Development of an IEC neutron source for NDE

This paper concerns the development of a neutron so based on the inertial electrostatic confinement (IEC) of a low density fusion plasma in a gridded, spherically-focusing device. With the motivation of using such sources for nondestructive evaluation (NDE) applications, the focus of the development is on : Small size devices, sealed operation with D{sub 2} or D{sub 2}/T{sub 2} mixtures, Power-utilization and neutron-output optimization, and integration into an assay system. In this paper, we describe an experimental system that has been established for the development and testing of IEC neutron sources, and we present preliminary results of tests conducted for 25-cm and 15-cm diameter IEC devices

Star Forming Dense Cloud Cores in the TeV {\gamma}-ray SNR RX J1713.7-3946

RX J1713.7-3946 is one of the TeV {\gamma}-ray supernova remnants (SNRs) emitting synchrotron X rays. The SNR is associated with molecular gas located at ~1 kpc. We made new molecular observations toward the dense cloud cores, peaks A, C and D, in the SNR in the 12CO(J=2-1) and 13CO(J=2-1) transitions at angular resolution of 90". The most intense core in 13CO, peak C, was also mapped in the 12CO(J=4-3) transition at angular resolution of 38". Peak C shows strong signs of active star formation including bipolar outflow and a far-infrared protostellar source and has a steep gradient with a r^{-2.2$\pm$0.4} variation in the average density within radius r. Peak C and the other dense cloud cores are rim-brightened in synchrotron X rays, suggesting that the dense cloud cores are embedded within or on the outer boundary of the SNR shell. This confirms the earlier suggestion that the X rays are physically associated with the molecular gas (Fukui et al. 2003). We present a scenario where the densest molecular core, peak C, survived against the blast wave and is now embedded within the SNR. Numerical simulations of the shock-cloud interaction indicate that a dense clump can indeed survive shock erosion, since shock propagation speed is stalled in the dense clump. Additionally, the shock-cloud interaction induces turbulence and magnetic field amplification around the dense clump that may facilitate particle acceleration in the lower-density inter-clump space leading to the enhanced synchrotron X rays around dense cores.Comment: 22 pages, 7 figures, to accepted in The Astrophysical Journal. A full color version with higher resolution figures is available at http://www.a.phys.nagoya-u.ac.jp/~sano/ApJ10/ms_sano.pd

Probing MHD Shocks with high-J CO observations: W28F

Context. Observing supernova remnants (SNRs) and modelling the shocks they are associated with is the best way to quantify the energy SNRs re-distribute back into the Interstellar Medium (ISM). Aims. We present comparisons of shock models with CO observations in the F knot of the W28 supernova remnant. These comparisons constitute a valuable tool to constrain both the shock characteristics and pre-shock conditions. Methods. New CO observations from the shocked regions with the APEX and SOFIA telescopes are presented and combined. The integrated intensities are compared to the outputs of a grid of models, which were combined from an MHD shock code that calculates the dynamical and chemical structure of these regions, and a radiative transfer module based on the 'large velocity gradient' (LVG) approximation. Results. We base our modelling method on the higher J CO transitions, which unambiguously trace the passage of a shock wave. We provide fits for the blue- and red-lobe components of the observed shocks. We find that only stationary, C-type shock models can reproduce the observed levels of CO emission. Our best models are found for a pre-shock density of 104 cm-3, with the magnetic field strength varying between 45 and 100 {\mu}G, and a higher shock velocity for the so-called blue shock (\sim25 km s-1) than for the red one (\sim20 km s-1). Our models also satisfactorily account for the pure rotational H2 emission that is observed with Spitzer.Comment: 8 pages, 6 figures, 1 table, accepted for A&A SOFIA/GREAT Special Issu

Predicting disease progression in behavioral variant frontotemporal dementia

Introduction: The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods: One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups ("fast progressors" vs. "slow progressors"), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results: We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion: Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

IntroductionDementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).MethodsAtlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer’s disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).ResultsThe binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.DiscussionResults suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer’s disease

