Large Data Analysis Research Articles

변전소의 PMU 생성 빅 데이터의 통계적 분석을 통한 안정적 운용 탐색

In order to monitor, analyze, and control RESs operated in connection with the power system in real-time, the installation and distribution of PMUs are expanding. Accordingly, there is a need for a method that can effectively use the large-capacity big data generated by the PMU. In Gangwon-do, more than 3GW of RES is expected to be connected by 2025. In this paper, in order to cope with the volatility of RESs and to improve reliability of power system in Gangwon-do, the effect on the power system is effectively investigated through the PMU-generated big data of S/S connected with RESs. First, big data is collected using the PMU and F/R installed in the S/S. The instantaneous voltage change rate is calculated, and statistical techniques such as boxplot, kernel density, correlation index, and harmonic average are implemented using R programming, which is specialized for large data statistics and analysis. Finally, the power quality is analyzed through the implemented technique. So, it is thought to be helpful in research on the stable operation of RESs linked systems.

Real-World Anticoagulatory Treatment After Transcatheter Aortic Valve Replacement: A Retrospective, Observational Study on 4,800 Patients.

Background: Transcatheter aortic valve replacement (TAVR) has developed to the therapy of choice for patients with symptomatic severe aortic stenosis who are unsuitable for surgical aortic valve replacement and elderly patients with intermediate or high operative risk. However, the optimal anticoagulant therapy post-TAVR still remains a matter of debate.Aims: This study sought to investigate current anticoagulant treatment patterns and clinical outcome in patients undergoing TAVR.Methods: In a retrospective study based on anonymized health claims data of approximately seven million Germans with statutory health insurance (InGef database), anticoagulant treatment regimens were assessed using any drug prescription post discharge within the first 90 days after TAVR procedure. Clinical events between 30 days and 6 months were examined by treatment regime.Results: The study population comprised 4,812 patients with TAVR between 2014 and 2018: 29.4% received antiplatelet monotherapy, 17.8% dual antiplatelet therapy, 17.4% oral anticoagulation (OAC) plus antiplatelet therapy, 12.9% OAC monotherapy, 2.2% triple therapy and 19.2% did not receive any anticoagulatory drugs. Sixty-four percentage of patients with OAC received direct oral anticoagulants (DOAC). Hence, 68% of all patients were treated non-adherent to current guidelines. Forty percentage of patients with OAC prior to TAVR did not have any OAC after TAVR. The adjusted risk of all-cause mortality was significantly increased in patients with OAC (HR 1.40, 95% CI 1.03–1.90, p = 0.03) and no anticoagulatory treatment (HR 3.95, 95% CI 2.95–5.27, p < 0.0001) when compared to antiplatelet monotherapy.Conclusions: This large real-world data analysis demonstrates substantial deviations from guideline recommendations and treatment after TAVR. Considering relevant differences in clinical outcome across treatment groups, major effort is warranted to examine underlying causes and improve guideline adherence.

Data parsing in mass spectrometry imaging using R Studio and Cardinal: A tutorial

Mass spectrometry imaging (MSI) has emerged as a rapidly expanding field in the MS community. The analysis of large spectral data is further complicated by the added spatial dimension of MSI. A plethora of resources exist for expert users to begin parsing MSI data in R, but there is a critical lack of guidance for absolute beginners. This tutorial is designed to serve as a one-stop guide to start using R with MSI data and describe the possibilities that data science can bring to MSI analysis.

Multi-Index Ecoacoustics Analysis for Terrestrial Soundscapes: A New Semi-Automated Approach Using Time-Series Motif Discovery and Random Forest Classification

High rates of biodiversity loss caused by human-induced changes in the environment require new methods for large scale fauna monitoring and data analysis. While ecoacoustic monitoring is increasingly being used and shows promise, analysis and interpretation of the big data produced remains a challenge. Computer-generated acoustic indices potentially provide a biologically meaningful summary of sound, however, temporal autocorrelation, difficulties in statistical analysis of multi-index data and lack of consistency or transferability in different terrestrial environments have hindered the application of those indices in different contexts. To address these issues we investigate the use of time-series motif discovery and random forest classification of multi-indices through two case studies. We use a semi-automated workflow combining time-series motif discovery and random forest classification of multi-index (acoustic complexity, temporal entropy, and events per second) data to categorize sounds in unfiltered recordings according to the main source of sound present (birds, insects, geophony). Our approach showed more than 70% accuracy in label assignment in both datasets. The categories assigned were broad, but we believe this is a great improvement on traditional single index analysis of environmental recordings as we can now give ecological meaning to recordings in a semi-automated way that does not require expert knowledge and manual validation is only necessary for a small subset of the data. Furthermore, temporal autocorrelation, which is largely ignored by researchers, has been effectively eliminated through the time-series motif discovery technique applied here for the first time to ecoacoustic data. We expect that our approach will greatly assist researchers in the future as it will allow large datasets to be rapidly processed and labeled, enabling the screening of recordings for undesired sounds, such as wind, or target biophony (insects and birds) for biodiversity monitoring or bioacoustics research.

