Search

Suzy E Meijer,1,2 Olga Klebanov-Akopyn,3 Vera Pavlov,2,3 Shachar Laks,4 David Hazzan,4 Aviram Nissan,2– 4 Douglas Zippel3,4 1Department of Infectious Disease, Sourasky Medical Center, Tel Aviv, Israel; 2The Surgical Oncology Laboratory, Hadassah-Hebrew University Hospital, Jerusalem, Israel; 3The Surgical Oncology Laboratory, The Chaim Sheba Medical Center, Ramat Gan, Israel; 4Department of General and Oncological Surgery – Surgery C, The Chaim Sheba Medical Center, Ramat Gan, IsraelCorrespondence: Douglas ZippelDepartment of General & Oncological Surgery-Surgery C, The Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, IsraelTel +972-3-530-2714Fax +972-3-5341562Email dov.zippel@sheba.health.gov.ilPurpose: Minimal residual disease (MRD) refers to micrometastases that are undetectable by conventional means and is a potential source of disease relapse. This study aimed to detect the presence of breast cancer (BC) biomarkers (MGB-1, MGB-2, CK-19, NY-BR-1) using real-time polymerase chain reaction (RT-PCR) in peripheral blood mononuclear cells (PBMC) of BC patients and the impact of a positive assay on clinical outcome.Patients and Methods: Patients in the analysis included females > 18 years of age with biopsy-proven carcinoma of the breast. A 10 mL sample of venous blood was obtained from 10 healthy controls and 25 breast cancer patients. Comparisons of peripheral blood markers were made with clinicopathological variables.Results: High-quality RNA was extracted from all samples with a mean RNA concentration of 224.8± 155.3 ng/μL. Each of the molecular markers examined was highly expressed in the primary breast tumors (n = 3, positive controls) with none of the markers detected in healthy negative controls. The NY-BR-1 marker was expressed in one (4%) patient with metastatic disease with no MGB-1 and MGB-2 detected in any sample derived from the study patients. The CK-19 marker was detected in 16 (64%) of the BC cases. No correlation was found between CK-19 expression and tumor stage (P = 0.07) or nodal status (P = 0.32). No correlation was identified in the BC patients between CK-19 expression and receptor status in the BC primary tumor.Conclusion: This study showed high expression of all 4 markers NY-BR-1, MGB-1, MGB-2 and CK-19 in the PBMCs derived from breast cancer patients. CK-19 was detected in 64% of the stage I–III cases operated with curative intent, the only recurrent events occurring in the CK-19-positive cases. Our data confirm the need to enhance techniques for detection of MRD, which may better predict patients at risk for relapse.Keywords: breast cancer, circulating tumor cells, cancer biomarkers, early detection, predictive potential

Copper-nickel sulfide ores are one of the main sources of platinum. One of the ways to extract precious metals from such ores involves melting of a concentrate in electric ore smelting furnaces, where the melt is divided into matte and slag. Platinum is generally concentrated in matte; however, some of it remains in the slag, thus leading to metal losses. In order to reduce platinum losses, the forms of platinum in these phases should be studied. It was found that during the melting of this ore, iron, nickel, and copper are reduced. The mineral composition of matte was studied. Platinum in matte is present in the form of intermetallics with Fe and Ni. The PtFe intermetallic is a dispersed needle formation with a length of 20 to 500 μm and a thickness of up to 10 μm. The size effect is revealed: the content of platinum in the PtFe intermetallic decreases with decreasing the thickness of needle formations. The decreases in the content of platinum in dispersed needle formations can be explained by an increase in the thermodynamic activity and changing properties of the dispersed substance and a corresponding increase in solubility. It was found that matte drops, together with their associated platinumcontaining particles of no more than 5-7 μm in size, were carried into the slag by gas bubbles using flotation. The conditions for the rise of a matte drop, together with a bubble in the slag, consist in the fact that the adhesive force of the drop with the bubble and the buoyancy force acting on the bubble must be greater than the gravity of the drop.

The Institute of Economics and Industrial Engineering (IEIE) of SB RAS and the Novosibirsk State University have accumulated many years of experience in the development and use of different types of point and spatial dynamic input-output models (DIOM) in the analysis and forecasting of the economy of Russia and its regions. In particular, the CAII system has been developed (Complex Analysis of Interindustrial Information), including DIOM taking into account the investment lag (DIOM-CAII), and model-software complex with spatial input-output models (further - MSC), including input-output inter-regional optimization model (IOIRM). Each type of the models has its advantages and limitations for use in the analysis and forecasting. The paper describes the differences and common features of data support of DIOM-CAII and MSC and summarizes the features of long-term forecasting of the Russian economy with the use of DIOM-CAII and IOIRM at the macroeconomic level. Authors provide a brief comparative analysis of the results of forecast calculations for the development of the Russian economy for the period 2014-2031 with allocation of the methodical features of the MSC and DIOM-CAII.

