190 research outputs found

    Correlated percolation and the correlated resistor network

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    We present some exact results on percolation properties of the Ising model, when the range of the percolating bonds is larger than nearest-neighbors. We show that for a percolation range to next-nearest neighbors the percolation threshold Tp is still equal to the Ising critical temperature Tc, and present the phase diagram for this type of percolation. In addition, we present Monte Carlo calculations of the finite size behavior of the correlated resistor network defined on the Ising model. The thermal exponent t of the conductivity that follows from it is found to be t = 0.2000 +- 0.0007. We observe no corrections to scaling in its finite size behavior.Comment: 16 pages, REVTeX, 6 figures include

    Affect fluctuations examined with ecological momentary assessment in patients with current or remitted depression and anxiety disorders

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    BACKGROUND: There is increasing interest in day-to-day affect fluctuations of patients with depressive and anxiety disorders. Few studies have compared repeated assessments of positive affect (PA) and negative affect (NA) across diagnostic groups, and fluctuation patterns were not uniformly defined. The aim of this study is to compare affect fluctuations in patients with a current episode of depressive or anxiety disorder, in remitted patients and in controls, using affect instability as a core concept but also describing other measures of variability and adjusting for possible confounders. METHODS: Ecological momentary assessment (EMA) data were obtained from 365 participants of the Netherlands Study of Depression and Anxiety with current (n = 95), remitted (n = 178) or no (n = 92) DSM-IV defined depression/anxiety disorder. For 2 weeks, five times per day, participants filled-out items on PA and NA. Affect instability was calculated as the root mean square of successive differences (RMSSD). Tests on group differences in RMSSD, within-person variance, and autocorrelation were performed, controlling for mean affect levels. RESULTS: Current depression/anxiety patients had the highest affect instability in both PA and NA, followed by remitters and then controls. Instability differences between groups remained significant when controlling for mean affect levels, but differences between current and remitted were no longer significant. CONCLUSIONS: Patients with a current disorder have higher instability of NA and PA than remitted patients and controls. Especially with regard to NA, this could be interpreted as patients with a current disorder being more sensitive to internal and external stressors and having suboptimal affect regulation

    Magnetic behaviour of Eu_2CuSi_3: Large negative magnetoresistance above Curie temperature

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    We report here the results of magnetic susceptibility, electrical-resistivity, magnetoresistance (MR), heat-capacity and ^{151}Eu Mossbauer effect measurements on the compound, Eu_2CuSi_3, crystallizing in an AlB_2-derived hexagonal structure. The results establish that Eu ions are divalent, undergoing long-range ferromagnetic-ordering below (T_C=) 37 K. An interesting observation is that the sign of MR is negative even at temperatures close to 3T_C, with increasing magnitude with decreasing temperature exhibiting a peak at T_C. This observation, being made for a Cu containing magnetic rare-earth compound for the first time, is of relevance to the field of collosal magnetoresistance.Comment: To appear in PRB, RevTex, 4 pages text + 6 psFigs. Related to our earlier work on Gd systems (see cond-mat/9811382, cond-mat/9811387, cond-mat/9812069, cond-mat/9812365

    Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale

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    Sensory processing is associated with gamma frequency oscillations (30–80 Hz) in sensory cortices. This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. We find that resetting of the oscillation phase at stimulus onset is important for a reliable representation of the stimulus and that there is a tradeoff between the resolution of the neural representation of the stimulus and robustness to time-warp. Author Summary Sensory processing of time-varying stimuli, such as speech, is associated with high-frequency oscillatory cortical activity, the functional significance of which is still unknown. One possibility is that the oscillations are part of a stimulus-encoding mechanism. Here, we investigate a computational model of such a mechanism, a spiking neuronal network whose intrinsic oscillations interact with external input (waveforms simulating short speech segments in a single acoustic frequency band) to encode stimuli that extend over a time interval longer than the oscillation's period. The network implements a temporally sparse encoding, whose robustness to time warping and neuronal noise we quantify. To our knowledge, this study is the first to demonstrate that a biophysically plausible model of oscillations occurring in the processing of auditory input may generate a representation of signals that span multiple oscillation cycles.National Science Foundation (DMS-0211505); Burroughs Wellcome Fund; U.S. Air Force Office of Scientific Researc

