scholarly journals Semi-Automated Methods for BIBFRAME Work Entity Description

2021 ◽  
pp. 1-15
Author(s):  
Jim Hahn
Keyword(s):  
Automated Methods

Automation of Two-Stage Factor VIII Assay

1978 ◽  
Vol 39 (02) ◽  
pp. 455-465 ◽  
Author(s):  
Yvonne Stirling ◽  
D J Howarth ◽  
Marguerite Vickers ◽  
W R S North ◽  
T W Meade
Keyword(s):  
Factor Viii ◽  
Optical Density ◽  
Electric Circuit ◽  
Clotting Time ◽  
Two Stage ◽  
Laboratory Error ◽  
Premature Closure ◽  
Factor Viii Concentrates ◽  
Experimental Findings ◽  
Automated Methods

SummaryTwo automated methods for two-stage factor VIII assays have been compared with one another, and evaluated in practice. The Depex method records the clotting time when an electric circuit is completed by the formation of a fibrin thread across a hook-type electrode; the Electra method is based on an optical density technique of clot detection. The two methods gave comparable results for measured levels of factor VIII when haemophilic or “normal” plasmas were assayed. Results from the two methods in practice also suggest that both are valid at low and “normal” factor VIII levels. The Electra method is also probably suitable for assays of concentrates; however, the Depex method appears to give falsely high values in these circumstances, and experimental findings suggest that the reason may be that increased viscosity due to the high fibrinogen levels in factor VIII concentrates causes premature closure of the circuit between the two ends of the Depex electrode. The main advantage of the Depex method is that, provided 3 or 4 machines are available, a given number of assays can be completed more quickly than on Electra. The main advantages of Electra are that it is probably subject to less laboratory error than Depex, and that it is suitable for assaying concentrates as well as haemophilic and “normal” plasmas.

Feasibility and Evaluation of Automated Methods for Radiolabeling of Radiopharmaceutical Kits with Gallium-68

2019 ◽  
Vol 12 (3) ◽  
pp. 229-237 ◽  
Author(s):  
Alban Revy ◽  
François Hallouard ◽  
Sandrine Joyeux-Klamber ◽  
Andrea Skanjeti ◽  
Catherine Rioufol ◽  
...  
Keyword(s):  
The European Union ◽  
Preparation Time ◽  
Limit Of Quantification ◽  
Manual Method ◽  
Personnel Costs ◽  
Automated Method ◽  
Gallium 68 ◽  
The Right ◽  
First Time ◽  
Automated Methods

Objective: Recent gallium-68 labeled peptides are of increasing interest in PET imaging in nuclear medicine. Somakit TOC® is a radiopharmaceutical kit registered in the European Union for the preparation of [68Ga]Ga-DOTA-TOC used for the diagnosis of neuroendocrine tumors. Development of a labeling process using a synthesizer is particularly interesting for the quality and reproducibility of the final product although only manual processes are described in the Summary of Product (SmPC) of the registered product. The aim of the present study was therefore to evaluate the feasibility and value of using an automated synthesizer for the preparation of [68Ga]Ga-DOTA-TOC according to the SmPC of the Somakit TOC®. Methods: Three methods of preparation were compared; each followed the SmPC of the Somakit TOC®. Over time, overheads, and overexposure were evaluated for each method. Results: Mean±SD preparation time was 26.2±0.3 minutes for the manual method, 28±0.5 minutes for the semi-automated, and 40.3±0.2 minutes for the automated method. Overcost of the semi-automated method is 0.25€ per preparation for consumables and from 0.58€ to 0.92€ for personnel costs according to the operator (respectively, technician or pharmacist). For the automated method, overcost is 70€ for consumables and from 4.06€ to 6.44€ for personnel. For the manual method, extremity exposure was 0.425mSv for the right finger, and 0.350mSv for the left finger; for both the semi-automated and automated method extremity exposure were below the limit of quantification. Conclusion: The present study reports for the first time both the feasibility of using a [68Ga]- radiopharmaceutical kit with a synthesizer and the limits for the development of a fully automated process.

