Efficient time step parallelization of full multigrid techniques

This paper deals with parallelization methods for time-dependent problems where the time steps are shared out among the processors. A Full Multigrid technique serves as solution algorithm, hence information of the preceding time step and of the coarser grid is necessary to compute the solution at each new grid level. Applying the usual extrapolation formula to process this information, the parallelization will not be very efficient. We developed another extrapolation technique which causes a much higher parallelization effect. Test examples show that no essential loss of exactness appears, such that the method presented here shall be well-applicable

Nonlinear Diffusion on the 2D Euclidean Motion Group

Linear and nonlinear diffusion equations are usually considered on an image, which is in fact a function on the translation group. In this paper we study diffusion on orientation scores, i.e. on functions on the Euclidean motion group SE(2). An orientation score is obtained from an image by a linear invertible transformation. The goal is to enhance elongated structures by applying nonlinear left-invariant diffusion on the orientation score of the image. For this purpose we describe how we can use Gaussian derivatives to obtain regularized left-invariant derivatives that obey the non-commutative structure of the Lie algebra of SE(2). The Hessian constructed with these derivatives is used to estimate local curvature and orientation strength and the diffusion is made nonlinearly dependent on these measures. We propose an explicit finite difference scheme to apply the nonlinear diffusion on orientation scores. The experiments show that preservation of crossing structures is the main advantage compared to approaches such as coherence enhancing diffusion

Tensor field interpolation with PDEs

We present a unified framework for interpolation and regularisation of scalar- and tensor-valued images. This framework is based on elliptic partial differential equations (PDEs) and allows rotationally invariant models. Since it does not require a regular grid, it can also be used for tensor-valued scattered data interpolation and for tensor field inpainting. By choosing suitable differential operators, interpolation methods using radial basis functions are covered. Our experiments show that a novel interpolation technique based on anisotropic diffusion with a diffusion tensor should be favoured: It outperforms interpolants with radial basis functions, it allows discontinuity-preserving interpolation with no additional oscillations, and it respects positive semidefiniteness of the input tensor data

Human amylase gene copy number variation as a determinant of metabolic state

Introduction Humans have multiple genes encoding amylase that are broadly divided into salivary (AMY1) and pancreatic (AMY2) genes. They exhibit some of the greatest copy numbers of any human gene, an expansion possibly driven by increased dietary starch intake. Within the population, amylase gene copy number is highly variable and there is evidence of an inverse association between AMY1 copy number and BMI. Areas covered We examine the evidence for the link between AMY1 and BMI, its potential mechanisms, and the metabolic effects of salivary and pancreatic amylase, both in the gastrointestinal tract and the blood. Expert commentary Salivary amylase may influence postprandial ‘cephalic phase’ insulin release, which improves glucose tolerance, while serum amylase may have insulin-sensitizing properties. This could explain the favorable metabolic status associated with higher AMY1 copy number. The association with BMI is harder to explain and is potentially mediated by increased flux of undigested starch into the ileum, with resultant effects on short-chain fatty acids (SCFAs), changes in gut microbiota and effects on appetite and energy expenditure in those with low copy number. Future research on the role of amylase as a determinant of metabolic health and BMI may lead to novel therapies to target obesity

Frontal and Parietal Contributions to Probabilistic Association Learning

Neuroimaging studies have shown both dorsolateral prefrontal (DLPFC) and inferior parietal cortex (iPARC) activation during probabilistic association learning. Whether these cortical brain regions are necessary for probabilistic association learning is presently unknown. Participants' ability to acquire probabilistic associations was assessed during disruptive 1 Hz repetitive transcranial magnetic stimulation (rTMS) of the left DLPFC, left iPARC, and sham using a crossover single-blind design. On subsequent sessions, performance improved relative to baseline except during DLPFC rTMS that disrupted the early acquisition beneficial effect of prior exposure. A second experiment examining rTMS effects on task-naive participants showed that neither DLPFC rTMS nor sham influenced naive acquisition of probabilistic associations. A third experiment examining consecutive administration of the probabilistic association learning test revealed early trial interference from previous exposure to different probability schedules. These experiments, showing disrupted acquisition of probabilistic associations by rTMS only during subsequent sessions with an intervening night's sleep, suggest that the DLPFC may facilitate early access to learned strategies or prior task-related memories via consolidation. Although neuroimaging studies implicate DLPFC and iPARC in probabilistic association learning, the present findings suggest that early acquisition of the probabilistic cue-outcome associations in task-naive participants is not dependent on either region

Adaptive structure tensors and their applications

The structure tensor, also known as second moment matrix or Förstner interest operator, is a very popular tool in image processing. Its purpose is the estimation of orientation and the local analysis of structure in general. It is based on the integration of data from a local neighborhood. Normally, this neighborhood is defined by a Gaussian window function and the structure tensor is computed by the weighted sum within this window. Some recently proposed methods, however, adapt the computation of the structure tensor to the image data. There are several ways how to do that. This article wants to give an overview of the different approaches, whereas the focus lies on the methods based on robust statistics and nonlinear diffusion. Furthermore, the dataadaptive structure tensors are evaluated in some applications. Here the main focus lies on optic flow estimation, but also texture analysis and corner detection are considered

