National trends in incidence and geographic distribution of melanoma and keratinocyte carcinoma in the Russian Federation

Keratinocyte Carcinomas (KC), including basal cell and cutaneous squamous cell carcinomas, are the most common skin cancers in Fitzpatrick phototype I-III individuals, while melanoma is one of the deadliest skin cancer types. The incidence of both melanoma and KC is increasing in Russia. KCs’ incidence increases from north-to-south across the Russian Federation. In contrast, while melanoma’s incidence increases from north-to-south in the eastern part of the country, in the west of Russia a reverse latitude gradient trend is noted, where northern more affluent regions of Russia display higher rates of melanoma than the southern jurisdictions. Furthermore, our detailed analysis of incidence by jurisdiction highlights that affluent northern capital cities have higher rates of melanoma than the surrounding regions. The observed melanoma incidence trends in the western portion of Russia are similar to the findings in the western Europe and opposite of the findings in Canada

Heterogeneous somatostatin-expressing neuron population in mouse ventral tegmental area

Publisher Copyright: © Nagaeva et al.The cellular architecture of the ventral tegmental area (VTA), the main hub of the brain reward system, remains only partially characterized. To extend the characterization to inhibitory neurons, we have identified three distinct subtypes of somatostatin (Sst)-expressing neurons in the mouse VTA. These neurons differ in their electrophysiological and morphological properties, anatomical localization, as well as mRNA expression profiles. Importantly, similar to cortical Sst-containing interneurons, most VTA Sst neurons express GABAergic inhibitory markers, but some of them also express glutamatergic excitatory markers and a subpopulation even express dopaminergic markers. Furthermore, only some of the proposed marker genes for cortical Sst neurons were expressed in the VTA Sst neurons. Physiologically, one of the VTA Sst neuron subtypes locally inhibited neighboring dopamine neurons. Overall, our results demonstrate the remarkable complexity and heterogeneity of VTA Sst neurons and suggest that these cells are multifunctional players in the midbrain reward circuitry.Peer reviewe

Burden and geographic distribution of oral cavity and oropharyngeal cancers in the Russian Federation

BackgroundThe global incidence of lip and oral cavity cancers (OCCs) and oropharyngeal cancers (OPCs) is steadily increasing. While tobacco and alcohol consumption are established risk factors, a considerable proportion of these cancers has become attributed to human papilloma virus (HPV) infection. We aimed to describe the occurrence and identify potential risk factors of OCCs and OPCs across the Russian Federation during 2007-2018.MethodsWe conducted an ecological analysis using publicly accessible data from the P.A. Herzen Moscow Oncology Research Institute. Incidence and mortality rates by jurisdiction were mapped for geospatial analysis. We pre-defined 11 potential contributing risk factors and used univariable and multivariable Poisson regression model with backwards stepwise variable selection to identify associated factors with OCC and OPC.ResultsA total of 190,585 individuals were diagnosed with OCCs and OPCs in Russia between 2007-2018. Non-uniform geographic distribution of cancer cases was noted where the Far Eastern Federal District had the highest rate of OCC and the Central Federal District of OPCs. Districts with high weekly alcohol consumption had significantly higher incidence and mortality rates in both sexes. Districts with high rates of daily smoking had higher incidence of OCC among females, and those with low smoking trends had lower mortality rates for OCCs and OPCs.ConclusionWe detail the burden of OCCs and OPCs across Russia, with the aim of elucidating modifiable risk factors and proposing evidence-based prevention strategies. Tobacco/alcohol sales control measures and smoking/drinking cessation programs should continue to be prioritized as public health measures, especially for females

The ALICE experiment at the CERN LHC

ALICE (A Large Ion Collider Experiment) is a general-purpose, heavy-ion detector at the CERN LHC which focuses on QCD, the strong-interaction sector of the Standard Model. It is designed to address the physics of strongly interacting matter and the quark-gluon plasma at extreme values of energy density and temperature in nucleus-nucleus collisions. Besides running with Pb ions, the physics programme includes collisions with lighter ions, lower energy running and dedicated proton-nucleus runs. ALICE will also take data with proton beams at the top LHC energy to collect reference data for the heavy-ion programme and to address several QCD topics for which ALICE is complementary to the other LHC detectors. The ALICE detector has been built by a collaboration including currently over 1000 physicists and engineers from 105 Institutes in 30 countries. Its overall dimensions are 161626 m3 with a total weight of approximately 10 000 t. The experiment consists of 18 different detector systems each with its own specific technology choice and design constraints, driven both by the physics requirements and the experimental conditions expected at LHC. The most stringent design constraint is to cope with the extreme particle multiplicity anticipated in central Pb-Pb collisions. The different subsystems were optimized to provide high-momentum resolution as well as excellent Particle Identification (PID) over a broad range in momentum, up to the highest multiplicities predicted for LHC. This will allow for comprehensive studies of hadrons, electrons, muons, and photons produced in the collision of heavy nuclei. Most detector systems are scheduled to be installed and ready for data taking by mid-2008 when the LHC is scheduled to start operation, with the exception of parts of the Photon Spectrometer (PHOS), Transition Radiation Detector (TRD) and Electro Magnetic Calorimeter (EMCal). These detectors will be completed for the high-luminosity ion run expected in 2010. This paper describes in detail the detector components as installed for the first data taking in the summer of 2008

