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Most ferns occur in moist and shaded environments; their relatively narrow band of survival conditions increase their extinction risk under Anthropocene-linked climate change. Osmundastrum cinnamomeum (L.) C. Presl is a perennial fern that has a natural distribution primarily encompassing the East Asian mountains. In this study, we characterized the vegetation and environmental factors in low- and high-elevation mountain ranges and montane wetlands to elucidate the optimal habitat for O. cinnamomeum. We found that populations of O. cinnamomeum developed better in high-elevation mountains than in low-elevation mountains. However, the low light intensity in high mountain forests reduces opportunities for reproduction. The montane wetlands recorded the highest biomass among the three habitat types, and the investment in reproduction was also significantly higher. The montane wetlands also showed higher light intensity, substrate fertility, and water content. Overall, the montane wetlands were found to be the optimal habitat for O. cinnamomeum. At lower elevations, lower precipitation, higher temperatures, human disturbance, and low substrate water could be factors limiting the expansion of this fern's distribution. Our study points to the importance of conserving montane wetlands to prevent the extinction of ferns.

Montane wetlands are important carbon sinks for mitigating climate change. In this study, we estimated carbon sequestration rates and carbon storage in montane fen wetlands of the Odaesan National Park of Korea. We measured the depth of the organic layer at the three wetlands using a probe and collected 12 cores from four sites with different dominant plant species. The physicochemical characteristics of the cores were analyzed, and the 4 cores were dated with 210Pb using the constant rate of supply model. Based on physicochemical analyses and 210Pb dating, the carbon sequestration rate and carbon storage were calculated, ranging from 58.29 to 125.31 g C m−2 y−1 and 14.13–138.00 t C, respectively. Among the four sites studied, the carbon storage was highest at the Phragmites australis and Sphagnum palustre dominant site and the carbon sequestration rate was highest at the Salix koriyanagi and Sphagnum palustre dominant site. The carbon sequestration rate was substantially different before and after 1980 in montane wetlands in Korea (p

Copy number variation is a well-known genetic variation. microRNAs (miRNAs/miRs) are non-coding RNAs that mediate gene expression by regulating target mRNAs. In the present study, copy number deletions encompassing miRNA coding regions were investigated to determine the association between the deletion of miRNA and its phenotypic effects. A total of 38 human miRNAs in copy number variants were identified and miR-650, which is functional in the human osteosarcoma MG-63 cell line, was selected. Overexpression of miR-650 decreased the expression of inhibitor of growth family member 4 (ING4) in the MG-63 cells and increased interleukin (IL)6 transcription, as well as IL6 secretion in IL1B-stimulated cells. Furthermore, miR-650 downregulated the amount of nuclear factor of κ light polypeptide gene enhancer in B cells inhibitor α and increased the transcriptional activity of nuclear factor (NF)κB. Downregulation of ING4 also increased the production of IL6, similar to miR-650 over-expression. Taken together, these data indicate that miR-650 plays a significant role in the production of IL6 by regulating ING4 expression and NFκB signaling in IL1B-stimulated MG-63 osteosarcoma cells. [ABSTRACT FROM AUTHOR]

Owing to the increasing complexity of managing IT infrastructure caused by rapid technological advancements, organizations are transforming their datacenter management environments from on-premises to the cloud. Datacenters operating in the cloud environment have large amounts of IT infrastructure, such as servers, storage devices, and network equipment, and are operational on all days of the year, thus causing power overconsumption problems. However, efforts to reduce power consumption are not the first priority as datacenters seek stable operation to avoid violating their service level agreements. Therefore, a research model that reduces power consumption of the datacenter while enabling stable operation by utilizing virtual machine (VM) consolidation is proposed here. To obtain the optimal solution for the proposed VM consolidation model, an adaptive harmony search methodology is developed, which expends less effort to set the parameters of the model compared to existing harmony search methods. Comparative experiments were conducted to validate the accuracy and performance of the proposed model. As a result, Original harmony search (HS) showed better performance than the existing heuristic algorithm, and novel self-adaptive (NS)-HS showed the best result among Adaptive HS. In addition, when considering workload stability, it showed better results in terms of datacenters (DC) stability than otherwise.

Real wars involve a considerable number of uncertainties when determining firing scheduling. This study proposes a robust optimization model that considers uncertainties in wars. In this model, parameters that are affected by enemy’s behavior and will, i.e., threats from enemy targets and threat time from enemy targets, are assumed as uncertain parameters. The robust optimization model considering these parameters is an intractable model with semi-infinite constraints. Thus, this study proposes an approach to obtain a solution by reformulating this model into a tractable problem; the approach involves developing a robust optimization model using the scenario concept and finding a solution in that model. Here, the combinations that express uncertain parameters are assumed by scenarios. This approach divides problems into master and subproblems to find a robust solution. A genetic algorithm is utilized in the master problem to overcome the complexity of global searches, thereby obtaining a solution within a reasonable time. In the subproblem, the worst scenarios for any solution are searched to find the robust solution even in cases where all scenarios have been expressed. Numerical experiments are conducted to compare robust and nominal solutions for various uncertainty levels to verify the superiority of the robust solution.

