Preoperative localization of the sensorimotor area using independent component analysis of resting-state fMRI

Analysis of resting-state functional magnetic resonance imaging (fMRI) data is based on detecting low-frequency signal fluctuations in functionally connected brain areas. These synchronous fluctuations in resting-state networks have been observed in several studies with healthy subjects. In this study, we explored if independent component analysis (ICA) can be used to localize the sensorimotor area from resting-state fMRI data in patients with brain tumors. Finger-tapping activation task and resting-state blood-oxygenation-level-dependent fMRI data were acquired from 8 patients with brain tumors and 10 healthy volunteers. Sensorimotor task independent components (IC_task) were used to verify resting-state independent components (IC_rest) individually. In addition, sensorimotor IC_rests were compared between the groups and no significant differences were detected in volume, spatial correlation or temporal correlation. These results show that it is possible to localize a sensorimotor area from resting-state data using ICA in patients with brain tumors. This offers a complementary method for assessing the sensorimotor area in subjects with brain tumors who have difficulties in performing motor paradigms.     

BODIL: a molecular modeling environment for structure-function analysis and drug design

BODIL is a molecular modeling environment geared to help the user to quickly identify key features of proteins critical to molecular recognition, especially (1) in drug discovery applications, and (2) to understand the structural basis for function. The program incorporates state-of-the-art graphics, sequence and structural alignment methods, among other capabilities needed in modern structure–function–drug target research. BODIL has a flexible design that allows on-the-fly incorporation of new modules, has intelligent memory management, and fast multi-view graphics. A beta version of BODIL and an accompanying tutorial are available at http://www.abo.fi/fak/mnf/bkf/research/johnson/bodil.html     

Correlated adiabatic and isocurvature cosmic microwave background fluctuations in the wake of the results from the wilkinson microwave anisotropy probe

In general correlated models, in addition to the usual adiabatic component with a spectral index n(ad1) there is another adiabatic component with a spectral index n(ad2) generated by entropy perturbation during inflation. We extend the analysis of a correlated mixture of adiabatic and isocurvature cosmic microwave background fluctuations of the Wilkinson Microwave Anisotropy Probe (WMAP) group, who set the two adiabatic spectral indices equal. Allowing n(ad1) and n(ad2) to vary independently we find that the WMAP data favor models where the two adiabatic components have opposite spectral tilts. Using the WMAP data only, the 2sigma upper bound for the isocurvature fraction f(iso) of the initial power spectrum at k(0)=0.05 Mpc(-1) increases somewhat, e.g., from 0.76 of n(ad2)=n(ad1) models to 0.84 with a prior n(iso)<1.84 for the isocurvature spectral index.     

Topological Quantum Field Theory on non-Abelian gerbes

The infinitesimal symmetries of a fully decomposed non-Abelian gerbe can be generated in terms of a nilpotent BRST operator, which is here constructed. The appearing fields find a natural interpretation in terms of the universal gerbe, a generalisation of the universal bundle. We comment on the construction of observables in the arising Topological Quantum Field Theory. It is also shown how the BRST operator and the trace part of a suitably truncated set of fields on the non-Abelian gerbe reduce directly to the coboundary operator and the pertinent cochains of the underlying ?ech–de Rham complex.     

An estimate on the non-real spectrum of a singular indefinite Sturm-Liouville operator

It will be shown with the help of the Birman-Schwinger principle that the non-real spectrum of the singular indefinite Sturm-Liouville operator sgn(·)(−d²/dx² + q) with a real potential q ∈ L¹ ∩ L² is contained in a circle around the origin with radius . (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Estimation of level set trees using adaptive partitions

We present methods for the estimation of level sets, a level set tree, and a volume function of a multivariate density function. The methods are such that the computation is feasible and estimation is statistically efficient in moderate dimensional cases (\(d\approx 8\)) and for moderate sample sizes (\(n\approx \) 50,000). We apply kernel estimation together with an adaptive partition of the sample space. We illustrate how level set trees can be applied in cluster analysis and in flow cytometry.     

Brain MRI tissue classification based on local Markov random fields

A new method for tissue classification of brain magnetic resonance images (MRI) of the brain is proposed. The method is based on local image models where each models the image content in a subset of the image domain. With this local modeling approach, the assumption that tissue types have the same characteristics over the brain needs not to be evoked. This is important because tissue type characteristics, such as T1 and T2 relaxation times and proton density, vary across the individual brain and the proposed method offers improved protection against intensity non-uniformity artifacts that can hamper automatic tissue classification methods in brain MRI. A framework in which local models for tissue intensities and Markov Random Field (MRF) priors are combined into a global probabilistic image model is introduced. This global model will be an inhomogeneous MRF and it can be solved by standard algorithms such as iterative conditional modes. The division of the whole image domain into local brain regions possibly having different intensity statistics is realized via sub-volume probabilistic atlases. Finally, the parameters for the local intensity models are obtained without supervision by maximizing the weighted likelihood of a certain finite mixture model. For the maximization task, a novel genetic algorithm almost free of initialization dependency is applied. The algorithm is tested on both simulated and real brain MR images. The experiments confirm that the new method offers a useful improvement of the tissue classification accuracy when the basic tissue characteristics vary across the brain and the noise level of the images is reasonable. The method also offers better protection against intensity non-uniformity artifact than the corresponding method based on a global (whole image) modeling scheme.     

Raman spectroscopy of acetic acid monomer and dimers isolated in solid argon

Acetic acid (AA) monomer and its dimers were studied by means of Raman spectroscopy combined with the matrix isolation technique. All fundamental bands of CH₃COOH monomer were identified, including the CH₃ torsional mode. Additionally, three overtone or combination modes were observed as a result of their enhanced intensities by Fermi resonance (FR). Twenty bands of the cyclic dimer (C_2h) were identified and assigned, among which appear all intermolecular modes. Bands due to two different higher energy forms of the dimer were also identified. The experimental assignments are supported by ab initio calculations. Copyright © 2011 John Wiley & Sons, Ltd.     

Limit properties of monotone matrix functions

The basic objects in this paper are monotonically nondecreasing $n\times n$ matrix functions $D(·)$ defined on some open interval $?=(a\text{,}b)$ of $\mathbb{R}$ and their limit values $D(a)$ and $D(b)$ at the endpoints a and b which are, in general, selfadjoint relations in ${\mathbb{C}}^n$. Certain space decompositions induced by the matrix function $D(·)$ are made explicit by means of the limit values $D(a)$ and $D(b)$. They are a consequence of operator inequalities involving these limit values and the notion of strictness (or definiteness) of monotonically nondecreasing matrix functions. This treatment provides a geometric approach to the square-integrability of solutions of definite canonical systems of differential equations.