Derived crop coefficients for winter wheat using different reference evpotranspiration estimates methods

This paper reports the results of using three empirical methods (Makkink, Priestley-Taylor and Hargreaves) for estimating the reference evapotranspiration (ET0) in the semi-arid region of Tensift Al Haouz, Marrakech (center of Morocco). The Penman-Monteith equation, standardized by the Food and Agriculture Organization (FAO-PM), is used to evaluate the three empirical methods. The obtained ET0 data were used to estimate crop water requirement (ET) of winter wheat using the crop coefficient (K-c) approach and results were compared with ET measured by the Eddy Covariance technique. The result showed that using the original empirical coefficients a, alpha and C-m in Hargreaves, Priestley-Taylor and Makkink equations, respectively, the Hargreaves method agreed fairly well with FAO-PM method at the test site. Conversely, the Priestley-Taylor and Makkink methods underestimate the ET by about 20 and 18 %. After adjustment of the original values of two parameters alpha and C-m coefficients in Priestley-Taylor and Makkink equations, the underestimation of ET was reduced to 9% and 4% for the Priestley Taylor and Makkink methods, respectively, which led to an improvement of 55% and 76% of the obtained values compared with the original values

XingGAN for Person Image Generation

We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i.e., translating the pose of a given person to a desired one. The proposed Xing generator consists of two generation branches that model the person's appearance and shape information, respectively. Moreover, we propose two novel blocks to effectively transfer and update the person's shape and appearance embeddings in a crossing way to mutually improve each other, which has not been considered by any other existing GAN-based image generation work. Extensive experiments on two challenging datasets, i.e., Market-1501 and DeepFashion, demonstrate that the proposed XingGAN advances the state-of-the-art performance both in terms of objective quantitative scores and subjective visual realness. The source code and trained models are available at https://github.com/Ha0Tang/XingGAN.Comment: Accepted to ECCV 2020, camera ready (16 pages) + supplementary (6 pages

The pregnane X receptor drives sexually dimorphic hepatic changes in lipid and xenobiotic metabolism in response to gut microbiota in mice.

The gut microbiota-intestine-liver relationship is emerging as an important factor in multiple hepatic pathologies, but the hepatic sensors and effectors of microbial signals are not well defined. By comparing publicly available liver transcriptomics data from conventional vs. germ-free mice, we identified pregnane X receptor (PXR, NR1I2) transcriptional activity as strongly affected by the absence of gut microbes. Microbiota depletion using antibiotics in Pxr <sup>+/+</sup> vs Pxr <sup>-/-</sup> C57BL/6J littermate mice followed by hepatic transcriptomics revealed that most microbiota-sensitive genes were PXR-dependent in the liver in males, but not in females. Pathway enrichment analysis suggested that microbiota-PXR interaction controlled fatty acid and xenobiotic metabolism. We confirmed that antibiotic treatment reduced liver triglyceride content and hampered xenobiotic metabolism in the liver from Pxr <sup>+/+</sup> but not Pxr <sup>-/-</sup> male mice. These findings identify PXR as a hepatic effector of microbiota-derived signals that regulate the host's sexually dimorphic lipid and xenobiotic metabolisms in the liver. Thus, our results reveal a potential new mechanism for unexpected drug-drug or food-drug interactions. Video abstract

Liver PPARα is crucial for whole-body fatty acid homeostasis and is protective against NAFLD.

OBJECTIVE: Peroxisome proliferator-activated receptor α (PPARα) is a nuclear receptor expressed in tissues with high oxidative activity that plays a central role in metabolism. In this work, we investigated the effect of hepatocyte PPARα on non-alcoholic fatty liver disease (NAFLD). DESIGN: We constructed a novel hepatocyte-specific PPARα knockout (Pparα(hep-/-)) mouse model. Using this novel model, we performed transcriptomic analysis following fenofibrate treatment. Next, we investigated which physiological challenges impact on PPARα. Moreover, we measured the contribution of hepatocytic PPARα activity to whole-body metabolism and fibroblast growth factor 21 production during fasting. Finally, we determined the influence of hepatocyte-specific PPARα deficiency in different models of steatosis and during ageing. RESULTS: Hepatocyte PPARα deletion impaired fatty acid catabolism, resulting in hepatic lipid accumulation during fasting and in two preclinical models of steatosis. Fasting mice showed acute PPARα-dependent hepatocyte activity during early night, with correspondingly increased circulating free fatty acids, which could be further stimulated by adipocyte lipolysis. Fasting led to mild hypoglycaemia and hypothermia in Pparα(hep-/-) mice when compared with Pparα(-/-) mice implying a role of PPARα activity in non-hepatic tissues. In agreement with this observation, Pparα(-/-) mice became overweight during ageing while Pparα(hep-/-) remained lean. However, like Pparα(-/-) mice, Pparα(hep-/-) fed a standard diet developed hepatic steatosis in ageing. CONCLUSIONS: Altogether, these findings underscore the potential of hepatocyte PPARα as a drug target for NAFLD