Towards a Large Set of Steiner Quadruple Systems

1991 ◽  
Vol 4 (2) ◽  
pp. 182-195 ◽  
Author(s):  
Tuvi Etzion ◽  
Alan Hartman
Keyword(s):  
Large Set ◽  
Steiner Quadruple Systems

Ab initio band theory approach to electron energy loss near edge structures

1988 ◽  
Vol 46 ◽  
pp. 506-507
Author(s):  
Xudong Weng ◽  
O.F. Sankey ◽  
Peter Rez
Keyword(s):  
Ab Initio ◽  
Local Density ◽  
Interpolation Method ◽  
Atomic Orbital ◽  
Plane Waves ◽  
Large Set ◽  
Theory Approach ◽  
Band Theory ◽  
Atomic Orbitals ◽  
Pseudopotential Calculations

Single electron band structure techniques have been applied successfully to the interpretation of the near edge structures of metals and other materials. Among various band theories, the linear combination of atomic orbital (LCAO) method is especially simple and interpretable. The commonly used empirical LCAO method is mainly an interpolation method, where the energies and wave functions of atomic orbitals are adjusted in order to fit experimental or more accurately determined electron states. To achieve better accuracy, the size of calculation has to be expanded, for example, to include excited states and more-distant-neighboring atoms. This tends to sacrifice the simplicity and interpretability of the method.In this paper. we adopt an ab initio scheme which incorporates the conceptual advantage of the LCAO method with the accuracy of ab initio pseudopotential calculations. The so called pscudo-atomic-orbitals (PAO's), computed from a free atom within the local-density approximation and the pseudopotential approximation, are used as the basis of expansion, replacing the usually very large set of plane waves in the conventional pseudopotential method. These PAO's however, do not consist of a rigorously complete set of orthonormal states.

Molecular Views of Ice-Embedded Lumbricus Terrestris Erythrocruorin Obtained By Invariant Classification

1990 ◽  
Vol 48 (1) ◽  
pp. 450-451
Author(s):  
Michael schatz ◽  
Joachim Jäger ◽  
Marin van Heel
Keyword(s):  
Image Interpretation ◽  
Lumbricus Terrestris ◽  
Reference Image ◽  
Large Set ◽  
Multivariate Statistical ◽  
Data Set ◽  
Low Contrast ◽  
Negative Stain ◽  
Reference Images ◽  
Statistical Resolution

Lumbricus terrestris erythrocruorin is a giant oxygen-transporting macromolecule in the blood of the common earth worm (worm "hemoglobin"). In our current study, we use specimens (kindly provided by Drs W.E. Royer and W.A. Hendrickson) embedded in vitreous ice (1) to avoid artefacts encountered with the negative stain preparation technigue used in previous studies (2-4).Although the molecular structure is well preserved in vitreous ice, the low contrast and high noise level in the micrographs represent a serious problem in image interpretation. Moreover, the molecules can exhibit many different orientations relative to the object plane of the microscope in this type of preparation. Existing techniques of analysis requiring alignment of the molecular views relative to one or more reference images often thus yield unsatisfactory results.We use a new method in which first rotation-, translation- and mirror invariant functions (5) are derived from the large set of input images, which functions are subsequently classified automatically using multivariate statistical techniques (6). The different molecular views in the data set can therewith be found unbiasedly (5). Within each class, all images are aligned relative to that member of the class which contributes least to the classes′ internal variance (6). This reference image is thus the most typical member of the class. Finally the aligned images from each class are averaged resulting in molecular views with enhanced statistical resolution.

A Double Interaction Brush Model for Snow Conditions

2019 ◽  
Vol 47 (2) ◽  
pp. 118-140
Author(s):  
Artem Kusachov ◽  
Fredrik Bruzelius ◽  
Mattias Hjort ◽  
Bengt J. H. Jacobson
Keyword(s):  
Low Complexity ◽  
Large Set ◽  
Tire Model ◽  
Vehicle Handling ◽  
Real Time Applications ◽  
Snow Conditions ◽  
Double Interaction ◽  
Tire Models ◽  
Force Characteristics ◽  
Model Approach

