Standard Data Format Research Articles

Interactive Middleware Services for Heterogeneous Systems

Computing has become more invisible, widespread and ubiquitous since the inception of the Internet of Things (IoT) and Web of Things. Multiple devices that surround us meet user’s requirements everywhere. Multiple Middleware Framework (MF) designs have come into existence because of the rapid development of interactive services in Heterogeneous Systems. This resulted in the delivery of interactive services throughout Heterogeneous Environments (HE). Users are given free navigation between devices in a widespread environment and continuously interact with each other from any chosen device. Numerous interactive devices with recent interactive platforms (for example, Smart Phones, Mobile Phones, Personal Computer (PC) and Personal Digital Assistant (PDA)) are available in the market. For easy access to information and services irrespective of the device used for working and even at the drastic change of the environment, the execution of applications on a broad spectrum of computing devices is propelled by the availability of the above-mentioned platforms. Different applications that need interoperability to coordinate and correspond with each other should be facilitated. Using a standard interface and data format, HE must link various devices from various platforms together to communicate with each other. To aid the interactive services performed by a middleware framework that operates on Application Programming Interface (API) over HEs, this issue aims to endorse an Adaptable Service Application Programming Interface (ASAPI).

Towards an Ontology for a Lightweight Support System for Production System Rough Planning

To shorten times-to-markets, also the planners of production systems have to be supported appropriately. For this, open, adaptable systems are needed which support continuous flows of information, e.g. by leveraging standard data formats – and, which are easy to use for planners without specific knowledge about software development or ontology engineering. This paper introduces a support system for production system rough planning, especially for, but not limited to, assembly systems, which consists of two main components: a standard-based ontology as explicitly formulated external data model and a relatively universal software system working on the ontology. Thereby, focus of this contribution is mainly on the ontology, so on manufacturing modeling.

Regulation of Quality of Life and Immune Function in Patients with Thyroid Cancer Treated by Deep Learning Technology.

Background In order to explore the regulation of quality of life and immune function in patients with thyroid cancer after radiotherapy, a method based on deep learning technology was proposed. A deep learning detection method for thyroid cancer is proposed. Methods It mainly includes three main modules: data preprocessing, thyroid cancer regional detection module, and thyroid cancer benign and malignant classification module. The data set in the experiment comes from LIDC-IDRI and is processed by the data preprocessing module to generate a standard data format that can be processed by the framework. The treatment of thyroid cancer can help patients relapse malignant thyroid cancer and prevent recurrence in advance. Results The results showed that most patients are diagnosed because of obvious swelling of local thyroid mass and conscious compression symptoms in the neck. At this time, they often miss the best treatment time, so as to reduce the surgical effect. Conclusions The metastasis and invasion of cancer cells are fast, the cancerous lesions are easy to form adhesion with the surrounding tracheal tissue, and the cancer cells invade the surrounding soft tissue, which is also easy to cause the cancerous tissue not to be completely removed. Clinical Trial Registration. Therefore, deep learning technology is used to treat residual cancerous lesions to ensure the surgical effect.

AutoLog: Anomaly detection by deep autoencoding of system logs

The use of system logs for detecting and troubleshooting anomalies of production systems has been known since the early days of computers. In spite of the advances in the area, the analysis of log files emitted by real-life systems poses many peculiar challenges. Up-to-date tools, such as log management and Security Information and Event Management (SIEM) products, capitalize on standard data formats, logging protocols and dictionaries of threat signatures, which hardly fit to logs of industrial and proprietary systems.This paper addresses the analysis of logs emitted by computer systems with a focus on anomaly detection. The proposed approach, named AutoLog, consists in sampling the logs at regular intervals and to compute numeric scores. Scores collected under normative operations are used to train a semi-supervised deep autoencoder, which serves as a baseline to classify future scores. The approach is not constrained by the structure of underlying logs and does not need for anomalies at training time. The results obtained in detecting anomalies of two industrial systems and the public BG/L and Hadoop datasets widely used as benchmarks, indicate that the recall of AutoLog ranges between 0.96 and 0.99, while the precision is within 0.93 and 0.98. A comparative study with isolation forest, one-class SVM, decision tree, vanilla autoencoder and variational autoencoder is conducted to demonstrate the validity of the proposal.

Managing of Unassigned Mass Spectrometric Data by Neural Network for Cancer Phenotypes Classification.

