Relationships Between Cytoplasmic Filaments and Nuclear Envelope in Teleost (Pimelodus maculatus) Endothelial Cells

In the present report a characteristic pattern showed by cytoplasmic filaments (intermediate-sized and actin-like) in the perinuclear area of a freshwater teleost (Pimelodus maculatus) endothelial cells is described for the first time. Thus, many intermediate-sized filaments are directly inserted in the nuclear envelope, but others are connected to one another and to the nucleus through microfilaments. It is suggested that these particular relationships between the nucleus and cytoplasmic filaments are responsible not only for nuclear anchorage, but also for nuclear movements     

Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential

Despite its century-old use, the interpretation of local field potentials (LFPs), the low-frequency part of electrical signals recorded in the brain, is still debated. In cortex the LFP appears to mainly stem from transmembrane neuronal currents following synaptic input, and obvious questions regarding the ‘locality’ of the LFP are: What is the size of the signal-generating region, i.e., the spatial reach, around a recording contact? How far does the LFP signal extend outside a synaptically activated neuronal population? And how do the answers depend on the temporal frequency of the LFP signal? Experimental inquiries have given conflicting results, and we here pursue a modeling approach based on a well-established biophysical forward-modeling scheme incorporating detailed reconstructed neuronal morphologies in precise calculations of population LFPs including thousands of neurons. The two key factors determining the frequency dependence of LFP are the spatial decay of the single-neuron LFP contribution and the conversion of synaptic input correlations into correlations between single-neuron LFP contributions. Both factors are seen to give low-pass filtering of the LFP signal power. For uncorrelated input only the first factor is relevant, and here a modest reduction (<50%) in the spatial reach is observed for higher frequencies (>100 Hz) compared to the near-DC () value of about . Much larger frequency-dependent effects are seen when populations of pyramidal neurons receive correlated and spatially asymmetric inputs: the low-frequency () LFP power can here be an order of magnitude or more larger than at 60 Hz. Moreover, the low-frequency LFP components have larger spatial reach and extend further outside the active population than high-frequency components. Further, the spatial LFP profiles for such populations typically span the full vertical extent of the dendrites of neurons in the population. Our numerical findings are backed up by an intuitive simplified model for the generation of population LFP.     

Firing-rate models capture essential response dynamics of LGN relay cells

Firing-rate models provide a practical tool for studying signal processing in the early visual system, permitting more thorough mathematical analysis than spike-based models. We show here that essential response properties of relay cells in the lateral geniculate nucleus (LGN) can be captured by surprisingly simple firing-rate models consisting of a low-pass filter and a nonlinear activation function. The starting point for our analysis are two spiking neuron models based on experimental data: a spike-response model fitted to data from macaque (Carandini et al. J. Vis., 20(14), 1–2011, 2007), and a model with conductance-based synapses and afterhyperpolarizing currents fitted to data from cat (Casti et al. J. Comput. Neurosci., 24(2), 235–252, 2008). We obtained the nonlinear activation function by stimulating the model neurons with stationary stochastic spike trains, while we characterized the linear filter by fitting a low-pass filter to responses to sinusoidally modulated stochastic spike trains. To account for the non-Poisson nature of retinal spike trains, we performed all analyses with spike trains with higher-order gamma statistics in addition to Poissonian spike trains. Interestingly, the properties of the low-pass filter depend only on the average input rate, but not on the modulation depth of sinusoidally modulated input. Thus, the response properties of our model are fully specified by just three parameters (low-frequency gain, cutoff frequency, and delay) for a given mean input rate and input regularity. This simple firing-rate model reproduces the response of spiking neurons to a step in input rate very well for Poissonian as well as for non-Poissonian input. We also found that the cutoff frequencies, and thus the filter time constants, of the rate-based model are unrelated to the membrane time constants of the underlying spiking models, in agreement with similar observations for simpler models.     

Epidemiology and Reporting Characteristics of Systematic Reviews of Biomedical Research: A Cross-Sectional Study

BackgroundSystematic reviews (SRs) can help decision makers interpret the deluge of published biomedical literature. However, a SR may be of limited use if the methods used to conduct the SR are flawed, and reporting of the SR is incomplete. To our knowledge, since 2004 there has been no cross-sectional study of the prevalence, focus, and completeness of reporting of SRs across different specialties. Therefore, the aim of our study was to investigate the epidemiological and reporting characteristics of a more recent cross-section of SRs.ConclusionsAn increasing number of SRs are being published, and many are poorly conducted and reported. Strategies are needed to help reduce this avoidable waste in research.     

