Impact of inactivated poliovirus vaccine on mucosal immunity: implications for the polio eradication endgame.

The polio eradication endgame aims to bring transmission of all polioviruses to a halt. To achieve this aim, it is essential to block viral replication in individuals via induction of a robust mucosal immune response. Although it has long been recognized that inactivated poliovirus vaccine (IPV) is incapable of inducing a strong mucosal response on its own, it has recently become clear that IPV may boost immunity in the intestinal mucosa among individuals previously immunized with oral poliovirus vaccine. Indeed, mucosal protection appears to be stronger following a booster dose of IPV than oral poliovirus vaccine, especially in older children. Here, we review the available evidence regarding the impact of IPV on mucosal immunity, and consider the implications of this evidence for the polio eradication endgame. We conclude that the implementation of IPV in both routine and supplementary immunization activities has the potential to play a key role in halting poliovirus transmission, and thereby hasten the eradication of polio

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousands of articles for new information about modelled entities is a painstaking and low-reward task. Text mining can be used to help a curator extract relevant information from this literature in a systematic way. We propose the application of text mining methods for the neuroscience literature. Specifically, two computational neuroscientists annotated a corpus of entities pertinent to neuroscience using active learning techniques to enable swift, targeted annotation. We then trained machine learning models to recognise the entities that have been identified. The entities covered are Neuron Types, Brain Regions, Experimental Values, Units, Ion Currents, Channels, and Conductances and Model organisms. We tested a traditional rule-based approach, a conditional random field and a model using deep learning named entity recognition, finding that the deep learning model was superior. Our final results show that we can detect a range of named entities of interest to the neuroscientist with a macro average precision, recall and F1 score of 0.866, 0.817 and 0.837 respectively. The contributions of this work are as follows: 1) We provide a set of Named Entity Recognition (NER) tools that are capable of detecting neuroscience entities with performance above or similar to prior work. 2) We propose a methodology for training NER tools for neuroscience that requires very little training data to get strong performance. This can be adapted for any sub-domain within neuroscience. 3) We provide a small corpus with annotations for multiple entity types, as well as annotation guidelines to help others reproduce our experiments

Commercial chicken breeds exhibit highly divergent patterns of linkage disequilibrium

The analysis of linkage disequilibrium (LD) underpins the development of effective genotyping technologies, trait mapping and understanding of biological mechanisms such as those driving recombination and the impact of selection. We apply the Malécot-Morton model of LD to create additive LD maps that describe the high-resolution LD landscape of commercial chickens. We investigated LD in chickens (Gallus gallus) at the highest resolution to date for broiler, white egg and brown egg layer commercial lines. There is minimal concordance between breeds of fine-scale LD patterns (correlation coefficient <0.21), and even between discrete broiler lines. Regions of LD breakdown, which may align with recombination hot spots, are enriched near CpG islands and transcription start sites (P<2.2 × 10?16), consistent with recent evidence described in finches, but concordance in hot spot locations between commercial breeds is only marginally greater than random. As in other birds, functional elements in the chicken genome are associated with recombination but, unlike evidence from other bird species, the LD landscape is not stable in the populations studied. The development of optimal genotyping panels for genome-led selection programmes will depend on careful analysis of the LD structure of each line of interest. Further study is required to fully elucidate the mechanisms underlying highly divergent LD patterns found in commercial chickens

Evolutionary Dynamics of Co-Segregating Gene Clusters Associated with Complex Diseases

