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

Learning dynamic representations of the functional connectome in neurobiological networks

The static synaptic connectivity of neuronal circuits stands in direct contrast to the dynamics of their function. As in changing community interactions, different neurons can participate actively in various combinations to effect behaviors at different times. We introduce an unsupervised approach to learn the dynamic affinities between neurons in live, behaving animals, and to reveal which communities form among neurons at different times. The inference occurs in two major steps. First, pairwise non-linear affinities between neuronal traces from brain-wide calcium activity are organized by non-negative tensor factorization (NTF). Each factor specifies which groups of neurons are most likely interacting for an inferred interval in time, and for which animals. Finally, a generative model that allows for weighted community detection is applied to the functional motifs produced by NTF to reveal a dynamic functional connectome. Since time codes the different experimental variables (e.g., application of chemical stimuli), this provides an atlas of neural motifs active during separate stages of an experiment (e.g., stimulus application or spontaneous behaviors). Results from our analysis are experimentally validated, confirming that our method is able to robustly predict causal interactions between neurons to generate behavior. Code is available at https://github.com/dyballa/dynamic-connectomes.Comment: Accepted at ICLR 2

Magnetic field generation in finite beam plasma system

For finite systems boundaries can introduce remarkable novel features. A well known example is the Casimir effect [1, 2] that is observed in quantum electrodynamic systems. In classical systems too novel effects associated with finite boundaries have been observed, for example the surface plasmon mode [3] that appears when the plasma has a finite extension. In this work a novel instability associated with the finite transverse size of a beam owing through a plasma system has been shown to exist. This instability leads to distinct characteristic features of the associated magnetic field that gets generated. For example, in contrast to the well known unstable Weibel mode of a beam plasma system which generates magnetic field at the skin depth scale, this instability generates magnetic field at the scales length of the transverse beam dimension [4]. The existence of this new instability is demonstrated by analytical arguments and by simulations conducted with the help of a variety of Particle - In - Cell (PIC) codes (e.g. OSIRIS, EPOCH, PICPSI). Two fluid simulations have also been conducted which confirm the observations. Furthermore, laboratory experiments on laser plasma system also provides evidence of such an instability mechanism at work

Performance Limits of Stochastic Sub-Gradient Learning, Part II: Multi-Agent Case

The analysis in Part I revealed interesting properties for subgradient learning algorithms in the context of stochastic optimization when gradient noise is present. These algorithms are used when the risk functions are non-smooth and involve non-differentiable components. They have been long recognized as being slow converging methods. However, it was revealed in Part I that the rate of convergence becomes linear for stochastic optimization problems, with the error iterate converging at an exponential rate $\alpha^i$ to within an $O(\mu)-$neighborhood of the optimizer, for some $\alpha \in (0,1)$ and small step-size $\mu$. The conclusion was established under weaker assumptions than the prior literature and, moreover, several important problems (such as LASSO, SVM, and Total Variation) were shown to satisfy these weaker assumptions automatically (but not the previously used conditions from the literature). These results revealed that sub-gradient learning methods have more favorable behavior than originally thought when used to enable continuous adaptation and learning. The results of Part I were exclusive to single-agent adaptation. The purpose of the current Part II is to examine the implications of these discoveries when a collection of networked agents employs subgradient learning as their cooperative mechanism. The analysis will show that, despite the coupled dynamics that arises in a networked scenario, the agents are still able to attain linear convergence in the stochastic case; they are also able to reach agreement within $O(\mu)$ of the optimizer

Solar wind collisional heating

To properly describe heating in weakly collisional turbulent plasmas such as the solar wind, inter-particle collisions should be taken into account. Collisions can convert ordered energy into heat by means of irreversible relaxation towards the thermal equilibrium. Recently, Pezzi et al. (Phys. Rev. Lett., vol. 116, 2016, p. 145001) showed that the plasma collisionality is enhanced by the presence of fine structures in velocity space. Here, the analysis is extended by directly comparing the effects of the fully nonlinear Landau operator and a linearized Landau operator. By focusing on the relaxation towards the equilibrium of an out of equilibrium distribution function in a homogeneous force-free plasma, here it is pointed out that it is significant to retain nonlinearities in the collisional operator to quantify the importance of collisional effects. Although the presence of several characteristic times associated with the dissipation of different phase space structures is recovered in both the cases of the nonlinear and the linearized operators, the influence of these times is different in the two cases. In the linearized operator case, the recovered characteristic times are systematically larger than in the fully nonlinear operator case, this suggesting that fine velocity structures are dissipated slower if nonlinearities are neglected in the collisional operator

Locus model for space-time fabric and quantum indeterminacies

A simple locus model for the space-time fabric is presented and is compared with quantum foam and random walk models. The induced indeterminacies in momentum are calculated and it is shown that these space-time fabric indeterminacies are, in most cases, negligible compared with the quantum mechanical indeterminacies. This result restricts the possibilities of an experimental observation of the space-time fabric

Some Remarks about the Complexity of Epidemics Management

Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that the assumptions underlying the established theory of epidemics management are too idealistic. For an improvement of procedures and organizations involved in fighting epidemics, extended models of epidemics management are required. The necessary extensions consist in a representation of the management loop and the potential frictions influencing the loop. The effects of the non-deterministic frictions can be taken into account by including the measures of robustness and risk in the assessment of management options. Thus, besides of the increased structural complexity resulting from the model extensions, the computational complexity of the task of epidemics management - interpreted as an optimization problem - is increased as well. This is a serious obstacle for analyzing the model and may require an additional pre-processing enabling a simplification of the analysis process. The paper closes with an outlook discussing some forthcoming problems

Ferromagnetic and insulating behavior of LaCoO3 films grown on a (001) SrTiO3 substrate. A simple ionic picture explained ab initio

This paper shows that the oxygen vacancies observed experimentally in thin films of LaCoO3 subject to tensile strain are thermodynamically stable according to ab initio calculations. By using DFT calculations, we show that oxygen vacancies on the order of 6 % forming chains perpendicular to the (001) direction are more stable than the stoichiometric solution. These lead to magnetic Co2+ ions surrounding the vacancies that couple ferromagnetically. The remaining Co3+ cations in an octahedral environment are non magnetic. The gap leading to a ferromagnetic insulating phase occurs naturally and we provide a simple ionic picture to explain the resulting electronic structure.Comment: 7 pages, 7 figure

Wave-like Decoding of Tail-biting Spatially Coupled LDPC Codes Through Iterative Demapping

For finite coupling lengths, terminated spatially coupled low-density parity-check (SC-LDPC) codes show a non-negligible rate-loss. In this paper, we investigate if this rate loss can be mitigated by tail-biting SC-LDPC codes in conjunction with iterative demapping of higher order modulation formats. Therefore, we examine the BP threshold of different coupled and uncoupled ensembles. A comparison between the decoding thresholds approximated by EXIT charts and the density evolution results of the coupled and uncoupled ensemble is given. We investigate the effect and potential of different labelings for such a set-up using per-bit EXIT curves, and exemplify the method for a 16-QAM system, e.g., using set partitioning labelings. A hybrid mapping is proposed, where different sub-blocks use different labelings in order to further optimize the decoding thresholds of tail-biting codes, while the computational complexity overhead through iterative demapping remains small.Comment: presentat at the International Symposium on Turbo Codes & Iterative Information Processing (ISTC), Brest, Sept. 201