Desktop Workstations Research Articles

Collaborative augmented reconstruction of 3D neuron morphology in mouse and human brains

Digital reconstruction of the intricate 3D morphology of individual neurons from microscopic images is a crucial challenge in both individual laboratories and large-scale projects focusing on cell types and brain anatomy. This task often fails in both conventional manual reconstruction and state-of-the-art artificial intelligence (AI)-based automatic reconstruction algorithms. It is also challenging to organize multiple neuroanatomists to generate and cross-validate biologically relevant and mutually agreed upon reconstructions in large-scale data production. Based on collaborative group intelligence augmented by AI, we developed a collaborative augmented reconstruction (CAR) platform for neuron reconstruction at scale. This platform allows for immersive interaction and efficient collaborative editing of neuron anatomy using a variety of devices, such as desktop workstations, virtual reality headsets and mobile phones, enabling users to contribute anytime and anywhere and to take advantage of several AI-based automation tools. We tested CAR’s applicability for challenging mouse and human neurons toward scaled and faithful data production.

TopoRoot+: computing whorl and soil line traits of field-excavated maize roots from CT imaging

BackgroundThe use of 3D imaging techniques, such as X-ray CT, in root phenotyping has become more widespread in recent years. However, due to the complexity of the root structure, analyzing the resulting 3D volumes to obtain detailed architectural root traits remains a challenging computational problem. When it comes to image-based phenotyping of excavated maize root crowns, two types of root features that are notably missing from existing methods are the whorls and soil line. Whorls refer to the distinct areas located at the base of each stem node from which roots sprout in a circular pattern (Liu S, Barrow CS, Hanlon M, Lynch JP, Bucksch A. Dirt/3D: 3D root phenotyping for field-grown maize (zea mays). Plant Physiol. 2021;187(2):739–57. https://doi.org/10.1093/plphys/kiab311.). The soil line is where the root stem meets the ground. Knowledge of these features would give biologists deeper insights into the root system architecture (RSA) and the below- and above-ground root properties.ResultsWe developed TopoRoot+, a computational pipeline that produces architectural traits from 3D X-ray CT volumes of excavated maize root crowns. Building upon the TopoRoot software (Zeng D, Li M, Jiang N, Ju Y, Schreiber H, Chambers E, et al. Toporoot: A method for computing hierarchy and fine-grained traits of maize roots from 3D imaging. Plant Methods. 2021;17(1). https://doi.org/10.1186/s13007-021-00829-z.) for computing fine-grained root traits, TopoRoot + adds the capability to detect whorls, identify nodal roots at each whorl, and compute the soil line location. The new algorithms in TopoRoot + offer an additional set of fine-grained traits beyond those provided by TopoRoot. The addition includes internode distances, root traits at every hierarchy level associated with a whorl, and root traits specific to above or below the ground. TopoRoot + is validated on a diverse collection of field-grown maize root crowns consisting of nine genotypes and spanning across three years. TopoRoot + runs in minutes for a typical volume size of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:40{0}^{3}$$\\end{document} on a desktop workstation. Our software and test dataset are freely distributed on Github.ConclusionsTopoRoot + advances the state-of-the-art in image-based phenotyping of excavated maize root crowns by offering more detailed architectural traits related to whorls and soil lines. The efficiency of TopoRoot + makes it well-suited for high-throughput image-based root phenotyping.

ETENLNC: An end to end lncRNA identification and analysis framework to facilitate construction of known and novel lncRNA regulatory networks

Long non-coding RNAs (lncRNAs) play crucial roles in the regulation of gene expression and maintenance of genomic integrity through various interactions with DNA, RNA, and proteins. The availability of large-scale sequence data from various high-throughput platforms has opened possibilities to identify, predict, and functionally annotate lncRNAs. As a result, there is a growing demand for an integrative computational framework capable of identifying known lncRNAs, predicting novel lncRNAs, and inferring the downstream regulatory interactions of lncRNAs at the genome-scale. We present ETENLNC (End-To-End-Novel-Long-NonCoding), a user-friendly, integrative, open-source, scalable, and modular computational framework for identifying and analyzing lncRNAs from raw RNA-Seq data. ETENLNC employs six stringent filtration steps to identify novel lncRNAs, performs differential expression analysis of mRNA and lncRNA transcripts, and predicts regulatory interactions between lncRNAs, mRNAs, miRNAs, and proteins. We benchmarked ETENLNC against six existing tools and optimized it for desktop workstations and high-performance computing environments using data from three different species. ETENLNC is freely available on GitHub: https://github.com/EvolOMICS-TU/ETENLNC.

