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We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing “curve” skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.

 

Abstract This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells. This paper is motivated by the pavement cell growth process, and building a quantitative morphogenesis model. We propose a deep feature based segmentation method to accurately detect and label each cell region. An adjacency graph based method is used to extract sub-cellular features of the segmented cells. Finally, the robust graph based tracking algorithm using multiple cell features is proposed for associating cells at different time instances. We also demonstrate the generality of our tracking method on C. elegans fluorescent nuclei imagery. Extensive experiment results are provided and demonstrate the robustness of the proposed method. The code is available on and the method is available as a service through the BisQue portal. 

Leopard seals ( Hydrurga leptonyx ) are top predators that can exert substantial top-down control of their Antarctic prey species. However, population trends and genetic diversity of leopard seals remain understudied, limiting our understanding of their ecological role. We investigated the genetic diversity, effective population size and demographic history of leopard seals to provide fundamental data that contextualizes their predatory influence on Antarctic ecosystems. Ninety leopard seals were sampled from the northern Antarctic Peninsula during the austral summers of 2008–2019 and a 405bp segment of the mitochondrial control region was sequenced for each individual. We uncovered moderate levels of nucleotide (π = 0.013) and haplotype (Hd = 0.96) diversity, and the effective population size was estimated at around 24,000 individuals (NE = 24,376; 95% CI: 16,876–33,126). Consistent with findings from other ice-breeding pinnipeds, Bayesian skyline analysis also revealed evidence for population expansion during the last glacial maximum, suggesting that historical population growth may have been boosted by an increase in the abundance of sea ice. Although leopard seals can be found in warmer, sub-Antarctic locations, the species’ core habitat is centered on the Antarctic, making it inherently vulnerable to the loss of sea ice habitat due to climate change. Therefore, detailed assessments of past and present leopard seal population trends are needed to inform policies for Antarctic ecosystems. 

Evaluating physiological responses in the context of a species’ life history, demographics, and ecology is essential to understanding the health of individuals and populations. Here, we measured the main mammalian glucocorticoid, cortisol, in an elusive Antarctic apex predator, the leopard seal ( Hydrurga leptonyx ). We also examined intraspecific variation in cortisol based on life history (sex), morphometrics (body mass, body condition), and ecological traits ( δ 15 N, δ 13 C). To do this, blood samples, life history traits, and morphometric data were collected from 19 individual leopard seals off the Western Antarctic Peninsula. We found that adult leopard seals have remarkably high cortisol concentrations (100.35 ± 16.72 μg/dL), showing the highest circulating cortisol concentration ever reported for a pinniped: 147 μg/dL in an adult male. Leopard seal cortisol concentrations varied with sex, body mass, and diet. Large adult females had significantly lower cortisol (94.49 ± 10.12 μg/dL) than adult males (120.85 ± 6.20 μg/dL). Similarly, leopard seals with higher isotope values (i.e., adult females, δ 15 N: 11.35 ± 0.69‰) had lower cortisol concentrations than seals with lower isotope values (i.e., adult males, δ 15 N: 10.14 ± 1.65‰). Furthermore, we compared cortisol concentrations across 26 closely related Arctoid taxa (i.e., mustelids, bears, and pinnipeds) with comparable data. Leopard seals had the highest mean cortisol concentrations that were 1.25 to 50 times higher than other Arctoids. More broadly, Antarctic ice seals (Lobodontini: leopard seal, Ross seal, Weddell seal, crabeater seal) had higher cortisol concentrations compared to other pinnipeds and Arctoid species. Therefore, high cortisol is a characteristic of all lobodontines and may be a specialized adaptation within this Antarctic-dwelling clade. Together, our results highlight exceptionally high cortisol concentrations in leopard seals (and across lobodontines) and reveal high variability in cortisol concentrations among individuals from a single location. This information provides the context for understanding how leopard seal physiology changes with life history, ecology, and morphology and sets the foundation for assessing their physiology in the context of a rapidly changing Antarctic environment. 

