An atlas of DNA methylomes in porcine adipose and muscle tissues

It is evident that epigenetic factors, especially DNA methylation, have essential roles in obesity development. Here, using pig as a model, we investigate the systematic association between DNA methylation and obesity. We sample eight variant adipose and two distinct skeletal muscle tissues from three pig breeds living within comparable environments but displaying distinct fat level. We generate 1,381 Gb of sequence data from 180 methylated DNA immunoprecipitation libraries, and provide a genome-wide DNA methylation map as well as a gene expression map for adipose and muscle studies. The analysis shows global similarity and difference among breeds, sexes and anatomic locations, and identifies the differentially methylated regions. The differentially methylated regions in promoters are highly associated with obesity development via expression repression of both known obesity-related genes and novel genes. This comprehensive map provides a solid basis for exploring epigenetic mechanisms of adipose deposition and muscle growth

Ecological Dynamics and Regeneration Expansion of Treeline Ecotones in Response to Climate Change in Northern Bhutan Himalayas

The alpine treeline ecotones are an early indicator of vegetation’s response to changes in climate, and the advancement of diffuse treeline ecotones has been associated with mean annual warming temperatures. However, the knowledge of how tree demographic size, age and population distribution, and regeneration decrease with increasing elevation and mean annual temperature remain fragmentary in Bhutan. There was no explanation of how treelines migrate in response to the climate. Therefore, the objectives of this study were to investigate tree demographic size and age and population distribution, as well as the regeneration expansion of treeline ecotones of Abies densa trees in response to climate change. Demographic data from thirty transect bands from treeline ecotones and reconstructed mean annual temperatures from tree-rings were used. Regression analysis was used to establish a relationship between elevation/temperature and demographic tree size and age, as well as to determine recruitment frequency distributions and whether these could be driven by climate change. The tree demography indicated that the treeline ecotone in our sampling site is temperature limited. Hence, cooler temperatures at higher elevations should drive decreases in basal diameter, age and recruitment frequencies. From the dendroecological analysis, the diffuse treeline ecotones appear to be climbing on average 1.00 m per year in Northern Bhutan. We also found that the recruitment frequency has increased over recent years (1850–2017), as temperatures continue to rise. The thermal treeline ecotones will be likely to serve as a line of bioclimatic reference against which other zones of bioclimate can be defined. With documented responses of treeline ecotones toward mean annual temperatures, the expectation is that additional warming will continue to influence regeneration expansion in the future. This dynamic response of treeline ecotones towards the climate acts as an indicator of climate change. Information about climbing treelines and altered ecotones should be a vital part of the material for decision makers to consider, to assess impacts and threats to Himalayan alpine biota

Dendroclimatic Reconstruction of Mean Annual Temperatures over Treeline Regions of Northern Bhutan Himalayas

The Himalayan region is likely particularly exposed to climate change indicated by the high regional rate of change. The number of high-resolution, well-calibrated, and long-term paleoclimate reconstructions are however regrettably few, to set this change in a longer-term context. The dendroclimatic reconstructions over Himalaya that do exist have only reconstructed summer season temperatures, and rarely or never attempted to reconstruct mean annual temperatures. The paucity of long meteorological records is a matter of concern when developing chronologies of climate sensitive tree-ring data in Bhutan, but the chronologies would theoretically be of high potential for extending short meteorological records back in time using trees in high-elevation ecotones. The objectives of this study were to explore dendroclimatic signals in tree-ring width chronologies of Abies densa growing in these extreme ecotones and to reconstruct, if possible, annual temperatures over Northern Bhutan. A point-by-point regression analysis revealed that the regional composite chronology was significantly and positively correlated with temperatures of all months of the current year, i.e., January to December. The chronology was highly correlated with annual temperatures (calibration period R = 0.67 and validation period R = 0.50; p < 0.001) allowing a reconstruction of temperature over Northern Bhutan (NB-TEMR). The NB-TEMR reveals some common variations with summer temperature reconstructions of the Northern Hemisphere as well as the Himalayan region, particularly w.r.t to the recent warming trend. The reconstruction covers the period of 1765 to 2017. This reconstruction reveals a warming trend since 1850 with higher rates of warming 1935 to 2017, but with a pause around 1940–1970. The warming is consistent with reduced volcanic activity and increase of greenhouse gases. We anticipate that our new reconstruction of annual mean temperature could be an important contribution for future climate change studies and assessments of climate models

Automated Detection Method to Extract <i>Pedicularis</i> Based on UAV Images

Pedicularis has adverse effects on vegetation growth and ecological functions, causing serious harm to animal husbandry. In this paper, an automated detection method is proposed to extract Pedicularis and reveal the spatial distribution. Based on unmanned aerial vehicle (UAV) images, this paper adopts logistic regression, support vector machine (SVM), and random forest classifiers for multi-class classification. One-class SVM (OCSVM), isolation forest, and positive and unlabeled learning (PUL) algorithms are used for one-class classification. The results are as follows: (1) The accuracy of multi-class classifiers is better than that of one-class classifiers, but it requires all classes that occur in the image to be exhaustively assigned labels. Among the one-class classifiers that only need to label positive or positive and labeled data, the PUL has the highest F score of 0.9878. (2) PUL performs the most robustly to change features in one-class classifiers. All one-class classifiers prove that the green band is essential for extracting Pedicularis. (3) The parameters of the PUL are easy to tune, and the training time is easy to control. Therefore, PUL is a promising one-class classification method for Pedicularis extraction, which can accurately identify the distribution range of Pedicularis to promote grassland administration