ABSTRACT
With the advancing energy transition, icing is a growing problem in the wind turbine sector. The development of systems to detect and mitigate icing makes it necessary to understand its basic behavior and characteristics. This paper proposes a method for the continuous and full-field measurement of the icing process of rotating blades, using a single line laser profile scanner. Inside of a climate chamber, a rotor is driven by a motor, while a system of nozzles provides a fine water dust, which leads to ice accumulating on simple NACA blades, which in turn is measured by a triangulation laser. The measurement data are cleared from outliers and presented as a surface in 3D space. An alpha shape is used to reconstruct and extract the volume of the ice between a reference and a measurement surface, using the corresponding Matlab function. Appropriate input parameters for the function and offsetting of the reference surface to improve the results are compared and discussed. The resulting system is able to detect small changes in the ice layer thickness in the sub-millimeter range.
ABSTRACT
We report the structure-based pathogenicity relationship identifier (SPRI), a novel computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutational clusters. SPRI can effectively extract properties determining pathogenicity encoded in protein structures, and can identify deleterious missense mutations of germ line origin associated with Mendelian diseases, as well as mutations of somatic origin associated with cancer drivers. It compares favorably to other methods in predicting deleterious mutations. Furthermore, SPRI can discover spatially organized pathogenic higher-order spatial clusters (patHOS) of deleterious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and driver mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole human proteome.
Subject(s)
Mutation, Missense , Neoplasms , Humans , Virulence , Mutation , Neoplasms/genetics , Computational Biology/methodsABSTRACT
BACKGROUND: A method of guiding an intermediate catheter from a new alpha-type guiding catheter placed in the ascending aorta to the carotid artery is evaluated in neuroendovascular treatment in challenging anatomic directions of the guiding catheter, such as the bovine aortic arch and type III aortic arch. METHODS: The existing 8-Fr guiding catheter was given a strong bending shape at the tip to make it an alpha type. The total length of the catheter was 85 cm. This guiding catheter was inserted into the ascending aorta to verify whether a 6-Fr intermediate or aspiration catheter could be coaxially guided into the right and left internal carotid arteries. A silicone vascular model was used for evaluation and in actual clinical cases. RESULTS: Creating an alpha shape of the catheter at the aortic arch was very easy. The inner catheter could be easily guided from the brachiocephalic artery to the right common carotid artery by pushing the alpha shape guiding catheter toward the aortic valve. The catheter was easily guided into the left common carotid artery when the α-guide was pulled a little bit backward. The 0.071-inch lumen aspiration catheter reached the bilateral middle cerebral arteries. CONCLUSIONS: The 8-Fr alpha shape guiding catheter quickly guides the inner catheter into the bovine and the type III aortic arch by looking up from the ascending aorta.
Subject(s)
Aorta, Thoracic , Carotid Artery Diseases , Humans , Aorta, Thoracic/surgery , Aorta/surgery , Catheterization , Carotid Arteries , CathetersABSTRACT
The k0 standardization method is one of the most commonly used neutron activation analyses to determine the concentrations of elements in the sample. To perform this method, it is required to know the neutron spectrum parameters such as epithermal neutron flux shape factor (α) and thermal to epithermal neutron flux ratio (f). In this study, the α and f parameters were determined using the Cd-ratio dual monitor method and bare triple monitor method. In both methods, irradiation studies were performed in the central thimble at the ITU Triga Mark II research reactor. A gamma-ray counting system (HpGe detector) was used for measuring foil activation values. The selected monitors were 197Au and 94Zr for the Cd-ratio dual monitor method and 197Au/94Zr/96Zr for the bare triple monitor method. The α and f parameters were -0.221 ± 0.018 and 41.826 ± 4.701 for 197Au/94Zr (724 keV) monitors, -0.231 ± 0.019 and 42.626 ± 4.791 for 197Au/94Zr (756 keV) monitors, 0.228 ± 0.025 and 43.917 ± 3.596 for 197Au/94Zr/96Zr monitors, respectively.
