Magnetic field - temperature phase diagram of quasi-two-dimensional organic superconductor lambda-(BETS)_2 GaCl_4 studied via thermal conductivity

The thermal conductivity kappa of the quasi-two-dimensional (Q2D) organic superconductor lambda-(BETS)_2 GaCl_4 was studied in the magnetic field H applied parallel to the Q2D plane. The phase diagram determined from this bulk measurement shows notable dependence on the sample quality. In dirty samples the upper critical field H_{c2} is consistent with the Pauli paramagnetic limiting, and a sharp change is observed in kappa(H) at H_{c2 parallel}. In contrast in clean samples H_{c2}(T) shows no saturation towards low temperatures and the feature in kappa(H) is replaced by two slope changes reminiscent of second-order transitions. The peculiarity was observed below ~ 0.33T_c and disappeared on field inclination to the plane when the orbital suppression of superconductivity became dominant. This behavior is consistent with the formation of a superconducting state with spatially modulated order parameter in clean samples.Comment: 10 pages, 8 figures, new figure (Fig.5) and references added, title change

Anisotropy of magnetothermal conductivity in Sr2RuO4

The dependence of in-plane and interplane thermal conductivities of Sr2RuO4 on temperature, as well as magnetic field strength and orientation, is reported. We found no notable anisotropy in the thermal conductivity for the magnetic field rotation parallel to the conducting plane in the whole range of experimental temperatures and fields, except in the vicinity of the upper critical field Hc2, where the anisotropy of the Hc2 itself plays a dominant role. This finding imposes strong constraints on the possible models of superconductivity in Sr2RuO4 and supports the existence of a superconducting gap with a line of nodes running orthogonal to the Fermi surface cylinder.Comment: published in Phys. Rev. Lett. 4pages, 4 eps figures, LaTe

Monte-Carlo dosimetry on a realistic cell monolayer geometry exposed to alpha particles

The energy and specific energy absorbed in the main cell compartments (nucleus and cytoplasm) in typical radiobiology experiments are usually estimated by calculations as they are not accessible for a direct measurement. In most of the work, the cell geometry is modelled using the combination of simple mathematical volumes. We propose a method based on high resolution confocal imaging and ion beam analysis (IBA) in order to import realistic cell nuclei geometries in Monte-Carlo simulations and thus take into account the variety of different geometries encountered in a typical cell population. Seventy-six cell nuclei have been imaged using confocal microscopy and their chemical composition has been measured using IBA. A cellular phantom was created from these data using the ImageJ image analysis software and imported in the Geant4 Monte-Carlo simulation toolkit. Total energy and specific energy distributions in the 76 cell nuclei have been calculated for two types of irradiation protocols: a 3 MeV alpha particle microbeam used for targeted irradiation and a 239Pu alpha source used for large angle random irradiation. Qualitative images of the energy deposited along the particle tracks have been produced and show good agreement with images of DNA double strand break signalling proteins obtained experimentally. The methodology presented in this paper provides microdosimetric quantities calculated from realistic cellular volumes. It is based on open-source oriented software that is publicly available

A comparison of the radiosensitisation ability of 22 different element metal oxide nanoparticles using clinical megavoltage X-rays

Background: A wide range of nanoparticles (NPs), composed of different elements and their compounds, are being developed by several groups as possible radiosensitisers, with some already in clinical trials. However, no systematic experimental survey of the clinical X-ray radiosensitising potential of different element nanoparticles has been made. Here, we directly compare the irradiation-induced (10 Gy of 6-MV X-ray photon) production of hydroxyl radicals, superoxide anion radicals and singlet oxygen in aqueous solutions of the following metal oxide nanoparticles: Al2O3, SiO2, Sc2O3, TiO2, V2O5, Cr2O3, MnO2, Fe3O4, CoO, NiO, CuO, ZnO, ZrO2, MoO3, Nd2O3, Sm2O3, Eu2O3, Gd2O3, Tb4O7, Dy2O3, Er2O3 and HfO2. We also examine DNA damage due to these NPs in unirradiated and irradiated conditions. Results: Without any X-rays, several NPs produced more radicals than water alone. Thus, V2O5 NPs produced around 5-times more hydroxyl radicals and superoxide radicals. MnO2 NPs produced around 10-times more superoxide anions and Tb4O7 produced around 3-times more singlet oxygen. Lanthanides produce fewer hydroxyl radicals than water. Following irradiation, V2O5 NPs produced nearly 10-times more hydroxyl radicals than water. Changes in radical concentrations were determined by subtracting unirradiated values from irradiated values. These were then compared with irradiation-induced changes in water only. Irradiation-specific increases in hydroxyl radical were seen with most NPs, but these were only significantly above the values of water for V2O5, while the Lanthanides showed irradiation-specific decreases in hydroxyl radical, compared to water. Only TiO2 showed a trend of irradiation-specific increase in superoxides, while V2O5, MnO2, CoO, CuO, MoO3 and Tb4O7 all demonstrated significant irradiation-specific decreases in superoxide, compared to water. No irradiation-specific increases in singlet oxygen were seen, but V2O5, NiO, CuO, MoO3 and the lanthanides demonstrated irradiation-specific decreases in singlet oxygen, compared to water. MoO3 and CuO produced DNA damage in the absence of radiation, while the highest irradiation-specific DNA damage was observed with CuO. In contrast, MnO2, Fe3O4 and CoO were slightly protective against irradiation-induced DNA damage. Conclusions: Beyond identifying promising metal oxide NP radiosensitisers and radioprotectors, our broad comparisons reveal unexpected differences that suggest the surface chemistry of NP radiosensitisers is an important criterion for their success

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution;(B) incorporating demographics & clinical covariates;(C) the impact of the size of the training data set;(D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of sample predictions. The highest performance for A 42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution;(B) incorporating demographics & clinical covariates;(C) the impact of the size of the training data set;(D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of sample predictions. The highest performance for A 42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status