RESUMO
The National Forestry Commission of Mexico continuously monitors forest structure within the country's continental territory by the implementation of the National Forest and Soils Inventory (INFyS). Due to the challenges involved in collecting data exclusively from field surveys, there are spatial information gaps for important forest attributes. This can produce bias or increase uncertainty when generating estimates required to support forest management decisions. Our objective is to predict the spatial distribution of tree height and tree density in all Mexican forests. We performed wall-to-wall spatial predictions of both attributes in 1-km grids, using ensemble machine learning across each forest type in Mexico. Predictor variables include remote sensing imagery and other geospatial data (e.g., mean precipitation, surface temperature, canopy cover). Training data is from the 2009 to 2014 cycle (n > 26,000 sampling plots). Spatial cross validation suggested that the model had a better performance when predicting tree height r 2 = .35 [.12, .51] (mean [min, max]) than for tree density r 2 = .23 [.05, .42]. The best predictive performance when mapping tree height was for broadleaf and coniferous-broadleaf forests (model explained ~50% of variance). The best predictive performance when mapping tree density was for tropical forest (model explained ~40% of variance). Although most forests had relatively low uncertainty for tree height predictions, e.g., values <60%, arid and semiarid ecosystems had high uncertainty, e.g., values >80%. Uncertainty values for tree density predictions were >80% in most forests. The applied open science approach we present is easily replicable and scalable, thus it is helpful to assist in the decision-making and future of the National Forest and Soils Inventory. This work highlights the need for analytical tools that help us exploit the full potential of the Mexican forest inventory datasets.
RESUMO
Cystic biliary atresia (CBA), a rare cystic expansion of atretic extrahepatic bile ducts in young infants, overlaps in age at presentation and imaging features with early choledochal cysts (CC). Treatment and prognosis differ; histologic differences are unsettled. We compared 10 patients with CBA, 1975 to 2015, to an age-similar cohort of 13 infants, and to older patients who had surgery for CC. Operative details, imaging, and clinical courses were correlated to pathologic specimens. Immunostains for smooth muscle actin and myosin heavy chain were used to evaluate cyst walls and atretic segments. CBA cysts typically lacked epithelium and inflammation; cyst walls had an inner, dense cicatricial layer associated with myofibroblastic (MF) hyperplasia that often delaminated producing a grossly visible inner cyst wall. Seven proximal biliary remnants in CBA featured circumferential peribiliary MF hyperplasia/fibrosis with little or no inflammation, similar to isolated BA. Extrahepatic atresia was usually both proximal and distal to the cyst. Features in 10/13 CC from infants and 8/8 CC in older patients had mostly preserved uninjured epithelium and no subepithelial cicatrix. Mural smooth muscle (absent in CBA) was present to some extent in CC at all ages. Unexpectedly, focal MF hyperplasia and laminar sclerosis was present in a few CC in infants, resembling CBA. CBA and infant CC are distinct histologic entities that occasionally overlap. CBA bile duct injury mimics non-CBA. Cystification is an aberrant manifestation of stromal proliferation in BA. The current management approach assuming CBA and CC in infants are 2 separate disease processes is supported but caution is advised.