Influence of robotic technologies on the quality of training of specialists-gynecologists

The article is devoted to the use of modern robotic simulation technologies in the training of gynecological specialists. Based on the results of student learning, a positive dynamics was revealed in the process of mastering the practical skills of hysterectomy using the Da Vinci robot assistant, which confirms the promising and relevance of simulation training in medical education.Статья посвящена использованию современных роботизированных симуляционных технологий в подготовке специалистов гинекологического профиля. На основании результатов обучения студентов была выявлена положительная динамика в процессе освоения практических навыков гистерэктомии при использовании робота-ассистента Da Vinci, что подтверждает перспективность и актуальность симуляционного тренинга в медицинском образовании

Визуальная веб-ориентированная среда динамического управления потоками данных в кампусных программно-конфигурируемых сетях

Nowadays new innovative approaches based on the technology of software defined networks (SDN) are gaining popularity in the field of computer networks (CN). SDN provide a flexible approach to the processing and control of data flows in CN by separating the control plane and data plane, as well as centralizing the representation of the entire network. In this paper, we propose a software infrastructure and a visual web-oriented environment (SIVE) for dynamic control of data flows in campus SDN based on OpenFlow protocol. It was proposed to use the SIVE as an integrated segment of the campus network of Ryazan State Radio Engineering University. The aim of the work is the development of the SIVE architecture in the form of UML class diagram description, as well as the creation of software methods for organizing effective network interaction of various software systems in SDN based on OpenFlow protocol. A hardware-software test bench based on HP Aruba 2920-24G equipment was developed to confirm the efficiency and reliability of the proposed SIVE. The offered SIVE is the basis for the development of a large class of software systems and SDN components based on OpenFlow protocol.В настоящее время в области компьютерных сетей (КС) широкую популярность получают инновационные подходы, основанные на технологии программно-конфигурируемых сетей (ПКС). ПКС позволяют обеспечить гибкий подход в обработке и управлении потоков данных в КС за счет разделения плоскости управления и передачи данных, а также централизации представления всей сети. В данной работе предложен прототип программной инфраструктуры и визуальной веб-ориентированной среды (ПИВС) динамического управления потоками данных в ПКС на основе протокола OpenFlow. Предложено использовать ПИВС в качестве интегрированного сегмента кампусной сети Рязанского государственного радиотехнического университета. Целью работы является разработка архитектуры ПИВС в виде описания UML диаграмм классов, а также создание программных методов для организации эффективного сетевого взаимодействия различных программных систем в ПКС на основе протокола OpenFlow. Для подтверждения эффективности и надежности предложенной ПИВС разработан программно-аппаратный стенд на базе оборудования HP Aruba 2920-24G. Предлагаемая в работе ПИВС является основой для разработки большого класса программных систем и компонентов ПКС на основе протокола OpenFlow

Visual Web-Oriented Environment of Dynamic Control of Data Flow in Campus of Software Defined Networks

Nowadays new innovative approaches based on the technology of software defined networks (SDN) are gaining popularity in the field of computer networks (CN). SDN provide a flexible approach to the processing and control of data flows in CN by separating the control plane and data plane, as well as centralizing the representation of the entire network. In this paper, we propose a software infrastructure and a visual web-oriented environment (SIVE) for dynamic control of data flows in campus SDN based on OpenFlow protocol. It was proposed to use the SIVE as an integrated segment of the campus network of Ryazan State Radio Engineering University. The aim of the work is the development of the SIVE architecture in the form of UML class diagram description, as well as the creation of software methods for organizing effective network interaction of various software systems in SDN based on OpenFlow protocol. A hardware-software test bench based on HP Aruba 2920-24G equipment was developed to confirm the efficiency and reliability of the proposed SIVE. The offered SIVE is the basis for the development of a large class of software systems and SDN components based on OpenFlow protocol

Clustering and Differential Alignment Algorithm: Identification of Early Stage Regulators in the <i>Arabidopsis thaliana</i> Iron Deficiency Response

<div><p>Time course transcriptome datasets are commonly used to predict key gene regulators associated with stress responses and to explore gene functionality. Techniques developed to extract causal relationships between genes from high throughput time course expression data are limited by low signal levels coupled with noise and sparseness in time points. We deal with these limitations by proposing the Cluster and Differential Alignment Algorithm (CDAA). This algorithm was designed to process transcriptome data by first grouping genes based on stages of activity and then using similarities in gene expression to predict influential connections between individual genes. Regulatory relationships are assigned based on pairwise alignment scores generated using the expression patterns of two genes and some inferred delay between the regulator and the observed activity of the target. We applied the CDAA to an iron deficiency time course microarray dataset to identify regulators that influence 7 target transcription factors known to participate in the <i>Arabidopsis thaliana</i> iron deficiency response. The algorithm predicted that 7 regulators previously unlinked to iron homeostasis influence the expression of these known transcription factors. We validated over half of predicted influential relationships using qRT-PCR expression analysis in mutant backgrounds. One predicted regulator-target relationship was shown to be a direct binding interaction according to yeast one-hybrid (Y1H) analysis. These results serve as a proof of concept emphasizing the utility of the CDAA for identifying unknown or missing nodes in regulatory cascades, providing the fundamental knowledge needed for constructing predictive gene regulatory networks. We propose that this tool can be used successfully for similar time course datasets to extract additional information and infer reliable regulatory connections for individual genes.</p></div

Regulatory interactions inference algorithms.

<p>Regulatory interactions inference algorithms.</p

Regulatory relationships predicted by the CDAA.

<p>Predicted regulations between 7 early stage transcription factors and 7 known iron homeostasis transcription factors. Edges indicating positive regulations are green and edges indicating negative regulations are red.</p

Expression validation of predicted targets in mutant regulator backgrounds.

<p>Root tissue was collected from seedlings grown 4 days on iron sufficient media and transferred to iron deficient media for 3 days. Expression values are normalized to <i><i>β</i>-tubulin</i> and to WT (Col-0) expression for each gene. Error bars indicate ±SEM (n = 4). Mutant backgrounds are (A)<i>obp4-1</i>, (B)<i>wrky57-3</i>, (C)<i>etf9-1</i>, (D)<i>col4-1</i>, (E)<i>asil2-1</i>, and (F)<i>myb55-1</i>. Asterisk indicates significant difference from WT (Student’s t-test, <i>p</i> < 0.05).</p