Subject(s)
Cadmium , Neutrons , Neutron Activation Analysis/methods , Gamma RaysABSTRACT
Robots with visual sensors have been used in various goods logistics, such as bin picking or uploading. However, there are more and more demands for the automatic blanking and loading, and it is necessary to solve the problem of object pose estimation in changing accommodation space. This paper proposes a method for pose estimation in the accommodation space using alpha-shape algorithm and improved fruit fly optimization algorithm (FOA). The alpha-shape volume variety of object and measured space is set to the objective function, and the pose variety of object is set to six variables of improved FOA. The experiments were performed by setting parameters of improved FOA and considering the four space types represented the common accommodation shapes. Compared with previous work using convex hull, the new study using alpha-shape algorithm not only keeps the object in the accommodation space, but also maintains the object pose which is at the bottom of the space and can meet the practical requirement of object placement by robot arms.
ABSTRACT
Proteins' three-dimensional (3D) structures are analyzed traditionally using geometric descriptors such as torsional angles and inter-atomic distances. In this study a measure that is borrowed from computational geometry, aspect ratio of each tetrahedron in alpha shapes of proteins, is utilized. This geometric descriptor differentiates alpha and beta structural classes of proteins when combined with principal components analysis. The method converts the structures of individual proteins, 3D coordinates of the atoms, to points on a plane. It has a high degree of accuracy in differentiating R and T structures of hemoglobin. Therefore, it is anticipated that the geometric measure can be used successfully in a method that is extended to solve classification problems in machine learning.
Subject(s)
Models, Molecular , Protein Conformation , Proteins/chemistryABSTRACT
Comparisons of 3D shapes have recently been applied to diverse anatomical structures using landmarking techniques. However, discerning evolutionary patterns can be challenging for structures lacking homologous landmarks. We used alpha shape analyses to quantify vaginal shape complexity in 40 marine mammal specimens including cetaceans, pinnipeds, and sirenians. We explored phylogenetic signal and the potential roles of natural and sexual selection on vaginal shape evolution. Complexity scores were consistent with qualitative observations. Cetaceans had a broad range of alpha complexities, while pinnipeds were comparatively simple and sirenians were complex. Intraspecific variation was found. Three-dimensional surface heat maps revealed that shape complexity was driven by invaginations and protrusions of the vaginal wall. Phylogenetic signal was weak and metrics of natural selection (relative neonate size) and sexual selection (relative testes size, sexual size dimorphism, and penis morphology) did not explain vaginal complexity patterns. Additional metrics, such as penile shape complexity, may yield interesting insights into marine mammal genital coevolution. We advocate for the use of alpha shapes to discern patterns of evolution that would otherwise not be possible in 3D anatomical structures lacking homologous landmarks.
ABSTRACT
The functioning of proteins requires highly specific dynamics, which depend critically on the details of how amino acids are packed. Hinge motions are the most common type of large motion, typified by the opening and closing of enzymes around their substrates. The packing and geometries of residues are characterized here by graph theory. This characterization is sufficient to enable reliable hinge predictions from a single static structure, and notably, this can be from either the open or the closed form of a structure. This new method to identify hinges within protein structures is called PACKMAN. The predicted hinges are validated by using permutation tests on B-factors. Hinge prediction results are compared against lists of manually curated hinge residues, and the results suggest that PACKMAN is robust enough to reproduce the known conformational changes and is able to predict hinge regions equally well from either the open or the closed forms of a protein. A group of 167 protein pairs with open and closed structures has been investigated Examples are shown for several additional proteins, including Zika virus nonstructured (NS) proteins where there are 6 hinge regions in the NS5 protein, 5 hinge regions in the NS2B bound in the NS3 protease complex and 5 hinges in the NS3- helicase protein. Results obtained from this method can be important for generating conformational ensembles of protein targets for drug design. PACKMAN is freely accessible at (https://PACKMAN.bb.iastate.edu/).