Application of green analytical chemistry to a green chemistry process: Magnetic resonance and Raman spectroscopic process monitoring of continuous ethanolic fermentation

Compact ¹H NMR and Raman spectrometers were used for real-time process monitoring of alcoholic fermentation in a continuous flow reactor. Yeast cells catalyzing the sucrose conversion were immobilized in alginate beads floating in the reactor. The spectrometers proved to be robust and could be easily attached to the reaction apparatus. As environmentally friendly analysis methods, ¹H NMR and Raman spectroscopy were selected to match the resource- and energy-saving process. Analyses took only a few seconds to minutes compared to chromatographic procedures and were, therefore, suitable for real-time control realized as a feedback loop. Both compact spectrometers were successfully implemented online. Raman spectroscopy allowed for faster spectral acquisition and higher quantitative precision, NMR yielded more resolved signals thus higher specificity. By using the software Matlab for automated data loading and processing, relevant parameters such as the ethanol, glycerol, and sugar content could be easily obtained. The subsequent multivariate data analysis using partial linear least-squares regression type 2 enabled the quantitative monitoring of all reactants within a single model in real time.     

Structure and energy of fusion stalks: the role of membrane edges

Fusion of lipid bilayers proceeds via a sequence of distinct structural transformations. Its early stage involves a localized, hemifused intermediate in which the proximal but not yet the distal monolayers are connected. Whereas the so-called stalk model most successfully accounts for the properties of the hemifused intermediate, there is still uncertainty about its microscopic structure and energy. We reanalyze fusion stalks using the theory of membrane elasticity. In our calculations, a short (cylindrical micelle-like) tether connects the two proximal monolayers of the hemifused membranes. The shape of the stalk and the length of the tether are calculated such as to minimize the overall free energy and to avoid the formation of voids within the hydrocarbon core. Our free energy expression is based on three internal degrees of freedom of a perturbed lipid layer: thickness, splay, and tilt deformations. Based on exactly the same model, we compare fusion stalks with and without the ability included to form sharp edges at the interfacial region between the hydrocarbon core and the polar environment. Requiring the interface to be smooth everywhere, our detailed calculations recover previous results: the stalk energies are far too high to account for the experimental observation of fusion intermediates. However, if we allow the interface to be nonsmooth, we find a remarkable reduction of the stalk free energy down to more realistic values. The corresponding structure of a nonsmooth stalk exhibits sharp edges at the transition regions between the bilayer and tether parts. In addition to that, a corner is formed at each of the two distal monolayers. We discuss the mechanism how membrane edges reduce the energy of fusion stalks.     

Analysis of multidimensional neural activity via CNN-UM

In this paper we show that the Cellular Nonlinear Network Universal Machine (CNN-UM) is an excellent tool for analyzing time series of multidimensional binary signals. The developed algorithm is dedicated to process electrophysiological multi-neuron recordings: our aim is to find specific multidimensional activity patterns, which may reflect higher order functional cell-assemblies. The analysis consists of two parts: first, the occurrences of different patterns are counted, then the statistical significance of each occurrence frequency is calculated separately.     

The Formation of Multi-synaptic Connections by the Interaction of Synaptic and Structural Plasticity and Their Functional Consequences

Cortical connectivity emerges from the permanent interaction between neuronal activity and synaptic as well as structural plasticity. An important experimentally observed feature of this connectivity is the distribution of the number of synapses from one neuron to another, which has been measured in several cortical layers. All of these distributions are bimodal with one peak at zero and a second one at a small number (3–8) of synapses.In this study, using a probabilistic model of structural plasticity, which depends on the synaptic weights, we explore how these distributions can emerge and which functional consequences they have.We find that bimodal distributions arise generically from the interaction of structural plasticity with synaptic plasticity rules that fulfill the following biological realistic constraints: First, the synaptic weights have to grow with the postsynaptic activity. Second, this growth curve and/or the input-output relation of the postsynaptic neuron have to change sub-linearly (negative curvature). As most neurons show such input-output-relations, these constraints can be fulfilled by many biological reasonable systems.Given such a system, we show that the different activities, which can explain the layer-specific distributions, correspond to experimentally observed activities.Considering these activities as working point of the system and varying the pre- or postsynaptic stimulation reveals a hysteresis in the number of synapses. As a consequence of this, the connectivity between two neurons can be controlled by activity but is also safeguarded against overly fast changes.These results indicate that the complex dynamics between activity and plasticity will, already between a pair of neurons, induce a variety of possible stable synaptic distributions, which could support memory mechanisms.