Ecological and growth characteristics of trees after resumption of management in abandoned substitution forest in Japan

Since the 1950s, secondary (substitution) forests known as Satoyama woods have been abandoned due to changes in human lifestyle. The aim of this study was to investigate the relationships between human activity and substitution forests to better understand the traditional management required to prevent succession to evergreen forest. An objective was to identify the tree species, their numbers of trunks (NT), and the basal area (BA) (collectively, the stand density) in the woods today, half a century after people abandoned the substitution forests. Another goal was to compare, over a six-year period, the figures for total NT, BA, and the number of living, dead or fallen trunks between an abandoned substitution forest (a control plot) and a mown plot. NT decreased from 700 trunks/ha to 600 trunks/ha on the control, and from 600 trunks/ha to 400 trunks/ha on the mown plot at ground level over six years. The total BA increased annually on the control plot but decreased from 48m2/ha to 38m2/ha on the mown plot over six years. Many hydrophytes (Alnus japonica, etc.), Quercus serrata, and other trees species were found dead on the mown plots. All Quercus myrsinaefolia (evergreen trees) were still alive by the sixth year. These results demonstrate that the vegetation in these forests succeeded to Quercetum myrsinaefoliae, Tyoische Subass., which is therefore shown as the potential vegetation of succession over this timescale. If it is desired to maintain the traditional vegetation type, then the study suggests that it is necessary to manage the substitution forest. This is in order to prevent succession to evergreen forest and can be achieved by cutting Pleioblastus chino, climbing plants, and shade plants (evergreen trees)

Improving Compound–Protein Interaction Prediction by Self-Training with Augmenting Negative Samples

Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine-learning methods for CPI predictions. However, their performance, particularly their generalizability against external data, often suffers from a data imbalance attributed to the lack of experimentally validated inactive (negative) samples. In this study, we developed a self-training method for augmenting both credible and informative negative samples to improve the performance of models impaired by data imbalances. The constructed model demonstrated higher performance than those constructed with other conventional methods for solving data imbalances, and the improvement was prominent for external datasets. Moreover, examination of the prediction score thresholds for pseudo-labeling during self-training revealed that augmenting the samples with ambiguous prediction scores is beneficial for constructing a model with high generalizability. The present study provides guidelines for improving CPI predictions on real-world data, thus facilitating drug discovery

kGCN: a graph-based deep learning framework for chemical structures

Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multi-modal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo

Membrane-associated prostaglandin E synthase-1 is upregulated by proinflammatory cytokines in chondrocytes from patients with osteoarthritis

Prostaglandin E synthase (PGES) including isoenzymes of membrane-associated PGES (mPGES)-1, mPGES-2, and cytosolic PGES (cPGES) is the recently identified terminal enzyme of the arachidonic acid cascade. PGES converts prostaglandin (PG)H(2 )to PGE(2 )downstream of cyclooxygenase (COX). We investigated the expression of PGES isoenzyme in articular chondrocytes from patients with osteoarthritis (OA). Chondrocytes were treated with various cytokines and the expression of PGES isoenzyme mRNA was analyzed by the reverse transcription–polymerase chain reaction and Northern blotting, whereas Western blotting was performed for protein expression. The subcellular localization of mPGES-1 was determined by immunofluorescent microscopy. Conversion of arachidonic acid or PGH(2 )to PGE(2 )was measured by enzyme-linked immunosorbent assay. Finally, the expression of mPGES-1 protein in OA articular cartilage was assessed by immunohistochemistry. Expression of mPGES-1 mRNA in chondrocytes was significantly induced by interleukin (IL)-1β or tumor necrosis factor (TNF)-α, whereas other cytokines, such as IL-4, IL-6, IL-8, IL-10, and interferon-γ, had no effect. COX-2 was also induced under the same conditions, although its pattern of expression was different. Expression of cPGES, mPGES-2, and COX-1 mRNA was not affected by IL-1β or TNF-α. The subcellular localization of mPGES-1 and COX-2 almost overlapped in the perinuclear region. In comparison with 6-keto-PGF(1α )and thromboxane B(2), the production of PGE(2 )was greater after chondrocytes were stimulated by IL-1β or TNF-α. Conversion of PGH(2 )to PGE(2 )(PGES activity) was significantly increased in the lysate from IL-1β-stimulated chondrocytes and it was inhibited by MK-886, which has an inhibitory effect on mPGES-1 activity. Chondrocytes in articular cartilage from patients with OA showed positive immunostaining for mPGES-1. These results suggest that mPGES-1 might be important in the pathogenesis of OA. It might also be a potential new target for therapeutic strategies that specifically modulate PGE(2 )synthesis in patients with OA

Gastric Carcinoid with Hypergastrinemia: Report of Three Cases

We report 3 cases of gastric carcinoids with hypergastrinemia. Case 1: A 60-year-old man had a 2 cm carcinoid of the stomach and underwent partial resection. Involvement of the muscularis propria and lymph nodes metastasis were observed microscopically. Follow-up gastroscopy revealed another carcinoid lesion and total gastrectomy was performed. Case 2: A 67-year-old woman with multiple carcinoids of the entire stomach underwent antrectomy. No growth of residual tumors has been detected so far. Case 3: A 61-year-old man had a tumor near the esophagogastric junction and underwent total gastrectomy. Carcinoid component was diffusely intermingled with adenocarcinoma in the tumor and invaded into the subserosa. In all 3 cases, the serum gastrin level was high and atrophic gastritis was microscopically observed. Carcinoid tumor in Case 3 was different from those in Cases 1 and 2 and interestingly, gastric carcinoid with hypergastrinemia showed various types of appearance

The Portrayal of Indonesian Image in 2007 Kompas Selected Short Stories: Social Problems, Criticisms and Hopes

Article aimed at exploring social problems reflected in 15 selected short stories printed in Kompas during 2007 both explicitly and implicitly. Specifically, this research is focused on the mapping of dominant social problems raised by the short stories, the social criticisms strongly voiced by the authors and the hopes of a better situation implicitly reflected in these interesting short stories. This study applies the Defamiliarization Effect promoted by Bertolt Brecht and Negative Dialectics or Negative Knowledge by Theodor Adorno, specifically in analyzing the literary works as a criticism tool. The result of the research shows that phenomena of social problems current lately in Indonesian context like identity, poverty, corruption, religious tensions, moral degradation, politics dirtiness, minority group problems, social security, natural disasters and the like are clearly seen and teased in these writings