Deep Learning for Time Series Classification of Parkinson's Disease Eye Tracking Data

Eye-tracking is an accessible and non-invasive technology that provides information about a subject's motor and cognitive abilities. As such, it has proven to be a valuable resource in the study of neurodegenerative diseases such as Parkinson's disease. Saccade experiments, in particular, have proven useful in the diagnosis and staging of Parkinson's disease. However, to date, no single eye-movement biomarker has been found to conclusively differentiate patients from healthy controls. In the present work, we investigate the use of state-of-the-art deep learning algorithms to perform Parkinson's disease classification using eye-tracking data from saccade experiments. In contrast to previous work, instead of using hand-crafted features from the saccades, we use raw $\sim1.5\,s$ long fixation intervals recorded during the preparatory phase before each trial. Using these short time series as input we implement two different classification models, InceptionTime and ROCKET. We find that the models are able to learn the classification task and generalize to unseen subjects. InceptionTime achieves $78\%$ accuracy, while ROCKET achieves $88\%$ accuracy. We also employ a novel method for pruning the ROCKET model to improve interpretability and generalizability, achieving an accuracy of $96\%$. Our results suggest that fixation data has low inter-subject variability and potentially carries useful information about brain cognitive and motor conditions, making it suitable for use with machine learning in the discovery of disease-relevant biomarkers.Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 12 page

Shiitake spent mushroom substrate as a sustainable feedstock for developing highly efficient nitrogen-doped biochars for treatment of dye-contaminated water

Edible white-rot mushrooms are organisms that are cultivated at an industrial scale using wood-based substrates. The mushroom industry has an estimated annual production of 34 Mt of edible mushrooms, and approximately 70 wt% of the substrate is left as waste known as spent mushroom substrate (SMS). The huge volumes of SMS generated by mushroom farms hinder proper recycling, meaning that combustion or open-field burning are common disposal practices. This paper shows a concept that could help reduce the environmental impact of the mushroom industry. SMS from the cultivation of shiitake mushroom was used as a carbon precursor for the production of nitrogen-doped activated biochar that was used to remove reactive orange-16 (RO-16) azo dye from water, as well as contaminants from two synthetic effluents and real sewage water. Melamine was used as a nitrogen dopant and phosphoric acid as an activating agent. Samples without the addition of melamine were used for comparison. The doping/impregnation process was carried out in one-step, followed by pyrolysis at 700 and 900 ◦C for 1 h. BET, Raman spectroscopy, X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM) were used for the characterization of the biochars. The specific surface area of the doped samples was slightly lower, i.e., 1011 m2 /g (SMS-700 ◦C), 810 m2 /g (SMS-700 ◦C + N), 1095 m2 /g (SMS900 ◦C), and 943 m2 /g (SMS-900 ◦C + N). Raman spectroscopic analysis showed that the N-doped biochars had more defective carbon structures than the non-doped ones. XPS analysis showed that doping with melamine led to the formation of N-functionalities on the surface of the biochar particles. The kinetics of adsorption were well represented by the Avrami model. The adsorption isotherms were well-fitted by the Liu model. The maximum adsorption capacities (qmax) of RO-16 were much higher for the N-doped biochars, i.e., 120 mg/g (SMS-700 ◦C), 216 mg/g (SMS-700 ◦C + N), 168 mg/g (SMS-900 ◦C), and 393 mg/g (SMS-900 ◦C + N). N-doped biochar samples were more effective for the removal of contaminants from synthetic effluents and sewage water. Ndoped biochar produced at 900 ◦C showed good recyclability. This work concludes that SMS is a valuable waste that could be used for the production of activated carbon and that N-doping helped to improve the adsorption performance to a great extent

Analysing user physiological responses for affective video summarisation

This is the post-print version of the final paper published in Displays. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2009 Elsevier B.V.Video summarisation techniques aim to abstract the most significant content from a video stream. This is typically achieved by processing low-level image, audio and text features which are still quite disparate from the high-level semantics that end users identify with (the ‘semantic gap’). Physiological responses are potentially rich indicators of memorable or emotionally engaging video content for a given user. Consequently, we investigate whether they may serve as a suitable basis for a video summarisation technique by analysing a range of user physiological response measures, specifically electro-dermal response (EDR), respiration amplitude (RA), respiration rate (RR), blood volume pulse (BVP) and heart rate (HR), in response to a range of video content in a variety of genres including horror, comedy, drama, sci-fi and action. We present an analysis framework for processing the user responses to specific sub-segments within a video stream based on percent rank value normalisation. The application of the analysis framework reveals that users respond significantly to the most entertaining video sub-segments in a range of content domains. Specifically, horror content seems to elicit significant EDR, RA, RR and BVP responses, and comedy content elicits comparatively lower levels of EDR, but does seem to elicit significant RA, RR, BVP and HR responses. Drama content seems to elicit less significant physiological responses in general, and both sci-fi and action content seem to elicit significant EDR responses. We discuss the implications this may have for future affective video summarisation approaches

Employment outcomes in people with bipolar disorder : a systematic review

Objective: Employment outcome in bipolar disorder is an under investigated, but important area. The aim of this study was to identify the long-term employment outcomes of people with bipolar disorder. Method: A systematic review using the Medline, PsychInfo and Web of Science databases. Results: Of 1962 abstracts retrieved, 151 full text papers were read. Data were extracted from 25 papers representing a sample of 4892 people with bipolar disorder and a mean length of follow-up of 4.9 years. Seventeen studies had follow-up periods of up to 4 years and eight follow-up of 5–15 years. Most studies with samples of people with established bipolar disorder suggest approximately 40–60% of people are in employment. Studies using work functioning measures mirrored this result. Bipolar disorder appears to lead to workplace underperformance and 40–50% of people may suffer a slide in their occupational status over time. Employment levels in early bipolar disorder were higher than in more established illness. Conclusion: Bipolar disorder damages employment outcome in the longer term, but up to 60% of people may be in employment. Whilst further studies are necessary, the current evidence provides support for extending the early intervention paradigm to bipolar disorder

