Approach For Fast Computation Research Articles

Air monitoring by nanopore sequencing.

While the air microbiome and its diversity are essential for human health and ecosystem resilience, comprehensive air microbial diversity monitoring has remained rare, so that little is known about the air microbiome's composition, distribution, or functionality. Here we show that nanopore sequencing-based metagenomics can robustly assess the air microbiome in combination with active air sampling through liquid impingement and tailored computational analysis. We provide fast and portable laboratory and computational approaches for air microbiome profiling, which we leverage to robustly assess the taxonomic composition of the core air microbiome of a controlled greenhouse environment and of a natural outdoor environment. We show that long-read sequencing can resolve species-level annotations and specific ecosystem functions through de novo metagenomic assemblies despite the low amount of fragmented DNA used as an input for nanopore sequencing. We then apply our pipeline to assess the diversity and variability of an urban air microbiome, using Barcelona, Spain, as an example; this randomized experiment gives first insights into the presence of highly stable location-specific air microbiomes within the city's boundaries, and showcases the robust microbial assessments that can be achieved through automatable, fast, and portable nanopore sequencing technology.

Fast computation approach of electron-impact ionization and excitation cross-sections for atoms and ions with medium- and high-<i>Z</i> elements

The atomic data of medium- and high-<i>Z</i> elements, such as electron-impact ionization and excitation cross-sections, possess extensive applications in fields such as fusion science and X-ray interactions with matter. There are atoms and ions in high energy density plasma, with different charge states and energy states ranging from ground states to highly excited states, and the cross-sections of each charge state and energy state need to be calculated. The bottlenecks limiting computational performance are the inevitable relativistic effects of medium- and high-<i>Z</i> elements and the extremely complex electronic configurations. Taking tantalum (Ta) for example, by using the relativistic Dirac-Fock theory and distorted wave model, we compute the electron-impact ionization and excitation cross-sections of Ta from the ground state atom up to Ta<sup>72+</sup> with the incident electron energy range of 1–150 keV. The detailed configuration accounting (DCA) reaction channel cross-sections are derived by summing and weighting the original detailed level accounting (DLA) cross-sections. After examining the data, two regularities are found. In terms of DLA, the pre-averaging DCA cross-sections have varying initial DLA energy levels but are typically close to each other. There is not a straightforward function that can explain the discrepancies between them. In terms of DCA, inner subshells typically contribute very little to the total cross-section as their ionization and excitation cross-sections are orders of magnitude smaller than those of outer subshells. We provide two techniques to reduce the computational costs based on the regularities. To minimize the total number of DLA reaction channels used in the computation, the initial DLA energy levels can be randomly sampled. Through a Monte Carlo numerical experiment, we determine the appropriate number of sampling points that can reduce the total number of DLA channels by an order of magnitude while maintaining a 5% error margin. In terms of impact ionization, since small cross-section DCA channels are insignificant, only a tiny portion of the DCA channels are required to preserve a 95% accuracy of the entire cross-section. It is possible to use the analytical Binary Encounter Bethe (BEB) formula to determine which DCA channels should be neglected before the computation to reduce computational costs. In terms of electron-impact excitation, just the cross-sections of the same excited subshells as the preserved ionized subshells, which are determined in the previous electron-impact ionization (EII) calculations, are needed. Finally, we compare our EII results with theoretical and experimental results. In the low incident electron energy range of below 2 keV, our results accord with the theoretical result of the 6s EII cross-section of the Ta atom and the experimental result of the total EII cross-section of the Ta<sup>1+</sup> ion. In the high energy range of below 150 keV, our results are also consistent with the theoretical result of the 1s EII cross-section of the Ta atom and the experimental result of the 1s EII cross-section of the Cu atom. Our results reasonably match the previous experimental and theoretical results in low-energy range and high-energy range, inner subshell and outer subshell, indicating the accuracy of our calculation. The proposed optimizing strategy can be applied to various medium- to high-<i>Z</i> elements and is compatible to most computation codes.

