Seven multiparous Holstein cows with a ruminal fistula were used to investigate the changes in rumen microbiota, gene expression of the ruminal epithelium, and blood biomarkers of metabolism and inflammation during the transition period. Samples of ruminal digesta, biopsies of ruminal epithelium, and blood were obtained during -14 through 28d in milk (DIM). A total of 35 genes associated with metabolism, transport, inflammation, and signaling were evaluated by quantitative reverse transcription-PCR. Among metabolic-related genes, expression of HMGCS2 increased gradually from -14 to a peak at 28 DIM, underscoring its central role in epithelial ketogenesis. The decrease of glucose and the increase of nonesterified fatty acids and β-hydroxybutyrate in the blood after calving confirmed the state of negative energy balance. Similarly, increases in bilirubin and decreases in albumin concentrations after calving were indicative of alterations in liver function and inflammation. Despite those systemic signs, lower postpartal expression of TLR2, TLR4, CD45, and NFKB1 indicated the absence of inflammation within the epithelium. Alternatively, these could reflect an adaptation to react against inducers of the immune system arising in the rumen (e.g., bacterial endotoxins). The downregulation of RXRA, INSR, and RPS6KB1 between -14 and 10 DIM indicated a possible increase in insulin resistance. However, the upregulation of IRS1 during the same time frame could serve to restore sensitivity to insulin of the epithelium as a way to preserve its proliferative capacity. The upregulation of TGFB1 from -14 and 10 DIM coupled with upregulation of both EGFR and EREG from 10 to 28 DIM indicated the existence of 2 waves of epithelial proliferation. However, the downregulation of TGFBR1 from -14 through 28 DIM indicated some degree of cell proliferation arrest. The downregulation of OCLN and TJP1 from -14 to 10 DIM indicated a loss of tight-junction integrity. The gradual upregulation of membrane transporters MCT1 and UTB to peak levels at 28 DIM reflected the higher intake and fermentability of the lactation diet. In addition, those changes in the diet after calving resulted in an increase of butyrate and a decrease of ruminal pH and acetate, which partly explain the increase of Anaerovibrio lipolytica, Prevotella bryantii, and Megasphaera elsdenii and the decrease of fibrolytic bacteria (Fibrobacter succinogenes, Butyrivibrio proteoclasticus). Overall, these multitier changes revealed important features associated with the transition into lactation. Alterations in ruminal epithelium gene expression could be driven by nutrient intake-induced changes in microbes; microbial metabolism; and the systemic metabolic, hormonal, and immune changes. Understanding causes and mechanisms driving the interaction among ruminal bacteria and host immunometabolic responses merits further study.

Deep imitation learning enables robots to learn from expert demonstrations to perform tasks such as lane following or obstacle avoidance. However, in the traditional imitation learning framework, one model only learns one task, and thus it lacks of the capability to support a robot to perform various different navigation tasks with one model in indoor environments. This paper proposes a new framework, Shared Multi-headed Imitation Learning(SMIL), that allows a robot to perform multiple tasks with one model without switching among different models. We model each task as a sub-policy and design a multi-headed policy to learn the shared information among related tasks by summing up activations from all sub-policies. Compared to single or non-shared multi-headed policies, this framework is able to leverage correlated information among tasks to increase performance. We have implemented this framework using a robot based on NVIDIA TX2 and performed extensive experiments in indoor environments with different baseline solutions. The results demonstrate that SMIL has doubled the performance over nonshared multi-headed policy.

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Croplands are decreasing due to the expansion of urban areas into rural communities and to some extent due to sand accumulations. Increases in population numbers, new development, in addition to the accumulation of sand and soil salinity are the major driving force leading to abandonment and shrinking of cropland. The aim of this study was to investigate and assess to what extent agricultural lands are affected by urban development in the Al Hassa oasis, Eastern region in Saudi Arabia by employing Landsat time series data of years 1988, 2000 and 2017 as the main source of information. A set of ground truth, control points (GCPs) was also used besides population census data. Unsupervised classifications approach, Normalized Difference Vegetation Index (NDVI) and change detection methods were used here. Urban area during 2000-2017 exhibits much higher increase compared to 1988-2000, while the arable lands declined to −3.4% in 1988-2000 and increased to 22% during 2000-2017. The data analysis results provided new accurate numerical information supported by a graphical representation in regard to the decrease and increase in urban and agricultural lands. Therefore the findings of this study should be considered by decision maker for improving and development the agriculture activities in rural and urban communities.

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