A microdosimetric analysis of the interactions of mono-energetic neutrons with human tissue

Nuclear reactions induced during high-energy radiotherapy produce secondary neutrons that, due to their carcinogenic potential, constitute an important risk for the development of iatrogenic cancer. Experimental and epidemiological findings indicate a marked energy dependence of neutron relative biological effectiveness (RBE) for carcinogenesis, but little is reported on its physical basis. While the exact mechanism of radiation carcinogenesis is yet to be fully elucidated, numerical microdosimetry can be used to predict the biological consequences of a given irradiation based on its microscopic pattern of energy depositions. Building on recent studies, this work investigated the physics underlying neutron RBE by using the microdosimetric quantity dose-mean lineal energy $\left({\bar{y}}_D\right)$ as a proxy. A simulation pipeline was constructed to explicitly calculate the ${\bar{y}}_D$ of radiation fields that consisted of (i) the open source Monte Carlo toolkit Geant4, (ii) its radiobiological extension Geant4-DNA, and (iii) a weighted track-sampling algorithm. This approach was used to study mono-energetic neutrons with initial kinetic energies between 1 eV and 10 MeV at multiple depths in a tissue-equivalent phantom. Spherical sampling volumes with diameters between 2 nm and 1 $\mathrm{\mu }$m were considered. To obtain a measure of RBE, the neutron ${\bar{y}}_D$ values were divided by those of 250 keV X-rays that were calculated in the same way. Qualitative agreement was found with published radiation protection factors and simulation data, allowing for the dependencies of neutron RBE on depth and energy to be discussed in the context of the neutron interaction cross sections and secondary particle distributions in human tissue.     

Artifact quantification of venous stents in the MRI environment: Differences between braided and laser-cut designs

PurposeTo quantify B₀- and B₁-induced imaging artifacts of braided venous stents and to compare the artifacts to a set of laser-cut stents used in venous interventions.MethodsThree prototypes of braided venous stents with different geometries were tested in vitro. B₀ field distortion maps were measured via the frequency shift $\mathrm{\Delta }f$ using multi-echo imaging. B₁ distortions were quantified using the double angle method. The relative amplitudes $B_1^{\mathit{rel}}$ were calculated to compare the intraluminal alteration of B₁. Measurements were repeated with the stents in three different orientations: parallel, diagonal and orthogonal to B₀.ResultsAt 1.5 T, the braided stents induced a maximum frequency shift of $\left|\mathrm{\Delta }f\left(\mathit{x}\right)\right|<100\mathrm{H}\mathrm{z}$. Signal voids were limited to a distance of 2 mm to the stent walls at an echo time of 3 ms. No substantial difference in the B₀ field distortions was seen between laser-cut and braided venous stents. $B_1^{\mathit{rel}}$ maps showed strongly varying distortion patterns in the braided stents with the mean intraluminal $B_1^{\mathit{rel}}$ ranging from $\left(63\pm 18\right)\%$ in prototype 1 to $\left(98\pm 38\right)\%$ in prototype 2. Compared to laser-cut stents the braided stents showed a 5 to 9 times higher coefficient of variation of the intraluminal $B_1^{\mathit{rel}}$.ConclusionBraided venous stent prototypes allow for MR imaging of the intraluminal area without substantial signal voids due to B₀-induced artifacts. Whereas B₁ is attenuated homogeneously in laser-cut stents, the B₁ distortion in braided stents is more inhomogeneous and shows areas with enhanced amplitude. This could potentially be used in braided stent designs for intraluminal signal amplification.     