Rapid multiple protein sequence search by parallel and heterogeneous computation.

Protein sequence database search and multiple sequence alignment generation is a fundamental task in many bioinformatics analyses. As the data volume of sequences continues to grow rapidly, there is an increasing need for efficient and scalable multiple sequence query algorithms for super-large databases without expensive time and computational costs. We introduce Chorus, a novel protein sequence query system that leverages parallel model and heterogeneous computation architecture to enable users to query thousands of protein sequences concurrently against large protein databases on a desktop workstation. Chorus achieves over 100× speedup over BLASTP without sacrificing sensitivity. We demonstrate the utility of Chorus through a case study of analyzing a ∼1.5-TB large-scale metagenomic datasets for novel CRISPR-Cas protein discovery within 30 min. Chorus is open-source and its code repository is available at https://github.com/Bio-Acc/Chorus.

Scalable enforcement of geometric non-interference constraints for gradient-based optimization

Many design optimization problems include constraints to prevent intersection of the geometric shape being optimized with other objects or with domain boundaries. When applying gradient-based optimization to such problems, the constraint function must provide an accurate representation of the domain boundary and be smooth, amenable to numerical differentiation, and fast-to-evaluate for a large number of points. We propose the use of tensor-product B-splines to construct an efficient-to-evaluate level set function that locally approximates the signed distance function for representing geometric non-interference constraints. Adapting ideas from the surface reconstruction methods, we formulate an energy minimization problem to compute the B-spline control points that define the level set function given an oriented point cloud sampled over a geometric shape. Unlike previous explicit non-interference constraint formulations, our method requires an initial setup operation, but results in a more efficient-to-evaluate and scalable representation of geometric non-interference constraints. This paper presents the results of accuracy and scaling studies performed on our formulation. We demonstrate our method by solving a medical robot design optimization problem with non-interference constraints. We achieve constraint evaluation times on the order of 10-6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10^{-6}$$\\end{document} seconds per point on a modern desktop workstation, and a maximum on-surface error of less than 1.0% of the minimum bounding box diagonal for all examples studied. Overall, our method provides an effective formulation for non-interference constraint enforcement with high computational efficiency for gradient-based design optimization problems whose solutions require at least hundreds of evaluations of constraints and their derivatives.

Robust fast direct integral equation solver for three-dimensional doubly periodic scattering problems with a large number of layers

Frequency-domain wave scattering problems that arise in acoustics and electromagnetism can be often described by the Helmholtz equation. This work presents a boundary integral equation method for the Helmholtz equation in 3-D multilayered media with many doubly periodic smooth layers. Compared with conventional quasi-periodic Green's function method, the new method is robust at all scattering parameters. A periodizing scheme is used to decompose the solution into near- and far-field contributions. The near-field contribution uses the free-space Green's function in an integral equation on the interface in the unit cell and its immediate eight neighbors; the far-field contribution uses proxy point sources that enclose the unit cell. A specialized high-order quadrature is developed to discretize the underlying surface integral operators to keep the number of unknowns per layer small. We achieve overall linear computational complexity in the number of layers by reducing the linear system into block tridiagonal form and then solving the system directly via block LU decomposition. The new solver is capable of handling a 100-interface structure with 961.3k unknowns to 10−5 accuracy in less than 2 hours on a desktop workstation.

Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery

The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions.

