No abstract

Hepatozoon spp. are Apicomplexan protozoa that parasitize a wide diversity of vertebrate hosts. In Brazil, few studies have reported the occurrence of Hepatozoon spp. in rodent species. Additionally, an evaluation of the population structure and distribution of Hepatozoon species over several Brazilian biomes has not yet been performed. The present work aimed to investigate the genetic diversity of Hepatozoon spp. in rodents from 31 genera sampled in five Brazilian biomes. Samples were submitted to PCR assays for Hepatozoon spp. targeting two regions of the 18S rRNA gene. Infection by Hepatozoon spp. was detected in 195 (42.2%) rodents comprising 24 genera. Phylogenetic analyses of 18S rRNA sequences grouped all sequences in the clade of Hepatozoon spp. previously detected in rodents and reptiles, apart from those detected in domestic/wild carnivores. These data raise two non-exclusive hypotheses: (i) rodents play an important role as intermediate or paratenic hosts for Hepatozoon infections in reptiles; and (ii) rodents do not seem to participate in the epidemiology of Hepatozoon infections of domestic/wild canids and felids in Brazil. TCS analyses performed with available 18S rRNA Hepatozoon sequences detected in rodents from Brazil showed the occurrence of six haplotypes, which were distributed in two large groups: one from rodents inhabiting the coastal region of Brazil and Mato Grosso state, and another from rodents from the central region of the country. A wide survey of the South American territory will help to elucidate the evolutionary history of Hepatozoon spp. parasitizing Rodentia in the American continent.

No abstract

The flipped classroom technique has been applied to a part (“Strength of Materials”) of a second‐year compulsory course called “Technology of Materials.” Due to the number of students, the course is divided into two groups – one taught with the traditional methodology (98 students), and the other taught with the flipped one (97 students). In the traditional methodology, the teacher explains the lesson and solves the problems, with students as passive actors in the learning process. In the developed flipped classroom model, the students have edited videos on an institutional online platform, available before each face‐to‐face session. In addition, a linked activity is used to check the students’ knowledge before class. The in‐class time is dedicated to briefly reviewing the concepts explained in the video, with a special emphasis on the errors detected in the link activity, followed by groups of students solving problems. The aim of this study is to present quantitative results of the effect of the flipped classroom in engineering with a focus on the gender of the students. The results show that the flipped classroom model has a direct impact on student grades, especially for female students, which presents significant differences when compared with males of the same group. In addition, the grade standard deviation values were lower, ensuring a better general level. The students of the flipped classroom group also attended the exams in a higher ratio than others, as these students are likely to feel more confident in the knowledge they have acquired.

Keywords:Active surfaces Cortical panellation Diffusion MRI Nonlinear registration Segmentation Susceptibility distortion Current methods for processing diffusion MRI (dMRI) to map the connectivity of the human brain require precise delineations of anatomical structures. This requirement has been approached by either segmenting the data in native dMRI space or mapping the structural information from Tl -weighted (Tl w) images. The characteristic features of diffusion data in terms of signal-to-noise ratio, resolution, as well as the geometrical distortions caused by the inhomogeneity of magnetic susceptibility across tissues hinder both solutions. Unifying the two approaches, we propose regseg, a surface-to-volume nonlinear registration method that segments homogeneous regions within multivariate images by mapping a set of nested reference-surfaces. Accurate surfaces are extracted from a Tl w image of the subject, using as target image the bivariate volume comprehending the fractional anisotropy (FA) and the apparent diffusion coefficient (ADC) maps derived from the dMRI dataset. We first verify the accuracy of regseg on a general context using digital phantoms distorted with synthetic and random deformations. Then we establish an evaluation framework using undistorted dMRI data from the Human Connectome Project (HCP) and realistic deformations derived from the inhomogeneity fieldmap corresponding to each subject. We analyze the performance of regseg computing the misregistration error of the surfaces estimated after being mapped with regseg onto 16 datasets from the HCP. The distribution of errors shows a 95% CI of 0.56-0.66 mm, that is below the dMRI resolution (1.25 mm, isotropic). Finally, we cross-compare the proposed tool against a nonlinear i>0-to-T2w registration method, thereby obtaining a significantly lower misregistration error with regseg. The accurate mapping of structural information in dMRI space is fundamental to increase the reliability of network building in connectivity analyses, and to improve the performance of the emerging structure-informed techniques for dMRI data processing.