Mass spectrometric profiling provides information on the protein and metabolic composition of biological samples. However, the weak efficiency of computational algorithms in correlating tandem spectra to molecular components (proteins and metabolites) dramatically limits the use of “omics” profiling for the classification of nosologies. The development of machine learning methods for the intelligent analysis of raw mass spectrometric (HPLC-MS/MS) measurements without involving the stages of preprocessing and data identification seems promising. In our study, we tested the application of neural networks of two types, a 1D residual convolutional neural network (CNN) and a 3D CNN, for the classification of three cancers by analyzing metabolomic-proteomic HPLC-MS/MS data. In this work, we showed that both neural networks could classify the phenotypes of gender-mixed oncology, kidney cancer, gender-specific oncology, ovarian cancer, and the phenotype of a healthy person by analyzing ‘omics’ data in ‘mgf’ data format. The created models effectively recognized oncopathologies with a model accuracy of 0.95. Information was obtained on the remoteness of the studied phenotypes. The closest in the experiment were ovarian cancer, kidney cancer, and prostate cancer/kidney cancer. In contrast, the healthy phenotype was the most distant from cancer phenotypes and ovarian and prostate cancers. The neural network makes it possible to not only classify the studied phenotypes, but also to determine their similarity (distance matrix), thus overcoming algorithmic barriers in identifying HPLC-MS/MS spectra. Neural networks are versatile and can be applied to standard experimental data formats obtained using different analytical platforms.

FCLQC: fast and concurrent lossless quality scores compressor

BackgroundAdvances in sequencing technology have drastically reduced sequencing costs. As a result, the amount of sequencing data increases explosively. Since FASTQ files (standard sequencing data formats) are huge, there is a need for efficient compression of FASTQ files, especially quality scores. Several quality scores compression algorithms are recently proposed, mainly focused on lossy compression to boost the compression rate further. However, for clinical applications and archiving purposes, lossy compression cannot replace lossless compression. One of the main challenges for lossless compression is time complexity, where it takes thousands of seconds to compress a 1 GB file. Also, there are desired features for compression algorithms, such as random access. Therefore, there is a need for a fast lossless compressor with a reasonable compression rate and random access functionality.ResultsThis paper proposes a Fast and Concurrent Lossless Quality scores Compressor (FCLQC) that supports random access and achieves a lower running time based on concurrent programming. Experimental results reveal that FCLQC is significantly faster than the baseline compressors on compression and decompression at the expense of compression ratio. Compared to LCQS (baseline quality score compression algorithm), FCLQC shows at least 31x compression speed improvement in all settings, where a performance degradation in compression ratio is up to 13.58% (8.26% on average). Compared to general-purpose compressors (such as 7-zip), FCLQC shows 3x faster compression speed while having better compression ratios, at least 2.08% (4.69% on average). Moreover, the speed of random access decompression also outperforms the others. The concurrency of FCLQC is implemented using Rust; the performance gain increases near-linearly with the number of threads.ConclusionThe superiority of compression and decompression speed makes FCLQC a practical lossless quality score compressor candidate for speed-sensitive applications of DNA sequencing data. FCLQC is available at https://github.com/Minhyeok01/FCLQC and is freely available for non-commercial usage.

Evaluation of Compression Algorithms for 3D Pavement Image Data

A standard interchangeable data format was recently developed through the Transportation Pooled-Fund Study TPF-5(299) to meet different requirements and needs for two- and three-dimensional (2D/3D) pavement image data storage and exchange. Considering pavement images occupy large amounts of storage space, efficient compression algorithms for both 8-bit 2D and higher-bit 3D images become one of the key components for a successful implementation of the standard. Highway agencies and industry vendors exclusively use the Joint Photographic Experts Group (JPEG) or its successor JPEG 2000 standard for 2D image compression, while the compression of 3D 16-bit images remains to be proprietary. The goal of this paper is to evaluate suitable algorithms for compressing 3D pavement images. An overview of the state-of-the-art pavement image data collection technologies was performed, and the relevant image data formats were identified. Subsequently, five compression algorithms suitable for 16-bit image data were selected based on results from a comprehensive survey of the TPF-5(299) technical community. The compressions of several representative field images were implemented and evaluated in terms of the computation efficiency and retaining of image quality. The expected benefits of this study include facilitating standard and efficient pavement image data collection and accelerating the development of consistent pavement condition evaluation.

OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies

The rapid pace of innovation in biological imaging and the diversity of its applications have prevented the establishment of a community-agreed standardized data format. We propose that complementing established open formats such as OME-TIFF and HDF5 with a next-generation file format such as Zarr will satisfy the majority of use cases in bioimaging. Critically, a common metadata format used in all these vessels can deliver truly findable, accessible, interoperable and reusable bioimaging data.

The Rat Genome Database (RGD) facilitates genomic and phenotypic data integration across multiple species for biomedical research

Model organism research is essential for discovering the mechanisms of human diseases by defining biologically meaningful gene to disease relationships. The Rat Genome Database (RGD, (https://rgd.mcw.edu)) is a cross-species knowledgebase and the premier online resource for rat genetic and physiologic data. This rich resource is enhanced by the inclusion and integration of comparative data for human and mouse, as well as other human disease models including chinchilla, dog, bonobo, pig, 13-lined ground squirrel, green monkey, and naked mole-rat. Functional information has been added to records via the assignment of annotations based on sequence similarity to human, rat, and mouse genes. RGD has also imported well-supported cross-species data from external resources. To enable use of these data, RGD has developed a robust infrastructure of standardized ontologies, data formats, and disease- and species-centric portals, complemented with a suite of innovative tools for discovery and analysis. Using examples of single-gene and polygenic human diseases, we illustrate how data from multiple species can help to identify or confirm a gene as involved in a disease and to identify model organisms that can be studied to understand the pathophysiology of a gene or pathway. The ultimate aim of this report is to demonstrate the utility of RGD not only as the core resource for the rat research community but also as a source of bioinformatic tools to support a wider audience, empowering the search for appropriate models for human afflictions.

Evolution of Data Formats in Very-High-Energy Gamma-Ray Astronomy

Most major scientific results produced by ground-based gamma-ray telescopes in the last 30 years have been obtained by expert members of the collaborations operating these instruments. This is due to the proprietary data and software policies adopted by these collaborations. However, the advent of the next generation of telescopes and their operation as observatories open to the astronomical community, along with a generally increasing demand for open science, confront gamma-ray astronomers with the challenge of sharing their data and analysis tools. As a consequence, in the last few years, the development of open-source science tools has progressed in parallel with the endeavour to define a standardised data format for astronomical gamma-ray data. The latter constitutes the main topic of this review. Common data specifications provide equally important benefits to the current and future generation of gamma-ray instruments: they allow the data from different instruments, including legacy data from decommissioned telescopes, to be easily combined and analysed within the same software framework. In addition, standardised data accessible to the public, and analysable with open-source software, grant fully-reproducible results. In this article, we provide an overview of the evolution of the data format for gamma-ray astronomical data, focusing on its progression from private and diverse specifications to prototypical open and standardised ones. The latter have already been successfully employed in a number of publications paving the way to the analysis of data from the next generation of gamma-ray instruments, and to an open and reproducible way of conducting gamma-ray astronomy.

On the Use of Virtual Reality for Medical Imaging Visualization.

Advanced visualization of medical imaging has been a motive for research due to its value for disease analysis, surgical planning, and academical training. More recently, attention has been turning toward mixed reality as a means to deliver more interactive and realistic medical experiences. However, there are still many limitations to the use of virtual reality for specific scenarios. Our intent is to study the current usage of this technology and assess the potential of related development tools for clinical contexts. This paper focuses on virtual reality as an alternative to today's majority of slice-based medical analysis workstations, bringing more immersive three-dimensional experiences that could help in cross-slice analysis. We determine the key features a virtual reality software should support and present today's software tools and frameworks for researchers that intend to work on immersive medical imaging visualization. Such solutions are assessed to understand their ability to address existing challenges of the field. It was understood that most development frameworks rely on well-established toolkits specialized for healthcare and standard data formats such as DICOM. Also, game engines prove to be adequate means of combining software modules for improved results. Virtual reality seems to remain a promising technology for medical analysis but has not yet achieved its true potential. Our results suggest that prerequisites such as real-time performance and minimum latency pose the greatest limitations for clinical adoption and need to be addressed. There is also a need for further research comparing mixed realities and currently used technologies.

Commentary: Strategies to Address COVID-19 Vaccine Hesitancy and Mitigate Health Disparities in Minority Populations.

Commentary: Strategies to Address COVID-19 Vaccine Hesitancy and Mitigate Health Disparities in Minority Populations.

