Using Open Source Libraries in the Development of Control Systems Based on Machine Vision

The possibility of the boundaries detection in the images of crushed ore particles using a convolutional neural network is analyzed. The structure of the neural network is given. The construction of training and test datasets of ore particle images is described. Various modifications of the underlying neural network have been investigated. Experimental results are presented. © 2020, IFIP International Federation for Information Processing.Foundation for Assistance to Small Innovative Enterprises in Science and Technology, FASIEFunding. The work was performed under state contract 3170ΓC1/48564, grant from the FASIE

A Bayesian approach to modelling heterogeneous calcium responses in cell populations

Calcium responses have been observed as spikes of the whole-cell calcium concentration in numerous cell types and are essential for translating extracellular stimuli into cellular responses. While there are several suggestions for how this encoding is achieved, we still lack a comprehensive theory. To achieve this goal it is necessary to reliably predict the temporal evolution of calcium spike sequences for a given stimulus. Here, we propose a modelling framework that allows us to quantitatively describe the timing of calcium spikes. Using a Bayesian approach, we show that Gaussian processes model calcium spike rates with high fidelity and perform better than standard tools such as peri-stimulus time histograms and kernel smoothing. We employ our modelling concept to analyse calcium spike sequences from dynamically-stimulated HEK293T cells. Under these conditions, different cells often experience diverse stimuli time courses, which is a situation likely to occur in vivo. This single cell variability and the concomitant small number of calcium spikes per cell pose a significant modelling challenge, but we demonstrate that Gaussian processes can successfully describe calcium spike rates in these circumstances. Our results therefore pave the way towards a statistical description of heterogeneous calcium oscillations in a dynamic environmen

Disentangling astroglial physiology with a realistic cell model in silico

Electrically non-excitable astroglia take up neurotransmitters, buffer extracellular K+ and generate Ca2+ signals that release molecular regulators of neural circuitry. The underlying machinery remains enigmatic, mainly because the sponge-like astrocyte morphology has been difficult to access experimentally or explore theoretically. Here, we systematically incorporate multi-scale, tri-dimensional astroglial architecture into a realistic multi-compartmental cell model, which we constrain by empirical tests and integrate into the NEURON computational biophysical environment. This approach is implemented as a flexible astrocyte-model builder ASTRO. As a proof-of-concept, we explore an in silico astrocyte to evaluate basic cell physiology features inaccessible experimentally. Our simulations suggest that currents generated by glutamate transporters or K+ channels have negligible distant effects on membrane voltage and that individual astrocytes can successfully handle extracellular K+ hotspots. We show how intracellular Ca2+ buffers affect Ca2+ waves and why the classical Ca2+ sparks-and-puffs mechanism is theoretically compatible with common readouts of astroglial Ca2+ imaging

A Dopaminergic Gene Cluster in the Prefrontal Cortex Predicts Performance Indicative of General Intelligence in Genetically Heterogeneous Mice

Background: Genetically heterogeneous mice express a trait that is qualitatively and psychometrically analogous to general intelligence in humans, and as in humans, this trait co-varies with the processing efficacy of working memory (including its dependence on selective attention). Dopamine signaling in the prefrontal cortex (PFC) has been established to play a critical role in animals ’ performance in both working memory and selective attention tasks. Owing to this role of the PFC in the regulation of working memory, here we compared PFC gene expression profiles of 60 genetically diverse CD-1 mice that exhibited a wide range of general learning abilities (i.e., aggregate performance across five diverse learning tasks). Methodology/Principal Findings: Animals ’ general cognitive abilities were first determined based on their aggregate performance across a battery of five diverse learning tasks. With a procedure designed to minimize false positive identifications, analysis of gene expression microarrays (comprised of <25,000 genes) identified a small number (,20) of genes that were differentially expressed across animals that exhibited fast and slow aggregate learning abilities. Of these genes, one functional cluster was identified, and this cluster (Darpp-32, Drd1a, and Rgs9) is an established modulator of dopamine signaling. Subsequent quantitative PCR found that expression of these dopaminegic genes plus one vascular gene (Nudt6) were significantly correlated with individual animal’s general cognitive performance. Conclusions/Significance: These results indicate that D1-mediated dopamine signaling in the PFC, possibly through it

Ultrasound-guided diagnostic breast biopsy methodology: retrospective comparison of the 8-gauge vacuum-assisted biopsy approach versus the spring-loaded 14-gauge core biopsy approach

