Putting the learning into e-learning

There has been an increase in the use of e-learning as a form of delivering higher education. Much of the innovation has been carried out in what has been called an ‘evaluation bypass’ and has seemingly been popular because of its economic efficiency. The literature on new technologies tends to be written by those committed to the innovation. They tend to present innovation as a good, regardless of what the innovation is, and ‘resistors’ as in some senses deviant. Using the example of the HEFCE funded multimedia project ‘Doing Political Research’ this paper argues that some degree of scepticism about new innovation can be seen as a positive response. Furthermore the paper argues that the cost saving arguments put forward by proponents of innovation are illusory. E-learning can be as costly as other means. However, it does offer alternative ways to teach and can be particularly effective at reaching isolated learners. The conclusion is that for e-learning to be effective it must place learning first

Efficient data management for putting forward data centric sciences

International audienceThe novel and multidisciplinary data centric and scientific movement promises new and not yet imagined applications that rely on massive amounts of evolving data that need to be cleaned, integrated, and analysed for modelling, prediction, and critical decision making purposes. This paper explores the key challenges and opportunities for data management in this new scientific context, and discusses how data management can best contribute to data centric sciences applications through clever data science strategies

Unbiased Forward Genetic Screening with Chemical Mutagenesis to Uncover Drug-Target Interactions

The steadily increasing throughput in next-generation sequencing technologies is revolutionizing a number of fields in biology. One application requiring massive parallel sequencing is forward genetic screening based on chemical mutagenesis. Such screens interrogate the entire genome in an entirely unbiased fashion and can be applied to a number of research questions. CRISPR/Cas9-based screens, which are largely limited to a gene's loss of function, have already been very successful in identifying drug targets and pathways related to the drug's mode of action. By inducing single nucleotide changes using an alkylating reagent, it is possible to generate amino acid changes that perturb the interaction between a drug and its direct target, resulting in drug resistance. This chemogenomic approach combined with latest sequencing technologies allows deconvolution of drug targets and characterization of drug-target binding interfaces at amino acid resolution, therefore nicely complementing existing biochemical approaches. Here we describe a general protocol for a chemical mutagenesis-based forward genetic screen applicable for drug-target deconvolution