Dirac constraints in field theory and exterior differential systems

The usual treatment of a (first order) classical field theory such as electromagnetism has a little drawback: It has a primary constraint submanifold that arise from the fact that the dynamics is governed by the antisymmetric part of the jet variables. So it is natural to ask if there exists a formulation of this kind of field theories which avoids this problem, retaining the versatility of the known approach. The following paper deals with a family of variational problems, namely, the so called non standard variational problems, which intends to capture the data necessary to set up such a formulation for field theories; moreover, we will formulate a multisymplectic structure for the family of non standard variational problems, and we will relate this with the (pre)symplectic structure arising on the space of sections of the bundle of fields. In this setting the Dirac theory of constraints will be studied, obtaining among other things a novel characterization of the constraint manifold which arises in this theory, as generators of an exterior differential system associated to the equations of motion and the chosen slicing. Several examples of application of this formalism are discussed: Two of them motivated from the physical point of view, that is, electromagnetism and Poisson sigma models, and two examples of mathematical application. In the case of electromagnetism, it is shown that this formulation avoids the problems arising in the usual approach.Comment: 51 pages, aims; added examples and references, typos added, improved some content, to appear in "Journal of Geometric Mechanics"

The impact of consent on observational research: a comparison of outcomes from consenters and non consenters to an observational study

Background Public health benefits from research often rely on the use of data from personal medical records. When neither patient consent nor anonymisation is possible, the case for accessing such records for research purposes depends on an assessment of the probabilities of public benefit and individual harm. Methods In the late 1990s, we carried out an observational study which compared the care given to affluent and deprived women with breast cancer. Patient consent was not required at that time for review of medical records, but was obtained later in the process prior to participation in the questionnaire study. We have re-analysed our original results to compare the whole sample with those who later provided consent. Results Two important findings emerged from the re-analysis of our data which if presented initially would have resulted in insufficient and inaccurate reporting. Firstly, the reduced dataset contains no information about women presenting with locally advanced or metastatic cancer and we would have been unable to demonstrate one of our initial key findings: namely a larger number of such women in the deprived group. Secondly, our re-analysis of the consented women shows that significantly more women from deprived areas (51 v 31%, p = 0.018) received radiotherapy compared to women from more affluent areas. Previously published data from the entire sample demonstrated no difference in radiotherapy treatment between the affluent and deprived groups. Conclusion The risk benefit assessment made regarding the use of medical records without consent should include the benefits of obtaining research evidence based on 100% of the population and the possibility of inappropriate or insufficient findings if research is confined to consented populations

Serotonergic innervation of the amygdala is increased in autism spectrum disorder and decreased in Williams syndrome.

BackgroundWilliams syndrome (WS) and autism spectrum disorder (ASD) are neurodevelopmental disorders that demonstrate overlapping genetic associations, dichotomous sociobehavioral phenotypes, and dichotomous pathological differences in neuronal distribution in key social brain areas, including the prefrontal cortex and the amygdala. The serotonergic system is critical to many processes underlying neurodevelopment and is additionally an important neuromodulator associated with behavioral variation. The amygdala is heavily innervated by serotonergic projections, suggesting that the serotonergic system is a significant mediator of neuronal activity. Disruptions to the serotonergic system, and atypical structure and function of the amygdala, are implicated in both WS and ASD.MethodsWe quantified the serotonergic axon density in the four major subdivisions of the amygdala in the postmortem brains of individuals diagnosed with ASD and WS and neurotypical (NT) brains.ResultsWe found opposing directions of change in serotonergic innervation in the two disorders, with ASD displaying an increase in serotonergic axons compared to NT and WS displaying a decrease. Significant differences (p < 0.05) were observed between WS and ASD data sets across multiple amygdala nuclei.LimitationsThis study is limited by the availability of human postmortem tissue. Small sample size is an unavoidable limitation of most postmortem human brain research and particularly postmortem research in rare disorders.ConclusionsDifferential alterations to serotonergic innervation of the amygdala may contribute to differences in sociobehavioral phenotype in WS and ASD. These findings will inform future work identifying targets for future therapeutics in these and other disorders characterized by atypical social behavior

A Sparse Stress Model

Force-directed layout methods constitute the most common approach to draw general graphs. Among them, stress minimization produces layouts of comparatively high quality but also imposes comparatively high computational demands. We propose a speed-up method based on the aggregation of terms in the objective function. It is akin to aggregate repulsion from far-away nodes during spring embedding but transfers the idea from the layout space into a preprocessing phase. An initial experimental study informs a method to select representatives, and subsequent more extensive experiments indicate that our method yields better approximations of minimum-stress layouts in less time than related methods.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

Comparison of the direct effects of human adipose- and bone-marrow-derived stem cells on postischemic cardiomyoblasts in an in vitro simulated ischemia-reperfusion model.