Fast Computational Approaches to Derive Fractional Pressure Ratio in Patients with Extracranial or Intracranial Symptomatic Stenosis

We aimed to evaluate the performance of fast and straightforward μQFR computation in cerebrovascular stenosis lesions. A total of 30 patients with symptomatic stenosis lesions of 50-70% luminal stenosis and underwent FPR assessment at our hospital were included in this study. μQFR was applied to the interrogated vessel. An artificial intelligence algorithm was proposed for automatic delineation of lumen contours of cerebrovascular stenosis. We used invasive fractional pressure ratios (FPRs) as a reference standard. Pearson's correlation coefficient (r) was used to assess the correlation strength between μQFR and FPR, while Bland-Altman plots were used to evaluate the agreement between μQFR and FPR. An analysis of receiver-operating characteristics (ROC) was used to evaluate the performance of μQFR. Our result displayed strong positive correlations (r = 0.92; p<0.001) between μQFR and pressure wire FPR. An excellent agreement was observed between μQFR and FPR with a mean difference of 0.01 + 0.08; p=0.263; range from -0.16 to 0.14. The overall accuracy for identifying an FPR of <0.7 was 92% (95% CI: 85% to 100%). The area under the receiver-operating characteristic curve was higher for μQFR (0.92 [95% CI: 0.81 to 0.98]) than diameter stenosis (DS) (0.88 [95% CI: 0.75 to 0.95]. The positive likelihood ratio was 3.9 for μQFR with a negative likelihood ratio of 0, respectively. The μQFR computation has a strong correlation and agrees with the FPR calculated from the pressure wire. Therefore, μQFR may provide an essential therapeutic aid in patients with symptomatic stenosis lesions.

A fast method for obtaining convex combination coefficients

This technical communiqué presents a novel approach for fast computation of convex combination coefficients based on ray shooting. Such a problem arises in the real-time implementation of many set-based control techniques, which require a computationally efficient solution to cope with systems of high dimension or fast dynamics. The proposed method poses no constraints on the polytope structure and requires only a list of vertices and boundary hyperplanes. In test cases involving polytopes of various dimensions and numbers of vertices, the proposed method provided up to seven-fold reductions in the 95% percentile of computation time, compared with a state-of-the-art technique.

A fast serial computation approach for computing viewshed of a region by detecting key spots on the terrain

Computing a viewshed for a space (a polygon), rather than a single location (a point), requires analysis of the maximum terrain extent observable from any location within the space. For a digital elevation model (DEM), it is the sum of viewshed analyses for all the pixels inside the confined space. As the raster spatial resolution increases, the process becomes computationally expensive. In this study, we propose a new serial computation approach, named Regional Viewshed by Aspect Peaks (RVAP), in which key pixels that define the cumulative visibility map on a digital elevation model (DEM) are found, making the process computationally affordable. We compare our proposed method next to 1) a 5-cell grid interval method 2) and an all-encompassing but slow complete approach where the viewshed is computed for all the pixels and then overlaid. Six regions-of-interests (ROIs) on two Shuttle radar (∼555 × 555 km dimensions, 90-meter resolution) DEMs are selected and categorized by their terrain ruggedness index (TRI) (i.e. slightly rugged, mildly rugged, highly rugged). The proposed approach is much faster, while its coverage is identical to the all-encompassing approach. Moreover, as the TRI increases, the accuracy of the proposed algorithm is not diminished, while its execution time decreases.

Eliminating Transition State Calculations for Faster and More Accurate Reactivity Prediction in Sulfa-Michael Additions Relevant to Human Health and the Environment.

Fast and accurate computational approaches to predicting reactivity in sulfa-Michael additions are required for high-throughput screening in toxicology (e.g., predicting excess aquatic toxicity and skin sensitization), chemical synthesis, covalent drug design (e.g., targeting cysteine), and data set generation for machine learning. The kinetic glutathione chemoassay is a time-consuming in chemico method used to extract kinetic data in the form of log(kGSH) for organic electrophiles. In this work, we use density functional theory to compare the use of transition states (TSs) and enolate intermediate structures following C–S bond formation in the prediction of log(kGSH) for a diverse group of 1,4 Michael acceptors. Despite the widespread use of transition state calculations in the literature to predict sulfa-Michael reactivity, we observe that intermediate structures show much better performance for the prediction of log(kGSH), are faster to calculate, and easier to obtain than TSs. Furthermore, we show how linear combinations of atomic charges from the isolated Michael acceptors can further improve predictions, even when using inexpensive semiempirical quantum chemistry methods. Our models can be used widely in the chemical sciences (e.g., in the prediction of toxicity relevant to the environment and human health, synthesis planning, and the design of cysteine-targeting covalent inhibitors), and represent a low-cost, sustainable approach to reactivity assessment.

