Metacognitive processes accompanying the first stages of autobiographical retrieval in the self-memory system.

According to Conway's view, Autobiographical memory (AM) construction is accompanied by control processes. These processes range from filtering out relevant memories according to the current context, to generating or elaborating appropriate retrieval cues. These processes can be conceptualised as metacognition, the ability to control and monitor cognitive processes. Experimentally, little has been carried out to support the idea that metacognition is involved in AM. To assess this, we designed a task, the Feeling of Retrieval. Participants had to predict whether cue words would facilitate AM access (i.e., fluent access cues) or not (i.e., limited access cues) in a limited time (either 1 (Exp. 2) or 2 (Exp. 1) s). Later, they retrieved memories in response to both types of cues. Results show that cues judged as fluent access led to better AM generation, as illustrated by AM retrieval latency and a subjective measure of the ease with which the AMs were retrieved. These rapid predictions may rely on epistemic feelings and / or other mnemonic cues such as a partial retrieval of information. This metacognitive access to the earliest stages of AM retrieval illustrates the ability to monitor AM processes as proposed by Conway (2005).

Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.

Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by microscopic analysis; a specialised and time-consuming task limited in taxonomical precision and sampling frequency, therefore restricting data quality used to inform climate change and pollen forecasting models. We build on the growing work using AI (artificial intelligence) for automated pollen classification to design a flexible network that can deal with the uncertainty of broad-scale environmental applications. We combined imaging flow cytometry with Guided Deep Learning to identify and accurately categorise pollen in environmental samples; here, pollen grains captured within c. 5500 Cal yr BP old lake sediments. Our network discriminates not only pollen included in training libraries to the species level but, depending on the sample, can classify previously unseen pollen to the likely phylogenetic order, family and even genus. Our approach offers valuable insights into the development of a widely transferable, rapid and accurate exploratory tool for pollen classification in 'real-world' environmental samples with improved accuracy over pure deep learning techniques. This work has the potential to revolutionise many aspects of palynology, allowing a more detailed spatial and temporal understanding of pollen in the environment with improved taxonomical resolution.

Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D.

Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and complexity of imaging experiments while limiting the number of channels remaining to address the study's objectives. We demonstrate that the excitation light reflected during routine confocal microscopy contains sufficient information to achieve accurate, label-free cell segmentation in 2D and 3D. This is achieved using a simple convolutional neural network trained to predict the probability that reflected light pixels belong to either nucleus, cytoskeleton, or background classifications. We demonstrate the approach across diverse lymphoid tissues and provide video tutorials demonstrating deployment in Python and MATLAB or via standalone software for Windows.

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25-5.0 µg/mL) and/or carbendazim (0.8-1.6 µg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the "DeepFlow" neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for 'mononucleates', 'binucleates', 'mononucleates with MN' and 'binucleates with MN', respectively. Successful classifications of 'trinucleates' (90%) and 'tetranucleates' (88%) in addition to 'other or unscorable' phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.

Activity Mapping of Children in Play Using Multivariate Analysis of Movement Events.

PURPOSE: (i) To develop an automated measurement technique for the assessment of both the form and intensity of physical activity undertaken by children during play. (ii) To profile the varying activity across a cohort of children using a multivariate analysis of their movement patterns. METHODS: Ankle-worn accelerometers were used to record 40 min of activity during a school recess, for 24 children over five consecutive days. Activity events of 1.1 s duration were identified within the acceleration time trace and compared with a reference motif, consisting of a single walking stride acceleration trace, obtained on a treadmill operating at a speed of 4 km h. Dynamic time warping of motif and activity events provided metrics of comparative movement duration and intensity, which formed the data set for multivariate mapping of the cohort activity using a principal component analysis (PCA). RESULTS: The two-dimensional PCA plot provided clear differentiation of children displaying diverse activity profiles and clustering of those with similar movement patterns. The first component of the PCA correlated to the integrated intensity of movement over the 40-min period, whereas the second component informed on the temporal phasing of activity. CONCLUSIONS: By defining movement events and then quantifying them by reference to a motion-standard, meaningful assessment of highly varied activity within free play can be obtained. This allows detailed profiling of individual children's activity and provides an insight on social aspects of play through identification of matched activity time profiles for children participating in conjoined play.

Physical activity, motor competence and movement and gait quality: A principal component analysis.

