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Objective To identify and classify de novo Parkinson’s disease patients compared to healthy controls (HC). Background PD patients experience visual symptoms and retinal degeneration. Studies using spectral-domain optical coherence tomography (SD-OCT) have shown retinal thinning in PD, even at the beginning of disease. This study investigated the utility of these retinal changes in de novo Ldopa-naive PD patients, to evaluate if the profile of retinal thinning could serve as a classification biomarker. Methods SD-OCT data were collected in de novo Ldopa-naive PD patients at the University Medical Center Groningen. 5x5 mm macular scans of right and left eyes were made. These were compared to age-matched HC scans. Good quality scans (≥4) were segmented by Iowa Reference Algorithms [1]; each retina was segmented into 10 individual cell layers. Results 121 PD, 110 HC were included. A random forest classification of all cell layers, across both eyes was run. Data was split into 102 training, 26 validation and 31 testing. Total test accuracy was 0.74, Out of the box accuracy 0.64. True positive rate: area under the curve receiver operating characteristic (AUROC) of 0.82, to classify PD compared to HC. Conclusions Retinal cell layer changes could play an important role in a model of classifying PD; presenting with significant differences in medication naive, newly diagnosed patients, being able to provide a high true positive classification, with automatically segmented retinal cell layer data, from straightforward SD-OCT retinal imaging.

Deception detection is valuable in the security domain to distinguish truth from lies. It is desirable in many security applications, such as suspect and witness interviews and airport passenger screening. Interviewers are constantly trying to assess the credibility of a statement, usually based on intuition without objective technical support. However, psychological research has shown that humans can hardly perform better than random guessing. Deception detection is a multi-disciplinary research area with an interest from different fields, such as psychology and computer science. In the last decade, several developments have helped to improve the accuracy of lie detection (e.g., with a concealed information test, increasing the cognitive load, or measurements with motion capture suits) and relevant cues have been discovered (e.g., eye blinking or fiddling with the fingers). With an increasing presence of mobile phones and bodycams in society, a mobile, stand-off, automatic deception detection methodology based on various cues from the whole body would create new application opportunities. In this paper, we study the feasibility of measuring these visual cues automatically on different parts of the body, laying the groundwork for stand-off deception detection in more flexible and mobile deployable sensors, such as body-worn cameras. We give an extensive overview of recent developments in two communities: in the behavioral-science community the developments that improve deception detection with a special attention to the observed relevant non-verbal cues, and in the computer-vision community the recent methods that are able to measure these cues. The cues are extracted from several body parts: the eyes, the mouth, the head and the full-body pose. We performed an experiment using several state-of-the-art video-content-analysis (VCA) techniques to assess the quality of robustly measuring these visual cues.

The cultural diversity of people encountered by front-line investigators has increased substantially over the last decade. Increasingly, investigators must try to resolve their suspicions by evaluating a person's behaviour through the lens of that person's social and cultural norms. In this chapter, we consider what is known about cross-cultural deception and deception detection. In the first section, we examine cultural differences in perceptions of deception and review evidence suggesting that the accuracy of deception judgements deteriorates when made across cultures. We examine the roots of this poor performance, showing how eight cultural norms lead to behaviours that appear suspicious to judges from other cultures. In the second section, we review evidence suggesting that verbal and non-verbal cues to deception vary across cultures. In particular, we show that the observed variation in cues is consistent with, and can be predicted by, what is known about cultural differences in fundamental interpersonal and cognitive processes. In our conclusion, we speculate about likely areas of development in this line of research.

Bronchial hyperresponsiveness (BHR) and peak expiratory flow (PEF) variability are associated expressions of airway lability, yet probably reflect different underlying pathophysiologic mechanisms. We investigated whether both measures can be used interchangeably to identify subjects who are susceptible to ambient air pollution, Data on BHR (greater than or equal to 20% fall in FEV1), PEF variability (ampl%mean PEF > 5% on any day during an 8-d period with low air pollution levels) and diary data on upper and lower respiratory symptoms, cough, and phlegm were collected in 189 subjects (48-73 yr). The acute effects (lag0) of particulate matter with a diameter less than 10 mu m (PM10), black smoke, SO2 and NO, on the prevalence of symptoms were estimated with logistic regression. In subjects with airway lability, both when expressed as PEF variability (69%) and BHR (28%), the prevalence of symptoms increased significantly with increasing levels of air pollution, especially in those with the greater PEF variability (n = 55, 29%). We found no such consistent positive associations in adults without airway lability. PEF variability, and to a smaller extent BHR, can be used to Identify adults who are susceptible to air pollution. Though odds ratios were rather low (ranging from 1.13 to 1.41), the impact on public health can be substantial because it applies to large populations.