ABSTRACT Commonly used tire models for vehicle-handling simulations are derived from the assumption of a flat and solid surface. Snow surfaces are nonsolid and may move under the tire. This results in inaccurate tire models and simulation results that are too far from the true phenomena. This article describes a physically motivated tire model that takes the effect of snow shearing into account. The brush tire model approach is used to describe an additional interaction between the packed snow in tire tread pattern voids with the snow road surface. Fewer parameters and low complexity make it suitable for real-time applications. The presented model is compared with test track tire measurements from a large set of different tires. Results suggest higher accuracy compared with conventional tire models. Moreover, the model is also proven to be capable of correctly predicting the self-aligning torque given the force characteristics.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

Domina Paupertas: the praise of voluntary poverty by St. Francis of Assisi and its reflection in late medieval Spanish painting

2014 ◽  
Vol 14 (2) ◽  
pp. 105
Author(s):  
José María Salvador González
Keyword(s):  
San Francisco ◽  
Middle Ages ◽  
Jesus Christ ◽  
Large Set ◽  
Late Medieval ◽  
Late Middle Ages ◽  
Perfect Model ◽  
Material Goods ◽  
Potential Impact ◽  
Francis Of Assisi

As is well known, St. Francis of Assisi heroically embraced evangelical poverty, renouncing material goods and living in abject poverty, in imitation of Jesus Christ. Furthermore, through his writings and oral testimonies collected by his disciples, the saint fervently urged Christians to live to some degree voluntary poverty , of which Christ was the perfect model. By basing this reading on some Poverello’s quotations, this paper intends to show the potential impact that these exhortations from San Francisco to poverty may have had in the late medieval Spanish painting, in some iconographic themes so significantly Franciscan as the Nativity and the Passion of the Redeemer. Through the analysis of a large set of paintings representing both issues, we will attempt to put into light if the teachings of St. Francis on evangelical poverty are reflected somehow in Spanish painting of the late Middle Ages.

A new algorithm for finding CRM-model coefficients

2019 ◽  
Vol 5 (3) ◽  
pp. 164-185 ◽  
Author(s):  
Alexander D. Bekman ◽  
Sergey V. Stepanov ◽  
Alexander A. Ruchkin ◽  
Dmitry V. Zelenin
Keyword(s):  
Approximate Solution ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Complex Form ◽  
Approximate Solutions ◽  
The Other ◽  
Programming Algorithm ◽  
Stochastic Algorithms ◽  
Large Set ◽  
Flow Rates

The quantitative evaluation of producer and injector well interference based on well operation data (profiles of flow rates/injectivities and bottomhole/reservoir pressures) with the help of CRM (Capacitance-Resistive Models) is an optimization problem with large set of variables and constraints. The analytical solution cannot be found because of the complex form of the objective function for this problem. Attempts to find the solution with stochastic algorithms take unacceptable time and the result may be far from the optimal solution. Besides, the use of universal (commercial) optimizers hides the details of step by step solution from the user, for example&nbsp;— the ambiguity of the solution as the result of data inaccuracy.<br> The present article concerns two variants of CRM problem. The authors present a new algorithm of solving the problems with the help of “General Quadratic Programming Algorithm”. The main advantage of the new algorithm is the greater performance in comparison with the other known algorithms. Its other advantage is the possibility of an ambiguity analysis. This article studies the conditions which guarantee that the first variant of problem has a unique solution, which can be found with the presented algorithm. Another algorithm for finding the approximate solution for the second variant of the problem is also considered. The method of visualization of approximate solutions set is presented. The results of experiments comparing the new algorithm with some previously known are given.

Web-based Tools for Drug Repurposing: Successful Examples of Collaborative Research

2020 ◽  
Vol 28 (1) ◽  
pp. 181-195
Author(s):  
Quentin Vanhaelen
Keyword(s):  
User Interfaces ◽  
Collaborative Research ◽  
Meta Analysis ◽  
A Priori ◽  
Drug Repurposing ◽  
Data Access ◽  
Estimation Methods ◽  
Large Set ◽  
Computational Tools ◽  
Web Based

: Computational approaches have been proven to be complementary tools of interest in identifying potential candidates for drug repurposing. However, although the methods developed so far offer interesting opportunities and could contribute to solving issues faced by the pharmaceutical sector, they also come with their constraints. Indeed, specific challenges ranging from data access, standardization and integration to the implementation of reliable and coherent validation methods must be addressed to allow systematic use at a larger scale. In this mini-review, we cover computational tools recently developed for addressing some of these challenges. This includes specific databases providing accessibility to a large set of curated data with standardized annotations, web-based tools integrating flexible user interfaces to perform fast computational repurposing experiments and standardized datasets specifically annotated and balanced for validating new computational drug repurposing methods. Interestingly, these new databases combined with the increasing number of information about the outcomes of drug repurposing studies can be used to perform a meta-analysis to identify key properties associated with successful drug repurposing cases. This information could further be used to design estimation methods to compute a priori assessment of the repurposing possibilities.

