Summed Parallel Infinite Impulse Response (SPIIR) Filters For Low-Latency Gravitational Wave Detection

With the upgrade of current gravitational wave detectors, the first detection of gravitational wave signals is expected to occur in the next decade. Low-latency gravitational wave triggers will be necessary to make fast follow-up electromagnetic observations of events related to their source, e.g., prompt optical emission associated with short gamma-ray bursts. In this paper we present a new time-domain low-latency algorithm for identifying the presence of gravitational waves produced by compact binary coalescence events in noisy detector data. Our method calculates the signal to noise ratio from the summation of a bank of parallel infinite impulse response (IIR) filters. We show that our summed parallel infinite impulse response (SPIIR) method can retrieve the signal to noise ratio to greater than 99% of that produced from the optimal matched filter. We emphasise the benefits of the SPIIR method for advanced detectors, which will require larger template banks.Comment: 9 pages, 6 figures, for PR

Teacher Use of Computer-Assisted Instruction for Young Inattentive Students: Implications for Implementation and Teacher Preparation

Teacher preparation and training appear limited in the area of computer-assisted instruction (CAI) as well as more general instruction and management for students with disabilities including those with attention problems. Research suggests that CAI is a promising intervention for young inattentive students, with several inherent advantages; however, there are a number of implementation challenges that may interfere with more extensive and effective use in the classroom. Lessons learned from a recent randomized controlled trial of a CAI intervention highlight some of these challenges and suggest strategies for addressing them. Implications for instruction are discussed with regard to selection of appropriate CAI programs, integration into the classroom, and strengthening teachers’ more general management skills for inattentive students. Recommendations for pre-service training and professional development in CAI are provided

DREADD activation of pedunculopontine cholinergic neurons reverses motor deficits and restores striatal dopamine signaling in parkinsonian rats

The brainstem-based pedunculopontine nucleus (PPN) traditionally associates with motor function, but undergoes extensive degeneration during Parkinson’s disease (PD), which correlates with axial motor deficits. PPN-Deep Brain Stimulation (DBS) can alleviate certain symptoms, but its mecha-nism(s) of action remains unknown. We previously characterized rats hemi-intranigrally injected with the proteasomal inhibitor lactacystin, as an accurate preclinical model of PD. Here we used a combination of chemogenetics with Positron Emission Tomography (PET) imaging for in vivo in-terrogation of discrete neural networks in this rat model of PD. Stimulation of excitatory DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) expressed within PPN cholinergic neurons activated residual nigrostriatal dopaminergic neurons to produce pro-found motor recovery, which correlated with striatal dopamine efflux as well as restored dopamine receptor (DR) 1- and DR2-based medium spiny neuron (MSN) activity, as was ascertained with c-Fos-based immunohistochemistry and stereological cell counts. By revealing that the improved axi-al-related motor functions seen in PD patients receiving PPN-DBS may be due to stimulation of remaining PPN cholinergic neurons interacting with dopaminergic ones in both the Substantia Nigra pars compacta (SNpc) and the striatum, our data strongly favor the PPN cholinergic-midbrain dopaminergic connectome as mechanism for PPN-DBS’s therapeutic effects. These findings have implications for refining PPD-DBS as a promising treatment modality available to PD patients

The novel mu-opioid antagonist, GSK1521498, reduces ethanol consumption in C57BL/6J mice.

RATIONALE Using the drinking-in-the-dark (DID) model, we compared the effects of a novel mu-opioid receptor antagonist, GSK1521498, with naltrexone, a licensed treatment of alcohol dependence, on ethanol consumption in mice. OBJECTIVE We test the ability of GSK1521498 to reduce alcohol consumption and compare its intrinsic efficacy to that of naltrexone by comparing the two drugs at doses matched for equivalent receptor occupancy. METHODS Thirty-six C57BL/6J mice were tested in a DID procedure. In 2-day cycles, animals experienced one baseline, injection-free session, and one test session when they received two injections, one of test drug and one placebo. All animals received GSK1521498 (0, 0.1, 1 and 3 mg/kg, i.p., 30 min pre-treatment) and naltrexone (0, 0.1, 1 and 3 mg/kg, s.c. 10 min pre-treatment) in a cross-over design. Receptor occupancies following the same doses were determined ex vivo in separate groups by autoradiography, using [3H]DAMGO. Binding in the region of interest was measured integrally by computer-assisted microdensitometry and corrected for non-specific binding. RESULTS Both GSK1521498 and naltrexone dose-dependently decreased ethanol consumption. When drug doses were matched for 70-75 % receptor occupancy, GSK1521498 3 mg/kg, i.p., caused a 2.5-fold greater reduction in alcohol consumption than naltrexone 0.1 mg/kg, s.c. Both GSK1521498 and naltrexone significantly reduced sucrose consumption at a dose of 1 mg/kg but not 0.1 mg/kg. In a test of conditioned taste aversion, GSK1521498 (3 mg/kg) reduced sucrose consumption 24 h following exposure to a conditioning injection. CONCLUSIONS Both opioid receptor antagonists reduced alcohol consumption but GK1521498 has higher intrinsic efficacy than naltrexone