OBJECTIVE: While novel analytical methods have been used to examine movement behaviours, to date, no studies have examined whether a frequency-based measure, such a spectral purity, is useful in explaining key facets of human movement. The aim of this study was to investigate movement and gait quality, physical activity and motor competence using principal component analysis. METHODS: Sixty-five children (38 boys, 4.3 ±â€¯0.7y, 1.04 ±â€¯0.05 m, 17.8 ±â€¯3.2 kg, BMI; 16.2 ±â€¯1.9 kg∙m2) took part in this study. Measures included accelerometer-derived physical activity and movement quality (spectral purity), motor competence (Movement Assessment Battery for Children 2nd edition; MABC2), height, weight and waist circumference. All data were subjected to a principal component analysis, and the internal consistency of resultant components were assessed using Cronbach's alpha. RESULTS: Two principal components, with excellent internal consistency (Cronbach α >0.9) were found; the 1st principal component, termed "movement component", contained spectral purity, traffic light MABC2 score, fine motor% and gross motor% (α = 0.93); the 2nd principal component, termed "anthropometric component", contained weight, BMI, BMI% and body fat% (α = 0.91). CONCLUSION: The results of the present study demonstrate that accelerometric analyses can be used to assess motor competence in an automated manner, and that spectral purity is a meaningful, indicative, metric related to children's movement quality.

The origin of heterogeneous nanoparticle uptake by cells.

Understanding nanoparticle uptake by biological cells is fundamentally important to wide-ranging fields from nanotoxicology to drug delivery. It is now accepted that the arrival of nanoparticles at the cell is an extremely complicated process, shaped by many factors including unique nanoparticle physico-chemical characteristics, protein-particle interactions and subsequent agglomeration, diffusion and sedimentation. Sequentially, the nanoparticle internalisation process itself is also complex, and controlled by multiple aspects of a cell's state. Despite this multitude of factors, here we demonstrate that the statistical distribution of the nanoparticle dose per endosome is independent of the initial administered dose and exposure duration. Rather, it is the number of nanoparticle containing endosomes that are dependent on these initial dosing conditions. These observations explain the heterogeneity of nanoparticle delivery at the cellular level and allow the derivation of simple, yet powerful probabilistic distributions that accurately predict the nanoparticle dose delivered to individual cells across a population.

Objective profiling of varied human motion based on normative assessment of magnetometer time series data.

OBJECTIVE: To quantify varied human motion and obtain an objective assessment of relative performance across a cohort. APPROACH: A wrist-worn magnetometer was used to record and quantify the complex motion patterns of 55 children aged 10 to 12 years old, generated during a fundamental movement skills programme. Sensor-based quantification of the physical activity used dynamic time warping of the magnetometer time series data for pairs of children. Pairwise comparison across the whole cohort produced a similarity matrix of all child to child correlations. Normative assessment scores were based on the Euclidean distance between n participants within an n - 1 multi-variate space, created from multi-dimensional scaling of the similarity matrix. The sensor-based scores were compared to the current standardised assessment which involves binary scoring of technique, outcome and time components by trained assessors. MAIN RESULTS: Visualisation of the relative performance using the first three axes of the multi-dimensional matrix, shows a 'performance sphere' in which children sit on concentric shells of increasing radius as performance deteriorates. Good agreement between standard and sensor scores is found, with Spearman rank correlation coefficients of the overall activity score in the range of 0.62-0.71 for different cohorts and a kappa statistic of 0.34 for categorisation of all 55 children into lower, middle, upper tertile and top 5% bands. SIGNIFICANCE: By using multi-dimensional analysis of similarity measures between participants rather than direct parameterisation of the physiological data, complex and varied patterns of physical motion can be quantified, allowing objective and robust profiling of relative function across participant groups.

Profiling Movement and Gait Quality Characteristics in Pre-School Children.

There is a dearth of suitable metrics capable of objectively quantifying motor competence. Further, objective movement quality characteristics during free play have not been investigated in pre-school children. The aims of this study were to characterize children's free play physical activity and investigate how gait quality characteristics cluster with free play in pre-school children (3-5 years old). Sixty-one children (39 boys; 4.3 ± 0.7 years, 1.04 ± 0.05 m, 17.8 ± 3.2 kg) completed the movement assessment battery for children and took part in free play while wearing an ankle- and hip-mounted accelerometer. Characteristics of movement quality were profiled using a clustering algorithm. Spearman's rho and the Mann-Whitney U tests were used to assess relationships between movement quality characteristics and motor competence classification differences in integrated acceleration and spectral purity, respectively. Significant differences were found between motor competency classifications for spectral purity and integrated acceleration (p < .001). Spectral purity was hierarchically clustered with motor competence and integrated acceleration. Significant positive correlations were found between spectral purity, integrated acceleration and motor competence (p < .001). This is the first study to report spectral purity in pre-school children and the results suggest that the underlying frequency component of movement is clustered with motor competence.

