A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Video analysis of animal behaviour is widely used in fields such as ecology, ecotoxicology, and evolutionary research. However, when tracking multiple animals, occlusion and crossing are problematic, especially when the identity of each individual needs to be preserved. We present a new algorithm, ToxId, which preserves the identity of multiple animals by linking trajectory segments using their intensity histogram and Hu-moments. We verify the performance and accuracy of our algorithm using video sequences with different animals and experimental conditions. The results show that our algorithm achieves state-of-the-art accuracy using an efficient approach without the need of learning processes, complex feature maps or knowledge of the animal shape. ToxId is also computationally efficient, has low memory requirements, and operates without accessing future or past frames.

/pdf/toxid-an-efficient-algorithm-to-solve-occlusions-when-p0ng4vlqio.pdf

ToxId: an efficient algorithm to solve occlusions when tracking multiple animals

Big behavior: challenges and opportunities in a new era of deep behavior profiling

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.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2017.0375

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

Animals exhibit astonishingly complex behaviors. Studying the subtle features of these behaviors requires quantitative, high-throughput, and accurate systems that can cope with the often rich perplexing data. Here, we present a Multi-Animal Tracker (MAT) that provides a user-friendly, end-to-end solution for imaging, tracking, and analyzing complex behaviors of multiple animals simultaneously. At the core of the tracker is a machine learning algorithm that provides immense flexibility to image various animals (e.g., worms, flies, zebrafish, etc.) under different experimental setups and conditions. Focusing on C. elegans worms, we demonstrate the vast advantages of using this MAT in studying complex behaviors. Beginning with chemotaxis, we show that approximately 100 animals can be tracked simultaneously, providing rich behavioral data. Interestingly, we reveal that worms’ directional changes are biased, rather than random – a strategy that significantly enhances chemotaxis performance. Next, we show that worms can integrate environmental information and that directional changes mediate the enhanced chemotaxis towards richer environments. Finally, offering high-throughput and accurate tracking, we show that the system is highly suitable for longitudinal studies of aging- and proteotoxicity-associated locomotion deficits, enabling large-scale drug and genetic screens. Together, our tracker provides a powerful and simple system to study complex behaviors in a quantitative, high-throughput, and accurate manner.

/pdf/a-multi-animal-tracker-for-studying-complex-behaviors-10nhr5a7aj.pdf

A multi-animal tracker for studying complex behaviors.

Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software that allows (1) automated identification of C. elegans worms, (2) extraction of morphological features and quantification of fluorescent signals, and (3) machine learning techniques for high-level analysis. We examined the power of WorMachine using five separate representative assays: supervised classification of binary-sex phenotype, scoring continuous-sexual phenotypes, quantifying the effects of two different RNA interference treatments, and measuring intracellular protein aggregation. WorMachine is suitable for analysis of a variety of biological questions and provides an accurate and reproducible analysis tool for measuring diverse phenotypes. It serves as a “quick and easy,” convenient, high-throughput, and automated solution for nematode research.

/pdf/wormachine-machine-learning-based-phenotypic-analysis-tool-2nua63su50.pdf

WorMachine: machine learning-based phenotypic analysis tool for worms.

Cellular mechanisms that act in concert to maintain protein homeostasis (proteostasis) are vital for organismal functionality and survival. Nevertheless, subsets of aggregation-prone proteins form toxic aggregates (proteotoxicity) that in some cases, underlie the development of neurodegenerative diseases. Proteotoxic aggregates are often deposited in the vicinity of the nucleus, a process that is cytoskeleton-dependent. Accordingly, cytoskeletal dysfunction contributes to pathological hallmarks of various neurodegenerative diseases. Here, we asked whether the linker of nucleoskeleton and cytoskeleton (LINC) complex, which bridges these filaments across the nuclear envelope, is needed for the maintenance of proteostasis. Employing model nematodes, we discovered that knocking down LINC components impairs the ability of the worm to cope with proteotoxicity. Knocking down anc-1, which encodes a key component of the LINC complex, modulates the expression of transcription factors and E3 ubiquitin ligases, thereby affecting the rates of protein ubiquitination and impairing proteasome-mediated protein degradation. Our results establish a link between the LINC complex, protein degradation, and neurodegeneration-associated proteotoxicity.

Gene expression modulation by the linker of nucleoskeleton and cytoskeleton complex contributes to proteostasis.

Summary Cellular mechanisms that act in concert to maintain protein homeostasis (proteostasis) are vital for survival. Nevertheless, subsets of aggregation-prone proteins form toxic aggregates (proteotoxicity) that sometimes underlie the development of neurodegenerative diseases. Proteotoxic aggregates are often deposited in the vicinity of the nucleus, a process that is cytoskeleton-dependent. Accordingly, cytoskeletal dysfunction contributes to pathological hallmarks of various neurodegenerative diseases. Thus, we asked whether the Linker of Nucleoskeleton and Cytoskeleton (LINC) complex, which bridges these filaments across the nuclear envelope, is needed for the maintenance of proteostasis. Employing model nematodes, we discovered that knocking down LINC components impairs the ability of the worm to cope with proteotoxicity. Knocking down anc-1, which encodes a key component of the LINC complex, modulates the expression of transcription factors and E3 ubiquitin ligases, thereby changing the rates of protein ubiquitination and degradation. Our results establish a link between the LINC complex and aging-associated proteotoxicity.

https://www.biorxiv.org/content/biorxiv/early/2019/02/27/548859.full.pdf

Gene Expression Modulation by the Linker of Nucleoskeleton and Cytoskeleton Complex Contributes to Proteostasis

Lowering the activity of the Insulin/IGF-1 Signaling (IIS) cascade results in elevated stress resistance, enhanced protein homeostasis (proteostasis) and extended lifespan of worms, flies and mice. In the nematode Caenorhabditis elegans (C. elegans), the longevity phenotype that stems from IIS reduction is entirely dependent upon the activities of a subset of transcription factors including the Forkhead factor DAF-16/FOXO (DAF-16), Heat Shock Factor-1 (HSF-1), SKiNhead/Nrf (SKN-1) and ParaQuat Methylviologen responsive (PQM-1). While DAF-16 determines lifespan exclusively during early adulthood and governs proteostasis in early adulthood and midlife, HSF-1 executes these functions foremost during development. Despite the central roles of SKN-1 as a regulator of lifespan and proteostasis, the temporal requirements of this transcription factor were unknown. Here we employed conditional knockdown techniques and discovered that in C. elegans, SKN-1 is primarily important for longevity and proteostasis during late larval development through early adulthood. Our findings indicate that events that occur during late larval developmental through early adulthood affect lifespan and proteostasis and suggest that subsequent to HSF-1, SKN-1 sets the conditions, partially overlapping temporally with DAF-16, that enable IIS reduction to promote longevity and proteostasis. Our findings raise the intriguing possibility that HSF-1, SKN-1 and DAF-16 function in a coordinated and sequential manner to promote healthy aging.

https://www.biorxiv.org/content/biorxiv/early/2020/11/24/2020.11.24.395707.full.pdf

Amir Levine

Papers

A multi-animal tracker for studying complex behaviors.

WorMachine: machine learning-based phenotypic analysis tool for worms.

Gene expression modulation by the linker of nucleoskeleton and cytoskeleton complex contributes to proteostasis.

Gene Expression Modulation by the Linker of Nucleoskeleton and Cytoskeleton Complex Contributes to Proteostasis

Temporal Requirements of SKN-1/NRF as a Regulator of Lifespan and Proteostasis in Caenorhabditis elegans