BACKGROUND: The distribution of human disease-associated mutations is not random across the human genome. Despite the fact that natural selection continually removes disease-associated mutations, an enrichment of these variants can be observed in regions of low recombination. There are a number of mechanisms by which such a clustering could occur, including genetic perturbations or demographic effects within different populations. Recent genome-wide association studies (GWAS) suggest that single nucleotide polymorphisms (SNPs) associated with complex disease traits are not randomly distributed throughout the genome, but tend to cluster in regions of low recombination. PRINCIPAL FINDINGS: Here we investigated whether deleterious mutations have accumulated in regions of low recombination due to the impact of recent positive selection and genetic hitchhiking. Using publicly available data on common complex diseases and population demography, we observed an enrichment of hitchhiked disease associations in conserved gene clusters subject to selection pressure. Evolutionary analysis revealed that these conserved gene clusters arose by multiple concerted rearrangements events across the vertebrate lineage. We observed distinct clustering of disease-associated SNPs in evolutionary rearranged regions of low recombination and high gene density, which harbor genes involved in immunity, that is, the interleukin cluster on 5q31 or RhoA on 3p21. CONCLUSIONS: Our results suggest that multiple lineage specific rearrangements led to a physical clustering of functionally related and linked genes exhibiting an enrichment of susceptibility loci for complex traits. This implies that besides recent evolutionary adaptations other evolutionary dynamics have played a role in the formation of linked gene clusters associated with complex disease traits

A Project Portfolio Management Approach to Tacklingthe Exploration/Exploitation Trade-off

Organizational ambidexterity (OA) is an essen-tial capability for surviving in dynamic business environ-ments that advocates the simultaneous engagement inexploration and exploitation. Over the last decades,knowledge on OA has substantially matured, coveringinsights into antecedents, outcomes, and moderators of OA.However, there is little prescriptive knowledge that offersguidance on how to put OA into practice and to tackle thetrade-off between exploration and exploitation. To addressthis gap, the authors adopt the design science researchparadigm and propose an economic decision model asartifact. The decision model assists organizations inselecting and scheduling exploration and exploitation pro-jects to become ambidextrous in an economically reason-able manner. As for justificatory knowledge, the decisionmodel draws from prescriptive knowledge on projectportfolio management and value-based management, andfrom descriptive knowledge related to OA to structure thefield of action. To evaluate the decision model, its designspecification is discussed against theory-backed designobjectives and with industry experts. The paper alsoinstantiates the decision model as a software prototype andapplies the prototype to a case based on real-world data

The Interaction between Early Life Epilepsy and Autistic-Like Behavioral Consequences: A Role for the Mammalian Target of Rapamycin (mTOR) Pathway

Early life seizures can result in chronic epilepsy, cognitive deficits and behavioral changes such as autism, and conversely epilepsy is common in autistic children. We hypothesized that during early brain development, seizures could alter regulators of synaptic development and underlie the interaction between epilepsy and autism. The mammalian Target of Rapamycin (mTOR) modulates protein translation and is dysregulated in Tuberous Sclerosis Complex, a disorder characterized by epilepsy and autism. We used a rodent model of acute hypoxia-induced neonatal seizures that results in long term increases in neuronal excitability, seizure susceptibility, and spontaneous seizures, to determine how seizures alter mTOR Complex 1 (mTORC1) signaling. We hypothesized that seizures occurring at a developmental stage coinciding with a critical period of synaptogenesis will activate mTORC1, contributing to epileptic networks and autistic-like behavior in later life. Here we show that in the rat, baseline mTORC1 activation peaks during the first three postnatal weeks, and induction of seizures at postnatal day 10 results in further transient activation of its downstream targets phospho-4E-BP1 (Thr37/46), phospho-p70S6K (Thr389) and phospho-S6 (Ser235/236), as well as rapid induction of activity-dependent upstream signaling molecules, including BDNF, phospho-Akt (Thr308) and phospho-ERK (Thr202/Tyr204). Furthermore, treatment with the mTORC1 inhibitor rapamycin immediately before and after seizures reversed early increases in glutamatergic neurotransmission and seizure susceptibility and attenuated later life epilepsy and autistic-like behavior. Together, these findings suggest that in the developing brain the mTORC1 signaling pathway is involved in epileptogenesis and altered social behavior, and that it may be a target for development of novel therapies that eliminate the progressive effects of neonatal seizures