432 research outputs found

    Automated identification and behaviour classification for modelling social dynamics in group-housed mice

    Get PDF
    Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results. This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing. A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups. The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup. Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic

    Automated riverbed composition analysis using deep learning on underwater images

    Get PDF
    The sediment of alluvial riverbeds plays a significant role in river systems both in engineering and natural processes. However, the sediment composition can show high spatial and temporal heterogeneity, even on river-reach scale, making it difficult to representatively sample and assess. Conventional sampling methods are inadequate and time-consuming for effectively capturing the variability of bed surface texture in these situations. In this study, we overcome this issue by adopting an image-based deep-learning (DL) algorithm. The algorithm was trained to recognise the main sediment classes in videos that were taken along cross sections underwater in the Danube. A total of 27 riverbed samples were collected and analysed for validation. The introduced DL-based method is fast, i.e. the videos of 300–400 m long sections can be analysed within minutes with continuous spatial sampling distribution (i.e. the whole riverbed along the path is mapped with images in ca. 0.3–1 m2 overlapping windows). The quality of the trained algorithm was evaluated (i) mathematically by dividing the annotated images into test and validation sets and also via (ii) intercomparison with other direct (sieving of physical samples) and indirect sampling methods (wavelet-based image processing of the riverbed images), focusing on the percentages of the detected sediment fractions. For the final evaluation, the sieving analysis of the collected physical samples were considered the ground truth. After correcting for samples affected by bed armouring, comparison of the DL approach with 14 physical samples yielded a mean classification error of 4.5 %. In addition, based upon the visual evaluation of the footage, the spatial trend in the fraction changes was also well captured along the cross sections. Suggestions for performing proper field measurements are also given; furthermore, possibilities for combining the algorithm with other techniques are highlighted, briefly showcasing the multi-purpose nature of underwater videos for hydromorphological assessment.</p

    Glaciological history and structural evolution of the Shackleton Ice Shelf system, East Antarctica, over the past 60 years

    Get PDF
    The discovery of Antarctica's deepest subglacial trough beneath the Denman Glacier, combined with high rates of basal melt at the grounding line, has caused significant concern over its vulnerability to retreat. Recent attention has therefore been focusing on understanding the controls driving Denman Glacier's dynamic evolution. Here we consider the Shackleton system, comprised of the Shackleton Ice Shelf, Denman Glacier, and the adjacent Scott, Northcliff, Roscoe and Apfel glaciers, about which almost nothing is known. We widen the context of previously observed dynamic changes in the Denman Glacier to the wider region of the Shackleton system, with a multi-decadal time frame and an improved biannual temporal frequency of observations in the last 7 years (2015–2022). We integrate new satellite observations of ice structure and airborne radar data with changes in ice front position and ice flow velocities to investigate changes in the system. Over the 60-year period of observation we find significant rift propagation on the Shackleton Ice Shelf and Scott Glacier and notable structural changes in the floating shear margins between the ice shelf and the outlet glaciers, as well as features indicative of ice with elevated salt concentration and brine infiltration in regions of the system. Over the period 2017–2022 we observe a significant increase in ice flow speed (up to 50 %) on the floating part of Scott Glacier, coincident with small-scale calving and rift propagation close to the ice front. We do not observe any seasonal variation or significant change in ice flow speed across the rest of the Shackleton system. Given the potential vulnerability of the system to accelerating retreat into the overdeepened, potentially sediment-filled bedrock trough, an improved understanding of the glaciological, oceanographic and geological conditions in the Shackleton system are required to improve the certainty of numerical model predictions, and we identify a number of priorities for future research. With access to these remote coastal regions a major challenge, coordinated internationally collaborative efforts are required to quantify how much the Shackleton region is likely to contribute to sea level rise in the coming centuries.</p

