SET translocation is associated with increase in caspase cleaved amyloid precursor protein in CA1 of Alzheimer and Down syndrome patients.

Caspase cleaved amyloid precursor protein (APPcc) and SET are increased and mislocalized in the neuronal cytoplasm in Alzheimer Disease (AD) brains. Translocated SET to the cytoplasm can induce tau hyperphosphorylation. To elucidate the putative relationships between mislocalized APPcc and SET, we studied their level and distribution in the hippocampus of 5 controls, 3 Down syndrome and 10 Alzheimer patients. In Down syndrome and Alzheimer patients, APPcc and SET levels were increased in CA1 and the frequency of both localizations in the neuronal cytoplasm was high in CA1, and low in CA4. As the increase of APPcc is already present at early stages of AD, we overexpressed APPcc in CA1 and the dentate gyrus neurons of adult mice with a lentiviral construct. APPcc overexpression in CA1 and not in the dentate gyrus induced endogenous SET translocation and tau hyperphosphorylation. These data suggest that increase in APPcc in CA1 neurons could be an early event leading to the translocation of SET and the progression of AD through tau hyperphosphorylation.

Comparison between detailed digital and conventional soil maps of an area with complex geology

Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.

Vehicular Traffic Flow Map, 1988

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1990

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1992

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1994

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1996

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 1998

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2000

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2002

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2004

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2006

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Vehicular Traffic Flow Map, 2008

Traffic volumes represented on this map are annual average daily traffic volumes between major traffic generators: highway junctions and cities.

Interstate Route Flow Map, 1992

Interstate Route Flow represented on this map are annual average daily traffic volumes between major traffic.