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Graph matching aims at finding the vertex correspondence between two unlabeled graphs that maximizes the total edge weight correlation. This amounts to solving a computationally intractable quadratic assignment problem. In this paper, we propose a new spectral method, graph matching by pairwise eigen-alignments (GRAMPA). Departing from prior spectral approaches that only compare top eigenvectors, or eigenvectors of the same order, GRAMPA first constructs a similarity matrix as a weighted sum of outer products between all pairs of eigenvectors of the two graphs, with weights given by a Cauchy kernel applied to the separation of the corresponding eigenvalues, then outputs a matching by a simple rounding procedure. The similarity matrix can also be interpreted as the solution to a regularized quadratic programming relaxation of the quadratic assignment problem. For the Gaussian Wigner model in which two complete graphs on n vertices have Gaussian edge weights with correlation coefficient [Formula omitted], we show that GRAMPA exactly recovers the correct vertex correspondence with high probability when [Formula omitted]. This matches the state of the art of polynomial-time algorithms and significantly improves over existing spectral methods which require [Formula omitted] to be polynomially small in n. The superiority of GRAMPA is also demonstrated on a variety of synthetic and real datasets, in terms of both statistical accuracy and computational efficiency. Universality results, including similar guarantees for dense and sparse Erdos-Rényi graphs, are deferred to a companion paper., Author(s): Zhou Fan [sup.1], Cheng Mao [sup.2], Yihong Wu [sup.1], Jiaming Xu [sup.3] Author Affiliations: (1) https://ror.org/03v76x132, grid.47100.32, 0000 0004 1936 8710, Department of Statistics and Data Science, Yale University, [...]

Anomalous Hall effect (AHE) sensor has attracted significant attention in recent years due to its simpler structure and better sensitivity as compared to semiconductor-based Hall sensors. However, up to now, most of the work has been focused on its basic functionalities, with very little attention given to optimizing its performance for practical applications. In this work, we systematically investigated how the lateral dimension affects the performance of CoFeB-based AHE sensors. By adjusting the sensor width while keeping the length and other parameters constant, it is found that although the output signal amplitude decreases with increasing the sensor width, the noise and power consumption also decrease simultaneously, leading to an overall enhancement of the sensor performances with a sensitivity, detectivity, and power efficiency of 8960 Ω/T, 205 nT / Hz @ 1 Hz , and 2009 V/WT, respectively. To illustrate the potential applications of AHE sensors, we demonstrate a proof-of-concept experiment to showcase the potential AHE sensor in pulse detection. [ABSTRACT FROM AUTHOR]

Dry–wet alternate constructed wetland is an effective method to remove nitrogen elements in the tail water of sewage treatment plant. According to the reaeration mechanism of wet–dry alternative constructed wetland, the denitrification effect and influence characteristics of the dry–wet alternate constructed wetland on the tail water of sewage treatment plant were studied under low temperature. The results showed that the removal rates of each nitrogen element in the constructed wetland reached 20–40% with the dry–wet alternate time at 10°C. Under the influence of dry–wet alternation of constructed wetland, dissolved oxygen (DO) was significantly correlated with the dry–wet alternation time (p < 0.05), and DO was correlated with each nitrogen element removal rate (p < 0.05), which means constructed wetlands use alternate dry and wet operation, which can significantly affect the content of DO and nitrogen removal efficiency in constructed wetlands, providing a technical basis for regulating the operation effect of constructed wetland ecosystems.

