CoCalc як засіб навчання нейромережевого моделювання в спецкурсі «Основи математичної інформатики»

Обговорено роль нейромережевого моделювання у змісті навчання спецкурсу «Основи математичної інформатики» для студентів технічних університетів – майбутніх фахівців з інформаційних технологій, спрямованого на подолання розриву між теоретичною інформатикою та її прикладними застосуваннями: програмною, системною та комп’ютерною інженерією. Обґрунтовано вибір CoCalc як засобу навчання основи математичної інформатики у цілому та нейромережевого моделювання зокрема. Наведено елементи методики використання CoCalc у навчанні теми «Нейронні мережі та розпізнавання образів» спецкурсу «Основи математичної інформатики». Наведено програмний код мовою CoffeeScript, що реалізує основні компоненти штучної нейронної мережі: нейрони, синаптичні з’єднання, функції активації (тангенціальна, сигмоїдальна, ступінчаста) та їх похідні, методи обчислення вагових коефіцієнтів мережі та ін. Обговорено особливості застосування теореми Колмогорова для визначення архітектури багатошарових нейронних мереж. Подано приклади реалізації диз’юнктивного логічного елементу та апроксимації довільної функції з використанням тришарової нейронної мережі. За результатами моделювання зроблено висновок про межі застосування побудованих мереж, в яких вони зберігають адекватність. Запропоновано рамкову тематику індивідуальних навчально-дослідних проектів із побудови штучних нейронних мереж.The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the special course “Foundations of Mathematic Informatics” are shown. The program code was presented in a CofeeScript language, which implements the basic components of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of calculating the network`s weights, etc. The features of the Kolmogorov–Arnold representation theorem application were discussed for determination the architecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a three-layer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed

A compartment model of VEGF distribution in blood, healthy and diseased tissues

<p>Abstract</p> <p>Background</p> <p>Angiogenesis is a process by which new capillaries are formed from pre-existing blood vessels in physiological (e.g., exercise, wound healing) or pathological (e.g., ischemic limb as in peripheral arterial disease, cancer) contexts. This neovascular mechanism is mediated by the vascular endothelial growth factor (VEGF) family of cytokines. Although VEGF is often targeted in anti-angiogenic therapies, there is little knowledge about how its concentration may vary between tissues and the vascular system. A compartment model is constructed to study the VEGF distribution in the tissue (including matrix-bound, cell surface receptor-bound and free VEGF isoforms) and in the blood. We analyze the sensitivity of this distribution to the secretion rate, clearance rate and vascular permeability of VEGF.</p> <p>Results</p> <p>We find that, in a physiological context, VEGF concentration varies approximately linearly with the VEGF secretion rate. VEGF concentration in blood but not in tissue is dependent on the vascular permeability of healthy tissue. Model simulations suggest that relative VEGF increases are similar in blood and tissue during exercise and return to baseline within several hours. In a pathological context (tumor), we find that blood VEGF concentration is relatively insensitive to increased vascular permeability in tumors, to the secretion rate of VEGF by tumors and to the clearance. However, it is sensitive to the vascular permeability in the healthy tissue. Finally, the VEGF distribution profile in healthy tissue reveals that about half of the VEGF is complexed with the receptor tyrosine kinase VEGFR2 and the co-receptor Neuropilin-1. In diseased tissues, this binding can be reduced to 15% while VEGF bound to the extracellular matrix and basement membranes increases.</p> <p>Conclusion</p> <p>The results are of importance for physiological conditions (e.g., exercise) and pathological conditions (e.g., peripheral arterial disease, coronary artery disease, cancer). This mathematical model can serve as a tool for understanding the VEGF distribution in physiological and pathological contexts as well as a foundation to investigate pro- or anti-angiogenic strategies.</p

Pharmacokinetics and pharmacodynamics of VEGF-neutralizing antibodies

<p>Abstract</p> <p>Background</p> <p>Vascular endothelial growth factor (VEGF) is a potent regulator of angiogenesis, and its role in cancer biology has been widely studied. Many cancer therapies target angiogenesis, with a focus being on VEGF-mediated signaling such as antibodies to VEGF. However, it is difficult to predict the effects of VEGF-neutralizing agents. We have developed a whole-body model of VEGF kinetics and transport under pathological conditions (in the presence of breast tumor). The model includes two major VEGF isoforms VEGF₁₂₁and VEGF₁₆₅, receptors VEGFR1, VEGFR2 and co-receptors Neuropilin-1 and Neuropilin-2. We have added receptors on parenchymal cells (muscle fibers and tumor cells), and incorporated experimental data for the cell surface density of receptors on the endothelial cells, myocytes, and tumor cells. The model is applied to investigate the action of VEGF-neutralizing agents (called "anti-VEGF") in the treatment of cancer.</p> <p>Results</p> <p>Through a sensitivity study, we examine how model parameters influence the level of free VEGF in the tumor, a measure of the response to VEGF-neutralizing drugs. We investigate the effects of systemic properties such as microvascular permeability and lymphatic flow, and of drug characteristics such as the clearance rate and binding affinity. We predict that increasing microvascular permeability in the tumor above 10^-5cm/s elicits the undesired effect of increasing tumor interstitial VEGF concentration beyond even the baseline level. We also examine the impact of the tumor microenvironment, including receptor expression and internalization, as well as VEGF secretion. We find that following anti-VEGF treatment, the concentration of free VEGF in the tumor can vary between 7 and 233 pM, with a dependence on both the density of VEGF receptors and co-receptors and the rate of neuropilin internalization on tumor cells. Finally, we predict that free VEGF in the tumor is reduced following anti-VEGF treatment when VEGF₁₂₁comprises at least 25% of the VEGF secreted by tumor cells.</p> <p>Conclusions</p> <p>This study explores the optimal drug characteristics required for an anti-VEGF agent to have a therapeutic effect and the tumor-specific properties that influence the response to therapy. Our model provides a framework for investigating the use of VEGF-neutralizing drugs for personalized medicine treatment strategies.</p

