Relationship between periodontal disease and coronary heart disease: A bibliometric analysis

Periodontal disease (PD) and coronary heart disease (CHD) are both prevalent diseases worldwide and cause patients physical and mental suffering and a global burden. Recent studies have suggested a link between PD and CHD, but there is less research in this field from the perspective of bibliometrics. This study aimed to quantitatively analyze the literature on PD and CHD to summarize intellectual bases, research hotspots, and emerging trends and pave the way for future research

A Survey on Intelligent Iterative Methods for Solving Sparse Linear Algebraic Equations

Efficiently solving sparse linear algebraic equations is an important research topic of numerical simulation. Commonly used approaches include direct methods and iterative methods. Compared with the direct methods, the iterative methods have lower computational complexity and memory consumption, and are thus often used to solve large-scale sparse linear equations. However, there are numerous iterative methods, parameters and components needed to be carefully chosen, and an inappropriate combination may eventually lead to an inefficient solution process in practice. With the development of deep learning, intelligent iterative methods become popular in these years, which can intelligently make a sufficiently good combination, optimize the parameters and components in accordance with the properties of the input matrix. This survey then reviews these intelligent iterative methods. To be clearer, we shall divide our discussion into three aspects: a method aspect, a component aspect and a parameter aspect. Moreover, we summarize the existing work and propose potential research directions that may deserve a deep investigation

AutoAMG(θ\theta): An Auto-tuned AMG Method Based on Deep Learning for Strong Threshold

Algebraic Multigrid (AMG) is one of the most used iterative algorithms for solving large sparse linear equations $Ax=b$. In AMG, the coarse grid is a key component that affects the efficiency of the algorithm, the construction of which relies on the strong threshold parameter $\theta$. This parameter is generally chosen empirically, with a default value in many current AMG solvers of 0.25 for 2D problems and 0.5 for 3D problems. However, for many practical problems, the quality of the coarse grid and the efficiency of the AMG algorithm are sensitive to $\theta$; the default value is rarely optimal, and sometimes is far from it. Therefore, how to choose a better $\theta$ is an important question. In this paper, we propose a deep learning based auto-tuning method, AutoAMG($\theta$) for multiscale sparse linear equations, which are widely used in practical problems. The method uses Graph Neural Networks (GNNs) to extract matrix features, and a Multilayer Perceptron (MLP) to build the mapping between matrix features and the optimal $\theta$, which can adaptively output $\theta$ values for different matrices. Numerical experiments show that AutoAMG($\theta$) can achieve significant speedup compared to the default $\theta$ value

LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images

Remote sensing images are known of having complex backgrounds, high intra-class variance and large variation of scales, which bring challenge to semantic segmentation. We present LoG-CAN, a multi-scale semantic segmentation network with a global class-aware (GCA) module and local class-aware (LCA) modules to remote sensing images. Specifically, the GCA module captures the global representations of class-wise context modeling to circumvent background interference; the LCA modules generate local class representations as intermediate aware elements, indirectly associating pixels with global class representations to reduce variance within a class; and a multi-scale architecture with GCA and LCA modules yields effective segmentation of objects at different scales via cascaded refinement and fusion of features. Through the evaluation on the ISPRS Vaihingen dataset and the ISPRS Potsdam dataset, experimental results indicate that LoG-CAN outperforms the state-of-the-art methods for general semantic segmentation, while significantly reducing network parameters and computation. Code is available at~\href{https://github.com/xwmaxwma/rssegmentation}{https://github.com/xwmaxwma/rssegmentation}.Comment: Accepted at ICASSP 202

Identification of Glycopeptides with Multiple Hydroxylysine O-Glycosylation Sites by Tandem Mass Spectrometry

Glycosylation is one of the most common post-translational modifications in proteins, existing in ∼50% of mammalian proteins. Several research groups have demonstrated that mass spectrometry is an efficient technique for glycopeptide identification; however, this problem is still challenging because of the enormous diversity of glycan structures and the microheterogeneity of glycans. In addition, a glycopeptide may contain multiple glycosylation sites, making the problem complex. Current software tools often fail to identify glycopeptides with multiple glycosylation sites, and hence we present GlycoMID, a graph-based spectral alignment algorithm that can identify glycopeptides with multiple hydroxylysine O-glycosylation sites by tandem mass spectra. GlycoMID was tested on mass spectrometry data sets of the bovine collagen α-(II) chain protein, and experimental results showed that it identified more glycopeptide-spectrum matches than other existing tools, including many glycopeptides with two glycosylation sites

Case report: Treatment-resistant depression with acute psychosis in an adolescent girl with Cushing's syndrome

Cushing's syndrome (CS) is a rare disease with multiple somatic signs and a high prevalence of co-occurring depression. However, the characteristics of depression secondary to CS and the differences from major depression have not been described in detail. In this case, we report a 17-year-old girl with treatment-resistant depression with a series of atypical features and acute psychotic episodes, which is a rare condition secondary to CS. This case showed a more detailed profile of depression secondary to CS and highlighted the differences with major depression in clinical features, and it will improve insight into the differential diagnosis especially when the symptoms are not typical

A unique iridium(III) complex-based chemosensor for multi-signal detection and multi-channel imaging of hypochlorous acid in liver injury

Although hypochlorous acid (HOCl) has long been associated with a number of inflammatory diseases in mammalian bodies, the functions of HOCl in specific organs at abnormal conditions, such as liver injury, remain unclear due to its high reactivity and the lack of effective methods for its detection. Herein, a unique Ir(III) complex-based chemosensor, Ir-Fc, was developed for highly sensitive and selective detection of HOCl. Ir-Fc was designed by incorporating a ferrocene (Fc) quencher to a Ir(III) complex through a HOCl-responsive linker. In the presence of HOCl, the fast cleavage of Fc moiety in less than 1s led to the enhancement of photoluminescence (PL) and electrochemical luminescence (ECL), by which the concentration of HOCl was determined by both PL and ECL analysis. Taking advantages of excellent properties of Ir(III) complexes, optical and electrochemical analyses of the response of Ir-Fc towards HOCl were fully investigated. Followed by the measurements of low cytotoxicity of Ir-Fc by MTT analysis, one-photon (OP), two-photon (TP) and lifetime imaging experiments were conducted to visualise the generation of HOCl in live microphage and HepG2 cells, and in zebrafish and mouse, respectively. Furthermore, the generation and distribution of HOCl in liver cells and liver injury of zebrafish and mouse were investigated. The results demonstrated the applicability of Ir-Fc as an effective chemosensor for imaging of HOCl generation in mitochondria of cells and liver injury in vivo, implying the potential of Ir-Fc for biomedical diagnosis and monitoring applications