The is a group of ancient fungi with global distribution. In the current study we accessed mucoralean fungi isolated from two countries on opposite sides of the Earth and in different hemispheres: South Korea and Brazil. isolates were obtained from freshwater, soil, invertebrates, and fruit seeds and identified using phenotypic techniques combined with the DNA sequence data.
View Article and Find Full Text PDFEurotiales fungi are thought to be distributed worldwide but there is a paucity of information about their occurrence on diverse substrates or hosts and at specific localities. Some of the Eurotiales, including Aspergillus and Penicillium species, produce an array of secondary metabolites of use for agricultural, medicinal, and pharmaceutical applications. Here, we carried out a survey of the Eurotiales in South Korea, focusing on soil, freshwater, and plants (dried persimmon fruits and seeds of Perilla frutescens, known commonly as shiso).
View Article and Find Full Text PDFNeuroscience research with public health relevance to emotional disorders examines brain-behavior relations. Joe LeDoux's legacy advances these efforts in ways that remain truly unique. While recognized for his basic science research, he also inspires applied researchers, guiding an agenda for clinical scientists: understanding the pathophysiology of altered subjective experiences in emotional disorders.
View Article and Find Full Text PDFBackground: Selective serotonin reuptake inhibitors (SSRIs) are used for the treatment of several conditions including anxiety disorders, but the basic neurobiology of serotonin function remains unclear. The amygdala and prefrontal cortex are strongly innervated by serotonergic projections and have been suggested to play an important role in anxiety expression. However, serotonergic function in behaviour and SSRI-mediated neurobiological changes remain incompletely understood.
View Article and Find Full Text PDFClustering is widely used in bioinformatics and many other fields, with applications from exploratory analysis to prediction. Many types of data have associated uncertainty or measurement error, but this is rarely used to inform the clustering. We present Dirichlet Process Mixtures with Uncertainty (DPMUnc), an extension of a Bayesian nonparametric clustering algorithm which makes use of the uncertainty associated with data points.
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