The Presence of VEGF Receptors on the Luminal Surface of Endothelial Cells Affects VEGF Distribution and VEGF Signaling

Vascular endothelial growth factor (VEGF) is a potent cytokine that binds to specific receptors on the endothelial cells lining blood vessels. The signaling cascade triggered eventually leads to the formation of new capillaries, a process called angiogenesis. Distributions of VEGF receptors and VEGF ligands are therefore crucial determinants of angiogenic events and, to our knowledge, no quantification of abluminal vs. luminal receptors has been performed. We formulate a molecular-based compartment model to investigate the VEGF distribution in blood and tissue in humans and show that such quantification would lead to new insights on angiogenesis and VEGF-dependent diseases. Our multiscale model includes two major isoforms of VEGF (VEGF121 and VEGF165), as well as their receptors (VEGFR1 and VEGFR2) and the non-signaling co-receptor neuropilin-1 (NRP1). VEGF can be transported between tissue and blood via transendothelial permeability and the lymphatics. VEGF receptors are located on both the luminal and abluminal sides of the endothelial cells. In this study, we analyze the effects of the VEGF receptor localization on the endothelial cells as well as of the lymphatic transport. We show that the VEGF distribution is affected by the luminal receptor density. We predict that the receptor signaling occurs mostly on the abluminal endothelial surface, assuming that VEGF is secreted by parenchymal cells. However, for a low abluminal but high luminal receptor density, VEGF binds predominantly to VEGFR1 on the abluminal surface and VEGFR2 on the luminal surface. Such findings would be pertinent to pathological conditions and therapies related to VEGF receptor imbalance and overexpression on the endothelial cells and will hopefully encourage experimental receptor quantification for both luminal and abluminal surfaces on endothelial cells

СoCalc як інструмент підготовки для моделювання нейронних мереж у спеціальному курсі "Основи математичної інформатики"

The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities –future IT-specialists and directed to breaking thegap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the special course “Foundations of Mathematic Informatics” are shown. The program code was presented in a CofeeScript language, which implements the basic components of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of calculating the network`s weights, etc. The features of the Kolmogorov’s theorem application were discussed for determination the architecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a three-layer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed.У статті розглянута роль моделювання нейронної мережі в навчальному процесі спеціального курсу "Основи математичної інформатики". Курс був розроблений для студентів технічних університетів -майбутніх спеціалістів з інформаційних технологій та спрямований на подолання розриву між теоретичною інформатикою та її прикладними програмами: програмною, системною та комп’ютерною інженерією. CoCalc розглядається як навчальний інструмент математичної інформатики в цілому та, зокрема, для моделювання нейронних мереж. Показані елементи методики використання CoCalc при вивченні теми "Нейронні мережі та розпізнавання образів" спеціального курсу "Основи математичної інформатики". Код програми був представлений на мові CofeeScript, в якій реалізуються основні компоненти штучної нейронної мережі: нейрони, синаптичні з'єднання, функції активації (тангенціальні, сигмоїдні, ступінчасті) та їх похідні, методи розрахунку ваги мережі та ін. Обговорювалися особливості застосування теореми Колмогорова для визначення архітектури багатошарових нейронних мереж. В якості прикладів було наведено реалізацію диз'юнктивного логічного елемента та наближення довільної функції за допомогою тришарової нейронної мережі. Згідно результатів моделювання, було зроблено висновок щодо меж використання побудованих мереж, в яких вони зберігають свою адекватність. Запропоновано основні теми окремих досліджень штучних нейронних мереж

Peristaltic Transport of a Couple Stress Fluid: Some Applications to Hemodynamics

The present paper deals with a theoretical investigation of the peristaltic transport of a couple stress fluid in a porous channel. The study is motivated towards the physiological flow of blood in the micro-circulatory system, by taking account of the particle size effect. The velocity, pressure gradient, stream function and frictional force of blood are investigated, when the Reynolds number is small and the wavelength is large, by using appropriate analytical and numerical methods. Effects of different physical parameters reflecting porosity, Darcy number, couple stress parameter as well as amplitude ratio on velocity profiles, pumping action and frictional force, streamlines pattern and trapping of blood are studied with particular emphasis. The computational results are presented in graphical form. The results are found to be in good agreement with those of Shapiro et. al \cite{r25} that was carried out for a non-porous channel in the absence of couple stress effect. The present study puts forward an important observation that for peristaltic transport of a couple stress fluid during free pumping when the couple stress effect of the fluid/Darcy permeability of the medium, flow reversal can be controlled to a considerable extent. Also by reducing the permeability it is possible to avoid the occurrence of trapping phenomenon

Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

This open access book relates to the III Annual Conference hosted by the Russian Federal Ministry of Education and Science in December 2016. This event has summarized, analyzed and discussed the interim results, academic outputs and scientific achievements of the Russian Federal Targeted Programme for Research and Development in priority areas of development of the Russian Scientific and Technological Complex for 2014-2020. It contains 75 selected papers from 6 areas considered priority by the Federal programme: computer science, ecology & environment sciences; energy and energy efficiency; life sciences; nanoscience & nanotechnology; and transport & communications. The chapters report the results of the 3-years research projects supported by the Programme and finalized in 2016