Search

Composite materials are popular because of their high performance capabilities, but new material development is time-consuming. To accelerate this process, researchers studying material informatics, an academic discipline combining computational science and material science, have developed less time-consuming approaches for predicting possible material combinations. However, these processes remain problematic because some materials are not suited for them. The limitations of specific candidates for new composites may cause potential new material pairs to be overlooked. To solve this problem, we developed a new method to predict possible composite material pairs by considering more materials than previous techniques. We predicted possible material pairs by conducting link predictions of material word co-occurrence networks while assuming that co-occurring material word pairs in scientific papers on composites were reported as composite materials. As a result, we succeeded in predicting the co-occurrence of material words with high specificity. Nodes tended to link to many other words, generating new links in the created co-occurrence material word network; notably, the number of material words co-occurring with graphene increased rapidly. This phenomenon confirmed that graphene is an attractive composite component. We expect our method to contribute to the accelerated development of new composite materials. [ABSTRACT FROM AUTHOR]

Identifying promising research as early as possible is vital to determine which research deserves investment. Additionally, developing a technology for automatically predicting future research trends is necessary because of increasing digital publications and research fragmentation. In previous studies, many researchers have performed the prediction of scientific indices using specially designed features for each index. However, this does not capture real research trends. It is necessary to develop a more integrated method to capture actual research trends from various directions. Recent deep learning technology integrates different individual models and makes it easier to construct more general-purpose models. The purpose of this paper is to show the possibility of integrating multiple prediction models for scientific indices by network-based representation learning. This paper will conduct predictive analysis of multiple future scientific impacts by embedding a heterogeneous network and showing that a network embedding method is a promising tool for capturing and expressing scientific trends. Experimental results show that the multiple heterogeneous network embedding improved 1.6 points than a single citation network embedding. Experimental results show better results than baseline for the number of indices, including the author h-index, the journal impact factor (JIF), and the Nature Index after three years from publication. These results suggest that distributed representations of a heterogeneous network for scientific papers are the basis for the automatic prediction of scientific trends. [ABSTRACT FROM AUTHOR]

Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth. [ABSTRACT FROM AUTHOR]

Electrochemically reduced water (ERW) is produced near a cathode during electrolysis and exhibits an alkaline pH, contains richly dissolved hydrogen, and contains a small amount of platinum nanoparticles. ERW has reactive oxygen species (ROS)-scavenging activity and recent studies demonstrated that hydrogen-dissolved water exhibits ROS-scavenging activity. Thus, the antioxidative capacity of ERW is postulated to be dependent on the presence of hydrogen levels; however, there is no report verifying the role of dissolved hydrogen in ERW. In this report, we clarify whether the responsive factor for antioxidative activity in ERW is dissolved hydrogen. The intracellular ROS scavenging activity of ERW and hydrogen-dissolved water was tested by both fluorescent stain method and immuno spin trapping assay. We confirm that ERW possessed electrolysis intensity-dependent intracellular ROS-scavenging activity, and ERW exerts significantly superior ROS-scavenging activity in HT1080 cells than the equivalent level of hydrogen-dissolved water. ERW retained its ROS-scavenging activity after removal of dissolved hydrogen, but lost its activity when autoclaved. An oxygen radical absorbance capacity assay, the 2,2-diphenyl-1-picrylhydrazyl assay and chemiluminescence assay could not detect radical-scavenging activity in both ERW and hydrogen-dissolved water. These results indicate that ERW contains electrolysis-dependent hydrogen and an additional antioxidative factor predicted to be platinum nanoparticles. [ABSTRACT FROM AUTHOR]

Motilin and ghrelin constitute a peptide family, and these hormones are important for the regulation of gastrointestinal motility. In this study, we examined the effect of motilin and ghrelin on gastric acid secretion in anesthetized suncus (house musk shrew, Suncus murinus), a ghrelin- and motilin-producing mammal. We first established a gastric lumen-perfusion system in the suncus and confirmed that intravenous (i.v.) administration of histamine (1 mg/kg body weight) stimulated acid secretion. Motilin (0.1, 1.0, and 10 μg/kg BW) stimulated the acid output in a dose-dependent manner in suncus, whereas ghrelin (0.1, 1.0, and 10 μg/kg BW) alone did not induce acid output. Furthermore, in comparison with the vehicle administration, the co-administration of low-dose (1 μg/kg BW) motilin and ghrelin significantly stimulated gastric acid secretion, whereas either motilin (1 μg/kg BW) or ghrelin (1 μg/kg BW) alone did not significantly induce gastric acid secretion. This indicates an additive role of ghrelin in motilin-induced gastric acid secretion. We then investigated the pathways of motilin/motilin and ghrelin-stimulated acid secretion using receptor antagonists. Treatment with YM 022 (a CCK-B receptor antagonist) and atropine (a muscarinic acetylcholine receptor antagonist) had no effect on motilin or motilin-ghrelin co-administration-induced acid output. In contrast, famotidine (a histamine H2 receptor antagonist) completely inhibited motilin-stimulated acid secretion and co-administration of motilin and ghrelin induced gastric acid output. This is the first report demonstrating that motilin stimulates gastric secretion in mammals. Our results also suggest that motilin and co-administration of motilin and ghrelin stimulate gastric acid secretion via the histamine-mediated pathway in suncus. [ABSTRACT FROM AUTHOR]