Increased levels of a pro-inflammatory IgG receptor in the midbrain of people with schizophrenia

Background: There is growing evidence that neuroinflammation may contribute to schizophrenia neuropathology. Elevated pro-inflammatory cytokines are evident in the midbrain from schizophrenia subjects, findings that are driven by a subgroup of patients, characterised as a “high inflammation” biotype. Cytokines trigger the release of antibodies, of which immunoglobulin G (IgG) is the most common. The level and function of IgG is regulated by its transporter (FcGRT) and by pro-inflammatory IgG receptors (including FcGR3A) in balance with the anti-inflammatory IgG receptor FcGR2B. Testing whether abnormalities in IgG activity contribute to the neuroinflammatory abnormalities schizophrenia patients, particularly those with elevated cytokines, may help identify novel treatment targets. Methods: Post-mortem midbrain tissue from healthy controls and schizophrenia cases (n = 58 total) was used to determine the localisation and abundance of IgG and IgG transporters and receptors in the midbrain of healthy controls and schizophrenia patients. Protein levels of IgG and FcGRT were quantified using western blot, and gene transcript levels of FcGRT, FcGR3A and FcGR2B were assessed using qPCR. The distribution of IgG in the midbrain was assessed using immunohistochemistry and immunofluorescence. Results were compared between diagnostic (schizophrenia vs control) and inflammatory (high vs low inflammation) groups. Results: We found that IgG and FcGRT protein abundance (relative to β-actin) was unchanged in people with schizophrenia compared with controls irrespective of inflammatory subtype. In contrast, FcGRT and FcGR3A mRNA levels were elevated in the midbrain from “high inflammation” schizophrenia cases (FcGRT; p = 0.02, FcGR3A; p < 0.0001) in comparison to low-inflammation patients and healthy controls, while FcGR2B mRNA levels were unchanged. IgG immunoreactivity was evident in the midbrain, and approximately 24% of all individuals (control subjects and schizophrenia cases) showed diffusion of IgG from blood vessels into the brain. However, the intensity and distribution of IgG was comparable across schizophrenia cases and control subjects. Conclusion: These findings suggest that an increase in the pro-inflammatory Fcγ receptor FcGR3A, rather than an overall increase in IgG levels, contribute to midbrain neuroinflammation in schizophrenia patients. However, more precise information about IgG-Fcγ receptor interactions is needed to determine their potential role in schizophrenia neuropathology

PDEs for tensor image processing

Methods based on partial differential equations (PDEs) belong to those image processing techniques that can be extended in a particularly elegant way to tensor fields. In this survey paper the most important PDEs for discontinuity-preserving denoising of tensor fields are reviewed such that the underlying design principles becomes evident. We consider isotropic and anisotropic diffusion filters and their corresponding variational methods, mean curvature motion, and selfsnakes. These filters preserve positive semidefiniteness of any positive semidefinite initial tensor field. Finally we discuss geodesic active contours for segmenting tensor fields. Experiments are presented that illustrate the behaviour of all these methods

Free fatty acids link metabolism and regulation of the insulin-sensitizing fibroblast growth factor-21

OBJECTIVE—Fibroblast growth factor (FGF)-21 improves insulin sensitivity and lipid metabolism in obese or diabetic animal models, while human studies revealed increased FGF-21 levels in obesity and type 2 diabetes. Given that FGF-21 has been suggested to be a peroxisome proliferator–activator receptor (PPAR) –dependent regulator of fasting metabolism, we hypothesized that free fatty acids (FFAs), natural agonists of PPAR, might modify FGF-21 levels. RESEARCH DESIGN AND METHODS—The effect of fatty acids on FGF-21 was investigated in vitro in HepG2 cells. Within a randomized controlled trial, the effects of elevated FFAs were studied in 21 healthy subjects (13 women and 8 men). Within a clinical trial including 17 individuals, the effect of insulin was analyzed using an hyperinsulinemic-euglycemic clamp and the effect of PPAR activation was studied subsequently in a rosiglitazone treatment trial over 8 weeks. RESULTS—Oleate and linoleate increased FGF-21 expression and secretion in a PPAR-dependent fashion, as demonstrated by small-interfering RNA–induced PPAR knockdown, while palmitate had no effect. In vivo, lipid infusion induced an increase of circulating FGF-21 in humans, and a strong correlation between the change in FGF-21 levels and the change in FFAs was observed. An artificial hyperinsulinemia, which was induced to delineate the potential interaction between elevated FFAs and hyperinsulinemia, revealed that hyperinsulinemia also increased FGF-21 levels in vivo, while rosiglitazone treatment had no effect. CONCLUSIONS—The results presented here offer a mechanism explaining the induction of the metabolic regulator FGF-21 in the fasting situation but also in type 2 diabetes and obesity