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Integrative, three-dimensional \(in\) \(silico\) modeling of gas exchange in the human alveolus
(2024)
The lung plays a vital role by exchanging respiratory gases. At the core of this gas exchange is a simple yet crucial passive diffusion process occurring within the alveoli. These balloon-like structures, connected to the peripheral airways, are surrounded by a dense network
of small capillaries. Here, inhaled air comes into close proximity with deoxygenated blood coming from the heart, enabling the exchange of oxygen and carbon dioxide across their concentration gradients.
The efficiency of gas exchange can be measured through indicators such as the diffusion capacity of the lung for oxygen and the reaction half-time. A notable discrepancy exists in humans between physiological estimates of diffusion capacity and the theoretical maximum capacity under optimal structural conditions (morphological estimate). This discrepancy is influenced by a range of interrelated factors, including structural elements like the surface area and thickness of the diffusion barrier, as well as physiological factors such as blood flow dynamics. To unravel the different roles of these factors, we investigated how morphological and physiological properties of the human alveolar micro-environment collectively and individually influence the process of gas exchange. To this end, we developed an integrative in silico approach combining 3D morphological modeling and simulation of blood flow and of oxygen transport.
At the core of our approach lies the simulation software Alvin, serving as an interactive platform for the underlying mathematical model of oxygen transport within the alveolus. Developed by integrating and expanding existing mathematical models, our spatio-temporal model produces results in agreement with experimental data. Alvin allows for real-time parameter adjustments and the execution of multiple simultaneous simulation instances and provides detailed quantitative feedback, offering an immersive exploration of the simulated gas exchange process. The morphological and physiological parameters at play were further investigated with a focus on the microvasculature. By compiling a stereological database from the literature and 3D geometric modeling, we created a sheet-flow model as a realistic representation of the morphology of the human alveolar capillary network. Blood flow was simulated using computational fluid dynamics. Our findings were in line with previous estimations and highlighted the crucial role of viscosity models in predicting pressure drop across the microvasculature. Furthermore, we showcased how our approach can be harnessed to explore structural details, such as the connectivity of the alveolar capillary network with the vascular tree, using blood flow indices. It is important to emphasize that
so far we have relied on different data sources and that experimental validation is needed to move forward.
Integration of our findings into Alvin allowed quantification of the simulated gas exchange process through the diffusion capacity for oxygen and reaction half-time. In addition to evaluating the collective influences of the morphological and physiological properties, our interactive software facilitates the assessment of individual parameter value changes. Exploring blood volume and surface area available for gas exchange revealed linear correlations with diffusion capacity. The blood flow velocity had a positive, non-linear effect on diffusion capacity. The reaction half-time confirmed that under normal conditions, the gas exchange process is not diffusion-limited. Collectively, our alveolar model yielded a diffusion capacity value that fell in the middle of previous physiological and morphological estimates, implying that alveolar-level phenomena contribute to 50% of the diffusion capacity limitations that occur in vivo.
In summary, our integrative in silico approach disentangles various structural and functional influences on alveolar gas exchange, complementing traditional investigations in respiratory
research. We further showcase its utility in teaching and the interpretation of published data. To advance our understanding, future work should prioritize obtaining a cohesive experimental data set and identifying an appropriate viscosity model for blood flow simulations.
(1) Background: C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2) Methods: Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database — representing n = 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in n = 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3) Results: We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4) Conclusions: CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets.
Summary
Embryos develop in a concerted sequence of spatiotemporal arrangements of cells. In the preimplantation mouse embryo, the distribution of the cells in the inner cell mass evolves from a salt-and-pepper pattern to spatial segregation of two distinct cell types. The exact properties of the salt-and-pepper pattern have not been analyzed so far. We investigate the spatiotemporal distribution of NANOG- and GATA6-expressing cells in the ICM of the mouse blastocysts with quantitative three-dimensional single-cell-based neighborhood analyses. A combination of spatial statistics and agent-based modeling reveals that the cell fate distribution follows a local clustering pattern. Using ordinary differential equations modeling, we show that this pattern can be established by a distance-based signaling mechanism enabling cells to integrate information from the whole inner cell mass into their cell fate decision. Our work highlights the importance of longer-range signaling to ensure coordinated decisions in groups of cells to successfully build embryos.
