A Non-Perturbative Pairwise-Additive Analysis of Charge Transfer Contributions to Intermolecular Interaction Energies

Energy decomposition analysis (EDA) based on absolutely localized molecular orbitals (ALMOs) decomposes the interaction energy between molecules into physically interpretable components like geometry distortion, frozen interactions, polarization, and charge transfer (CT, also sometimes called charge delocalization) interactions. In this work, a numerically exact scheme to decompose the CT interaction energy into pairwise additive terms is introduced for the ALMO-EDA using density functional theory. Unlike perturbative pairwise charge-decomposition analysis, the new approach does not break down for strongly interacting systems, or show significant exchange-correlation functional dependence in the decomposed energy components. Both the energy lowering and the charge flow associated with CT can be decomposed. Complementary occupied-virtual orbital pairs (COVPs) that capture the dominant donor and acceptor CT orbitals are obtained for the new decomposition. It is applied to systems with different types of interactions including DNA base-pairs, borane-ammonia adducts, and transition metal hexacarbonyls. While consistent with most existing understanding of the nature of CT in these systems, the results also reveal some new insights into the origin of trends in donor-acceptor interactions

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Two-Qubit Separability Probabilities and Beta Functions

Due to recent important work of Zyczkowski and Sommers (quant-ph/0302197 and quant-ph/0304041), exact formulas are available (both in terms of the Hilbert-Schmidt and Bures metrics) for the (n^2-1)-dimensional and (n(n-1)/2-1)-dimensional volumes of the complex and real n x n density matrices. However, no comparable formulas are available for the volumes (and, hence, probabilities) of various separable subsets of them. We seek to clarify this situation for the Hilbert-Schmidt metric for the simplest possible case of n=4, that is, the two-qubit systems. Making use of the density matrix (rho) parameterization of Bloore (J. Phys. A 9, 2059 [1976]), we are able to reduce each of the real and complex volume problems to the calculation of a one-dimensional integral, the single relevant variable being a certain ratio of diagonal entries, nu = (rho_{11} rho_{44})/{rho_{22} rho_{33})$. The associated integrand in each case is the product of a known (highly oscillatory near nu=1) jacobian and a certain unknown univariate function, which our extensive numerical (quasi-Monte Carlo) computations indicate is very closely proportional to an (incomplete) beta function B_{nu}(a,b), with a=1/2, b=sqrt{3}in the real case, and a=2 sqrt{6}/5, b =3/sqrt{2} in the complex case. Assuming the full applicability of these specific incomplete beta functions, we undertake separable volume calculations.Comment: 17 pages, 4 figures, paper is substantially rewritten and reorganized, with the quasi-Monte Carlo integration sample size being greatly increase

High-Sensitivity C Reactive Protein: Associations with Cardiovascular Risk Factors and Tracking in Female Adolescents and Young Adults

Objective. We assessed adolescent anthropometry, lipids, insulin, glucose, and blood pressures to identify factors associated with high-sensitivity C-reactive protein (hsCRP) and its tracking in young adults. Methods. Ten-year prospective study of 589 schoolgirls, 321 black, 268 white. Results. HsCRP did not differ (P > .08) by race or oral contraceptive use. HsCRP tracked from age 16 to 25 (r = 0.77), 16 to 26 (r = 0.50), 24 to 26 (r = 0.66), and 25 to 26 (r = 0.71), all P ≤ .02. By stepwise regression, at age 16, waist circumference accounted for 44.8% of hsCRP variance; BMI accounted for 33.1%, 34.4%, and 31.1% at ages 24, 25, and 26, P < .0001 for all. Changes in cholesterol and BMI were associated with change in hsCRP from age 24–26 (partial R2 = 12.3%  P < .0001, 6.6%  P = .0012). Changes in BMI and triglyceride (partial R2 = 8.5%  P = .0001, 3.3%, P = .0045) were associated with change in hsCRP from age 25 to 26. Conclusions. HsCRP tracks from age 16 to 26, with BMI, waist circumference, and cholesterol as major determinants

Approaching the basis set limit for DFT calculations using an environment-adapted minimal basis with perturbation theory: formulation, proof of concept, and a pilot implementation

