Using parallel pre-trained types of DCNN model to predict breast cancer with color normalization

Objective Breast cancer is the most common among women, and it causes many deaths every year. Early diagnosis increases the chance of cure through treatment. The traditional manual diagnosis requires effort and time from pathological experts, as it needs a joint experience of a number of pathologists. Diagnostic mistakes can lead to catastrophic results and endanger the lives of patients. The presence of an expert system that is able to specify whether the examined tissue is healthy or not, thus improves the quality of diagnosis and saves the time of experts. In this paper, a model capable of classifying breast cancer anatomy by making use of a pre-trained DCNN has been proposed. To build this model, first of all the image should be color stained by using Vahadane algorithm, then the model which combines three pre-trained DCNN (Xception, NASNet and Inceptoin_Resnet_V2) should be built in parallel, then the three branches should be aggregated to take advantage of each other. The suggested model was tested under different values of threshold ratios and also compared with other models. Results The proposed model on the BreaKHis dataset achieved 98% accuracy, which is better than the accuracy of other models used in this field.

Bevacizumab (Avegra®) for the treatment of gynecological cancers: joint experience of several centers

Tumors of the female reproductive system are among the five most common cancers in women worldwide. Up to 60 % of these patients require chemotherapy to reduce the risk of recurrence. Currently, an additional treatment option is available for patients with advanced cancers of the female reproductive system, namely vascular endothelial growth factor inhibitor bevacizumab. To assess the efficacy and safety of Avegra®, Russian generic of bevacizumab, we summarized the experience of 5 specialized healthcare institutions that used it for the treatment of 178 patients with tumors of the female reproductive system. The analysis of side effects in 1,074 treatment cycles demonstrated that Avegra® had a good safety profile. Clinical effects of Avegra® were similar to those of the original drug.

Experience of designing a low-pressing turbocharger compressor using the modern version of a Universal modelling method

The paper presents the joint experience of the NPO “Turbotekhnika” and the R&D Laboratory “Gas Dynamics of Turbomachines” of SPbPU for designing a centrifugal compressor operating at pressure ratio 1.61 and a mass flow rate of 0.62 kg/s. The design was executed using the Universal modelling method and the inviscid quasi-three-dimensional calculation program 3DM.023. At the first step, by the preliminary design program, the stage dimensions were determined. The expected gas-dynamic characteristics are calculated. At the final design step, stator elements were offered by NPO “Turbotekhnika” and the configuration of the impeller blades was optimized based on the non-viscous quasi-three-dimensional calculations. NPO “Turbotekhnika” designed, manufactured and tested the 140E compressor at blade velocities 150, 200, 250 and 300 m/s. A comparison and analysis of experimental and calculated characteristics is presented. The design pressure ratio was calculated almost exactly for the design flow rate at a blade velocity of 300 m/s. The expected efficiency was confirmed. The mathematical model slightly overestimates the efficiency and the pressure ratio for the off-design flow rates.

Life Hacks, How Tos, and Guides in the Qualitative Research Data Collection Tutorial

A review of the textbook by A.V. Vanke, E.V. Polukhina and A.V. Strelnikova “How to Collect Data in Field Qualitative Research”. The authors of the book describe a joint experience of participation in field research projects, choosing the genre of “how to do” with many examples of field situations, strategies, tools and documents. The problems of access to the field, visual materials collecting, issues of data archiving, and many other topical aspects of research work are reviewed here. The book shows the logic of a field project step by step, from constructing a research design to leaving the field and maintaining connections with it.

Forced migrations of Georgia and Ukraine: reasons for the emergence, process features, implications for countries

The purpose of this paper is to analyze the causes, current realities and consequences of forced migrations of Georgia and Ukraine in order to identify key strategies for resolving the socio-economic problems of internally displaced persons and transforming them from a “problem vulnerable group” into an indicator of socio-economic development of the countries. Methodology. The study was conducted using a set of multidisciplinary approaches and methods. The paper was used philosophical, general scientific and concrete scientific research methods. Among them: methods of analysis and synthesis, comparative geographical method, mathematical and statistical, graphical method, problem method and some others. Results. Forced migration in Georgia and Ukraine is associated with armed conflicts, which were supported by the separatist sentiments of the part of the population. The center of the military confrontation in Georgia in 1989-1992 was the Tskhinvali region (South Ossetia), and in 1992-1993 – Abkhazia. In 2008, an armed conflict arose between Georgia, Russia and the separatist groups of South Ossetia, referred to as the Russian-Georgian war. In 2014, the Donbass (Donetsk and Luhansk regions) became a center of hostilities in Ukraine. Because of such military conflicts, millions of people have become internally displaced persons – a socially vulnerable category of the population. Analysis of the structural features of the IDPs of Georgia and Ukraine shows the unevenness of their territorial distribution: IDPs are settled in large cities close to their previous homes and almost completely ignore the countryside. Such resettlement speaks about possible intentions to return to their previous places of residence. As a result of the displacement of IDPs, a large burden on the social and economic spheres is observed in parts of Donetsk and Luhansk, as well as Kharkiv regions, the city of Kiev. A similar picture is observed in Georgia: IDPs are settled closer to the occupied regions of Abkhazia and the Tskhinvali region, in large cities and districts of Tbilisi, Samegrelo-Zemo, Svaneti and Imereti. IDPs have become a real challenge for both countries, with which the government could not finally cope. Unresolved in both countries are the housing problems of IDPs. In Ukraine, in addition to the housing problem, IDPs face acute problems with determining their status, social benefits, and employment. Ignoring the solution of these problems leads to increased tension in society, conflicts at the local level between the migrants and the local population, and, as a consequence, to repeated internal displacement. Resolving the problems of forced migrants should be the first line of policy with IDPs. Scientific novelty. a comprehensive comparative analysis of forced migration of Georgia and Ukraine related to military conflicts in the paper was carried out for the first time. The methodology of human-geographical research of migration processes, in particular forced migration, has been improved. The application of the joint experience of Georgia and Ukraine in solving the problems of IDPs was further developed. Practical significance. Studying the joint experience of countries in solving the socio-economic problems of IDPs, the features of their integration and adaptation will contribute to a deeper analysis of these processes at all levels of territorial organization, ensuring the erasing of borders between such a category as "migrants" and "ordinary population". This paper is only a part of the comprehensive study of the problems of IDPs, so the next step will be a study of the features of IDP integration in the host communities of Georgia and the implementation of this experience for Ukraine.

Software training for the next generation of physicists: joint experience of LHCb and ALICE

The need for good software training is essential in the HEP community. Unfortunately, current training is non-homogeneous and the definition of a common baseline is unclear, making it difficult for newcomers to proficiently join large collaborations such as ALICE or LHCb. In the last years, both collaborations have started separate efforts to tackle this issue through training workshops, via Analysis Tutorials (organized by the ALICE Juniors since 2014) and the Starterkit (organized by LHCb students since 2015). In 2017, ALICE and LHCb have for the first time joined efforts to provide combined training by identifying common topics, such as version control systems (Git) and programming languages (e.g. Python). Given the positive experience and feedback, this collaboration will be repeated in the future. We will illustrate the teaching methods, experience and feedback from our first common training workshop. We will also discuss our efforts to extend our format to other HEP experiments for future iterations.