Highlights
• The local cell neighborhood and global ICM population composition correlate
• ICM cells show characteristics of local clustering in early and mid mouse blastocysts
• ICM patterning requires integration of signals from cells beyond the first neighbors
Young grapevines (Vitis vinifera) suffer and eventually can die from the crown gall disease caused by the plant pathogen Allorhizobium vitis (Rhizobiaceae). Virulent members of A. vitis harbor a tumor-inducing plasmid and induce formation of crown galls due to the oncogenes encoded on the transfer DNA. The expression of oncogenes in transformed host cells induces unregulated cell proliferation and metabolic and physiological changes. The crown gall produces opines uncommon to plants, which provide an important nutrient source for A. vitis harboring opine catabolism enzymes. Crown galls host a distinct bacterial community, and the mechanisms establishing a crown gall–specific bacterial community are currently unknown. Thus, we were interested in whether genes homologous to those of the tumor-inducing plasmid coexist in the genomes of the microbial species coexisting in crown galls. We isolated 8 bacterial strains from grapevine crown galls, sequenced their genomes, and tested their virulence and opine utilization ability in bioassays. In addition, the 8 genome sequences were compared with 34 published bacterial genomes, including closely related plant-associated bacteria not from crown galls. Homologous genes for virulence and opine anabolism were only present in the virulent Rhizobiaceae. In contrast, homologs of the opine catabolism genes were present in all strains including the nonvirulent members of the Rhizobiaceae and non-Rhizobiaceae. Gene neighborhood and sequence identity of the opine degradation cluster of virulent and nonvirulent strains together with the results of the opine utilization assay support the important role of opine utilization for cocolonization in crown galls, thereby shaping the crown gall community.
Abstract
Cell lineage decisions occur in three-dimensional spatial patterns that are difficult to identify by eye. There is an ongoing effort to replicate such patterns using mathematical modeling. One approach uses long ranging cell-cell communication to replicate common spatial arrangements like checkerboard and engulfing patterns. In this model, the cell-cell communication has been implemented as a signal that disperses throughout the tissue. On the other hand, machine learning models have been developed for pattern recognition and pattern reconstruction tasks. We combined synthetic data generated by the mathematical model with spatial summary statistics and deep learning algorithms to recognize and reconstruct cell fate patterns in organoids of mouse embryonic stem cells. Application of Moran’s index and pair correlation functions for in vitro and synthetic data from the model showed local clustering and radial segregation. To assess the patterns as a whole, a graph neural network was developed and trained on synthetic data from the model. Application to in vitro data predicted a low signal dispersion value. To test this result, we implemented a multilayer perceptron for the prediction of a given cell fate based on the fates of the neighboring cells. The results show a 70% accuracy of cell fate imputation based on the nine nearest neighbors of a cell. Overall, our approach combines deep learning with mathematical modeling to link cell fate patterns with potential underlying mechanisms.
Author summary
Mammalian embryo development relies on organized differentiation of stem cells into different lineages. Particularly at the early stages of embryogenesis, cells of different fates form three-dimensional spatial patterns that are difficult to identify by eye. Pattern quantification and mathematical modeling have produced first insights into potential mechanisms for the cell fate arrangements. However, these approaches have relied on classifications of the patterns such as inside-out or random, or used summary statistics such as pair correlation functions or cluster radii. Deep neural networks allow characterizing patterns directly. Since the tissue context can be readily reproduced by a graph, we implemented a graph neural network to characterize the patterns of embryonic stem cell organoids as a whole. In addition, we implemented a multilayer perceptron model to reconstruct the fate of a given cell based on its neighbors. To train and test the models, we used synthetic data generated by our mathematical model for cell-cell communication. This interplay of deep learning and mathematical modeling in combination with summary statistics allowed us to identify a potential mechanism for cell fate determination in mouse embryonic stem cells. Our results agree with a mechanism with a dispersion of the intercellular signal that links a cell’s fate to those of the local neighborhood.
Ultrastructural analysis of wild-type and RIM1α knockout active zones in a large cortical synapse
(2022)
Rab3A-interacting molecule (RIM) is crucial for fast Ca\(^{2+}\)-triggered synaptic vesicle (SV) release in presynaptic active zones (AZs). We investigated hippocampal giant mossy fiber bouton (MFB) AZ architecture in 3D using electron tomography of rapid cryo-immobilized acute brain slices in RIM1α\(^{−/−}\) and wild-type mice. In RIM1α\(^{−/−}\), AZs are larger with increased synaptic cleft widths and a 3-fold reduced number of tightly docked SVs (0–2 nm). The distance of tightly docked SVs to the AZ center is increased from 110 to 195 nm, and the width of their electron-dense material between outer SV membrane and AZ membrane is reduced. Furthermore, the SV pool in RIM1α\(^{−/−}\) is more heterogeneous. Thus, RIM1α, besides its role in tight SV docking, is crucial for synaptic architecture and vesicle pool organization in MFBs.
