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Babesia bovis, an apicomplexan intraerythrocytic protozoan parasite, causes serious economic loss to cattle industries around the world. Infection with this parasite leads to accumulation of infected red blood cells (iRBCs) in the brain microvasculature that results in severe clinical complications known as cerebral babesiosis. Throughout its growth within iRBCs, the parasite exports various proteins to the iRBCs that lead to the formation of protrusions known as "ridges" on the surface of iRBCs, which serve as sites for cytoadhesion to endothelial cells. Spherical body proteins (SBPs; proteins secreted from spherical bodies, which are organelles specific to Piroplasmida) are exported into iRBCs, and four proteins (SBP1-4) have been reported to date. In this study, we elucidated the function of SBP3 using an inducible gene knockdown (KD) system. Localization of SBP3 was assessed by immunofluorescence assay, and only partial colocalization was detected between SBP3 and SBP4 inside the iRBCs. In contrast, colocalization was observed with VESA-1, which is a major parasite ligand responsible for the cytoadhesion. Immunoelectron microscopy confirmed localization of SBP3 at the ridges. SBP3 KD was performed using the glmS system, and effective KD was confirmed by Western blotting, immunofluorescence assay, and RNA-seq analysis. The SBP3 KD parasites showed severe growth defect suggesting its importance for parasite survival in the iRBCs. VESA-1 on the surface of iRBCs was scarcely detected in SBP3 KD parasites, whereas SBP4 was still detected in the iRBCs. Moreover, abolition of ridges on the iRBCs and reduction of iRBCs cytoadhesion to the bovine brain endothelial cells were observed in SBP3 KD parasites. Immunoprecipitation followed by mass spectrometry analysis detected the host Band 3 multiprotein complex, suggesting an association of SBP3 with iRBC cytoskeleton proteins. Taken together, this study revealed the vital role of SBP3 in ridge formation and its significance in the pathogenesis of cerebral babesiosis.

Abstract As a powerful, non-destructive analysis tool based on thermal neutron capture reaction, prompt gamma neutron activation analysis (PGNAA) indeed requires the appropriate neutron source. Neutrons produced by electron Linac-based neutron sources should be thermalized to be appropriate for PGNAA. As a result, thermalization devices (TDs) are used for the usual fast neutron beam to simultaneously maximize the thermal neutron flux and minimize the non- thermal neutron flux at the beam port of TD. To achieve the desired thermal neutron flux, the optimized geometry of TD including the proper materials for moderators and collimator, as well as the optimized dimensions are required. In this context, TD optimization using only Monte Carlo approaches such as MCNP is a multi-parameter problem and time-consuming task. In this work, multilayer perceptron (MLP) neural network has been applied in combination with Q-learning algorithm to optimize the geometry of TD containing collimator and two moderators. Using MLP, both thickness and diameter of the collimator at the beam port of TD have first been optimized for different input electron energies of Linac as well as for moderators’ thickness values and the collimator. Then, the MLP has been learned by the thermal and non-thermal neutron flux simultaneously at the beam port of TD calculated by MCNPX2.6 code. After selecting the optimized geometry of the collimator, a combination of Q-learning algorithm and MLP artificial neural network have been used to find the optimal moderators’ thickness for different input electron energies of Linac. Results verify that the final optimum setup can be obtained based on the prepared dataset in a considerably smaller number of simulations compared to conventional calculation methods as implemented in MCNP.

Abstract The increasing use of nuclear technology in various fields makes it necessary to provide the required safety to work with this industry. Gamma source is one of the most widely used sources in industry and medicine. Finding a lost gamma source in a gamma irradiation room without human presence is challenging due to the particular arrangements and barriers in the room for radiation shielding and requires an efficient and robust method. In this paper, locating and routing the lost gamma source in the gamma irradiation room containing radiation blocking barriers are done simultaneously by using two methods, convolutional neural network (CNN) and Q-learning, which are powerful algorithms for deep learning and machine learning. Environment simulation with gamma source was performed using Geant4 simulation. The results show that by combining these two methods in geometries with radiation blocking barriers, in addition to locating with 90% accuracy, routing can also be performed. Although the presence of thick barriers in the room reduces the accuracy, increases the time required to finding the lost gamma source or the inefficiency of other methods, nevertheless, the results show that combination of CNN and Q-learning reduces the time and greatly increases the accuracy.

Abstract Background Young Middle Eastern male refugees are currently among the most vulnerable groups in Europe. Most of them have experienced potentially traumatic events (PTEs) such as rape, torture, or violent assaults. Compared to their peers, young refugees suffer more from internalizing and externalizing symptoms, especially when unaccompanied. Little is known about the cumulative impact of experiencing different types of PTEs on mental health outcomes (polytraumatization) of young male refugees from the Middle East. We investigated (1) whether there is a dose–response relationship between multiple PTE types experienced and mental health outcomes, (2) whether individual types of PTEs are particularly important, and (3) to what extent these are differentially associated with mental health outcomes among unaccompanied or accompanied peers. Methods In total, 151 young Middle Eastern male refugees in Germany (M age = 16.81 years, SD age = 2.01) answered questionnaires on PTEs, mental health, and post-migration stress. Results Hierarchical regression analyses revealed, while controlling for age, duration of stay, unaccompanied status, and post-migration stress, (1) a dose–effect between PTE types on both internalizing and externalizing symptoms. Moreover, (2) regarding internalizing symptoms, violent family separation and experiencing life-threatening medical problems were particularly crucial. The latter was driven by unaccompanied refugees, who also reported higher levels of substance use. Conclusions The results extend findings from the literature and suggest that not only may greater polytraumatization be related to greater depression among refugees, but also to a range of other mental health outcomes from the internalizing and externalizing symptom domains. Furthermore, the results highlight the mental health risks that unaccompanied and accompanied refugee adolescents face after exposure to PTEs, and provide information for practitioners as well as researchers about event types that may be particularly relevant.

The red fluorescent protein (rfp)-blasticidin deaminase (bsd) fusion gene was transfected into Babesia ovata by electroporation with the plasmid DNA and selected with 15 µg/mL of blasticidin S under the in vitro culture condition. The transfected parasite with episomal DNA was selected and cultured for further analysis based on the presence of the rfp-bsd fusion gene by PCR and expression of the fusion protein by immunofluorescence antibody test under fluorescence microscopy for 2 months after the transfection. The results are the first, to our knowledge, to demonstrate the expression and stability of the episomal rfp-bsd fusion gene under the control of actin promoter as a selectable marker for the transfection system in B. ovata. [ABSTRACT FROM AUTHOR]