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Interface and visualization tools usually provide static representations of biological pathways, which can be a severe limitation: fixed pathway boundaries are used without consensus about the elements that should be included in a particular pathway; one cannot generate new pathways or produce selective views of existing pathways. Also, the tools are not capable of integrating multiple levels that conceptually can be distinguished in biological systems.

We present ReConn, an interface and visualization tool for a flexible analysis of large data at multiple biological levels. ReConn (Reactome Connector) is an open source extension to Cytoscape which allows user friendly interaction with the Reactome database. ReConn can use both predefined Reactome pathways as well as generate new pathways. A pathway can be derived by starting from any given metabolite and existing pathways can be extended by adding related reactions. The tool can also retrieve alternative routes between elements of a biological network. Such an option is potentially applicable in the design and analysis of knockout experiments. ReConn displays information about multiple levels of the system in one view. With these dynamic features ReConn addresses all of the above mentioned limitations of the interface tools.

Geotechnical systems often examine interactions that occur between continuum bodies and granular soils. The systems and interactions can be accurately simulated by using multiscale coupling approaches. The model for the continuum bodies is often constructed into a mesh. The meshing, however, is time consuming for a huge spatial system and if distorted is subject to adjustments. A mesh-free approach can be used to eliminate these drawbacks. In this study, a mesh-free approach for simulating continuum–granular systems is presented. This approach combines element-free Galerkin (EFG) and discrete element (DE) methods to approximate the interactions. The capabilities of the coupled EFG–DE method are validated through its solving two example problems: the cantilever beam–disc system and Cundall’s Nine Disc Test. The proposed approach appears to be an efficient and promising tool to model multiscale, multibody contacting problems.

In this paper, a micro-to-macro multiscale approach with peridynamics is proposed to study metal-ceramic composites. Since the volume fraction varies in the spatial domain, these composites are called spatially tailored materials (STMs). Microstructure uncertainties, including porosity, are considered at the microscale when conducting peridynamic modeling and simulation. The collected dataset is used to train probabilistic machine learning models via Gaussian process regression, which can stochastically predict material properties. The machine learning models play a role in passing the information from the microscale to the macroscale. Then, at the macroscale, peridynamics is employed to study the mechanics of STM structures with various volume fraction distributions.

The magnetic resonance imaging technique is mostly used for visualizing and detecting brain tumor, which requires accurate segmentation of brain MR images into white matter, gray matter, cerebrospinal fluid, necrotic tissue, tumor, and edema. But brain image segmentation is a challenging task because of unknown noise and intensity inhomogeneity in brain MR images. This paper proposed a technique for the segmentation and the detection of a tumor, cystic component and edema in brain MR images using multiscale intuitionistic fuzzy roughness (MSIFR). Application of linear scale-space theory and intuitionistic fuzzy image representation deals with noise and intensity inhomogeneity in brain MR images. Intuitionistic fuzzy roughness calculated at proper scale is used to find optimum valley points for segmentation of brain MR images. The algorithm is applied to the real brain MR images from various hospitals and also to the benchmark set of the synthetic MR images from brainweb. The algorithm segments synthetic brain MR image into three regions, gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) and also separates tumor, cystic component and edema accurately in real brain MR images. The results of segmentation of proposed algorithm for synthetic images are compared with nonlocal fuzzy c-means (NLFCM), rough set based algorithms, intervalued possibilistic fuzzy c-means (IPFCM), robust modified Gaussian mixture model with rough set (RMGMMRS) and three algorithms, recursive bias corrected possibilistic fuzzy c-means (RBCPFCM), recursive bias corrected possibilistic neighborhood fuzzy c-means (RBCPNFCM) and recursive bias corrected separately weighted possibilistic neighborhood fuzzy c-means (RBCSPNFCM). The quantitative and qualitative evaluation demonstrates the superiority of the proposed algorithm.

Nowadays, the number of patients with brain tumors is steadily increasing, diagnosis and isolation of the tumor play an important role in the process of treatment and surgery. Due to the high error of manual segmentation of the tumor, algorithms that perform this operation with less error are of great importance. Convolutional neural networks have made great progress in the field of medical imaging. The use of imaging techniques and pattern recognition in the diagnosis and automatic determination of brain tumors by MRI imaging reduces errors, human error and speeds up detection. The artificial convolutional neural network (CNN) has been widely used in the diagnosis of intelligent cancers and has significantly reduced the error rate. Therefore, in this paper, we present a new method using a combination of convolutional and multi-scale artificial neural network that has significantly increased the accuracy of tumor diagnosis. This study presents a multidisciplinary convolution neural network (MCNN) approach to classifying tumors that can be used as an important part of automated diagnosis systems for accurate cancer diagnosis. Based on the MCNN structure, which presents the MRI image to several deep convolutional neural networks of varying sizes and resolutions, the stage of extracting classical hand-made features is avoided. This approach proposes better classification rates than the classical methods. This study uses a multi-scale convolution technique to achieve a detection accuracy of 95/4%, which shows the efficiency of the proposed method.