Deep Learning-Based Detection of Glottis Segmentation Failures.

Medical image segmentation is crucial for clinical applications, but challenges persist due to noise and variability. In particular, accurate glottis segmentation from high-speed videos is vital for voice research and diagnostics. Manual searching for failed segmentations is labor-intensive, prompting interest in automated methods. This paper proposes the first deep learning approach for detecting faulty glottis segmentations. For this purpose, faulty segmentations are generated by applying both a poorly performing neural network and perturbation procedures to three public datasets. Heavy data augmentations are added to the input until the neural network's performance decreases to the desired mean intersection over union (IoU). Likewise, the perturbation procedure involves a series of image transformations to the original ground truth segmentations in a randomized manner. These data are then used to train a ResNet18 neural network with custom loss functions to predict the IoU scores of faulty segmentations. This value is then thresholded with a fixed IoU of 0.6 for classification, thereby achieving 88.27% classification accuracy with 91.54% specificity. Experimental results demonstrate the effectiveness of the presented approach. Contributions include: (i) a knowledge-driven perturbation procedure, (ii) a deep learning framework for scoring and detecting faulty glottis segmentations, and (iii) an evaluation of custom loss functions.

Genomes of multicellular algal sisters to land plants illuminate signaling network evolution.

Zygnematophyceae are the algal sisters of land plants. Here we sequenced four genomes of filamentous Zygnematophyceae, including chromosome-scale assemblies for three strains of Zygnema circumcarinatum. We inferred traits in the ancestor of Zygnematophyceae and land plants that might have ushered in the conquest of land by plants: expanded genes for signaling cascades, environmental response, and multicellular growth. Zygnematophyceae and land plants share all the major enzymes for cell wall synthesis and remodifications, and gene gains shaped this toolkit. Co-expression network analyses uncover gene cohorts that unite environmental signaling with multicellular developmental programs. Our data shed light on a molecular chassis that balances environmental response and growth modulation across more than 600 million years of streptophyte evolution.

Constitutive activation of ABA receptors in Arabidopsis reveals unique regulatory circuitries.

Abscisic acid (ABA) is best known for regulating the responses to abiotic stressors. Thus, applications of ABA signaling pathways are considered promising targets for securing yield under stress. ABA levels rise in response to abiotic stress, mounting physiological and metabolic responses that promote plant survival under unfavorable conditions. ABA elicits its effects by binding to a family of soluble receptors found in monomeric and dimeric states, differing in their affinity to ABA and co-receptors. However, the in vivo significance of the biochemical differences between these receptors remains unclear. We took a gain-of-function approach to study receptor-specific functionality. First, we introduced activating mutations that enforce active ABA-bound receptor conformation. We then transformed Arabidopsis ABA-deficient mutants with the constitutive receptors and monitored suppression of the ABA deficiency phenotype. Our findings suggest that PYL4 and PYL5, monomeric ABA receptors, have differential activity in regulating transpiration and transcription of ABA biosynthesis and stress response genes. Through genetic and metabolic data, we demonstrate that PYR1, but not PYL5, is sufficient to activate the ABA positive feedback mechanism. We propose that ABA signaling - from perception to response - flows differently when triggered by different PYLs, due to tissue and transcription barriers, thus resulting in distinct circuitries.

Divergent responses in desiccation experiments in two ecophysiologically different Zygnematophyceae.

Water scarcity can be considered a major stressor on land, with desiccation being its most extreme form. Land plants have found two different solutions to this challenge: avoidance and tolerance. The closest algal relatives to land plants, the Zygnematophyceae, use the latter, and how this is realized is of great interest for our understanding of the conquest of land. Here, we worked with two representatives of the Zygnematophyceae, Zygnema circumcarinatum SAG 698-1b and Mesotaenium endlicherianum SAG 12.97, who differ in habitats and drought resilience. We challenged both algal species with severe desiccation in a laboratory setup until photosynthesis ceased, followed by a recovery period. We assessed their morphological, photophysiological, and transcriptomic responses. Our data pinpoint global differential gene expression patterns that speak of conserved responses, from calcium-mediated signaling to the adjustment of plastid biology, cell envelopes, and amino acid pathways, between Zygnematophyceae and land plants despite their strong ecophysiological divergence. The main difference between the two species appears to rest in a readjustment of the photobiology of Zygnema, while Mesotaenium experiences stress beyond a tipping point.

Environmental gradients reveal stress hubs pre-dating plant terrestrialization.

Plant terrestrialization brought forth the land plants (embryophytes). Embryophytes account for most of the biomass on land and evolved from streptophyte algae in a singular event. Recent advances have unravelled the first full genomes of the closest algal relatives of land plants; among the first such species was Mesotaenium endlicherianum. Here we used fine-combed RNA sequencing in tandem with a photophysiological assessment on Mesotaenium exposed to a continuous range of temperature and light cues. Our data establish a grid of 42 different conditions, resulting in 128 transcriptomes and ~1.5 Tbp (~9.9 billion reads) of data to study the combinatory effects of stress response using clustering along gradients. Mesotaenium shares with land plants major hubs in genetic networks underpinning stress response and acclimation. Our data suggest that lipid droplet formation and plastid and cell wall-derived signals have denominated molecular programmes since more than 600 million years of streptophyte evolution-before plants made their first steps on land.

Chromosome-level genomes of multicellular algal sisters to land plants illuminate signaling network evolution.

