The omega-3 postbiotic trans-10-cis-15-octadecadienoic acid attenuates contact hypersensitivity in mice through downregulation of vascular endothelial growth factor A.

Intestinal bacteria metabolize dietary substances to produce bioactive postbiotics, among which some are recognized for their role in promoting host health. We here explored the postbiotic potential of two omega-3 α-linolenic acid-derived metabolites: trans-10-cis-15-octadecadienoic acid (t10,c15-18:2) and cis-9-cis-15-octadecadienoic acid (c9,c15-18:2). Dietary intake of lipids rich in omega-3 α-linolenic acid elevated levels of t10,c15-18:2 and c9,c15-18:2 in the serum and feces of mice, an effect dependent on the presence of intestinal bacteria. Notably, t10,c15-18:2 mitigated skin inflammation in mice that became hypersensitive after exposure to 2,4-dinitrofluorobenzene, an experimental model for allergic contact dermatitis. In particular, t10,c15-18:2-but not c9,c15-18:2-attenuated ear swelling and edema, characteristic symptoms of contact hypersensitivity. The anti-inflammatory effects of t10,c15-18:2 were due to its ability to suppress the release of vascular endothelial growth factor A from keratinocytes, thereby mitigating the enhanced vascular permeability induced by hapten stimulation. Our study identified retinoid X receptor as a functional receptor that mediates the downregulation of skin inflammation upon treatment with t10,c15-18:2. Our results suggest that t10,c15-18:2 holds promise as an omega-3 fatty acid-derived postbiotic with potential therapeutic implications for alleviating the skin edema seen in allergic contact dermatitis-induced inflammation.

Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling.

A deep learning architecture, denoted as CNNsLSTM, is proposed for hourly rainfall-runoff modeling in this study. The architecture involves a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory (LSTM) network. In the proposed framework, multiple layers of the CNN component process long-term hourly meteorological time series data, while the LSTM component handles short-term meteorological time series data and utilizes the extracted features from the 1D-CNN. In order to demonstrate the effectiveness of the proposed approach, it was implemented for hourly rainfall-runoff modeling in the Ishikari River watershed, Japan. A meteorological dataset, including precipitation, air temperature, evapotranspiration, longwave radiation, and shortwave radiation, was utilized as input. The results of the proposed approach (CNNsLSTM) were compared with those of previously proposed deep learning approaches used in hydrologic modeling, such as 1D-CNN, LSTM with only hourly inputs (LSTMwHour), a parallel architecture of 1D-CNN and LSTM (CNNpLSTM), and the LSTM architecture, which uses both daily and hourly input data (LSTMwDpH). Meteorological and runoff datasets were separated into training, validation, and test periods to train the deep learning model without overfitting, and evaluate the model with an independent dataset. The proposed approach clearly improved estimation accuracy compared to previously utilized deep learning approaches in rainfall = runoff modeling. In comparison with the observed flows, the median values of the Nash-Sutcliffe efficiency for the test period were 0.455-0.469 for 1D-CNN, 0.639-0.656 for CNNpLSTM, 0.745 for LSTMwHour, 0.831 for LSTMwDpH, and 0.865-0.873 for the proposed CNNsLSTM. Furthermore, the proposed CNNsLSTM reduced the median root mean square error (RMSE) of 1D-CNN by 50.2%-51.4%, CNNpLSTM by 37.4%-40.8%, LSTMwHour by 27.3%-29.5%, and LSTMwDpH by 10.6%-13.4%. Particularly, the proposed CNNsLSTM improved the estimations for high flows (≧75th percentile) and peak flows (≧95th percentile). The computational speed of LSTMwDpH is the fastest among the five architectures. Although the computation speed of CNNsLSTM is slower than LSTMwDpH's, it is still 6.9-7.9 times faster than that of LSTMwHour. Therefore, the proposed CNNsLSTM would be an effective approach for flood management and hydraulic structure design, mainly under climate change conditions that require estimating hourly river flows using meteorological datasets.

Induction of unique macrophage subset by simultaneous stimulation with LPS and IL-4.