Analysing discrete dislocation data using alignment and curvature tensors

Analysis of large scale discrete dislocation data requires the characterisation of complex dislocation networks by suitable average quantities. In the current work, we suggest dislocation alignment tensors and closely related curvature tensors as easily extractable and intelligible measures of geometrical and topological characteristics of dislocation distributions. We provide formulae for extracting these measures from discrete dislocation data based on straight segments. Examples for interpreting and visualising these measures are provided for a simple configuration and two more involved results from discrete dislocation simulations. We suggest the alignment and curvature tensors for wider use in plasticity research.

ANALYSIS OF EXISTING SOLUTIONS FOR IMPROVING THE MEASURING AND COMPUTING COMPLEXES OF HEAT SUPPLY NETWORKS

In the presented article, the team of authors considers the existing methods and the main modern technical solutions that are currently implemented in different countries in the diagnosis of heat supply networks. There is a selection of the main directions in the development and design of heat supply networks, which have already been implemented or supported by scientific teams from different countries. Various methods and technical features of diagnostics are reviewed, strengths and weaknesses of the presented solutions are highlighted. The reviewed works were subjected to detailed analysis, which revealed the presence of a high interest of the scientific and industrial community in the integration and improvement of existing digital technologies in the development of heat supply systems, which would be closely related to forecasting and modeling processes in this industry. The team of authors highlights the main vectors for the development of this sector, citing an example of a significant increase in the degree of digitalization of final products, which makes it possible to use data analytics to obtain effective technical solutions regarding heat supply networks. Separately, the positive experience of different countries in this industry is noted when using neural networks not only in the design of heat supply networks, but also as a target industry as a whole. Assumptions are put forward about the need for a detailed analysis of the existing foreign and domestic experience, as well as scientific developments in this area, in order to determine the most suitable technical solutions on the territory of the Russian Federation, which will take into account the climatic characteristics of the country and be based on methods of large data analysis, computer vision and simulation. modeling.

Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)

Background. Efficient analysis of large image data produced in greenhouse phenotyping experiments is often challenged by a large variability of optical plant and background appearance which requires advanced classification model methods and reliable ground truth data for their training. In the absence of appropriate computational tools, generation of ground truth data has to be performed manually, which represents a time-consuming task. Methods. Here, we present a efficient GUI-based software solution which reduces the task of plant image segmentation to manual annotation of a small number of image regions automatically pre-segmented using k-means clustering of Eigen-colors (kmSeg). Results. Our experimental results show that in contrast to other supervised clustering techniques k-means enables a computationally efficient pre-segmentation of large plant images in their original resolution. Thereby, the binary segmentation of plant images in fore- and background regions is performed within a few minutes with the average accuracy of 96–99% validated by a direct comparison with ground truth data. Conclusions. Primarily developed for efficient ground truth segmentation and phenotyping of greenhouse-grown plants, the kmSeg tool can be applied for efficient labeling and quantitative analysis of arbitrary images exhibiting distinctive differences between colors of fore- and background structures.

