Integrating Social Circles and Network Representation Learning for Item Recommendation

With the increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms. More and more researchers utilize trust relationships of users to improve the performance of recommendation algorithms. However, most of existing social-network-based recommendation algorithms ignore the following problems: (1) In different domains, users tend to trust different friends. (2) the performance of recommendation algorithms is limited by the coarse-grained trust relationships. In this paper, we propose a novel recommendation algorithm that integrates social circles and network representation learning for item recommendation. Specifically, we first infer domain-specific social trust circles based on original users’ rating information and social network information. Next, we adopt network representation technique to embed domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained trust relationships between users. Finally, we integrate the fine-gained trust relationships into domain-specific matrix factorization model to learn latent user and item feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms

Bilateral-ViT For Robust Fovea Localization

The fovea is an important anatomical landmark of the retina. Detecting the location of the fovea is essential for the analysis of many retinal diseases. However, robust fovea localization remains a challenging problem, as the fovea region often appears fuzzy, and retina diseases may further obscure its appearance. This paper proposes a novel Vision Transformer (ViT) approach that integrates information both inside and outside the fovea region to achieve robust fovea localization. Our proposed network, named Bilateral-Vision-Transformer (Bilateral-ViT), consists of two network branches: a transformer-based main network branch for integrating global context across the entire fundus image and a vessel branch for explicitly incorporating the structure of blood vessels. The encoded features from both network branches are subsequently merged with a customized Multi-scale Feature Fusion (MFF) module. Our comprehensive experiments demonstrate that the proposed approach is significantly more robust for diseased images and establishes the new state of the arts using the Messidor and PALM datasets.Comment: This work has been accepted for oral presentation by ISBI202

Autophagy regulates the maturation of hematopoietic precursors in the embryo

An understanding of the mechanisms regulating embryonic hematopoietic stem cell (HSC) development would facilitate their regeneration. The aorta-gonad-mesonephros region is the site for HSC production from hemogenic endothelial cells (HEC). While several distinct regulators are involved in this process, it is not yet known whether macroautophagy (autophagy) plays a role in hematopoiesis in the pre-liver stage. Here, we show that different states of autophagy exist in hematopoietic precursors and correlate with hematopoietic potential based on the LC3-RFP-EGFP mouse model. Deficiency of autophagy-related gene 5 (Atg5) specifically in endothelial cells disrupts endothelial to hematopoietic transition (EHT), by blocking the autophagic process. Using combined approaches, including single-cell RNA-sequencing (scRNA-seq), we have confirmed that Atg5 deletion interrupts developmental temporal order of EHT to further affect the pre-HSC I maturation, and that autophagy influences hemogenic potential of HEC and the formation of pre-HSC I likely via the nucleolin pathway. These findings demonstrate a role for autophagy in the formation/maturation of hematopoietic precursors.</p

Say What You Are Looking At: An Attention-Based Interactive System for Autistic Children

Gaze-following is an effective way for intention understanding in human–robot interaction, which aims to follow the gaze of humans to estimate what object is being observed. Most of the existing methods require people and objects to appear in the same image. Due to the limitation in the view of the camera, these methods are not applicable in practice. To address this problem, we propose a method of gaze following that utilizes a geometric map for better estimation. With the help of the map, this method is competitive for cross-frame estimation. On the basis of this method, we propose a novel gaze-based image caption system, which has been studied for the first time. Our experiments demonstrate that the system follows the gaze and describes objects accurately. We believe that this system is competent for autistic children’s rehabilitation training, pension service robots, and other applications.</jats:p

SARS-CoV-2 N protein induced acute kidney injury in diabetic db/db mice is associated with a Mincle-dependent M1 macrophage activation

“Cytokine storm” is common in critically ill COVID-19 patients, however, mechanisms remain largely unknown. Here, we reported that overexpression of SARS-CoV-2 N protein in diabetic db/db mice significantly increased tubular death and the release of HMGB1, one of the damage-associated molecular patterns (DAMPs), to trigger M1 proinflammatory macrophage activation and production of IL-6, TNF-α, and MCP-1 via a Mincle-Syk/NF-κB-dependent mechanism. This was further confirmed in vitro that overexpression of SARS-CoV-2 N protein caused the release of HMGB1 from injured tubular cells under high AGE conditions, which resulted in M1 macrophage activation and production of proinflammatory cytokines via a Mincle-Syk/NF-κB-dependent mechanism. This was further evidenced by specifically silencing macrophage Mincle to block HMGB1-induced M1 macrophage activation and production of IL-6, TNF-α, and MCP-1 in vitro. Importantly, we also uncovered that treatment with quercetin largely improved SARS-CoV-2 N protein-induced AKI in db/db mice. Mechanistically, we found that quercetin treatment significantly inhibited the release of a DAMP molecule HMGB1 and inactivated M1 pro-inflammatory macrophage while promoting reparative M2 macrophage responses by suppressing Mincle-Syk/NF-κB signaling in vivo and in vitro. In conclusion, SARS-CoV-2 N protein-induced AKI in db/db mice is associated with Mincle-dependent M1 macrophage activation. Inhibition of this pathway may be a mechanism through which quercetin inhibits COVID-19-associated AKI