    The Role of Circadian Entrainment in Rice Blast Disease

    Get PDF
    A circadian clock is present in some capacity in almost all forms of life, and is useful for a wide array of traits, but crucially allows organisms to predict future conditions and adapt their behaviour to synchronise with, and thrive under, their dynamic environment. Accordingly, plant environmental stress responses are gated in a circadian manner, including that for pathogenic defence and immunity. Comparatively less work has been carried out on the plant pathogens, but there are increasing reports of pathogens capable of rhythmically altering their behaviour and virulence-related traits. Magnaporthe oryzae, the fungal pathogen responsible for the destruction of enough rice to feed at least 60 M people annually, has been shown to possess some circadian clock components, and based on bioinformatic analyses, likely contains all the core, accessory, and circadian-associated genes. M. oryzae displays a conidial banding pattern, reminiscent of the model clock species, N. crassa, and (after sufficient entrainment) this pattern can continue to occur under free running conditions for a number of days, with a period of approximately 24 h. This rhythm is also presented on a range of nutrient-rich and poor media, suggesting a nutritionally-compensated circadian rhythm in M. oryzae. This onset of conidial banding is partially determined by the presence of secreted metabolites, the sensation of which is facilitated by the circadian clock, predominantly via WC2. The entraining light conditions that M. oryzae is exposed to can significantly alter its vegetative growth, conidiation and conidial development, and even pathogenicity. Further, inoculation timing (dawn or dusk) plays a role in both the virulence of M. oryzae, and in the susceptibility of the plant host, seemingly in a species-by-species manner, where rice is most susceptible at dawn, and barley most susceptible at dusk. For M. oryzae, pre-inoculation entrainment to darkness predominantly favours dawn inoculations, and those exposed to prolonged periods of light prefer dusk inoculation. Upon mutation of the core clock genes, WC2 and FRQ, vegetative growth, conidiation and conidial development, photoadaptation, and pathogenicity were all significantly altered compared to the wild type, suggesting an important role of the clock in the general fitness of M. oryzae. This work discusses how entraining light cycles and the circadian clock impacts the growth, development, conidiation, virulence, and ultimate severity in the economically important rice blast disease

    Visual Preference, Sensitivity, Perceived Complexity and Similarity of Images Varying in Natural Scene Statistics

    Full text link
    Introduction: Our visual system is optimised to process natural scenes, but it is still unclear which properties of natural scenes drive these adaptations. Natural scenes are characterised by distance-dependent regularities in their spatial structure such that nearby regions are more similar in their spatial properties, compared to more distal regions. These regularities have been linked to the notions of scale invariance and self-similarity, commonly indexed by the two different scaling techniques: the slope alpha of the Fourier amplitude spectrum (1/f^alpha) and the box-counting fractal dimension (D). The two measures capture either more photometric (amplitude) or more geometric (density) of contrast variations in an image. Aims: The current study aims to examine the role of photometric (spectral contrast amplitude) and geometric (density of spatial contrast variations) properties in visual preference, sensitivity, perceived complexity and similarity in synthetic noise images. We also examine the effects of prolonged exposure (visual adaptation) to natural scene statistics on preference, discrimination sensitivity and perceived complexity. Methods: Visual preference, sensitivity, perceived complexity and similarity were studied separately. The stimuli varied in their amplitude spectral slope (alpha = 0.25, 0.75, 1.25,1.75,2.25) and image type (Greyscale, GS; Threshold, TH; Edges, ED). Stimuli systematically varied in their photometric and geometric properties, thus allowing analyses of their relative contributions. We used a traditional visual adaptation paradigm where participants were exposed to an initial adaptation period (150s, 300s) followed by test trials (AFC, rating tasks) with top-up adaptation periods (5s, 10s). Results & Discussion: Visual sensitivity, preference, perceived complexity and similarity were strongly modulated by the variations in the amplitude spectra of synthetic noise images. The visual preference and perceived complexity were similar, if not identical for different image types, suggesting that these effects are driven mostly by the geometric image properties. Visual preferences were influenced by adaptation, with post-adaptation preference shifting towards the adaptor. Slightly enhanced discrimination performance was observed after adapting to and testing at alpha = 2.25, albeit inconsistently. Overall, the effects of adaptation on sensitivity was neither strong nor robust requiring at least 300s of adaptation for the effects to occur. There were no effects of adaptation on perceived complexity. There were also considerable individual differences in the effects of adaptation, particularly in the case of visual preference and sensitivity. Overall, the results suggest that visual sensitivity, preference, perceived complexity and similarity are affected by variations in natural scene statistics

