Diffusion Tensor Imaging in Alzheimer's disease

Attentional control and Information processing speed are central concepts in cognitive psychology and neuropsychology. Functional neuroimaging and neuropsychological assessment have depicted theoretical models considering attention as a complex and non-unitary process. One of its component processes, Attentional set-shifting ability, is commonly assessed using the Trail Making Test (TMT). Performance in the TMT decreases with increasing age in adults, Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Besides, speed of information processing (SIP) seems to modulate attentional performance. While neural correlates of attentional control have been widely studied, there are few evidences about the neural substrates of SIP in these groups of patients. Different authors have suggested that it could be a property of cerebral white matter, thus, deterioration of the white matter tracts that connect brain regions related to set-shifting may underlie the age-related, MCI and AD decrease in performance. The aim of this study was to study the anatomical dissociation of attentional and speed mechanisms. Diffusion tensor imaging (DTI) provides a unique insight into the cellular integrity of the brain, offering an in vivo view into the microarchitecture of cerebral white matter. At the same time, the study of ageing, characterized by white matter decline, provides the opportunity to study the anatomical substrates speeded or slowed information processing. We hypothesized that FA values would be inversely correlated with time to completion on Parts A and B of the TMT, but not the derived scores B/A and B-A

Nuclear Shell Model Calculations of Neutralino-Nucleus Cross Sections for Silicon 29 and Germanium 73

We present the results of detailed nuclear shell model calculations of the spin-dependent elastic cross section for neutralinos scattering from \si29 and \ge73. The calculations were performed in large model spaces which adequately describe the configuration mixing in these two nuclei. As tests of the computed nuclear wave functions, we have calculated several nuclear observables and compared them with the measured values and found good agreement. In the limit of zero momentum transfer, we find scattering matrix elements in agreement with previous estimates for \si29 but significantly different than previous work for \ge73. A modest quenching, in accord with shell model studies of other heavy nuclei, has been included to bring agreement between the measured and calculated values of the magnetic moment for \ge73. Even with this quenching, the calculated scattering rate is roughly a factor of 2 higher than the best previous estimates; without quenching, the rate is a factor of 4 higher. This implies a higher sensitivity for germanium dark matter detectors. We also investigate the role of finite momentum transfer upon the scattering response for both nuclei and find that this can significantly change the expected rates. We close with a brief discussion of the effects of some of the non-nuclear uncertainties upon the matrix elements.Comment: 31 pages, figures avaiable on request, UCRL-JC-11408

Nuclear spin structure in dark matter search: The finite momentum transfer limit

Spin-dependent elastic scattering of weakly interacting massive dark matter particles (WIMP) off nuclei is reviewed. All available, within different nuclear models, structure functions S(q) for finite momentum transfer (q>0) are presented. These functions describe the recoil energy dependence of the differential event rate due to the spin-dependent WIMP-nucleon interactions. This paper, together with the previous paper ``Nuclear spin structure in dark matter search: The zero momentum transfer limit'', completes our review of the nuclear spin structure calculations involved in the problem of direct dark matter search.Comment: 39 pages, 12 figures, a review in revtex

A machine learning platform to optimize the translation of personalized network models to the clinic

PURPOSE Dynamic network models predict clinical prognosis and inform therapeutic intervention by elucidating disease-driven aberrations at the systems level. However, the personalization of model predictions requires the profiling of multiple model inputs, which hampers clinical translation. PATIENTS AND METHODS We applied APOPTO-CELL, a prognostic model of apoptosis signaling, to showcase the establishment of computational platforms that require a reduced set of inputs. We designed two distinct and complementary pipelines: a probabilistic approach to exploit a consistent subpanel of inputs across the whole cohort (Ensemble) and a machine learning approach to identify a reduced protein set tailored for individual patients (Tree). Development was performed on a virtual cohort of 3,200,000 patients, with inputs estimated from clinically relevant protein profiles. Validation was carried out in an in-house stage III colorectal cancer cohort, with inputs profiled in surgical resections by reverse phase protein array (n = 120) and/or immunohistochemistry (n = 117). RESULTS Ensemble and Tree reproduced APOPTO-CELL predictions in the virtual patient cohort with 92% and 99% accuracy while decreasing the number of inputs to a consistent subset of three proteins (40% reduction) or a personalized subset of 2.7 proteins on average (46% reduction), respectively. Ensemble and Tree retained prognostic utility in the in-house colorectal cancer cohort. The association between the Ensemble accuracy and prognostic value (Spearman ρ = 0.43; P = .02) provided a rationale to optimize the input composition for specific clinical settings. Comparison between profiling by reverse phase protein array (gold standard) and immunohistochemistry (clinical routine) revealed that the latter is a suitable technology to quantify model inputs. CONCLUSION This study provides a generalizable framework to optimize the development of network-based prognostic assays and, ultimately, to facilitate their integration in the routine clinical workflow

Thermal properties of Fe-54

We study the thermal properties of Fe-54 with the Brown-Richter interaction in the complete 1p0f model space. Monte Carlo calculations show a peak in the heat capacity and rapid increases in both the moment of inertia and M1 strength near a temperature of 1.1 MeV that are associated with the vanishing of proton-proton and neutron-neutron monopole pair correlations; neutron-proton correlations persist to higher temperatures. Our results are consistent with a Fermi gas level density whose back-shift vanishes with increasing temperature.Comment: 10 pages (RevTeX) and 2 figures (uuencoded postscript). Caltech preprint MAP-171 (originally submitted May 1994

Overview of the PALM model system 6.0

In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Largeeddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue