Added AMIME project

Author: karingare

projects.html

@@ -68,6 +68,20 @@ <h3>Metagenomics-driven enzyme discovery</h3>
 
                     <h3>Primer Design</h3>
                     <p>By sequencing of PCR amplified taxonomic marker genes we can accurately profile the microbial diversity within many samples in parallel. However, to minimize biases it is important to use primers that bind as non-selectively as possible. We have developed the program DegePrime (<a href="http://www.ncbi.nlm.nih.gov/pubmed/24928874">Hugerth et al - 1</a>) that given a sequence alignmentfro each position finds the primer with a maximum degeneracy given by the user that matches as many sequences as possible. We used this to develop the primer pair 341F-805R that was show by another group to be the best primer pair for bacterial diversity studies out of 512 pairs tested. We also used DegePrime to greatly improve the coverage of a popular primer pair for assessing bacterial and archaeal diversity (515F-805R) and for developing a new primer pair for 18S rRNA gene surveys (574F-1123R) (<a href="http://www.ncbi.nlm.nih.gov/pubmed/24755918">Hugerth et al - 2</a>).</p>
+
+					<h3>Artificial intelligence-assisted plankton Monitoring with Imaging flow cytometry and MEtabarcoding</h3>
+                <p>Single-celled eukaryotic plankton (protists) form the productive base of marine ecosystems and are key drivers of global biogeochemical cycles of carbon and nutrients. Consequently, plankton have fundamental impacts on both fish stocks and the climate and understanding the factors that control the abundance and distribution of different
+plankton species is of great concern. Monitoring of eukaryotic plankton has traditionally been conducted by
+manual microscopic detection. Recently, alternative approaches have emerged such as high-throughput imaging
+and DNA metabarcoding. While promising, these methods have their challenges, not least in how to translate
+between these disparate datasets. In this project we will utilize state-of-the-art image analysis and deep learning
+approaches to maximise the information gained from these types of data and to translate between them. We will
+leverage on existing imaging data from the new Imaging FlowCytobot (IFCB) instrument mounted on the research
+vessel Svea as well as generate new parallel IFCB and DNA metabarcoding datasets for 500 water samples
+spanning the Baltic Sea, Kattegat and Skagerrak. The methodology developed in the project will advance plankton
+research and ecology in general and plankton monitoring in Sweden in particular. It will bridge the gap between
+imaging and DNA-based diversity data and increase the information output from both approaches.</p>
+
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