Agent-Based Simulation of the COVID-19 Epidemic in Russia.

The COVID-19 pandemic has created a public health emergency in Russia and across the world. The wavelike spread of the new coronavirus infection, caused by newly emerging variants of the coronavirus, has led to a high incidence rate in all subjects of the Russian Federation. It is becoming extremely topical to get the opportunity to manage the development of the epidemic and assess the impact of certain regulatory measures on this process. This will help government agencies make informed decisions to control the burden on healthcare organizations. It is often impossible to obtain such assessments without using modern mathematical models.

Applying interval stability concept to empirical model of middle Pleistocene transition.

Interval stability is a novel method for the study of complex dynamical systems, allowing for the estimation of their stability to strong perturbations. This method describes how large perturbation should be to disrupt the stable dynamical regime of the system (attractor). In our work, interval stability is used for the first time to study the properties of a real natural system: to analyze the stability of the earth's climate system during the last 2.6×106 years. The main abrupt shift in global climate during this period is the middle Pleistocene transition (MPT), which occurred about 1×106 years ago as a change of the periodicity of glacial cycles from 41 to 100 kyr. On the basis of the empirical nonlinear stochastic model proposed in our recent work, we demonstrate that the global climate stability to any perturbations decreases throughout the Pleistocene period (including the MPT), enhancing its response to fast (with a millennial scale or less) internal disturbances.

Data-driven stochastic model for cross-interacting processes with different time scales.

In this work, we propose a new data-driven method for modeling cross-interacting processes with different time scales represented by time series with different sampling steps. It is a generalization of a nonlinear stochastic model of an evolution operator based on neural networks and designed for the case of time series with a constant sampling step. The proposed model has a more complex structure. First, it describes each process by its own stochastic evolution operator with its own time step. Second, it takes into account possible nonlinear connections within each pair of processes in both directions. These connections are parameterized asymmetrically, depending on which process is faster and which process is slower. They make this model essentially different from the set of independent stochastic models constructed individually for each time scale. All evolution operators and connections are trained and optimized using the Bayesian framework, forming a multi-scale stochastic model. We demonstrate the performance of the model on two examples. The first example is a pair of coupled oscillators, with the couplings in both directions which can be turned on and off. Here, we show that inclusion of the connections into the model allows us to correctly reproduce observable effects related to coupling. The second example is a spatially distributed data generated by a global climate model running in the middle 19th century external conditions. In this case, the multi-scale model allows us to reproduce the coupling between the processes which exists in the observed data but is not captured by the model constructed individually for each process.

[On the tactics of management and treatment of patients with congenital cerebral hernias in their localization in the maxillofacial region]. / O taktike vedeniya i lecheniya bol'nykh s vrozhdennymi mozgovymi gryzhami pri ikh lokalizatsii v chelyustno-litsevoi oblasti.

The analysis of the state of two patients with congenital cerebral hernias was carried out, which made it possible to establish differences in the effect of hernias on the state of the body. In the first case, the hernia is localized in the nasal cavity, and after its removal, the postoperative cerebrospinal fluid (CSF) leakage was stopped by a flap of the mucous membrane from the opposite side of the nasal septum. In the second case clinical analysis and computed tomography made it possible to state that the hernia was in the retromaxillary space and did not affect the patient's condition. Computed tomography shows signs of moderate blood pressure on the adjacent formations, and removal of the hernia and stopping the subsequent CSF leakage were impossible. The presented observations demonstrate an ambiguous approach to resolving the issue of surgical intervention in such cases.

Forced signal and predictability in a prototype climate model: Implications for fingerprinting based detection in the presence of multidecadal natural variability.

Advanced numerical models used for climate prediction are known to exhibit biases in their simulated climate response to variable concentrations of the atmospheric greenhouse gases and aerosols that force a non-uniform, in space and time, secular global warming. We argue here that these biases can be particularly pronounced due to misrepresentation, in these models, of the multidecadal internal climate variability characterized by large-scale, hemispheric-to-global patterns. This point is illustrated through the development and analysis of a prototype climate model comprised of two damped linear oscillators, which mimic interannual and multidecadal internal climate dynamics and are set into motion via a combination of stochastic driving, representing weather noise, and deterministic external forcing inducing a secular climate change. The model time series are paired with pre-specified patterns in the physical space and form, conceptually, a spatially extended time series of the zonal-mean near-surface temperature, which is further contaminated by a spatiotemporal noise simulating the rest of climate variability. The choices of patterns and model parameters were informed by observations and climate-model simulations of the 20th century near-surface air temperature. Our main finding is that the intensity and spatial patterns of the internal multidecadal variability associated with the slow-oscillator model component greatly affect (i) the ability of modern pattern-recognition/fingerprinting methods to isolate the forced response of the climate system in the 20th century ensemble simulations and (ii) climate-system predictability, especially decadal predictability, as well as the estimates of this predictability using climate models in which the internal multidecadal variability is underestimated or otherwise misrepresented.