The negative impact of juvenile undernourishment on adult behavior has been well reported for vertebrates, but relatively little is known about invertebrates. In honeybees, nutrition has long been known to affect task performance and timing of behavioral transitions. Whether and how a dietary restriction during larval development affects the task performance of adult honeybees is largely unknown. We raised honeybees in-vitro, varying the amount of a standardized diet (150 µl, 160 µl, 180 µl in total). Emerging adults were marked and inserted into established colonies. Behavioral performance of nurse bees and foragers was investigated and physiological factors known to be involved in the regulation of social organization were quantified. Surprisingly, adult honeybees raised under different feeding regimes did not differ in any of the behaviors observed. No differences were observed in physiological parameters apart from weight. Honeybees were lighter when undernourished (150 µl), while they were heavier under the overfed treatment (180 µl) compared to the control group raised under a normal diet (160 µl). These data suggest that dietary restrictions during larval development do not affect task performance or physiology in this social insect despite producing clear effects on adult weight. We speculate that possible effects of larval undernourishment might be compensated during the early period of adult life.
Background
Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method.
Results
Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality.
Conclusions
Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.
Introduction
Neurotransmitter release at presynaptic active zones (AZs) requires concerted protein interactions within a dense 3D nano-hemisphere. Among the complex protein meshwork the (M)unc-13 family member Unc-13 of Drosophila melanogaster is essential for docking of synaptic vesicles and transmitter release.
Methods
We employ minos-mediated integration cassette (MiMIC)-based gene editing using GFSTF (EGFP-FlAsH-StrepII-TEV-3xFlag) to endogenously tag all annotated Drosophila Unc-13 isoforms enabling visualization of endogenous Unc-13 expression within the central and peripheral nervous system.
Results and discussion
Electrophysiological characterization using two-electrode voltage clamp (TEVC) reveals that evoked and spontaneous synaptic transmission remain unaffected in unc-13\(^{GFSTF}\) 3rd instar larvae and acute presynaptic homeostatic potentiation (PHP) can be induced at control levels. Furthermore, multi-color structured-illumination shows precise co-localization of Unc-13\(^{GFSTF}\), Bruchpilot, and GluRIIA-receptor subunits within the synaptic mesoscale. Localization microscopy in combination with HDBSCAN algorithms detect Unc-13\(^{GFSTF}\) subclusters that move toward the AZ center during PHP with unaltered Unc-13\(^{GFSTF}\) protein levels.
Understanding the causal relationship between genotype and phenotype is a major objective in biology. The main interest is in understanding trait architecture and identifying loci contributing to the respective traits. Genome-wide association mapping (GWAS) is one tool to elucidate these relationships and has been successfully used in many different species. However, most studies concentrate on marginal marker effects and ignore epistatic and gene-environment interactions. These interactions are problematic to account for, but are likely to make major contributions to many phenotypes that are not regulated by independent genetic effects, but by more sophisticated gene-regulatory networks. Further complication arises from the fact that these networks vary in different natural accessions. However, understanding the differences of gene regulatory networks and gene-gene interactions is crucial to conceive trait architecture and predict phenotypes.
The basic subject of this study – using data from the Arabidopsis 1001 Genomes Project – is the analysis of pre-mature stop codons. These have been incurred in nearly one-third of the ~ 30k genes. A gene-gene interaction network of the co-occurrence of stop codons has been built and the over and under representation of different pairs has been statistically analyzed. To further classify the significant over and under- represented gene-gene interactions in terms of molecular function of the encoded proteins, gene ontology terms (GO-SLIM) have been applied. Furthermore, co- expression analysis specifies gene clusters that co-occur over different genetic and phenotypic backgrounds. To link these patterns to evolutionary constrains, spatial location of the respective alleles have been analyzed as well. The latter shows clear patterns for certain gene pairs that indicate differential selection.
