An Ecological and Evolutionary Framework for Commensalism in Anthropogenic Environments

Acknowledgements We would like to thank Jean-Denis Vigne, members of the Searle lab, and SNEEB at Cornell University for a stimulating environment and many early discussions and comments. We would also like to thank Maeve McMahon for comments on the manuscript.Peer reviewedPublisher PD

Comparing Natural Language Processing Techniques for Alzheimer's Dementia Prediction in Spontaneous Speech

Alzheimer's Dementia (AD) is an incurable, debilitating, and progressive neurodegenerative condition that affects cognitive function. Early diagnosis is important as therapeutics can delay progression and give those diagnosed vital time. Developing models that analyse spontaneous speech could eventually provide an efficient diagnostic modality for earlier diagnosis of AD. The Alzheimer's Dementia Recognition through Spontaneous Speech task offers acoustically pre-processed and balanced datasets for the classification and prediction of AD and associated phenotypes through the modelling of spontaneous speech. We exclusively analyse the supplied textual transcripts of the spontaneous speech dataset, building and comparing performance across numerous models for the classification of AD vs controls and the prediction of Mental Mini State Exam scores. We rigorously train and evaluate Support Vector Machines (SVMs), Gradient Boosting Decision Trees (GBDT), and Conditional Random Fields (CRFs) alongside deep learning Transformer based models. We find our top performing models to be a simple Term Frequency-Inverse Document Frequency (TF-IDF) vectoriser as input into a SVM model and a pre-trained Transformer based model `DistilBERT' when used as an embedding layer into simple linear models. We demonstrate test set scores of 0.81-0.82 across classification metrics and a RMSE of 4.58.Comment: Submitted to INTERSPEECH 2020: Alzheimer's Dementia Recognition through Spontaneous Speech The ADReSS Challenge Worksho

Flexible surface electrodes targeting biopotential signals from forearm muscles for control of prosthetic hands: Part 1 - Characterisation of semg electrodes

This study is Part 1 of two studies which investigate the use of various flexible surface sensors as an alternative to the gold standard Ag/AgCl surface electromyography (sEMG) electrodes in identifying movement intention from a user during common hand gestures. Three conductive textiles, two commercial conductive elastomers and one E-skin elastomer produced on site were tested as biopotential electrodes to establish the efficacy of each in gathering movement intention from the human brain at the level of the muscle. Testing was performed in vivo on two participants across three hand gestures, with results demonstrating that sEMG electrodes made from a commercially sourced conductive fabric can outperform the traditional Ag/AgCl sEMG electrodes, obtaining substantially larger peak and RMS measurements. Given the disadvantages of Ag/AgCl electrodes over long usage periods, namely their tendency to dry out and significant skin preparation, resulting in variable impedances and skin irritation respectively, the incorporation of flexible surface EMG electrodes in hand prosthetic control systems would increase functionality of the prosthetic devices, consequently increasing the quality of life of prosthetic hand users

Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset

Clinical coding is currently a labour-intensive, error-prone, but critical administrative process whereby hospital patient episodes are manually assigned codes by qualified staff from large, standardised taxonomic hierarchies of codes. Automating clinical coding has a long history in NLP research and has recently seen novel developments setting new state of the art results. A popular dataset used in this task is MIMIC-III, a large intensive care database that includes clinical free text notes and associated codes. We argue for the reconsideration of the validity MIMIC-III's assigned codes that are often treated as gold-standard, especially when MIMIC-III has not undergone secondary validation. This work presents an open-source, reproducible experimental methodology for assessing the validity of codes derived from EHR discharge summaries. We exemplify the methodology with MIMIC-III discharge summaries and show the most frequently assigned codes in MIMIC-III are under-coded up to 35%

P3_13 Solar Sails

The theory of a solar sail is introduced and a simple model of a 1000m2 solar sail weighing 2000kg launched from near the Earth is considered. It is found that the solar sail can reach a speed of 26km s-1 in 6.33 years but beyond that acceleration is very small, making a solar sail viable for exploring the edge of the solar system

P3_12 The Age of Sail

The maximum speed of a cargo ship powered entirely by wind is examined and found to be around 50% of wind speed, evaluating to between 5 and 8 knots. Available solar power is also examined as a comparison and shown to give an increase in speed. It is shown to be feasible to power a ship with renewable power but only if speed is a minimal concern

P3_10 The Extinction Criteria

This paper examines the possible early warning available before a “rogue†meteorite impact likely to cause the extinction of humanity

Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models

Brief Hospital Course (BHC) summaries are succinct summaries of an entire hospital encounter, embedded within discharge summaries, written by senior clinicians responsible for the overall care of a patient. Methods to automatically produce summaries from inpatient documentation would be invaluable in reducing clinician manual burden of summarising documents under high time-pressure to admit and discharge patients. Automatically producing these summaries from the inpatient course, is a complex, multi-document summarisation task, as source notes are written from various perspectives (e.g. nursing, doctor, radiology), during the course of the hospitalisation. We demonstrate a range of methods for BHC summarisation demonstrating the performance of deep learning summarisation models across extractive and abstractive summarisation scenarios. We also test a novel ensemble extractive and abstractive summarisation model that incorporates a medical concept ontology (SNOMED) as a clinical guidance signal and shows superior performance in 2 real-world clinical data sets

P3_11 Smallest Violin

This paper investigates the length of violin string needed for "the world's smallest violin playing the world’s saddest song.†It is found that the length required for an E string is 0.0108m and a violin of this size capable of producing the required frequency is unlikely to be possible