This work presents Atomistic Topology Operations in MATLAB (atom), an open source library of modular MATLAB routines which comprise a general and flexible framework for manipulation of atomistic systems. The purpose of the atom library is simply to facilitate common operations performed for construction, manipulation, or structural analysis. Due to the data structure used, atoms and molecules can be operated upon based on different chemical names or attributes, such as atom- or molecule-ID, name, residue name, charge, positions, etc. Furthermore, the Bond Valence Method and a neighbor-distance analysis can be performed to assign many chemical properties of inorganic molecules. Apart from reading and writing common coordinate files (.pdb, .xyz, .gro, .cif) and trajectories (.dcd, .trr, .xtc; binary formats are parsed via third-party packages), the atom library can also be used to generate topology files with bonding and angle information taking the periodic boundary conditions into account, and supports basic Gromacs, NAMD, LAMMPS, and RASPA2 topology file formats. Focusing on clay-mineral systems, the library supports CLAYFF (Cygan, 2004) but can also generate topology files for the INTERFACE forcefield (Heinz, 2005, 2013) for Gromacs and NAMD.

Spatially resolved transcriptomics (SRT) is a growing field that links gene expression to anatomical context. SRT approaches that use next-generation sequencing (NGS) combine RNA sequencing with histological or fluorescent imaging to generate spatial maps of gene expression in intact tissue sections. These technologies directly couple gene expression measurements with high-resolution histological or immunofluorescent images that contain rich morphological information about the tissue under study. While broad access to NGS-based spatial transcriptomic technology is now commercially available through the Visium platform from the vendor 10× Genomics, computational tools for extracting image-derived metrics for integration with gene expression data remain limited. We developed VistoSeg as a MATLAB pipeline to process, analyze and interactively visualize the high-resolution images generated in the Visium platform. VistoSeg outputs can be easily integrated with accompanying transcriptomic data to facilitate downstream analyses in common programing languages including R and Python. VistoSeg provides user-friendly tools for integrating image-derived metrics from histological and immunofluorescent images with spatially resolved gene expression data. Integration of this data enhances the ability to understand the transcriptional landscape within tissue architecture. VistoSeg is freely available at http://research.libd.org/VistoSeg/.

In this paper, a new method based on a genetic algorithm and Minkowski Island fractal is proposed for multiband antennas. Three-antenna configurations are chosen to validate the proposed optimization procedure. The first configuration is a wide-band antenna, operating in the WLAN (wireless local area network) UNII-2C band. The second configuration is a dual-band antenna, operating in the WLAN UNII-2 and UNII-2C bands. In contrast, the third is a tri-band antenna operating in the UNII-2, UNII-2C, and UNII-3 bands. The optimization process is accelerated by using the Computer Simulation Technology (CST) Application Programming Interface which allows all genetic operators to be performed in MATLAB while the numerical calculations are running in the internal CST Finite-Difference Time-Domain -solver using parallel computing with GPU acceleration. All three designed configurations are manufactured using a $\textstyle0.8\;\text{mm}$ thick FR4 epoxy substrate with a relative dielectric constant of $4.8$. The return loss and the radiation pattern’s measurements agree well with the simulation results. Further, the methodology presented can be very effective in terms of size reduction; the designed antennas are $24 \times 24 \times 0.8\;{\textrm{m}}{{\textrm{m}}^3}$ ($460\;{\textrm{m}}{{\textrm{m}}^3}$).

This work presents a $\textsf{Python}$ EMD package named AdvEMDpy that is both more flexible and generalises existing empirical mode decomposition (EMD) packages in $\textsf{Python}$, $\textsf{R}$, and $\textsf{MATLAB}$. It is aimed specifically for use by the insurance and financial risk communities, for applications such as return modelling, claims modelling, and life insurance applications with a particular focus on mortality modelling. AdvEMDpy both expands upon the EMD options and methods available, and improves their statistical robustness and efficiency, providing a robust, usable, and reliable toolbox. Unlike many EMD packages, AdvEMDpy allows customisation by the user, to ensure that a broader class of linear, non-linear, and non-stationary time series analyses can be performed. The intrinsic mode functions (IMFs) extracted using EMD contain complex multi-frequency structures which warrant maximum algorithmic customisation for effective analysis. A major contribution of this package is the intensive treatment of the EMD edge effect which is the most ubiquitous problem in EMD and time series analysis. Various EMD techniques, of varying intricacy from numerous works, have been developed, refined, and, for the first time, compiled in AdvEMDpy. In addition to the EMD edge effect, numerous pre-processing, post-processing, detrended fluctuation analysis (localised trend estimation) techniques, stopping criteria, spline methods, discrete-time Hilbert transforms (DTHT), knot point optimisations, and other algorithmic variations have been incorporated and presented to the users of AdvEMDpy. This paper and the supplementary materials provide several real-world actuarial applications of this package for the user’s benefit.

Analysis of numerous filamentous structures in an image is often limited by the ability of algorithms to accurately segment complex structures or structures within a dense population. It is even more problematic if these structures continuously grow when recording a time-series of images. To overcome these issues we present DSeg; an image analysis program designed to process time-series image data, as well as single images, to segment filamentous structures. The program includes a robust binary level-set algorithm modified to use size constraints, edge intensity, and past information. We verify our algorithms using synthetic data, differential interference contrast images of filamentous prokaryotes, and transmission electron microscopy images of bacterial adhesion fimbriae. DSeg includes automatic segmentation, tools for analysis, and drift correction, and outputs statistical data such as persistence length, growth rate, and growth direction. The program is available at Sourceforge.

