Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.

We developed Graphical Representation of Ancestral Sequence Predictions (GRASP) to infer and explore ancestral variants of protein families with more than 10,000 members. GRASP uses partial order graphs to represent homology in very large datasets, which are intractable with current inference tools and may, for example, be used to engineer proteins by identifying ancient variants of enzymes. We demonstrate that (1) across three distinct enzyme families, GRASP predicts ancestor sequences, all of which demonstrate enzymatic activity, (2) within-family insertions and deletions can be used as building blocks to support the engineering of biologically active ancestors via a new source of ancestral variation, and (3) generous inclusion of sequence data encompassing great diversity leads to less variance in ancestor sequence.GRASP is the central tool in the GRASP-suite, which is freely available at

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The inter-relationship between arousal events and body and/or limb movements during sleep may significantly impact the performance and clinical interpretation of actigraphy. As such, the objective of this study was to quantify the temporal association between arousals and body/limb movement. From this, we aim to determine whether actigraphy can predict arousal events in children, and identify the impact of arousal-related movements on estimates of sleep/wake periods. Thirty otherwise healthy children (5-16 years, median 9 years, 21 male) with suspected sleep apnoea were studied using full polysomnography and customised raw tri-axial accelerometry measured at the left fingertip, left wrist, upper thorax, left ankle and left great toe. Raw data were synchronised to within 0.1 s of the polysomnogram. Movements were then identified using a custom algorithm. On average 67.5% of arousals were associated with wrist movement. Arousals associated with movement were longer than those without movement (mean duration: 12.2 s versus 7.9 s respectively, p < 0.01); movements during wake and arousal were longer than other sleep movements (wrist duration: 6.26 s and 9.89 s versus 2.35 s respectively, p < 0.01); and the movement index (movements/h) did not predict apnoea-hypopnoea index (ρ  =  -0.11). Movements associated with arousals are likely to unavoidably contribute to actigraphy's poor sensitivity for wake. However, as sleep-related movements tend to be shorter than those during wake or arousal, incorporating movement duration into the actigraphy scoring algorithm may improve sleep staging performance. Although actigraphy-based measurements cannot reliably predict all arousal events, actigraphy can likely identify longer events that may have the greatest impact on sleep quality.

Actigraphy is a useful alternative to the gold standard polysomnogram for non-invasively measuring sleep and wakefulness. However, it is unable to accurately assess sleep fragmentation due to its inability to differentiate restless sleep from wakefulness and quiet wake from sleep. This presents significant limitations in the assessment of sleep-related breathing disorders where sleep fragmentation is a common symptom. We propose that this limitation may be caused by hardware constraints and movement representation techniques. Our objective was to determine if multisite tri-axial accelerometry improves sleep and wake classification. Twenty-four patients aged 6-15 years (median: 8 years, 16 male) underwent a diagnostic polysomnogram while simultaneously recording motion from the left wrist and index fingertip, upper thorax and left ankle and great toe using a custom accelerometry system. Movement was quantified using several features and two feature selection techniques were employed to select optimal features for restricted feature set sizes. A heuristic was also applied to identify movements during restless sleep. The sleep and wake classification performance was then assessed and validated against the manually scored polysomnogram using discriminant analysis. Tri-axial accelerometry measured at the wrist significantly improved the wake detection when compared to uni-axial accelerometry (specificity at 85% sensitivity: 71.3(14.2)% versus 55.2(24.7)%, p < 0.01). Multisite accelerometry significantly improved the performance when compared to the single wrist placement (specificity at 85% sensitivity: 82.1(12.5)% versus 71.3(14.2)%, p < 0.05). Our results indicate that multisite accelerometry offers a significant performance benefit which could be further improved by analysing movement in raw multisite accelerometry data.

Actigraphy is increasingly used to non-invasively estimate sleep quality in children with a suspected sleep disorder. Commercial actigraphs summarise wrist movement, conventionally measured with a uni-axial accelerometer, within a fixed epoch (typically 30s). Wake is subsequently identified as epochs of increased activity and sleep is identified as epochs of inactivity. This classification framework has some distinct limitations: actigraphy misclassifies activity during sleep as wake, and inactivity during wake (i.e. quiet rest) as sleep. In this thesis we will address these limitations by investigating three hypotheses. Firstly, uni-axial accelerometry measured solely at the wrist restricts prediction accuracy, since movements orthogonal to the measurement axis, or occurring elsewhere on the body, cannot be detected. Utilising multisite tri-axial accelerometry may consequently improve sleep and wake prediction. Secondly, there are movement characteristics that can di↵erentiate sleep from wake because the physiological nature of these movements di↵er. Identifying these characteristics may reduce false wake detections. Finally, physiological and pathological events such as apnoeas, hypopneas and transient arousals may be associated with sleep movements that contribute to false wake detections. Exploring this association may consequently explain the presence of some sleep movements.In order to address the hypotheses, 38 participants (27 male, aged 5 16 years) were recruited from children attending the sleep laboratory for suspected sleep-disordered breathing. These children were studied concurrently with polysomnography and a custom system (synchronised to within 0.1s) that recorded raw tri-axial accelerometry data (8 bit, 100Hz, ±2G) simultaneously at the left index fingertip, left wrist, upper thorax, left ankle and left great toe.The first analysis compared the accuracy of predicting sleep and wake epochs with uniaxial, tri-axial, and multisite accelerometry. Tri-axial versions of the conventional 30s epoch summaries were derived and compared to conventional uni-axial accelerometry. Multisite data were explored and verified using two feature selection algorithms with the tri-axial summaries for each accelerometer. Classification performance was significantly improved when incorporating additional accelerometers, and measuring movement with tri-axial accelerometry (Kappa agreement for multisite, tri-axial and uni-axial accelerometry: 0.565(0.231), 0.402(0.141) and 0.268(0.210), p < 0.05). Tri-axial accelerometry has clear benefits with no increase in cost or invasiveness. Although multisite accelerometry provides additional performance benefits, these benefits come at the expense of system complexity and patient discomfort.iii Moving away from epoch-by-epoch predictions, the second analysis assessed wake detection on a movement-by-movement basis. Localised spectral characteristics of raw segmented wrist movements were identified using the discrete wavelet transform. Characteristics that significantly di↵...

Actigraphy is effective at monitoring circadian rhythms, but often misidentifies periods of restless sleep (defined here as sleep periods with movement) as wake, and periods of quiet wake as sleep. This limitation restricts the effectiveness of actigraphy for investigating sleep disorders. Our objective in this study was to investigate a time-frequency representation of movement during sleep and wake which could ultimately aid in improving classification performance by reducing false wake detections. As a pilot study, we investigate the characteristics of manually labelled movements from six patients (aged 6-12 years, 3 male) during sleep and wake using the over complete discrete wavelet decomposition. The difference between the median wavelet coefficients were analyzed for 30 movement segments from six movement categories during sleep and wake. We found that, in general, the temporal location of high energy coefficients and the energy of the high frequency bands differed between movements during sleep and wake. This indicates that we are able to differentiate movement during sleep and wake with a time-frequency representation. This representation may improve the sleep and wake classification performance by identifying movements specific to sleep and wake. This will likely improve the poor specificity inherent in conventional actigraphy.