Development of a novel endolysin, PanLys.1, for the specific inhibition of

Objective The objective of this study was to develop a novel endolysin (PanLys.1) for the specific killing of the ruminal hyper-ammonia-producing bacterium Peptostreptococcus anaerobius (P. anaerobius). Methods Whole genome sequences of P. anaerobius strains and related bacteriophages were collected from the National Center for Biotechnology Information database, and the candidate gene for PanLys.1 was isolated based on amino acid sequences and conserved domain database (CDD) analysis. The gene was overexpressed using a pET system in Escherichia coli BL21 (DE3). The lytic activity of PanLys.1 was evaluated under various conditions (dosage, pH, temperature, NaCl, and metal ions) to determine the optimal lytic activity conditions. Finally, the killing activity of PanLys.1 against P. anaerobius was confirmed using an in vitro rumen fermentation system. Results CDD analysis showed that PanLys.1 has a modular design with a catalytic domain, amidase-2, at the N-terminal, and a cell wall binding domain, from the CW-7 superfamily, at the C-terminal. The lytic activity of PanLys.1 against P. anaerobius was the highest at pH 8.0 (p<0.05) and was maintained at 37°C to 45°C, and 0 to 250 mM NaCl. The activity of PanLys.1 significantly decreased (p<0.05) after Mn2+ or Zn2+ treatment. The relative abundance of P. anaerobius did not decrease after administration PanLys.1 under in vitro rumen conditions. Conclusion The application of PanLys.1 to modulate P. anaerobius in the rumen might not be feasible because its lytic activity was not observed in in vitro rumen system

Essential oil mixture on rumen fermentation and microbial community – an study

Objective The objective of this study was to investigate the effects of essential oil mixture (EOM) supplementation on rumen fermentation characteristics and microbial changes in an in vitro. Methods Three experimental treatments were used: control (CON, no additive), EOM 0.1 (supplementation of 1 g EOM/kg of substrate), and EOM 0.2 (supplementation of 2 g EOM/kg of substrate). An in vitro fermentation experiment was carried out using strained rumen fluid for 12 and 24 h incubation periods. At each time point, in vitro dry matter digestibility (IVDMD), neutral detergent fiber digestibility (IVNDFD), pH, ammonia nitrogen (NH3-N), and volatile fatty acid (VFA) concentrations, and relative microbial diversity were estimated. Results After 24 h incubation, treatments involving EOM supplementation led to significantly higher IVDMD (treatments and quadratic effect; p = 0.019 and 0.008) and IVNDFD (linear effect; p = 0.068) than did the CON treatment. The EOM 0.2 supplementation group had the highest NH3-N concentration (treatments; p = 0.032). Both EOM supplementations did not affect total VFA concentration and the proportion of individual VFAs; however, total VFA tended to increase in EOM supplementation groups, after 12 h incubation (linear; p = 0.071). Relative protozoa abundance significantly increased following EOM supplementation (treatments, p<0.001). Selenomonas ruminantium and Ruminococcus albus (treatments; p<0.001 and p = 0.005), abundance was higher in the EOM 0.1 treatment group than in CON. The abundance of Butyrivibrio fibrisolvens, fungi and Ruminococcus flavefaciens (treatments; p< 0.001, p<0.001, and p = 0.005) was higher following EOM 0.2 treatment. Conclusion The addition of newly developed EOM increased IVDMD, IVNDFD, and tended to increase total VFA indicating that it may be used as a feed additive to improve rumen fermentation by modulating rumen microbial communities. Further studies would be required to investigate the detailed metabolic mechanism underlying the effects of EOM supplementation

Higher emissions scenarios lead to more extreme flooding in the United States

Understanding projected changes in flooding across the contiguous United States (CONUS) helps increase our capability to adapt to and mitigate against this hazard. Here, we assess future changes in flooding across CONUS using outputs from 28 global climate models and four scenarios of the Coupled Model Intercomparison Project Phase 6. We find that CONUS is projected to experience an overall increase in flooding, especially under higher emission scenarios; there are subregional differences, with the Northeast and Southeast (Great Plains of the North and Southwest) showing higher tendency towards increasing (decreasing) flooding due to changes in flood processes at the seasonal scale. Moreover, even though trends may not be detected in the historical period, these projected future trends highlight the current needs for incorporating climate change in the future infrastructure designs and management of the water resources

Evaluation of the Analysis of Record for Calibration (AORC) Rainfall across Louisiana