Rates of Discordant Information in the National Cancer Database

Publications utilizing the National Cancer Database (NCDB) have risen over 1000-fold over the past decade. While some studies address identical questions, sample sizes and thusly conclusions are often different. Furthermore, there is little correlation between NCDB analysis and level I data. Many of these publications are not adequately addressing the methodology with which they create their sample from the initial dataset. Our goal in this analysis was to address the rate of discordant information in the NCDB, primarily with TNM staging, to underscore the needs to adequately display methods.We utilized the NCDB Non-Small Cell Cancer Database to demonstrate the rate of errors and referenced the NCDB data dictionary to compile terms. We compared rates of differing information between NCD terms of "Tumor Size" to T stage ("TNM We also compared group stage ("TNM_Stage Group") to N stage (TNM_CLIN_N"), M stage ("TNM_CLIN_M"). We further assessed differences between "TNM_CLIN_M" and listed sites of metastatic disease as well as comparing whether Radiation administrations was concordant with given doses.We evaluated the entire database with an initial size of N = 121,930 patients. After including only complete information only on TNM and tumor size criteria we evaluated concordance for 106262 patients. We noted marked discordant rates between T stage and Tumor size. Specifically, between tumors that were staged T1 did not meet correlate with their "Tumor Size" criteria in 12.5% of the time, T2 tumors did not meet size criteria in 8.5% of instances. Patients that were stage as M0 were only given a group stage of IV in 2.5% of instances while in 0.75% of instances was a group stage IV given when there was no identified metastatic site. For N stage, there was an error rate of approximately 2.6% between reported stage. With regard to "RAD Administered" there was a 1.7% error rate in dose being reported when it was listed that no radiation was administered.This demonstration serves to show that proper explanation of how the primary sample size is achieved is paramount. Depending on how one builds their data set (i.e., utilizing tumor size vs T1) could alter sample sizes and lead to the variance noted in NCDB studies evaluating similar questions. A comprehensive detailing of, or attachment of precise methodology can result in more overall transparency in analysis of large database oncology data.

PyActigraphy: Open-source python package for actigraphy data visualization and analysis.

Over the past 40 years, actigraphy has been used to study rest-activity patterns in circadian rhythm and sleep research. Furthermore, considering its simplicity of use, there is a growing interest in the analysis of large population-based samples, using actigraphy. Here, we introduce pyActigraphy, a comprehensive toolbox for data visualization and analysis including multiple sleep detection algorithms and rest-activity rhythm variables. This open-source python package implements methods to read multiple data formats, quantify various properties of rest-activity rhythms, visualize sleep agendas, automatically detect rest periods and perform more advanced signal processing analyses. The development of this package aims to pave the way towards the establishment of a comprehensive open-source software suite, supported by a community of both developers and researchers, that would provide all the necessary tools for in-depth and large scale actigraphy data analyses.

Mineral quantification at deposit scale using drill-core hyperspectral data: A case study in the Iberian Pyrite Belt

Drill-core analysis is paramount for the characterization of deposits in mineral exploration. Over the past years, the use of hyperspectral (HS) sensors has rapidly increased to improve the reliability and efficiency of core logging. However, scanning drill-core samples of an entire mineral deposit entails several complex challenges, from transport logistics to large scale data management and analysis. Hence, academic studies on new applications of drill-core HS data at a mineral deposit scale remain rare.We present a semi-automated workflow for large scale interpretation of HS data, founded on a novel approach of mineral mapping based on a supervised dictionary learning technique. This approach exploits the complementary information from scanning electron microscopy based automated mineralogy and hyperspectral imaging techniques for estimating mineral quantities along all boreholes. We propose that it is effectively possible to propagate the mineral quantification to the entire borehole from small samples with high resolution mineralogical information strategically selected throughout the deposit.We showcase this approach on data acquired in the Elvira shale-hosted volcanogenic massive sulphide (VMS) deposit located at the Iberian Pyrite Belt (IPB), where 7000 m of drill-core were acquired along 80 boreholes. Resulting maps provide insights on the controls on the mineral assemblages and chemical composition of specific minerals across the whole volume at several spatial scales, from large scale variations within apparently homogeneous black shales to small scale mineral composition variations, of potential use as vectors towards mineralization. This approach adds value to the core data, allowing for a better understanding of the geological setting of the Elvira deposit and providing valuable insights for future exploration targeting in the region.This approach based on machine learning can easily be transposed to different ore deposits with a limited input from a geologist.

Large spatial data modeling and analysis: A Krylov subspace approach

AbstractEstimating the parameters of spatial models for large spatial datasets can be computationally challenging, as it involves repeated evaluation of sizable spatial covariance matrices. In this paper, we aim to develop Krylov subspace‐based methods that are computationally efficient for large spatial data. Specifically, we approximate the inverse and the log‐determinant of the spatial covariance matrix in the log‐likelihood function via conjugate gradient and stochastic Lanczos on a Krylov subspace. These methods reduce the computational complexity from to and for dense and sparse matrices, respectively. Moreover, we quantify the difference between the approximated log‐likelihood function and the original log‐likelihood function and establish the consistency of parameter estimates. Simulation studies are conducted to examine the computational efficiency as well as the finite‐sample properties. For illustration, our methodology is applied to analyze a large dataset comprising LiDAR estimates of forest canopy height in western Alaska.

