Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti

Machine learning techniques are presented for automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia cathedral in Kyiv (Ukraine). A new image dataset of these carved Glagolitic and Cyrillic letters (CGCL) was assembled and pre-processed for recognition and prediction by machine learning methods. The dataset consists of more than 4000 images for 34 types of letters. The explanatory data analysis of CGCL and notMNIST datasets shown that the carved letters can hardly be differentiated by dimensionality reduction methods, for example, by t-distributed stochastic neighbor embedding (tSNE) due to the worse letter representation by stone carving in comparison to hand writing. The multinomial logistic regression (MLR) and a 2D convolutional neural network (CNN) models were applied. The MLR model demonstrated the area under curve (AUC) values for receiver operating characteristic (ROC) are not lower than 0.92 and 0.60 for notMNIST and CGCL, respectively. The CNN model gave AUC values close to 0.99 for both notMNIST and CGCL (despite the much smaller size and quality of CGCL in comparison to notMNIST) under condition of the high lossy data augmentation. CGCL dataset was published to be available for the data science community as an open source resource.Comment: 11 pages, 9 figures, accepted for 25th International Conference on Neural Information Processing (ICONIP 2018), 14-16 December, 2018 (Siem Reap, Cambodia

Acute Coronary Syndrome during the Pandemic New Coronavirus Infection

The experience of managing patients with COVID-19 around the world has shown that, although  respiratory symptoms predominate  during the manifestation of infection, then many patients can develop serious damage  to the cardiovascular system. However, coronary artery disease (CHD) remains the leading cause of death worldwide. The purpose of the review is to clarify the possible pathogenetic links between COVID-19 and acute coronary syndrome (ACS), taking into account which will help to optimize the management of patients with comorbid  pathology. Among the body's responses to SARS-CoV-2 infection, which increase the likelihood of developing  ACS,  the role of systemic inflammation, the quintessence  of which is a "cytokine storm" that can destabilize  an atherosclerotic  plaque is discussed.  Coagulopathy, typical for patients with Covid-19, is based on immunothrombosis, caused by a complex  interaction between neutrophilic  extracellular  traps and von Willebrandt  factor in conditions  of systemic inflammation. The implementation  of a modern strategy  for managing patients with ACS,  focused on the priority of percutaneous interventions (PCI), during  a pandemic is experiencing great  difficulties  due to the formation  of time delays  before  the start of invasive  procedures  due to the epidemiological situation. Despite this, the current European,  American and Russian recommendations for the management of infected patients with ACS confirm the inviolability of the position of PCI as the first choice for treating patients with ACS and the undesirability  of replacing  invasive treatment with thrombolysis

Some Aspects of Mast Cells Carboxypeptidase A3 Participation in the Pathogenesis of COVID-19

Background: This study aimed to determine the involvement of carboxypeptidase A3 (CPA3) in developing lung damage in patients with COVID-19. Methods and Results: The study included samples of autopsy material from the lungs of patients who died as a result of severe COVID-19 (the main group [MG] and persons who died from external causes (the control group [CG]). Immunohistochemical staining for CPA3 was carried out. A quantitative study of CPA3-positive mast cells (MCs) and the degree of their degranulation was carried out using a ×40 objective lens with an analysis of ≥50 fields of view with further conversion to 1 mm². Significant representation of CPA3-positive MCs per 1 mm2 of CPA3-positive MCs, CPA3-positive MCs with signs of degranulation (SD), and co-adjacent MCs was found in the MG compared to the CG (P=0.01 in all cases). In the main group, positive correlations were identified between the total number of CPA3-positive MCs, CPA3-positive MCs with SD and the blood hemoglobin level shortly before death (r=0.491 [P=0.008] and r=0.521 [P=0.004], respectively). Co-adjacent CPA3-positive MCs were negatively correlated with blood eosinophils at the beginning of hospitalization (r=-0.420 [P=0.023]). Also, the number of separately lying, CPA3-positive MCs negatively correlated with the blood monocyte shortly before death (r=-0.384 [P=0.044]). A positive correlation was established between the total number of CPA3-positive MCs, CPA3-positive MCs with SD, and adjacent CPA3-positive MCs with total blood protein in patients at the beginning of hospitalization (r=0.431 [P=0.020], r=0.449 [P=0.015] and r=0.456 [P=0.013], respectively). In addition, the study demonstrated a positive correlation between CPA3-positive MCs with SD and the total number of CPA3-positive MCs with blood aPTT levels (r=0.304 [P=0.045] and r=0.375 [P=0.045], respectively). A negative correlation was also found between the total number of CPA3-positive MCs and the blood INR level (r=-0.812 [P=0.050]). Finally, in patients at the beginning of hospitalization, a negative correlation was found between CPA3-positive MCs with SD, CPA3-positive MCs without SD, separately located CPA3-positive MCs, adjacent CPA3-positive MCs, and the total number of CPA3-positive MCs with blood amylase (r=-0.550 [P=0.002], r=-0.452 [P=0.045], r=-0.485 [P=0.030], r=-0.622 [P=0.008], and r=-0.590 [P=0.006], respectively). Conclusion: Our study identifies the potential involvement of CPA3 in the pathogenesis of severe COVID-19. However, many aspects of its participation remain unclear and require further study

