Cooperative molecular motors moving back and forth

We use a two-state ratchet model to study the cooperative bidirectional motion of molecular motors on cytoskeletal tracks with randomly alternating polarities. Our model is based on a previously proposed model [Badoual et al., {\em Proc. Natl. Acad. Sci. USA} {\bf 99}, 6696 (2002)] for collective motor dynamics and, in addition, takes into account the cooperativity effect arising from the elastic tension that develops in the cytoskeletal track due to the joint action of the walking motors. We show, both computationally and analytically, that this additional cooperativity effect leads to a dramatic reduction in the characteristic reversal time of the bidirectional motion, especially in systems with a large number of motors. We also find that bidirectional motion takes place only on (almost) a-polar tracks, while on even slightly polar tracks the motion is unidirectional. We argue that the origin of these observations is the sensitive dependence of the cooperative dynamics on the difference between the number of motors typically working in and against the instantaneous direction of motion.Comment: Accepted for publication in Phys. Rev.

Three-Prong Distribution of Massive Narrow QCD Jets

We study the planar-flow distributions of narrow, highly boosted, massive QCD jets. Using the factorization properties of QCD in the collinear limit, we compute the planar-flow jet function from the one-to-three splitting function at tree-level. We derive the leading-log behavior of the jet function analytically. We also compare our semi-analytic jet function with parton-shower predictions using various generators.Comment: 59 pages, 9 figure

Graph diffusion distance: a difference measure for weighted graphs based on the graph Laplacian exponential kernel

pre-printWe propose a novel difference metric, called the graph diffusion distance (GDD), for quantifying the difference between two weighted graphs with the same number of vertices. Our approach is based on measuring the average similarity of heat diffusion on each graph. We compute the graph Laplacian exponential kernel matrices, corresponding to repeatedly solving the heat diffusion problem with initial conditions localized to single vertices. The GDD is then given by the Frobenius norm of the difference of the kernels, at the diffusion time yielding the maximum difference. We study properties of the proposed distance on both synthetic examples, and on real-data graphs representing human anatomical brain connectivity

Faster Family-wise Error Control for Neuroimaging with a Parametric Bootstrap

In neuroimaging, hundreds to hundreds of thousands of tests are performed across a set of brain regions or all locations in an image. Recent studies have shown that the most common family-wise error (FWE) controlling procedures in imaging, which rely on classical mathematical inequalities or Gaussian random field theory, yield FWE rates that are far from the nominal level. Depending on the approach used, the FWER can be exceedingly small or grossly inflated. Given the widespread use of neuroimaging as a tool for understanding neurological and psychiatric disorders, it is imperative that reliable multiple testing procedures are available. To our knowledge, only permutation joint testing procedures have been shown to reliably control the FWER at the nominal level. However, these procedures are computationally intensive due to the increasingly available large sample sizes and dimensionality of the images, and analyses can take days to complete. Here, we develop a parametric bootstrap joint testing procedure. The parametric bootstrap procedure works directly with the test statistics, which leads to much faster estimation of adjusted \emph{p}-values than resampling-based procedures while reliably controlling the FWER in sample sizes available in many neuroimaging studies. We demonstrate that the procedure controls the FWER in finite samples using simulations, and present region- and voxel-wise analyses to test for sex differences in developmental trajectories of cerebral blood flow

“It's Not What You Say, But How You Say it”: A Reciprocal Temporo-frontal Network for Affective Prosody

Humans communicate emotion vocally by modulating acoustic cues such as pitch, intensity and voice quality. Research has documented how the relative presence or absence of such cues alters the likelihood of perceiving an emotion, but the neural underpinnings of acoustic cue-dependent emotion perception remain obscure. Using functional magnetic resonance imaging in 20 subjects we examined a reciprocal circuit consisting of superior temporal cortex, amygdala and inferior frontal gyrus that may underlie affective prosodic comprehension. Results showed that increased saliency of emotion-specific acoustic cues was associated with increased activation in superior temporal cortex [planum temporale (PT), posterior superior temporal gyrus (pSTG), and posterior superior middle gyrus (pMTG)] and amygdala, whereas decreased saliency of acoustic cues was associated with increased inferior frontal activity and temporo-frontal connectivity. These results suggest that sensory-integrative processing is facilitated when the acoustic signal is rich in affective information, yielding increased activation in temporal cortex and amygdala. Conversely, when the acoustic signal is ambiguous, greater evaluative processes are recruited, increasing activation in inferior frontal gyrus (IFG) and IFG STG connectivity. Auditory regions may thus integrate acoustic information with amygdala input to form emotion-specific representations, which are evaluated within inferior frontal regions

Disrupted anatomic networks in the 22q11.2 deletion syndrome

AbstractThe 22q11.2 deletion syndrome (22q11DS) is an uncommon genetic disorder with an increased risk of psychosis. Although the neural substrates of psychosis and schizophrenia are not well understood, aberrations in cortical networks represent intriguing potential mechanisms. Investigations of anatomic networks within 22q11DS are sparse. We investigated group differences in anatomic network structure in 48 individuals with 22q11DS and 370 typically developing controls by analyzing covariance patterns in cortical thickness among 68 regions of interest using graph theoretical models. Subjects with 22q11DS had less robust geographic organization relative to the control group, particularly in the occipital and parietal lobes. Multiple global graph theoretical statistics were decreased in 22q11DS. These results are consistent with prior studies demonstrating decreased connectivity in 22q11DS using other neuroimaging methodologies