Computer-aided heuristics in orthodontics

During the decision-making process, physicians rely on heuristics that consist of simple, useful procedures for solving problems, intuitive shortcuts that produce reliable decisions based on limited information. In clinical situations characterized by a high degree of uncertainty such as thoseencountered in orthodontics, cognitive biases and judgment errors related to heuristics are not uncommon. This study aimed at promoting trust in the effective interface between the intuitive reasoning of the orthodontic practitioner and the computational heuristics emerging from simple statistical models. We propose an integrative model based on the interaction between clinical reasoning and 2 computational tools, cluster analysis and fast-and-frugal trees, to extract a structured craniofacial representation of untreated subjects with Class III malocclusion and to forecast the worsening of the malocclusion over time. Cluster analysis of cephalometric values from 144 growing subjects with Class III malocclusion followed longitudinally (T1: mean age, 10.2±1.9years; T2: mean age, 13.8±2.7years) produced 3 morphologic subgroups with predominant sagittal, vertical, and slight maxillomandibular imbalances. Fast-and-frugal trees applied to different subgroups extracted heuristics that improved the prediction of key features associated with adverse craniofacial growth. Provided that cephalometric values are placed in the appropriate framework, the matching between simple and fast computational approaches and clinical reasoning could help the intuitive logic, perception, and cognitive inferences of orthodontic practitioners on the outcome of patients affected by Class III disharmony, decreasing errors associated with flawed judgments and improving the accuracy of decision making.

Fast and Novel Computational Methods for Multi-scale and Multi-physics: FETI and POD-ROM

In this paper, the finite element tearing and interconnecting (FETI) method and proper orthogonal decomposition-based reduced order model (POD-ROM) are suggested as fast and novel computational approaches for multi-scale and multi-physics analysis. FETI is one of non-overlapping domain decomposition methods, facilitating the parallel computation. POD-ROM reduces the number of degrees of freedom (DoFs) to several representative POD modes which completely describe the behavior of the full order model. The computational time for the analyses using large number of DoFs can be decreased by using the previously mentioned methods. The present FETI-local and POD-ROM are found to reduce computational time significantly in each example.

Analysis of protein missense alterations by combining sequence- and structure-based methods.

BackgroundDifferent types of in silico approaches can be used to predict the phenotypic consequence of missense variants. Such algorithms are often categorized as sequence based or structure based, when they necessitate 3D structural information. In addition, many other in silico tools, not dedicated to the analysis of variants, can be used to gain additional insights about the possible mechanisms at play.MethodsHere we applied different computational approaches to a set of 20 known missense variants present on different proteins (CYP, complement factor B, antithrombin and blood coagulation factor VIII). The tools that were used include fast computational approaches and web servers such as PolyPhen‐2, PopMusic, DUET, MaestroWeb, SAAFEC, Missense3D, VarSite, FlexPred, PredyFlexy, Clustal Omega, meta‐PPISP, FTMap, ClusPro, pyDock, PPM, RING, Cytoscape, and ChannelsDB.ResultsWe observe some conflicting results among the methods but, most of the time, the combination of several engines helped to clarify the potential impacts of the amino acid substitutions.ConclusionCombining different computational approaches including some that were not developed to investigate missense variants help to predict the possible impact of the amino acid substitutions. Yet, when the modified residues are involved in a salt‐bridge, the tools tend to fail, even when the analysis is performed in 3D. Thus, interactive structural analysis with molecular graphics packages such as Chimera or PyMol or others are still needed to clarify automatic prediction.

TM Scattering by Arbitrary-Shaped Multilayer Cylinders Having Embedded Inhomogeneities

We present an efficient approach for the solution of the 2-D forward electromagnetic scattering problem related to arbitrary-shaped multilayer cylinders having embedded inhomogeneities. For this purpose, we combine the method of moments (MoM) with the previously introduced approach that enables simple and fast computation of the field scattered from arbitrary-shaped multilayer cylinders through the expansion of the field into a series of cylindrical functions. The MoM is applied for the computation of the field scattered from the embedded inhomogeneity only by discretizing it. In order to apply MoM in such a manner, first, the above-mentioned series representation approach is applied straightforwardly for the computation of the field scattered from the multilayer background. Then, we propose an adaptation of this approach for fast computation of the integral of the background Green's function over each cell of the MoM mesh. This adaptation also deals with the singularity of the Green's function. The efficiency of the method is shown in various numerical simulations.

Fast computation of desired thermal dose: Application to focused ultrasound-induced lesion planning

Computation of desired thermal dose can improve the safety and effectiveness of thermal therapeutic interventions while fast bio-heat in silico simulation can enhance the real-time surgical feedback. However, the existing methods are either computationally expensive or numerically inaccurate. This article presents a new methodology for fast and accurate desired thermal dose computation. New iterative algorithms are developed to adjust control parameters to efficiently obtain the desired lesion volumes, based on efficient solutions of bio-heat transfer in soft tissue. The variation of thermal dose is investigated using single- and multi-lesion configurations, and convergence analysis is presented. The clinically relevant scenario is demonstrated using a patient-specific simulation of focused ultrasound treatment of hepatic cancers to achieve multi-lesion planning (consistent and common lesion volumes in our case) for predictable and repeatable heat exposure sequence. The proposed method provides a common approach for fast iterative computation of single- or multiple-desired thermal lesions.