<p>Abstract</p> <p>Background</p> <p>Ultrasound-guided diagnostic breast biopsy technology represents the current standard of care for the evaluation of indeterminate and suspicious lesions seen on diagnostic breast ultrasound. Yet, there remains much debate as to which particular method of ultrasound-guided diagnostic breast biopsy provides the most accurate and optimal diagnostic information. The aim of the current study was to compare and contrast the 8-gauge vacuum-assisted biopsy approach and the spring-loaded 14-gauge core biopsy approach.</p> <p>Methods</p> <p>A retrospective analysis was done of all ultrasound-guided diagnostic breast biopsy procedures performed by either the 8-gauge vacuum-assisted biopsy approach or the spring-loaded 14-gauge core biopsy approach by a single surgeon from July 2001 through June 2009.</p> <p>Results</p> <p>Among 1443 ultrasound-guided diagnostic breast biopsy procedures performed, 724 (50.2%) were by the 8-gauge vacuum-assisted biopsy technique and 719 (49.8%) were by the spring-loaded 14-gauge core biopsy technique. The total number of false negative cases (i.e., benign findings instead of invasive breast carcinoma) was significantly greater (P = 0.008) in the spring-loaded 14-gauge core biopsy group (8/681, 1.2%) as compared to in the 8-gauge vacuum-assisted biopsy group (0/652, 0%), with an overall false negative rate of 2.1% (8/386) for the spring-loaded 14-gauge core biopsy group as compared to 0% (0/148) for the 8-gauge vacuum-assisted biopsy group. Significantly more (P < 0.001) patients in the spring-loaded 14-gauge core biopsy group (81/719, 11.3%) than in the 8-gauge vacuum-assisted biopsy group (18/724, 2.5%) were recommended for further diagnostic surgical removal of additional tissue from the same anatomical site of the affected breast in an immediate fashion for indeterminate/inconclusive findings seen on the original ultrasound-guided diagnostic breast biopsy procedure. Significantly more (P < 0.001) patients in the spring-loaded 14-gauge core biopsy group (54/719, 7.5%) than in the 8-gauge vacuum-assisted biopsy group (9/724, 1.2%) personally requested further diagnostic surgical removal of additional tissue from the same anatomical site of the affected breast in an immediate fashion for a benign finding seen on the original ultrasound-guided diagnostic breast biopsy procedure.</p> <p>Conclusions</p> <p>In appropriately selected cases, the 8-gauge vacuum-assisted biopsy approach appears to be advantageous to the spring-loaded 14-gauge core biopsy approach for providing the most accurate and optimal diagnostic information.</p

A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation

Few models exist that accurately reproduce the complex rhythms of the thalamocortical system that are apparent in measured scalp EEG and at the same time, are suitable for large-scale simulations of brain activity. Here, we present a neural mass model of the thalamocortical system during natural non-REM sleep, which is able to generate fast sleep spindles (12–15 Hz), slow oscillations (<1 Hz) and K-complexes, as well as their distinct temporal relations, and response to auditory stimuli. We show that with the inclusion of detailed calcium currents, the thalamic neural mass model is able to generate different firing modes, and validate the model with EEG-data from a recent sleep study in humans, where closed-loop auditory stimulation was applied. The model output relates directly to the EEG, which makes it a useful basis to develop new stimulation protocols

A Neuron-Glial Perspective for Computational Neuroscience

International audienceThere is growing excitement around glial cells, as compelling evidence point to new, previously unimaginable roles for these cells in information processing of the brain, with the potential to affect behavior and higher cognitive functions. Among their many possible functions, glial cells could be involved in practically every aspect of the brain physiology in health and disease. As a result, many investigators in the field welcome the notion of a Neuron-Glial paradigm of brain function, as opposed to Ramon y Cayal's more classical neuronal doctrine which identifies neurons as the prominent, if not the only, cells capable of a signaling role in the brain. The demonstration of a brain-wide Neuron-Glial paradigm however remains elusive and so does the notion of what neuron-glial interactions could be functionally relevant for the brain computational tasks. In this perspective, we present a selection of arguments inspired by available experimental and modeling studies with the aim to provide a biophysical and conceptual platform to computational neuroscience no longer as a mere prerogative of neuronal signaling but rather as the outcome of a complex interaction between neurons and glial cells

A Statistical View on Calcium Oscillations.

Transient rises and falls of the intracellular calcium concentration have been observed in numerous cell types and under a plethora of conditions. There is now a growing body of evidence that these whole-cell calcium oscillations are stochastic, which poses a significant challenge for modelling. In this review, we take a closer look at recently developed statistical approaches to calcium oscillations. These models describe the timing of whole-cell calcium spikes, yet their parametrisations reflect subcellular processes. We show how non-stationary calcium spike sequences, which e.g. occur during slow depletion of intracellular calcium stores or in the presence of time-dependent stimulation, can be analysed with the help of so-called intensity functions. By utilising Bayesian concepts, we demonstrate how values of key parameters of the statistical model can be inferred from single cell calcium spike sequences and illustrate what information whole-cell statistical models can provide about the subcellular mechanistic processes that drive calcium oscillations. In particular, we find that the interspike interval distribution of HEK293 cells under constant stimulation is captured by a Gamma distribution