Penicillium rubens germination on desiccated and nutrient-depleted conditions depends on the water activity during sporogenesis

Fungal growth often appears in a surrounding where water and nutrients are scarce. The impact of this environment during sporogenesis on subsequent growth is often neglected.This study investigates the effect of water availability during sporogenesis on subsequent early growth. Therefore, a carbon-depleted substrate was constructed. Humidity is then the only parameter of interest. The water conditions during sporogenesis, and during subsequent growth, were varied. This is a stressing environment: no carbon source is present, and water provided solely via the vapour.The lag time, $t_l$, and initial growth rate, ${\mu }_{fp}$, of the germ tubes were monitored.The effect of $a_w$ history on germination and initial growth depends on the $RH$ of the environment. Only at low $RH$ do spores produced at low $a_w$ have a smaller $t_l$ and higher ${\mu }_{fp}$ compared to those grown at high $a_w$. This result was remarkably pronounced when the substrate was also made hydrophobic: growth only occurred when spores were developed at low $a_w$ and placed in high $RH$.Spores grown on lowered $a_w$ attract more water. It is hypothesized that this attraction affects subsequent growth behaviour, and is the reason why growth on hydrophobic glass only prevails in the condition of high $RH$ and lowered $a_w$ history.We demonstrate the influence of cultivation conditions on germination, which becomes more pronounced in a more desiccated environment.     

Crowding-induced protein destabilization in the absence of soft attractions

It is generally assumed that volume exclusion by macromolecular crowders universally stabilizes the native states of proteins and destabilization suggests soft attractions between crowders and protein. Here we show that proteins can be destabilized even by crowders that are purely repulsive. With a coarse-grained sequence-based model, we study the folding thermodynamics of two sequences with different native folds, a helical hairpin and a β-barrel, in a range of crowder volume fractions, ${\phi }_{\text{c}}$. We find that the native state, N, remains structurally unchanged under crowded conditions, while the size of the unfolded state, U, decreases monotonically with ${\phi }_{\text{c}}$. Hence, for all ${\phi }_{\text{c}}>0$, U is entropically disfavored relative to N. This entropy-centric view holds for the helical hairpin protein, which is stabilized under all crowded conditions as quantified by changes in either the folding midpoint temperature, $T_{\text{m}}$, or the free energy of folding. We find, however, that the β-barrel protein is destabilized under low-T, low-${\phi }_{\text{c}}$ conditions. This destabilization can be understood from two characteristics of its folding: 1) a relatively compact U at $T<T_{\text{m}}$, such that U is only weakly disfavored entropically by the crowders; and 2) a transient, compact, and relatively low-energy nonnative state that has a maximum population of only a few percent at ${\phi }_{\text{c}}=0$, but increasing monotonically with ${\phi }_{\text{c}}$. Overall, protein destabilization driven by hard-core effects appears possible when a compaction of U leads to even a modest population of compact nonnative states that are energetically competitive with N.     

A deep learning approach for magnetic resonance fingerprinting: Scaling capabilities and good training practices investigated by simulations.

MR fingerprinting (MRF) is an innovative approach to quantitative MRI. A typical disadvantage of dictionary-based MRF is the explosive growth of the dictionary as a function of the number of reconstructed parameters, an instance of the curse of dimensionality, which determines an explosion of resource requirements. In this work, we describe a deep learning approach for MRF parameter map reconstruction using a fully connected architecture. Employing simulations, we have investigated how the performance of the Neural Networks (NN) approach scales with the number of parameters to be retrieved, compared to the standard dictionary approach. We have also studied optimal training procedures by comparing different strategies for noise addition and parameter space sampling, to achieve better accuracy and robustness to noise. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-$B_1$, and IR-bSSFP-$B_1$. A comparison between NN and the dictionary approaches in reconstructing parameter maps as a function of the number of parameters to be retrieved was performed using a numerical brain phantom. Results demonstrated that training with random sampling and different levels of noise variance yielded the best performance. NN performance was at least as good as the dictionary-based approach in reconstructing parameter maps using Gaussian noise as a source of artifacts: the difference in performance increased with the number of estimated parameters because the dictionary method suffers from the coarse resolution of the parameter space sampling. The NN proved to be more efficient in memory usage and computational burden, and has great potential for solving large-scale MRF problems.