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We show how repulsive interactions of deconfined quarks as well as confined hadrons have an influence on the baryon number susceptibilities and the curvature of the chiral pseudo-critical line in effective models of QCD. We discuss implications and constraints for the vector interaction strength from comparisons to lattice QCD and comment on earlier constraints, extracted from the curvature of the transition line of QCD and compact star observables. Our results clearly point to a strong vector repulsion in the hadronic phase and near-zero repulsion in the deconfined phase.
We study the phase structure of QCD at finite temperature within a Polyakov-loop extended quark–meson model. Such a model describes the chiral as well as the confinement-deconfinement dynamics. In the present investigation, based on the approach and results put forward in [1], [2], [3], [4], both matter and glue fluctuations are included. We present results for the order parameters as well as some thermodynamic observables and find very good agreement with recent results from lattice QCD.
Within the statistical model, the net strangeness conservation and incomplete total strangeness equilibration lead to the suppression of strange particle multiplicities. Furthermore, suppression effects appear to be stronger in small systems. By treating the production of strangeness within the canonical ensemble formulation we developed a simple model which allows to predict the excitation function of K+/π+ ratio in nucleus–nucleus collisions. In doing so we assumed that different values of K+/π+, measured in p + p and Pb + Pb interactions at the same collision energy per nucleon, are driven by the finite size effects only. These predictions may serve as a baseline for experimental results from NA61/SHINE at the CERN SPS and the future CBM experiment at FAIR.
The fluctuations in the ideal quantum gases are studied using the strongly intensive measures Δ[A,B] and Σ[A,B] defined in terms of two extensive quantities A and B. In the present Letter, these extensive quantities are taken as the motional variable, A=X, the system energy E or transverse momentum PT, and number of particles, B=N. This choice is most often considered in studying the event-by-event fluctuations and correlations in high energy nucleus–nucleus collisions. The recently proposed special normalization ensures that Δ and Σ are dimensionless and equal to unity for fluctuations given by the independent particle model. In statistical mechanics, the grand canonical ensemble formulation within the Boltzmann approximation gives an example of independent particle model. Our results demonstrate the effects due to the Bose and Fermi statistics. Estimates of the effects of quantum statistics in the hadron gas at temperatures and chemical potentials typical for thermal models of hadron production in high energy collisions are presented. In the case of massless particles and zero chemical potential the Δ and Σ measures are calculated analytically.
The diagnosis of drug induced liver injury (DILI) is based primarily on the exclusion of alternative causes. To assess the frequency of alternative causes in initially suspected DILI cases, we searched the Medline database with the following terms: drug hepatotoxicity, drug induced liver injury, and hepatotoxic drugs. For each term, we used the first 100 publications. We reviewed references, selected those reports relevant to our study, and retrieved finally 15 publications related to DILI and alternative causes. A total of 2,906 cases of initially assumed DILI were analyzed in these 15 publications, with diagnoses missed in 14% of the cases due to overt alternative causes. In another 11%, the diagnosis of DILI could not be established because of confounding variables. Alternative diagnoses included hepatitis B, C, and E, CMV, EBV, ischemic hepatitis, autoimmune hepatitis, primary biliary cirrhosis, primary sclerosing cholangitis, hemochromatosis, Wilson’s disease, Gilbert’s syndrome, fatty liver, non alcoholic steatohepatitis, alcoholic liver diseases, cardiac and thyroid causes, rhabdomyolysis, polymyositis, postictal state, tumors, lymphomas, chlamydial and HIV infections. Causality assessment methods applied in these 15 publications were the CIOMS (Council for International Organizations of Medical Sciences) scale alone (n = 5) or combined with the Maria and Victorino (MV) scale (n = 1), the DILIN (Drug-Induced Liver Injury Network) method (n = 4), or the Naranjo scale (n = 1); the qualitative CIOMS method alone (n = 3) or combined with the MV scale (n = 1). In conclusion, alternative diagnoses are common in primarily suspected DILI cases and should be excluded early in future cases, requiring a thorough clinical and causality assessment.
