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

Dendritic cells in cancer immunology and immunotherapy

Dendritic cells (DCs) are a diverse group of specialized antigen-presenting cells with key roles in the initiation and regulation of innate and adaptive immune responses. As such, there is currently much interest in modulating DC function to improve cancer immunotherapy. Many strategies have been developed to target DCs in cancer, such as the administration of antigens with immunomodulators that mobilize and activate endogenous DCs, as well as the generation of DC-based vaccines. A better understanding of the diversity and functions of DC subsets and of how these are shaped by the tumour microenvironment could lead to improved therapies for cancer. Here we will outline how different DC subsets influence immunity and tolerance in cancer settings and discuss the implications for both established cancer treatments and novel immunotherapy strategies.S.K.W. is supported by a European Molecular Biology Organization Long- Term Fellowship (grant ALTF 438– 2016) and a CNIC–International Postdoctoral Program Fellowship (grant 17230–2016). F.J.C. is the recipient of a PhD ‘La Caixa’ fellowship. Work in the D.S. laboratory is funded by the CNIC, by the European Research Council (ERC Consolidator Grant 2016 725091), by the European Commission (635122-PROCROP H2020), by the Ministerio de Ciencia, Innovación e Universidades (MCNU), Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional (FEDER) (SAF2016-79040-R), by the Comunidad de Madrid (B2017/BMD-3733 Immunothercan- CM), by FIS- Instituto de Salud Carlos III, MCNU and FEDER (RD16/0015/0018-REEM), by Acteria Foundation, by Atresmedia (Constantes y Vitales prize) and by Fundació La Marató de TV3 (201723). The CNIC is supported by the Instituto de Salud Carlos III, the MCNU and the Pro CNIC Foundation, and is a Severo Ochoa Centre of Excellence (SEV-2015-0505).S

Biological and Non-Biological Methods for Lignocellulosic Biomass Deconstruction

Owing to their abundance and cost-effectiveness, lignocellulosic materials have attracted increasing attention in clean energy technologies over the last decade. However, the complex polymer structure in these residues makes it difficult to extract the fermentable sugars. Therefore, various pretreatment regimes have been used resulting in the breaking of lignocelluloses’ physical and chemical structures, thereby enhancing the availability of the polysaccharides which are subsequently hydrolysed into different biocommodities. This chapter provides an evaluation of some of the latest exploited methodologies that are used in the pretreatment of lignocellulosic materials. Moreover, the chapter discusses the advantages and disadvantages of each method

A Probabilistic, Distributed, Recursive Mechanism for Decision-making in the Brain

Decision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT). Using it to simulate the random-dot-motion task, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT. Its architecture maps to the recurrent cortico-basal-ganglia-thalamo-cortical loops, whose components are all implicated in decision-making. We show that the dynamics of its mapped computations match those of neural activity in the sensorimotor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus. This also predicts which aspects of neural dynamics are and are not part of inference. Our single-equation algorithm is probabilistic, distributed, recursive, and parallel. Its success at capturing anatomy, behaviour, and electrophysiology suggests that the mechanism implemented by the brain has these same characteristics

Saccades actively maintain perceptual continuity

Copyright © 2004 Nature Publishing GroupPeople make saccades-rapid eye movements to a new fixation-approximately three times per second. This would seemingly disrupt perceptual continuity, yet our brains construct a coherent, stable view of the world from these successive fixations. There is conflicting evidence regarding the effects of saccades on perceptual continuity: some studies report that they are disruptive, with little information carryover between saccades; others report that carryover is substantial. Here we show that saccades actively contribute to perceptual continuity in humans in two different ways. When bistable stimuli are presented intermittently, saccades executed during the blank interval shorten the duration of states of ambiguous figures, indicating that saccades can erase immediately past perceptual states. On the other hand, they prolong the McCollough effect, indicating that saccades strengthen learned contingencies. Our results indicate that saccades help, rather than hinder, perceptual continuity.John Ross and Anna Ma-Wyat