The Barents and Chukchi Seas: Comparison of two Arctic shelf ecosystems

This paper compares and contrasts the ecosystems of the Barents and Chukchi Seas. Despite their similarity in a number of features, the Barents Sea supports a vast biomass of commercially important fish, but the Chukchi does not. Here we examine a number of aspects of these two seas to ascertain how they are similar and how they differ. We then indentify processes and mechanisms that may be responsible for their similarities and differences.Both the Barents and Chukchi Seas are high latitude, seasonally ice covered, Arctic shelf-seas. Both have strongly advective regimes, and receive water from the south. Water entering the Barents comes from the deep, ice-free and "warm" Norwegian Sea, and contains not only heat, but also a rich supply of zooplankton that supports larval fish in spring. In contrast, Bering Sea water entering the Chukchi in spring and early summer is cold. In spring, this Bering Sea water is depleted of large, lipid-rich zooplankton, thus likely resulting in a relatively low availability of zooplankton for fish. Although primary production on average is similar in the two seas, fish biomass density is an order of magnitude greater in the Barents than in the Chukchi Sea. The Barents Sea supports immense fisheries, whereas the Chukchi Sea does not. The density of cetaceans in the Barents Sea is about double that in the Chukchi Sea, as is the density of nesting seabirds, whereas, the density of pinnipeds in the Chukchi is about double that in the Barents Sea. In the Chukchi Sea, export of carbon to the benthos and benthic biomass may be greater. We hypothesize that the difference in fish abundance in the two seas is driven by differences in the heat and plankton advected into them, and the amount of primary production consumed in the upper water column. However, we suggest that the critical difference between the Chukchi and Barents Seas is the pre-cooled water entering the Chukchi Sea from the south. This cold water, and the winter mixing of the Chukchi Sea as it becomes ice covered, result in water temperatures below the physiological limits of the commercially valuable fish that thrive in the southeastern Bering Sea. If climate change warms the Barents Sea, thereby increasing the open water area via reducing ice cover, productivity at most trophic levels is likely to increase. In the Chukchi, warming should also reduce sea ice cover, permitting a longer production season. However, the shallow northern Bering and Chukchi Seas are expected to continue to be ice-covered in winter, so water there will continue to be cold in winter and spring, and is likely to continue to be a barrier to the movement of temperate fish into the Chukchi Sea. Thus, it is unlikely that large populations of boreal fish species will become established in this Arctic marginal sea. © 2012 Elsevier B.V

Evaluation of stress resistance among young men after autogenous training

At 60 students - two weeks prior to the first final employment and before the first final employment carried out test of cardiorespiratory synchronism. On dynamics of the regulation-adaptive status determined a level stress resistance. After the first final, before the second, 30 of examinees passed within two weeks autogenous training by Schultz's technique, and 30 didn't. Then, at them again carried out test of cardiorespiratory synchronism, twice, and on dynamics of the regulatory-adaptive status determined a level stress resistance. By results of an evaluation stress resistance on dynamics of the regulatory-adaptive status autogenous training at the end of one academic year did not cause authentic change stress resistance of students with high and low levels of stress resistance. At the same time it raises stress resistance at persons with a moderate level stress resistance. Under the action of the stress factor from those not held autogenic training index of regulatory-adaptive status was 39,5 ± 0,7, and held it at 62,6 ± 0,3

