Spectral-Based Graph Neural Networks for Complementary Item Recommendation

Modeling complementary relationships greatly helps recommender systems to accurately and promptly recommend the subsequent items when one item is purchased. Unlike traditional similar relationships, items with complementary relationships may be purchased successively (such as iPhone and Airpods Pro), and they not only share relevance but also exhibit dissimilarity. Since the two attributes are opposites, modeling complementary relationships is challenging. Previous attempts to exploit these relationships have either ignored or oversimplified the dissimilarity attribute, resulting in ineffective modeling and an inability to balance the two attributes. Since Graph Neural Networks (GNNs) can capture the relevance and dissimilarity between nodes in the spectral domain, we can leverage spectral-based GNNs to effectively understand and model complementary relationships. In this study, we present a novel approach called Spectral-based Complementary Graph Neural Networks (SComGNN) that utilizes the spectral properties of complementary item graphs. We make the first observation that complementary relationships consist of low-frequency and mid-frequency components, corresponding to the relevance and dissimilarity attributes, respectively. Based on this spectral observation, we design spectral graph convolutional networks with low-pass and mid-pass filters to capture the low-frequency and mid-frequency components. Additionally, we propose a two-stage attention mechanism to adaptively integrate and balance the two attributes. Experimental results on four e-commerce datasets demonstrate the effectiveness of our model, with SComGNN significantly outperforming existing baseline models.Comment: Accepted by AAAI-2

Deep Semantic Graph Matching for Large-scale Outdoor Point Clouds Registration

Current point cloud registration methods are mainly based on local geometric information and usually ignore the semantic information contained in the scenes. In this paper, we treat the point cloud registration problem as a semantic instance matching and registration task, and propose a deep semantic graph matching method (DeepSGM) for large-scale outdoor point cloud registration. Firstly, the semantic categorical labels of 3D points are obtained using a semantic segmentation network. The adjacent points with the same category labels are then clustered together using the Euclidean clustering algorithm to obtain the semantic instances, which are represented by three kinds of attributes including spatial location information, semantic categorical information, and global geometric shape information. Secondly, the semantic adjacency graph is constructed based on the spatial adjacency relations of semantic instances. To fully explore the topological structures between semantic instances in the same scene and across different scenes, the spatial distribution features and the semantic categorical features are learned with graph convolutional networks, and the global geometric shape features are learned with a PointNet-like network. These three kinds of features are further enhanced with the self-attention and cross-attention mechanisms. Thirdly, the semantic instance matching is formulated as an optimal transport problem, and solved through an optimal matching layer. Finally, the geometric transformation matrix between two point clouds is first estimated by the SVD algorithm and then refined by the ICP algorithm. Experimental results conducted on the KITTI Odometry dataset demonstrate that the proposed method improves the registration performance and outperforms various state-of-the-art methods.Comment: 12 pages, 6 figure

CRISPR dynamics during the interaction between bacteria and phage in the first year of life

Gut microbiomes in infancy have a profound impact on health in adulthood. CRISPRs play an essential role in the interaction between bacteria and phages. However, the dynamics of CRISPRs in gut microbiomes during early life are poorly understood. In this study, using shotgun metagenomic sequencing data from 82 Swedish infants’ gut microbiomes, 1882 candidate CRISPRs were identified, and their dynamics were analysed. We found large-scale turnover of CRISPRs and their spacers during the first year of life. As well as changes in relative abundance of the bacteria containing CRISPR, acquisition, loss and mutation of spacers were observed within the same CRISPR array sampled over time. Accordingly, the inferred interaction network of bacteria and phage was distinct at different times. This research underpins CRISPR dynamics and their potential role in the interaction between bacteria and phage in early life

Observation and simulation study on the rapid intensification mechanism of Typhoon “Mekkhala” (2006)

