A computational method for large‐scale differential symmetric Stein equation

2018 ◽  
Vol 42 (16) ◽  
pp. 5438-5445
Author(s):  
Yaprak Güldoğan Dericioğlu ◽  
Muhammet Kurulay
Keyword(s):  
Large Scale ◽  
Computational Method ◽  
Stein Equation

scholarly journals A geometric diffuse-interface method for droplet spreading

2020 ◽  
Vol 476 (2233) ◽  
pp. 20190222
Author(s):  
Darryl D. Holm ◽  
Lennon Ó Náraigh ◽  
Cesare Tronci
Keyword(s):  
Numerical Simulations ◽  
Large Scale ◽  
Computational Cost ◽  
Computational Method ◽  
Diffuse Interface ◽  
Precursor Film ◽  
Droplet Spreading ◽  
Wetting Fluids ◽  
Thin Film Equation ◽  
Wetting Fluid

This paper exploits the theory of geometric gradient flows to introduce an alternative regularization of the thin-film equation valid in the case of large-scale droplet spreading—the geometric diffuse-interface method. The method possesses some advantages when compared with the existing models of droplet spreading, namely the slip model, the precursor-film method and the diffuse-interface model. These advantages are discussed and a case is made for using the geometric diffuse-interface method for the purpose of numerical simulations. The mathematical solutions of the geometric diffuse interface method are explored via such numerical simulations for the simple and well-studied case of large-scale droplet spreading for a perfectly wetting fluid—we demonstrate that the new method reproduces Tanner’s Law of droplet spreading via a simple and robust computational method, at a low computational cost. We discuss potential avenues for extending the method beyond the simple case of perfectly wetting fluids.

scholarly journals Thermoelectric Properties of InA Nanowires from Full-Band Atomistic Simulations

Molecules ◽  
2020 ◽  
Vol 25 (22) ◽  
pp. 5350
Author(s):  
Damiano Archetti ◽  
Neophytos Neophytou
Keyword(s):  
Surface Roughness ◽  
Thermoelectric Power ◽  
Power Factor ◽  
Large Scale ◽  
Large Diameter ◽  
Computational Method ◽  
Thermoelectric Power Factor ◽  
Full Band ◽  
Low Dimensional ◽  
Power Factor Improvement

In this work we theoretically explore the effect of dimensionality on the thermoelectric power factor of indium arsenide (InA) nanowires by coupling atomistic tight-binding calculations to the Linearized Boltzmann transport formalism. We consider nanowires with diameters from 40 nm (bulk-like) down to 3 nm close to one-dimensional (1D), which allows for the proper exploration of the power factor within a unified large-scale atomistic description across a large diameter range. We find that as the diameter of the nanowires is reduced below d < 10 nm, the Seebeck coefficient increases substantially, as a consequence of strong subband quantization. Under phonon-limited scattering conditions, a considerable improvement of ~6× in the power factor is observed around d = 10 nm. The introduction of surface roughness scattering in the calculation reduces this power factor improvement to ~2×. As the diameter is decreased to d = 3 nm, the power factor is diminished. Our results show that, although low effective mass materials such as InAs can reach low-dimensional behavior at larger diameters and demonstrate significant thermoelectric power factor improvements, surface roughness is also stronger at larger diameters, which takes most of the anticipated power factor advantages away. However, the power factor improvement that can be observed around d = 10 nm could prove to be beneficial as both the Lorenz number and the phonon thermal conductivity are reduced at that diameter. Thus, this work, by using large-scale full-band simulations that span the corresponding length scales, clarifies properly the reasons behind power factor improvements (or degradations) in low-dimensional materials. The elaborate computational method presented can serve as a platform to develop similar schemes for two-dimensional (2D) and three-dimensional (3D) material electronic structures.

scholarly journals Prediction of hot spots in protein–DNA binding interfaces based on supervised isometric feature mapping and extreme gradient boosting

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Ke Li ◽  
Sijia Zhang ◽  
Di Yan ◽  
Yannan Bin ◽  
Junfeng Xia
Keyword(s):  
Feature Selection ◽  
Manifold Learning ◽  
Hot Spots ◽  
Large Scale ◽  
Computational Method ◽  
Gradient Boosting ◽  
Feature Mapping ◽  
Accessible Information ◽  
Extreme Gradient Boosting ◽  
Isometric Feature Mapping

