Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding

The problem of representing nodes in a signed network as low-dimensional vectors, known as signed network embedding (SNE), has garnered considerable attention in recent years. While several SNE methods based on graph convolutional networks (GCN) have been proposed for this problem, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real-world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which corrects for incorrect embedding propagation in GCN by utilizing the trustworthiness on edge signs for high-order relationships inferred by the balance theory. The proposed approach consists of three modules: (M1) generation of each node's extended ego-network; (M2) measurement of trustworthiness on edge signs; and (M3) trustworthiness-aware propagation of embeddings. Furthermore, TrustSGCN learns the node embeddings by leveraging two well-known societal theories, i.e., balance and status. The experiments on four real-world signed network datasets demonstrate that TrustSGCN consistently outperforms five state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN.Comment: 12 pages, 8 figures, 9 table

COMPARISON OF VOLATILE COMPOSITION OF SUPERCRITICAL CARBON DIOXIDE EXTRACT FROM RHIZOMES OF KOREAN MEDICINAL PLANT 'CHUN-KUNG' (CNIDIUM OFFICINALE MAKINO) BY DIRECT-AND SPME-GC/MS

Objective: The main purpose of this study was to evaluate the volatile composition of supercritical fluid extract (SFE) obtained from Cnidium officinale Makino rhizomes. Methods: GC/MS analyses were carried out with the direct- and solid phase microextraction (SPME) of SFE obtained from rhizomes. In addition, GC/MS analysis was performed for the rhizomes of C. officinale using SPME. Results: SPME-GC/MS analysis of the rhizomes revealed the separation of 23 components. Among these, β-phellandrene (20.38%), dictyotene (12.98%), β-pinene (10.59%), β-selinene (9.45%), eugenol (7.71%) and β-farnesene (7.09%) were found to be major components. In the SFE analyzed by direct-GC/MS, linoleic acid (19.26%), 2-methoxy-4-vinylphenol (18.98%), hexadecanoic acid (12.15%), and β-selinene (9.44%) were identified as major components. Whereas, 3,4-dihydrocoumarin (16.94%), shyobunone (14.07%), dictyotene (10.65%), p-cresol (10.17%), zierone (6.36%) and umbellulone (5.71%) were major components in the SFE analyzed by SPME-GC/MS. Conclusion: The present study clearly suggested that the SPME-GC/MS analysis of SFE provided the separation of more number with diverse groups of compounds than the direct-GC/MS

A carbon nanotubes-silicon nanoparticles network for high performance lithium rechargeable battery anodes

As an effort to address the chronic capacity fading of Si anodes and thus achieve their robust cycling performance, herein, we develop a unique electrode in which silicon nanoparticles are embedded in the carbon nanotubes network. Utilizing robust contacts between silicon nanoparticles and carbon nanotubes, the composite electrodes exhibit excellent electrochemical performance : 95.5% capacity retention after 140 cycles as well as rate capability such that at the C-rate increase from 0.1C to 1C to 10C, the specific capacities of 850, 698, and 312 mAh/g are obtained, respectively. The present investigation suggests a useful design principle for silicon as well as other high capacity alloying electrodes that undergo large volume expansions during battery operations.

Genetic variation of aldolase from Korean isolates of Plasmodium vivax and its usefulness in serodiagnosis

Background: The malaria aldolase is widely used as rapid diagnostic test (RDT), but the efficacy in aspect of its serological effectiveness in diagnosis is not known. The genetic variation of Korean isolates was analysed and recombinant aldolase was evaluated as a serological antigen in Plasmodium vivax malaria. Methods: Genomic DNA was purified and the aldolase gene of P. vivax from 25 patients’ blood samples was amplified. The samples came from 5 epidemic areas; Bucheon-si, Gimpo-si, Paju-si of Gyeonggido, Gangwha-gun of Incheon metropolitan city, and Cheorwon of Gangwon-do, South Korea. The antigenicity of the recombinant aldolase was tested by western blot and enzyme-linked immunosorbent assay (ELISA). Results: Sequence analysis of 25 Korean isolates of P. vivax showed that the open reading frame (ORF) of 1,110 nucleotides encoded a deduced protein of 369 amino acids (aa). This ORF showed 100% homology with the P. vivax Sal I strain (XM_00165894) and P. vivax WDK strain (AF247063), 87.4% homology with Plasmodium falciparum (AF179421), 90.6% homology with Plasmodium chabaudi (AF247060), 89.5% homology with Plasmodium vinckei (AF247061), and 96.7% homology with Plasmodium knowlesi. A single nucleotide polymorphism (SNP) at nucleotide 180 (G to A, n = 5) was also observed in the isolates. The expressed recombinant protein had a molecular weight of approximately 31 kDa (monomeric form) and 62 kDa (dimeric form) as analysed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis. Among 109 P. vivax patients, 32 (29.4%) had positive in an enzyme-linked absorbance assay (ELISA). This result showed significant correlation between ELISA and an indirect fluorescent antibody test (IFAT) (P < 0.0001). Conclusions: The aldolase gene from Korean isolates of P. vivax showed one SNP at nucleotide position 180; this SNP mutant was discovered in only the western part of Han River, and included the regions of Ganghwa, Gimpo, and Bucheon. Based on the results, the relationship between antibody production against aldolase and the pattern of disease onset should be more investigated before using aldolase for serodiagnosis

