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1.
Front Behav Neurosci ; 17: 1232546, 2023.
Article in English | MEDLINE | ID: mdl-38033480

ABSTRACT

The IntelliCage allows automated testing of cognitive abilities of mice in a social home cage environment without handling by human experimenters. Restricted water access in combination with protocols in which only correct responses give access to water is a reliable learning motivator for hippocampus-dependent tasks assessing spatial memory and executive function. However, water restriction may negatively impact on animal welfare, especially in poor learners. To better comply with the 3R principles, we previously tested protocols in which water was freely available but additional access to sweetened water could be obtained by learning a task rule. While this purely appetitive motivation worked for simple tasks, too many mice lost interest in the sweet reward during more difficult hippocampus-dependent tasks. In the present study, we tested a battery of increasingly difficult spatial tasks in which water was still available without learning the task rule, but rendered less attractive either by adding bitter tasting quinine or by increasing the amount of work to obtain it. As in previous protocols, learning of the task rule provided access to water sweetened with saccharin. The two approaches of dual motivation were tested in two cohorts of female C57BL/6 N mice. Compared to purely appetitive motivation, both novel protocols strongly improved task engagement and increased task performance. Importantly, neither of the added disincentives had an adverse impact on liquid consumption, health status or body weight of the animals. Our results show that it is possible to refine test protocols in the IntelliCage so that they challenge cognitive functions without restricting access to water.

2.
Methods Mol Biol ; 406: 213-27, 2007.
Article in English | MEDLINE | ID: mdl-18287694

ABSTRACT

A comprehensive Arabidopsis genomic resource has been developed at the Nottingham Arabidopsis Stock Centre (NASC) to support the international plant community. This browser, termed AtEnsembl, provides a detailed and user-friendly interface for accessing a wide range of Arabidopsis-based genomic information and post-genomic resources using the Ensembl browser. The resource aims to provide the broadest possible range of Ensembl features, including pointers to germplasm as well as representations of gene and protein information, links to Affymetrix gene expression data, and extensive data download capabilities.


Subject(s)
Arabidopsis/genetics , Databases, Genetic , Arabidopsis/metabolism , Arabidopsis Proteins/genetics , Arabidopsis Proteins/metabolism , Computational Biology/methods , Gene Expression Regulation, Plant , Genome, Plant/genetics , Genomics/methods , Information Storage and Retrieval/methods , Oligonucleotide Array Sequence Analysis
3.
Plant Methods ; 2: 1, 2006 Jan 09.
Article in English | MEDLINE | ID: mdl-16401339

ABSTRACT

Appropriate biological interpretation of microarray data calls for relevant experimental annotation. The widely accepted MIAME guidelines provide a generic, organism-independant standard for minimal information about microarray experiments. In its overall structure, MIAME is very general and specifications cover mostly technical aspects, while relevant organism-specific information useful to understand the underlying experiments is largely missing. If plant biologists want to use results from published microarray experiments, they need detailed information about biological aspects, such as growth conditions, harvesting time or harvested organ(s). Here, we propose MIAME/Plant, a standard describing which biological details to be captured for describing microarray experiments involving plants. We expect that a more detailed and more systematic annotation of microarray experiments will greatly increase the use of transcriptome data sets for the scientific community. The power and value of systematic annotation of microarray data is convincingly demonstrated by data warehouses such as Genevestigator(R) or NASCArrays, and better experimental annotation will make these applications even more powerful.

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