Isolation of fungi from bats of the Amazon basin

A total of 2,886 bats captured in the Amazon Basin of Brazil were processed for the isolation of fungi. From the livers, spleens, and lungs of 155 bats (5.4%), 186 fungal isolates of the genera Candida (123 isolates), Trichosporon (26 isolates), Torulopsis (25 isolates), Kluyveromyces (11 isolates), and Geotrichum (1 isolate) were recovered. Seven known pathogenic species were present: Candida parapsilosis, C. guilliermondii, C. albicans, C. stellatoidea, C. pseudotropicalis, Trichosporon beigelii, and Torulopsis glabrata. Twenty-three culture-positive bats showed identical fungal colonization in multiple organs or mixed colonization in a single organ. The fungal isolation rates for individual bat species varied from 1 fungus per 87 bats to 3 fungi per 13 bats, and the mycoflora diversity for members of an individual fungus-bearing bat species varied from 16 fungi per 40 bats to 7 fungi per 6 bats. Of the 38 fungal species isolated, 36 had not been previously described as in vivo bat isolates. Of the 27 culture-positive bat species, 21 had not been previously described as mammalian hosts for medically or nonmedically important fungi.     

A predictive model for freshwater bioassessment (Mondego River,Portugal)

We sampled macroinvertebrates at 75 locations in the Mondego river catchment, Central Portugal, and developed a predictive model for water quality assessment of this basin, based on the Reference Condition Approach. Sampling was done from June to September 2001. Fifty-five sites were identified as “Reference sites” and 20 sites were used as “Test sites” to test the model. At each site we also measured 40 habitat variables to characterize water physics and chemistry, habitat type, land use, stream hydrology and geographic location. Macroinvertebrates were generally identified to species or genus level; a total of 207 taxa were found. By Unweighted Pair Group Method with Arithmetic mean (UPGMA) clustering and analysis of species contribution to similarities percentage (SIMPER), two groups of reference sites were established. Using Discriminant Analysis (stepwise forward), four variables correctly predicted 78% of the reference sites to the appropriate group: stream order, pool quality, substrate quality and current velocity. Test sites’ environmental quality was established from their relative distance to reference sites, in MDS ordination space, using a series of bands (BEAST methodology). The model performed well at upstream sites, but at downstream sites it was compromised by the lack of reference sites. As with the English RIVPACS predictive model, the Mondego model should be continually improved with the addition of new reference sites. The adaptation of the Mondego model methodology to the Water Framework Directive is possible and would consist mainly of the integration of the WFD typology and increasing the number of ellipses that define quality bands. Handling editor: K. Martens     

Perspective Texture Synthesis Based on Improved Energy Optimization

Perspective texture synthesis has great significance in many fields like video editing, scene capturing etc., due to its ability to read and control global feature information. In this paper, we present a novel example-based, specifically energy optimization-based algorithm, to synthesize perspective textures. Energy optimization technique is a pixel-based approach, so it’s time-consuming. We improve it from two aspects with the purpose of achieving faster synthesis and high quality. Firstly, we change this pixel-based technique by replacing the pixel computation with a little patch. Secondly, we present a novel technique to accelerate searching nearest neighborhoods in energy optimization. Using k- means clustering technique to build a search tree to accelerate the search. Hence, we make use of principal component analysis (PCA) technique to reduce dimensions of input vectors. The high quality results prove that our approach is feasible. Besides, our proposed algorithm needs shorter time relative to other similar methods.