A Standardized Workflow Based on the STAVIRO Unbaited Underwater Video System for Monitoring Fish and Habitat Essential Biodiversity Variables in Coastal Areas

Essential Biodiversity Variables (EBV) related to benthic habitats and high trophic levels such as fish communities must be measured at fine scale but monitored and assessed at spatial scales that are relevant for policy and management actions. Local scales are important for assessing anthropogenic impacts, and conservation-related and fisheries management actions, while reporting on the conservation status of biodiversity to formulate national and international policies requires much broader scales. Measurements must account for the fact that coastal habitats and fish communities are heterogeneously distributed locally and at larger scales. Assessments based on in situ monitoring generally suffer from poor spatial replication and limited geographical coverage, which is challenging for area-wide assessments. Requirements for appropriate monitoring comprise cost-efficient and standardized observation protocols and data formats, spatially scalable and versatile data workflows, data that comply with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, while minimizing the environmental impact of measurements. This paper describes a standardized workflow based on remote underwater video that aims to assess fishes (at species and community levels) and habitat-related EBVs in coastal areas. This panoramic unbaited video technique was developed in 2007 to survey both fishes and benthic habitats in a cost-efficient manner, and with minimal effect on biodiversity. It can be deployed in areas where low underwater visibility is not a permanent or major limitation. The technique was consolidated and standardized and has been successfully used in varied settings over the last 12 years. We operationalized the EBV workflow by documenting the field protocol, survey design, image post-processing, EBV production and data curation. Applications of the workflow are illustrated here based on some 4,500 observations (fishes and benthic habitats) in the Pacific, Indian and Atlantic Oceans, and Mediterranean Sea. The STAVIRO’s proven track-record of utility and cost-effectiveness indicates that it should be considered by other researchers for future applications.

Dataset of cortical activity recorded with high spatial resolution from anesthetized rats

Publicly available neural recordings obtained with high spatial resolution are scarce. Here, we present an electrophysiological dataset recorded from the neocortex of twenty rats anesthetized with ketamine/xylazine. The wideband, spontaneous recordings were acquired with a single-shank silicon-based probe having 128 densely-packed recording sites arranged in a 32 × 4 array. The dataset contains the activity of a total of 7126 sorted single units extracted from all layers of the cortex. Here, we share raw neural recordings, as well as spike times, extracellular spike waveforms and several properties of units packaged in a standardized electrophysiological data format. For technical validation of our dataset, we provide the distributions of derived single unit properties along with various spike sorting quality metrics. This large collection of in vivo data enables the investigation of the high-resolution electrical footprint of cortical neurons which in turn may aid their electrophysiology-based classification. Furthermore, the dataset might be used to study the laminar-specific neuronal activity during slow oscillation, a brain rhythm strongly involved in neural mechanisms underlying memory consolidation and sleep.

An IFC schema extension for BIM-based description of wastewater treatment plants

Building information modeling (BIM) may advantageously be used to support life-cycle management of engineering structures. The Industry Foundation Classes (IFC) are a standardized BIM data format issued by the International Organization for Standardization to describe engineering structures, such as buildings, tunnels, and bridges. Although often employed for describing basic features of structures, the IFC standard is not capable of sufficiently describing the planning procedure of wastewater treatment plants, which is determined by functional aspects. This paper presents an IFC schema extension that supports BIM-based planning of wastewater treatment plants. A semantic model, serving as a formal basis for the IFC schema extension, is defined that contains the semantics describing the information used for planning of wastewater treatment plants. The IFC schema extension proposed in this study enables BIM-based description of wastewater treatment plants in compliance with the IFC standard, and it may serve as basis for wastewater treatment plant simulations.

Improved Chroma Resampling Algorithm Based on Interpolation Method

An improved algorithm based on bilinear interpolation was proposed. It could deal with the shortcoming of chroma component information loss in the original video image chroma resampling algorithm. With the bilinear interpolation method, the chroma component matrix in the YCbCr color space was scaled in both the horizontal and vertical directions. It could reduce information loss while meeting the corresponding standard data format. The new algorithm could retain more information from the source image’s color, which improved image recovery. In addition, the paper proposed a new format with a higher compression ratio on the basis of ensuring visual performance. Experimental results showed that, compared with the original chroma resampling algorithm, the processing result of the improved algorithm had a significant improvement in the performance of parameters such as composite peak signal noise ratio (CPSNR).