Application of a Validated QSTR Model for Repurposing COX-2 Inhibitor Coumarin Derivatives as Potential Antitumor Agents

2019 ◽  
Vol 19 (13) ◽  
pp. 1121-1128 ◽  
Author(s):  
Gulcin Tugcu ◽  
Hande Sipahi ◽  
Ahmet Aydin
Keyword(s):  
Antitumor Activity ◽  
Drug Development ◽  
Linear Model ◽  
Cell Lines ◽  
Cyclooxygenase 2 ◽  
Large Set ◽  
Coumarin Derivatives ◽  
Antitumor Compounds ◽  
Cyclooxygenase 2 Inhibitors ◽  
Potential Antitumor

Background: The discovery of novel potent molecules for both cancer prevention and treatment has been continuing over the past decade. In recent years, identification of new, potent, and safe anticancer agents through drug repurposing has been regarded as an expeditious alternative to traditional drug development. The cyclooxygenase-2 is known to be over-expressed in several types of human cancer. For this reason cyclooxygenase-2 inhibition may be useful tool for cancer chemotherapy. Objective: The first aim of the study was to develop a validated linear model to predict antitumor activity. Subsequently, applicability of the model for repurposing these cyclooxygenase-2 inhibitors as antitumor compounds to abridge drug development process. Method: We performed a quantitative structure-toxicity relationship (QSTR) study on a set of coumarin derivatives using a large set of molecular descriptors. A linear model predicting growth inhibition on leukemia CCRF cell lines was developed and consequently validated internally and externally. Accordingly, the model was applied on a set of 143 cyclooxygenase-2 inhibitor coumarin derivatives to explore their antitumor activity. Results: The results indicated that the developed QSAR model would be useful for estimating inhibitory activity of coumarin derivatives on leukemia cell lines. Electronegativity was found to be a prominent property of the molecules in describing antitumor activity. The applicability domain of the developed model highlighted the potential antitumor compounds. Conclusion: The promising results revealed that applied integrated in silico approach for repurposing by combining both the biological activity similarity and the molecular similarity via the computational method could be efficiently used to screen potential antitumor compounds among cyclooxygenase-2 inhibitors.

Mathematical and Computational Techniques for Drug Discovery: Promises and Developments

2019 ◽  
Vol 18 (32) ◽  
pp. 2774-2799 ◽  
Author(s):  
Krishnan Balasubramanian
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Quantum Chemical ◽  
Intrinsically Disordered Proteins ◽  
Hepatitis Virus ◽  
Disordered Proteins ◽  
Computational Techniques ◽  
Large Set ◽  
Structural Protein ◽  
Intrinsically Disordered

We review various mathematical and computational techniques for drug discovery exemplifying some recent works pertinent to group theory of nested structures of relevance to phylogeny, topological, computational and combinatorial methods for drug discovery for multiple viral infections. We have reviewed techniques from topology, combinatorics, graph theory and knot theory that facilitate topological and mathematical characterizations of protein-protein interactions, molecular-target interactions, proteomics, genomics and statistical data reduction procedures for a large set of starting chemicals in drug discovery. We have provided an overview of group theoretical techniques pertinent to phylogeny, protein dynamics especially in intrinsically disordered proteins, DNA base permutations and related algorithms. We consider computational techniques derived from high level quantum chemical computations such as QM/MM ONIOM methods, quantum chemical optimization of geometries complexes, and molecular dynamics methods for providing insights into protein-drug interactions. We have considered complexes pertinent to Hepatitis Virus C non-structural protein 5B polymerase receptor binding of C5-Arylidebne rhodanines, complexes of synthetic potential vaccine molecules with dengue virus (DENV) and HIV-1 virus as examples of various simulation studies that exemplify the utility of computational tools. It is demonstrated that these combinatorial and computational techniques in conjunction with experiments can provide promising new insights into drug discovery. These techniques also demonstrate the need to consider a new multiple site or allosteric binding approach to drug discovery, as these studies reveal the existence of multiple binding sites.

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