A Review of Emerging Analytical Techniques for Objective Physical Activity Measurement in Humans.

Physical inactivity is one of the most prevalent risk factors for non-communicable diseases in the world. A fundamental barrier to enhancing physical activity levels and decreasing sedentary behavior is limited by our understanding of associated measurement and analytical techniques. The number of analytical techniques for physical activity measurement has grown significantly, and although emerging techniques may advance analyses, little consensus is presently available and further synthesis is therefore required. The objective of this review was to identify the accuracy of emerging analytical techniques used for physical activity measurement in humans. We conducted a search of electronic databases using Web of Science, PubMed, and Google Scholar. This review included studies written in English and published between January 2010 and December 2014 that assessed physical activity using emerging analytical techniques and reported technique accuracy. A total of 2064 papers were initially retrieved from three databases. After duplicates were removed and remaining articles screened, 50 full-text articles were reviewed, resulting in the inclusion of 11 articles that met the eligibility criteria. Despite the diverse nature and the range in accuracy associated with some of the analytic techniques, the rapid development of analytics has demonstrated that more sensitive information about physical activity may be attained. However, further refinement of these techniques is needed.

Quantitative Time Profiling of Children's Activity and Motion.

INTRODUCTION: The aim of this study was to establish children's mechanical movement patterns during a standardized assessment of fitness by using an accelerometer. Further to this, our objective was to use the information from the accelerometer to profile individual time courses of exercise, across the cohort. METHODS: A multistage fitness test study was performed with 103 children (mean ± SD age = 10.3 ± 0.6 yr). Children wore an ankle-mounted accelerometer, and gait data were collected on radial acceleration traces obtained at a frequency of 40 Hz. Time-resolved metrics of foot impact force, maximum leg lift angle, and stride frequency were used to profile children's performance across the test duration. A whole-history metric of stride quality, based on the changing ratio of stride length to stride frequency, was used in bivariate analyses of physical performance and body metrics. RESULTS: Stride angle derived by our protocol was found to have a strong positive correlation with integrated acceleration, synonymous with counts, widely used in the sport science community (r = 0.81, 0.79, and 0.80 across different stages of the multistage fitness test). Accelerometer data show that differing performance in the test is related to the children's ability to accurately control their gait, with high performers displaying a linearly increasing speed, delivered through stride extension and well matched to the demand level of the test. A negative correlation was found between stride quality and body measures of body mass index (r = -0.61) and body mass (r = -0.60). CONCLUSION: Profiles of the gait parameters provide information on the mechanics of child's motion, allowing detailed assessment of multiple parameter during increasing intensities of exercise.

Profiling movement quality and gait characteristics according to body-mass index in children (9-11 y).

Obese children move less and with greater difficulty than their normal-weight counterparts. Whilst the effect of high BMI on cardiovascular fitness is well known, the effect on movement quality characteristics during a standardised fitness test has not been investigated. The aims of this study were, to characterise the movement quality of children performing the multi-stage fitness test (MSFT), and, report how movement quality characteristics cluster according to weight status. One hundred and three children (10.3±0.6 y, 1.42±0.08m, 37.8±9.3kg, BMI; 18.5±3.3kgm(2)) performed the MSFT whilst wearing an ankle mounted accelerometer. BMI groups were used to classify children as underweight (UW), normal weight (NW), overweight (OW) and obese (OB). Characteristics of movement were profiled using a clustering algorithm. Spearman's rho was used to assess relationship with BMI group, and a Mann-Whitney U test was used to assess differences between BMI groups. Obese children had significantly lower spectral purity than every other group and significantly lower time to exhaustion (TTE) than UW and NW children (P<0.05). BMI was clustered with stride profile and TTE with spectral purity. Significant negative correlations (P<0.05) were found between BMI and TTE (r=-0.25), spectral purity (r=-0.24), integrated acceleration (r=-0.22), stride angle (r=-0.23) and stride variability (r=-0.22). This was the first study to report the spectral purity of children's gait. Further analysis unveiled key performance characteristics that differed between BMI groups. These were (i) representative of children's performance during the MSFT and, (ii) significantly negatively correlated with BMI.