674 Machine Learning and Automation in Burn Care: A Systematic Review

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S193-S193
Author(s):  
Samantha Huang ◽  
Justin Dang ◽  
Clifford C Sheckter ◽  
Haig A Yenikomshian ◽  
Justin Gillenwater
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Total Body Surface Area ◽  
Burn Wound ◽  
Burn Care ◽  
Surgeon Experience ◽  
Automated Software ◽  
Burn Depth ◽  
Automated Methods ◽  
Equal Weighting

Abstract Introduction Current methods of burn evaluation and treatment are subjective and dependent on surgeon experience, with high rates of inter-rater variability leading to inaccurate diagnoses and treatment. Machine learning (ML) and automated methods are being used to develop more objective and accurate methods for burn diagnosis and triage. Defined as a subfield of artificial intelligence that applies algorithms capable of knowledge acquisition, machine learning draws patterns from data, which it can then apply to clinically relevant tasks. This technology has the potential to improve burn management by quantitating diagnoses, improving diagnostic accuracy, and increasing access to burn care. The aim of this systematic review is to summarize the literature regarding machine learning and automated methods for burn wound evaluation and treatment. Methods A systematic review of articles available on PubMed and MEDLINE (OVID) was performed. Keywords used in the search process included burns, machine learning, deep learning, burn classification technology, and mobile applications. Reviews, case reports, and opinion papers were excluded. Data were extracted on study design, study objectives, study models, devices used to capture data, machine learning, or automated software used, expertise level and number of evaluators, and ML accuracy of burn wound evaluation. Results The search identified 592 unique titles. After screening, 35 relevant articles were identified for systematic review. Nine studies used machine learning and automated software to estimate percent total body surface area (%TBSA) burned, 4 calculated fluid requirements, 18 estimated burn depth, 5 estimated need for surgery, 6 predicted mortality, and 2 evaluated scarring in burn patients. Devices used to estimate %TBSA burned showed an accuracy comparable to or better than traditional methods. Burn depth estimation sensitivities resulted in unweighted means >81%, which increased to >83% with equal weighting applied. Mortality prediction sensitivity had an unweighted mean of 96.75%, which increased to 99.35% with equal weighting. Conclusions Machine learning and automated technology are promising tools that provide objective and accurate measures of evaluating burn wounds. Existing methods address the key steps in burn care management; however, existing data reporting on their robustness remain in the early stages. Further resources should be dedicated to leveraging this technology to improve outcomes in burn care.

scholarly journals The Effectiveness of Multi-Label Classification and Multi-Output Regression in Social Trait Recognition

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4127
Author(s):  
Will Farlessyost ◽  
Kelsey-Ryan Grant ◽  
Sara R. Davis ◽  
David Feil-Seifer ◽  
Emily M. Hand
Keyword(s):  
Social Interactions ◽  
First Impressions ◽  
Social Traits ◽  
Judicial Proceedings ◽  
Facial Attributes ◽  
Future Work ◽  
Automated Methods ◽  
Insight Into ◽  
Social Trait ◽  
Better Than

First impressions make up an integral part of our interactions with other humans by providing an instantaneous judgment of the trustworthiness, dominance and attractiveness of an individual prior to engaging in any other form of interaction. Unfortunately, this can lead to unintentional bias in situations that have serious consequences, whether it be in judicial proceedings, career advancement, or politics. The ability to automatically recognize social traits presents a number of highly useful applications: from minimizing bias in social interactions to providing insight into how our own facial attributes are interpreted by others. However, while first impressions are well-studied in the field of psychology, automated methods for predicting social traits are largely non-existent. In this work, we demonstrate the feasibility of two automated approaches—multi-label classification (MLC) and multi-output regression (MOR)—for first impression recognition from faces. We demonstrate that both approaches are able to predict social traits with better than chance accuracy, but there is still significant room for improvement. We evaluate ethical concerns and detail application areas for future work in this direction.

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