Macrophages manifest as various subtypes that play diverse and important roles in immunosurveillance and the maintenance of immunological homeostasis in various tissues. Many in vitro studies divide macrophages into two broad groups: M1 macrophages induced by lipopolysaccharide (LPS), and M2 macrophages induced by interleukin 4 (IL-4). However, considering the complex and diverse microenvironment in vivo, the concept of M1 and M2 is not enough to explain diversity of macrophages. In this study, we analyzed the functions of macrophages induced by simultaneous stimulation with LPS and IL-4 (termed LPS/IL-4-induced macrophages). LPS/IL-4-induced macrophages were a homogeneous population showing a mixture of the characteristics of M1 and M2 macrophages. In LPS/IL-4-induced macrophages, expression of cell-surface M1 markers (I-Ab) was higher than in M1 macrophages, but lower expression of iNOS, and expression of M1-associated genes (Tnfα and Il12p40) were decreased in comparison to expression in M1 macrophages. Conversely, expression of the cell-surface M2 marker CD206 was lower on LPS/IL-4-induced macrophages than on M2 macrophages and expression of M2-associated genes (Arg1, Chi3l3, and Fizz1) varied, with Arg1 being greater than, Fizz1 being lower than, and Chi3l3 being comparable to that in M2 macrophages. Glycolysis-dependent phagocytic activity of LPS/IL-4-induced macrophages was strongly enhanced as was that of M1 macrophages; however, the energy metabolism of LPS/IL-4-induced macrophages, such as activation state of glycolytic and oxidative phosphorylation, was quite different from that of M1 or M2 macrophages. These results indicate that the macrophages induced by LPS and IL-4 had unique properties.

Rapid Alkene-Alkene Photo-Cross-Linking on the Base-Flipping-Out Field in Duplex DNA.

Specific chemical reactions by enzymes acting on a nucleobase are realized by flipping the target base out of the helix. Similarly, artificial oligodeoxynucleotides (ODNs) can also induce the base flipping and a specific chemical reaction. We now report an easily prepared and unique structure-providing photo-cross-linking reaction by taking advantage of the base-flipping-out field formed by alkene-type base-flipping-inducing artificial bases. Two 3-arylethenyl-5-methyl-2-pyridone nucleosides with the Ph or An group were synthesized and incorporated into the ODNs. We found that the two Ph derivatives provided the cross-linked product in a high yield only by a 10 s photoirradiation when their alkenes overlap each other in the duplex DNA. The highly efficient reaction enabled forming a cross-linked product even when using the duplex with a low Tm value.

Capabilities of deep learning models on learning physical relationships: Case of rainfall-runoff modeling with LSTM.

This study investigates the relationships which deep learning methods can identify between the input and output data. As a case study, rainfall-runoff modeling in a snow-dominated watershed by means of a long short-term memory (LSTM) network is selected. Daily precipitation and mean air temperature were used as model input to estimate daily flow discharge. After model training and verification, two experimental simulations were conducted with hypothetical inputs instead of observed meteorological data to clarify the response of the trained model to the inputs. The first numerical experiment showed that even without input precipitation, the trained model generated flow discharge, particularly winter low flow and high flow during the snow melting period. The effects of warmer and colder conditions on the flow discharge were also replicated by the trained model without precipitation. Additionally, the model reflected only 17-39% of the total precipitation mass during the snow accumulation period in the total annual flow discharge, revealing a strong lack of water mass conservation. The results of this study indicated that a deep learning method may not properly learn the explicit physical relationships between input and target variables, although they are still capable of maintaining strong goodness-of-fit results.

Coupling hydroclimate-hydraulic-sedimentation models to estimate flood inundation and sediment transport during extreme flood events under a changing climate.

Extreme flood events are disastrous and can cause serious damages to society. Flood frequency obtained based on historical flow records may also be changing under future climate conditions. The associated flood inundation and environmental transport processes will also be affected. In this study, an integrated numerical modeling framework is proposed to investigate the inundation and sedimentation during multiple flood events (2,5,10, 20, 50, 100, 200-year) under future climate change scenarios in a watershed system in northern California, USA. The proposed modeling framework couples physical models of various spatial resolution: kilometers to several hundred kilometers climatic processes, hillslope scale hydrological processes in a watershed, and centimeters to meters scale hydrodynamic and sediment transport processes in a riverine system. The modeling results show that compared to the flows during historical periods, extreme events become more extreme in the 21st century and higher flows tend to be larger and smaller flows tend to be smaller in the system. Flood inundation in the study area, especially during 200-year events, is projected to increase in the future. More sediment will be trapped as the flow increases and the deposition will also increase in the settling basin. Sediment trap efficiency values are within 37.5-65.4% for the historical conditions, within 32.4-68.8% in the first half of the 21st century, and within 34.9-69.3% in the second half of the 21st century. The results highlight the impact of climate change on extreme flood events, the resulting sedimentation, and reflected the importance of incorporating the coupling of physical models into the adaptive watershed and river system management.