Modern radiotherapy has increased demand for dose delivery verification. In this paper transmission portal dosimetry was considered. Portal detectors are a promising tool for 2D dosimetric verification and they are nowadays one of the most widely investigated topics. In this study an Electronic Portal Imaging Device (EPID) was positioned below the patient and the transmission images were captured during the irradiation. The principle of this verification consists of comparison of the acquired images with images predicted on the basis of the entrance fluence map and the tissue distribution in the patient. Such verification is not performed at any radiotherapy department in the Czech Republic. There is no system available for the prediction of transmission portal images. Even worldwide, there is still a lack of commercially available solutions. The aim of this paper is to present a new method of prediction of transmission portal images by means of the Monte Carlo (MC) method and the mathematical programming language MATLAB. The MC code EGSnrc (Electron Gamma Shower) was used. The validity of the presented method was verified by comparison of the predicted images with the acquired ones. The acquisition of EPID images was performed at the Hospital Na Bulovce. Three different validation tests were performed. In the first case, the EPID was irradiated by regular and irregular fields while there was nothing present in the beam path. In the second case, a water-equivalent phantom was added to the EPID and was irradiated by a number of irregular fields. In the third case, a real patient was present in the beam path and the EPID images were acquired during the patient’s treatment. The patient was irradiated by 8 treatment fields and the portal images were acquired during 5 treatment fractions. All of the acquired images were compared with the MC predicted ones by gamma analysis with gamma criteria of 3%, 3 mm. The average gamma values were 0.31−0.4, 0.34−0.4 and 0.35−0.61 in the first, second and third case, respectively. The results validate the developed method and demonstrate that MC is an effective tool for portal dose image prediction. MC may be favourably used for in vivo transmission dosimetry.

A Late Jurassic sauropod bone-bed comprising thousands of individual skeletal elements in the Morrison Formation of Montana, USA, is re-analysed to further elucidate its taphonomic history. New data gained from detailed contextual sampling is assembled and presented to test the hypothesis of a drought-induced, mass-mortality assemblage, and its subsequent depositional emplacement by a debris flow. Large dinosaur bone-beds are well-known in the Morrison, yet the Mother's Day Quarry site is unique for the formation in depicting an apparent debris flow deposit. Use of a computer program tailored to the data allows three-dimensional reconstruction of the bone-bed and recreates the palaeoslope of the deposit, adding support to the debris flow hypothesis. Such digital manipulation of field data may allow a deeper understanding of other bone-bed origination events.

In this work we introduce PRIN-3D (PRoto-code for Internal flows modeled by Navier-Stokes equations in 3-Dimensions), a new high level algebraic language (Matlab®) based code, by discussing some fundamental aspects regarding its basic solving kernel and by describing the design of an innovative advection scheme. The main focus was on designing a memory and computationally efficient code that, due to the typical conciseness of the Matlab coding language, could allow for fast and effective implementation of new models or algorithms. Innovative numerical methods are discussed in the paper. The pressure equation is derived with a quasi-segregation technique leading to an iterative scheme obtained within the framework of a global preconditioning procedure. Different levels of parallelization are obtainable by exploiting special pressure variable ordering patterns that lead to a block-structured Poisson-like matrix. Moreover, the new advection scheme has the potential of a controllable artificial diffusivity. Preliminary results are shown including a fully three-dimensional internal laminar flow evolving in a relatively complex geometry and a 3D methane-air flame simulated with the aid of libraries based on the Flamelet model.

Fiber alignment plays a critical role in the structure and function of cells and tissues. While fiber alignment quantification is important to experimental analysis and several different methods for quantifying fiber alignment exist, many studies focus on qualitative rather than quantitative analysis perhaps due to the complexity of current fiber alignment methods. Speed and sensitivity were compared in edge detection and fast Fourier transform (FFT) for measuring actin fiber alignment in cells exposed to shear stress. While edge detection using matrix multiplication was consistently more sensitive than FFT, image processing time was significantly longer. However, when MATLAB functions were used to implement edge detection, MATLAB's efficient element-by-element calculations and fast filtering techniques reduced computation cost 100 times compared to the matrix multiplication edge detection method. The new computation time was comparable to the FFT method, and MATLAB edge detection produced well-distributed fiber angle distributions that statistically distinguished aligned and unaligned fibers in half as many sample images. When the FFT sensitivity was improved by dividing images into smaller subsections, processing time grew larger than the time required for MATLAB edge detection. Implementation of edge detection in MATLAB is simpler, faster, and more sensitive than FFT for fiber alignment quantification.

Subjectivity in visual selection of hinge and inflection points leads to significant errors in analyses of fold shapes in profile sections. This article gives a method for precise determination of these points. The method: (1) imports a fold image into Matlab, (2) digitizes points on the image, (3) increases the number of available points by using an interpolation algorithm, (4) fits a polynomial curve to the points, and (5) searches for the hinge and the inflection points mathematically. Tests on several folds confirm that the ‘HingeInflex’ is a rapid, robust and user-friendly method for precise selection of the hinge and inflection points.