Based on Doppler Weather radar observations and numerical simulations applying the Weather Research and Forecasting (WRF) system, this study focused on the rapid intensification (RI) of Typhoon “Mekkhala” (2006) in the inshore area in 2020. The simulated track of the typhoon relatively matched with the observation, with a slight eastward bias compared to the observed track. During the phase of RI, there was a slight weakening of vertical wind shear between 200–500 hPa. The temporary decrease in vertical wind shear became a favorable factor for the intensification of the typhoon. In general, vertical wind shear of the lower atmosphere is the key to supporting the RI of Typhoon Mekkhala. In the middle troposphere, the southward component of the vertical wind shear suddenly increases, indicates that the inflow of southern wind to the core of the typhoon had strengthened. Thus, the strengthening of the moisture transport by enhanced southern wind, contributed to the intensification of the typhoon. During the intensification of the typhoon, the low-level vorticity was significantly enhanced, and the high vorticity values expanded from the lower to higher troposphere. The vertical distribution of vorticity transformed from symmetry to asymmetry. The development of secondary circulation on both sides of the typhoon is a dynamic factor for intensification

CRISPR dynamics during the interaction between bacteria and phage in the first year of life

Gut microbiomes in infancy have a profound impact on health in adulthood. CRISPRs play an essential role in the interaction between bacteria and phages. However, the dynamics of CRISPRs in gut microbiomes during early life are poorly understood. In this study, using shotgun metagenomic sequencing data from 82 Swedish infants' gut microbiomes, 1882 candidate CRISPRs were identified, and their dynamics were analysed. We found large-scale turnover of CRISPRs and their spacers during the first year of life. As well as changes in relative abundance of the bacteria containing CRISPR, acquisition, loss and mutation of spacers were observed within the same CRISPR array sampled over time. Accordingly, the inferred interaction network of bacteria and phage was distinct at different times. This research underpins CRISPR dynamics and their potential role in the interaction between bacteria and phage in early life.</p

Symmetric Dense Inception Network for Simultaneous Cell Detection and Classification in Multiplex Immunohistochemistry Images

Deep-learning based automatic analysis of the multiplex immunohistochemistry (mIHC) enables distinct cell populations to be localized on a large scale, providing insights into disease biology and therapeutic targets. However, standard deep-learning pipelines performed cell detection and classification as two-stage tasks, which is computationally inefficient and faces challenges to incorporate neighbouring tissue context for determining the cell identity. To overcome these limitations and to obtain a more accurate mapping of cell phenotypes, we presented a symmetric dense inception neural network for detecting and classifying cells in mIHC slides simultaneously. The model was applied with a novel stop-gradient strategy and a loss function accounted for class imbalance. When evaluated on an ovarian cancer dataset containing 6 cell types, the model achieved an F1 score of 0.835 in cell detection, and a weighted F1-score of 0.867 in cell classification, which outperformed separate models trained on individual tasks by 1.9% and 3.8% respectively. Taken together, the proposed method boosts the learning efficiency and prediction accuracy of cell detection and classification by simultaneously learning from both tasks

Coordination of H2O2 on praseodymia nanorods and its application in sensing cholesterol

The advancement of functional nanomaterials has promoted the development of biomarker sensors underpinning promising analytical tools for a range of bioanalytes such as cholesterol. In this work, we established a light-on fluorescent probe for cholesterol in human serum by coordination of H2O2 on the surface of praseodymia nanorods (Pr6O11 NRs). The distinctive interactions of various nucleotides and H2O2 with praseodymia were examined, whereby good fluorescent quenching and recovery capability were observed. A highly sensitive and selective cholesterol detection was achieved in serum samples with a detection limit of 0.1 mu M and recovery of 97.2-101.3%, respectively, due to the high oxygen mobility of praseodymia. The result suggests strong potential for work towards a key probe for a portable clinical test system for cholesterol as well as other H2O2-deriving biomarkers, potentially addressing the ever-increasing demand for the prevention of cardiovascular disease. (C) 2022 Vietnam National University, Hanoi. Published by Elsevier B.V.This work was supported by the Natural Science Foundation of Shandong Province (Grant ZR2017LB028) , Key R&D Program of Shandong Province (Grant 2018GSF118032) , and Fundamental Research Funds for the Central Universities (Grant 18CX02125A) in China. The project with reference number of ENE2017-82451-C3-2-R from Ministry of Science, Innovation and Universities of Spain is also acknowledged. This work has been co-financed by the 2014-2020 ERDF Operational Programme and by the Department of Economy, Knowledge, Business and University of the Regional Government of Andalusia with reference number of FEDER-UCA18-107316

