Adaptive, locally-linear models of complex dynamics

The dynamics of complex systems generally include high-dimensional, non-stationary and non-linear behavior, all of which pose fundamental challenges to quantitative understanding. To address these difficulties we detail a new approach based on local linear models within windows determined adaptively from the data. While the dynamics within each window are simple, consisting of exponential decay, growth and oscillations, the collection of local parameters across all windows provides a principled characterization of the full time series. To explore the resulting model space, we develop a novel likelihood-based hierarchical clustering and we examine the eigenvalues of the linear dynamics. We demonstrate our analysis with the Lorenz system undergoing stable spiral dynamics and in the standard chaotic regime. Applied to the posture dynamics of the nematode $C. elegans$ our approach identifies fine-grained behavioral states and model dynamics which fluctuate close to an instability boundary, and we detail a bifurcation in a transition from forward to backward crawling. Finally, we analyze whole-brain imaging in $C. elegans$ and show that the stability of global brain states changes with oxygen concentration.Comment: 25 pages, 16 figure

The Automatic Tracking Of <i>Caenorhabditis elegans</i> And Its Use In Determining Genetic Function

Even with its simple nervous system, the nematode worm Caenorhabditis elegans can display a range of complex behaviours. Movement can be viewed as the main output of the C. elegans nervous system, and aberrations in the worm’s locomotion can be used as an indicator for genetic function in mutant strains of C. elegans. Automated tracking of C. elegans locomotion has been used to determine phenotypic fingerprints for ~300 mutant C. elegans strains. Two methods of creating phenotypic fingerprints were used. The first based on pre-determined micro-behaviours previously described in worms, but never before analysed using automated tracking. The second used the tracking data itself to determine micro-motifs, repeated sets of behaviours observed at least twice in at least two mutant or wild-type strains. Both methods of clustering successfully grouped together strains with mutations in genes known to interact together, verifying that the technique is able to detect meaningful connections between mutant strains. The following step was to determine whether the technique can be used to establish connections between genes on unknown function. A pair of strains with mutations in DEG/ENaC subunit encoding genes clustered strongly together using the micro-motif method, due to similar defects in their behaviours upon turning. The function of these genes, asic-2 and acd-5, was unknown. Upon further investigation it was found that the two genes are expressed in different classes of neurons, the IL2s in the case of asic-2 and the ASIs in the case of acd-5. Following investigation into behaviours known to be modulated by these two neuron classes it was found that the mutant strains displayed mutant phenotypes in similar behaviours, but that their mutant phenotypes are opposing. Mutations in asic-2 cause increased lifespan and healthspan and a reduction in dauer entry in response to exogenous, purified ascarosides. Mutations in acd-5 cause decreased lifespan and healthspan and a reduction in dauer entry in response to crude dauer pheromone. This suggested that the two genes were unlikely to be working in the same pathway, but do function in similar pathways. Calcium imaging is a technique used in C. elegans to measure responses in excitable cells, in this case in neurons. Many calcium indicators are available for use in this technique, one in particular is GCaMP. GCaMP has undergone many rounds of targeted mutations with the aim to increase the molecule’s dynamic range and dissociation constant. At the time of commencing this project, new variants of GCaMP, known as GCaMP6s, became available, and had yet to be tested in C. elegans neurons. The effectiveness of a total of 6 new variants was tested in the gentle touch neurons of C. elegans. It was found that the alterations made to GCaMP5G in order to make the GCaMP6 variants did not result in improved dynamic range or dissociation constant in the PLM of C. elegans

High-throughput, single-worm tracking and analysis in Caenorhabditis elegans

Caenorhabditis elegans, a millimeter-sized, soil-dwelling nematode, is a model organism for biology research. Its whole genome has been sequenced. The lineage and fate, for each one of the cells in wild-type (N2) worms, is known. The connectivity, for all 302 neurons of wild-type hermaphrodites, has been mapped. Many of its genes have homologs within other organisms, including humans. C. elegans have a well-defined repertoire of observed behaviors. For these reasons, and due to a wealth of experimental data, C. elegans is a well-suited organism for mapping genetics to phenotype. This thesis details a system for relating genetics to phenotype. I present a methodology for semi-automated, high-throughput, high resolution investigation of gene effects on behavior and morphology using C. elegans. In the first section beyond the introduction, Chapter 2, I describe a new singleworm tracking system (hardware and software), titled Worm Tracker 2.0 (WT2), which was used to collect videos of worm behavior with high throughput. While multi-worm tracking systems exist, including ones that enable higher experimental throughput by recording multiple worms at once, their videos have insufficient resolution to resolve worm bodies well and these systems have been limited to only simple measurements. While other single-worm tracking systems also exist, they present, among other limitations, significant costs precluding high experimental throughput. I designed and built the hardware and software for a less expensive unit, which is approximately 1/4 the cost of previous single-worm trackers. This enabled us to purchase eight such units for high-throughput of experimentation. Other novelty for our system includes the ability to track worms at all larval stages and the ability to follow single-worms swimming. In Chapter 3, I describe a novel automated analysis for the worm videos collected using the aforementioned single-worm tracker. While analysis exists for other single-worm tracking systems, several limitations precluded adaptation. Our worm videos are on food and the worms are of variable size. Several previous algorithms attempted to deal with worms on food but, for our purposes, suffer from poor resolution at the head and tail, areas necessary to obtain significant phenotypic information. The analysis I built uses a novel algorithm driven by a need to obtain high-accuracy and precise worm contours (and their consequent skeletons) in our difficult conditions (e.g., on food and swimming environments) with invariance to worm size (bounded by a minimal limit of resolution). This accuracy was necessary due to the sheer size of the data set collected, roughly 1/3 of a billion frames, which precludes manual verification. In the final section, Chapter 4, I describe the results from my analysis of our collected data. Using our trackers we collected more than 12,000 videos, each 15 minutes in length, at 640x480 20-30Hz resolution, representing over 300 mutant strains matched to wild-type controls. This large set was filtered to obtain high-quality data and remove strains specific to private data sets (prepared for future publications). The filtered analysis covers 330 worm groups compiled from 300 mutant strains, 2 wild isolates, three descendants of N2, along with our N2 controls divided into hourly, daily, and monthly groups. A subset of 79 strains, representing 76 genes with no previously characterized phenotype, show significant measures in my analysis. Further sensitivity of the analysis is explored through measures of habituation, small morphological changes due to growth, and a phenotypic comparison of the three descendants from the ancestral, wild-type N2. With the sensitivity explored, I present an N2 phenotypic reference compiled from 1,218 worms, recorded over three years. Statistics of this set define a reference measure of the N2 phenotype (specific to the Schafer Lab wild type) with broad implications for performing and controlling C. elegans experiments. Three genes, implicated in mechanosensation as a result of genetic sequence but lacking any observed phenotypic support, reveal locomotory phenotypes in our analysis. This prompts a large clustering of all 330 groups, to assess the predictive capabilities of our system. The N2 groups cluster together in a large exclusive aggregate. Further support for the predictive capabilities of the clustering emerge among multiple published pathways that also form exclusive clusters. I end by discussing a set of genes, predicted to be acetylcholine receptors through genetic sequence and functional heterologous expression, which now receive further support through strong aggregation within their own exclusive phenotypic cluster.This work was supported by the Gates Trust and the Medical Research Council