Subject(s)
Enzymes/ultrastructure , Protein Conformation , Proteins/ultrastructure , Viral Nonstructural Proteins/ultrastructure , Algorithms , Computer Simulation , Enzymes/chemistry , Molecular Dynamics Simulation , Proteins/chemistry , Viral Nonstructural Proteins/chemistry , Zika Virus/chemistry , Zika Virus/ultrastructureABSTRACT
BACKGROUND: Epidermal growth factor receptor (EGFR) mutation-induced drug resistance is a difficult problem in lung cancer treatment. Studying the molecular mechanisms of drug resistance can help to develop corresponding treatment strategies and benefit new drug design. METHODS: In this study, Rosetta was employed to model the EGFR mutant structures. Then Amber was carried out to conduct molecular dynamics (MD) simulation. Afterwards, we used Computational Geometry Algorithms Library (CGAL) to compute the alpha shape model of the mutants. RESULTS: We analyzed the EGFR mutation-induced drug resistance based on the motion trajectories obtained from MD simulation. We computed alpha shape model of all the trajectory frames for each mutation type. Solid angle was used to characterize the curvature of the atoms at the drug binding site. We measured the knob level of the drug binding pocket of each mutant from two ways and analyzed its relationship with the drug response level. Results show that 90 % of the mutants can be grouped correctly by setting a certain knob level threshold. CONCLUSIONS: There is a strong correlation between the geometric properties of the drug binding pocket of the EGFR mutants and the corresponding drug responses, which can be used to predict the response of a new EGFR mutant to a drug molecule.
ABSTRACT
In biomechanical studies examining joint kinematics the most common measurement is range of motion (ROM), yet other techniques, such as the finite helical axis (FHA), may elucidate the changes in the 3D motion pathology more effectively. One of the deficiencies with the FHA technique is in quantifying the axes generated throughout a motion sequence. This study attempted to solve this issue via a computational geometric technique known as the alpha shape, which bounds a set of point data within a closed boundary similar to a convex hull. The purpose of this study was to use the alpha shape as an additional tool to visualize and quantify FHA dispersion between intact and injured cadaveric spine movements and compare these changes to the gold-standard ROM measurements. Flexion-extension, axial rotation, and lateral bending were simulated with five C5-C6 motion segments using a spinal loading simulator and Optotrak motion tracking system. Specimens were first tested intact followed by a simulated injury model. ROM and the FHAs were calculated post-hoc, with alpha shapes and convex hulls generated from the anatomic planar intercept points of the FHAs. While both ROM and the boundary shape areas increased with injury (p<0.05), no consistent geometric trends in the alpha shape growth were identified. The alpha shape area was sensitive to the alpha value chosen and values examined below 2.5 created more than one closed boundary. Ultimately, the alpha shape presents as a useful technique to quantify sequences of joint kinematics described by scatter plots such as FHA intercept data.
Subject(s)
Cervical Vertebrae/physiology , Range of Motion, Articular , Aged , Algorithms , Biomechanical Phenomena , Cadaver , Computer Simulation , Humans , Image Processing, Computer-Assisted/methods , Movement , Rotation , SoftwareABSTRACT
Epidermal growth factor receptor (EGFR) mutation-induced drug resistance leads to a limited efficacy of tyrosine kinase inhibitors during lung cancer treatments. In this study, we explore the correlations between the local surface geometric properties of EGFR mutants and the progression-free survival (PFS). The geometric properties include local surface changes (four types) of the EGFR mutants compared with the wild-type EGFR, and the convex degrees of these local surfaces. Our analysis results show that the Spearman׳s rank correlation coefficients between the PFS and three types of local surface properties are all greater than 0.6 with small P-values, implying a high significance. Moreover, the number of atoms with solid angles in the ranges of [0.71, 1], [0.61, 1] or [0.5, 1], indicating the convex degree of a local EGFR surface, also shows a strong correlation with the PFS. Overall, these characteristics can be efficiently applied to the prediction of drug resistance in lung cancer treatments, and easily extended to other cancer treatments.