Visuospatial exploration and art therapy intervention in patients with Parkinson's disease: an exploratory therapeutic protocol.

Though abnormalities of visuospatial function occur in Parkinson's disease, the impact of such deficits on functional independence and psychological wellbeing has been historically under- recognized, and effective treatments for this impairment are unknown. These symptoms can be encountered at any stage of the disease, affecting many activities of daily living, and negatively influencing mood, self-efficacy, independence, and overall quality of life. Furthermore, visuospatial dysfunction has been recently linked to gait impairment and falls, symptoms that are known to be poor prognostic factors. Here, we aim to present an original modality of neurorehabilitation designed to address visuospatial dysfunction and related symptoms in Parkinson's disease, known as "Art Therapy". Art creation relies on sophisticated neurologic mechanisms including shape recognition, motion perception, sensory-motor integration, abstraction, and eye-hand coordination. Furthermore, art therapy may enable subjects with disability to understand their emotions and express them through artistic creation and creative thinking, thus promoting self-awareness, relaxation, confidence and self-efficacy. The potential impact of this intervention on visuospatial dysfunction will be assessed by means of combined clinical, behavioral, gait kinematic, neuroimaging and eye tracking analyses. Potential favorable outcomes may drive further trials validating this novel paradigm of neurorehabilitation.

Random dynamical models from time series.

In this work we formulate a consistent Bayesian approach to modeling stochastic (random) dynamical systems by time series and implement it by means of artificial neural networks. The feasibility of this approach for both creating models adequately reproducing the observed stationary regime of system evolution, and predicting changes in qualitative behavior of a weakly nonautonomous stochastic system, is demonstrated on model examples. In particular, a successful prognosis of stochastic system behavior as compared to the observed one is illustrated on model examples, including discrete maps disturbed by non-Gaussian and nonuniform noise and a flow system with Langevin force.

Prognosis of qualitative system behavior by noisy, nonstationary, chaotic time series.

An approach to prognosis of qualitative behavior of an unknown dynamical system (DS) from weakly nonstationary chaotic time series (TS) containing significant measurement noise is proposed. The approach is based on construction of a global time-dependent parametrized model of discrete evolution operator (EO) capable of reproducing nonstationary dynamics of a reconstructed DS. A universal model in the form of artificial neural network (ANN) with certain prior limitations is used for the approximation of the EO in the reconstructed phase space. Probabilistic prognosis of the system behavior is performed using Monte Carlo Markov chain (MCMC) analysis of the posterior Bayesian distribution of the model parameters. The classification of qualitatively different regimes is supposed to be dictated by the application, i.e., it is assumed that some classifier function is predefined that maps a point of a model parameter space to a finite set of different behavior types. The ability of the approach to provide prognosis for times comparable to the observation time interval is demonstrated. Some restrictions as well as possible advances of the proposed approach are discussed.

Abnormal metabolic brain networks in Tourette syndrome.

OBJECTIVES: To identify metabolic brain networks that are associated with Tourette syndrome (TS) and comorbid obsessive-compulsive disorder (OCD). METHODS: We utilized [(18)F]-fluorodeoxyglucose and PET imaging to examine brain metabolism in 12 unmedicated patients with TS and 12 age-matched controls. We utilized a spatial covariance analysis to identify 2 disease-related metabolic brain networks, one associated with TS in general (distinguishing TS subjects from controls), and another correlating with OCD severity (within the TS group alone). RESULTS: Analysis of the combined group of patients with TS and healthy subjects revealed an abnormal spatial covariance pattern that completely separated patients from controls (p < 0.0001). This TS-related pattern (TSRP) was characterized by reduced resting metabolic activity of the striatum and orbitofrontal cortex associated with relative increases in premotor cortex and cerebellum. Analysis of the TS cohort alone revealed the presence of a second metabolic pattern that correlated with OCD in these patients. This OCD-related pattern (OCDRP) was characterized by reduced activity of the anterior cingulate and dorsolateral prefrontal cortical regions associated with relative increases in primary motor cortex and precuneus. Subject expression of OCDRP correlated with the severity of this symptom (r = 0.79, p < 0.005). CONCLUSION: These findings suggest that the different clinical manifestations of TS are associated with the expression of 2 distinct abnormal metabolic brain networks. These, and potentially other disease-related spatial covariance patterns, may prove useful as biomarkers for assessing responses to new therapies for TS and related comorbidities.

Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series.

An alternative approach to determining embedding dimension when reconstructing dynamic systems from a noisy time series is proposed. The available techniques of determining embedding dimension (the false nearest-neighbor method, calculation of the correlation integral, and others) are known [H. D. I. Abarbanel, (Springer-Verlag, New York, 1997)] to be inefficient, even at a low noise level. The proposed approach is based on constructing a global model in the form of an artificial neural network. The required amount of neurons and the embedding dimension are chosen so that the description length should be minimal. The considered approach is shown to be appreciably less sensitive to the level and origin of noise, which makes it also a useful tool for determining embedding dimension when constructing stochastic models.

Repeat instability in the 27-39 CAG range of the HD gene in the Venezuelan kindreds: Counseling implications.

The instability of the CAG repeat size of the HD gene when transmitted intergenerationally has critical implications for genetic counseling practices. In particular, CAG repeats between 27 and 35 have been the subject of debate based on small samples. To address this issue, we analyzed allelic instability in the Venezuelan HD kindreds, the largest and most informative families ascertained for HD. We identified 647 transmissions. Our results indicate that repeats in the 27-35 CAG range are highly stable. Out of 69 transmitted alleles in this range, none expand into any penetrant ranges. Contrastingly, 14% of alleles transmitted from the incompletely penetrant range (36-39 CAGs) expand into the completely penetrant range, characterized by alleles with 40 or more CAG repeats. At least 12 of the 534 transmissions from the completely penetrant range contract into the incompletely penetrant range of 36-39 CAG repeats. In these kindreds, none of the individuals with 27-39 CAGs were symptomatic, even though they ranged in age from 11 to 82 years. We expect these findings to be helpful in updating genetic counseling practices.

Markov chain Monte Carlo method in Bayesian reconstruction of dynamical systems from noisy chaotic time series.

The impossibility to use the MCMC (Markov chain Monte Carlo) methods for long noisy chaotic time series (TS) (due to high computational complexity) is a serious limitation for reconstruction of dynamical systems (DSs). In particular, it does not allow one to use the universal Bayesian approach for reconstruction of a DS in the most interesting case of the unknown evolution operator of the system. We propose a technique that makes it possible to use the MCMC methods for Bayesian reconstruction of a DS from noisy chaotic TS of arbitrary long duration.

Implicit and explicit aspects of sequence learning in pre-symptomatic Huntington's disease.

Learning deficits may be part of the early symptoms of Huntington's disease (HD). Here we characterized implicit and explicit aspects of sequence learning in 11 pre-symptomatic HD gene carriers (pHD) and 11 normal controls. Subjects moved a cursor on a digitizing tablet and performed the following tasks: SEQ: learning to anticipate the appearance of a target sequence in two blocks; VSEQ: learning a sequence by attending to the display without moving for one block, and by moving to the sequence in a successive block (VSEQ test). Explicit learning was measured with declarative scores and number of anticipatory movements. Implicit learning was measured as a strategy change reflected in movement time. By the end of SEQ, pHD had a significantly lower number of correct anticipatory movements and lower declarative scores than controls, while in VSEQ and VSEQ test these indices improved. During all three tasks, movement time changed in controls, but not in pHD. These results suggest that both explicit and implicit aspects of sequence learning may be impaired before the onset of motor symptoms. However, when attentional demands decrease, explicit, but not implicit, learning may improve.

Thalamic metabolism and symptom onset in preclinical Huntington's disease.

