Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation

We present a three-dimensional graph convolutional network (3DGCN), which predicts molecular properties and biochemical activities, based on 3D molecular graph. In the 3DGCN, graph convolution is unified with learning operations on the vector to handle the spatial information from molecular topology. The 3DGCN model exhibits significantly higher performance on various tasks compared with other deep-learning models, and has the ability of generalizing a given conformer to targeted features regardless of its rotations in the 3D space. More significantly, our model also can distinguish the 3D rotations of a molecule and predict the target value, depending upon the rotation degree, in the protein-ligand docking problem, when trained with orientation-dependent datasets. The rotation distinguishability of 3DGCN, along with rotation equivariance, provides a key milestone in the implementation of three-dimensionality to the field of deep-learning chemistry that solves challenging biochemical problems.Comment: 39 pages, 14 figures, 5 table

Systematic review of new medics’ clinical task experience by country

OBJECTIVES: There is a need for research which informs on the overall size and significance of clinical skills deficits among new medics, globally. There is also the need for a meta-review of the similarities and differences between countries in the clinical skills deficits of new medics. DESIGN: A systematic review of published literature produced 68 articles from Google/Scholar, of which 9 met the inclusion criteria (quantitative clinical skills data about new medical doctors). PARTICIPANTS: 1329 new medical doctors (e.g., foundation year-1s, interns, PGY1s). SETTING: Ten countries/regions. MAIN OUTCOME MEASURES: 123 data points and representation of a broad range of clinical procedures. RESULTS: The average rate of inexperience with a wide range of clinical procedures was 35.92% (lower CI 30.84%, upper CI 40.99%). The preliminary meta-analysis showed that the overall deficit in experience is significantly different from 0 in all countries. Focusing on a smaller selection of clinical skills such as catheterisation, IV cannulation, nasogastric tubing and venepuncture, the average rate of inexperience was 26.75% (lower CI 18.55%, upper CI 35.54%) and also significant. England presented the lowest average deficit (9.15%), followed by New Zealand (18.33%), then South Africa (19.53%), Egypt, Kuwait, Gulf Cooperation Council countries and Ireland (21.07%), after which was Nigeria (37.99%), then USA (38.5%), and Iran (44.75%). CONCLUSION: A meta-analysis is needed to include data not yet in the public domain from more countries. These results provide some support for the UK General Medical Council’s clear, detailed curriculum, which has been heralded by other countries as good practice

LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks

Recently, deep neural networks have achieved remarkable performance on the task of object detection and recognition. The reason for this success is mainly grounded in the availability of large scale, fully annotated datasets, but the creation of such a dataset is a complicated and costly task. In this paper, we propose a novel method for weakly supervised object detection that simplifies the process of gathering data for training an object detector. We train an ensemble of two models that work together in a student-teacher fashion. Our student (localizer) is a model that learns to localize an object, the teacher (assessor) assesses the quality of the localization and provides feedback to the student. The student uses this feedback to learn how to localize objects and is thus entirely supervised by the teacher, as we are using no labels for training the localizer. In our experiments, we show that our model is very robust to noise and reaches competitive performance compared to a state-of-the-art fully supervised approach. We also show the simplicity of creating a new dataset, based on a few videos (e.g. downloaded from YouTube) and artificially generated data.Comment: To appear in AMV18. Code, datasets and models available at https://github.com/Bartzi/loan

Mechanisms of intermittent state transitions in a coupled heterogeneous oscillator model of epilepsy

This is the final version of the article. Available from BioMed Central/SpringerOpen via the DOI in this record.We investigate the dynamic mechanisms underlying intermittent state transitions in a recently proposed neural mass model of epilepsy. A low dimensional model is constructed, which preserves two key features of the neural mass model, namely (i) coupling between oscillators and (ii) heterogeneous proximity of these oscillators to a bifurcation between distinct limit cycles. We demonstrate that state transitions due to intermittency occur in the abstract model. This suggests that there is a general bifurcation mechanism responsible for this behaviour and that this is independent of the precise form of the evolution equations. Such abstractions of neural mass models allow a deeper insight into underlying dynamic and physiological mechanisms, and also allow the more efficient exploration of large scale brain dynamics in disease.MG acknowledges funding from the EPSRC through a postdoctoral prize fellowship

Class reconstruction driven adversarial domain adaptation for hyperspectral image classification

We address the problem of cross-domain classification of hyperspectral image (HSI) pairs under the notion of unsupervised domain adaptation (UDA). The UDA problem aims at classifying the test samples of a target domain by exploiting the labeled training samples from a related but different source domain. In this respect, the use of adversarial training driven domain classifiers is popular which seeks to learn a shared feature space for both the domains. However, such a formalism apparently fails to ensure the (i) discriminativeness, and (ii) non-redundancy of the learned space. In general, the feature space learned by domain classifier does not convey any meaningful insight regarding the data. On the other hand, we are interested in constraining the space which is deemed to be simultaneously discriminative and reconstructive at the class-scale. In particular, the reconstructive constraint enables the learning of category-specific meaningful feature abstractions and UDA in such a latent space is expected to better associate the domains. On the other hand, we consider an orthogonality constraint to ensure non-redundancy of the learned space. Experimental results obtained on benchmark HSI datasets (Botswana and Pavia) confirm the efficacy of the proposal approach

Temporally correlated fluctuations drive epileptiform dynamics

Published onlineJournal ArticleMacroscopic models of brain networks typically incorporate assumptions regarding the characteristics of afferent noise, which is used to represent input from distal brain regions or ongoing fluctuations in non-modelled parts of the brain. Such inputs are often modelled by Gaussian white noise which has a flat power spectrum. In contrast, macroscopic fluctuations in the brain typically follow a 1/f(b) spectrum. It is therefore important to understand the effect on brain dynamics of deviations from the assumption of white noise. In particular, we wish to understand the role that noise might play in eliciting aberrant rhythms in the epileptic brain. To address this question we study the response of a neural mass model to driving by stochastic, temporally correlated input. We characterise the model in terms of whether it generates "healthy" or "epileptiform" dynamics and observe which of these dynamics predominate under different choices of temporal correlation and amplitude of an Ornstein-Uhlenbeck process. We find that certain temporal correlations are prone to eliciting epileptiform dynamics, and that these correlations produce noise with maximal power in the δ and θ bands. Crucially, these are rhythms that are found to be enhanced prior to seizures in humans and animal models of epilepsy. In order to understand why these rhythms can generate epileptiform dynamics, we analyse the response of the model to sinusoidal driving and explain how the bifurcation structure of the model gives rise to these findings. Our results provide insight into how ongoing fluctuations in brain dynamics can facilitate the onset and propagation of epileptiform rhythms in brain networks. Furthermore, we highlight the need to combine large-scale models with noise of a variety of different types in order to understand brain (dys-)function.This work was supported by the European Commission through the FP7 Marie Curie Initial Training Network 289146 (NETT: Neural Engineering Transformative Technologies), by the Spanish Ministry of Economy and Competitiveness and FEDER (project FIS2012-37655-C02-01). J.G.O. also acknowledges support from the ICREA Academia programme, the Generalitat de Catalunya (project 2014SGR0947), and the “María de Maeztu” Programme for Units of Excellence in R&D (Spanish Ministry of Economy and Competitiveness, MDM-2014-0370) M.G. gratefully acknowledges the financial support of the EPSRC via grant EP/N014391/1. The contribution of M.G. was generously supported by a Wellcome Trust Institutional Strategic Support Award (WT105618MA)