Hy-DeFake: Hypergraph Neural Networks for Detecting Fake News in Online Social Networks

Nowadays social media is the primary platform for people to obtain news and share information. Combating online fake news has become an urgent task to reduce the damage it causes to society. Existing methods typically improve their fake news detection performances by utilizing textual auxiliary information (such as relevant retweets and comments) or simple structural information (i.e., graph construction). However, these methods face two challenges. First, an increasing number of users tend to directly forward the source news without adding comments, resulting in a lack of textual auxiliary information. Second, simple graphs are unable to extract complex relations beyond pairwise association in a social context. Given that real-world social networks are intricate and involve high-order relations, we argue that exploring beyond pairwise relations between news and users is crucial for fake news detection. Therefore, we propose constructing an attributed hypergraph to represent non-textual and high-order relations for user participation in news spreading. We also introduce a hypergraph neural network-based method called Hy-DeFake to overcome the challenges. Our proposed method captures semantic information from news content, credibility information from involved users, and high-order correlations between news and users to learn distinctive embeddings for fake news detection. The superiority of Hy-DeFake is demonstrated through experiments conducted on four widely-used datasets, and it is compared against six baselines using four evaluation metrics

Integrated photonics modular arithmetic processor

Integrated photonics computing has emerged as a promising approach to overcome the limitations of electronic processors in the post-Moore era, capitalizing on the superiority of photonic systems. However, present integrated photonics computing systems face challenges in achieving high-precision calculations, consequently limiting their potential applications, and their heavy reliance on analog-to-digital (AD) and digital-to-analog (DA) conversion interfaces undermines their performance. Here we propose an innovative photonic computing architecture featuring scalable calculation precision and a novel photonic conversion interface. By leveraging Residue Number System (RNS) theory, the high-precision calculation is decomposed into multiple low-precision modular arithmetic operations executed through optical phase manipulation. Those operations directly interact with the digital system via our proposed optical digital-to-phase converter (ODPC) and phase-to-digital converter (OPDC). Through experimental demonstrations, we showcase a calculation precision of 9 bits and verify the feasibility of the ODPC/OPDC photonic interface. This approach paves the path towards liberating photonic computing from the constraints imposed by limited precision and AD/DA converters.Comment: 23 pages, 9 figure

A Lightweight Reconstruction Network for Surface Defect Inspection

Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and $\mathit{L}1$ loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.Comment: Journal of Mathematical Imaging and Vision(JMIV

Depletion of DNMT3A Suppressed Cell Proliferation and Restored PTEN in Hepatocellular Carcinoma Cell

Promoter hypermethylation mediated by DNA methyltransferases (DNMTs) is the main reason for epigenetic inactivation of tumor suppressor genes (TSGs). Previous studies showed that DNMT1 and DNMT3B play an important role in CpG island methylation in tumorigenesis. Little is known about the role of DNMT3A in this process, especially in hepatocellular carcinoma (HCC). In the present study, increased DNMT3A expression in 3 out of 6 HCC cell lines and 16/25 (64%) HCC tissues implied that DNMT3A is involved in hepatocellular carcinogenesis. Depletion of DNMT3A in HCC cell line SMMC-7721 inhibited cell proliferation and decreased the colony formation (about 65%). Microarray data revealed that 153 genes were upregulated in DNMT3A knockdown cells and that almost 71% (109/153) of them contain CpG islands in their 5′ region. 13 of them including PTEN, a crucial tumor suppressor gene in HCC, are genes involved in cell cycle and cell proliferation. Demethylation of PTEN promoter was observed in DNMT3A-depleted cells implying that DNMT3A silenced PTEN via DNA methylation. These results provide insights into the mechanisms of DNMT3A to regulate TSGs by an epigenetic approach in HCC

Colorectal cancer screening with fecal occult blood test: A 22-year cohort study.

The aim of the present study was to investigate the efficacy of colorectal cancer (CRC) screening with a three-tier fecal occult blood test (FOBT) in the Chinese population. The study was performed between 1987 and 2008 at the Beijing Military General Hospital, in a cohort of army service males and females aged >50 years. Between 1987 and 2005, a three-tier screening program, comprising guaiac-based FOBTs (gFOBTs), followed by immunochemical FOBTs for positive guaiac test samples and then colonoscopy for positive immunochemical test subjects, was performed annually. The cohort was followed up until 2008. The cohort included 5,104 subjects, of which, 3,863 subjects participated in screening (screening group) and 1,241 did not (non-screening group). The two groups did not differ in age, gender or other major risk factors for colon cancer. Overall, 36 CRCs occurred in the screening group and 21 in the non-screening group. Compared with the non-screening group, the relative risk for the incidence and mortality of CRC was 0.51 [95% confidence interval (CI), 0.30-0.87] and 0.36 (95% CI, 0.18-0.71), respectively, in the screening group. The general sensitivity of this three-tier FOBT was 80.6% (95% CI, 65.3-91.1). Thus, annual screening using the three-tier FOBT program may reduce the CRC incidence and mortality rate

Mechanisms Of Intrinsic Epileptogenesis In Human Gelastic Seizures With Hypothalamic Hamartoma

Human hypothalamic hamartoma (HH) is a rare developmental malformation often characterized by gelastic seizures, which are refractory to medical therapy. Ictal EEG recordings from the HH have demonstrated that the epileptic source of gelastic seizures lies within the HH lesion itself. Recent advances in surgical techniques targeting HH have led to dramatic improvements in seizure control, which further supports the hypothesis that gelastic seizures originate within the HH. However, the basic cellular and molecular mechanisms of epileptogenesis in this subcortical lesion are poorly understood. Since 2003, Barrow Neurological Institute has maintained a multidisciplinary clinical program to evaluate and treat patients with HH. This program has provided a unique opportunity to investigate the basic mechanisms of epileptogenesis using surgically resected HH tissue. The first report on the electrophysiological properties of HH neurons was published in 2005. Since then, ongoing research has provided additional insights into the mechanisms by which HH generate seizure activity. In this review, we summarize this progress and propose a cellular model that suggests that GABA-mediated excitation contributes to epileptogenesis in HH lesions

Megadalton-Node Assembly by Binding of Skb1 to the Membrane Anchor Slf1

The plasma membrane contains both dynamic and static microdomains. Given the growing appreciation of cortical microdomains in cell biology, it is important to determine the organizational principles that underlie assembly of compartmentalized structures at the plasma membrane. The fission yeast plasma membrane is highly compartmentalized by distinct sets of cortical nodes, which control signaling for cell cycle progression and cytokinesis. The mitotic inhibitor Skb1 localizes to a set of cortical nodes that provide spatial control over signaling for entry into mitosis. However, it has been unclear whether these nodes contain other proteins and how they might be organized and tethered to the plasma membrane. Here we show that Skb1 forms nodes by interacting with the novel protein Slf1, which is a limiting factor for node formation in cells. Using quantitative fluorescence microscopy and in vitro assays, we demonstrate that Skb1-Slf1 nodes are megadalton structures that are anchored to the membrane by a lipid-binding region in the Slf1 C-terminus. We propose a mechanism for higher-order node formation by Skb1 and Slf1, with implications for macromolecular assemblies in diverse cell types