Workflow scheduling Dotaz Zobrazit nápovědu
Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users-to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives-cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results' quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.
- Klíčová slova
- Edge computing, Firefly algorithm, Genetic operator, Quasi-reflection-based learning, Swarm intelligence, Workflow scheduling,
- Publikační typ
- časopisecké články MeSH
OBJECTIVES: The analysis of organic acids in urine is an important part of the diagnosis of inherited metabolic disorders (IMDs), for which gas chromatography coupled with mass spectrometry is still predominantly used. METHODS: Ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for urinary organic acids, acylcarnitines and acylglycines was developed and validated. Sample preparation consists only of dilution and the addition of internal standards. Raw data processing is quick and easy using selective scheduled multiple reaction monitoring mode. A robust standardised value calculation as a data transformation together with advanced automatic visualisation tools are applied for easy evaluation of complex data. RESULTS: The developed method covers 146 biomarkers consisting of organic acids (n=99), acylglycines (n=15) and acylcarnitines (n=32) including all clinically important isomeric compounds present. Linearity with r2>0.98 for 118 analytes, inter-day accuracy between 80 and 120 % and imprecision under 15 % for 120 analytes were achieved. Over 2 years, more than 800 urine samples from children tested for IMDs were analysed. The workflow was evaluated on 93 patient samples and ERNDIM External Quality Assurance samples involving a total of 34 different IMDs. CONCLUSIONS: The established LC-MS/MS workflow offers a comprehensive analysis of a wide range of organic acids, acylcarnitines and acylglycines in urine to perform effective, rapid and sensitive semi-automated diagnosis of more than 80 IMDs.
- Klíčová slova
- inherited metabolic disorders, liquid chromatography, mass spectrometry, organic acidurias,
- MeSH
- chromatografie kapalinová metody MeSH
- dítě MeSH
- lidé MeSH
- metabolické nemoci * MeSH
- organické látky MeSH
- průběh práce MeSH
- tandemová hmotnostní spektrometrie * metody MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- acylcarnitine MeSH Prohlížeč
- organické látky MeSH
Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network.
Trapped ion mobility spectrometry (TIMS) adds an additional separation dimension to mass spectrometry (MS) imaging, however, the lack of fragmentation spectra (MS2) impedes confident compound annotation in spatial metabolomics. Here, we describe spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF), a dataset-dependent acquisition strategy that augments TIMS-MS imaging datasets with MS2 spectra. The fragmentation experiments are systematically distributed across the sample and scheduled for multiple collision energies per precursor ion. Extendable data processing and evaluation workflows are implemented into the open source software MZmine. The workflow and annotation capabilities are demonstrated on rat brain tissue thin sections, measured by matrix-assisted laser desorption/ionisation (MALDI)-TIMS-MS, where SIMSEF enables on-tissue compound annotation through spectral library matching and rule-based lipid annotation within MZmine and maps the (un)known chemical space by molecular networking. The SIMSEF algorithm and data analysis pipelines are open source and modular to provide a community resource.
- MeSH
- algoritmy MeSH
- iontová mobilní spektrometrie * MeSH
- krysa rodu Rattus MeSH
- metabolomika * metody MeSH
- software MeSH
- spektrometrie hmotnostní - ionizace laserem za účasti matrice metody MeSH
- zvířata MeSH
- Check Tag
- krysa rodu Rattus MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Lipid A, a crucial component of lipopolysaccharides (LPS), plays a pivotal role in the pathogenesis of Gram-negative bacteria. Lipid A patterns are recognized by mammals and can induce immunostimulatory effects. However, the outcome of the interaction is highly dependent on the chemical composition of individual lipid A species. The diversity of potential fatty acyl and polar headgroup combinations in this complex saccharolipid presents a significant analytical challenge. Current mass spectrometry (MS)-based lipid A methods are focused on either direct matrix-assisted laser desorption/ionization (MALDI)-MS screening or comprehensive structural elucidation by tandem mass spectrometry (MS/MS) hyphenated with separation techniques. In this study, we developed an alternative workflow for rapid lipid A profiling covering the entire analysis pipeline from sample preparation to data analysis. This workflow is based on microextraction and subsequent MALDI-MS/MS analysis of uropathogenic Escherichia coli utilizing trapped ion mobility spectrometry (TIMS), followed by mzmine data processing. The additional TIMS dimension served for enhanced sensitivity, selectivity, and structural elucidation through mobility-resolved fragmentation via parallel accumulation-serial fragmentation (PASEF) in parallel reaction monitoring (prm)-mode. Furthermore, mzmine enabled automated MS/MS acquisition by adapting the spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF) strategy for MALDI spot analysis. It also facilitated robust lipid A annotation through a newly developed extension of the rule-based lipid annotation module, allowing for the custom generation of lipid classes, including specific fragmentation rules. In this study, the first publication of lipid A species' collision cross section (CCS) values is reported, which will enhance high-confidence lipid A annotation in future studies.
- MeSH
- gramnegativní bakterie * chemie MeSH
- iontová mobilní spektrometrie * metody MeSH
- lipid A * analýza chemie MeSH
- spektrometrie hmotnostní - ionizace laserem za účasti matrice * metody MeSH
- tandemová hmotnostní spektrometrie metody MeSH
- uropatogenní Escherichia coli * chemie MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- lipid A * MeSH
