FCS-HGNN: Flexible Multi-type Community Search in Heterogeneous Information Networks

Community Search (CS), a crucial task in network science, has attracted considerable interest owing to its prowess in unveiling personalized communities, thereby finding applications across diverse domains. Existing research primarily focuses on traditional homogeneous networks, which cannot be directly applied to heterogeneous information networks (HINs). However, existing research also has some limitations. For instance, either they solely focus on single-type or multi-type community search, which severely lacking flexibility, or they require users to specify meta-paths or predefined community structures, which poses significant challenges for users who are unfamiliar with community search and HINs. In this paper, we propose an innovative method, FCS-HGNN, that can flexibly identify either single-type or multi-type communities in HINs based on user preferences. We propose the heterogeneous information transformer to handle node heterogeneity, and the edge-semantic attention mechanism to address edge heterogeneity. This not only considers the varying contributions of edges when identifying different communities, but also expertly circumvents the challenges presented by meta-paths, thereby elegantly unifying the single-type and multi-type community search problems. Moreover, to enhance the applicability on large-scale graphs, we propose the neighbor sampling and depth-based heuristic search strategies, resulting in LS-FCS-HGNN. This algorithm significantly improves training and query efficiency while maintaining outstanding community effectiveness. We conducted extensive experiments on five real-world large-scale HINs, and the results demonstrated that the effectiveness and efficiency of our proposed method, which significantly outperforms state-of-the-art methods.Comment: 13 page

A web knowledge-driven multimodal retrieval method in computational social systems: unsupervised and robust graph convolutional hashing

Multimodal retrieval has received widespread consideration since it can commendably provide massive related data support for the development of computational social systems (CSSs). However, the existing works still face the following challenges: 1) rely on the tedious manual marking process when extended to CSS, which not only introduces subjective errors but also consumes abundant time and labor costs; 2) only using strongly aligned data for training, lacks concern for the adjacency information, which makes the poor robustness and semantic heterogeneity gap difficult to be effectively fit; and 3) mapping features into real-valued forms, which leads to the characteristics of high storage and low retrieval efficiency. To address these issues in turn, we have designed a multimodal retrieval framework based on web-knowledge-driven, called unsupervised and robust graph convolutional hashing (URGCH). The specific implementations are as follows: first, a “secondary semantic self-fusion” approach is proposed, which mainly extracts semantic-rich features through pretrained neural networks, constructs the joint semantic matrix through semantic fusion, and eliminates the process of manual marking; second, a “adaptive computing” approach is designed to construct enhanced semantic graph features through the knowledge-infused of neighborhoods and uses graph convolutional networks for knowledge fusion coding, which enables URGCH to sufficiently fit the semantic modality gap while obtaining satisfactory robustness features; Third, combined with hash learning, the multimodality data are mapped into the form of binary code, which reduces storage requirements and improves retrieval efficiency. Eventually, we perform plentiful experiments on the web dataset. The results evidence that URGCH exceeds other baselines about $1\%$ – $3.7\%$ in mean average precisions (MAPs), displays superior performance in all the aspects, and can meaningfully provide multimodal data retrieval services to CSS

Recent advances in arterial spin labeling perfusion MRI in patients with vascular cognitive impairment

Cognitive impairment (CI) is a major health concern in aging populations. It impairs patients’ independent life and may progress to dementia. Vascular cognitive impairment (VCI) encompasses all cerebrovascular pathologies that contribute to cognitive impairment (CI). Moreover, the majority of CI subtypes involve various aspects of vascular dysfunction. Recent research highlights the critical role of reduced cerebral blood flow (CBF) in the progress of VCI, and the detection of altered CBF may help to detect or even predict the onset of VCI. Arterial spin labeling (ASL) is a non-invasive, non-ionizing perfusion MRI technique for assessing CBF qualitatively and quantitatively. Recent methodological advances enabling improved signal-to-noise ratio (SNR) and data acquisition have led to an increase in the use of ASL to assess CBF in VCI patients. Combined with other imaging modalities and biomarkers, ASL has great potential for identifying early VCI and guiding prediction and prevention strategies. This review focuses on recent advances in ASL-based perfusion MRI for identifying patients at high risk of VCI

The origin of human pathogenicity and biological interactions in Chaetothyriales

Fungi in the order Chaetothyriales are renowned for their ability to cause human infections. Nevertheless, they are not regarded as primary pathogens, but rather as opportunists with a natural habitat in the environment. Extremotolerance is a major trend in the order, but quite diferent from black yeasts in Capnodiales which focus on endurance, an important additional parameter is advancing toxin management. In the ancestral ecology of rock colonization, the association with metabolite-producing lichens is signifcant. Ant-association, dealing with pheromones and repellents, is another mainstay in the order. The phylogenetically derived family, Herpotrichiellaceae, shows dual ecology in monoaromatic hydrocarbon assimilation and the ability to cause disease in humans and cold-blooded vertebrates. In this study, data on ecology, phylogeny, and genomics were collected and analyzed in order to support this hypothesis on the evolutionary route of the species of Chaetothyriales. Comparing the ribosomal tree with that of enzymes involved in toluene degradation, a signifcant expansion of cytochromes is observed and the toluene catabolism is found to be complete in some of the Herpotrichiellaceae. This might enhance human systemic infection. However, since most species have to be traumatically inoculated in order to cause disease, their invasive potential is categorized as opportunism. Only in chromoblastomycosis, true pathogenicity might be surmised. The criterion would be the possible escape of agents of vertebrate disease from the host, enabling dispersal of adapted genotypes to subsequent generations.info:eu-repo/semantics/publishedVersio

Discrimination of As Sources in Greenhouse Soils in Northeast China Based on Multivariate Statistics

The aims of this research were to determine the accumulation status of total As and discriminate the sources of As by the methods of multivariate statistics. Our results showed that total arsenic contents in top soils increased with cultivation duration, and had a significant positive correlation with cultivation years (R2=0.5192, P=0.009). Based on multivariate statistics, soil organic matters (SOM) and As in greenhouse soils may originate from the same source. It could be concluded that agrichemicals, especially chicken manures may cause As enrichment in greenhouse top soils

Detection of Yeast-like Symbionts in Brown Planthopper Reared on Different Resistant Rice Varieties Combining DGGE and Absolute Quantitative Real-Time PCR

The brown planthopper (BPH), Nilaparvata lugens, is a serious pest of rice throughout Asia. Yeast-like symbionts (YLS) are endosymbionts closely linked with the development of BPH and the adapted mechanism of BPH virulence to resistant plants. In this study, we used semi-quantitative DGGE and absolute quantitative real-time PCR (qPCR) to quantify the number of the three YLS strains (Ascomycetes symbionts, Pichia-like symbionts, and Candida-like symbionts) that typically infect BPH in the nymphal stages and in newly emerged female adults. The quantities of each of the three YLS assessed increased in tandem with the developing nymphal instar stages, peaking at the fourth instar stage, and then declined significantly at the fifth instar stage. However, the amount of YLS present recovered sharply within the emerging adult females. Additionally, we estimated the quantities of YLS for up to eight generations after their inoculation onto resistant cultivars (Mudgo, ASD7, and RH) to reassociate the dynamics of YLS with the fitness of BPH. The minimum number of each YLS was detected in the second generation and gradually increased from the third generation with regard to resistant rice varieties. In addition, the Ascomycetes symbionts of YLS were found to be the most abundant of the three YLS strains tested for all of the development stages of BPH