Surface soil carbon storage in urban green spaces in three major South Korean cities

Quantifying and managing carbon (C) storage in urban green space (UGS) soils is associated with the ecosystem services necessary for human well-being and the national C inventory report of the Intergovernmental Panel on Climate Change (IPCC). Here, the soil C stocks at 30-cm depths in different types of UGS’s (roadside, park, school forest, and riverside) were studied in three major South Korean cities that have experienced recent, rapid development. The total C of 666 soil samples was analyzed, and these results were combined with the available UGS inventory data. Overall, the mean soil bulk density, C concentration, and C density at 30-cm depths were 1.22 g·cm−3, 7.31 g·C·kg−1, and 2.13 kg·C·m−2, respectively. The UGS soil C stock (Gg·C) at 30-cm depths was 105.6 for Seoul, 43.6 for Daegu, and 26.4 for Daejeon. The lower C storage of Korean UGS soils than those of other countries is due to the low soil C concentration and the smaller land area under UGS. Strategic management practices that augment the organic matter supply in soil are expected to enhance C storage in South Korean UGS soilsScopu

Chromia coating with nanofluid deposition and sputtering for accident tolerance, CHF enhancement

In Fukushima accident, zirconium cladding was rapidly oxidized with high temperature steam, which ultimately led to hydrogen explosion. To overcome materialistic limitation, accident tolerant fuel (ATF) was suggested to improve safety response of nuclear power plants during accidents by modifying cladding surface with various coating materials. When chrome was coated on cladding surface, it showed fewer weight gain by high temperature oxidation compared to bare zirconium cladding. Chrome forms chrome oxide or chromia (Cr2O3) when oxidized, and this layer prevents further oxidation thus protecting inner material from oxidizing. However, previous studies indicated that implementation of chrome containing alloys have major drawbacks such as excessive coating thickness or degraded critical heat flux (CHF). Instead, direct coating of chromia was suggested in this study with the expectation of CHF enhancement compared to other chrome alloy coatings. Chromia nanoparticles were coated on nichrome wire surface with boiling deposition of chromia nanofluid. Another method was applying RF sputtering with chromia target. Chrome coating with DC sputtering were also tested for comparison. Verification of chromia coating was conducted by three steps: CHF measurement with wire pool boiling, high temperature oxidation in furnace to compare the oxidation resistance of specimens, and surface investigation. Surface characteristics investigation were conducted with measurement of contact angle by sessile drop method, capillary wicking height, and scanning electron microscope image. Experimental results show that chromia coating significantly increased CHF. Weight gain by oxidation indicate chromia nanofluid coating had improved oxidation resistance property

Data on the expression and insulin-stimulated phosphorylation of IRS-1 by miR-96 in L6-GLUT4myc myocytes

Diets containing a high saturated fatty acid (SFA) increase the risk of metabolic diseases, and microRNAs (miRNAs) induced by SFA have been implicated in the pathogenesis of insulin resistance and type 2 diabetes. In a previous report, miR-96 is found to be upregulated by SFA and involved in the suppression of insulin signaling intermediates, leading to insulin resistance in hepatocytes (Yang et al., 2016) [1]. This article presents the accompanying data collected from L6-GLUT4myc myocytes to determine the effects of miR-96 on insulin signaling in skeletal muscle cells. The transfection of miR-96 decreased the expression of IRS-1 in myocytes. Accordingly, miR-96 inhibited the insulin-stimulated phosphorylation of IRS-1, which led to an impairment of insulin signaling. More detailed analysis and understanding of the roles of miR-96 in diet-induced insulin resistance can be found in 'Induction of miR-96 by dietary saturated fatty acids exacerbates hepatic insulin resistance through the suppression of INSR and IRS-1' (Yang et al., 2016) [1]. Keywords: MicroRNAs, miR-96, Myocyte, IRS-1, INS

Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models

Interest in anaerobic co-digestion (AcoD) has increased significantly in recent decades owing to enhanced biogas productivity due to the utilization of different organic wastes, such as food waste and sewage sludge. In this study, a robust AcoD model for biogas prediction is developed using deep learning (DL). We propose a hybrid DL architecture, i.e., DA-LSTM-VSN, wherein a dual-stage-attention (DA)-based long short-term memory (LSTM) network is integrated with variable selection networks (VSNs). To enhance the model predictability, we perform hyperparameter optimization. The model accuracy is validated using long-term AcoD monitoring data measured over two years of municipal wastewater treatment plant operation and then compared with those of two other DL-based models (i.e., DA-LSTM and the standard LSTM). In addition, the feature importance (FI) is analyzed to investigate the relative contribution of input variables to biogas production prediction. Finally, we demonstrate the successful application of the validated DL model to the AcoD process optimization. Results show that the model accuracy improved significantly by incorporating DA into LSTM, i.e., the coefficient of determination (R2) increased from 0.38 to 0.68; however, the R2 can be further increased to 0.76 by combining DA-LSTM with a VSN. For the biogas prediction of the AcoD model, the VSN contributes significantly by employing the discontinuous time series of measurement data on biodegradable organic-associated variables during AcoD. In addition, the VSN allows the AcoD model to be interpretable via FI analysis using its weighted input features. The FI results show that the relative importance is vital to variables associated with food waste leachate, whereas it is marginal for those associated with the primary and chemically assisted sedimentation sludges. In conclusion, the AcoD model proposed herein can be utilized in practical applications as a robust tool because it can provide the optimal sludge conditions to improve biogas production. This is because it facilitates the time-series biogas prediction at the full scale using unprocessed datasets with either missing value imputation or outlier removal