Abstract Background Identification of hot spots in protein-DNA interfaces provides crucial information for the research on protein-DNA interaction and drug design. As experimental methods for determining hot spots are time-consuming, labor-intensive and expensive, there is a need for developing reliable computational method to predict hot spots on a large scale. Results Here, we proposed a new method named sxPDH based on supervised isometric feature mapping (S-ISOMAP) and extreme gradient boosting (XGBoost) to predict hot spots in protein-DNA complexes. We obtained 114 features from a combination of the protein sequence, structure, network and solvent accessible information, and systematically assessed various feature selection methods and feature dimensionality reduction methods based on manifold learning. The results show that the S-ISOMAP method is superior to other feature selection or manifold learning methods. XGBoost was then used to develop hot spots prediction model sxPDH based on the three dimensionality-reduced features obtained from S-ISOMAP. Conclusion Our method sxPDH boosts prediction performance using S-ISOMAP and XGBoost. The AUC of the model is 0.773, and the F1 score is 0.713. Experimental results on benchmark dataset indicate that sxPDH can achieve generally better performance in predicting hot spots compared to the state-of-the-art methods.

Million-Finite-Element-Order Large-Scale Computation of Residual Stress in Complicated Weld Structures

2010 ◽  
Author(s):  
Masahito Mochizuki ◽  
Shinsuke Itoh
Keyword(s):  
Residual Stress ◽  
Finite Element ◽  
Large Scale ◽  
Base Plate ◽  
Computational Method ◽  
Element Order ◽  
Welding Deformation ◽  
Weld Distortion ◽  
Steam Separator ◽  
Reactor Internals

Shroud head in a steam separator of ABWR is connected to more than 300 pipes, which are attached by fillet welds. Although the welding causes welding deformation, it is impossible to correct this deformation to the shroud head because the shroud head material is very thick; more than 50 mm. Thus, it is necessary to clarify the mechanism of welding deformation so that it can be quantitatively predicted and controlled. SCC prevention is also essential by residual stress control in reactor internals. The objective of this paper is to predict welding deformation and residual stress for a shroud head, and to investigate the influence of factors such as the inclination angle of the base plate, to which the pipes are connected, on welding deformation and residual stress. Million-finite-element-order large-scale computational method of residual stress and weld distortion has been developed in order to apply directy to complicated weld structures. Details of algorithm and some applications are introduced.

scholarly journals Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yang Li ◽  
Zheng Wang ◽  
Li-Ping Li ◽  
Zhu-Hong You ◽  
Wen-Zhun Huang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Sequence ◽  
Large Scale ◽  
False Positive Rate ◽  
Computational Method ◽  
Evolutionary Information ◽  
Local Alignment ◽  
Protein Interaction Data ◽  
Sequence Information ◽  
Protein Protein Interactions

AbstractVarious biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.

scholarly journals A systematic sequencing-based approach for microbial contaminant detection and functional inference

BMC Biology ◽  
2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Sung-Joon Park ◽  
Satoru Onizuka ◽  
Masahide Seki ◽  
Yutaka Suzuki ◽  
Takanori Iwata ◽  
...  
Keyword(s):  
Large Scale ◽  
Microbial Contamination ◽  
Precise Determination ◽  
Computational Method ◽  
Host Cells ◽  
Functional Inference ◽  
Apoptotic Pathways ◽  
Molecular Landscape ◽  
Multiple Species ◽  
Ngs Data

Abstract Background Microbial contamination poses a major difficulty for successful data analysis in biological and biomedical research. Computational approaches utilizing next-generation sequencing (NGS) data offer promising diagnostics to assess the presence of contaminants. However, as host cells are often contaminated by multiple microorganisms, these approaches require careful attention to intra- and interspecies sequence similarities, which have not yet been fully addressed. Results We present a computational approach that rigorously investigates the genomic origins of sequenced reads, including those mapped to multiple species that have been discarded in previous studies. Through the analysis of large-scale synthetic and public NGS samples, we estimate that 1000–100,000 contaminating microbial reads are detected per million host reads sequenced by RNA-seq. The microbe catalog we established included Cutibacterium as a prevalent contaminant, suggesting that contamination mostly originates from the laboratory environment. Importantly, by applying a systematic method to infer the functional impact of contamination, we revealed that host-contaminant interactions cause profound changes in the host molecular landscapes, as exemplified by changes in inflammatory and apoptotic pathways during Mycoplasma infection of lymphoma cells. Conclusions We provide a computational method for profiling microbial contamination on NGS data and suggest that sources of contamination in laboratory reagents and the experimental environment alter the molecular landscape of host cells leading to phenotypic changes. These findings reinforce the concept that precise determination of the origins and functional impacts of contamination is imperative for quality research and illustrate the usefulness of the proposed approach to comprehensively characterize contamination landscapes.