Relationship of serum beta-synuclein with blood biomarkers and brain atrophy

Background: Recent data support beta-synuclein as a blood biomarker to study synaptic degeneration in Alzheimer's disease (AD). Methods: We provide a detailed comparison of serum beta-synuclein immunoprecipitation - mass spectrometry (IP-MS) with the established blood markers phosphorylated tau 181 (p-tau181) (Simoa) and neurofilament light (NfL) (Ella) in the German FTLD consortium cohort (n = 374) and its relation to brain atrophy (magnetic resonance imaging) and cognitive scores. Results: Serum beta-synuclein was increased in AD but not in frontotemporal lobar degeneration (FTLD) syndromes. Beta-synuclein correlated with atrophy in temporal brain structures and was associated with cognitive impairment. Serum p-tau181 showed the most specific changes in AD but the lowest correlation with structural alterations. NfL was elevated in all diseases and correlated with frontal and temporal brain atrophy. Discussion: Serum beta-synuclein changes differ from those of NfL and p-tau181 and are strongly related to AD, most likely reflecting temporal synaptic degeneration. Beta-synuclein can complement the existing panel of blood markers, thereby providing information on synaptic alterations. Highlights: Blood beta-synuclein is increased in Alzheimer's disease (AD) but not in frontotemporal lobar degeneration (FTLD) syndromes. Blood beta-synuclein correlates with temporal brain atrophy in AD. Blood beta-synuclein correlates with cognitive impairment in AD. The pattern of blood beta-synuclein changes in the investigated diseases is different to phosphorylated tau 181 (p-tau181) and neurofilament light (NfL)

Pittsburgh compound B imaging and cerebrospinal fluid amyloid-β in a multicentre European memory clinic study

The aim of this study was to assess the agreement between data on cerebral amyloidosis, derived using Pittsburgh compound B positron emission tomography and (i) multi-laboratory INNOTEST enzyme linked immunosorbent assay derived cerebrospinal fluid concentrations of amyloid-β 42 ; (ii) centrally measured cerebrospinal fluid amyloid-β 42 using a Meso Scale Discovery enzyme linked immunosorbent assay; and (iii) cerebrospinal fluid amyloid-β 42 centrally measured using an antibody-independent mass spectrometry-based reference method. Moreover, we examined the hypothesis that discordance between amyloid biomarker measurements may be due to interindividual differences in total amyloid-β production, by using the ratio of amyloid-β 42 to amyloid-β 40 . Our study population consisted of 243 subjects from seven centres belonging to the Biomarkers for Alzheimer’s and Parkinson’s Disease Initiative, and included subjects with normal cognition and patients with mild cognitive impairment, Alzheimer’s disease dementia, frontotemporal dementia, and vascular dementia. All had Pittsburgh compound B positron emission tomography data, cerebrospinal fluid INNOTEST amyloid-β 42 values, and cerebrospinal fluid samples available for reanalysis. Cerebrospinal fluid samples were reanalysed (amyloid-β 42 and amyloid-β 40 ) using Meso Scale Discovery electrochemiluminescence enzyme linked immunosorbent assay technology, and a novel, antibody-independent, mass spectrometry reference method. Pittsburgh compound B standardized uptake value ratio results were scaled using the Centiloid method. Concordance between Meso Scale Discovery/mass spectrometry reference measurement procedure findings and Pittsburgh compound B was high in subjects with mild cognitive impairment and Alzheimer’s disease, while more variable results were observed for cognitively normal and non-Alzheimer’s disease groups. Agreement between Pittsburgh compound B classification and Meso Scale Discovery/mass spectrometry reference measurement procedure findings was further improved when using amyloid-β 42/40 . Agreement between Pittsburgh compound B visual ratings and Centiloids was near complete. Despite improved agreement between Pittsburgh compound B and centrally analysed cerebrospinal fluid, a minority of subjects showed discordant findings. While future studies are needed, our results suggest that amyloid biomarker results may not be interchangeable in some individuals

Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes

Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context. Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging. Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes. Interventions: N.A. Main outcomes and measures: Cohen's kappa, accuracy, and F1-score to assess model performance. Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy. Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best