Autismus und soziale Kognition: eine Übersicht funktioneller Bildgebungsstudien

Störungen des autistischen Spektrums (frühkindlicher Autismus, Asperger-Syndrom und High-functioning-Autismus) gehören zu den tiefgreifenden Entwicklungsstörungen und weisen ein gemeinsames Muster von erheblichen Beeinträchtigungen der sozialen Interaktion auf. Dazu gehören Einschränkungen der Gesichter- und Emotionswahrnehmung und der sozialen Attribution („theory of mind“), allgemein gesprochen der „sozialen Kognition“. Einige Studien zeigen, dass sich diese Einschränkungen bereits in der frühen Kindheit zeigen, wobei als Ursache eine frühe Schädigung der neuronalen Entwicklung vermutet wird. Damit übereinstimmend konnten sowohl strukturelle als auch funktionelle Veränderungen des Gehirns bei Kindern, Jugendlichen und Erwachsenen mit Autismus nachgewiesen werden. Die vorliegende Arbeit gibt einen systematischen Überblick über die existierenden funktionellen Bildgebungsstudien mit experimentellen Paradigmen der sozialen Kognition bzw. Gesichterdiskriminierung, Emotionserkennung und sozialen Attribution („theory of mind“) bei Störungen des autistischen Spektrums. Autism spectrum disorders (autism, Asperger's syndrome, high-functioning autism) are characterized by a common pattern of marked impairments in social interactions. Deficits have been described in face processing, facial emotion recognition, and social attribution ("theory of mind") or generally speaking in social cognition. Some studies have shown that these impairments are already detectable in early childhood, leading to the assumption that the underlying cause is an early disruption of neuronal development. Accordingly, neuroimaging data have revealed alterations of structure and function in the brains of autistic children, adolescents, and adults. The present review gives a systematic overview of the existing literature on functional imaging studies using experimental paradigms of social cognition, i.e. face discrimination, facial emotion recognition, and theory of mind in autistic disorders

A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms.

Increasing occurrence of harmful algal blooms across the land-water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective analysis and identification of specific conditions supporting phytoplankton growth. A novel iterative Random Forests (iRF) machine-learning model was developed and applied to two example cases along the California coast to identify key stable interactions: (1) phytoplankton abundance in response to various drivers due to coastal conditions and land-sea nutrient fluxes, (2) microbial community structure during algal blooms. In Example 1, watershed derived nutrients were identified as the least significant interacting variable associated with Monterey Bay phytoplankton abundance. In Example 2, through iRF analysis of field-based 16S OTU bacterial community and algae datasets, we independently found stable interactions of prokaryote abundance patterns associated with phytoplankton abundance that have been previously identified in laboratory-based studies. Our study represents the first iRF application to marine algal blooms that helps to identify ocean, microbial, and terrestrial conditions that are considered dominant causal factors on bloom dynamics

A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms.

Increasing occurrence of harmful algal blooms across the land-water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective analysis and identification of specific conditions supporting phytoplankton growth. A novel iterative Random Forests (iRF) machine-learning model was developed and applied to two example cases along the California coast to identify key stable interactions: (1) phytoplankton abundance in response to various drivers due to coastal conditions and land-sea nutrient fluxes, (2) microbial community structure during algal blooms. In Example 1, watershed derived nutrients were identified as the least significant interacting variable associated with Monterey Bay phytoplankton abundance. In Example 2, through iRF analysis of field-based 16S OTU bacterial community and algae datasets, we independently found stable interactions of prokaryote abundance patterns associated with phytoplankton abundance that have been previously identified in laboratory-based studies. Our study represents the first iRF application to marine algal blooms that helps to identify ocean, microbial, and terrestrial conditions that are considered dominant causal factors on bloom dynamics

Factors influencing treatment of veterans with advanced prostate cancer

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/168287/1/cncr33485.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/168287/2/cncr33485_am.pd