Influence of radiation on Hemarthria compressa's genetic variations

Using the material of Hemarthria compressa (L.F.) R.Br. cv. YA’AN, we carried out this research to study the influence of radiation on the genetic variation of plants. Genetic difference was analyzed with expressed sequence tag-simple sequence repeat (EST-SSR) molecular marker through the comparison of 60Co-γ radiation on H. compressa seed stems and original variety. By using 20 primer pairs, 176 polymerase chain reaction (PCR)-amplifications with clear and consistent bands were obtained. The results showed that 155 of 176 bands were polymorphic, which indicating an 88.07% polymorphism rate, and each pair of primers had 8.8 amplified bands on average; the amplitude of polymorphism information content was 0.4709–0.6952 with an average value 0.6081. The genetic similarity coefficient of H. compressa and its mutants ranged from 0.4318 to 0.8239 with an average of 0.6671. As a consequence, existence of genetic differences between the mutants and the basic material was proved.We gratefully acknowledge financial support from the Modern Agro-industry Technology Research System (CARS-34) and the Sichuan Province Breeding Research grant (2016NZ0098-11).Peer reviewe

The impact of investor sentiment for the U.S. stock market based on Fama-French 3-factor model

Particularly, it is difficult to accurately measure investor sentiment due to the inherent complexity and dynamic change. This paper tests the impact of investors’ behavior in the U.S. equity market. By using monthly data from February 2014 to December 2018, the impacts of investor sentiment are examined. Besides, Fama-French risk factors are investigated in a new multiple factor asset pricing model. Specifically, the investor sentiment is measured by six-variable composite index. Empirical results indicate that the investor sentiment is a composition of systemic risk. In this case, the Fama-French three factor model with investor sentiment factor can fully explains the return of stocks in the USA stock market. By comparing the trend of investor sentiment and market index, investor sentiment will affect asset pricing and market volatility, i.e., verifies the effectiveness of investor sentiment index in the U.S, stock market

Mobile Robot Path Planning Based on a Generalized Wavefront Algorithm

This study develops a generalized wavefront algorithm for conducting mobile robot path planning. The algorithm combines multiple target point sets, multilevel grid costs, logarithmic expansion around obstacles, and subsequent path optimization. The planning performances obtained with the proposed algorithm, the A∗ algorithm, and the rapidly exploring random tree (RRT) algorithm optimized using a Bézier curve are compared using simulations with different grid map environments comprising different numbers of obstacles with varying shapes. The results demonstrate that the generalized wavefront algorithm generates smooth and safe paths around obstacles that meet the required kinematic conditions associated with the actual maneuverability of mobile robots and significantly reduces the planned path length compared with the results obtained with the A∗ algorithm and the optimized RRT algorithm with a computation time acceptable for real-time applications. Therefore, the generated path is not only smooth and effective but also conforms to actual robot maneuverability in practical applications

Damage Localization of Piles Based on Complex Continuous Wavelet Transform: Numerical Example and Experimental Verification

A new signal processing method called complex continuous wavelet transform (CCWT) is introduced in this paper to localize pile damage because it clearly reveals inherent characteristics of response signals. In this method, CCWT is first performed on the response signal to obtain the wavelet coefficient matrix. The resultant coefficients are then employed to calculate phase angles at different frequency bands with an aim of pile damage localization. However, the CCWT method is only demonstrated via laboratory tests on pile specimens, and its application on actual piles has not been examined. Moreover, various factors such as pile-soil interaction need to be considered when the CCWT method is applied on actual piles. To address these issues, a numerical example of 3D finite element pile model followed by a parameter analysis and an experimental verification on an actual pile are investigated. The results demonstrate that the CCWT method is capable of localizing pile damage under different damage scenarios. However, there are still some interference points in the grayscale images of phase angles and the reduction of interference points needs to be addressed by mutual verification with other pile damage detection methods and engineering experience