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The Global ETD Search service is a free service for researchers
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Showing 1 to 3 of 3 (0.046 seconds)Spelling suggestions: "subject:"3Rs principle"" "subject:"3Rs aprinciple""
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Approcci innovativi alla modellizzazione della corteccia cerebrale: analisi automatizzate della citoarchitettonica corticale / INNOVATIVE APPROACHES TO THE MODELING OF THE CEREBRAL CORTEX: AUTOMATED ANALYSIS OF CORTICAL CYTOARCHITECTONICSDE GIORGIO, ANDREA 04 December 2017 (has links)
In questa tesi descriviamo una procedura automatizzata per l’analisi della corteccia motoria dello scimpanzè, del Macaca fascicularis e del cavallo, basata su un nuovo metodo computerizzato di analisi delle sezioni colorate attraverso il metodo di Nissl, al fine di studiare la corteccia cerebrale in specie differenti. Le microfotografie delle sezioni sono state elaborate con una procedura standardizzata usando il software ImageJ. Questa procedura ha previsto la suddivisione degli strati corticali, dal primo al sesto, in diversi frames. Per misurare la complessità delle cellule nervose (cioè quanto una cellula fosse diversa dalle adiacenti) abbiamo utilizzato un modello di rappresentazione statistica non-parametrica che mostra come la complessità può essere espressa in termini di un adeguato indice di dispersione statistica quale il MAD (mean absolute deviation).
Abbiamo quindi dimostrato che gli strati piramidali della corteccia motoria del cavallo sono più irregolari di quelli di scimpanzè e Macaca fascicularis. La combinazione dell’analisi automatica delle immagini e delle analisi statistiche consente pertanto di confrontare e classificare la complessità della corteccia motoria attraverso diverse specie. Il modello viene proposto come strumento al fine di contribuire a stabilire le somiglianze cerebrali tra umani e animali, rispettando il principio delle 3R. / In this thesis we describe an automated procedure based on a new computerized method of partitioning Nissl-stained sections of the motor cortex of the chimpanzee, crab-eating monkey, and horse, to study the neocortex in different species. Microphotographs of the sections were first processed using a standard procedure in ImageJ, then the stained neuronal profiles were analyzed within continuously adjoining frames from the first to the sixth layer of neocortex. To measure the neuronal complexity (how a given cell is different from its neighbors) we used a general non-parametric data representation model showing that the complexity can be expressed in terms of a suitable measure of statistical dispersion such as the mean absolute deviation. We demonstrated that the pyramidal layers of the motor cortex of the horse are more irregular than those of the monkeys studied. The combination of automated image analysis and statistical analysis made it possible to compare and rank the motor cortex complexity across different species. Therefore, we are confident that our work will help to establish brain similarities between humans and animals used for alimentary purpose, whose brain is often discarded. This, in turn, will allow to carry out the experimental brain research obeying the 3Rs principle.
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Semi-automated Ontology Generation for Biocuration and Semantic SearchWächter, Thomas 01 February 2011 (has links) (PDF)
Background:
In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed.
Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing.
Motivation:
The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences.
Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods.
Results:
The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results.
To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org.
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Semi-automated Ontology Generation for Biocuration and Semantic SearchWächter, Thomas 27 October 2010 (has links)
Background:
In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed.
Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing.
Motivation:
The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences.
Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods.
Results:
The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results.
To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org.
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