The neural basis for the transition from preclinical to symptomatic Huntington's disease (HD) is unknown. We used serial positron emission tomography (PET) imaging in preclinical HD gene carriers (p-HD) to assess the metabolic changes that occur during this period. Twelve p-HD subjects were followed longitudinally with [11C]-raclopride and [18F]-fluorodeoxyglucose PET imaging, with scans at baseline, 18 and 44 months. Progressive declines in striatal D2-receptor binding were correlated with concurrent changes in regional metabolism and in the activity of an HD-related metabolic network. We found that striatal D2 binding declined over time (P < 0.005). The activity of a reproducible HD-related metabolic covariance pattern increased between baseline and 18 months (P < 0.003) but declined at 44 months (P < 0.04). These network changes coincided with progressive declines in striatal and thalamic metabolic activity (P < 0.01). Striatal metabolism was abnormally low at all time points (P < 0.005). By contrast, thalamic metabolism was elevated at baseline (P < 0.01), but fell to subnormal levels in the p-HD subjects who developed symptoms. These findings were confirmed with an MRI-based atrophy correction for each individual PET scan. Increases in network expression and thalamic glucose metabolism may be compensatory for early neuronal losses in p-HD. Declines in these measures may herald the onset of symptoms in gene carriers.

Modified Bayesian approach for the reconstruction of dynamical systems from time series.

Some recent papers were concerned with applicability of the Bayesian (statistical) approach to reconstruction of dynamic systems (DS) from experimental data. A significant merit of the approach is its universality. But, being correct in terms of meeting conditions of the underlying theorem, the Bayesian approach to reconstruction of DS is hard to realize in the most interesting case of noisy chaotic time series (TS). In this work we consider a modification of the Bayesian approach that can be used for reconstruction of DS from noisy TS. We demonstrate efficiency of the modified approach for solution of two types of problems: (1) finding values of parameters of a known DS by noisy TS; (2) classification of modes of behavior of such a DS by short TS with pronounced noise.

Creatine in Huntington disease is safe, tolerable, bioavailable in brain and reduces serum 8OH2'dG.

In a randomized, double-blind, placebo-controlled study in 64 subjects with Huntington disease (HD), 8 g/day of creatine administered for 16 weeks was well tolerated and safe. Serum and brain creatine concentrations increased in the creatine-treated group and returned to baseline after washout. Serum 8-hydroxy-2'-deoxyguanosine (8OH2'dG) levels, an indicator of oxidative injury to DNA, were markedly elevated in HD and reduced by creatine treatment.

The role of radiotracer imaging in Parkinson disease.

Radiotracer imaging (RTI) of the nigrostriatal dopaminergic system is a widely used but controversial biomarker in Parkinson disease (PD). Here the authors review the concepts of biomarker development and the evidence to support the use of four radiotracers as biomarkers in PD: [18F]fluorodopa PET, (+)-[11C]dihydrotetrabenazine PET, [123I]beta-CIT SPECT, and [18F]fluorodeoxyglucose PET. Biomarkers used to study disease biology and facilitate drug discovery and early human trials rely on evidence that they are measuring relevant biologic processes. The four tracers fulfill this criterion, although they do not measure the number or density of dopaminergic neurons. Biomarkers used as diagnostic tests, prognostic tools, or surrogate endpoints must not only have biologic relevance but also a strong linkage to the clinical outcome of interest. No radiotracers fulfill these criteria, and current evidence does not support the use of imaging as a diagnostic tool in clinical practice or as a surrogate endpoint in clinical trials. Mechanistic information added by RTI to clinical trials may be difficult to interpret because of uncertainty about the interaction between the interventions and the tracer.

[Results of a complex evaluation of accommodative asthenopia in using different-design video monitors]. / Rezul'taty kompleksnoi otsenki akkomodativnoi astenopii pri rabote s videomonitorami razlichnoi konstruktsii.

Eighty volunteer computer operators were examined experimentally under the conditions of working with different-type video monitors, i.e. electron-beam (EB), liquid-crystal (LC), including different visual loads (typing, computer games, and gaze fixation). Experiments lasted for as long as 6 hours. The absolute accommodation volume (AAV), relative accommodation reserve (RAR) and ergograms were comparatively evaluated. A lower AAV was registered in all types of visual loads under the conditions of using different-type monitors, and it made up 0.27-1.08 diopters. A maximum reduction of AAV was detected for gaze fixation and while working with liquid-crystal monitors. The RAR reduction ranged from 0.31 to 0.63 diopters. A maximum RAR reduction was noted for gaze fixation and while using the electronic-beam monitors. The nature of changed ergography curves occurred within the interval between I and IIb types of ergograms. In case of typing, the ergogram deviations were registered to a greater extent for the liquid-crystal monitors. The value of deviations for computer games was bigger, at the experiment end, for EB rather than for LC.