Hourly-scale coastal sea level modeling in a changing climate using long short-term memory neural network.

In this study, a coastal sea level estimation model was developed at an hourly temporal scale using the long short-term memory (LSTM) network, which is a type of recurrent neural networks. The model incorporates the effects of various phenomena on the coastal sea level such as the gravitational attractions of the sun and the moon, seasonality, storm surges, and changing climate. The relative positions of the moon and the sun from the target location at each hour were utilized to reflect the gravitational attractions of the sun and the moon in the model simulation. The wind speed and direction, mean sea level pressure (MSLP), and air temperature near the target point at each hour were used to consider the effects of storm surges and seasonality of the coastal sea level. In addition to the hourly local variables, the annual global mean air temperature was considered as input to the model to reflect the effect of global warming on the coastal sea level. The model was implemented using several input lengths of the annual global mean air temperature to estimate the coastal sea level at the Osaka gauging station in Japan. Several statistics such as the mean, the Nash-Sutcliffe efficiency, and the root mean square error were used to evaluate model performance. The results show that the proposed model accurately reconstructed the effects of the gravitational attractions of the sun and the moon on the coastal sea levels. The model also considered the effects of fluctuations in the wind speed and MSLP although the coastal sea levels during were underestimated strong winds and low MSLP conditions. Lastly, introducing a longer duration annual global mean air temperature improved model accuracy. Consequently, the best results show 0.720 of the NSE value for the test process.

Alkyne-Alkyne Photo-cross-linking on the Flipping-out Field.

The base flip-inducing nucleic acids are expected to create a specific field for various chemical reactions. We now report a novel type of base-flip-inducing oligodeoxynucleotide and photo-cross-linking reaction. Two 3-arylethynyl-5-methyl-2-pyridone nucleosides, Ph and An, were synthesized, and their properties were investigated. The alkyne-alkyne photo-cross-linking rapidly proceeded by taking advantage of the base-flipping-out field where two alkynes overlap each other. This photo-cross-linking would be a new candidate to form cross-linked DNAs.

Numerical reconstruction of the intense precipitation and moisture transport fields for six tropical cyclones affecting the eastern United States.

Tropical cyclones (TCs) are intense atmospheric vortices that form over the warm tropical oceans. They are recognized for their ability to generate intense precipitation that may in turn create disastrous floods. This article first assesses the suitability of a regional atmospheric model, the Weather Research and Forecasting (WRF) model, to simulate the intense precipitation depth (PD) fields of six North Atlantic TCs that affected the eastern United States during 2002-2016. Due to the strong nonlinearity involved in tropical cyclones' dynamics and thermodynamics, which causes tropical cyclones' tracks to be very sensitive to the different modeling choices, placing the PD fields in the observed locations was challenging. This involved trying several simulation start dates and combinations of the WRF model's parameterization schemes for each storm simulated. Model performance was evaluated by comparing the simulated PD fields with the observed PD fields obtained from the NCEP Stage IV precipitation dataset. In addition to qualitative comparisons, three quantitative metrics were used to quantify the WRF model performance in simulating a PD field's location, structure and intensity. The sensitivity of the simulation results to the choice of the parameterization schemes was then illustrated using Hurricane Gustav (2008). Eventually, the most satisfactory simulations were used to investigate the mechanisms responsible for the generation of intense precipitation in these TCs. More specifically, the vertically integrated vapor transport field and its divergence were calculated using the model outputs, and it was found that horizontal moisture convergence played a central role in the generation of intense precipitation in these TCs.

Assessing the impacts of future climate change on the hydroclimatology of the Gediz Basin in Turkey by using dynamically downscaled CMIP5 projections.

