Dynamic Analysis of Vascular Morphogenesis Using Transgenic Quail Embryos

Background: One of the least understood and most central questions confronting biologists is how initially simple clusters or sheet-like cell collectives can assemble into highly complex three-dimensional functional tissues and organs. Due to the limits of oxygen diffusion, blood vessels are an essential and ubiquitous presence in all amniote tissues and organs. Vasculogenesis, the de novo self-assembly of endothelial cell (EC) precursors into endothelial tubes, is the first step in blood vessel formation [1]. Static imaging and in vitro models are wholly inadequate to capture many aspects of vascular pattern formation in vivo, because vasculogenesis involves dynamic changes of the endothelial cells and of the forming blood vessels, in an embryo that is changing size and shape. Methodology/Principal Findings: We have generated Tie1 transgenic quail lines Tg(tie1:H2B-eYFP) that express H2B-eYFP in all of their endothelial cells which permit investigations into early embryonic vascular morphogenesis with unprecedented clarity and insight. By combining the power of molecular genetics with the elegance of dynamic imaging, we follow the precise patterning of endothelial cells in space and time. We show that during vasculogenesis within the vascular plexus, ECs move independently to form the rudiments of blood vessels, all while collectively moving with gastrulating tissues that flow toward the embryo midline. The aortae are a composite of somatic derived ECs forming its dorsal regions and the splanchnic derived ECs forming its ventral region. The ECs in the dorsal regions of the forming aortae exhibit variable mediolateral motions as they move rostrally; those in more ventral regions show significant lateral-to-medial movement as they course rostrally. Conclusions/Significance: The present results offer a powerful approach to the major challenge of studying the relative role(s) of the mechanical, molecular, and cellular mechanisms of vascular development. In past studies, the advantages of the molecular genetic tools available in mouse were counterbalanced by the limited experimental accessibility needed for imaging and perturbation studies. Avian embryos provide the needed accessibility, but few genetic resources. The creation of transgenic quail with labeled endothelia builds upon the important roles that avian embryos have played in previous studies of vascular development

Motion magnification in coronal seismology

We introduce a new method for the investigation of low-amplitude transverse oscillations of solar plasma non-uniformities, such as coronal loops, individual strands in coronal arcades, jets, prominence fibrils, polar plumes, and other contrast features, observed with imaging instruments. The method is based on the two-dimensional dual tree complex wavelet transform (DT$\mathbb{C}$WT). It allows us to magnify transverse, in the plane-of-the-sky, quasi-periodic motions of contrast features in image sequences. The tests performed on the artificial data cubes imitating exponentially decaying, multi-periodic and frequency-modulated kink oscillations of coronal loops showed the effectiveness, reliability and robustness of this technique. The algorithm was found to give linear scaling of the magnified amplitudes with the original amplitudes provided they are sufficiently small. Also, the magnification is independent of the oscillation period in a broad range of the periods. The application of this technique to SDO/AIA EUV data cubes of a non-flaring active region allowed for the improved detection of low-amplitude decay-less oscillations in the majority of loops.Comment: Accepted for publication in Solar Physic

Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a deterministic or non-parametric way, we propose a novel approach that models future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. Future frame synthesis is challenging, as it involves low- and high-level image and motion understanding. We propose a novel network structure, namely a Cross Convolutional Network to aid in synthesizing future frames; this network structure encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, as well as on real-wold videos. We also show that our model can be applied to tasks such as visual analogy-making, and present an analysis of the learned network representations.Comment: The first two authors contributed equally to this wor

Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions

Computer-aided diagnosis offers a promising solution to reduce variation in colonoscopy performance. Pooled miss rates for polyps are as high as 22%, and associated interval colorectal cancers after colonoscopy are of concern. Optical biopsy, whereby in-vivo classification of polyps based on enhanced imaging replaces histopathology, has not been incorporated into routine practice because it is limited by interobserver variability and generally only meets accepted standards in expert settings. Real-time decision-support software has been developed to detect and characterise polyps, and also to offer feedback on the technical quality of inspection. Some of the current algorithms, particularly with recent advances in artificial intelligence techniques, match human expert performance for optical biopsy. In this Review, we summarise the evidence for clinical applications of computer-aided diagnosis and artificial intelligence in colonoscopy

Automatically tracking feeding behavior in populations of foraging worms

C. elegans feeds on bacteria and other small microorganisms which it ingests using its pharynx, a neuromuscular pump. Currently, measuring feeding behavior requires tracking a single animal, indirectly estimating food intake from population-level metrics, or using restrained animals. Therefore, to enable large throughput feeding measurements of unrestrained, crawling worms on agarose plates, we developed an imaging protocol and a complementary image analysis tool called PharaGlow. We image up to 50 freely moving worms simultaneously and extract locomotion and feeding behaviors. Our tool reliably detects pharyngeal pumping in adult worms with a maximum deviation of 5% in the number of pumps compared to an expert annotator. We demonstrate the tool's robustness and highthroughput capabilities by measuring feeding in different use-case scenarios. This includes tracing pharyngeal dynamics during development, revealing their highly conserved nature throughout all life cycle stages. We also observed pumping after food deprivation, corroborating previous studies in which starvation time strongly influences pumping. Finally, we further validated our behavioral tracker by exploring two previously characterized pumping defective mutants: unc-31 and eat-18. Remarkably, our analysis of eat-18 mutants identified unreported defects in pumping and overall locomotion regulation, highlighting the potential of this toolkit. Pharaglow therefore enables the observation and analysis of the temporal dynamics of food intake with high-throughput and precision in a user-friendly system

