Positional asphyxia.

Background. The concept of "positional asphyxia" is considered as a separate type of mechanical asphyxia. Objective. Determination of morphological features characteristic of deaths resulting from "positional asphyxia". Results. The concept of "positional asphyxia" – a separate type of mechanical asphyxia, and the risk factors leading to death. We give expert-WIDE cases of death as a result of long-term human presence in the you-stimulated position prevents the normal respiratory movements. The analysis of morphological changes in the internal organs, arising from the death from "positional asphyxia". Conclusion. These changes in the lungs (edema and coloring) can be regarded as so-called "carmine pulmonary edema", and which is one of the types of signs of compression asphyxia from compression of the chest and abdomen. This feature developed with compression of the chest, which prevents operation of the intercostal muscles and the respiratory motion performs only the diaphragm, with a broken blood movement from the lungs into the systemic circulation and the inflow of venous blood to the lungs. As seen from the cases of "carmine pulmonary edema" may develop in terms of positional asphyxia due to similar pathophysiological factors, and it can be regarded as a specific sign of positional asphyxia

Tests to determine optimal performance.

<p>(A) Performance indicator versus number of computers: examples for small <i>i</i>- and <i>e</i>- networks (100 cells). Ordinate, frequency (1 / runtime). Large balls, the optimal number of computers; n_t, the number of cores per processor. Scalability tests were performed on a cluster of 12 computers, each with 4-core processors. (B-C) Similar tests as in (A) for a medium (B, 1000 cells) and larger (C, 4000 cells) network. Other notations as in (A).</p

Network organisation versus rhythm genesis and synchronisation.

<p>(A-B) Frequency (A) and synchronization (B) indicators <i>versus</i> the relative radius of <i>e</i>-network and <i>i</i>-network (relative to their 'reference' radiii 250 μm and 200 μm, respectively). (C) Spiking raster plots of the ‘basic-set’ (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005467#pcbi.1005467.s002" target="_blank">S2 File</a> Biophysical model) networks, including the BSD type synaptic weight distribution. (D) Spiking raster plots for ‘basic-set’ (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005467#pcbi.1005467.s002" target="_blank">S2 File</a>) networks (ratio = 1), but with the BSS type synaptic distribution. (E) Spiking raster plots of ‘basic-set’ networks, but with the network radii increased two-fold (corresponds to the abscissa value of 2 in A-B). (F) Spiking raster plots for ‘basic-set’ networks, but with the total numbers of both <i>e</i>-neurons and <i>i</i>-neurons increased 1.5-fold.</p

Exploring network memorisation, recall, and the effects of astroglial signalling.

<p>(A) External input patterns (EPs) used in simulations, as indicated. (B) <i>Top</i><b>,</b> four successive network stages (<i>i-iv</i>) of memorisation and recall, and the corresponding EPs, as indicated. <i>Middle trace</i>, dynamics of the recall quality (colours depict network stages). <i>Bottom</i>, spiking raster plots depicting the overall dynamics of <i>e-</i> and <i>i</i>-networks corresponding to the four stages as above. (C) Example of the <i>ee</i> synaptic connections matrixes corresponding to the end of stages <i>i</i> and <i>ii</i>, as shown in (B). In simulations shown in (A-C) astrocytes are switched off. (D) <i>Left</i>, Color-coded time map of astrocyte calcium dynamics during stage <i>i</i> shown in (B). <i>Middle</i>. Spiking raster plot of <i>e</i>- and <i>i</i>-networks that corresponds to the astrocyte calcium dynamics shown on the left. <i>Right</i>. The hypothetical relationship between the <i>ei</i>-connection synaptic released probability and the astrocyte calcium concentration.</p

Structure of ARACHNE and simulated network types.

<p>(A) General diagram of the ARACHNE simulator. In brief, local computer generates the model and the HPC configuration as input.mat file, which is sent to the remote computer with master and slaves N clusters. Each slave computer has M processors. After the parallel computation has run the results recorded in output.mat file are sent back to the local computer. (B) Diagram depicting three key network types: principal neurons (<i>e-neurons)</i>, interneurons (<i>i-neurons</i>) and astrocytes (<i>a-cell</i>); <i>R</i>_<i>e</i> and <i>R</i>_<i>i</i>, the network size (radius), respectively. (C) A network fragment depicted by dotted area in (B); different types of cell-cell signalling types are indicated including an <i>aa</i> connection reflecting (mostly) astrocyte gap junctions.</p