Developing machine-learning methods for the analysis of electromagnetic brain activity

Traditionally, analysis of electromagnetic brain activity focuses on modeling the data-generating process and identifying which components in the measured signal are associated with experimental manipulations. At the beginning of XXI century, machine-learning based approaches aiming to infer brain states from the measurements started to gain increasing popularity. These methods rely on extracting complex multivariate patterns allowing to predict experimental conditions from the measurements. This thesis summarizes how such prediction-based methods can be applied to measurements of electromagnetic brain activity in a way that allows to advance our understanding of the underlying neural processes. Because these techniques belong to a class of inverse probability problems and do not model the data-generating process directly, interpreting the learning outcomes in terms of the underlying neurophysiological processes is not straightforward. Instead, predictive models allow testing the generalization properties of brain activity across e.g. experimental tasks (Publication I) and individuals (Publication II), as well as employ model comparison techniques to gain additional insights about the statistical properties of the data- generating process indirectly i.e. by comparing models with different structural constraints (Publication II). Moreover, projecting relevant model parameters learned from the data back into the input space can provide additional insights into the data-generating process and thus complement traditional approaches. These approaches are implemented in an open-sourceacademic software described in Publication III

MNEflow: Neural networks for EEG/MEG decoding and interpretation

| openaire: EC/H2020/678578/EU//HRMEGMNEflow is a Python package for applying deep neural networks to multichannel electroencephalograpic (EEG) and magnetoencephalographic (MEG) measurements. This software comprises Tensorflow-based implementations of several popular convolutional neural network (CNN) models for EEG–MEG data and introduces a flexible pipeline enabling easy application of the most common preprocessing, validation, and model interpretation approaches. The software aims to save time and computational resources required for applying neural networks to the analysis of EEG and MEG data.Peer reviewe

Evidence for a general performance-monitoring system in the human brain

Adaptive behavior relies on the ability of the brain to form predictions and monitor action outcomes. In the human brain, the same system is thought to monitor action outcomes regardless of whether the information originates from internal (e.g., proprioceptive) and external (e.g., visual) sensory channels. Neural signatures of processing motor errors and action outcomes communicated by external feedback have been studied extensively; however, the existence of such a general action-monitoring system has not been tested directly. Here, we use concurrent EEG-MEG measurements and a probabilistic learning task to demonstrate that event-related responses measured by electroencephalography and magnetoencephalography display spatiotemporal patterns that allow an effective transfer of a multivariate statistical model discriminating the outcomes across the following conditions: (a) erroneous versus correct motor output, (b) negative versus positive feedback, (c) high- versus low-surprise negative feedback, and (d) erroneous versus correct brain-computer-interface output. We further show that these patterns originate from highly-overlapping neural sources in the medial frontal and the medial parietal cortices. We conclude that information about action outcomes arriving from internal or external sensory channels converges to the same neural system in the human brain, that matches this information to the internal predictions.Peer reviewe

Research of influencing constructive parameters of an autoGrader’s operating device on its performance

The article touches upon the theme of an autograder’s performance. There is considered a working process of an earth-moving machine. The data on the influence of constructive parameters of the autograder’s blade on the total force of resistance to soil’s movement and pulling power is presented. There are identified functional dependencies of a criterion of maximizing operational performance of the autograder. On the basis of present data there is provided a method of improving operational performance

Adaptive neural network classifier for decoding MEG signals

| openaire: EC/H2020/678578/EU//HRMEGWe introduce two Convolutional Neural Network (CNN )classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification. The proposed networks outperform traditional classifiers as well as more complex neural networks when decoding evoked and induced responses to different stimuli across subjects. Importantly, these models can successfully generalize to new subjects in real-time classification enabling more efficient brain–computer interfaces (BCI).Peer reviewe