Quantitative Analysis of Style in Mihai Eminescu's Poetry

Quantitative stylistic methods aim to express certain aspects of a text in numeric form, thus allowing the introduction of fast, powerful and accurate computational approaches for analysis. While in the case of literature, the validity and usefulness of such studies is highly controversial, one cannot deny the opportunities brought forward by computational methods: first, the exploration of large sets of documents in search of patterns otherwise difficult to discover by human readers; second, the possibility of opening up new perspectives by uncovering latent features of texts. In this study, we investigate the poetic work of one of the most important Romanian poets, Mihai Eminescu, through a variety of quantitative methods addressing lexical, morphological, semantic and emotional aspects of text. We propose a comparison between the results of the computational approach and established interpretations of Eminescu’s work in order to assess the viability of computational methods in poetic style studies.

A minimalistic approach for fast computation of geodesic distances on triangular meshes

The computation of geodesic distances is an important research topic in Geometry Processing and 3D Shape Analysis as it is a basic component of many methods used in these areas. In this work, we present a minimalistic parallel algorithm based on front propagation to compute approximate geodesic distances on meshes. Our method is practical and simple to implement, and does not require any heavy pre-processing. The convergence of our algorithm depends on the number of discrete level sets around the source points from which distance information propagates. To appropriately implement our method on GPUs taking into account memory coalescence problems, we take advantage of a graph representation based on a breadth-first search traversal that works harmoniously with our parallel front propagation approach. We report experiments that show how our method scales with the size of the problem. We compare the mean error and processing time obtained by our method with such measures computed using other methods. Our method produces results in competitive times with almost the same accuracy, especially for large meshes. We also demonstrate its use for solving two classical geometry processing problems: the regular sampling problem and the Voronoi tessellation on meshes.

Fast computation approach for parameterized design and simulation of vertical ground heat exchangers and GCHP systems

Vertical ground heat exchanger (GHE) is an expensive part of vertical GCHP systems. Fast and accurate computation of fluid and ground temperatures is desired for the long-term energy performance prediction of GCHP and optimal sizing of bore field. Hourly simulations and iteration of parameterized design in time domain with thermal response factors is quite time-consuming. In this paper, a new thermal response factors, δ-function is proposed for computing fluid and ground temperatures of bore field. δ-function is a non-dimensional ground temperature response and an accurate analytical solution of finite line source model to a unit rectangular heat pulse. Computation of δ-function is quite fast due to its quite small integral range. The validity and accuracy of δ-function are examined by the simulation results of g-function. Through comparing hourly simulation of various periods between g- and δ-functions in both time and spectral domain methods, the combination of δ-function with Fast Fourier Transform spends the least computing time and is suitable to quickly and accurately compute fluid and ground temperatures in parameterized design and long-period hourly simulation of vertical heat exchanger and GCHP systems.

A Bayesian spatiotemporal approach to inform management unit appropriateness

One prerequisite for sustainable fisheries management is to match management actions with biological processes. Stocks are fundamental units for fisheries management. Understanding the spatial structure of fish stocks is critical for conducting defensible stock assessments, applying efficient management strategies, and ensuring the sustainability of fish stocks. Yellow perch (Perca flavescens) is an important fishery in the Great Lakes. The appropriateness of its management units (MUs) has been identified as of high concern by the Great Lakes Fisheries Commission. Here we established integrated nested Laplace approximations and stochastic partial differential equations as two powerful tools for modeling spatiotemporal patterns of fish relative biomass. These fast computational approaches were applied to fit a Bayesian hierarchical hurdle model to occurrence and positive mass of yellow perch caught in gill-net surveys. Yellow perch relative biomass index has clear temporal variation and spatial heterogeneity, with the two middle MUs for yellow perch within Lake Erie merging together. The method explicitly models the spatiotemporal correlation structure inherent in biomass survey data at a reasonable computational cost, and the estimated spatiotemporal correlation informs stock structure.

