Talent Identification at the limits of Peer Review an analysis of the EMBO Postdoctoral Fellowships Selection Process, bioRxiv, 2018-12-04
Scientific peer review is still the most common system for fund allocation despite having been shown in multiple instances to lack accuracy in identifying the most meritorious applications among high quality ones. This study evaluates two aspects of the selection process of the top- ranked applicants to the EMBO Long-Term Fellowship program in 2007. First, the accuracy of the system is evaluated by comparing the level of career progression of the candidates in 2017 with the original award decisions made in 2007. The second aspect, explores the relationship of career progression with indicators derived from the information available to evaluators at the time of application. The results obtained suggest that the peer review system is not substantially better than random selection in identifying the best candidates once an initial pre-selection of the most promising ones is performed. Not only that, the analysis of the indicators studied, some of which have not been analyzed in detail in the past, suggests that among other potential sources of uncertainty, the information available at the time of application is not sufficiently predictive of career progression. As previously described, however, we find differences in career progression between men and women. We propose a new mixed model of fellowship evaluation in which peer review is used to select high quality applications, and random allocation of funds is subsequently used to award fellowships among these top ranked candidates.
biorxiv scientific-communication-and-education 500+-users 2018Engineering Brain Parasites for Intracellular Delivery of Therapeutic Proteins, bioRxiv, 2018-12-03
Protein therapy has the potential to alleviate many neurological diseases; however, delivery mechanisms for the central nervous system (CNS) are limited, and intracellular delivery poses additional hurdles. To address these challenges, we harnessed the protist parasite Toxoplasma gondii, which can migrate into the CNS and secrete proteins into cells. Using a fusion protein approach, we engineered T. gondii to secrete therapeutic proteins for human neurological disorders. We tested two secretion systems, generated fusion proteins that localized to the secretory organelles of T. gondii and assessed their intracellular targeting in various mammalian cells including neurons. We show that T. gondii expressing GRA16 fused to the Rett syndrome protein MeCP2 deliver a fusion protein that mimics the endogenous MeCP2, binding heterochromatic DNA in neurons. This demonstrates the potential of T. gondii as a therapeutic protein vector, which could provide either transient or chronic, in situ synthesis and delivery of intracellular proteins to the CNS.
biorxiv synthetic-biology 500+-users 2018Statistical physics of liquid brains, bioRxiv, 2018-11-27
Liquid neural networks (or “liquid brains”) are a widespread class of cognitive living networks characterised by a common feature the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agent-agent connections are maintained, in contrast with standard neural systems. How is this class of systems capable of displaying cognitive abilities, from learning to decision-making? In this paper, the collective dynamics, memory and learning properties of liquid brains is explored under the perspective of statistical physics. Using a comparative approach, we review the generic properties of three large classes of systems, namely standard neural networks (“solid brains”), ant colonies and the immune system. It is shown that, despite their intrinsic physical differences, these systems share key properties with standard neural systems in terms of formal descriptions, but strongly depart in other ways. On one hand, the attractors found in liquid brains are not always based on connection weights but instead on population abundances. However, some liquid systems use fluctuations in ways similar to those found in cortical networks, suggesting a relevant role of criticality as a way of rapidly reacting to external signals.
biorxiv systems-biology 500+-users 2018A primer on deep learning in genomics, Nature Genetics, 2018-11-21
Deep learning methods are a class of machine learning techniques capable of identifying highly complex patterns in large datasets. Here, we provide a perspective and primer on deep learning applications for genome analysis. We discuss successful applications in the fields of regulatory genomics, variant calling and pathogenicity scores. We include general guidance for how to effectively use deep learning methods as well as a practical guide to tools and resources. This primer is accompanied by an interactive online tutorial.
nature genetics genetics 500+-users 2018