The filamentous and unicellular algae of the class Zygnematophyceae are the closest algal relatives of land plants. Inferring the properties of the last common ancestor shared by these algae and land plants allows us to identify decisive traits that enabled the conquest of land by plants. We sequenced four genomes of filamentous Zygnematophyceae (three strains of Zygnema circumcarinatum and one strain of Z. cylindricum) and generated chromosome-scale assemblies for all strains of the emerging model system Z. circumcarinatum. Comparative genomic analyses reveal expanded genes for signaling cascades, environmental response, and intracellular trafficking that we associate with multicellularity. Gene family analyses suggest that Zygnematophyceae share all the major enzymes with land plants for cell wall polysaccharide synthesis, degradation, and modifications; most of the enzymes for cell wall innovations, especially for polysaccharide backbone synthesis, were gained more than 700 million years ago. In Zygnematophyceae, these enzyme families expanded, forming co-expressed modules. Transcriptomic profiling of over 19 growth conditions combined with co-expression network analyses uncover cohorts of genes that unite environmental signaling with multicellular developmental programs. Our data shed light on a molecular chassis that balances environmental response and growth modulation across more than 600 million years of streptophyte evolution.

Crossroads in the evolution of plant specialized metabolism.

The monophyletic group of embryophytes (land plants) stands out among photosynthetic eukaryotes: they are the sole constituents of the macroscopic flora on land. In their entirety, embryophytes account for the majority of the biomass on land and constitute an astounding biodiversity. What allowed for the massive radiation of this particular lineage? One of the defining features of all land plants is the production of an array of specialized metabolites. The compounds that the specialized metabolic pathways of embryophytes produce have diverse functions, ranging from superabundant structural polymers and compounds that ward off abiotic and biotic challenges, to signaling molecules whose abundance is measured at the nanomolar scale. These specialized metabolites govern the growth, development, and physiology of land plants-including their response to the environment. Hence, specialized metabolites define the biology of land plants as we know it. And they were likely a foundation for their success. It is thus intriguing to find that the closest algal relatives of land plants, freshwater organisms from the grade of streptophyte algae, possess homologs for key enzymes of specialized metabolic pathways known from land plants. Indeed, some studies suggest that signature metabolites emerging from these pathways can be found in streptophyte algae. Here we synthesize the current understanding of which routes of the specialized metabolism of embryophytes can be traced to a time before plants had conquered land.

Lightweight Techniques to Improve Generalization and Robustness of U-Net Based Networks for Pulmonary Lobe Segmentation.

Lung lobe segmentation in chest CT is relevant to a wide range of clinical applications. However, existing segmentation pipelines often exhibit vulnerabilities and performance degradations when applied to external datasets. This is usually attributed to the size of the available dataset or model. We show that it is possible to enhance generalizability without huge resources by carefully curating the dataset and combining machine learning with medical expertise. Multiple machine learning techniques (self-supervision (SSL), attention (A), and data augmentation (DA)) are used to train a fast and fully-automated lung lobe segmentation model based on 2D U-Net. Our study involved evaluating these techniques on a diverse dataset collected under the RACOON project, encompassing 100 CT chest scans from patients with bacterial, viral, or SARS-CoV2 infections. We compare our model to a baseline U-Net trained on the same dataset. Our approach significantly improved segmentation accuracy (Dice score of 92.8% vs. 82.3%, p < 0.001). Moreover, our model achieved state-of-the-art performance (Dice score of 92.8% vs. 90.8% for the literature's state-of-the-art, p = 0.102) with reduced training examples (69 vs. 231 CT Scans). Among the techniques, data augmentation with expert knowledge displayed the most significant impact, enhancing the Dice score by +0.056. Notably, these enhancements are not limited to lobe segmentation but can be seamlessly integrated into various medical imaging segmentation tasks, demonstrating their versatility and potential for broader applications.

Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation.

Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation. However, deep learning models are not trusted in the clinical routine due to failing silently on out-of-distribution (OOD) data. We propose a lightweight OOD detection method that leverages the Mahalanobis distance in the feature space and seamlessly integrates into state-of-the-art segmentation pipelines. The simple approach can even augment pre-trained models with clinically relevant uncertainty quantification. We validate our method across four chest CT distribution shifts and two magnetic resonance imaging applications, namely segmentation of the hippocampus and the prostate. Our results show that the proposed method effectively detects far- and near-OOD samples across all explored scenarios.

Underwater CAM photosynthesis elucidated by Isoetes genome.

To conserve water in arid environments, numerous plant lineages have independently evolved Crassulacean Acid Metabolism (CAM). Interestingly, Isoetes, an aquatic lycophyte, can also perform CAM as an adaptation to low CO2 availability underwater. However, little is known about the evolution of CAM in aquatic plants and the lack of genomic data has hindered comparison between aquatic and terrestrial CAM. Here, we investigate underwater CAM in Isoetes taiwanensis by generating a high-quality genome assembly and RNA-seq time course. Despite broad similarities between CAM in Isoetes and terrestrial angiosperms, we identify several key differences. Notably, Isoetes may have recruited the lesser-known 'bacterial-type' PEPC, along with the 'plant-type' exclusively used in other CAM and C4 plants for carboxylation of PEP. Furthermore, we find that circadian control of key CAM pathway genes has diverged considerably in Isoetes relative to flowering plants. This suggests the existence of more evolutionary paths to CAM than previously recognized.