Estimation of wavenumber–frequency spectra of wall pressure due to turbulent flow over a flat plate using large-eddy simulation

The wavenumber–frequency spectra (WFS) of turbulent wall pressure fluctuations are used to design underwater acoustic systems, and a method is presented to determine them for flat plate flow using large-eddy simulations (LES). First, the Reynolds averaged Navier–Stokes (RANS) analysis is carried out for flow over the plate using k–ω shear-stress transport model. Flow parameters are extracted from the output of this analysis and used to impose boundary conditions for large-eddy simulations using a smaller domain with finer mesh. Mesh convergence studies are done to establish the adequacy of the proposed meshing scheme for estimating the turbulent boundary layer wall pressures. The stream-wise velocity profiles, turbulence intensities, and power spectra obtained using LES are compared with other computational and experimental results. The time history of fluctuating pressure on the wall at various stream-wise locations is used to estimate the WFS. Estimations are made of the convective ridge as well as sub-convective and low wavenumber portions of the spectrum. The computations are performed at momentum thickness-based Reynolds numbers (Rθ) of 5761, 7988, and 7709. The effect of downstream distance on the WFS is studied by computing it at three downstream locations and shown that the downstream distance has little effect on the WFS once the flow has become fully turbulent. The use of a desktop workstation for the estimation of WFS has not been reported earlier. The results show that the WFS of turbulent pressure due to flow over more complex geometries can be estimated using a similar method.

A dynamical low-rank approach to solve the chemical master equation for biological reaction networks

Solving the chemical master equation is an indispensable tool in understanding the behavior of biological and chemical systems. In particular, it is increasingly recognized that commonly used ODE models are not able to capture the stochastic nature of many cellular processes. Solving the chemical master equation directly, however, suffers from the curse of dimensionality. That is, both memory and computational effort scale exponentially in the number of species. In this paper we propose a dynamical low-rank approach that enables the simulation of large biological networks. The approach is guided by partitioning the network into biological relevant subsets and thus avoids the use of single species basis functions that are known to give inaccurate results for biological systems. We use the proposed method to gain insight into the nature of asynchronous vs. synchronous updating in Boolean models and successfully simulate a 41 species apoptosis model on a standard desktop workstation.

Anesthesia, Coming of Age in the World of Modern In Silico Drug Design.

Anesthesia, Coming of Age in the World of Modern In Silico Drug Design.

Sitting less and moving more for improved metabolic and brain health in type 2 diabetes: ‘OPTIMISE your health’ trial protocol

BackgroundClinical practice guidelines recommend that adults with type 2 diabetes (T2D) sit less and move more throughout the day. The 18-month OPTIMISE Your Health Clinical Trial was developed to support desk-based workers with T2D achieve these recommendations. The two-arm protocol consists of an intervention and control arms. The intervention arm receives 6 months health coaching, a sit-stand desktop workstation and an activity tracker, followed by 6 months of text message support, then 6 months maintenance. The control arm receives a delayed modified intervention after 12 months of usual care. This paper describes the methods of a randomised controlled trial (RCT) evaluating the effectiveness and cost-effectiveness of the intervention, compared to a delayed intervention control.MethodsThis is a two-arm RCT being conducted in Melbourne, Australia. Desk-based workers (≥0.8 full-time equivalent) aged 35–65 years, ambulatory, and with T2D and managed glycaemic control (6.5–10.0% HbA1c), are randomised to the multicomponent intervention (target n = 125) or delayed-intervention control (target n = 125) conditions. All intervention participants receive 6 months of tailored health coaching assisting them to “sit less” and “move more” at work and throughout the day, supported by a sit-stand desktop workstation and an activity tracker (Fitbit). Participants receive text message-based extended care for a further 6-months (6–12 months) followed by 6-months of non-contact (12–18 months: maintenance). Delayed intervention occurs at 12–18 months for the control arm. Assessments are undertaken at baseline, 3, 6, 12, 15 and 18-months. Primary outcomes are activPAL-measured sitting time (h/16 h day), glycosylated haemoglobin (HbA1c; %, mmol/mol) and, cognitive function measures (visual learning and new memory; Paired Associates Learning Total Errors [adjusted]). Secondary, exploratory, and process outcomes will also be collected throughout the trial.DiscussionThe OPTIMISE Your Health trial will provide unique insights into the benefits of an intervention aimed at sitting less and moving more in desk-bound office workers with T2D, with outcomes relevant to glycaemic control, and to cardiometabolic and brain health. Findings will contribute new insights to add to the evidence base on initiating and maintaining behaviour change with clinical populations and inform practice in diabetes management.Trial registrationANZCTRN12618001159246.

TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging

Background3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture.ResultsWe present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image.ConclusionsTopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.

ROA-CONS: Raccoon Optimization for Job Scheduling

High-performance computing comprises thousands of processing powers in order to deliver higher performance computation than a typical desktop computer or workstation in order to solve large problems in science, engineering, or business. The scheduling of these machines has an important impact on their performance. HPC’s job scheduling is intended to develop an operational strategy which utilises resources efficiently and avoids delays. An optimised schedule results in greater efficiency of the parallel machine. In addition, processes and network heterogeneity is another difficulty for the scheduling algorithm. Another problem for parallel job scheduling is user fairness. One of the issues in this field of study is providing a balanced schedule that enhances efficiency and user fairness. ROA-CONS is a new job scheduling method proposed in this paper. It describes a new scheduling approach, which is a combination of an updated conservative backfilling approach further optimised by the raccoon optimisation algorithm. This algorithm also proposes a technique of selection that combines job waiting and response time optimisation with user fairness. It contributes to the development of a symmetrical schedule that increases user satisfaction and performance. In comparison with other well-known job scheduling algorithms, the simulation assesses the effectiveness of the proposed method. The results demonstrate that the proposed strategy offers improved schedules that reduce the overall system’s job waiting and response times.

Adiabatic quantum linear regression

A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on classical computers. Adiabatic quantum computers have been shown to excel at solving optimization problems, and therefore, we believe, present a promising alternative to improve machine learning training times. In this paper, we present an adiabatic quantum computing approach for training a linear regression model. In order to do this, we formulate the regression problem as a quadratic unconstrained binary optimization (QUBO) problem. We analyze our quantum approach theoretically, test it on the D-Wave adiabatic quantum computer and compare its performance to a classical approach that uses the Scikit-learn library in Python. Our analysis shows that the quantum approach attains up to {2.8 times } speedup over the classical approach on larger datasets, and performs at par with the classical approach on the regression error metric. The quantum approach used the D-Wave 2000Q adiabatic quantum computer, whereas the classical approach used a desktop workstation with an 8-core Intel i9 processor. As such, the results obtained in this work must be interpreted within the context of the specific hardware and software implementations of these machines.

GPU_PBTE: an efficient solver for three and four phonon scattering rates on graphics processing units

Lattice thermal conductivity (LTC) is a key parameter for many technological applications. Based on the Peierls–Boltzmann transport equation (PBTE), many unique phonon transport properties of various materials were revealed. Accurate calculation of LTC with PBTE, however, is a time-consuming task, especially for compounds with a complex crystal structure or taking high-order phonon scattering into consideration. Graphical processing units (GPUs) have been extensively used to accelerate scientific simulations, making it possible to use a single desktop workstation for calculations that used to require supercomputers. Due to its fundamental differences from traditional processors, GPUs are especially suited for executing a large group of similar tasks with minimal communication, but require completely different algorithm design. In this paper, we provide a new algorithm optimized for GPUs, where a two-kernel method is used to avoid divergent branching. A new open-source code, GPU_PBTE, is developed based on the proposed algorithm. As demonstrations, we investigate the thermal transport properties of silicon and silicon carbide, and find that accurate and reliable LTC can be obtained by our software. GPU_PBTE performed on NVIDIA Tesla V100 can extensively improve double precision performance, making it two to three orders of magnitude faster than our CPU version performed on Intel Xeon CPU Gold 6248 @2.5 GHz. Our work also provides an idea of accelerating calculations with other novel hardware that may come out in the future.