    Ecosystem Resilience Monitoring and Early Warning Using Earth Observation Data: Challenges and Outlook

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    As the Earth system is exposed to large anthropogenic interferences, it becomes ever more important to assess the resilience of natural systems, i.e., their ability to recover from natural and human-induced perturbations. Several, often related, measures of resilience have been proposed and applied to modeled and observed data, often by different scientific communities. Focusing on terrestrial ecosystems as a key component of the Earth system, we review methods that can detect large perturbations (temporary excursions from a reference state as well as abrupt shifts to a new reference state) in spatio-temporal datasets, estimate the recovery rate after such perturbations, or assess resilience changes indirectly from stationary time series via indicators of critical slowing down. We present here a sequence of ideal methodological steps in the field of resilience science, and argue how to obtain a consistent and multi-faceted view on ecosystem or climate resilience from Earth observation (EO) data. While EO data offers unique potential to study ecosystem resilience globally at high spatial and temporal scale, we emphasize some important limitations, which are associated with the theoretical assumptions behind diagnostic methods and with the measurement process and pre-processing steps of EO data. The latter class of limitations include gaps in time series, the disparity of scales, and issues arising from aggregating time series from multiple sensors. Based on this assessment, we formulate specific recommendations to the EO community in order to improve the observational basis for ecosystem resilience research

    Expanding the clinical spectrum of biglycan-related Meester-Loeys syndrome

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    \ua9 The Author(s) 2024.Pathogenic loss-of-function variants in BGN, an X-linked gene encoding biglycan, are associated with Meester-Loeys syndrome (MRLS), a thoracic aortic aneurysm/dissection syndrome. Since the initial publication of five probands in 2017, we have considerably expanded our MRLS cohort to a total of 18 probands (16 males and 2 females). Segregation analyses identified 36 additional BGN variant-harboring family members (9 males and 27 females). The identified BGN variants were shown to lead to loss-of-function by cDNA and Western Blot analyses of skin fibroblasts or were strongly predicted to lead to loss-of-function based on the nature of the variant. No (likely) pathogenic missense variants without additional (predicted) splice effects were identified. Interestingly, a male proband with a deletion spanning the coding sequence of BGN and the 5’ untranslated region of the downstream gene (ATP2B3) presented with a more severe skeletal phenotype. This may possibly be explained by expressional activation of the downstream ATPase ATP2B3 (normally repressed in skin fibroblasts) driven by the remnant BGN promotor. This study highlights that aneurysms and dissections in MRLS extend beyond the thoracic aorta, affecting the entire arterial tree, and cardiovascular symptoms may coincide with non-specific connective tissue features. Furthermore, the clinical presentation is more severe and penetrant in males compared to females. Extensive analysis at RNA, cDNA, and/or protein level is recommended to prove a loss-of-function effect before determining the pathogenicity of identified BGN missense and non-canonical splice variants. In conclusion, distinct mechanisms may underlie the wide phenotypic spectrum of MRLS patients carrying loss-of-function variants in BGN

    Sensitivity of methods for estimating breeding values using genetic markers to the number of QTL and distribution of QTL variance

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    The objective of this simulation study was to compare the effect of the number of QTL and distribution of QTL variance on the accuracy of breeding values estimated with genomewide markers (MEBV). Three distinct methods were used to calculate MEBV: a Bayesian Method (BM), Least Angle Regression (LARS) and Partial Least Square Regression (PLSR). The accuracy of MEBV calculated with BM and LARS decreased when the number of simulated QTL increased. The accuracy decreased more when QTL had different variance values than when all QTL had an equal variance. The accuracy of MEBV calculated with PLSR was affected neither by the number of QTL nor by the distribution of QTL variance. Additional simulations and analyses showed that these conclusions were not affected by the number of individuals in the training population, by the number of markers and by the heritability of the trait. Results of this study show that the effect of the number of QTL and distribution of QTL variance on the accuracy of MEBV depends on the method that is used to calculate MEBV
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