Trichosporonosis Presenting as an Exophytic Cutaneous Mass Lesion

Mycopathologia - Opportunistic fungal pathogens have increased in frequency with the growing immunosuppressed population. New and emerging pathogens, including the rare yeasts, continue to cause...     

Neuronal-glial interactions: Quantitation of astrocytic influences on development of catecholamine neurons

Effects of astroglia on the morphological and biochemical differentiation of catecholamine neurons from embryonic rat mesencephalon were studied in vitro, and compared to results obtained with fibroblasts. Neurite outgrowth and complexity were measured using computer-assisted morphometry on tyrosine hydroxylase immunoreactive neurons growing on preformed monolayers of astrocytes or fibroblasts. The morphological differentiation of these neurons was stimulated by the presence of astrocytes, and this effect was evident in various cellular compartments, including the size of the cell soma, length of neurites and neuritic segments, and the numbers of these segments. Tyrosine hydroxylase activity was measured biochemically in these cultures and was also found to be stimulated by the presence of astroglial monolayers. The implication of these results for the understanding of specific neuron-glial interactions during embryonic brain development is discussed.Special issue dedicated to Dr. Paola S. Timiras     

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.