Efficiently mapping structure–property relationships of gas adsorption in porous materials: application to Xe adsorption

2017 ◽  
Vol 201 ◽  
pp. 221-232 ◽  
Author(s):  
A. R. Kaija ◽  
C. E. Wilmer
Keyword(s):  
Porous Materials ◽  
Void Fraction ◽  
Large Scale ◽  
Gas Adsorption ◽  
Structural Characteristics ◽  
Gas Storage ◽  
Computational Method ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Computational Screening

Designing better porous materials for gas storage or separations applications frequently leverages known structure–property relationships. Reliable structure–property relationships, however, only reveal themselves when adsorption data on many porous materials are aggregated and compared. Gathering enough data experimentally is prohibitively time consuming, and even approaches based on large-scale computer simulations face challenges. Brute force computational screening approaches that do not efficiently sample the space of porous materials may be ineffective when the number of possible materials is too large. Here we describe a general and efficient computational method for mapping structure–property spaces of porous materials that can be useful for adsorption related applications. We describe an algorithm that generates random porous “pseudomaterials”, for which we calculate structural characteristics (e.g., surface area, pore size and void fraction) and also gas adsorption properties via molecular simulations. Here we chose to focus on void fraction and Xe adsorption at 1 bar, 5 bar, and 10 bar. The algorithm then identifies pseudomaterials with rare combinations of void fraction and Xe adsorption and mutates them to generate new pseudomaterials, thereby selectively adding data only to those parts of the structure–property map that are the least explored. Use of this method can help guide the design of new porous materials for gas storage and separations applications in the future.

scholarly journals Real age prediction from the transcriptome with RAPToR

2021 ◽  
Author(s):  
Romain Bulteau ◽  
Mirko Francesconi
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Time Series Data ◽  
Expression Profiles ◽  
Computational Method ◽  
Model Systems ◽  
Developmental Expression ◽  
Model Organisms ◽  
Series Data ◽  
Developmental Variation

AbstractGenome-wide gene expression profiling is a powerful tool for exploratory analyses, providing a high dimensional picture of the state of a biological system. However, uncontrolled variation among samples can obscure and confound the effect of variables of interest. Uncontrolled developmental variation is often a major source of unknown expression variation in developmental systems. Existing methods to sort samples from transcriptomes require many samples to infer developmental trajectories and only provide a relative pseudo-time.Here we present RAPToR (Real Age Prediction from Transcriptome staging on Reference), a simple computational method to estimate the absolute developmental age of even a single sample from its gene expression with up to minutes precision. We achieve this by staging samples on high-resolution reference developmental expression profiles we build from existing time series data. We implemented RAPToR for the most common animal model systems: nematode, fruit fly, zebrafish, and mouse, and demonstrate application for non-model organisms. We show how developmental variation discovered by RAPToR can be exploited to increase power to detect differential expression and to untangle the signal of perturbations of interest even when it is completely confounded with development. We anticipate our RAPToR post-profiling staging strategy will be especially useful in large scale single organism profiling because it eliminates the need for synchronization or for a tedious and potentially difficult step of accurate staging before profiling.

scholarly journals Computational correction of off-targeting for CRISPR-Cas9 essentiality screens

2019 ◽  
Author(s):  
Alexendar R. Perez ◽  
Laura Sala ◽  
Richard K. Perez ◽  
Joana A. Vidigal
Keyword(s):  
Cell Cycle ◽  
Cell Proliferation ◽  
Dna Damage ◽  
Dna Damage Response ◽  
Large Scale ◽  
Computational Method ◽  
Seamless Integration ◽  
Guide Rnas ◽  
Cycle Arrest ◽  
Damage Response

Off-target cleavage by Cas9 can confound measurements of cell proliferation/viability in CRISPR assays by eliciting a DNA-damage response that includes cell cycle arrest1-3. This gene-independent toxicity has been documented in large scale assays2-4 and shown to be a source of false-positives when libraries are populated by promiscuous guide RNAs (gRNAs)7. To address this, we developed CSC, a computational method to correct for the effect of specificity on gRNA depletion. We applied CSC to screening data from the Cancer Dependency Map and show that it significantly improves the specificity of CRISPR-Cas9 essentiality screens while preserving known gene essentialities even for genes targeted by highly pro-miscuous guides. We packaged CSC in a Python software to allow its seamless integration into current CRISPR analysis pipelines and improve the sensitivity of essentiality screens for repetitive genomic loci.

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