Resumen pt: Tomar o tema alfabetizacao de criancas surdas, atualmente, e direcionar o olhar para a pratica docente que demanda rumos para alem do espaco escolar. Que...

Karstic cave systems in Slovenia receive substantial amounts of organic input from adjacent forest and freshwater systems. These caves host microbial communities that consist of distinct small colonies differing in colour and shape. Visible to the naked eye, the colonies cover cave walls and are strewn with light-reflecting water droplets. In this study, the diversity of prokaryotes constituting these unusual microbial communities in Pajsarjeva jama cave was examined. A molecular survey based on small subunit rRNA diversity showed a high diversity within the Bacteria, while members of Archaea were not recovered. A total of eight bacterial phyla were detected. The application of various species richness estimators confirmed the diverse nature of the microbial community sample. Members of Gammaproteobacteria were most abundant in the clone libraries constructed and were followed in abundance by members of Actinobacteria and Nitrospira. In addition, members of Alphaproteobacteria, Betaproteobacteria and Deltaproteobacteria as well as Acidobacteria, Verrucomicrobia, Planctomycetes, Chloroflexi and Gemmatimonadetes were identified in clone libraries. The high number of clones most closely related to environmental 16S rRNA gene clones showed the broad spectrum of unknown and yet to be cultivated microorganisms inhabiting these cave systems.

Aqueous solubility is an i mportant factor influencin g several a spects of the phar macokinetic profile of a drug. Nu merous p ublications pre sent di f- ferent methodologies for the de velopment of reliable computational models for the prediction of solubility fro m stru cture. The quality of s uch models c an be significantly affected by the accuracy of the em ployed experimental solubility data. In this work, the importance of the accuracy of the experimental solubility data used for mod el training was inv estigated. Three data sets were u sed as training sets - data set 1, containing solubility data collected from various lite- rature sources using a few criteria (n = 319), data set 2, created by substituting 28 valu es fro m data set 1 wit h unifor mly deter mined ex perimental d ata fro m one laboratory ( n = 319), and data set 3, cre ated by includi ng 56 ad ditional components, for which th e solubility was al so determined under uniform con- ditions in the same laboratory, in the d ata set 2 (n = 37 5). The sel ection of the most significant descriptors was performed by the heuristic method, using one- -parameter and multi-parameter analy sis. The correlations between th e most significant descriptors and solubility were established using multi-linear regres- sion analysis (MLR) for all t hree inve stigated data sets. Notable differen ces were observed between the equations corresponding to different data sets, sug- gesting that models updated with new experimental data need to be additionally optimized. It was succe ssfully shown that the inclusion of unifor m experimen- tal data co nsistently leads to an i mprovement in th e correla tion coefficie nts. These findings contribute to an emerging consensus that improving the reliabi- lity of solubility prediction requires the inclu sion of many diverse co mpounds for which solubility was measured under standardized conditions in the data set.