In computer vision, single-image super-resolution (SISR) has been extensively explored using convolutional neural networks (CNNs) on optical images, but images outside this domain, such as those from scientific experiments, are not well investigated. Experimental data is often gathered using non-optical methods, which alters the metrics for image quality. One such example is electron backscatter diffraction (EBSD), a materials characterization technique that maps crystal arrangement in solid materials, which provides insight into processing, structure, and property relationships. We present a broadly adaptable approach for applying state-of-art SISR networks to generate super-resolved EBSD orientation maps. This approach includes quaternion-based orientation recognition, loss functions that consider rotational effects and crystallographic symmetry, and an inference pipeline to convert network output into established visualization formats for EBSD maps. The ability to generate physically accurate, high-resolution EBSD maps with super-resolution enables high-throughput characterization and broadens the capture capabilities for three-dimensional experimental EBSD datasets.

 

Animals that display plasticity in behavioral, ecological, and morphological traits are better poised to cope with environmental disturbances. Here, we examined individual plasticity and intraspecific variation in the morphometrics, movement patterns, and dive behavior of an enigmatic apex predator, the leopard seal ( Hydrurga leptonyx ). Satellite/GPS tags and time-depth recorders were deployed on 22 leopard seals off the Western Antarctic Peninsula. Adult female leopard seals were significantly larger (454±59 kg) and longer (302±11 cm) than adult males (302±22 kg, 276±11 cm). As females were 50% larger than their male counterparts, leopard seals are therefore one of the most extreme examples of female-biased sexual size dimorphism in marine mammals. Female leopard seals also spent more time hauled-out on land and ice than males. In the austral spring/summer, three adult female leopard seals hauled-out on ice for 10+ days, which likely represent the first satellite tracks of parturition and lactation for the species. While we found sex-based differences in morphometrics and haul-out durations, other variables, including maximum distance traveled and dive parameters, did not vary by sex. Regardless of sex, some leopard seals remained in near-shore habitats, traveling less than 50 kilometers, while other leopard seals traveled up to 1,700 kilometers away from the tagging location. Overall, leopard seals were short (3.0±0.7 min) and shallow (29±8 m) divers. However, within this general pattern, some individual leopard seals primarily used short, shallow dives, while others switched between short, shallow dives and long, deep dives. We also recorded the single deepest and longest dive made by any leopard seal—1, 256 meters for 25 minutes. Together, our results showcased high plasticity among leopard seals tagged in a single location. These flexible behaviors and traits may offer leopard seals, an ice-associated apex predator, resilience to the rapidly changing Southern Ocean. 

Abstract This paper presents a deep-learning-based workflow to detect synapses and predict their neurotransmitter type in the primitive chordate Ciona intestinalis ( Ciona ) electron microscopic (EM) images. Identifying synapses from EM images to build a full map of connections between neurons is a labor-intensive process and requires significant domain expertise. Automation of synapse classification would hasten the generation and analysis of connectomes. Furthermore, inferences concerning neuron type and function from synapse features are in many cases difficult to make. Finding the connection between synapse structure and function is an important step in fully understanding a connectome. Class Activation Maps derived from the convolutional neural network provide insights on important features of synapses based on cell type and function. The main contribution of this work is in the differentiation of synapses by neurotransmitter type through the structural information in their EM images. This enables the prediction of neurotransmitter types for neurons in Ciona , which were previously unknown. The prediction model with code is available on GitHub. 

Precise measurement of physiological signals is critical for the effective monitoring of human vital signs. Recent developments in computer vision have demonstrated that signals such as pulse rate and respiration rate can be extracted from digital video of humans, increasing the possibility of contact-less monitoring. This paper presents a novel approach to obtaining physiological signals and classifying stress states from thermal video. The proposed network–”StressNet”–features a hybrid emission representation model that models the direct emission and absorption of heat by the skin and underlying blood vessels. This results in an information-rich feature representation of the face, which is used by spatio-temporal network for reconstructing the ISTI ( Initial Systolic Time Interval : a measure of change in cardiac sympathetic activity that is considered to be a quantitative index of stress in humans). The reconstructed ISTI signal is fed into a stress-detection model to detect and classify the individual’s stress state (i.e. stress or no stress). A detailed evaluation demonstrates that StressNet achieves estimated the ISTI signal with 95% accuracy and detect stress with average precision of 0.842.