Impact of children born with low birth weight on stunting and wasting in Sindh province of Pakistan: a propensity score matching approach

Low Birth Weight (LBW) is considered as a major public health issue and leading cause of neonatal death. Almost one in four newborns are reported as underweight in Pakistan. Children born with low birth weight are highly vulnerable to develop diseases and death and/or remain undernourished (i.e., stunted and wasted). This study determines the LBW newborns are more prone to develop stunting and wasting in province of Sindh, Pakistan. Moreover, regression-based estimation of the impact of LBW on the child health outcomes of under five years of age, may be prone to selection bias because of the nature of non-experimental data set, thus, propensity score matching methods are used in this study. Data for this study was used from Multiple Indicators Cluster Survey (MICS-2014). MICS is a two-stage, stratified cluster sampling household level data covering urban and rural areas and consists of 19,500 households from five administrative divisions and 28 districts of Sindh province of Pakistan. The total sample size of children less than five years of age after cleaning the data are 7781, of which 2095 are LBW having birth weight categorized as “smaller than average and very small” and 5686 are normal birth weight (NBW) having birth weight very large, larger than average, and average. This study employed propensity score matching (PSM) regression methods to understand whether the children born as low birth weight are more prone to stunting and wasting and/or both. In province of Sindh, moderate wasting children under five years were 21%, severe wasting 6% and both wasting and stunting 10%. The propensity score results are shown significant in all groups. Specifically, all four types of PSM methods confirm a significant difference in the potential outcome variables—meaning that a child born with LBW has a significant adverse effect on the potential child health outcome variables (stunting, wasting and both). Thus, the propensity score matching findings confirm a significant and adverse effect of LBW on potential health outcomes of under five children. Similarly, low birth weight children are significantly more likely to be moderately wasted (OR = 1.5, CI = 1.3–1.6) and severely wasted (OR = 1.6, CI = 1.3–2.0) and both (stunted and wasted, OR = 2.0, CI = 1.7–2.3) as compared to children with normal birth weight. Male children, if born with low birth weight, are significantly more likely to be moderately wasted (OR = 1.3, CI = 1.1–1.5) and both (wasted and stunted, OR = 1.3, CI = 1.1–1.5) than girls. This large data analysis finding proved that the LBW newborns are on higher risk to develop wasting and stunting in Pakistan.

Machine learning for the educational sciences

AbstractMachine learning (ML) provides a powerful framework for the analysis of high‐dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non‐linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows, as larger and more complex datasets become available through massive open online courses (MOOCs) and large‐scale investigations. The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. To provide educational researchers with an elaborate introduction to the topic, we provide an instructional summary of the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. Specifically, we (1) provide an overview of the types of data suitable for ML and (2) give practical advice for the application of ML methods. In each section, we provide analytical examples and reproducible R code. Also, we provide an extensive Appendix on ML‐based applications for education. This instructional summary will help educational scientists and practitioners to prepare for the promises and threats that come with the shift towards digitisation and large‐scale assessment in education. Context and implicationsRationale for this studyIn 2020, the worldwide SARS‐COV‐2 pandemic forced the educational sciences to perform a rapid paradigm shift with classrooms going online around the world—a hardly novel but now strongly catalysed development. In the context of data‐driven education, this paper demonstrates that the widespread adoption of machine learning techniques is central for the educational sciences and shows how these methods will become crucial tools in the collection and analysis of data and in concrete educational applications. Helping to leverage the opportunities and to avoid the common pitfalls of machine learning, this paper provides educators with the theoretical, conceptual and practical essentials.Why the new findings matterThe process of teaching and learning is complex, multifaceted and dynamic. This paper contributes a seminal resource to highlight the digitisation of the educational sciences by demonstrating how new machine learning methods can be effectively and reliably used in research, education and practical application.Implications for educational researchers and policy makersThe progressing digitisation of societies around the globe and the impact of the SARS‐COV‐2 pandemic have highlighted the vulnerabilities and shortcomings of educational systems. These developments have shown the necessity to provide effective educational processes that can support sometimes overwhelmed teachers to digitally impart knowledge on the plan of many governments and policy makers. Educational scientists, corporate partners and stakeholders can make use of machine learning techniques to develop advanced, scalable educational processes that account for individual needs of learners and that can complement and support existing learning infrastructure. The proper use of machine learning methods can contribute essential applications to the educational sciences, such as (semi‐)automated assessments, algorithmic‐grading, personalised feedback and adaptive learning approaches. However, these promises are strongly tied to an at least basic understanding of the concepts of machine learning and a degree of data literacy, which has to become the standard in education and the educational sciences.Demonstrating both the promises and the challenges that are inherent to the collection and the analysis of large educational data with machine learning, this paper covers the essential topics that their application requires and provides easy‐to‐follow resources and code to facilitate the process of adoption.

