The study was conducted to investigate the effect of essential oils on performance, egg quality, nutrient digestibility and yolk fatty acid profile in laying hens. A total of 960 Lohmann laying hens aged 53 weeks were enrolled, under 4 different treatment diets supplemented with 0, 50, 100 and 150 mg/kg essential oils (Enviva EO, Dupont Nutrition Biosciences ApS, Denmark), respectively. Each treatment was replicated 8 times with 30 birds each. Birds were fed dietary treatment diets for 12 weeks (54 to 65 weeks). For data recording and analysis, a 12-week period was divided into 3 periods of 4 weeks' duration each: period 1 (54 to 57 weeks), period 2 (58 to 61 weeks), and period 3 (62 to 65 weeks). For the diet supplemented with Enviva EO, hen-day egg production and the feed conversion ratio (FCR) were significantly improved (P < 0.05) at weeks 58 to 61, and the eggshell thickness was significantly increased (P < 0.05) at week 65. However, egg production, egg weight, feed intake, FCR and other egg quality parameters (albumen height, Haugh unit, egg yolk color and eggshell strength) were not affected by the dietary treatment. In addition, compared with the control diet, protein digestibility in the 100 mg/kg Enviva EO treatment group was significantly increased (P < 0.05), and fat digestibility in the 100 and 150 mg/kg Enviva EO treatment groups was significantly decreased (P < 0.05), but Enviva EO had no effect on energy apparent digestibility. Saturated fatty acid (SFA) and monounsaturated fatty acid (MUFA) gradually decreased and polyunsaturated fatty acid (PUFA) increased with Enviva EO supplementation, but the difference was not significant. The data suggested that the supplementation of essential oils (Enviva EO) in laying hen diet did not show a significant positive effect on performance and yolk fatty acid composition but it tended to increase eggshell thickness and protein digestibility, especially at the dose of 50 mg/kg.
Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation. Graphical Abstract
Purpose The impact of demand fluctuation during crisis events is crucial to the dynamic pricing and revenue management tactics of the hospitality industry. The purpose of this paper is to improve the accuracy of hotel demand forecast during periods of crisis or volatility, taking the 2019 social unrest in Hong Kong as an example. Design/methodology/approach Crisis severity, approximated by social media data, is combined with traditional time-series models, including SARIMA, ETS and STL models. Models with and without the crisis severity intervention are evaluated to determine under which conditions a crisis severity measurement improves hotel demand forecasting accuracy. Findings Crisis severity is found to be an effective tool to improve the forecasting accuracy of hotel demand during crisis. When the market is volatile, the model with the severity measurement is more effective to reduce the forecasting error. When the time of the crisis lasts long enough for the time series model to capture the change, the performance of traditional time series model is much improved. The finding of this research is that the incorporating social media data does not universally improve the forecast accuracy. Hotels should select forecasting models accordingly during crises. Originality/value The originalities of the study are as follows. First, this is the first study to forecast hotel demand during a crisis which has valuable implications for the hospitality industry. Second, this is also the first attempt to introduce a crisis severity measurement, approximated by social media coverage, into the hotel demand forecasting practice thereby extending the application of big data in the hospitality literature.
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