Recently developed density functionals have good accuracy for both thermochemistry (TC) and non-covalent interactions (NC) if very large atomic orbital basis sets are used. To approach the basis set limit with potentially lower computational cost, a new self-consistent field (SCF) scheme is presented that employs minimal adaptive basis (MAB) functions. The MAB functions are optimized on each atomic site by minimizing a surrogate function. High accuracy is obtained by applying a perturbative correction (PC) to the MAB calculation, similar to dual basis approaches. Compared to exact SCF results, using this MAB-SCF?(PC) approach with the same large target basis set produces &lt;0.15 kcal/mol root-mean-square deviations for most of the tested TC datasets, and &lt;0.1 kcal/mol for most of the NC datasets. The performance of density functionals near the basis set limit can be even better reproduced. With further improvement to its implementation, MAB-SCF?(PC) is a promising lower-cost substitute for conventional large-basis calculations as a method to approach the basis set limit of modern density functionals

Anaerobic methanotrophic communities thrive in deep submarine permafrost

Thawing submarine permafrost is a source of methane to the subsurface biosphere. Methane oxidation in submarine permafrost sediments has been proposed, but the responsible microorganisms remain uncharacterized. We analyzed archaeal communities and identified distinct anaerobic methanotrophic assemblages of marine and terrestrial origin (ANME-2a/b, ANME-2d) both in frozen and completely thawed submarine permafrost sediments. Besides archaea potentially involved in anaerobic oxidation of methane (AOM) we found a large diversity of archaea mainly belonging to Bathyarchaeota, Thaumarchaeota, and Euryarchaeota. Methane concentrations and δ13C-methane signatures distinguish horizons of potential AOM coupled either to sulfate reduction in a sulfate-methane transition zone (SMTZ) or to the reduction of other electron acceptors, such as iron, manganese or nitrate. Analysis of functional marker genes (mcrA) and fluorescence in situ hybridization (FISH) corroborate potential activity of AOM communities in submarine permafrost sediments at low temperatures. Modeled potential AOM consumes 72–100% of submarine permafrost methane and up to 1.2 Tg of carbon per year for the total expected area of submarine permafrost. This is comparable with AOM habitats such as cold seeps. We thus propose that AOM is active where submarine permafrost thaws, which should be included in global methane budgets

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction☆

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

“It’s like my life but more, and better!” - Playing with the Cathaby Shark Girls: MMORPGs, young people and fantasy-based social play

This article is available open access through the publisher’s website at the link below. Copyright @ 2011 A B Academic Publishers.Digital technology has opened up a range of new on-line leisure spaces for young people. Despite their popularity, on-line games and Massive Multiplayer Online Role Playing Games in particular are still a comparatively under-researched area in the fields of both Education and more broadly Youth Studies. Drawing on a Five year ethnographic study, this paper considers the ways that young people use the virtual spaces offered by MMORPGs. This paper suggests that MMORPGs represent significant arenas within which young people act out a range of social narratives through gaming. It argues that MMORPG have become important fantasy spaces which offer young people possibilities to engage in what were formally material practices. Although this form of play is grounded in the everyday it also extends material practices and offers new and unique forms of symbolic experimentation, thus I argue that game-play narratives cannot be divorced from the everyday lives of their participants

Fast and accurate modelling of longitudinal and repeated measures neuroimaging data

Despite the growing importance of longitudinal data in neuroimaging, the standard analysis methods make restrictive or unrealistic assumptions (e.g., assumption of Compound Symmetry—the state of all equal variances and equal correlations—or spatially homogeneous longitudinal correlations). While some new methods have been proposed to more accurately account for such data, these methods are based on iterative algorithms that are slow and failure-prone. In this article, we propose the use of the Sandwich Estimator (SwE) method which first estimates the parameters of interest with a simple Ordinary Least Square model and second estimates variances/covariances with the “so-called” SwE which accounts for the within-subject correlation existing in longitudinal data. Here, we introduce the SwE method in its classic form, and we review and propose several adjustments to improve its behaviour, specifically in small samples. We use intensive Monte Carlo simulations to compare all considered adjustments and isolate the best combination for neuroimaging data. We also compare the SwE method to other popular methods and demonstrate its strengths and weaknesses. Finally, we analyse a highly unbalanced longitudinal dataset from the Alzheimer's Disease Neuroimaging Initiative and demonstrate the flexibility of the SwE method to fit within- and between-subject effects in a single model. Software implementing this SwE method has been made freely available at http://warwick.ac.uk/tenichols/SwE