    Surface analysis and fingerprint recognition from multi-light imaging collections

    Get PDF
    Multi-light imaging captures a scene from a fixed viewpoint through multiple photographs, each of which are illuminated from a different direction. Every image reveals information about the surface, with the intensity reflected from each point being measured for all lighting directions. The images captured are known as multi-light image collections (MLICs), for which a variety of techniques have been developed over recent decades to acquire information from the images. These techniques include shape from shading, photometric stereo and reflectance transformation imaging (RTI). Pixel coordinates from one image in a MLIC will correspond to exactly the same position on the surface across all images in the MLIC since the camera does not move. We assess the relevant literature to the methods presented in this thesis in chapter 1 and describe different types of reflections and surface types, as well as explaining the multi-light imaging process. In chapter 2 we present a novel automated RTI method which requires no calibration equipment (i.e. shiny reference spheres or 3D printed structures as other methods require) and automatically computes the lighting direction and compensates for non-uniform illumination. Then in chapter 3 we describe our novel MLIC method termed Remote Extraction of Latent Fingerprints (RELF) which segments each multi-light imaging photograph into superpixels (small groups of pixels) and uses a neural network classifier to determine whether or not the superpixel contains fingerprint. The RELF algorithm then mosaics these superpixels which are classified as fingerprint together in order to obtain a complete latent print image, entirely contactlessly. In chapter 4 we detail our work with the Metropolitan Police Service (MPS) UK, who described to us with their needs and requirements which helped us to create a prototype RELF imaging device which is now being tested by MPS officers who are validating the quality of the latent prints extracted using our technique. In chapter 5 we then further developed our multi-light imaging latent fingerprint technique to extract latent prints from curved surfaces and automatically correct for surface curvature distortions. We have a patent pending for this method

    Image Data Augmentation from Small Training Datasets Using Generative Adversarial Networks (GANs)

    Get PDF
    The scarcity of labelled data is a serious problem since deep models generally require a large amount of training data to achieve desired performance. Data augmentation is widely adopted to enhance the diversity of original datasets and further improve the performance of deep learning models. Learning-based methods, compared to traditional techniques, are specialized in feature extraction, which enhances the effectiveness of data augmentation. Generative adversarial networks (GANs), one of the learning-based generative models, have made remarkable advances in data synthesis. However, GANs still face many challenges in generating high-quality augmented images from small datasets because learning-based generative methods are difficult to create reliable outcomes without sufficient training data. This difficulty deteriorates the data augmentation applications using learning-based methods. In this thesis, to tackle the problem of labelled data scarcity and the training difficulty of augmenting image data from small datasets, three novel GAN models suitable for training with a small number of training samples have been proposed based on three different mapping relationships between the input and output images, including one-to-many mapping, one-to-one mapping, and many-to-many mapping. The proposed GANs employ limited training data, such as a small number of images and limited conditional features, and the synthetic images generated by the proposed GANs are expected to generate images of not only high generative quality but also desirable data diversity. To evaluate the effectiveness of the augmented images generated by the proposed models, inception distances and human perception methods are adopted. Additionally, different image classification tasks were carried out and accuracies from using the original datasets and the augmented datasets were compared. Experimental results illustrate the image classification performance based on convolutional neural networks, i.e., AlexNet, GoogLeNet, ResNet and VGGNet, is comprehensively enhanced, and the scale of improvement is significant when a small number of training samples are involved
    • …
    corecore