Direct visualization of the putative portal in the Kaposi's sarcoma-associated herpesvirus capsid by cryoelectron tomography.

Genetic and biochemical studies have suggested the existence of a bacteriophage-like, DNA-packaging/ejecting portal complex in herpesviruses capsids, but its arrangement remained unknown. Here, we report the first visualization of a unique vertex in the Kaposi's sarcoma-associated herpesvirus (KSHV) capsid by cryoelectron tomography, thus providing direct structural evidence for the existence of a portal complex in a gammaherpesvirus. This putative KSHV portal is an internally localized, umbilicated structure and lacks all of the external machineries characteristic of portals in DNA bacteriophages.

A Hierarchical Bayesian Model for Measuring Motion Adaptation

Previous research has shown that motion imagery draws on the same neural circuits that are involved in perception of motion, thus leading to a motion aftereffect (Winawer et al., 2010). Imagined stimuli can induce a similar shift in participants’ psychometric functions as neural adaptation due to a perceived stimulus. However, these studies have been criticized on the grounds that they fail to exclude the possibility that the subjects might have guessed the experimental hypothesis, and behaved accordingly (Morgan et al., 2012). In particular, the authors claim that participants can adopt arbitrary response criteria, which results in similar changes of the central tendency μ of psychometric curves as those shown by Winawer et al. (2010).

Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Hierarchical self-assembly of nucleotide-appended oligopyrenotides into defined supramolecular objects

Supramolecular DNA assembly blends DNA building blocks with synthetic organic and inorganic molecules giving structural and functional advantages both to the initial self-assembly process and to the final construct. Synthetic molecules can bring a number of additional interactions into DNA nanotechnology. Incorporating extended aromatic molecules as connectors of DNA strands allows folding of these strands through π-π stacking (DNA “foldamers”). In previous work it was shown that short oligopyrenotides (phosphodiester-linked pyrene oligomers) behave as staircase-like foldamers, which cooperatively self-assemble into two-dimensional supramolecular polymers in aqueous medium. Herein, we demonstrate that a 10-mer DNA-sequence modified with 7 pyrene units (see illustration) forms dimensionally-defined supramolecular polymers under thermodynamic conditions in water. We present the self-assembly behavior, morphological studies, and the spectroscopic properties of the investigated DNA-sequences (illustrative AFM picture shown below).

Hierarchical self-assembly of nucleotide-appended oligopyrenotides into defined supramolecular objects

Supramolecular DNA assembly blends DNA building blocks with synthetic organic molecules giving structural and functional advantages. Incorporating extended aromatic molecules as connectors of DNA strands allows folding of these strands through π-π stacking (DNA 'foldamers'). In previous work it was shown that short oligopyrenotides behave as staircase-like foldamers, which cooperatively self-assemble into 2D supramolecular polymers in aqueous medium. Herein, we demonstrate that 10-mer DNA-sequence conjugated with seven pyrene unites forms dimensionally-defined supramolecular polymers under thermodynamic conditions in water. We present the self-assembly behavior, morphologycal studies (AFM and TEM), and the spectroscopic properties (UV/vis, CD) of the investigated pyrene - conjugated DNA-sequence.

The ovine cerebral venous system: comparative anatomy, visualization, and implications for translational research.

Cerebrovascular diseases are significant causes of death and disability in humans. Improvements in diagnostic and therapeutic approaches strongly rely on adequate gyrencephalic, large animal models being demanded for translational research. Ovine stroke models may represent a promising approach but are currently limited by insufficient knowledge regarding the venous system of the cerebral angioarchitecture. The present study was intended to provide a comprehensive anatomical analysis of the intracranial venous system in sheep as a reliable basis for the interpretation of experimental results in such ovine models. We used corrosion casts as well as contrast-enhanced magnetic resonance venography to scrutinize blood drainage from the brain. This combined approach yielded detailed and, to some extent, novel findings. In particular, we provide evidence for chordae Willisii and lateral venous lacunae, and report on connections between the dorsal and ventral sinuses in this species. For the first time, we also describe venous confluences in the deep cerebral venous system and an 'anterior condylar confluent' as seen in humans. This report provides a detailed reference for the interpretation of venous diagnostic imaging findings in sheep, including an assessment of structure detectability by in vivo (imaging) versus ex vivo (corrosion cast) visualization methods. Moreover, it features a comprehensive interspecies-comparison of the venous cerebral angioarchitecture in man, rodents, canines and sheep as a relevant large animal model species, and describes possible implications for translational cerebrovascular research.