Constraining dark energy fluctuations with supernova correlations

We investigate constraints on dark energy fluctuations using type Ia supernovae. If dark energy is not in the form of a cosmological constant, that is if the equation of state is not equal to -1, we expect not only temporal, but also spatial variations in the energy density. Such fluctuations would cause local variations in the universal expansion rate and directional dependences in the redshift-distance relation. We present a scheme for relating a power spectrum of dark energy fluctuations to an angular covariance function of standard candle magnitude fluctuations. The predictions for a phenomenological model of dark energy fluctuations are compared to observational data in the form of the measured angular covariance of Hubble diagram magnitude residuals for type Ia supernovae in the Union2 compilation. The observational result is consistent with zero dark energy fluctuations. However, due to the limitations in statistics, current data still allow for quite general dark energy fluctuations as long as they are in the linear regime.Comment: 18 pages, 6 figures, matches the published versio

Disorders of compulsivity: a common bias towards learning habits.

Why do we repeat choices that we know are bad for us? Decision making is characterized by the parallel engagement of two distinct systems, goal-directed and habitual, thought to arise from two computational learning mechanisms, model-based and model-free. The habitual system is a candidate source of pathological fixedness. Using a decision task that measures the contribution to learning of either mechanism, we show a bias towards model-free (habit) acquisition in disorders involving both natural (binge eating) and artificial (methamphetamine) rewards, and obsessive-compulsive disorder. This favoring of model-free learning may underlie the repetitive behaviors that ultimately dominate in these disorders. Further, we show that the habit formation bias is associated with lower gray matter volumes in caudate and medial orbitofrontal cortex. Our findings suggest that the dysfunction in a common neurocomputational mechanism may underlie diverse disorders involving compulsion.This study was funded by the WT fellowship grant for VV (093705/Z/ 10/Z) and Cambridge NIHR Biomedical Research Centre. VV and NAH are Wellcome Trust (WT) intermediate Clinical Fellows. YW is supported by the Fyssen Fondation and MRC Studentships. PD is supported by the Gatsby Charitable Foundation. JEG has received grants from the National Institute of Drug Abuse and the National Center for Responsible Gaming. TWR and BJS are supported on a WT Programme Grant (089589/Z/09/Z). The BCNI is supported by a WT and MRC grant.This is the final published version. It's also available from Molecular Psychiatry at http://www.nature.com/mp/journal/vaop/ncurrent/full/mp201444a.html

Pollution prevention in a zinc die casting company: A 10-year case study

In this project, pollution prevention methodologies were applied to a mass finishing process used for the cleaning and polishing of miniature zinc die-casts. While the original project objective had been to rely on the use of acids and filtration methods (cartridge and membrane) to minimize waste discharges, development testing and improvement data obtained throughout the year-long project indicated that acid use and cartridge filtration were no longer required; pH levels were found to be the key to simplifying the entire operation. Pollution prevention was achieved through (1) the elimination of mineral spirits to preclean parts; (2) the elimination of hydrochloric acid used to settle and remove sludge; (3) the elimination of a 100,000 l/month sewer discharge of metal-bearing wastewater; (4) the recovery of zinc metal for off-site recycling; and (5) the recycling of an aqueous-based soap. Favorable economic payback and reduced liabilities were also realized while product rejection rates decreased. The company has been using the process for 10 years. © 2001 Elsevier Science Ltd. All rights reserved

Investigation of time of combustion in a gas engine cylinder

http://www.archive.org/details/investigationoft00enanThesis (B.S.)--Armour Institute of Technolog

A model for self-organization of sensorimotor function : spinal interneuronal integration