Open Forest Data: Digitalizing and building an online repository

Poland is characterised by a relatively high variety of living organisms attributed to terrestrial and water environments. Currently, close to 57.000 species of living organisms are described that occur in Poland (Symonides 2008), including lowland and mountain species, those attributed to oceanic and continental areas, as well as species from forested and open habitats. Poland comprehensively represents biodiversity of living organisms on a continental scale and thus, is considered to have an important role for biodiversity maintenance. The Mammal Research Institute of Polish Academy of Sciences (MRI PAS), located in Białowieża Forest, a UNESCO Heritage Site, has been collecting biodiversity data for 90 years. However, a great amount of data gathered over the years, especially old data, is gradually being forgotten and hard to access. Old catalogues and databases have never been digitalized or publicly shared, and not many Polish scientists are aware of the existence of such resources, not to mention the rest of the scientific world. Recognizing the need for an online, interoperable platform, following FAIR data principles (findable, accessible, interoperable, reusable), where biodiversity and scientific data can be shared, MRI PAS took a lead in creation of an Open Forest Data (OFD) repository. OpenForestData.pl (Fig. 1) is a newly created (2020) digital repository, designed to provide access to natural sciences data and provide scientists with an infrastructure for storing, sharing and archiving their research outcomes. Creating such a platform is a part of an ongoing development of life sciences in Poland, aiming for an open, modern science, where data are published as free-access. OFD also allows for the consolidation of natural science data, enabling the use and processing of shared data, including API (Application Programming Interface) tools. OFD is indexed by the Directory of Open Repositories (OpenDOAR) and Registry of Research Data Repositories (re3data). The OFD platform is based entirely on reliable, globally recognized open source software: DATAVERSE, an interactive database app which supports sharing, storing, exploration, citation and analysis of scientific data; GEONODE, a content management geospatial system used for storing, publicly sharing and visualising vector and raster layers, GRAFANA, a system meant for storing and analysis of metrics and large scale measurement data, as well as visualisation of historical graphs at any time range and analysis for trends; and external tools for database storage (Orthanc) and data visualisation (Orthanc plugin Osimis Web Viewer and Online 3D Viewer (https://3dviewer.net/), which were integrated with the system mechanism of Dataverse. Furthermore, according to the need for specimen description, Darwin Core (Wieczorek et al. 2012) metadata schema was decided to be the most suitable for specimen and collections description and mapped into a Dataverse additional metadata block. The use of Darwin Core is based on the same file format, the Darwin Core Archive (DwC-A) which allows for sharing data using common terminology and provides the possibility for easy evaluation and comparison of biodiversity datasets. It allows the contributors to OFD to optionally choose Darwin Core for object descriptions making it possible to share biodiversity datasets in a standardized way for users to download, analyse and compare. Currently, OFD stores more than 10.000 datasets and objects from the collections of Mammal Research Institute of Polish Academy of Sciences and Forest Science Institute of Białystok University of Technology. The objects from natural collections were digitalized, described, catalogued and made public in free-access. OFD manages seven types of collection materials: 3D and 2D scans of specimen in Herbarium, Fungarium, Insect and Mammal Collections, images from microscopes (including stereoscopic and scanning electron microscopes), morphometric measurements, computed tomography and microtomography scans in Mammal Collection, mammal telemetry data, satellite imagery, geospatial climatic and environmental data, georeferenced historical maps. 3D and 2D scans of specimen in Herbarium, Fungarium, Insect and Mammal Collections, images from microscopes (including stereoscopic and scanning electron microscopes), morphometric measurements, computed tomography and microtomography scans in Mammal Collection, mammal telemetry data, satellite imagery, geospatial climatic and environmental data, georeferenced historical maps. In the OFD repository, researchers have the possibility to share data in standardized way, which nowadays is often a requirement during the publishing process of a scientific article. Beside scientists, OFD is designed to be open and free for students and specialists in nature protection, but also for officials, foresters and nature enthusiasts. Creation of the OFD repository supports the development of citizen science in Poland, increases visibility and access to published data, improves scientific collaboration, exchange and reuse of data within and across borders.

