Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation

We present a three-dimensional graph convolutional network (3DGCN), which predicts molecular properties and biochemical activities, based on 3D molecular graph. In the 3DGCN, graph convolution is unified with learning operations on the vector to handle the spatial information from molecular topology. The 3DGCN model exhibits significantly higher performance on various tasks compared with other deep-learning models, and has the ability of generalizing a given conformer to targeted features regardless of its rotations in the 3D space. More significantly, our model also can distinguish the 3D rotations of a molecule and predict the target value, depending upon the rotation degree, in the protein-ligand docking problem, when trained with orientation-dependent datasets. The rotation distinguishability of 3DGCN, along with rotation equivariance, provides a key milestone in the implementation of three-dimensionality to the field of deep-learning chemistry that solves challenging biochemical problems.Comment: 39 pages, 14 figures, 5 table

Network Topology Analysis of Post-Mortem Brain Microarrays Identifies More Alzheimer’s Related Genes and MicroRNAs and Points to Novel Routes for Fighting with the Disease

Network-based approaches are powerful and beneficial tools to study complex systems in their entirety, elucidating the essential factors that turn the multitude of individual elements into a functional system. In this study we used critical network topology descriptors and guilt-by-association rule to explore and understand the significant molecular players, drug targets and underlying biological mechanisms of Alzheimer’s disease. Analyzing two post-mortem brain gene microarrays (GSE4757 and GSE28146) with Pathway Studio software package we constructed and analyzed a set of protein-protein interaction, as well as miRNA-target networks. In a 4-step procedure the expression datasets were normalized using Robust Multi-array Average approach, while the modulation of gene expression by the disease was statistically evaluated by the empirical Bayes method from the limma Bioconductor package. Representative set of 214 seed-genes (p\u3c0.01) common for the three brain sections of the two microarrays was thus created. The Pathway Studio analysis of the networks built identified 15 new potential AD-related genes and 17 novel AD-involved microRNAs. Using KEGG pathways relevant in Alzheimer’s disease we built an integrated mechanistic network from the interactions between the overlapping genes in these pathways. Routes of possible disease initiation process were thus revealed through the CD4, DCN, and IL8 extracellular ligands. DAVID and IPA enrichment analysis uncovered a number of deregulated biological processes and pathways including neuron projection/differentiation, aging, oxidative stress, chemokine/ neurotrophin signaling, long-term potentiation and others. The findings in this study offer information of interest for subsequent experimental studies

A survey of current software for network analysis in molecular biology

Software for network motifs and modules is briefly reviewed, along with programs for network comparison. The three major software packages for network analysis, CYTOSCAPE, INGENUITY and PATHWAY STUDIO, and their associated databases, are compared in detail. A comparative test evaluated how these software packages perform the search for key terms and the creation of network from those terms and from experimental expression data

Identifying influential nodes in a wound healing-related network of biological processes using mean first-passage time

In this study we offer an approach to network physiology, which proceeds from transcriptomic data and uses gene ontology analysis to identify the biological processes most enriched in several critical time points of wound healing process (days 0, 3 and 7). The top-ranking differentially expressed genes for each process were used to build two networks: one with all proteins regulating the transcription of selected genes, and a second one involving the proteins from the signaling pathways that activate the transcription factors. The information from these networks is used to build a network of the most enriched processes with undirected links weighted proportionally to the count of shared genes between the pair of processes, and directed links weighted by the count of relationships connecting genes from one process to genes from the other. In analyzing the network thus built we used an approach based on random walks and accounting for the temporal aspects of the spread of a signal in the network (mean-first passage time, MFPT). The MFPT scores allowed identifying the top influential, as well as the top essential biological processes, which vary with the progress in the healing process. Thus, the most essential for day 0 was found to be the Wnt-receptor signaling pathway, well known for its crucial role in wound healing, while in day 3 this was the regulation of NF-kB cascade, essential for matrix remodeling in the wound healing process. The MFPT-based scores correctly reflected the pattern of the healing process dynamics to be highly concentrated around several processes between day 0 and day 3, and becoming more diffuse at day 7

Information inequalities and Generalized Graph Entropies

In this article, we discuss the problem of establishing relations between information measures assessed for network structures. Two types of entropy based measures namely, the Shannon entropy and its generalization, the R\'{e}nyi entropy have been considered for this study. Our main results involve establishing formal relationship, in the form of implicit inequalities, between these two kinds of measures when defined for graphs. Further, we also state and prove inequalities connecting the classical partition-based graph entropies and the functional-based entropy measures. In addition, several explicit inequalities are derived for special classes of graphs.Comment: A preliminary version. To be submitted to a journa

Особливості гістологічної будови та PH шкіри щурів молодого віку з різними видами опіків

Актуальність проблеми термічних та хімічних уражень визначається порівняно високою частотою їх в побуті і на виробництві, тяжкістю опікової травми, складністю і тривалістю лікування хворих з опіками, частою інвалідізацією та високою летальністю. Актуальність даної проблеми в першу чергу визначається частотою отримання опіків, відсутність методів швидкої діагностики, характеру та глибини структурних змін, важкості протікання. За даними Всесвітньої організації охорони здоров’я опіки за частотою займають третє місце серед інших травм, а в деяких країнах – друге, поступаючись лише транспортним травмам. При цитуванні документа, використовуйте посилання http://essuir.sumdu.edu.ua/handle/123456789/3576

Особливості загоєння шкіри із змодельованою механічною травмою при використанні хітозанового покриття

З інтенсифікацією життєдіяльності людини збільшується ризик травматизації людини в побуті, на виробництві, що пов’язаний з урбанізацією суспільства. В зв’язку з цим збільшується кількість випадків механічного ураження шкірних покривів, яке проявляється у вигляді саден, порізів, розривів шкіри. Вони виникають внаслідок дії чинника безпосередньо на шкіру. При цитуванні документа, використовуйте посилання http://essuir.sumdu.edu.ua/handle/123456789/3574

Використання методу "напівтонких зрізів" для вивчення структури зразків, які використовуються в електронній мікроскопії

Вивчення структури напівтонких зрізів незамінний метод морфологічних досліджень. Для того, щоб електронномікроскопічне дослідження досягло своїх прямих цілей (вивчення ультраструктури) майже завжди необхідно проводити вивчення напівтонкого зріза виготовленого препарата. Це дозволяє більш точно та прицільно оцінити топографію досліджуваного об’єкта на тканинному та клітинному рівнях, розширити границі дослідження, використати кольорове фарбування та раціонально вибрати ділянку для виготовлення ультратонких зрізів. При цитуванні документа, використовуйте посилання http://essuir.sumdu.edu.ua/handle/123456789/3577

A network model of interpersonal alignment in dialog

In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic) networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations. Keywords: alignment in communication; structural coupling; linguistic networks; graph distance measures; mutual information of graphs; quantitative network analysi