Benchmarking Combinations of Learning and Testing Algorithms for Active Automata Learning

2020 ◽  
pp. 3-22
Author(s):  
Bernhard K. Aichernig ◽  
Martin Tappler ◽  
Felix Wallner
Keyword(s):  
Automata Learning ◽  
Testing Algorithms

scholarly journals Assessment of common housekeeping genes as reference for gene expression studies using RT-qPCR in mouse choroid plexus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kim Hoa Ho ◽  
Annarita Patrizi
Keyword(s):  
Gene Expression ◽  
Choroid Plexus ◽  
Sensory Deprivation ◽  
Housekeeping Genes ◽  
Housekeeping Gene ◽  
Experimental Conditions ◽  
Brain Repair ◽  
Expression Studies ◽  
Testing Algorithms ◽  
Gene Expression Studies

AbstractChoroid plexus (ChP), a vascularized secretory epithelium located in all brain ventricles, plays critical roles in development, homeostasis and brain repair. Reverse transcription quantitative real-time PCR (RT-qPCR) is a popular and useful technique for measuring gene expression changes and also widely used in ChP studies. However, the reliability of RT-qPCR data is strongly dependent on the choice of reference genes, which are supposed to be stable across all samples. In this study, we validated the expression of 12 well established housekeeping genes in ChP in 2 independent experimental paradigms by using popular stability testing algorithms: BestKeeper, DeltaCq, geNorm and NormFinder. Rer1 and Rpl13a were identified as the most stable genes throughout mouse ChP development, while Hprt1 and Rpl27 were the most stable genes across conditions in a mouse sensory deprivation experiment. In addition, Rpl13a, Rpl27 and Tbp were mutually among the top five most stable genes in both experiments. Normalisation of Ttr and Otx2 expression levels using different housekeeping gene combinations demonstrated the profound effect of reference gene choice on target gene expression. Our study emphasized the importance of validating and selecting stable housekeeping genes under specific experimental conditions.

Current Developments in Diagnostic Assays for Laboratory Confirmation and Investigation of Botulism

2021 ◽  
Author(s):  
Dominick A. Centurioni ◽  
Christina T. Egan ◽  
Michael J. Perry
Keyword(s):  
Gold Standard ◽  
Botulinum Neurotoxin ◽  
Mouse Bioassay ◽  
Environmental Matrices ◽  
Animal Testing ◽  
Gold Standard Method ◽  
Diagnostic Assays ◽  
Technical Performance ◽  
Laboratory Confirmation ◽  
Testing Algorithms

Detection of botulinum neurotoxin or isolation of the toxin producing organism is required for the laboratory confirmation of botulism in clinical specimens. In an effort to reduce animal testing required by the gold standard method of botulinum neurotoxin detection, the mouse bioassay, many technologies have been developed to detect and characterize the causative agent of botulism. Recent advancements in these technologies have led to improvements in technical performance of diagnostic assays; however, many emerging assays have not been validated for the detection of all serotypes in complex clinical and environmental matrices. Improvements to culture protocols, endopeptidase-based assays, and a variety of immunological and molecular methods have provided laboratories with a variety of testing options to evaluate and incorporate into their testing algorithms. While significant advances have been made to improve these assays, additional work is necessary to evaluate these methods in various clinical matrices and to establish standardized criteria for data analysis and interpretation.

Variability in Testing for Antineutrophil Cytoplasmic Antibodies: A Survey of Participants in the College of American Pathologists Proficiency Testing Program

2016 ◽  
Vol 140 (6) ◽  
pp. 524-528
Author(s):  
William J. Karlon ◽  
Stanley J. Naides ◽  
John T. Crosson ◽  
Mohammad Qasim Ansari
Keyword(s):  
Proficiency Testing ◽  
Antineutrophil Cytoplasmic Antibodies ◽  
Testing Program ◽  
Acceptable Alternative ◽  
Disease States ◽  
Comprehensive Information ◽  
Testing Algorithms ◽  
Testing Practices ◽  
Current Guidelines ◽  
Multiplex Bead

Context.—Variability in testing for antineutrophil cytoplasmic antibodies (ANCAs) contributes to confusion and controversy related to testing for vasculitis and other ANCA-associated diseases. Objectives.—To survey laboratory testing practices regarding ANCA testing and to investigate differences in testing algorithms. Design.—Supplemental questions were sent to the 333 laboratories participating in the College of American Pathologists proficiency testing program for ANCA as part of the Special Immunology S2 Survey. Results.—A total of 315 laboratories submitted responses to the supplemental questions. Only 88 of 315 participants (28%) reported using a combination of indirect immunofluorescence (IFA) and enzyme immunoassay (EIA) techniques as recommended by current guidelines, with a few additional labs using IFA and multiplex bead assay as an acceptable alternative to EIA. Other labs reported using only IFA, EIA, or multiplex bead assays. Conclusions.—A wide variety of testing algorithms are in use for ANCA testing despite evidence to suggest that a combination of IFA and EIA testing provides the most comprehensive information. Laboratories should inform clinicians clearly about testing practices and utility of testing in specific disease states.

A new software architecture for developing and testing algorithms for space exploration missions

2011 ◽  
Vol 4 (2) ◽  
pp. 135-146
Author(s):  
Enrico Simetti ◽  
Enrica Zereik ◽  
Alessandro Sperindé ◽  
Sandro Torelli ◽  
Davide Ducco ◽  
...  
Keyword(s):  
Software Architecture ◽  
Space Exploration ◽  
Testing Algorithms

Synthesize Models for Quantitative Analysis Using Automata Learning

2019 ◽  
pp. 75-92
Author(s):  
Yu-Fang Chen ◽  
Hsiao-Chen Chung ◽  
Wen-Chi Hung ◽  
Ming-Hsien Tsai ◽  
Bow-Yaw Wang ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Automata Learning

scholarly journals Compositional learning of mutually recursive procedural systems

2021 ◽  
Author(s):  
Markus Frohme ◽  
Bernhard Steffen
Keyword(s):  
Finite Automata ◽  
Simultaneous Inference ◽  
Recursive Structure ◽  
Recursive Models ◽  
Membership Queries ◽  
Compositional Approach ◽  
Recursive Programs ◽  
Automata Learning ◽  
The Individual ◽  
Compositional Learning

AbstractThis paper presents a compositional approach to active automata learning of Systems of Procedural Automata (SPAs), an extension of Deterministic Finite Automata (DFAs) to systems of DFAs that can mutually call each other. SPAs are of high practical relevance, as they allow one to efficiently learn intuitive recursive models of recursive programs after an easy instrumentation that makes calls and returns observable. Key to our approach is the simultaneous inference of individual DFAs for each of the involved procedures via expansion and projection: membership queries for the individual DFAs are expanded to membership queries of the entire SPA, and global counterexample traces are transformed into counterexamples for the DFAs of concerned procedures. This reduces the inference of SPAs to a simultaneous inference of the DFAs for the involved procedures for which we can utilize various existing regular learning algorithms. The inferred models are easy to understand and allow for an intuitive display of the procedural system under learning that reveals its recursive structure. We implemented the algorithm within the LearnLib framework in order to provide a ready-to-use tool for practical application which is publicly available on GitHub for experimentation.

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