Subject(s)
Carcinoma, Non-Small-Cell Lung/genetics , Drug Resistance, Neoplasm , ErbB Receptors , Lung Neoplasms/genetics , Mutation , Neoplasm Proteins , ErbB Receptors/chemistry , ErbB Receptors/genetics , Female , Humans , Male , Neoplasm Proteins/chemistry , Neoplasm Proteins/genetics , Protein Structure, TertiaryABSTRACT
Elastic network models (ENM) are based on the idea that the geometry of a protein structure provides enough information for computing its fluctuations around its equilibrium conformation. This geometry is represented as an elastic network (EN) that is, a network of links between residues. A spring is associated with each of these links. The normal modes of the protein are then identified with the normal modes of the corresponding network of springs. Standard approaches for generating ENs rely on a cutoff distance. There is no consensus on how to choose this cutoff. In this work, we propose instead to filter the set of all residue pairs in a protein using the concept of alpha shapes. The main alpha shape we considered is based on the Delaunay triangulation of the Cα positions; we referred to the corresponding EN as EN(∞). We have shown that heterogeneous anisotropic network models, called αHANMs, that are based on EN(∞) reproduce experimental B-factors very well, with correlation coefficients above 0.99 and root-mean-square deviations below 0.1 Å(2) for a large set of high resolution protein structures. The construction of EN(∞) is simple to implement and may be used automatically for generating ENs for all types of ENMs.
Subject(s)
Proteins/chemistry , Algorithms , Anisotropy , Computer Simulation , Models, Chemical , Models, Molecular , Protein ConformationABSTRACT
In recent years, more 3D protein structures have become available, which has made the analysis of large molecular structures much easier. There is a strong demand for geometric models for the study of protein-related interactions. Alpha shape and Delaunay triangulation are powerful tools to represent protein structures and have advantages in characterizing the surface curvature and atom contacts. This review presents state-of-the-art applications of alpha shape and Delaunay triangulation in the studies on protein-DNA, protein-protein, protein-ligand interactions and protein structure analysis.
Subject(s)
Protein Interaction Domains and Motifs , Proteins/chemistry , Proteins/metabolism , Algorithms , Binding Sites , Computational Biology , DNA/genetics , DNA/metabolism , Ligands , Models, Molecular , Protein Binding , Protein Conformation , Protein Folding , Structural Homology, ProteinABSTRACT
The molecular basis of life rests on the activity of biological macromolecules, mostly nucleic acids and proteins. A perhaps surprising finding that crystallized over the last handful of decades is that geometric reasoning plays a major role in our attempt to understand these activities. In this paper, we address this connection between geometry and biology, focusing on methods for measuring and characterizing the shapes of macromolecules. We briefly review existing numerical and analytical approaches that solve these problems. We cover in more details our own work in this field, focusing on the alpha shape theory as it provides a unifying mathematical framework that enable the analytical calculations of the surface area and volume of a macromolecule represented as a union of balls, the detection of pockets and cavities in the molecule, and the quantification of contacts between the atomic balls. We have shown that each of these quantities can be related to physical properties of the molecule under study and ultimately provides insight on its activity. We conclude with a brief description of new challenges for the alpha shape theory in modern structural biology.
ABSTRACT
Predicting protein functions from structures is an important and challenging task. Although proteins are often thought to be packed as tightly as solids, closer examination based on geometric computation reveals that they contain numerous voids and pockets. Most of them are of random nature, but some are binding sites providing surfaces to interact with other molecules. A promising approach for function prediction is to infer functions through discovery of similarity in local binding pockets, as proteins binding to similar substrates/ligands and carrying out similar functions have similar physical constraints for binding and reactions. In this chapter, we describe computational methods to distinguish those surface pockets that are likely to be involved in important biological functions, and methods to identify key residues in these pockets. We further describe how to predict protein functions at large scale from structures by detecting binding surfaces similar in residue make-ups, shape, and orientation. We also describe a Bayesian Monte Carlo method that can separate selection pressure due to biological function from pressure due to protein folding. We show how this method can be used to reconstruct the evolutionary history of binding surfaces for detecting similar binding surfaces. In addition, we briefly discuss how the negative image of a binding pocket can be casted, and how such information can be used to facilitate drug discovery.