Seasonal Changes in Antibiotic Resistance Genes in Rivers and Reservoirs in South Korea

The fate of antibiotic resistance genes (ARGs) in aquatic environments, especially in rivers and reservoirs, is receiving growing attention in South Korea because reservoirs are an important source of drinking water in this country. Seasonal changes in the abundance of 11 ARGs and a mobile genetic element (int1) in two reservoirs in South Korea, located near drinking water treatment plants in Cheonan and Cheongju cities, were monitored for 6 mo. In these drinking water sources, total ARG concentrations reached 2.5 ?? 107 copies mL‒1, which is one order of magnitude higher than in influents of some wastewater treatment plants in South Korea. During the sampling periods in August, October, and November 2016 and January 2017, sulfonamides (sul1), ??-lactam antibiotics (blaTEM), and tetracycline (tetA) resistance genes were the most abundant genes at the two sites. The ARG abundance consistently increased in January relative to 16S ribosomal ribonucleic acid (rRNA) counts. General stress responses to oxidative stress and other environmental factors associated with the cold season could be significant drivers of ARG horizontal gene transfer in the environment. Accordingly, removal of ARGs as a key step in water treatment warrants more attention

Deep reinforcement learning in an ultrafiltration system: Optimizing operating pressure and chemical cleaning conditions

Enhancing engineering efficiency and reducing operating costs are permanent subjects that face all engineers over the world. To effectively improve the performance of filtration systems, it is necessary to determine an optimal operating condition beyond conventional methods of periodic and empirical operation. Herein, this paper proposes an effective approach to finding an optimal operating strategy using deep reinforcement learning (DRL), particularly for an ultrafiltration (UF) system. Deep learning was developed to represent the UF system utilizing a long-short term memory and provided an environment for DRL. DRL was designed to control three actions; operating pressure, cleaning time, and cleaning concentration. Ultimately, DRL proposed the UF system to actively change the operating pressure and cleaning conditions over time toward better water productivity and operating efficiency. DRL denoted similar to 20.9% of specific energy consumption can be reduced by increasing average water flux (39.5-43.7 L m(-2) h(-1)) and reducing operating pressure (0.617-0.540 bar). Moreover, the optimal action of DRL was reasonable to achieve better performance beyond the conventional operation. Crucially, this study demonstrated that due to the nature of DRL, the approach is tractable for engineering systems that have structurally complex relationships among operating conditions and resultants

Kitasatospora cheerisanensis sp. nov., a new species of the genus Kitasatospora that produces an antifungal agent

An actinomycete, strain YC75(T), which produced bafilomycin-like antifungal compounds, was identified as a member of the genus Kitasatospora on the basis of morphological and chemotaxonomic characteristics. The strain produced the aerial and fragmenting vegetative mycelia consisting of straight chains of 20 or more smooth-surfaced spores. Submerged spores were formed in tryptic soy broth. No soluble pigments were formed. Whole-cell hydrolysates contained glucose and mannose, but not galactose. The 16S rDNA sequence of YC75(T) was compared with those of the other representative kitasatosporae and streptomycetes. Strain YC75(T) formed a significant monophyletic clade with Kitasatospora phosalacinea. The levels of DNA relatedness between strain YC75(T) and representatives of the genus Kitasatospora ranged from 16 to 59% including K. phosalacinea (28 and 40%). It is clear from polyphasic evidence that the isolate should be classified as Kitasatospora cheerisanensis sp. nov., whose type strain is YC75(T) (= KCTC 2395(T)). The presence of galactose in whole-cell hydrolysates may not be a stable chemical marker for the genus Kitasatospora.

Utilization of Phytochemical and Molecular Diversity to Develop a Target-Oriented Core Collection in Tea Germplasm

Tea has received attention due to its phytochemicals. For the direct use of tea germplasm in breeding programs, a core collection that retains the genetic diversity and various phytochemicals in tea is needed. In this study, we evaluated the content of eight phytochemicals over two years and the genetic diversity through 33 SSR (simple sequence repeats) markers for 462 tea accessions (entire collection, ENC) and developed a target-oriented core collection (TOCC). Significant phytochemical variation was observed in the ENC between genotypes and years. The genetic diversity of ENC showed high levels of molecular variability. These results were incorporated into developing TOCCs. The TOCC showed a representation of the ENC, where the mean difference percentage, the variance difference percentage, the variable rate of coefficient of variance percentage, and the coincidence rate of range percentage were 7.88, 39.33, 120.79, and 97.43, respectively. The Shannon’s diversity index (I) and Nei’s gene diversity (H) of TOCC were higher than those of ENC. Furthermore, the accessions in TOCC were shown to be selected proportionally, thus accurately reflecting the distribution of the overall accessions for each phytochemical. This is the first report describing the development of a TOCC retaining the diversity of phytochemicals in tea germplasm. This TOCC will facilitate the identification of the genetic determinants of trait variability and the effective utilization of phytochemical diversity in crop improvement programs