The Prevalence of ADHD in a Population-Based Sample

Few studies of ADHD prevalence have used population-based samples, multiple informants, and DSM-IV criteria. In addition, children who are asymptomatic while receiving ADHD mediction often have been misclassified. Therefore, we conducted a population-based study to estimate the prevalence of ADHD in elementary school children using DSM-IV critera

The Shifting Subtypes of ADHD: Classification Depends on How Symptom Reports are Combined

Research on the correlates of ADHD subtypes has yielded inconsistent findings, perhaps because the procedures used to define subtypes vary across studies. We examined this possibility by investigating whether the ADHD subtype distribution in a community sample was sensitive to different methods for combining informant data. We conducted a study to screen all children in grades 1–5 (N=7847) in a North Carolina County for ADHD. Teachers completed a DSM-IV behavior rating scale and parents completed a structured telephone interview. We found substantial differences in the distribution of ADHD subtypes depending on whether one or both sources were used to define the subtypes. When parent and teacher data were combined, the procedures used substantially influenced subtype distribution. We conclude the ADHD subtype distribution is sensitive to how symptom information is combined and that standardization of the subtyping process is required to advance our understanding of the correlates of different ADHD subtypes

Reductions of Hidden Information Sources

In all but special circumstances, measurements of time-dependent processes reflect internal structures and correlations only indirectly. Building predictive models of such hidden information sources requires discovering, in some way, the internal states and mechanisms. Unfortunately, there are often many possible models that are observationally equivalent. Here we show that the situation is not as arbitrary as one would think. We show that generators of hidden stochastic processes can be reduced to a minimal form and compare this reduced representation to that provided by computational mechanics--the epsilon-machine. On the way to developing deeper, measure-theoretic foundations for the latter, we introduce a new two-step reduction process. The first step (internal-event reduction) produces the smallest observationally equivalent sigma-algebra and the second (internal-state reduction) removes sigma-algebra components that are redundant for optimal prediction. For several classes of stochastic dynamical systems these reductions produce representations that are equivalent to epsilon-machines.Comment: 12 pages, 4 figures; 30 citations; Updates at http://www.santafe.edu/~cm

Inferring hidden Markov models from noisy time sequences: a method to alleviate degeneracy in molecular dynamics

We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al., and generates so called causal state models, equivalent to hidden Markov models. This method is applicable to any continuous data which clusters around discrete values and exhibits multiple transitions between these values such as tethered particle motion data or Fluorescence Resonance Energy Transfer (FRET) spectra. The algorithms developed have been shown to perform well on simulated data, demonstrating the ability to recover the model used to generate the data under high noise, sparse data conditions and the ability to infer the existence of degenerate states. They have also been applied to new experimental FRET data of Holliday Junction dynamics, extracting the expected two state model and providing values for the transition rates in good agreement with previous results and with results obtained using existing maximum likelihood based methods.Comment: 19 pages, 9 figure

The Functional DRD3 Ser9Gly Polymorphism (rs6280) Is Pleiotropic, Affecting Reward as Well as Movement

Abnormalities of motivation and behavior in the context of reward are a fundamental component of addiction and mood disorders. Here we test the effect of a functional missense mutation in the dopamine 3 receptor (DRD3) gene (ser9gly, rs6280) on reward-associated dopamine (DA) release in the striatum. Twenty-six healthy controls (HCs) and 10 unmedicated subjects with major depressive disorder (MDD) completed two positron emission tomography (PET) scans with [11C]raclopride using the bolus plus constant infusion method. On one occasion subjects completed a sensorimotor task (control condition) and on another occasion subjects completed a gambling task (reward condition). A linear regression analysis controlling for age, sex, diagnosis, and self-reported anhedonia indicated that during receipt of unpredictable monetary reward the glycine allele was associated with a greater reduction in D2/3 receptor binding (i.e., increased reward-related DA release) in the middle (anterior) caudate (p<0.01) and the ventral striatum (p<0.05). The possible functional effect of the ser9gly polymorphism on DA release is consistent with previous work demonstrating that the glycine allele yields D3 autoreceptors that have a higher affinity for DA and display more robust intracellular signaling. Preclinical evidence indicates that chronic stress and aversive stimulation induce activation of the DA system, raising the possibility that the glycine allele, by virtue of its facilitatory effect on striatal DA release, increases susceptibility to hyperdopaminergic responses that have previously been associated with stress, addiction, and psychosis

Self-labeling techniques for semi-supervised time series classification: an empirical study

An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context