The use of a long-term and high-quality precipitation dataset is crucial for hydrologic modeling and flood risk management. This study evaluates the Analysis of Period of Record for Calibration (AORC) dataset, a newly released product with high temporal and spatial resolutions. Our study region is centered on Louisiana because of the major flooding it has been experiencing. We compare the AORC hourly precipitation to other widely used gridded rainfall products and rain-gauge observations. To evaluate the performance of rainfall products according to different weather conditions causing severe flooding, we stratify the analyses depending on whether precipitation is associated with a tropical cyclone (TC) or not. Compared to observations, our results show that the AORC has the highest correlation coefficients (i.e., values above 0.75) with respect to observations among all rainfall products for both TC and non-TC periods. When the skill metric is decomposed into the potential skill and biases, the AORC clearly shows the highest potential skill with relatively small biases for the whole period. In addition, the AORC performs better for the TC period compared to the non-TC ones. Our results suggest that AORC precipitation shows good potential to be viable for hydrologic modeling and simulations of TC and non-TC events

Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty

Extreme value modeling for extreme rainfall is one of the most important processes in the field of hydrology. For the improvement of extreme value modeling and its physical meaning, large-scale climate modes have been widely used as covariates of distribution parameters, as they can physically account for climate variability. This study proposes a novel procedure for extreme value modeling of rainfall based on the significant relationship between the long-term trend of the annual maximum (AM) daily rainfall and large-scale climate indices. This procedure is characterized by two main steps: (a) identifying significant seasonal climate indices (SCIs), which impact the long-term trend of AM daily rainfall using statistical approaches, such as ensemble empirical mode decomposition, and (b) selecting an appropriate generalized extreme value (GEV) distribution among the stationary GEV and nonstationary GEV (NS-GEV) using time and SCIs as covariates by comparing their model fit and uncertainty. Our findings showed significant relationships between the long-term trend of AM daily rainfall over South Korea and SCIs (i.e., the Atlantic Meridional Mode, Atlantic Multidecadal Oscillation in the fall season, and North Atlantic Oscillation in the summer season). In addition, we proposed a model selection procedure considering both the Akaike information criterion and residual bootstrap method to select an appropriate GEV distribution among a total of 59 GEV candidates. As a result, the NS-GEV with SCI covariates generally showed the best performance over South Korea. We expect that this study can contribute to estimating more reliable extreme rainfall quantiles using climate covariates

Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty

Extreme value modeling for extreme rainfall is one of the most important processes in the field of hydrology. For the improvement of extreme value modeling and its physical meaning, large-scale climate modes have been widely used as covariates of distribution parameters, as they can physically account for climate variability. This study proposes a novel procedure for extreme value modeling of rainfall based on the significant relationship between the long-term trend of the annual maximum (AM) daily rainfall and large-scale climate indices. This procedure is characterized by two main steps: (a) identifying significant seasonal climate indices (SCIs), which impact the long-term trend of AM daily rainfall using statistical approaches, such as ensemble empirical mode decomposition, and (b) selecting an appropriate generalized extreme value (GEV) distribution among the stationary GEV and nonstationary GEV (NS-GEV) using time and SCIs as covariates by comparing their model fit and uncertainty. Our findings showed significant relationships between the long-term trend of AM daily rainfall over South Korea and SCIs (i.e., the Atlantic Meridional Mode, Atlantic Multidecadal Oscillation in the fall season, and North Atlantic Oscillation in the summer season). In addition, we proposed a model selection procedure considering both the Akaike information criterion and residual bootstrap method to select an appropriate GEV distribution among a total of 59 GEV candidates. As a result, the NS-GEV with SCI covariates generally showed the best performance over South Korea. We expect that this study can contribute to estimating more reliable extreme rainfall quantiles using climate covariates

Effects of Red Ginseng Byproducts on Rumen Fermentation, Growth Performance, Blood Metabolites, and mRNA Expression of Heat Shock Proteins in Heat-Stressed Fattening Hanwoo Steers

The objective of this study was to evaluate the effects of dietary supplementation with red ginseng byproduct (RGB) on rumen fermentation, growth performance, blood metabolites, and mRNA expression of heat shock proteins (HSP) in fattening Hanwoo steers under heat stress. Two experimental total mixed rations (TMR) were prepared: (1) a TMR meeting the requirement of fattening beef having an average daily gain (ADG) 0.8 kg/day (CON) and (2) a TMR that included 2% RGB on a dry matter (DM) basis (GINSENG). In vitro rumen fermentation and in vivo growth experiments were conducted using two experimental diets. A total of 22 Hanwoo steers were distributed to two treatments (CON vs. GINSENG) in a completely randomized block design according to body weight (BW). The experiment was conducted during the summer season for five weeks. The final BW, ADG, DM intake, and feed conversion ratio did not differ between treatments in the growth trial. In the mRNA expression results, only HSP 90 showed an increasing tendency in the GINSENG group. The use of 2%DM RGB did not improve the growth performance or alleviate heat stress in fattening Hanwoo steers during the summer season

