process automation
Dotaz
Zobrazit nápovědu
- MeSH
- algoritmy MeSH
- automatizace MeSH
- lidé MeSH
- manažerské informační systémy MeSH
- rozhodování MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
Effective identification of previously implausible safety signals is a core component of successful pharmacovigilance. Timely, reliable, and efficient data ingestion and related processing are critical to this. The term 'black swan events' was coined by Taleb to describe events with three attributes: unpredictability, severe and widespread consequences, and retrospective bias. These rare events are not well understood at their emergence but are often rationalized in retrospect as predictable. Pharmacovigilance strives to rapidly respond to potential black swan events associated with medicine or vaccine use. Machine learning (ML) is increasingly being explored in data ingestion tasks. In contrast to rule-based automation approaches, ML can use historical data (i.e., 'training data') to effectively predict emerging data patterns and support effective data intake, processing, and organisation. At first sight, this reliance on previous data might be considered a limitation when building ML models for effective data ingestion in systems that look to focus on the identification of potential black swan events. We argue that, first, some apparent black swan events-although unexpected medically-will exhibit data attributes similar to those of other safety data and not prove algorithmically unpredictable, and, second, standard and emerging ML approaches can still be robust to such data outliers with proper awareness and consideration in ML system design and with the incorporation of specific mitigatory and support strategies. We argue that effective approaches to managing data on potential black swan events are essential for trust and outline several strategies to address data on potential black swan events during data ingestion.
- MeSH
- automatizace MeSH
- farmakovigilance * MeSH
- lidé MeSH
- retrospektivní studie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- MeSH
- informační systémy MeSH
- lidé MeSH
- SNOMED * MeSH
- software MeSH
- terminologie jako téma MeSH
- ultrasonografie MeSH
- Check Tag
- lidé MeSH
To ensure food safety and to prevent unnecessary foodborne complications this study reports fast, fully automated process for histamine determination. This method is based on magnetic separation of histamine with magnetic particles and quantification by the fluorescence intensity change of MSA modified CdSe Quantum dots. Formation of Fe2O3 particles was followed by adsorption of TiO2 on their surface. Magnetism of developed probe enabled rapid histamine isolation prior to its fluorescence detection. Quantum dots (QDs) of approx. 3 nm were prepared via facile UV irradiation. The fluorescence intensity of CdSe QDs was enhanced upon mixing with magnetically separated histamine, in concentration-dependent manner, with a detection limit of 1.6 μM. The linear calibration curve ranged between 0.07 and 4.5 mM histamine with a low LOD and LOQ of 1.6 μM and 6 μM. The detection efficiency of the method was confirmed by ion exchange chromatography. Moreover, the specificity of the sensor was evaluated and no cross-reactivity from nontarget analytes was observed. This method was successfully applied for the direct analysis of histamine in white wine providing detection limit much lower than the histamine maximum levels established by EU regulation in food samples. The recovery rate was excellent, ranging from 84 to 100% with an RSD of less than 4.0%. The main advantage of the proposed method is full automation of the analytical procedure that reduces the time and cost of the analysis, solvent consumption and sample manipulation, enabling routine analysis of large numbers of samples for histamine and highly accurate and precise results.
- MeSH
- fluorescence MeSH
- fluorescenční barviva chemie MeSH
- fluorescenční spektrometrie metody MeSH
- histamin analýza MeSH
- kontaminace potravin analýza MeSH
- kovové nanočástice chemie MeSH
- kvantové tečky chemie MeSH
- limita detekce MeSH
- magnetické jevy MeSH
- silany chemie MeSH
- sloučeniny kadmia chemie MeSH
- telur chemie MeSH
- titan chemie MeSH
- víno analýza MeSH
- železité sloučeniny chemie MeSH
- Publikační typ
- časopisecké články MeSH
The process of manual species identification is a daunting task, so much so that the number of taxonomists is seen to be declining. In order to assist taxonomists, many methods and algorithms have been proposed to develop semi-automated and fully automated systems for species identification. While semi-automated tools would require manual intervention by a domain expert, fully automated tools are assumed to be not as reliable as manual or semiautomated identification tools. Hence, in this study we investigate the accuracy of fully automated and semi-automated models for species identification. We have built fully automated and semi-automated species classification models using the monogenean species image dataset. With respect to monogeneans' morphology, they are differentiated based on the morphological characteristics of haptoral bars, anchors, marginal hooks and reproductive organs (male and female copulatory organs). Landmarks (in the semi-automated model) and shape morphometric features (in the fully automated model) were extracted from four monogenean species images, which were then classified using k-nearest neighbour and artificial neural network. In semi-automated models, a classification accuracy of 96.67 % was obtained using the k-nearest neighbour and 97.5 % using the artificial neural network, whereas in fully automated models, a classification accuracy of 90 % was obtained using the k-nearest neighbour and 98.8 % using the artificial neural network. As for the crossvalidation, semi-automated models performed at 91.2 %, whereas fully automated models performed slightly higher at 93.75 %.