Efficient Model Selection for Moisture Ratio Removal of Seaweed Using Hybrid Of Sparse And Robust Regression Analysis

The Internet of things ((IoT) consisted of physical devices networks such as sensors, home appliances, electronics, and software’s. It enables us to collect and exchange data in several fields. After data collection from IoT, variable selection is considered a major problem because many variables are involved in real life datasets. The current study focused on large data analysis of the problem of model selection, including interaction terms. The dataset used in this study is taken from solar drier with moisture ratio removal (%) as dependent variable while ambient temperature, chamber temperature, collector temperature, chamber relative humidity, ambient relative humidity, and solar radiation as independent variables. LASSO with Huber M, LASSO with Hampel M and LASSO with Bisquare M are proposed in this study. Comparison of proposed techniques are made with ridge regression and OLS (ordinary least square) after multicollinearity test and coefficient test. MAPE (mean absolute percentage error) is calculated for the efficient selected model to forecast. As a result, the model using LASSO with Bisquare-M provides a minimum MAPE value for the best efficient model. Thus, the resulting model with the selected variables can be used to predict Moisture Ratio Removal (%) to determine seaweed drying behavior.

The threshold conjecture for the energy critical hyperbolic Yang–Mills equation

This article represents the fourth and final part of a four-paper sequence whose aim is to prove the Threshold Conjecture as well as the more general Dichotomy Theorem for the energy critical 4+1-dimensional hyperbolic Yang–Mills equation. The Threshold Theorem asserts that topologically trivial solutions with energy below twice the ground state energy are global and scatter. The Dichotomy Theorem applies to solutions in arbitrary topological class with large energy, and provides two exclusive alternatives: Either the solution is global and scatters, or it bubbles off a soliton in either finite time or infinite time. Using the caloric gauge developed in the first paper, the continuation/scattering criteria established in the second paper, and the large data analysis in an arbitrary topological class at optimal regularity in the third paper, here we perform a blow-up analysis that shows that the failure of global well-posedness and scattering implies either the existence of a soliton with at most the same energy bubbling off, or the existence of a non-trivial self-similar solution. The proof is completed by showing that the latter solutions do not exist.

Assessing the Bowing Technique in Violin Beginners Using MIMU and Optical Proximity Sensors: A Feasibility Study.

Bowing is the fundamental motor action responsible for sound production in violin playing. A lot of effort is required to control such a complex technique, especially at the beginning of violin training, also due to a lack of quantitative assessments of bowing movements. Here, we present magneto-inertial measurement units (MIMUs) and an optical sensor interface for the real-time monitoring of the fundamental parameters of bowing. Two MIMUs and a sound recorder were used to estimate the bow orientation and acquire sounds. An optical motion capture system was used as the gold standard for comparison. Four optical sensors positioned on the bow stick measured the stick–hair distance. During a pilot test, a musician was asked to perform strokes using different sections of the bow at different paces. Distance data were used to train two classifiers, a linear discriminant (LD) classifier and a decision tree (DT) classifier, to estimate the bow section used. The DT classifier reached the best classification accuracy (94.2%). Larger data analysis on nine violin beginners showed that the orientation error was less than 2°; the bow tilt correlated with the audio information . The results confirmed that the interface provides reliable information on the bowing technique that might improve the learning performance of violin beginners.

A codesign framework for online data analysis and reduction

AbstractScience applications preparing for the exascale era are increasingly exploring in situ computations comprising of simulation‐analysis‐reduction pipelines coupled in‐memory. Efficient composition and execution of such complex pipelines for a target platform is a codesign process that evaluates the impact and tradeoffs of various application‐ and system‐specific parameters. In this article, we describe a toolset for automating performance studies of composed HPC applications that perform online data reduction and analysis. We describe Cheetah, a new framework for composing parametric studies on coupled applications, and Savanna, a runtime engine for orchestrating and executing campaigns of codesign experiments. This toolset facilitates understanding the impact of various factors such as process placement, synchronicity of algorithms, and storage versus compute requirements for online analysis of large data. Ultimately, we aim to create a catalog of performance results that can help scientists understand tradeoffs when designing next‐generation simulations that make use of online processing techniques. We illustrate the design of Cheetah and Savanna, and present application examples that use this framework to conduct codesign studies on small clusters as well as leadership class supercomputers.