Developing a BIM-Based MUVR Treadmill System for Architectural Design Review and Collaboration

Virtual reality (VR) is quickly becoming the medium of choice for various architecture, engineering, and construction applications, such as design visualization, construction planning, and safety training. In particular, this technology offers an immersive experience to enhance the way architects review their design with team members. Traditionally, VR has used a desktop PC or workstation setup inside a room, yielding the risk of two users bump into each other while using multiuser VR (MUVR) applications. MUVR offers shared experiences that disrupt the conventional single-user VR setup, where multiple users can communicate and interact in the same virtual space, providing more realistic scenarios for architects in the design stage. However, this shared virtual environment introduces challenges regarding limited human locomotion and interactions, due to physical constraints of normal room spaces. This study thus presented a system framework that integrates MUVR applications into omnidirectional treadmills. The treadmills allow users an immersive walking experience in the simulated environment, without space constraints or hurt potentialities. A prototype was set up and tested in several scenarios by practitioners and students. The validated MUVR treadmill system aims to promote high-level immersion in architectural design review and collaboration.

ENABLE HIGH PERFORMANCE COMPUTING IN CLOUD: A REVIEW

High Performance Computing (HPC) resulting whole computing power in a way that delivers much higher performance than it could get in typical desktop computer or workstation. High Performance Computing (HPC) allows scientists and engineers to solve complex science, engineering, and business problems using applications that require high bandwidth, enhanced networking, and very high compute capabilities. HPC's democratization has been driven particularly by cloud computing, which has given scientists access to supercomputing-like features as the pay as you go. This paper will provide an overview on benets, challenges and future of HPC in cloud.

MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series

Abstract. With MLAir (Machine Learning on Air data) we created a software environment that simplifies and accelerates the exploration of new machine learning (ML) models, specifically shallow and deep neural networks, for the analysis and forecasting of meteorological and air quality time series. Thereby MLAir is not developed as an abstract workflow, but hand in hand with actual scientific questions. It thus addresses scientists with either a meteorological or an ML background. Due to their relative ease of use and spectacular results in other application areas, neural networks and other ML methods are also gaining enormous momentum in the weather and air quality research communities. Even though there are already many books and tutorials describing how to conduct an ML experiment, there are many stumbling blocks for a newcomer. In contrast, people familiar with ML concepts and technology often have difficulties understanding the nature of atmospheric data. With MLAir we have addressed a number of these pitfalls so that it becomes easier for scientists of both domains to rapidly start off their ML application. MLAir has been developed in such a way that it is easy to use and is designed from the very beginning as a stand-alone, fully functional experiment. Due to its flexible, modular code base, code modifications are easy and personal experiment schedules can be quickly derived. The package also includes a set of validation tools to facilitate the evaluation of ML results using standard meteorological statistics. MLAir can easily be ported onto different computing environments from desktop workstations to high-end supercomputers with or without graphics processing units (GPUs).

Hybrid parallel iterative sparse linear solver framework for reservoir geomechanical and flow simulation

We discuss new developments of a hybrid parallel iterative sparse linear solver framework focused on petroleum reservoir flow and geomechanical simulation. It runs efficiently on several platforms, from desktop workstations to clusters of multicore nodes, with or without multiple GPUs, using a two-tier hierarchical architecture for distributed matrices and vectors. Results show good parallel scalability. Comparisons with a well-established library and a proprietary commercial solver indicate that our solver is competitive with the best available tools. We present results of the solver's application to simulations of real and synthetic reservoir models of up to billions of unknowns, running on CPUs and GPUs on up to 2000 processes.

A parallel, object-oriented framework for unsteady free-wake analysis of multi-rotor/wing systems

The development, validation, and applications of an object-oriented free-wake solver for multi-rotor and fixed-wing systems are outlined here. Advantages of utilizing the object-oriented philosophy for modeling the multi-rotor/wing free-wake problem are described. To explore the feasibility of utilizing conventional desktop workstations, the vortex lattice methodology’s time complexity is examined as an evolving n-body problem and the advantage gained from employing wake roll up models is demonstrated. Modifications in typical formulae to leverage features of multi-core systems and improvements in computational performance obtained from these modifications are illustrated using a roofline analysis. The free-wake solver incorporating these optimizations is then employed—after extensive validation—to simulate a distributed propulsion aircraft and a multi-rotor urban mobility vehicle. The paper also proposes a novel lattice skew parameter for monitoring instabilities in wake solutions commonly observed in rotorcraft free-wake analysis.