Universal performance bounds of restart

As has long been known to computer scientists, the performance of probabilistic algorithms characterized by relatively large runtime fluctuations can be improved by applying a restart, i.e., episodic interruption of a randomized computational procedure followed by initialization of its new statistically independent realization. A similar effect of restart-induced process acceleration could potentially be possible in the context of enzymatic reactions, where dissociation of the enzyme-substrate intermediate corresponds to restarting the catalytic step of the reaction. To date, a significant number of analytical results have been obtained in physics and computer science regarding the effect of restart on the completion time statistics in various model problems, however, the fundamental limits of restart efficiency remain unknown. Here we derive a range of universal statistical inequalities that offer constraints on the effect that restart could impose on the completion time of a generic stochastic process. The corresponding bounds are expressed via simple statistical metrics of the original process such as harmonic mean $h$, median value $m$ and mode $M$, and, thus, are remarkably practical. We test our analytical predictions with multiple numerical examples, discuss implications arising from them and important avenues of future work.Comment: 12 pages, 2 figure

Technology of Storage and Processing of Electronic Documents with Intellectual Search Properties

The technology of record, storage and processing of the texts, based on creation of integer index cycles is discussed. Algorithms of exact-match search and search similar on the basis of inquiry in a natural language are considered. The software realizing offered approaches is described, and examples of the electronic archives possessing properties of intellectual search are resulted

High protonic potential actuates a mechanism of production of reactive oxygen species in mitochondria

AbstractFormation of H2O2 has been studied in rat heart mitochondria, pretreated with H2O2 and aminotriazole to lower their antioxidant capacity. It is shown that the rate of H2O2 formation by mitochondria oxidizing 6 mM succinate is inhibited by a protonophorous uncoupler, ADP and phosphate, malonate, rotenone and myxothiazol, and is stimulated by antimycin A. The effect of ADP is abolished by carboxyatractylate and oligomycin. Addition of uncoupler after rotenone induces further inhibition of H2O2 production. Inhibition of H2O2 formation by uncoupler, malonate and ADP+Pi is shown to be proportional to the ΔΨ decrease by these compounds. A threshold ΔΨ value is found, above which a very strong increase in H2O2 production takes place. This threshold slightly exceeds the state 3 ΔΨ level. The data obtained are in line with the concept [Skulachev, V.P., Q. Rev. Biophys. 29 (1996), 169–202] that a high proton motive force in state 4 is potentially dangerous for the cell due to an increase in the probability of superoxide formation

Segmentation of muscle tissue in computed tomography images at the level of the L3 vertebra

With the increasing routine workload on radiologists associated with the need to analyze large numbers of images, there is a need to automate part of the analysis process. Sarcopenia is a condition in which there is a loss of muscle mass. To diagnose sarcopenia, computed tomography is most often used, from the images of which the volume of muscle tissue can be assessed. The first stage of the analysis is its contouring, which is performed manually, takes a long time and is not always performed with sufficient quality affecting the accuracy of estimates and, as a result, the patient’s treatment plan. The subject of the study is the use of computer vision approaches for accurate segmentation of muscle tissue from computed tomography images for the purpose of sarcometry. The purpose of the study is to develop an approach to solving the problem of segmentation of collected and annotated images. An approach is presented that includes the stages of image pre-processing, segmentation using neural networks of the U-Net family, and post-processing. In total, 63 different configurations of the approach are considered, which differ in terms of data supplied to the input models and model architectures. The influence of the proposed method of post-processing the resulting binary masks on the segmentation accuracy is also evaluated. The approach, which includes pre-processing with table masking and anisotropic diffusion filtering, segmentation with an Inception U-Net architecture model, and post-processing based on contour analysis, achieves a Dice similarity coefficient of 0.9379 and Intersection over Union of 0.8824. Nine other configurations, the experimental results for which are reflected in the article, also demonstrated high values of these metrics (in the ranges of 0.9356–0.9374 and 0.8794–0.8822, respectively). The approach proposed in the article based on preprocessed three-channel images allows us to achieve metrics of 0.9364 and 0.8802, respectively, using the lightweight U-Net segmentation model. In accordance with the described approach, a software module was implemented in Python. The results of the study confirm the feasibility of using computer vision to assess muscle tissue parameters. The developed module can be used to reduce the routine workload on radiologists

