Identification of Contact Angle and Heterogenity Detection in Tactile Images
Introduction. Automated analysis of tactile images registered by specialized medical tools is a novel domain, which results promptly find their applications in clinical practice. Medical Tactile Endosurgical Complex (MTEC) is currently the only commercially available device for intraoperative instrumental mechanoreceptoric palpation. One of the main challenges related to processing data generated by MTEC is heterogeneity detection in tactile images. This problem is highly important because it is a key step of localization of visually undetectable pathologies using instrumental palpation. Objectives. One of the main difficulties related to the problem of heterogeneity detection is a possibility to vary contact angle between mechanoreceptor and sample during tactile examination, so the aim of the research was to develop a method for automated contact angle identification and detection of heterogeneity in tactile images registered by MTEC. Methods. The proposed method of a tactile press contact angle estimation is based on classification with a specifically designed feature space. For heterogeneity detection we developed two different approaches. The first one is based on separation of heterogeneity detector into several components corresponding to similar contact angles. The second approach uses standard classification approach with contact angle as a high weighted element of the feature space. Results. Validation on a set of samples modeling normal tissues and pathologies showed high accuracy for contact angle identification. Both methods of heterogenity detection provided approximately the same accuracy clearly outperforming previously available methods in case of significant deviations of a contact angle from zero. Conclusion. The methods developed provide an accurat solution for problems of contact angle identification and detection of heterogeneity even in case of significant contact angle deviations, and such deviations are unavoidable in clinical practice, especially in minimally-invasive surgery.
The paper presents a formalized statement of the problem of selecting parameters and construction of a genomic classifier for medical test systemswith mathematical methods of machine learning without the use of biological and medical knowledge. A method is proposed to solve this problem. The results of testing the method using microarray datasets containing information on genome-wide transcriptome of the samples of estrogen positive breast tumors are discussed. Testing showed that the quality of classification provided by the constructed test system and implemented on the basis of assessments of expression of 12 genes is not inferior to the quality of classification carried out by such test systems as OncotypeDX and MammaPrint.
Genes with significant differential expression are traditionally used to reveal the genetic background underlying phenotypic differences between cancer cells. We hypothesized that informative marker sets can be obtained by combining genes with a relatively low degree of individual differential expression. We developed a method for construction of highly informative gene combinations aimed at the maximization of the cumulative informative power and identified sets of 2–5 genes efficiently predicting recurrence for ER-positive breast cancer patients. The gene combinations constructed on the basis of microarray data were successfully applied to data acquired by RNA-seq. The developed method provides the basis for the generation of highly efficient prognostic and predictive gene signatures for cancer and other diseases. The identified gene sets can potentially reveal novel essential segments of gene interaction networks and pathways implied in cancer progression.
The question about possibilities to use Twitter users’ moods to increase accuracy of stock price movement prediction draws attention of many researchers. In this paper we examine the possibility of analyzing Twitter users’ mood to improve accuracy of predictions for Gold and Silver stock market prices. We used a lexicon-based approach to categorize the mood of users expressed in Twitter posts and to analyze 755 million tweets downloaded from February 13, 2013 to September 29, 2013. As forecasting technique, we select Support Vector Machines (SVM), which have shown the best performance. Results of SVM application to prediction the stock market prices for Gold and Silver are discussed.
Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users’ moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach, which allow us to evaluate presence of eight basic emotions in more than 755 million tweets. The application of Support Vectors Machine and Neural Networks algorithms to predict DJIA and S&P500 indicators are discussed.
Background: Robotic surgery undergoes its wide acceptance achieved due to minimizing trauma to patients. However, currently robotic surgery lacks a tactile feedback, and it is an essential limiting factor for its further expansion. The problem can be solved by utilization of Medical Tactile Endosurgical complex (MTEC). This complex measures and displays tactile properties of tissues during endoscopic surgeries in real time. It was developed in Lomonosov Moscow State University and officially admitted for clinical use in 2012. Materials and method: MTEC palpation device performs tactile examinations via pressure sensors located in the operating head of the device under a soft membrane. Sensors wirelessly transmit measurement results to a computer in real time. Computer performs processing and output of tactile data to a standard monitor and to specialized tactile display from which data can be read simply via a finger. Processing includes automated analysis of registered tactile data aimed at the identification of heterogeneities which simplifies the identification of lesion boundaries. The utilization of MTEC in endoscopic surgery was tested from January 2015 to December 2015 in hospital No. 31 (Moscow). Nine elective surgeries were performed with da Vinci robotic system (Intuitive Surgical, USA): 2 gastrectomies, 2 stomach resections, 2 resections of pancreas, 2 prostatectomies and 1 right hemicolectomy. Patients’ ages were from 30 to 76. During the surgeries an assistant performed tactile examination under the guidance of surgeon. Operating surgeon identified boundaries of pathological process using a tactile display, and assistant inspected the visualization of tactile data. Results: MTEC was first tested on visually identifiable lesions in stomach and intestine (5 cases). In all cases it correctly localized boundaries of pathological processes. Then the method was applied to pancreas and prostate pathologies and also led to correct decisions in all cases, including a case in which the whole pancreas was involved in process and hence no boundaries were detectable. Conclusion: Utilization of MTEC provides a tactile feedback in robotic surgery thus increasing its capabilities by correct identification of boundaries of a pathological process in the absence of sufficient visual data.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
The geographic information system (GIS) is based on the first and only Russian Imperial Census of 1897 and the First All-Union Census of the Soviet Union of 1926. The GIS features vector data (shapefiles) of allprovinces of the two states. For the 1897 census, there is information about linguistic, religious, and social estate groups. The part based on the 1926 census features nationality. Both shapefiles include information on gender, rural and urban population. The GIS allows for producing any necessary maps for individual studies of the period which require the administrative boundaries and demographic information.
Existing approaches suggest that IT strategy should be a reflection of business strategy. However, actually organisations do not often follow business strategy even if it is formally declared. In these conditions, IT strategy can be viewed not as a plan, but as an organisational shared view on the role of information systems. This approach generally reflects only a top-down perspective of IT strategy. So, it can be supplemented by a strategic behaviour pattern (i.e., more or less standard response to a changes that is formed as result of previous experience) to implement bottom-up approach. Two components that can help to establish effective reaction regarding new initiatives in IT are proposed here: model of IT-related decision making, and efficiency measurement metric to estimate maturity of business processes and appropriate IT. Usage of proposed tools is demonstrated in practical cases.