Objectives: The study investigates the role of Cell Division Cycle Associated (CDCA) genes in colorectal cancer (COAD) by analyzing their differential expression, epigenetic alterations, prognostic significance, and functional associations. Methodology: This study employed a detailed in silico and in vitro experiments-based methodology. Results: RT-qPCR assays reveal significantly elevated mRNA levels of CDCA2, CDCA3, CDCA4, CDCA5, CDCA7, and CDCA8 genes in COAD cell lines compared to controls. Bisulfite sequencing indicates reduced promoter methylation of CDCA gene promoters in COAD cell lines, suggesting an epigenetic regulatory mechanism. Analysis of large TCGA datasets confirms increased CDCA gene expression in COAD tissues. Survival analysis using cSurvival database demonstrates negative correlations between CDCA gene expression and patient overall survival. Additionally, Lasso regression-based models of CDCA genes predict survival outcomes in COAD patients. Investigating immune modulation, CDCA gene expression inversely correlates with immune cell infiltration and immune modulators. miRNA-mRNA network analysis identifies regulatory miRNAs targeting CDCA genes, validated by RT-qPCR showing up-regulation of has-mir-10a-5p and has-mir-20a-5p in COAD cell lines and tissues. Drug sensitivity analysis suggests resistance to specific drugs in COAD patients with elevated CDCA gene expression. Furthermore, CDCA gene expression correlates with crucial functional states in COAD, including "angiogenesis, apoptosis, differentiation, hypoxia, inflammation, and metastasis." Additional in vitro experiments revealed that CDCA2 and CDCA3 knockdown in SW480 and SW629 cells significantly reduced cell proliferation and colony formation while enhancing cell migration. Conclusion: Overall, the study elucidates the multifaceted role of CDCA genes in COAD progression, providing insights into potential diagnostic, prognostic, and therapeutic implications. [ABSTRACT FROM AUTHOR]

The color of coffee fruits is influenced by several factors, including cultivar, ripening stage, and metabolite composition. However, the metabolic accumulation of pigments and the molecular mechanisms underlying peel coloration during the ripening process of Coffea arabica L. remain relatively understudied. In this study, UPLC-MS/MS-based metabolomics and RNA sequencing (RNA-seq)-based transcriptomics were integrated to investigate the accumulation of anthocyanins and carotenoids in the peel of Coffea arabica at different ripening stages: green peel (GP), green-yellow peel (GYRP), red peel (RP), and red-purple peel (RPP). This integration aimed at elucidating the molecular mechanisms associated with these changes. A total of ten anthocyanins, six carotenoids, and thirty-five xanthophylls were identified throughout the ripening process. The results demonstrated a gradual decrease in the total carotenoid content in the peel with fruit maturation, while anthocyanin content increased significantly. Notably, the accumulation of specific anthocyanins was closely associated with the transition of peel colors from green to red. Integrated metabolomics and transcriptomics analyses identified the GYRP stage as critical for this color transition. A weighted gene co-expression network analysis (WGCNA) revealed that enzyme-coding genes such as 3AT, BZ1, and lcyE, along with transcription factors including MYB, NAC, and bHLH, which interact with PHD and SET TR, may regulate the biosynthesis of anthocyanins and carotenoids, thereby influencing peel pigmentation. These findings provide valuable insights into the molecular mechanisms underlying the accumulation of anthocyanins and carotenoids in Coffea arabica peel during fruit maturation. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) and 3D reconstruction have numerous applications for indoor ground wheeled robots such as floor sweeping and food delivery. To advance research in leveraging semantic information and multi-sensor data to enhance the performances of SLAM and 3D reconstruction in complex indoor scenes, we propose a novel and complex indoor dataset named CID-SIMS, where semantic annotated RGBD images, inertial measurement unit (IMU) measurements, and wheel odometer data are provided from a ground wheeled robot viewpoint. The dataset consists of 22 challenging sequences captured in nine different scenes including office building and apartment environments. Notably, our dataset achieves two significant breakthroughs. Firstly, semantic information and multi-sensor data are provided meanwhile for the first time. Secondly, GeoSLAM is utilized for the first time to generate ground truth trajectories and 3D point clouds within two-centimeter accuracy. With spatial-temporal synchronous ground truth trajectories and 3D point clouds, our dataset is capable of evaluating SLAM and 3D reconstruction algorithms in a unified global coordinate system. We evaluate state-of-the-art SLAM and 3D reconstruction approaches on our dataset, demonstrating that our benchmark is applicable. The dataset is publicly available on https://cid-sims.github.io. [ABSTRACT FROM AUTHOR]