Voxel-based morphometry (VBM) and tensor-based morphometry (TBM) both rely on spatial normalization to a template and yet have different requirements for the level of registration accuracy. VBM requires only global alignment of brain structures, with limited degrees of freedom in transformation, whereas TBM performs best when the registration is highly deformable and can achieve higher registration accuracy. In addition, the registration accuracy varies over the whole brain, with higher accuracy typically observed in subcortical areas and lower accuracy seen in cortical areas. Hence, even the determinant of Jacobian of registration maps is spatially varying in their accuracy, and combining these with VBM by direct multiplication introduces errors in VBM maps where the registration is inaccurate. We propose a unified approach to combining these 2 morphometry methods that is motivated by these differing requirements for registration and our interest in harnessing the advantages of both. Our novel method uses local estimates of registration confidence to determine how to weight the influence of VBM-and TBM-like approaches. Results are shown on healthy and mild Alzheimer's subjects (N = 150) investigating age and group differences, and potential of differential diagnosis is shown on a set of Alzheimer's disease (N = 34) and frontotemporal dementia (N = 30) patients compared against controls (N = 14). These show that the group differences detected by our proposed approach are more descriptive than those detected from VBM, Jacobian-modulated VBM, and TBM separately, hence leveraging the advantages of both approaches in a unified framework. (C) 2015 Elsevier Inc. All rights reserved.

Regional analysis of cortical thickness has been studied extensively in building imaging biomarkers for early detection of Alzheimer's disease but not its interregional covariation of thickness. We present novel features based on the inter-regional covariation of cortical thickness. Initially, the cortical labels of each subject are partitioned into small patches (graph nodes) by spatial k-means clustering. A graph is then constructed by establishing a link between 2 nodes if the difference in thickness between the nodes is below a certain threshold. From this binary graph, a thickness network is computed using nodal degree, betweenness, and clustering coefficient measures. Fusing them with multiple kernel learning, it is observed that thickness network features discriminate mild cognitive impairment (MCI) converters from controls (CN) with an area under curve (AUC) of 0.83, 74% sensitivity and 76% specificity on a large subset obtained from the Alzheimer's Disease Neuroimaging Initiative data set. A comparison of predictive utility in Alzheimer's disease and/or CN classification (AUC of 0.92, 80% sensitivity [SENS] and 90% specificity [SPEC]), in discriminating CN from MCI (converters and nonconverters combined; AUC of 0.75, SENS and SPEC of 64% and 73%, respectively) and in discriminating between MCI nonconverters and MCI converters (AUC of 0.68, SENS and SPEC of 65% and 64%) is also presented. ThickNet features as defined here are novel, can be derived from a single magnetic resonance imaging scan, and demonstrate the potential for the computer-aided prognostic applications. (C) 2015 Elsevier Inc. All rights reserved.

In this article, we propose an approach to integrate cortical morphology measures for improving the discrimination of individuals with and without very mild Alzheimer's disease (AD). FreeSurfer was applied to scans collected from 83 participants with very mild AD and 124 cognitively normal individuals. We generated cortex thickness, white matter convexity (aka sulcal depth), and white matter surface metric distortion measures on a normalized surface atlas in this first study to integrate high resolution gray matter thickness and white matter surface geometric measures in identifying very mild AD. Principal component analysis was applied to each individual structural measure to generate eigenvectors. Discrimination power based on individual and combined measures are compared, based on stepwise logistic regression and 10-fold cross-validation. Global AD likelihood index and surface-based likelihood maps were also generated. Our results show complementary patterns on the cortical surface between thickness, which reflects gray matter atrophy, convexity, which reflects white matter sulcal depth changes and metric distortion, which reflects white matter surface area changes. The classifier integrating all 3 types of surface measures significantly improved classification performance compared with classification based on single measures. The principal component analysis-based approach provides a framework for achieving high discrimination power by integrating high-dimensional data, and this method could be very powerful in future studies for early diagnosis of diseases that are known to be associated with abnormal gyral and sulcal patterns. (C) 2015 Elsevier Inc. All rights reserved.

Cutaneous T-cell lymphoma (CTCL) is a heterogeneous group of malignancies characterized by accumulation of malignant T-cells within the skin. Retinoids, metabolic derivatives, and synthetic analogs of vitamin A embody an effective CTCL therapy with over three decades of clinical use. The established mechanism of action is induction of growth arrest and apoptosis. However, the natural role of retinoids in T-cell biology is imprinting gut-homing properties by inducing integrin alpha 4 beta 7 expression. How the natural role of retinoids relates to therapeutic effectiveness in CTCL has not been addressed and merits investigation. Here we provide evidence that retinoids, including Bexarotene, selectively induce CTCL lineages to increase integrin beta 7 expression and function prior to growth arrest and apoptosis. Interestingly, augmented CTCL cell adhesion obtained with retinoid exposure was potently attenuated by 1,25-dihydroxyvitamin D-3, a metabolic vitamin derivative involved in prompting immune cell skin homing. The integrin-dependent adhesion changes in CTCL cells occurred through synergistic activation of RAR and RXR nuclear receptors. These data explore the early cellular changes induced by retinoids that may be pivotal to sensitizing CTCL cells to growth arrest and apoptosis.

The pentablock is a Hartogs domain in C-3 over the symmetrized bidisc in C-2. The domain is a bounded inhomogeneous pseudoconvex domain, which does not have a C-1 boundary. Recently, Agler-Lykova-Young constructed a special subgroup of the group of holomorphic automorphisms of the pentablock, and Kosinski fully described the group of holomorphic automorphisms of the pentablock. The aim of the present study is to prove that any proper holomorphic self-mapping of the pentablock must be an automorphism. (C) 2014 Elsevier Inc. All rights reserved.

Highly sensitive detection of atmospheric methane (CH4) was performed by long optical pathlength absorption spectroscopy based on a novel compact dense-pattern multipass cell (DP-MPC) in conjunction with a fiber-coupled distributed feedback diode laser operating at 1.653 mu m. Wavelength modulation spectroscopy approach was used and a minimum detectable concentration (1 sigma) of 100 ppb was obtained with a lock-in time constant of 1 ms. A measurement precision of <79 ppb was achieved by average of five laser scans in 1 s. This newly developed DP-MPC realized 215 times multiple reflections between two very cheap and robust silver coated concave spherical mirrors separated by a distance of 12 cm (forming an absorption cell of 280 cm(3) volume), offering an effective optical path length of 26.4m, which is very suitable for applications to trace gas sensing in harsh environment, and weight-limited unmanned aerial vehicle (UAV) - or balloon-embedded field observations. (C) 2015 Elsevier B.V. All rights reserved.