Observational Study Design in Veterinary Pathology, Part 1: Study Design

Observational studies are the basis for much of our knowledge of veterinary pathology and are highly relevant to the daily practice of pathology. However, recommendations for conducting pathology-based observational studies are not readily available. In part 1 of this series, we offer advice on planning and conducting an observational study with examples from the veterinary pathology literature. Investigators should recognize the importance of creativity, insight, and innovation in devising studies that solve problems and fill important gaps in knowledge. Studies should focus on specific and testable hypotheses, questions, or objectives. The methodology is developed to support these goals. We consider the merits and limitations of different types of analytic and descriptive studies, as well as of prospective vs retrospective enrollment. Investigators should define clear inclusion and exclusion criteria and select adequate numbers of study subjects, including careful selection of the most appropriate controls. Studies of causality must consider the temporal relationships between variables and the advantages of measuring incident cases rather than prevalent cases. Investigators must consider unique aspects of studies based on archived laboratory case material and take particular care to consider and mitigate the potential for selection bias and information bias. We close by discussing approaches to adding value and impact to observational studies. Part 2 of the series focuses on methodology and validation of methods

Quantification of Normal Cell Fraction and Copy Number Neutral LOH in Clinical Lung Cancer Samples Using SNP Array Data

Technologies based on DNA microarrays have the potential to provide detailed information on genomic aberrations in tumor cells. In practice a major obstacle for quantitative detection of aberrations is the heterogeneity of clinical tumor tissue. Since tumor tissue invariably contains genetically normal stromal cells, this may lead to a failure to detect aberrations in the tumor cells.Using SNP array data from 44 non-small cell lung cancer samples we have developed a bioinformatic algorithm that accurately models the fractions of normal and tumor cells in clinical tumor samples. The proportion of normal cells in combination with SNP array data can be used to detect and quantify copy number neutral loss-of-heterozygosity (CNNLOH) in the tumor cells both in crude tumor tissue and in samples enriched for tumor cells by laser capture microdissection.Genome-wide quantitative analysis of CNNLOH using the CNNLOH Quantifier method can help to identify recurrent aberrations contributing to tumor development in clinical tumor samples. In addition, SNP-array based analysis of CNNLOH may become important for detection of aberrations that can be used for diagnostic and prognostic purposes

Recognition of subtle and universal facial expressions in a community-based sample of adults classified with intellectual disability

Background Across the USA and UK schemes now exist to aid the successful integration of adults with mild to moderate intellectual disabilities into general society. One factor that may prove important to the success of such schemes is social competence. Here, understanding the facial expressions of others is critical, as emotion recognition is a prerequisite to empathetic responding and an essential factor in social functioning. Yet research in this area is lacking, especially in community-based samples. Method We investigated the performance of 13 adults with mild to moderate intellectual disability (ID), relative to 13 age-matched controls, on three tasks of emotion recognition (emotion categorisation; recognition of valence; recognition of arousal), using a number of ‘basic’ (angry, happy) and more ‘subtle’ (compassionate, critical) emotional expressions, as well as the posers face in a default relaxed (i.e. ‘neutral’) state. Importantly, the sample was drawn from a community-based initiative, and was therefore representative of populations’ government schemes target. Results Across emotion recognition tasks the ID adults, as compared to controls, were significantly impaired when labelling the emotions displayed by the poser as well as recognising the associated ‘feelings’ conveyed by these faces. This was especially true for the neutral, compassionate and angry facial expressions. For example, ID adults demonstrated deficits in categorising neutral and subtle emotional expressions, as well as assessing the valence of such facial expressions. In addition, ID adults also struggled to assess arousal levels; especially those associated with compassionate and angry faces. Conclusion Given both basic and subtle emotions are conveyed in a range of daily situations, errors in interpreting such facial expressions and, relatedly, understanding what potential behaviours such expressions signify could contributing to the social difficulties ID adults face. This is important since current initiatives such as ‘personalisation’ do not appear to have schemes supporting training in this area and understanding the facial expressions of others is, after all, one of our most important non-verbal social communication tools

Is gender encoded in the smile? A computational framework for the analysis of the smile driven dynamic face for gender recognition

YesAutomatic gender classification has become a topic of great interest to the visual computing research community in recent times. This is due to the fact that computer-based automatic gender recognition has multiple applications including, but not limited to, face perception, age, ethnicity, identity analysis, video surveillance and smart human computer interaction. In this paper, we discuss a machine learning approach for efficient identification of gender purely from the dynamics of a person’s smile. Thus, we show that the complex dynamics of a smile on someone’s face bear much relation to the person’s gender. To do this, we first formulate a computational framework that captures the dynamic characteristics of a smile. Our dynamic framework measures changes in the face during a smile using a set of spatial features on the overall face, the area of the mouth, the geometric flow around prominent parts of the face and a set of intrinsic features based on the dynamic geometry of the face. This enables us to extract 210 distinct dynamic smile parameters which form as the contributing features for machine learning. For machine classification, we have utilised both the Support Vector Machine and the k-Nearest Neighbour algorithms. To verify the accuracy of our approach, we have tested our algorithms on two databases, namely the CK+ and the MUG, consisting of a total of 109 subjects. As a result, using the k-NN algorithm, along with tenfold cross validation, for example, we achieve an accurate gender classification rate of over 85%. Hence, through the methodology we present here, we establish proof of the existence of strong indicators of gender dimorphism, purely in the dynamics of a person’s smile