The asymmetric unit of the title compound, C28H42N2O5·H2O, consists of one half of the organic molecule and one half-molecule of water, both of which are located on a mirror plane which passes through the central C atoms and the hydroxyl group of the heterocyclic system. The hydroxyl group at the central ring is disordered over two equally occupied positions. The six-membered ring adopts a chair conformation, and the 2-hydroxybenzyl substituents occupy the sterically preferred equatorial positions. The aromatic rings make dihedral angles of 75.57 (9)° with the mean plane of the heterocyclic ring. The dihedral angle between the two aromatic rings is 19.18 (10)°. The molecular structure features two intramolecular phenolic O-H...N hydrogen bonds with graph-set motif S(6). In the crystal, molecules are connected via O-H...O hydrogen bonds into zigzag chains running along the a-axis direction.
Connecting narrative worlds
(2014)
Cross-frequency coupling (CFC) has been proposed to coordinate neural dynamics across spatial and temporal scales. Despite its potential relevance for understanding healthy and pathological brain function, the standard CFC analysis and physiological interpretation come with fundamental problems. For example, apparent CFC can appear because of spectral correlations due to common non-stationarities that may arise in the total absence of interactions between neural frequency components. To provide a road map towards an improved mechanistic understanding of CFC, we organize the available and potential novel statistical/modeling approaches according to their biophysical interpretability. While we do not provide solutions for all the problems described, we provide a list of practical recommendations to avoid common errors and to enhance the interpretability of CFC analysis.
Abstract Trial-to-trial variability and spontaneous activity of cortical recordings have been suggested to reflect intrinsic noise. This view is currently challenged by mounting evidence for structure in these phenomena: Trial-to-trial variability decreases following stimulus onset and can be predicted by previous spontaneous activity. This spontaneous activity is similar in magnitude and structure to evoked activity and can predict decisions. Allof the observed neuronal properties described above can be accounted for, at an abstract computational level, by the sampling-hypothesis, according to which response variability reflects stimulus uncertainty. However, a mechanistic explanation at the level of neural circuit dynamics is still missing.
In this study, we demonstrate that all of these phenomena can be accounted for by a noise-free self-organizing recurrent neural network model (SORN). It combines spike-timing dependent plasticity (STDP) and homeostatic mechanisms in a deterministic network of excitatory and inhibitory McCulloch-Pitts neurons. The network self-organizes to spatio-temporally varying input sequences.
We find that the key properties of neural variability mentioned above develop in this model as the network learns to perform sampling-like inference. Importantly, the model shows high trial-to-trial variability although it is fully deterministic. This suggests that the trial-to-trial variability in neural recordings may not reflect intrinsic noise. Rather, it may reflect a deterministic approximation of sampling-like learning and inference. The simplicity of the model suggests that these correlates of the sampling theory are canonical properties of recurrent networks that learn with a combination of STDP and homeostatic plasticity mechanisms.
Author Summary Neural recordings seem very noisy. If the exact same stimulus is shown to an animal multiple times, the neural response will vary. In fact, the activity of a single neuron shows many features of a stochastic process. Furthermore, in the absence of a sensory stimulus, cortical spontaneous activity has a magnitude comparable to the activity observed during stimulus presentation. These findings have led to a widespread belief that neural activity is indeed very noisy. However, recent evidence indicates that individual neurons can operate very reliably and that the spontaneous activity in the brain is highly structured, suggesting that much of the noise may in fact be signal. One hypothesis regarding this putative signal is that it reflects a form of probabilistic inference through sampling. Here we show that the key features of neural variability can be accounted for in a completely deterministic network model through self-organization. As the network learns a model of its sensory inputs, the deterministic dynamics give rise to sampling-like inference. Our findings show that the notorious variability in neural recordings does not need to be seen as evidence for a noisy brain. Instead it may reflect sampling-like inference emerging from a self-organized learning process.