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As an important evolutionary innovation and unique organ, the rumen has played a crucial role in ruminant adaptation to complex ecological environments. However, the cellular basis of its complex morphology and function remains largely unknown. In this study, we identified eight major cell types from seven representative prenatal and postnatal rumen samples using ~56 600 single-cell transcriptomes. We captured the dynamic changes and high heterogeneity in cellular and molecular profiles before, during, and after the appearance of keratinized stratified squamous epithelium with neatly arranged papillae and functional maturity. Basal cells, keratinocytes, differentiating keratinocytes, terminally differentiated keratinocytes, and special spinous cells provided the cellular basis for rumen epithelium formation. Notably, we obtained clear evidence of two keratinization processes involved in early papillogenesis and papillae keratinization and identified TBX3 as a potential marker gene. Importantly, enriched stratum spinosum cells played crucial roles in volatile fatty acid (VFA) metabolism and immune response. Our results provide a comprehensive transcriptional landscape of rumen development at single-cell resolution, as well as valuable insight into the interactions between dietary metabolism and the rumen.

Mangosteen fruit is fermented with five different strains (i.e. GRE (Y1), Lalvin RC212 (Y2), Lalvin D254 (Y3), CGMCC2.23 (Y4) and CGMCC2.4 (Y5)) of the yeast Saccharomyces cerevisiae to make mangosteen wines. A total of 36 volatile compounds of the mangosteen wines were identified by gas chromatography-mass spectrometry and gas chromatography-pulsed flame photometric detection. A total of 35 odour-active compounds were identified by gas chromatography-olfactometry analysis and by the detection frequency (DF) method. The compounds with high DF values included ethyl octanoate, ethyl hexanoate and 3-methyl-2-butene-1-thiol. Principal component analysis was used to characterise the differences of the flavour profiles of those mangosteen wines. The result demonstrated that the samples could be divided into three groups that were associated closely with aroma-active compounds.

Bayesian Model Averaging (BMA) is a statistical method used for multi-model ensemble forecast system. Firstly, the likelihood function of BMA is improved by eliminating the explicit constraint, that the sum of weights is 1, and use SCE-UA for the minimization of its, which presents a new method for solving the Bayesian model averaging, that is the BMA-SCE-UA method. With three land surface models of soil moisture simulation test of multiple numerical model. By comparing the common Expectation Maximization (EM) method with the SCE-UA method, the results show that: SCE-UA method can improve the simulation performance of soil moisture in a large extent, and the soil moisture obtained by the BMA collection simulation and observation matches well, no matter from the amplitude variation and seasonal variability, which makes it possible that generating high accuracy data set of soil moisture with the method of BMA-SCE-UA and using multiple land surface models.