RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion Recognition

Emotion recognition in conversation (ERC) has received increasing attention from researchers due to its wide range of applications. As conversation has a natural graph structure, numerous approaches used to model ERC based on graph convolutional networks (GCNs) have yielded significant results. However, the aggregation approach of traditional GCNs suffers from the node information redundancy problem, leading to node discriminant information loss. Additionally, single-layer GCNs lack the capacity to capture long-range contextual information from the graph. Furthermore, the majority of approaches are based on textual modality or stitching together different modalities, resulting in a weak ability to capture interactions between modalities. To address these problems, we present the relational bilevel aggregation graph convolutional network (RBA-GCN), which consists of three modules: the graph generation module (GGM), similarity-based cluster building module (SCBM) and bilevel aggregation module (BiAM). First, GGM constructs a novel graph to reduce the redundancy of target node information. Then, SCBM calculates the node similarity in the target node and its structural neighborhood, where noisy information with low similarity is filtered out to preserve the discriminant information of the node. Meanwhile, BiAM is a novel aggregation method that can preserve the information of nodes during the aggregation process. This module can construct the interaction between different modalities and capture long-range contextual information based on similarity clusters. On both the IEMOCAP and MELD datasets, the weighted average F1 score of RBA-GCN has a 2.17$\sim$5.21\% improvement over that of the most advanced method

Local minima in quantum systems

Finding ground states of quantum many-body systems is known to be hard for both classical and quantum computers. As a result, when Nature cools a quantum system in a low-temperature thermal bath, the ground state cannot always be found efficiently. Instead, Nature finds a local minimum of the energy. In this work, we study the problem of finding local minima in quantum systems under thermal perturbations. While local minima are much easier to find than ground states, we show that finding a local minimum is computationally hard for classical computers, even when the task is to output a single-qubit observable at any local minimum. In contrast, we prove that a quantum computer can always find a local minimum efficiently using a thermal gradient descent algorithm that mimics the cooling process in Nature. To establish the classical hardness of finding local minima, we consider a family of two-dimensional Hamiltonians such that any problem solvable by polynomial-time quantum algorithms can be reduced to finding ground states of these Hamiltonians. We prove that for such Hamiltonians, all local minima are global minima. Therefore, assuming quantum computation is more powerful than classical computation, finding local minima is classically hard and quantumly easy.Comment: 9+80 pages, 4 figure

Analysis of clinical outcomes in pediatric bacterial meningitis focusing on patients without cerebrospinal fluid pleocytosis

BackgroundCerebrospinal fluid (CSF) cell count and biochemical examinations and cultures form the basis for the diagnosis of bacterial meningitis. However, some patients do not have typical findings and are at a higher risk of being missed or having delayed treatment. To better understand the correlation between CSF results and outcomes, we evaluated CSF data focusing on the patients with atypical findings.MethodsThis study enrolled CSF culture-proven bacterial meningitis patients aged from 1 month to 18 years in a medical center. The patients were divided into “normal” and “abnormal” groups for each laboratory result and in combination. The correlations between the laboratory results and the outcomes were analyzed.ResultsA total of 175 children with confirmed bacterial meningitis were enrolled. In CSF examinations, 16.2% of patients had normal white blood cell counts, 29.5% had normal glucose levels, 24.5% had normal protein levels, 10.2% had normal results in two items, and 8.6% had normal results in all three items. In logistic regression analysis, a normal CSF leukocyte count and increased CSF protein level were related to poor outcomes. Patients with meningitis caused by Streptococcus pneumoniae and hyponatremia were at a higher risk of mortality and the development of sequelae.ConclusionsIn children with bacterial meningitis, nontypical CSF findings and, in particular, normal CSF leukocyte count and increased protein level may indicate a worse prognosis

Effects of Light Quality on the Chlorophyll Degradation Pathway in Rice Seedling Leaves

The objective of this study was to investigate the dynamics of chlorophyll (Chl), biosynthetic intermediates (protoporphyrin IX, magnesium protoporphyrin IX, and protochlorophyllide), degradation intermediates [chlorophyllide (Chlide), pheophytin (Phe), and pheophorbide (Pho)], and carotenoids (Car) in leaves of rice seedlings. Two rice varieties, 'Taichung Shen 10' ('TCS10') and 'IR1552', were grown under different light quality conditions controlled by light emitting diodes (LED). Lighting treatments for rice seedlings were included by red (R), blue (B), green (G), and red + blue (RB), with fluorescent lighting (FL) as the control and photosynthetic photon flux density being set at 105 µmol m-2 s-1. The results show that lower levels of Chl and Car in leaves were detected under G lighting, and light quality did not mediate porphyrins in biosynthetic pathways. Rice seedling leaves took Chl→Phe→Pho and Chl→Chlide→Pho as the major and minor degradation routes, respectively. Furthermore, higher Phe/Chlide ratios were observed under G and FL lighting conditions, indicating that green-enriched environments can up-regulate the minor degradation route in leaves

PlantPAN: Plant promoter analysis navigator, for identifying combinatorial cis-regulatory elements with distance constraint in plant gene groups

<p>Abstract</p> <p>Background</p> <p>The elucidation of transcriptional regulation in plant genes is important area of research for plant scientists, following the mapping of various plant genomes, such as <it>A. thaliana</it>, <it>O. sativa </it>and <it>Z. mays</it>. A variety of bioinformatic servers or databases of plant promoters have been established, although most have been focused only on annotating transcription factor binding sites in a single gene and have neglected some important regulatory elements (tandem repeats and CpG/CpNpG islands) in promoter regions. Additionally, the combinatorial interaction of transcription factors (TFs) is important in regulating the gene group that is associated with the same expression pattern. Therefore, a tool for detecting the co-regulation of transcription factors in a group of gene promoters is required.</p> <p>Results</p> <p>This study develops a database-assisted system, PlantPAN (Plant Promoter Analysis Navigator), for recognizing combinatorial <it>cis</it>-regulatory elements with a distance constraint in sets of plant genes. The system collects the plant transcription factor binding profiles from PLACE, TRANSFAC (public release 7.0), AGRIS, and JASPER databases and allows users to input a group of gene IDs or promoter sequences, enabling the co-occurrence of combinatorial transcription factor binding sites (TFBSs) within a defined distance (20 bp to 200 bp) to be identified. Furthermore, the new resource enables other regulatory features in a plant promoter, such as CpG/CpNpG islands and tandem repeats, to be displayed. The regulatory elements in the conserved regions of the promoters across homologous genes are detected and presented.</p> <p>Conclusion</p> <p>In addition to providing a user-friendly input/output interface, PlantPAN has numerous advantages in the analysis of a plant promoter. Several case studies have established the effectiveness of PlantPAN. This novel analytical resource is now freely available at <url>http://PlantPAN.mbc.nctu.edu.tw</url>.</p