Title: Diet and Microbes: Gut health for the brain and body
Date: 27-31 January 2020, Levi, Lapland, Finland
Course Website: https://www.helsinki.fi/en/conferences/nutrition-winter-school/nutrition-winter-school-2020
Grants: NuGO will make 4 course grants available for PhDs and postdocs (< 3 years after obtaining the PhD) covering the course fee of 550€. For more details about the grant and how to apply please visit: grants Winterschool Lapland 2020
Deadline grant application: 31 October 2019.
For detailed infomation about this symposium please visit the programme
Diet and Microbes: Gut health for the brain and body
27.1. – 31.1.2020, Levi, Lapland, Finland
Nutrition Winter School is a scientific seminar aimed at young post docs and PhD students from the fields of nutrition, medicine, and biochemistry. Ever since we organized this event for the first time in 2009, our goal has been to gather early-career scientists and top researchers from various backgrounds to enjoy scientific lectures and fruitful discussions under the magical atmosphere of the Finnish Lapland.
The 6th Nutrition Winter School will take place in Lapland at Levi, Kittilä, Finland from January 27th to 31st, 2020. This time the theme of the seminar is Diet and Microbes: Gut health for the brain and body. This theme is covered by internationally renowned experts from a clinical, experimental, and molecular biological point-of-view.
Confirmed speakers include:
Prof. Robert-Jan Brummer, Örebro University Dr Federica Facciotti, European Institute of Oncology
Prof. Karl-Heinz Herzig, University of Oulu Dr Jonna Jalanka, University of Helsinki
Prof. Stefan Kiechl, Medical University of Innsbruck Prof. Eero Mervaala, University of Helsinki
Dr Milla Pietiäinen, University of Helsinki Dr Justus Reunanen, University of Oulu
Dr Anu Ruusunen, University of Eastern Finland Prof Seppo Salminen, University of Turku
Prof Michael Schemann, Technical University of München Dr Filip Scheperjans, Helsinki University Hospital
Prof. Magnus Simrén, University of Gothenburg
Check out the preliminary program:
Early Bird registration and call for abstracts are now open! Be fast and book your place today!
Extra Q and As from Claus:
What other omics integration tools have you tested on your datasets (DIABLO, MINT, sMBPLS...)?
This particular data set is more than 10 years old. I think Le Cao had published some of her papers by then, but the mixOmics package didn’t exist yet and neither did DIABLO and MINT. In a way doing this seminar was one way for me get into these packages a bit more. At that time we used the ade4 and made4 packages, which I think are pre-runners of omicade4. I wasn’t aware of the sMBPLS package, but it looks very interesting so will check it out. I think there are slightly different cultures depending on which omics data you deal with. The metabolomics world seems very strong on variants of PLS, whereas the Gene Expression folk seem to love network analysis (which you can regard as multivariate analysis too, but is very different).
Is there any rule for a maximum number of variables that can be assessed according to the number of samples (when the number of variables is huge but the number of subjects do not exceed a few hundreds)?
No, I don’t think so. The number of variables will only ever grow in comparison to the number of samples and saying there is a maximum number would imply we (as statisticians) refuse to accept our responsibility to deal with that. Statistics has developed rapidly in this area in the last 20 years, and the best and most successful methods tend to make use of the high-dimensionality rather than seeing it as a curse.
A pdf copy of the webinar presentation is available in the attachment to this message for ECN members.
Sparse multivariate methods and integration of omics data sets
Friday 6th September, 16:00-17:00 CET
Multivariate methods like principle component analysis (PCA) or partial least squares (PLS) are essential in revealing structure in high-dimensional omics type data, where the number of variables is typically much larger than the number of samples (p>>n). As useful as these methods are to study the relationship between samples the high number of variables obscures the interpretation which genes, proteins or metabolites contribute to the patterns we see. Sparse methods enforce the loadings of most variables to be 0, while still explaining much of the variation in the data and thus enable an easier biological interpretation of the results. Dr Mayer will introduce sparse versions of some commonly used multivariate methods and illustrate their use in data examples.
In a second part he will present methods that simultaneously analyse two (or more) data sets like Canonical Correlation Analysis (CCA) or Co-Inertia Analysis (CIA). These tools allow to study the joint influence of two sets of variables (eg. a transcriptomic and a proteomic data set) on the variation within samples while showing the relationship between the data sets at the same time.
