurrence and metastasis