Control of musculoskeletal systems depends on integration of voluntary commands and somatosensory feedback in the complex neural circuits of the spinal cord. It has been suggested that the various connectivity patterns that have been identified experimentally may result from the many transcriptional types that have been observed in spinal interneurons. We ask instead whether the muscle-specific details of observed connectivity patterns can arise as a consequence of Hebbian adaptation during early development, rather than being genetically ordained. We constructed an anatomically simplified model musculoskeletal system with realistic muscles and sensors and connected it to a recurrent, random neuronal network consisting of both excitatory and inhibitory neurons endowed with Hebbian learning rules. We then generated a wide set of randomized muscle twitches typical of those described during fetal development and allowed the network to learn. Multiple simulations consistently resulted in diverse and stable patterns of activity and connectivity that included subsets of the interneurons that were similar to “archetypical” interneurons described in the literature. We also found that such learning led to an increased degree of cooperativity between interneurons when performing larger limb movements on which it had not been trained. Hebbian learning gives rise to rich sets of diverse interneurons whose connectivity reflects the mechanical properties of the system. At least some of the transcriptomic diversity may reflect the effects of this process rather than the cause of the connectivity. Such a learning process seems better suited to respond to the musculoskeletal mutations that underlie the evolution of new species. NEW & NOTEWORTHY We present a model of a self-organizing early spinal cord circuitry, which is attached to a biologically realistic sensorized musculoskeletal system. Without any a priori-defined connectivity or organization, learning induced by spontaneous, fetal-like motor activity results in the emergence of a well-functioning spinal interneuronal circuit whose connectivity patterns resemble in many respects those observed in the adult mammalian spinal cord. Hence, our result questions the importance of genetically controlled wiring for spinal cord function

Internet administration of the Dimensional Obsessive-Compulsive Scale: A psychometric evaluation

The Dimensional Obsessive-Compulsive Scale (DOCS) was designed to address the current limitations of existing obsessive-compulsive (OC) symptom measures and is a self-report questionnaire that assesses the severity of the four most empirically supported OC symptom dimensions. The aim of this study was to examine the psychometric properties of a Swedish version of the DOCS when administered via the Internet. Internal consistency, factor structure, and convergent and discriminant validity were examined in a sample consisting of 101 patients diagnosed with obsessive-compulsive disorder. The DOCS sensitivity to treatment effects were examined in a sample consisting of 48 patients treated with Internet-delivered cognitive behavioral therapy were the main intervention was exposure with response prevention. Results showed that the internal consistency was high. The DOCS also showed adequate convergent and discriminant validity, as well as fair sensitivity to treatment effects. The factor analysis supported the DOCS four-factor solution. In summary, the results from the present study give initial support that the DOCS can be administered via the Internet with adequate psychometric properties. © 2012 Elsevier Ltd

A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks

Recurrent circuitry components are distributed widely within the brain, including both excitatory and inhibitory synaptic connections. Recurrent neuronal networks have potential stability problems, perhaps a predisposition to epilepsy. More generally, instability risks making internal representations of information unreliable. To assess the inherent stability properties of such recurrent networks, we tested a linear summation, non-spiking neuron model with and without a “dynamic leak”, corresponding to the low-pass filtering of synaptic input current by the RC circuit of the biological membrane. We first show that the output of this neuron model, in either of its two forms, follows its input at a higher fidelity than a wide range of spiking neuron models across a range of input frequencies. Then we constructed fully connected recurrent networks with equal numbers of excitatory and inhibitory neurons and randomly distributed weights across all synapses. When the networks were driven by pseudorandom sensory inputs with varying frequency, the recurrent network activity tended to induce high frequency self-amplifying components, sometimes evident as distinct transients, which were not present in the input data. The addition of a dynamic leak based on known membrane properties consistently removed such spurious high frequency noise across all networks. Furthermore, we found that the neuron model with dynamic leak imparts a network stability that seamlessly scales with the size of the network, conduction delays, the input density of the sensory signal and a wide range of synaptic weight distributions. Our findings suggest that neuronal dynamic leak serves the beneficial function of protecting recurrent neuronal circuitry from the self-induction of spurious high frequency signals, thereby permitting the brain to utilize this architectural circuitry component regardless of network size or recurrency