Material scaling and frequency-selective enhancement of near-field radiative heat transfer for lossy metals in two dimensions via inverse design

The super-Planckian features of radiative heat transfer in the near-field are known to depend strongly on both material and geometric design properties. However, the relative importance and interplay of these two facets, and the degree to which they can be used to ultimately control energy flow, remains an open question. Recently derived bounds suggest that enhancements as large as $|\chi|^4 \lambda^{2} / \left(\left(4\pi\right)^{2} \Im\left[\chi\right]^{2} d^{2}\right)$ are possible between extended structures (compared to blackbody); but neither geometries reaching this bound, nor designs revealing the predicted material ($\chi$) scaling, have been previously reported. Here, exploiting inverse techniques, in combination with fast computational approaches enabled by the low-rank properties of elliptic operators for disjoint bodies, we investigate this relation between material and geometry on an enlarged space structures. Crucially, we find that the material proportionality given above does indeed emerge in realistic structures. In reaching this result, we also show that (in two dimensions) lossy metals such as tungsten, typically considered to be poor candidate materials for strongly enhancing heat transfer in the near infrared, can be structured to selectively realize flux rates that come within $50\%$ of those exhibited by an ideal pair of resonant lossless metals for separations as small as $2\%$ of a tunable design wavelength.

Kohn–Sham approach for fast hybrid density functional calculations in real-space numerical grid methods

Real-space methods have not been suitable for hybrid density functional calculations due to high cost coming from the nonlocality of Fock operator. Here we propose a practical approach for fast computation. The key is to use a strictly local Kohn–Sham potential that can be deduced from any hybrid functional using the optimized effective potential method. This new approach improved the computation speed of the global and range-separated hybrid methods up to 30 and 80 times, respectively. As a result, accurate prediction of the size-dependent excitonic spectra of Si quantum dots became feasible.

Advanced Dynamic Modeling of Three-Phase Mutually Coupled Switched Reluctance Machine

This paper proposes an advanced dynamic modelling approach of the mutually coupled switched reluctance motor (MCSRM) in the $dq$ reference system that can consider saturation, cross-coupling, and spatial harmonics. Different topologies and their operating principles are investigated and an idealized $dq$ -model considering the inductance harmonics is derived. A dynamic model is built based on flux-current lookup tables (LUTs) obtained from finite element analysis (FEA). A simplified method to inverse the two-dimensional LUTs is proposed. A fast computation approach is used to reduce the number of FEA simulations and calculation time to obtain the LUTs. Motor dynamic performances at different speeds are simulated by using the proposed dynamic model and the results are investigated and verified by FEA. The motor dynamic behavior can be accurately obtained in a short simulation time by using the proposed approach. Experiments are carried out on a 12/8 MCSRM, showing good accuracy of the proposed model.

Efficient optimization approach for fast GPU computation of Zernike moments

Our study focuses on accelerating the computation of Zernike moments on graphics processing units (GPUs). There are two ideas to achieve the goal. First is to implement a novel re-layout that involves reordering the image pixels and addressing the diagonal pixels in advance, so that computations of all pixels are allocated to an octant effectively. Second is to the leverage the constant memory to store precomputed values used across GPU threads. An in-depth study has been carried out to evaluate the performance in each case and to compare against GPU implementation of other algorithms and to discuss the bottleneck. The result shows that our approach is effective and achieves significant performance improvement compared to other GPU state-of-the-art implementations. Furthermore, our approach is suited for allocating the data flow into multiple GPUs.

Evaluation and Characterization of Trk Kinase Inhibitors for the Treatment of Pain: Reliable Binding Affinity Predictions from Theory and Computation.

Optimization of ligand binding affinity to the target protein of interest is a primary objective in small-molecule drug discovery. Until now, the prediction of binding affinities by computational methods has not been widely applied in the drug discovery process, mainly because of its lack of accuracy and reproducibility as well as the long turnaround times required to obtain results. Herein we report on a collaborative study that compares tropomyosin receptor kinase A (TrkA) binding affinity predictions using two recently formulated fast computational approaches, namely, Enhanced Sampling of Molecular dynamics with Approximation of Continuum Solvent (ESMACS) and Thermodynamic Integration with Enhanced Sampling (TIES), to experimentally derived TrkA binding affinities for a set of Pfizer pan-Trk compounds. ESMACS gives precise and reproducible results and is applicable to highly diverse sets of compounds. It also provides detailed chemical insight into the nature of ligand-protein binding. TIES can predict and thus optimize more subtle changes in binding affinities between compounds of similar structure. Individual binding affinities were calculated in a few hours, exhibiting good correlations with the experimental data of 0.79 and 0.88 from the ESMACS and TIES approaches, respectively. The speed, level of accuracy, and precision of the calculations are such that the affinity predictions can be used to rapidly explain the effects of compound modifications on TrkA binding affinity. The methods could therefore be used as tools to guide lead optimization efforts across multiple prospective structurally enabled programs in the drug discovery setting for a wide range of compounds and targets.