PATHOGENESIS, ACTUAL ASPECTS OF PREVENTION AND TREATMENT OF THE ANTIBIOTIC-ASSOCIATED DIARRHOEA

The article provides an overview of current Russian and foreign literature devoted to the problem of pathogenesis, and of the treatment and prevention of antibiotic-associated diarrhea. Antibiotic-associated diarrhea is one of the most relevant aspects of modern drug therapy in due to the frequent prescription of antibacterial agents. Antibiotic-associated diarrhea (according to WHO) is defined as the presence of three or more episodes of an unformed stool for two or more consecutive days that occurred during or after the end of antibiotic therapy. The risk of developing this disorder is highest when using aminopenicillins, as well as their combinations with clavulanic acid, cephalosporins, clindamycin. Despite the presence of a common etiologic factor — the intake of antibacterial agents, the immediate causes and mechanisms of antibiotic-associated diarrhea development in patients may be different. The article describes the main issues of the etiology and pathogenesis of this pathology, the risk factors for the development of antibiotic-associated diarrhea are named, that allows to predict this complication in certain categories of patients. Тhe virulence factors of Clostridium difficile, Klebsiella oxytoca, Candida spp. and the clinical manifestations associated with their effects are highlighted. The clinical variants of this disease are described: 1) pseudomembranous colitis; 2) segmental hemorrhagic colitis; 3) “mild illness”. Contemporary literature data on the possibilities of prevention, as well as effective methods of treatment of antibiotic-associated diarrhea, are presented. For the treatment and prevention of all clinical forms of antibiotic-associated diarrhea, most authors suggest the use of drugs that make up the deficiency of normal intestinal microbiota — probiotics and prebiotics. The problem of the benefits of adjuvant therapy with probiotics during the course of antibiotics for the prevention of antibiotic-associated diarrhea remains controversial, the effectiveness and safety of the use of various probiotic cultures for this purpose is being studied. The information presented in this review is intended to target physicians to the rational use of antibacterial agents, and to early diagnosis of their most frequent side effect, antibiotic-associated diarrhea

Climatic and ecological drivers of euphausiid community structure vary spatially in the Barents Sea: relationships from a long time series (1952–2009)

Euphausiids play an important role in transferring energy from ephemeral primary producers to fish, seabirds, and marine mammals in the Barents Sea ecosystem. Climatic impacts have been suggested to occur at all levels of the Barents Sea food-web, but adequate exploration of these phenomena on ecologically relevant spatial scales has not been integrated sufficiently. We used a time-series of euphausiid abundance data spanning 58 years, one of the longest biological time-series in the Arctic, to explore qualitative and quantitative relationships among climate, euphausiids, and their predators, and how these parameters vary spatially in the Barents Sea. We detected four main hydrographic regions, each with distinct patterns of interannual variability in euphausiid abundance and community structure. Assemblages varied primarily in the relative abundance of Thysanoessa inermis vs. T. raschii, or T. inermis vs. T. longicaudata, and Meganyctiphanes norvegica. Climate proxies and the abundance of capelin or cod explained 30–60% of the variability in euphausiid abundance in each region. Climate also influenced patterns of variability in euphausiid community structure, but correlations were generally weaker. Advection of boreal euphausiid taxa from the Norwegian Sea is clearly more prominent in warmer years than in colder years, and interacts with seasonal fish migrations to help explain spatial differences in primary drivers of euphausiid community structure. Non-linear effects of predators were common, and must be considered more carefully if a mechanistic understanding of the ecosystem is to be achieved. Quantitative relationships among euphausiid abundance, climate proxies, and predator stock-sizes derived from these time series are valuable for ecological models being used to predict impacts of climate change on the Barents Sea ecosystem, and how the system should be managed.We also gratefully acknowledge Chris Emblow for graphics support, and financial support from Statoil and Akvaplan-niva. Michael Greenacre's research is partially supported by grant MTM2012-37195 of the Spanish Ministry of Economy and Competitiveness. This manuscript was improved by comments from F. Buchholz