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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.
The electric dipole strength distribution in 130Te has been investigated using the method of Nuclear Resonance Fluorescence. The experiments were performed at the Darmstadt High Intensity Photon Setup using bremsstrahlung as photon source and at the High Intensity -Ray Source, where quasi-monochromatic and polarized photon beams are provided. Average decay properties of 130Te below the neutron separation energy are determined. Comparing the experimental data to the predictions of the statistical model indicate, that nuclear structure effects play an important role even at sufficiently high excitation energies. Preliminary results will be presented.
Fission fragment mass distributions were measured in heavy-ion induced fissions using 238U target nucleus. The measured mass distributions changed drastically with incident energy. The results are explained by a change of the ratio between fusion and qasifission with nuclear orientation. A calculation based on a fluctuation dissipation model reproduced the mass distributions and their incident energy dependence. Fusion probability was determined in the analysis, and the values were consistent with those determined from the evaporation residue cross sections.
The Facility for Antiproton and Ion Research (FAIR), under construction at Darmstadt will provide intense relativistic beams of exotic nuclei at its Superconducting-FRagment Separator. High-resolution in-beam γ-ray spectroscopy will be performed in the HISPEC experiment, using the European Advanced GAmma-ray Tracking Array (AGATA). The PreSPEC-AGATA campaign is the predecessor of HISPEC and runs from 2012 to 2014 at GSI Helmholtzzentrum für Schwerionenforschung GmbH. Up to19 AGATA modules were used at GSI's F Ragment Separator in 2012. We report on the status of the experiment including preliminary results from performance commissioning.
The n_TOF facility operates at CERN with the aim of addressing the request of high accuracy nuclear data for advanced nuclear energy systems as well as for nuclear astrophysics. Thanks to the features of the neutron beam, important results have been obtained on neutron induced fission and capture cross sections of U, Pu and minor actinides. Recently the construction of another beam line has started; the new line will be complementary to the first one, allowing to further extend the experimental program foreseen for next measurement campaigns.
The production of charmonia in the antiproton-nucleus reactions at plab = 3 − 10 GeV/c is studied within the Glauber model and the generalized eikonal approximation. The main reaction channel is charmonium formation in an antiproton-proton collision. The target mass dependence of the charmonium transparency ratio allows to determine the charmonium-nucleon cross section. The polarization effects in the production of χc2 states are evaluated.
The simultaneous description of the hadronic yields, pion, kaon and proton spectra, elliptic flows and femtoscopy scales in hydrokinetic model of A+A collisions is presented at different centralities for the top RHIC and LHC energies. The hydrokinetic model is used in its hybrid version that allows one to switch correctly to the UrQMD cascade at the isochronic hypersurface which separates the cascade stage and decaying hydrodynamic one. The results are compared with pure hybrid model where hydrodynamics and hadronic cascade are matching just at the non-space-like hypersurface of chemical freeze-out. The initial conditions are based on both Glauber- and KLN- Monte-Carlo simulations and results are compared. It seems that the observables, especially femtoscopy data, prefer the Glauber initial conditions. The modification of the particle number ratios caused, in particular, by the particle annihilations at the afterburn stage is analyzed.