The Gediz Basin is a Mediterranean watershed along the Aegean coast of Turkey, in which the most important economic activity is agriculture. Over the last few decades, this basin has been experiencing water-related problems such as water scarcity and competing use of water. This study assesses the impact of future climate change on the availability of water resources in the Gediz Basin during the 21st century by investigating the inflows into the major reservoir in the basin, Demirkopru Reservoir, which is the major source of irrigation water to the basin. The analysis in this study involves setting up a coupled hydro-climate model over the Gediz Basin by coupling the Weather Research and Forecasting (WRF) model to the physically-based Watershed Environmental Hydrology (WEHY) model. First, the WRF model is used to reconstruct the historical climatic variables over the basin by dynamically downscaling the ERA-Interim reanalysis dataset. The calibrated and validated WRF model is then used to dynamically downscale eight different future climate projections over the Gediz Basin to a much finer resolution (6 km), which is more appropriate for the hydrologic modeling of the basin. These climate projections are from four Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Climate Models (GCMs), namely, CCSM4, GFDL-ESM2M, HadGEM2-ES, and MIROC5, under two IPCC (The Intergovernmental Panel on Climate Change) representative concentration pathway scenarios (RCP4.5 and RCP8.5). The outputs from the WRF model are then input into the WEHY model, which is calibrated and validated over the basin, to simulate the hydrological processes within the basin and to obtain the projected future inflows into the Demirkopru Reservoir. Results of the future analysis over the 21st century (2017-2100) are then compared to the historical values (1985-2012) to investigate the impacts of future climate change on the hydroclimatology of the Gediz Basin.

Overexpression of PkINO1 improves ethanol resistance of Pichia kudriavzevii N77-4 isolated from the Korean traditional fermentation starter nuruk.

The yeast Pichia kudriavzevii N77-4 was isolated from the Korean traditional fermentation starter nuruk. In this study, fermentation performance and stress resistance ability of N77-4 was analyzed. N77-4 displayed superior thermotolerance (up to 44°C) in addition to enhanced acetic acid resistance compared to Saccharomyces cerevisiae. Moreover, N77-4 produced 7.4 g/L of ethanol with an overall production yield of 0.37 g/g glucose in 20 g/L glucose medium. However, in 250 g/L glucose medium the growth of N77-4 slowed down when the concentration of ethanol reached 14 g/L or more and ethanol production yield also decreased to 0.30 g/g glucose. An ethanol sensitivity test indicated that N77-4 was sensitive to the presence of 1% ethanol, which was not the case for S. cerevisiae. Furthermore, N77-4 displayed a severe growth defect in the presence of 6% ethanol. Because inositol biosynthesis is critical for ethanol resistance, expression levels of the PkINO1 encoding a key enzyme for inositol biosynthesis was analyzed under ethanol stress conditions. We found that ethanol stress clearly repressed PkINO1 expression in a dose-dependent manner and overexpression of PkINO1 improved the growth of N77-4 by 19% in the presence of 6% ethanol. Furthermore, inositol supplementation also enhanced the growth by 13% under 6% ethanol condition. These findings indicate that preventing downregulation in PkINO1 expression caused by ethanol stress improves ethanol resistance and enhances the utility of P. kudriavzevii N77-4 in brewing and fermentation biotechnology.

Long-term trend analysis on total and extreme precipitation over Shasta Dam watershed.

California's interconnected water system is one of the most advanced water management systems in the world, and understanding of long-term trends in atmospheric and hydrologic behavior has increasingly being seen as vital to its future well-being. Knowledge of such trends is hampered by the lack of long-period observation data and the uncertainty surrounding future projections of atmospheric models. This study examines historical precipitation trends over the Shasta Dam watershed (SDW), which lies upstream of one of the most important components of California's water system, Shasta Dam, using a dynamical downscaling methodology that can produce atmospheric data at fine time-space scales. The Weather Research and Forecasting (WRF) model is employed to reconstruct 159years of long-term hourly precipitation data at 3km spatial resolution over SDW using the 20th Century Reanalysis Version 2c dataset. Trend analysis on this data indicates a significant increase in total precipitation as well as a growing intensity of extreme events such as 1, 6, 12, 24, 48, and 72-hour storms over the period of 1851 to 2010. The turning point of the increasing trend and no significant trend periods is found to be 1940 for annual precipitation and the period of 1950 to 1960 for extreme precipitation using the sequential Mann-Kendall test. Based on these analysis, we find the trends at the regional scale do not necessarily apply to the watershed-scale. The sharp increase in the variability of annual precipitation since 1970s is also detected, which implies an increase in the occurrence of extreme wet and dry conditions. These results inform long-term planning decisions regarding the future of Shasta Dam and California's water system.