A Survey of Graph Neural Networks for Social Recommender Systems

Social recommender systems (SocialRS) simultaneously leverage user-to-item interactions as well as user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly effective in understanding users' tastes due to the effects of homophily and social influence. For this reason, SocialRS has increasingly attracted attention. In particular, with the advance of Graph Neural Networks (GNN), many GNN-based SocialRS methods have been developed recently. Therefore, we conduct a comprehensive and systematic review of the literature on GNN-based SocialRS. In this survey, we first identify 80 papers on GNN-based SocialRS after annotating 2151 papers by following the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). Then, we comprehensively review them in terms of their inputs and architectures to propose a novel taxonomy: (1) input taxonomy includes 5 groups of input type notations and 7 groups of input representation notations; (2) architecture taxonomy includes 8 groups of GNN encoder, 2 groups of decoder, and 12 groups of loss function notations. We classify the GNN-based SocialRS methods into several categories as per the taxonomy and describe their details. Furthermore, we summarize the benchmark datasets and metrics widely used to evaluate the GNN-based SocialRS methods. Finally, we conclude this survey by presenting some future research directions.Comment: GitHub repository with the curated list of papers: https://github.com/claws-lab/awesome-GNN-social-recsy

Screening models using multiple markers for early detection of late-onset preeclampsia in low-risk pregnancy

BACKGROUND: Our primary objective was to establish a cutoff value for the soluble fms-like tyrosine kinase 1(sFlt-1)/placental growth factor (PlGF) ratio measured using the Elecsys assay to predict late-onset preeclampsia in low-risk pregnancies. Our secondary objective was to evaluate the ability of combination models using Elecsys data, second trimester uterine artery (UtA) Doppler ultrasonography measurements, and the serum fetoplacental protein levels used for Down’s syndrome screening, to predict preeclampsia. METHODS: This prospective cohort study included 262 pregnant women with a low risk of preeclampsia. Plasma levels of pregnancy-associated plasma protein-A (PAPP-A) and serum levels of alpha-fetoprotein, unconjugated estriol, human chorionic gonadotropin, and inhibin-A were measured, and sFlt-1/PlGF ratios were calculated. All women underwent UtA Doppler ultrasonography at 20 to 24 weeks of gestation. RESULTS: Eight of the 262 women (3.0%) developed late-onset preeclampsia. Receiver operating characteristic curve analysis showed that the third trimester sFlt-1/PlGF ratio yielded the best detection rate (DR) for preeclampsia at a fixed false-positive rate (FPR) of 10%, followed by the second trimester sFlt-1/PlGF ratio, sFlt-1 level, and PlGF level. Binary logistic regression analysis was used to determine the five best combination models for early detection of late-onset preeclampsia. The combination of the PAPP-A level and the second trimester sFlt-1/PlGF ratio yielded a DR of 87.5% at a fixed FPR of 5%, the combination of second and third trimester sFlt-1/PlGF ratios yielded a DR of 87.5% at a fixed FPR of 10%, the combination of body mass index and the second trimester sFlt-1 level yielded a DR of 87.5% at a fixed FPR of 10%, the combination of the PAPP-A and inhibin-A levels yielded a DR of 50% at a fixed FPR of 10%, and the combination of the PAPP-A level and the third trimester sFlt-1/PlGF ratio yielded a DR of 62.5% at a fixed FPR of 10%. CONCLUSIONS: The combination of the PAPP-A level and the second trimester sFlt-1/PlGF ratio, and the combination of the second trimester sFlt-1 level with body mass index, were better predictors of late-onset preeclampsia than any individual marker

Resting-state EEG activity related to impulsivity in gambling disorder

Background and aims Impulsivity is a core feature of gambling disorder (GD) and is related to the treatment response. Thus, it is of interest to determine objective neurobiological markers associated with impulsivity in GD. We explored resting-state electroencephalographic (EEG) activity in patients with GD according to the degree of impulsivity. Methods In total, 109 GD subjects were divided into three groups according to Barratt impulsiveness scale-11 (BIS-11) scores: high (HI; 25th percentile of BIS-11 scores, n = 29), middle (MI; 26th–74th percentile, n = 57), and low-impulsivity (LI) groups (75th percentile, n = 23). We used generalized estimating equations to analyze differences in EEG absolute power considering group (HI, MI, and LI), brain region (frontal, central, and posterior), and hemisphere (left, midline, and right) for each frequency band (delta, theta, alpha, beta, and gamma). Results The results indicated that GD patients in the HI group showed decreased theta absolute power, and decreased alpha and beta absolute power in the left, right, particularly midline frontocentral regions. Discussion and conclusions This study is a novel attempt to reveal impulsive features in GD by neurophysiological methods. The results suggest different EEG patterns among GD patients according to the degree of impulsivity, raising the possibility of neurophysiological objective features in GD and helping clinicians in treating GD patients with impulsive features