Complement component C1q mediates mitochondria-driven oxidative stress in neonatal hypoxic-ischemic brain injury

Hypoxic–ischemic (HI) brain injury in infants is a leading cause of lifelong disability. We report a novel pathway mediating oxidative brain injury after hypoxia–ischemia in which C1q plays a central role. Neonatal mice incapable of classical or terminal complement activation because of C1q or C6 deficiency or pharmacologically inhibited assembly of membrane attack complex were subjected to hypoxia–ischemia. Only C1q−/− mice exhibited neuroprotection coupled with attenuated oxidative brain injury. This was associated with reduced production of reactive oxygen species (ROS) in C1q−/− brain mitochondria and preserved activity of the respiratory chain. Compared with C1q+/+ neurons, cortical C1q−/− neurons exhibited resistance to oxygen–glucose deprivation. However, postischemic exposure to exogenous C1q increased both mitochondrial ROS production and mortality of C1q−/− neurons. This C1q toxicity was abolished by coexposure to antioxidant Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid). Thus, the C1q component of complement, accelerating mitochondrial ROS emission, exacerbates oxidative injury in the developing HI brain. The terminal complement complex is activated in the HI neonatal brain but appeared to be nonpathogenic. These findings have important implications for design of the proper therapeutic interventions against HI neonatal brain injury by highlighting a pathogenic priority of C1q-mediated mitochondrial oxidative stress over the C1q deposition-triggered terminal complement activation

Reactivity margin evaluation software for WWR-c reactor

The WWR-c reactor reactivity margin can be calculated using a precision reactor model. The precision model based on the Monte Carlo method (Kolesov et al. 2011) is not well suited for operational calculations. The article describes the work on creating a software package for preliminary evaluations of the WWR-c reactor reactivity margin. The research has confirmed the possibility of using an artificial neural network to approximate the reactivity margin based on the reactor core condition. Computational experiments were conducted on training the artificial neural network using the precision model data and real reactor measured data. According to the results of the computational experiments, the maximum relative approximation error ∆k/k for fuel burnup was 3.13 and 3.56%, respectively. The mean computation time was 100 ms. The computational experiments showed it possible to construct the artificial neural network architecture. This architecture became the basis for building a software package for evaluating the WWR-c reactor reactivity margin – REST API based web-application – which has a convenient user interface for entering the core configuration. It is also possible to replenish the training sample with new measurements and train the artificial neuron network once again. The reactivity margin evaluation software is ready to be tested by the WWR-c reactor personnel and to be used as a component of the automated reactor refueling system. With minor modifications, the software package can be used for reactors of other types

Application of spiking neural networks for modelling the process of high-temperature hydrogen production in systems with gas-cooled reactors

Hydrogen energy is able to solve the problem of the dependence of modern industries on fossil fuels and significantly reduce the amount of harmful emissions. One of the ways to produce hydrogen is high-temperature water-steam electrolysis. Increasing the temperature of the steam involved in electrolysis makes the process more efficient. The key problem is the use of a reliable heat energy source capable of reaching high temperatures. High-temperature gas-cooled reactors with a gaseous coolant and a graphite moderator provide a solution to the problem of heating the electrolyte. Part of the heat energy is used for producing electrical energy required for electrolysis. Modern electrolyzers built as arrays of tubular or planar electrolytic cells with a nuclear energy source make it possible to produce hydrogen by decomposing water molecules, and the working temperature control leads to a decrease in the Nernst potential. The operation of such facilities is complicated by the need to determine the optimal parameters of the electrolysis cell, the steam flow rate, and the operating current density. To reduce the costs associated with the process optimization, it is proposed to use a low-temperature electrolysis system controlled by a spiking neural network. The results confirm the effectiveness of intelligent technologies that implement adaptive control of hybrid modeling processes in order to organize the most feasible hydrogen production in a specific process, the parameters of which can be modified depending on the specific use of the reactor thermal energy. In addition, the results of the study confirm the feasibility of using a combined functional structure made on the basis of spiking neurons to correct the parameters of the developed electrolytic system. The proposed simulation strategy can significantly reduce the consumption of computational resources in comparison with models based only on neural network prediction methods