How To Handle Speciose Clades? Mass Taxon-Sampling As A Strategy Towards Illuminating The Natural History Of Campanula (Campanuloideae)

Background Speciose clades usually harbor species with a broad spectrum of adaptive strategies and complex distribution patterns, and thus constitute ideal systems to disentangle biotic and abiotic causes underlying species diversification. The delimitation of such study systems to test evolutionary hypotheses is difficult because they often rely on artificial genus concepts as starting points. One of the most prominent examples is the bellflower genus Campanula with some 420 species, but up to 600 species when including all lineages to which Campanula is paraphyletic. We generated a large alignment of petD group II intron sequences to include more than 70% of described species as a reference. By comparison with partial data sets we could then assess the impact of selective taxon sampling strategies on phylogenetic reconstruction and subsequent evolutionary conclusions. Methodology/Principal Findings Phylogenetic analyses based on maximum parsimony (PAUP, PRAP), Bayesian inference (MrBayes), and maximum likelihood (RAxML) were first carried out on the large reference data set (D680). Parameters including tree topology, branch support, and age estimates, were then compared to those obtained from smaller data sets resulting from “classification-guided” (D088) and “phylogeny-guided sampling” (D101). Analyses of D088 failed to fully recover the phylogenetic diversity in Campanula, whereas D101 inferred significantly different branch support and age estimates. Conclusions/Significance A short genomic region with high phylogenetic utility allowed us to easily generate a comprehensive phylogenetic framework for the speciose Campanula clade. Our approach recovered 17 well-supported and circumscribed sub-lineages. Knowing these will be instrumental for developing more specific evolutionary hypotheses and guide future research, we highlight the predictive value of a mass taxon-sampling strategy as a first essential step towards illuminating the detailed evolutionary history of diverse clades.PubMedWoSScopu

Magnetoencephalography, functional connectivity and neural network topology in diffuse low-grade gliomas

Diffuse low-grade glioma not only impacts structural and functional connections in its direct vicinity, but also has a profound impact on the entire brain network. Magnetoencephalography (MEG) performed during a resting, task-free state is one of the methods that can be used to study functional connectivity. Frequency-specific analysis of functional connectivity further provides information on the topology of the brain network in terms of for instance efficiency, clustering and modularity. These network features are not only altered in low-grade glioma, also correlate with cognitive (dys)functioning and with the occurrence of epileptic seizures. Better understanding of the association between the tumor and the disruption of the neural network may in the future be used for diagnostic and prognostic purposes in low-grade glioma

Simulations of a Therapeutic Proton Beam with FLUKA Monte Carlo Code and Varian Eclipse Proton Planning Software

The sharp dose deposition from protons in a medium has some clear advantageous aspects for use in cancer treatment. When charged particles, for instance protons, traverse matter, most of their energy are deposited at the end of their range, causing a sharp peak, the Bragg peak, at a depth determined by the proton range in the medium. This range is a function of the initial proton energy and of the characteristics of the traversed medium. The purpose of this project has been to apply Monte Carlo software to simulate a proton beam resembling a therapeutic beam, and to study the interactions of this beam in phantoms of various design. The simulations were produced with FLUKA Monte Carlo code version 2011 2b. Further the purpose has been to apply Varian Eclipse proton planning (version 11) software to create treatment plans on similar phantoms, in order to study the differences between a time-effective, clinically optimized tool and an accurate, yet time consuming, Monte Carlo simulation tool. A fundamental criteria for a therapeutic beam is that the beam must have the ability to deliver a homogeneous dose to an extended volume. To meet this criteria, the energy of the applied protons must be spread out in order to create a plateau of dose covering the target volume, which is the tumour with some specified margins. This implies that an otherwise monoenergetic beam needs to be energy modulated in order to produce a weighted energy spectrum, resulting in a spread-out Bragg peak (SOBP). In this project, two different approaches were followed in order to obtain a dose deposition with a flat dose plateau at the desired depth. By (1) passively modulating the beam range by inserting material into the simulated beamline, which is the equivalent of the use of a range modifier positioned in the beamline, and by (2) actively modulating the proton energy from the beam source. The resulting dose distributions from these two approaches showed that the dose falloff at the distal part of the target volume was sharper with the active energy modulation approach than when passively using material to modulate the beam energy. The final goal of this project was obtained by the use of a commercial treatment planning system to produce simple treatment plans, and further; to compare the dose profiles to similar treatment plans created with a Monte Carlo simulated beam and geometry. The overall agreement between the two calculation methods were adequate, especially with respect to dose coverage within the defined target volume. However, when introducing different materials such as bone, air and aluminium into the geometry, the differences between the two methods became apparent and it illustrates the tentative limitations of a fast, clinical optimized, dose planning tool compared with a more accurate and detailed, hence tentatively slower, Monte Carlo simulation tool