Visual Representation Learning with Minimal Supervision

Computer vision intends to provide the human abilities of understanding and interpreting the visual surroundings to computers. An essential element to comprehend the environment is to extract relevant information from complex visual data so that the desired task can be solved. For instance, to distinguish cats from dogs the feature 'body shape' is more relevant than 'eye color' or the 'amount of legs'. In traditional computer vision it is conventional to develop handcrafted functions that extract specific low-level features such as edges from visual data. However, in order to solve a particular task satisfactorily we require a combination of several features. Thus, the approach of traditional computer vision has the disadvantage that whenever a new task is addressed, a developer needs to manually specify all the features the computer should look for. For that reason, recent works have primarily focused on developing new algorithms that teach the computer to autonomously detect relevant and task-specific features. Deep learning has been particularly successful for that matter. In deep learning, artificial neural networks automatically learn to extract informative features directly from visual data. The majority of developed deep learning strategies require a dataset with annotations which indicate the solution of the desired task. The main bottleneck is that creating such a dataset is very tedious and time-intensive considering that every sample needs to be annotated manually. This thesis presents new techniques that attempt to keep the amount of human supervision to a minimum while still reaching satisfactory performances on various visual understanding tasks. In particular, this thesis focuses on self-supervised learning algorithms that train a neural network on a surrogate task where no human supervision is required. We create an artificial supervisory signal by breaking the order of visual patterns and asking the network to recover the original structure. Besides demonstrating the abilities of our model on common computer vision tasks such as action recognition, we additionally apply our model to biomedical scenarios. Many research projects in medicine involve profuse manual processes that extend the duration of developing successful treatments. Taking the example of analyzing the motor function of neurologically impaired patients we show that our self-supervised method can help to automate tedious, visually based processes in medical research. In order to perform a detailed analysis of motor behavior and, thus, provide a suitable treatment, it is important to discover and identify the negatively affected movements. Therefore, we propose a magnification tool that can detect and enhance subtle changes in motor function including motor behavior differences across individuals. In this way, our automatic diagnostic system does not only analyze apparent behavior but also facilitates the perception and discovery of impaired movements. Learning a feature representation without requiring annotations significantly reduces human supervision. However, using annotated dataset leads generally to better performances in contrast to self-supervised learning methods. Hence, we additionally examine semi-supervised approaches which efficiently combine few annotated samples with large unlabeled datasets. Consequently, semi-supervised learning represents a good trade-off between annotation time and accuracy

Automatically tracking feeding behavior in populations of foraging C. elegans

Caenorhabditis elegans feeds on bacteria and other small microorganisms which it ingests using its pharynx, a neuromuscular pump. Currently, measuring feeding behavior requires tracking a single animal, indirectly estimating food intake from population-level metrics, or using restrained animals. To enable large throughput feeding measurements of unrestrained, crawling worms on agarose plates at a single worm resolution, we developed an imaging protocol and a complementary image analysis tool called PharaGlow. We image up to 50 unrestrained crawling worms simultaneously and extract locomotion and feeding behaviors. We demonstrate the tool’s robustness and high-throughput capabilities by measuring feeding in different use-case scenarios, such as through development, with genetic and chemical perturbations that result in faster and slower pumping, and in the presence or absence of food. Finally, we demonstrate that our tool is capable of long-term imaging by showing behavioral dynamics of mating animals and worms with different genetic backgrounds. The low-resolution fluorescence microscopes required are readily available in C. elegans laboratories, and in combination with our python-based analysis workflow makes this methodology easily accessible. PharaGlow therefore enables the observation and analysis of the temporal dynamics of feeding and locomotory behaviors with high-throughput and precision in a user-friendly system.eLife digest: A small worm called C. elegans is constantly hungry. It spends all its time looking for food or eating. Hunger and environmental factors, like light, influence its feeding behavior. Studying these worms has helped scientists learn how feeding affects health, longevity, and aging. Feeding studies might also help scientists learn how the nervous system works and how it controls feeding. Most studies have used one of two approaches. Scientists may measure how much food a group of C. elegans eat by measuring food before and after it is offered to the worms. Or they restrain individual worms and measure the movement of a tube-like muscle, called the pharynx, which the animals use to vacuum up food. Restraining the worms can alter their behavior or brain activity, and studying group feeding habits may miss individual differences, so neither is optimal. Ideally, scientists could measure the feeding activity of many free-ranging worms, but because the movements of the pharynx are small, that too can be a challenge. Bonnard, Liu et al. developed a software tool that automatically detects and measures feeding behavior in a group of about 30 free-ranging C. elegans simultaneously. In the experiments, Bonnard, Liu et al. genetically engineered worms expressing a fluorescent protein in their pharynx, making it possible to measure its movements with a microscope. They used the microscope to capture images of 30-50 animals at a time as they foraged for food in a dish. Then, they used the software to analyze the data they collected. Over three days and five imaging sessions, Bonnard and Liu et al. tracked the feeding behavior of about 1,000 animals under different conditions. The experiments show that the pharynx grows rapidly during early worm development when the worms quadruple their length, but the rate of pharynx muscle contractions stays the same. They also showed the technique could measure feeding behaviors in animals with different genetic backgrounds, ages, or those engaged in behaviors like mating. The tool allows for larger and longer-term studies of worm feeding behaviors than previous approaches. Bonnard, Liu et al. made their software, called PharaGlow, available for use by other researchers. The tool may make feeding measurements a routine part of C. elegans studies. It will allow scientists to gain new insights into the role of feeding in a range of processes, including aging, fitness, mating, and overall health. Follow-up studies could determine if these findings are general strategies that also apply to other animals