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Powerful and interpretable behavioural features for quantitative phenotyping of C. elegans

Behaviour is a sensitive and integrative readout of nervous system function and therefore an attractive measure for assessing the effects of mutation or drug treatment on animals. Video data provide a rich but high-dimensional representation of behaviour, and so the first step of analysis is often some form of tracking and feature extraction to reduce dimensionality while maintaining relevant information. Modern machine-learning methods are powerful but notoriously difficult to interpret, while handcrafted features are interpretable but do not always perform as well. Here, we report a new set of handcrafted features to compactly quantify Caenorhabditis elegans behaviour. The features are designed to be interpretable but to capture as much of the phenotypic differences between worms as possible. We show that the full feature set is more powerful than a previously defined feature set in classifying mutant strains. We then use a combination of automated and manual feature selection to define a core set of interpretable features that still provides sufficient power to detect behavioural differences between mutant strains and the wild-type. Finally, we apply the new features to detect time-resolved behavioural differences in a series of optogenetic experiments targeting different neural subsets

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

Automated, high-throughput, motility analysis in Caenorhabditis elegans and parasitic nematodes: Applications in the search for new anthelmintics

The scale of the damage worldwide to human health, animal health and agricultural crops resulting from parasitic nematodes, together with the paucity of treatments and the threat of developing resistance to the limited set of widely-deployed chemical tools, underlines the urgent need to develop novel drugs and chemicals to control nematode parasites. Robust chemical screens which can be automated are a key part of that discovery process. Hitherto, the successful automation of nematode behaviours has been a bottleneck in the chemical discovery process. As the measurement of nematode motility can provide a direct scalar readout of the activity of the neuromuscular system and an indirect measure of the health of the animal, this omission is acute. Motility offers a useful assay for high-throughput, phenotypic drug/chemical screening and several recent developments have helped realise, at least in part, the potential of nematode-based drug screening. Here we review the challenges encountered in automating nematode motility and some important developments in the application of machine vision, statistical imaging and tracking approaches which enable the automated characterisation of nematode movement. Such developments facilitate automated screening for new drugs and chemicals aimed at controlling human and animal nematode parasites (anthelmintics) and plant nematode parasites (nematicides)

Tools for Behavioral Phenotyping of C. elegans

Animal behavior is critical to survival and provides a window into how the brain makes decisions and integrates sensory information. A simple model organism that allows researchers to more precisely interrogate the relationships between behavior and the brain is the nematode C. elegans. However, current phenotyping tools have technical limitations that make observing, intervening in, and quantifying behavior in diverse settings difficult. In this thesis, I develop enabling technological systems to resolve these challenges. To address scaling issues in observation and intervention in long-term behavior, I develop a platform for long-term continuous imaging, online behavior quantification, and online behavior-conditional intervention. I show that this tool is easy to build and use and can operate in an automated fashion for days at a time. I then use this platform to understand the consequences of quiescence deprivation to C. elegans health. To quantify complex animal postures, and plant and stem cell aggregate morphology, I develop an app to enable fast, versatile and quantitative annotation and demonstrate that it is both ~ 130-fold faster and in some cases less error-prone than state-of-the-art computational methods. This app is agnostic to image content and allows freehand annotation of curves and other complex and non-uniform shapes while also providing an automated way to distribute annotation tasks. This tool may be used to generate ground truth sets for testing or creating automated algorithms. Finally, I quantify C. elegans behavior using quantitative machine-learning analysis and map the worm’s behavioral repertoire across multiple physical environments that more closely mimic C. elegans’ natural environment. From this analysis, I identified subtle behaviors that are not easily distinguishable by eye and built a tool that allows others to explore our video dataset and behaviors in a facile way. I also use this analysis to examine the richness of C. elegans behavior across selected environments and find that behavior diversity is not uniform across environments. This has important implications for choice of media for behavioral phenotyping, as it suggests that the appropriate media choice may increase our ability to distinguish behavioral phenotypes in C. elegans. Together, these tools enable novel behavior experiments at a larger scale and with more nuanced phenotyping compared to currently available tools.Ph.D