Exploring the Mechanism of Action of Herbal Medicine (Gan-Mai-Da-Zao Decoction) for Poststroke Depression Based on Network Pharmacology and Molecular Docking.

Background Poststroke depression (PSD) is the most common and serious neuropsychiatric complication occurring after cerebrovascular accidents, seriously endangering human health while also imposing a heavy burden on society. Nevertheless, it is difficult to control disease progression. Gan-Mai-Da-Zao Decoction (GMDZD) is effective for PSD, but its mechanism of action in PSD is unknown. In this study, we explored the mechanism of action of GMDZD in PSD treatment using network pharmacology and molecular docking. Material and methods. We obtained the active components of all drugs and their targets from the public database TCMSP and published articles. Then, we collected PSD-related targets from the GeneCards and OMIM databases. Cytoscape 3.8.2 was applied to construct PPI and composite target disease networks. In parallel, the DAVID database was used to perform GO and KEGG enrichment analyses to determine the biological processes enriched in the treatment-related drugs in vivo. Finally, molecular docking was used to verify the association between the main active ingredients and their targets. Results The network pharmacological analysis of GMDZD in PSD revealed 107 active ingredients with important biological effects, including quercetin, luteolin, kaempferol, naringenin, and isorhamnetin. In total, 203 potential targets for the treatment of this disease were screened, including STAT3, JUN, TNF, TPT53, AKT1, and EGFR. These drugs are widely enriched in a series of signaling pathways, such as TNF, HIF-1, and toll-like receptor. Moreover, molecular docking analysis showed that the core active components were tightly bound to their core targets, further confirming their anti-PSD effects. Conclusion This prospective study was based on the integrated analysis of large data using network pharmacology technology to explore the feasibility of GMDZD for PSD treatment that was successfully validated by molecular docking. It reflects the multicomponent and multitarget characteristics of Chinese medicine and, more importantly, brings hope for the clinical treatment of PSD.

LLAMA: a robust and scalable machine learning pipeline for analysis of large scale 4D microscopy data: analysis of cell ruffles and filopodia

BackgroundWith recent advances in microscopy, recordings of cell behaviour can result in terabyte-size datasets. The lattice light sheet microscope (LLSM) images cells at high speed and high 3D resolution, accumulating data at 100 frames/second over hours, presenting a major challenge for interrogating these datasets. The surfaces of vertebrate cells can rapidly deform to create projections that interact with the microenvironment. Such surface projections include spike-like filopodia and wave-like ruffles on the surface of macrophages as they engage in immune surveillance. LLSM imaging has provided new insights into the complex surface behaviours of immune cells, including revealing new types of ruffles. However, full use of these data requires systematic and quantitative analysis of thousands of projections over hundreds of time steps, and an effective system for analysis of individual structures at this scale requires efficient and robust methods with minimal user intervention.ResultsWe present LLAMA, a platform to enable systematic analysis of terabyte-scale 4D microscopy datasets. We use a machine learning method for semantic segmentation, followed by a robust and configurable object separation and tracking algorithm, generating detailed object level statistics. Our system is designed to run on high-performance computing to achieve high throughput, with outputs suitable for visualisation and statistical analysis. Advanced visualisation is a key element of LLAMA: we provide a specialised tool which supports interactive quality control, optimisation, and output visualisation processes to complement the processing pipeline. LLAMA is demonstrated in an analysis of macrophage surface projections, in which it is used to i) discriminate ruffles induced by lipopolysaccharide (LPS) and macrophage colony stimulating factor (CSF-1) and ii) determine the autonomy of ruffle morphologies.ConclusionsLLAMA provides an effective open source tool for running a cell microscopy analysis pipeline based on semantic segmentation, object analysis and tracking. Detailed numerical and visual outputs enable effective statistical analysis, identifying distinct patterns of increased activity under the two interventions considered in our example analysis. Our system provides the capacity to screen large datasets for specific structural configurations. LLAMA identified distinct features of LPS and CSF-1 induced ruffles and it identified a continuity of behaviour between tent pole ruffling, wave-like ruffling and filopodia deployment.