Regenerative therapies hold a promising and exciting future for the cure of yet untreatable diseases, and mesenchymal stem cells are in the forefront of this approach. However, the relative efficacy and the mechanism of action of different types of mesenchymal stem cells are still incompletely understood. We aimed to evaluate the effects of human adipose- (hASC) and bone-marrow-derived stem cells (hBMSCs) and adipose-derived stem cell conditioned media (ACM) on the viability of cardiomyoblasts in an in vitro ischemia-reperfusion (I-R) model. Flow cytometric viability analysis revealed that both cell treatments led to similarly increased percentages of living cells, while treatment with ACM did not (I-R model: 12.13 +/- 0.75%; hASC: 24.66 +/- 2.49%; hBMSC: 25.41 +/- 1.99%; ACM: 13.94 +/- 1.44%). Metabolic activity measurement (I-R model: 0.065 +/- 0.033; hASC: 0.652 +/- 0.089; hBMSC: 0.607 +/- 0.059; ACM: 0.225 +/- 0.013; arbitrary units) and lactate dehydrogenase assay (I-R model: 0.225 +/- 0.006; hASC: 0.148 +/- 0.005; hBMSC: 0.146 +/- 0.004; ACM: 0.208 +/- 0.009; arbitrary units) confirmed the flow cytometric results while also indicated a slight beneficial effect of ACM. Our results highlight that mesenchymal stem cells have the same efficacy when used directly on postischemic cells, and differences found between them in preclinical and clinical investigations are rather related to other possible causes such as their immunomodulatory or angiogenic properties

Human-in-the-Loop Mixup

Aligning model representations to humans has been found to improve robustness and generalization. However, such methods often focus on standard observational data. Synthetic data is proliferating and powering many advances in machine learning; yet, it is not always clear whether synthetic labels are perceptually aligned to humans - rendering it likely model representations are not human aligned. We focus on the synthetic data used in mixup: a powerful regularizer shown to improve model robustness, generalization, and calibration. We design a comprehensive series of elicitation interfaces, which we release as HILL MixE Suite, and recruit 159 participants to provide perceptual judgments along with their uncertainties, over mixup examples. We find that human perceptions do not consistently align with the labels traditionally used for synthetic points, and begin to demonstrate the applicability of these findings to potentially increase the reliability of downstream models, particularly when incorporating human uncertainty. We release all elicited judgments in a new data hub we call H-Mix

A Regularized Graph Layout Framework for Dynamic Network Visualization

Many real-world networks, including social and information networks, are dynamic structures that evolve over time. Such dynamic networks are typically visualized using a sequence of static graph layouts. In addition to providing a visual representation of the network structure at each time step, the sequence should preserve the mental map between layouts of consecutive time steps to allow a human to interpret the temporal evolution of the network. In this paper, we propose a framework for dynamic network visualization in the on-line setting where only present and past graph snapshots are available to create the present layout. The proposed framework creates regularized graph layouts by augmenting the cost function of a static graph layout algorithm with a grouping penalty, which discourages nodes from deviating too far from other nodes belonging to the same group, and a temporal penalty, which discourages large node movements between consecutive time steps. The penalties increase the stability of the layout sequence, thus preserving the mental map. We introduce two dynamic layout algorithms within the proposed framework, namely dynamic multidimensional scaling (DMDS) and dynamic graph Laplacian layout (DGLL). We apply these algorithms on several data sets to illustrate the importance of both grouping and temporal regularization for producing interpretable visualizations of dynamic networks.Comment: To appear in Data Mining and Knowledge Discovery, supporting material (animations and MATLAB toolbox) available at http://tbayes.eecs.umich.edu/xukevin/visualization_dmkd_201

A simple mathematical model of gradual Darwinian evolution: Emergence of a Gaussian trait distribution in adaptation along a fitness gradient

We consider a simple mathematical model of gradual Darwinian evolution in continuous time and continuous trait space, due to intraspecific competition for common resource in an asexually reproducing population in constant environment, while far from evolutionary stable equilibrium. The model admits exact analytical solution. In particular, Gaussian distribution of the trait emerges from generic initial conditions.Comment: 21 pages, 2 figures, as accepted to J Math Biol 2013/03/1