Surface-enhanced Raman Spectroscopy Facilitates the Detection of Microplastics &lt; 1 μm in the Environment

Micro- and nanoplastics are considered one of the top pollutants that threaten the environment, aquatic life and mammalian (including human) health. Unfortunately, the development of uncomplicated but reliable analytical methods that are sensitive to individual microplastic particles, with sizes smaller than 1 μm, remains incomplete. Here, we demonstrate the detection and identification of (single) micro- and nanoplastics, by using surface-enhanced Raman spectroscopy (SERS), with Klarite substrates. Klarite is an exceptional SERS substrate; it is shaped as a dense grid of inverted pyramidal cavities, made of gold. Numerical simulations demonstrate that these cavities (or pits) strongly focus incident light into intense hotspots. We show that Klarite has the potential to facilitate the detection and identification of synthesized and atmospheric/aquatic microplastic (single) particles, with sizes down to 360 nm. We find enhancement factors of up to two orders of magnitude for polystyrene analytes. In addition, we detect and identify microplastics with sizes down to 450 nm on Klarite, with samples extracted from ambient, airborne particles. Moreover, we demonstrate Raman mapping as a fast detection technique for sub-micron microplastic particles. The results show that SERS with Klarite is a facile technique that has the potential to detect and systematically measure nanoplastics in the environment. This research is an important step towards detecting nanoscale plastic particles that may cause toxic effects to mammalian and aquatic life when present in high concentrations

Susceptibility of acute myeloid leukemia cells to ferroptosis and evasion strategies

Acute myeloid leukemia (AML) is a highly aggressive hematologic malignancy with a 5-year survival rate of less than 30%. Continuous updating of diagnostic and therapeutic strategies has not been effective in improving the clinical benefit of AML. AML cells are prone to iron metabolism imbalance due to their unique pathological characteristics, and ferroptosis is a novel cell death mode that is dominated by three cellular biological processes: iron metabolism, oxidative stress and lipid metabolism. An in-depth exploration of the unique ferroptosis mechanism in AML can provide new insights for the diagnosis and treatment of this disease. This study summarizes recent studies on ferroptosis in AML cells and suggests that the metabolic characteristics, gene mutation patterns, and dependence on mitochondria of AML cells greatly increase their susceptibility to ferroptosis. In addition, this study suggests that AML cells can establish a variety of strategies to evade ferroptosis to maintain their survival during the process of occurrence and development, and summarizes the related drugs targeting ferroptosis pathway in AML treatment, which provides development directions for the subsequent mechanism research and clinical treatment of AML

Endogenous L-Carnosine Level in Diabetes Rat Cardiac Muscle

A novel method for quantitation of cardiac muscle carnosine levels using HPLC-UV is described. In this simple and reliable method, carnosine from the rat cardiac muscle and the internal standard, thymopentin, were extracted by protein precipitation with acetonitrile. The method was linear up to 60.96 μg·mL−1 for L-carnosine. The calibration curve was linear in concentration ranges from 0.5 to 60.96 μg·mL−1. The relative standard deviations obtained for intra- and interday precision were lower than 12% and the recoveries were higher than 90% for both carnosine and internal standard. We successfully applied this method to the analysis of endogenous carnosine in cardiac muscle of the diabetes rats and healthy control rats. The concentration of carnosine was significantly lower in the diabetes rats group, compared to that in the healthy control rats. These results support the usefulness of this method as a means of quantitating carnosine and illustrate the important role of L-carnosine in cardiac muscle