Bayesian inference of initial models in cryo-electron microscopy using pseudo-atoms.

Single-particle cryo-electron microscopy is widely used to study the structure of macromolecular assemblies. Tens of thousands of noisy two-dimensional images of the macromolecular assembly viewed from different directions are used to infer its three-dimensional structure. The first step is to estimate a low-resolution initial model and initial image orientations. This is a challenging global optimization problem with many unknowns, including an unknown orientation for each two-dimensional image. Obtaining a good initial model is crucial for the success of the subsequent refinement step. We introduce a probabilistic algorithm for estimating an initial model. The algorithm is fast, has very few algorithmic parameters, and yields information about the precision of estimated model parameters in addition to the parameters themselves. Our algorithm uses a pseudo-atomic model to represent the low-resolution three-dimensional structure, with isotropic Gaussian components as moveable pseudo-atoms. This leads to a significant reduction in the number of parameters needed to represent the three-dimensional structure, and a simplified way of computing two-dimensional projections. It also contributes to the speed of the algorithm. We combine the estimation of the unknown three-dimensional structure and image orientations in a Bayesian framework. This ensures that there are very few parameters to set, and specifies how to combine different types of prior information about the structure with the given data in a systematic way. To estimate the model parameters we use Markov chain Monte Carlo sampling. The advantage is that instead of just obtaining point estimates of model parameters, we obtain an ensemble of models revealing the precision of the estimated parameters. We demonstrate the algorithm on both simulated and real data

Disability Prevention Among Michigan Employers

This chapter briefly discusses the magnitude of the problem of disability in the workplace. It also presents an overview of the three and one-half year research project for which this Final Report is the product. It highlights the origins of the project and the major design elements that are reflected in this report. It concludes with a discussion of the remaining limitations of the research

Research Note, March 1968

This is issue 7: Natural Distribution of Western Larch and Subalpine Larchhttps://scholarworks.umt.edu/montana_forestry_notes/1006/thumbnail.jp

A unifying probabilistic framework for analyzing residual dipolar couplings

Residual dipolar couplings provide complementary information to the nuclear Overhauser effect measurements that are traditionally used in biomolecular structure determination by NMR. In a de novo structure determination, however, lack of knowledge about the degree and orientation of molecular alignment complicates the analysis of dipolar coupling data. We present a probabilistic framework for analyzing residual dipolar couplings and demonstrate that it is possible to estimate the atomic coordinates, the complete molecular alignment tensor, and the error of the couplings simultaneously. As a by-product, we also obtain estimates of the uncertainty in the coordinates and the alignment tensor. We show that our approach encompasses existing methods for determining the alignment tensor as special cases, including least squares estimation, histogram fitting, and elimination of an explicit alignment tensor in the restraint energy

Structural MRI Covariance Patterns Associated with Normal Aging and Neuropsychological Functioning

Structural magnetic resonance imaging (MRI) studies have shown dramatic age-associated changes in grey and white matter volume, but typically use univariate analyses that do not explicitly test the interrelationship among brain regions. The current study used a multivariate approach to identify covariance patterns of grey and white matter tissue density to distinguish older from younger adults. A second aim was to examine whether the expression of the age-associated covariance topographies is related to performance on cognitive tests affected by normal aging. Eighty-four young (mean age=24.0) and 29 older (mean age=73.1) participants were scanned with a 1.5T MRI machine and assessed with a cognitive battery. Images were spatially normalized and segmented to produce grey and white matter density maps. A multivariate technique, based on the subprofile scaling model, was used to capture sources of between- and within-group variation to produce a linear combination of principal components that represented a "pattern" or "network" that best discriminated between the two age groups. Univariate analyses were also conducted with statistical parametric maps. Grey and white matter covariance patterns were identified that reliably discriminated between the groups with greater than 0.90 sensitivity and specificity. The identified patterns were similar for the univariate and multivariate techniques, and involved widespread regions of the cortex and subcortex. Age and the expression of both patterns were significantly associated with performance on tests of attention, language, memory, and executive functioning. The results suggest that identifiable networks of grey and white matter regions systematically decline with age and that pattern expression is linked to age-related cognitive decline

Inferential NMR/X-ray-based structure determination of a dibenzo[a,d]cycloheptenone inhibitor-p38a MAP kinase complex in solution.

Complex problem: The crystal structure of p38α mitogen-activated protein kinase in complex with a dibenzo[a,d]cycloheptenone inhibitor was found to be incompatible with NMR data of the same complex in solution. By using inferential structure determination (ISD) with restraints from X-ray crystallography and NMR spectra, a structure that is compatible with both data sets and very close to the X-ray crystal structure was generated (see picture)

Cerebral Blood Flow and Gray Matter Volume Covariance Patterns of Cognition in Aging

Advancing age results in altered cognitive and neuroimaging-derived markers of neural integrity. Whether cognitive changes are the result of variations in brain measures remains unclear and relating the two across the lifespan poses a unique set of problems. It must be determined whether statistical associations between cognitive and brain measures truly exist and are not epiphenomenal due solely to their shared relationships with age. The purpose of this study was to determine whether cerebral blood flow (CBF) and gray matter volume (GMV) measures make unique and better predictions of cognition than age alone. Multivariate analyses identified brain-wide covariance patterns from 35 healthy young and 23 healthy older adults using MRI-derived measures of CBF and GMV related to three cognitive composite scores (i.e., memory, fluid ability, and speed/attention). These brain-cognitive relationships were consistent across the age range, and not the result of epiphenomenal associations with age and each imaging modality provided its own unique information. The CBF and GMV patterns each accounted for unique aspects of cognition and accounted for nearly all the age-related variance in the cognitive composite scores. The findings suggest that measures derived from multiple imaging modalities explain larger amounts of variance in cognition providing a more complete understanding of the aging brain

Examining the Multifactorial Nature of Cognitive Aging with Covariance Analysis of Positron Emission Tomography Data

Research has indicated that there may be age-related and Alzheimer's disease (AD) -related reductions in regional cerebral blood flow (rCBF) in the brain. This study explored differences in age- and AD-related rCBF patterns in the context of cognitive aging using a multivariate approach to the analysis of H215O PET data. First, an rCBF covariance pattern that distinguishes between a group of younger and older adults was identified. Individual subject's expression of the identified age-related pattern was significantly correlated with their performance on tests of memory, even after controlling for the effect of age. This finding suggests that subject expression of the covariance pattern explained additional variation in performance on the memory tasks. The age-related covariance pattern was then compared to an AD-related covariance pattern. There was little evidence that the two covariance patterns were similar, and the age-related pattern did a poor job of differentiating between cognitively-healthy older adults and those with probable AD. The findings from this study are consistent with the multifactorial nature of cognitive aging