The accumulation of human capital is an important factor of economic growth. It seems to be useful to include «human capital» as a factor of a macroeconomic model, as it helps to take into account the quality differentiation of the workforce. Most of the models usually distinguish labor force by the levels of education, while some of the factors remain unaccounted. Among them are health status and culture development level, which influence productivity level as well as gross product reproduction. Inclusion of the human capital block to the interindustry model can help to make it more reliable for economic development forecasting. The article presents a mathematical description of the extended dynamic input-output model (DIOM) with a human capital block. The extended DIOM is based on the Input-Output Model from The KAMIN system (the System of Integrated Analyses of Interindustrial Information) developed at the Institute of Economics and Industrial Engineering of the Siberian Branch of the Academy of Sciences of the Russian Federation and at the Novosibirsk State University. The extended input-output model can be used to analyze and forecast development of Russian economy.

The purpose of this study is to adapt methods of fuzzy sets to analyze the effectiveness of multistage investment projects. The problem solved by the study is as follows. Some innovative projects are characterized by the lack of profitability in the early stages of implementation and high risk associated with high uncertainty of assessment of expected future cash flows generated by the project. In this situation, the use of standard methods of analysis of economic efficiency of investment projects in high-tech industries, does not provide a comprehensive assessment of the appropriateness of investing, as well as to quantify the accuracy of the dynamics of the projected figures. All this requires the development of theory and methods of analysis of economic efficiency of innovation. Application of real options, as well as the fuzzy sets is, in our view, the direction of improving these methods. The fuzzy random pairs approach is developed in order to study fuzzy set properties of random pointwise set mappings. The articles proposes generalization of the fuzzy random pairs approach for research of stochastic processes. The generalization is initiated by an approach to exploration of uncertainty in research project supported with an RFBR grant no. 15-06-06914, which is based on application of the Geske model modification. Mathematical description of the generalization is carried out for an example of a real venture-backed investment project aimed at organization of methyl chloride to ethylene processing. The generalization essence is in the following: 1) time variable t in a random process ξ () t is replaced with a random value u , distributed uniformly within a segment [0; T ], which turns the process ξ () t into a bidimensional random value V = u ,ξ( ) ) 0;( u , defined on [ T ]× R ; 2) the random value V value is translated into a random pointwise set mapping using the interval translation; 3) in order to translate the random pointwise set mapping into a fuzzy set and to build its membership function a stochastic algorithm is used; 4) for fuzzy set exploration of the resulting pointwise set mapping the fuzzy random pairs approach is used. The solution of the Geske model is a stochastic process defined on a finite segment of time. The article contains main definitions and adaptations of abstract procedures of fuzzy set approach to the real investment project aimed at organization of methyl chloride to ethylene processing. A detailed research of this project attributes with the use of suggested fuzzy set approach lays beyond the frame of the article and should be the subject of an independent applied research.

In innovation processes play an important role are two types of uncertainty: uncertainty of goals and uncertainty of available resources. In mathematical language uncertainty of goals is usually described as a representation of the target function with random parameters (or a representation of the target as a random variable). The available resources are usually described by a set of valid values. Uncertainty of the resources in this case must be described by a random set. When dealing with many applications, be aware of the statistical relationship between the individual uncertainties. From the information point of view, this relationship is determined by a change in the entropy of each factor as a result of their mutual influence on each other. Properties of the entropy generated by the random variables, by now fairly well-known Application of entropy of random variables to study the uncertainty of economic processes is contained. However, the uncertainty generated by the random point-set maps, studied far less. At the same time, this type of uncertainty from the applied point of view is not less important. This article describes the method proposed by the authors study the uncertainty generated by the random point-set mappings, i.e. uncertainty of available resources. The main tool in the proposed method is a function of the entropy of the random set, which generalizes the notion of entropy of a random variable. On the basis of the entropy function is determined by the function of information link of random sets and all other features.

The article presents a comparative study of methods for modeling reproduction of fixed assets in various types of dynamic input-output models, which have been developed at the Novosibirsk State University and at the Institute of Economics and Industrial Engineering of the Siberian Division of Russian Academy of Sciences. The study compares the technique of information providing for the investment blocks of the models. Considered in detail mathematical description of the block of fixed assets reproduction in the Dynamic Input - Output Model included in the KAMIN system and the optimization interregional input - output model. Analyzes the peculiarities of information support of investment and fixed assets blocks of the Dynamic Input - Output Model included in the KAMIN system and the optimization interregional input - output model. In conclusion of the article provides suggestions for joint use of the analyzed models for Russian economy development forecasting. Provided the use of the KAMIN system’s models for short-term and middle-term forecasting and the optimization interregional input - output model to develop long-term forecasts based on the spatial structure of the economy.