An effective and elastic blockchain-based provenance preserving solution for the open data

PurposeIn the open data context, the shared data could come through many transformation processes, originating from many sources, which exposes the risk of non-authentic data. Moreover, each data set has different properties, shared under various licenses, which means the updated data could change its characteristics and related policies. This paper aims to introduce an effective and elastic solution to keep track of data changes and manage their characteristics within the open data platform. These changes have to be immutable to avoid violated modification and could be used as the certified provenance to improve the quality of data.Design/methodology/approachThis paper will propose a pragmatic solution that focuses on the combination of comprehensive knowledge archive network – the broadest used open data platform and hyperledger fabric blockchain to ensure all the changes are immutable and transparent. As using smart contracts plus a standard provenance data format, all processes are running automatically and could be extended to integrate with other provenance systems and so the introduced solution is quite flexible to be used in different open data ecosystems and real-world application domains.FindingsThe research involves some related studies about the provenance system. This study finds out that most of the studies are focused on the commercial sector or applicable to a specific domain and not relevant for the open-data section. To show that the proposed solution is a logical and feasible direction, this paper conducts an experimental sample to validate the result. The testing model is running successfully with an elastic system architect and promising overall performance.Originality/valueOpen data is the future of many businesses but still does not receive enough attention from the research community. The paper contributes a novel approach to protect the provenance of open data.

Fourier domain excision of periodic radio frequency interference

The discovery and study of pulsars and fast radio bursts (FRBs) in time-domain radio data is often hampered by radio frequency interference (RFI). Some of this terrestrial RFI is impulsive and bright, and relatively easy to identify and remove. Other anthropogenic signals, however, are weaker yet periodic, and their persistence can drown out astrophysical signals. Here we show that Fourier-domain excision of periodic RFI is an effective and powerful step in detecting weak cosmic signals. We find that applying the method significantly increases the signal-to-noise ratio of transient and periodic pulsar signals. In live studies we detected single pulses from pulsars and FRBs that would otherwise have remained buried in background noise. We show the method has no negative effects on pulsar pulse shape, and that it enhances timing campaigns. We demonstrate the method on real-life data from a number of large radio telescopes, and conclude that Fourier-domain RFI excision increases the effective sensitivity to astrophysical sources by a significant fraction, which can be even larger than an order of magnitude in the case of strong RFI. An accelerated implementation of the method runs on standard time-domain radio data formats and is publicly available.

Accessing the ECG Data of the Apple Watch and Accomplishing Interoperability Through FHIR.

Medical data generated by wearables and smartphones can add value to health care and medical research. This also applies to the ECG data that is created with Apple Watch 4 or later. However, Apple currently does not provide an efficient solution for accessing and sharing ECG raw data in a standardized data format. Our method aims to provide a solution that enables patients to share their Apple Watch's ECG data with any health care institution via an iPhone application. We achieved this by implementing a parser in Swift that converts the Apple Watch's raw ECG data into a FHIR observation. Furthermore, we added the capability of transmitting these observations to a specified server and equipping it with the patient's reference number. The result is a user-friendly iPhone application, enabling patients to share their Apple Watch's ECG data in a widely known health data standard with minimal effort. This allows the personnel involved in the patient's treatment to use data that was previously difficult to access for further analyses and processing. Our solution can facilitate research for new treatment methods, for example, utilizing the Apple Watch for continuous monitoring of heart activity and early detection of heart conditions.

MS Amanda 2.0: Advancements in the standalone implementation.

RationaleDatabase search engines are the preferred method to identify peptides in mass spectrometry data. However, valuable software is in this context not only defined by a powerful algorithm to separate correct from false identifications, but also by constant maintenance and continuous improvements.MethodsIn 2014, we presented our peptide identification algorithm MS Amanda, showing its suitability for identifying peptides in high‐resolution tandem mass spectrometry data and its ability to outperform widely used tools to identify peptides. Since then, we have continuously worked on improvements to enhance its usability and to support new trends and developments in this fast‐growing field, while keeping the original scoring algorithm to assess the quality of a peptide spectrum match unchanged.ResultsWe present the outcome of these efforts, MS Amanda 2.0, a faster and more flexible standalone version with the original scoring algorithm. The new implementation has led to a 3–5× speedup, is able to handle new ion types and supports standard data formats. We also show that MS Amanda 2.0 works best when using only the most common ion types in a particular search instead of all possible ion types.ConclusionsMS Amanda is available free of charge from https://ms.imp.ac.at/index.php?action=msamanda.