Tisaje de artículos elaborados con firmas acrílicas.

Características de la fibra acrílica y la preparación necesaria para el tisaje.Peer Reviewe

The happiness paradox: your friends are happier than you

Most individuals in social networks experience a so-called Friendship Paradox: they are less popular than their friends on average. This effect may explain recent findings that widespread social network media use leads to reduced happiness. However the relation between popularity and happiness is poorly understood. A Friendship paradox does not necessarily imply a Happiness paradox where most individuals are less happy than their friends. Here we report the first direct observation of a significant Happiness Paradox in a large-scale online social network of $39,110$ Twitter users. Our results reveal that popular individuals are indeed happier and that a majority of individuals experience a significant Happiness paradox. The magnitude of the latter effect is shaped by complex interactions between individual popularity, happiness, and the fact that users cluster assortatively by level of happiness. Our results indicate that the topology of online social networks and the distribution of happiness in some populations can cause widespread psycho-social effects that affect the well-being of billions of individuals.Comment: 15 pages, 3 figures, 2 table

Molecular genetic characterization of ataxic movement disorders in mouse and human

Deletion at ITPR1 underlies a young onset autosomal recessive ataxia in mice and a late onset autosomal dominant ataxia (SCA15) in humans. Data presented show the utility of investigating spontaneous mouse mutations in understanding human disease. Through linkage and sequence analysis a novel mutation in the gene encoding inositol 1,4,5-triphosphate receptor type 1 was identified to underlie a severe movement disorder in mice. The 18bp in frame deletion in Itpr1 exon 36 was shown to be allelic to that of another model, opisthotonos (Lane 1972). The Itpr1Δ18 mutation leads to a decreased to almost total lack in the normally high level of ITPR1 expression in cerebellar Purkinje cells. In addition, high density genome wide SNP genotype data in humans showed a SUMF1-ITPR1 deletion to segregate with spinocerebellar ataxia 15 (SCA15), a late-onset autosomal dominant disorder, which was previously mapped to the genomic region containing ITPR1; however, no causal mutations had been identified (Knight et al. 2003). With this deletion not observed in a control population, decreased ITPR1 protein levels in individuals carrying the deletion, and subsequent identification of similar deletions in additional spinocerebellar ataxia families, the data provide compelling evidence that heterozygous deletion in ITPR1 underlies SCA15. As demonstrated, high density genome wide SNP analysis can facilitate rapid detection of structural genomic mutations that may underlie disease when standard sequencing approaches are insufficient. The data suggest genetic alterations at ITPR1 underlie approximately over 1% of autosomal dominant SCA type III (ADCA III) cases for which currently no genetic cause has been identified. Data described herein add weight to a role for aberrant intracellular Ca2+ signaling in Purkinje cells in the pathogenesis of spinocerebellar ataxia

PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation

With the advent of convolutional neural networks~(CNN), supervised learning methods are increasingly being used for whole brain segmentation. However, a large, manually annotated training dataset of labeled brain images required to train such supervised methods is frequently difficult to obtain or create. In addition, existing training datasets are generally acquired with a homogeneous magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such datasets are unable to generalize on test data with different acquisition protocols. Modern neuroimaging studies and clinical trials are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is very difficult to standardize the gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input acquisition. Our approach relies on building approximate forward models of pulse sequences that produce a typical test image. For a given pulse sequence, we use its forward model to generate plausible, synthetic training examples that appear as if they were acquired in a scanner with that pulse sequence. Sampling over a wide variety of pulse sequences results in a wide variety of augmented training examples that help build an image contrast invariant model. Our method trains a single CNN that can segment input MRI images with acquisition parameters as disparate as $T_1$-weighted and $T_2$-weighted contrasts with only $T_1$-weighted training data. The segmentations generated are highly accurate with state-of-the-art results~(overall Dice overlap$=0.94$), with a fast run time~($\approx$ 45 seconds), and consistent across a wide range of acquisition protocols.Comment: Typo in author name corrected. Greves -> Grev

Coarse-grained numerical bifurcation analysis of lattice Boltzmann models

In this paper we study the earlier proposed coarse-grained bifurcation analysis approach. We extend the results obtained then for a one-dimensional FitzHugh–Nagumo lattice Boltzmann (LB) model in several ways. First, we extend the coarse-grained time stepper concept to enable the computation of periodic solutions and we use the more versatile Newton–Picard method rather than the Recursive Projection Method (RPM) for the numerical bifurcation analysis. Second, we compare the obtained bifurcation diagram with the bifurcation diagrams of the corresponding macroscopic PDE and of the lattice Boltzmann model. Most importantly, we perform an extensive study of the influence of the lifting or reconstruction step on the minimal successful time step of the coarse-grained time stepper and the accuracy of the results. It is shown experimentally that this time step must often be much larger than the time it takes for the higher-order moments to become slaved by the lowest-order moment, which somewhat contradicts earlier claims.

Fast and Sequence-Adaptive Whole-Brain Segmentation Using Parametric Bayesian Modeling

AbstractQuantitative analysis of magnetic resonance imaging (MRI) scans of the brain requires accurate automated segmentation of anatomical structures. A desirable feature for such segmentation methods is to be robust against changes in acquisition platform and imaging protocol. In this paper we validate the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four different datasets acquired with different scanners, field strengths and pulse sequences, demonstrating comparable accuracy to state-of-the-art methods on T1-weighted scans while being one to two orders of magnitude faster. The proposed algorithm is also shown to be robust against small training datasets, and readily handles images with different MRI contrast as well as multi-contrast data