Characterization of Endolysin LyJH307 with Antimicrobial Activity against Streptococcus bovis

Streptococcus bovis (S. bovis) is one of the critical initiators of acute acidosis in ruminants. Therefore, we aimed to develop and characterize the endolysin LyJH307, which can lyse ruminal S. bovis. We tested the bactericidal activity of recombinant LyJH307 against S. bovis JB1 under a range of pH, temperature, NaCl, and metal ion concentrations. In silico analyses showed that LyJH307 has a modular design with a distinct, enzymatically active domain of the NLPC/P60 superfamily at the N-terminal and a cell wall binding domain of the Zoocin A target recognition domain (Zoocin A_TRD) superfamily at the C-terminal. The lytic activity of LyJH307 against S. bovis JB1 was the highest at pH 5.5, and relatively higher under acidic, than under alkaline conditions. LyJH307 activity was also the highest at 39 &deg;C, but was maintained between 25&deg;C and 55&deg;C. LyJH307 bactericidal action was retained under 0-500 mM NaCl. While the activity of LyJH307 significantly decreased on treatment with ethylenediaminetetraacetic acid (EDTA), it was only restored with supplementation of 10 mM Ca2+. Analyses of antimicrobial spectra showed that LyJH307 lysed Lancefield groups D (S. bovis group and Enterococcus faecalis) and H (S. sanguinis) bacteria. Thus, LyJH307 might help to prevent acute ruminal acidosis

Evaluation of Statistical PMP Considering RCP Climate Change Scenarios in Republic of Korea

Extreme rainfall and floods have increased in frequency and severity in recent years, due to climate change and urbanization. Consequently, interest in estimating the probable maximum precipitation (PMP) has been burgeoning. The World Meteorological Organization (WMO) recommends two types of methods for calculating the PMP: hydrometeorological and statistical methods. This study proposes a modified Hershfield’s nomograph method and assesses the changes in PMP values based on the two representative concentration pathway (RCP4.5 and RCP8.5) scenarios in South Korea. To achieve the intended objective, five techniques were employed to compute statistical PMPs (SPMPs). Moreover, the most suitable statistical method was selected by comparing the calculated SPMP with the hydrometeorological PMP (HPMP), by applying statistical criteria. Accordingly, SPMPs from the five methods were compared with the HPMPs for the historical period of 2020 and the future period of 2100 for RCP 4.5 and 8.5 scenarios, respectively. The results confirmed that the SPMPs from the modified Hershfield’s nomograph showed the smallest MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error), which are the best results compared with the HPMP with an average SPMP/HPMP ratio of 0.988 for the 2020 historical period. In addition, Hershfield’s method with varying KM exhibits the worst results for both RCP scenarios, with SPMP/HPMP ratios of 0.377 for RCP4.5 and 0.304 for RCP8.5, respectively. On the contrary, the modified Hershfield’s nomograph was the most appropriate method for estimating the future SPMPs: the average ratios were 0.878 and 0.726 for the 2100 future period under the RCP 4.5 and 8.5 scenarios, respectively, in South Korea

The Use of Large-Scale Climate Indices in Monthly Reservoir Inflow Forecasting and Its Application on Time Series and Artificial Intelligence Models

Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential information about climate variability, as they usually have a direct or indirect correlation with hydrologic variables. This study aims to use large-scale climate indices in monthly reservoir inflow forecasting for considering climate variability. For this purpose, time series and artificial intelligence models, such as Seasonal AutoRegressive Integrated Moving Average (SARIMA), SARIMA with eXogenous variables (SARIMAX), Artificial Neural Network (ANN), Adaptive Neural-based Fuzzy Inference System (ANFIS), and Random Forest (RF) models were employed with two types of input variables, autoregressive variables (AR-) and a combination of autoregressive and exogenous variables (ARX-). Several statistical methods, including ensemble empirical mode decomposition (EEMD), were used to select the lagged climate indices. Finally, monthly reservoir inflow was forecasted by SARIMA, SARIMAX, AR-ANN, ARX-ANN, AR-ANFIS, ARX-ANFIS, AR-RF, and ARX-RF models. As a result, the use of climate indices in artificial intelligence models showed a potential to improve the model performance, and the ARX-ANN and AR-RF models generally showed the best performance among the employed models