Abstract Prediabetes (PD) is a high-risk state of developing type 2 diabetes, and cardiovascular and metabolic diseases. Metabolomics-based biomarker studies can provide advanced opportunities for prediction of PD over the conventional methods. Here, we aimed to identify metabolic markers and verify their abilities to predict PD, as compared to the performance of the traditional clinical risk factor (CRF) and previously reported metabolites in other population-based studies. Targeted metabolites quantification was performed in 1723 participants in the Korea Association REsource (KARE) cohort, from which 500 normal individuals were followed up for 6 years. We selected 12 significant metabolic markers, including five amino acids, four glycerophospholipids, two sphingolipids, and one acylcarnitine, at baseline, resulting in a predicted incidence of PD with an area under the curve (AUC) of 0.71 during follow-up. The performance of these metabolic markers compared to that of fasting glucose was significantly higher in obese patients (body mass index: BMI ≥ 25 kg/m2, 0.79 vs. 0.58, P

Introduction Obesity is growing global health concern and highly associated with increased risk of metabolic diseases including type 2 diabetes. We aimed to discover new differential DNA methylation patterns predisposing obesity and prioritize surrogate epigenetic markers in Koreans.Research design and methods We performed multistage epigenome-wide analyses to identify differentially expressed CpGs in obesity using the Illumina HumanMethylationEPIC array (EPIC). Forty-eight CpGs showed significant differences across three phases: 902 whole blood DNAs from two cohorts (phase 1: n=450, phase 2: n=377) and a hospital-based sample (phase 3: n=75). Samples from phase III participants were used to examine whether the 48 CpGs are significant in the fat tissue and influenced gene expression. Furthermore, we investigated the epigenetic effect of CpG loci in childhood obesity (n=94).Results Seven of the 48 CpGs exhibited similar changes in the fat tissue along with gene expression changes. In particular, hypomethylated CpG (cg13424229) on the GATA1 transcription factor cluster of CPA3 promoter was related to its increased gene expression and showed consistent effect in childhood obesity. Interestingly, subsequent analysis using RNA sequencing data from 21 preadipocytes and 26 adipocytes suggested CPA3 as a potential obesity-related gene. Moreover, expression patterns from RNA sequencing and public Gene Expression Omnibus showed the correlation between CPA3 and type 2 diabetes (T2D) and asthma.Conclusions Our finding prioritizes influential genes in obesity and provides new evidence for the role of CPA3 linking obesity, T2D, and asthma.

Alterations in DNA methylation and gene expression have been implicated in the development of human dilated cardiomyopathy (DCM). Differentially methylated probes (DMPs) and differentially expressed genes (DEGs) were identified between the left ventricle (LV, a pathological locus for DCM) and the right ventricle (RV, a proxy for normal hearts). The data in this DiB are for supporting our report entitled “Methylome analysis reveals alterations in DNA methylation in the regulatory regions of left ventricle development genes in human dilated cardiomyopathy” (Bong-Seok Jo, In-Uk Koh, Jae-Bum Bae, Ho-Yeong Yu, Eun-Seok Jeon, Hae-Young Lee, Jae-Joong Kim, Murim Choi, Sun Shim Choi, 2016) [1].

Glycated hemoglobin (HbA1c) is an indicator of the average blood glucose concentration. Failing to control HbA1c levels can accelerate the development of complications in patients with diabetes. Although metabolite profiles associated with HbA1c level in diabetes patients have been characterized using different platforms, more studies using high-throughput technology will be helpful to identify additional metabolites related to diabetes. Type 2 diabetes (T2D) patients were divided into two groups based on the HbA1c level: normal (HbA1c ≤6%) and high (HbA1c ≥9%) in both discovery and replication sets. A targeted metabolomics approach was used to quantify serum metabolites and multivariate logistic regression was used to identify significant differences between groups. The concentrations of 22 metabolites differed significantly between the two groups in the discovery set. In the replication set, the levels of 21 metabolites, including 16 metabolites identified in the discovery set, differed between groups. Among these, concentrations of eleven amino acids and one phosphatidylcholine (PC), lysoPC a C16:1, were higher and four metabolites, including three PCs (PC ae C36:1, PC aa C26:0, PC aa C34:2) and hexose, were lower in the group with normal HbA1c group than in the group with high HbA1c. Metabolites with high concentrations in the normal HbA1c group, such as glycine, valine, and PCs, may contribute to reducing HbA1c levels in patients with T2D. The metabolite signatures identified in this study provide insight into the mechanisms underlying changes in HbA1c levels in T2D.

The single nucleotide polymorphism rs9939609 of the gene FTO, which encodes fat mass and obesity-associated protein, is strongly associated with obesity and type 2 diabetes (T2D) in multiple populations; however, the underlying mechanism of this association is unclear. The present study aimed to investigate FTO genotype-dependent metabolic changes in obesity and T2D. To elucidate metabolic dysregulation associated with disease risk genotype, genomic and metabolomic datasets were recruited from 2,577 participants of the Korean Association REsource (KARE) cohort, including 40 homozygous carriers of the FTO risk allele (AA), 570 heterozygous carriers (AT), and 1,967 participants carrying no risk allele (TT). A total of 134 serum metabolites were quantified using a targeted metabolomics approach. Through comparison of various statistical methods, seven metabolites were identified that are significantly altered in obesity and T2D based on the FTO risk allele (adjusted p < 0.05). These identified metabolites are relevant to phosphatidylcholine metabolic pathway, and previously reported to be metabolic markers of obesity and T2D. In conclusion, using metabolomics with the information from genome-wide association studies revealed significantly altered metabolites depending on the FTO genotype in complex disorders. This study may contribute to a better understanding of the biological mechanisms linking obesity and T2D.