Dr Claus Mayer is a senior statistician working for Biomathematics and Statistics Scotland. His main area of research in recent years has been the analysis of high-dimensional genomics data with a particular emphasis on gene expression studies (microarrays, RNAseq) and related areas (proteomics, methylation studies. Dr Mayer has worked on methods of integrating/combining such omics data sets from different sources like combining high-dimensional data from different stages of an experiment in a group-sequential setting, conducting meta-analysis of comparable gene expression studies or integrating different types of omics data collected from the same samples. Dr Mayer has also investigated ways of quickly calculating overall summary statistics of pairwise (cross-) correlations within one or more high-dimensional data sets and has studied ways of turning such (partial) correlation structures into sparse biologically interpretable networks.
Sign up for the webinar at:
This year there will be an adventure room, speed-networking, refreshments and more!
Don't miss out, participation is limited, please sign up on the NuGOweek 2019 registration homepage.
Further information is given in the attached flyer.
Many thanks to Michel Combes for the flyer design:)
The Jupyter tutorial and supporting files for the webinar are attached below.
How to uncover biological patterns in omics data
Friday 28th June, 16:00 CET
This webinar is targeted at researchers who are using (or plan to use) omic data such as metabolomics, proteomics or transcriptomics to address their research questions. The first part of the webinar will cover how to use principal component analysis (PCA) as a tool for dimensionality reduction and detection of biases. In addition, guidance will be provided on how to select the number of meaningful components and how to interpret the loadings of principal components (if necessary). The second part of the webinar will explore how to complete basic clustering, including hierarchical clustering (e.g. as used in dendrograms), and how to extract groups from it. A practical example of the typical workflow for these analyses will be provided with an R script example for those who use R programming.
Extensive knowledge in R programming is not essential to participate in the webinar but the application of some of the methods is demonstrated in the R environment.
To sign up, please register directly at: https://attendee.gotowebinar.com/register/674439732259279361
Future Food is a Swiss research initiative that provides opportunities to educate and train new talent around the food value chain by leveraging the complementary strengths of the Swiss academic and industrial research communities.
The 'fellowship' is a postdoc programme for exceptionally qualified young researchers who propose projects targeting future food issues such as nutrition, production, packaging and digital health. The fellowship provides personal research funds for three years, enabling fellows to work on their projects in a research laboratory anywhere in Switzerland with a “champion” in academia (the host professor at ETH Zurich or EPFL) and in industry (the industry partner).
The aim is to award up to ten fellowships each year. The actual number depends on how many candidates successfully complete a selection process culminating with a face-to-face interview. In addition to scientific skills, the personality and motivation of the candidates will also be considered. However, the main criterion for selection is scientific excellence combined with industrial relevance. The call for new research or translational projects is conducted once every year.
For further information, visit:
D-limonene is a monoterpene abundant in citrus fruits known for its hypoglycaemic and hypolipidemic effects. The focus of the study is to evaluate mechanisms of action not yet explored, aiming to understand how this compound can modulate metabolism and prevent chronic diseases. For this purpose, a study in animal models will be carried out and different metabolomics platforms as well as metagenomic and gene expression analysis will be employed to assess d-limonene effects.
The ideal candidate must have:
• Obtained the PhD degree in the last 7 years;
• Have formal training in analytical chemistry (bachelor degree in chemistry, biochemistry,
pharmacy or related areas);
• Demonstrated experience in targeted and untargeted metabolomics. This includes the operation of qToF and triple quadrupole mass spectrometers and familiarity with data analysis softwares and mass spectrometry databases;
• Experience in R for data analysis;
• Experience with metabolomics analysis applied to nutrition/ metabolism of phytochemicals;
• Be the first author in at least one scientific publication in an indexed journal.
Applicants must send a CV, motivation letter and one recommendation letter to firstname.lastname@example.org until 25/04/2019. A final decision will be made until the 05/05/2019. The estimated date to start the new position is 01/07/2019.
FAPESP post-doctoral fellowships consist of monthly allowances of R$7373,10 and additional grant equivalent to 15% of the monthly allowances to cover costs related to the research project. The fellowship is granted for 2 years, with the possibility of extension for 2 extra years. Applicants living further than 350 Km from São Paulo are entitled to a relocation allowance and covering of travel costs.