Candida auris is a globally emerging fungal pathogen responsible for causing nosocomial outbreaks in healthcare associated settings. It is known to cause infection in all age groups and exhibits multi-drug resistance with high potential for horizontal transmission. Because of this reason combined with limited therapeutic choices available, C. auris infection has been acknowledged as a potential risk for causing a future pandemic, and thus seeking a promising strategy for its treatment is imperative. Here, we combined evolutionary information with reverse vaccinology approach to identify novel epitopes for vaccine design that could elicit CD4+ T-cell responses against C. auris. To this end, we extensively scanned the family of proteins encoded by C. auris genome. In addition, a pathogen may acquire substitutions in epitopes over a period of time which could cause its escape from the immune response thus rendering the vaccine ineffective. To lower this possibility in our design, we eliminated all rapidly evolving genes of C. auris with positive selection. We further employed highly conserved regions of multiple C. auris strains and identified two immunogenic and antigenic T-cell epitopes that could generate the most effective immune response against C. auris. The antigenicity scores of our predicted vaccine candidates were calculated as 0.85 and 1.88 where 0.5 is the threshold for prediction of fungal antigenic sequences. Based on our results, we conclude that our vaccine candidates have the potential to be successfully employed for the treatment of C. auris infection. However, in vivo experiments are imperative to further demonstrate the efficacy of our design.
Spatiotemporal dynamics of freshwater macrophytes in Bavarian lakes under environmental change
(2022)
Macrophytes are key components of freshwater ecosystems because they provide habitat, food, and improve the water quality. Macrophyte are vulnerable to environmental change as their physiological processes depend on changing environmental factors, which themselves vary within a geographical region and along lake depth. Their spatial distribution is not well understood and their importance is publicly little-known. In this thesis, I have investigated the spatiotemporal dynamics of freshwater macrophytes in Bavarian lakes to understand their diversity pattern along different scales and to predict and communicate potential consequences of global change on their richness.
In the introduction (Chapter 1), I provide an overview of the current scientific knowledge of the species richness patterns of macrophytes in freshwater lakes, the influences of climate and land-use change on macrophyte growth, and different modelling approaches of macrophytes.
The main part of the thesis starts with a study about submerged and emergent macrophyte species richness in natural and artificial lakes of Bavaria (Chapter 2). By analysing publicly available monitoring data, I have found a higher species richness of submerged macrophytes in natural lakes than in artificial lakes. Furthermore, I showed that the richness of submerged species is better explained by physio-chemical lake parameters than the richness of emergent species. In Chapter 3, I considered that submerged macrophytes grow along a depth gradient that provides a sharp environmental gradient on a short spatial scale. This study is the first comparative assessment of the depth diversity gradient (DDG) of macrophytes. I have found a hump-shaped pattern of different diversity components. Generalised additive mixed-effect models indicate that the shape of the DDG is influenced mainly by light quality, light quantity, layering depth, and lake area. I could not identify a general trend of the DDG within recent years, but single lakes show trends leading into different directions. In Chapter 4, I used a mechanistic eco-physiological model to explore changes in the distribution of macrophyte species richness under different scenarios of environmental conditions across lakes and with depths. I could replicate the hump-shaped pattern of potential species richness along depth. Rising temperature leads to increased species richness in all lake types, and depths. The effect of turbidity and nutrient change depends on depth and lake type. Traits that characterise “loser species” under increased turbidity and nutrients are a high light consumption and a high sensibility to disturbances. “Winner species” can be identified by a high biomass production. In Chapter 5, I discuss the image problem of macrophytes. Unawareness, ignorance, and the poor accessibility of macrophytes can lead to conflicts of use. I assumed that an increased engagement and education could counteract this. Because computer games can transfer knowledge interactively while creating an immersive experience, I present in the chapter an interactive single-player game for children.
Finally, I discuss the findings of this thesis in the light of their implications for ecological theory, their implications for conservation, and future research ideas (Chapter 6). The findings help to understand the regional distribution and the drivers of macrophyte species richness. By applying eco-physiological models, multiple environmental shaping factors for species richness were tested and scenarios of climate and land-use change were explored.
Biodiversity loss, as often found in intensively managed agricultural landscapes, correlates with reduced ecosystem functioning, for example, pollination by insects, and with altered plant composition, diversity, and abundance. But how does this change in floral resource diversity and composition relate to occurrence and resource use patterns of trap-nesting solitary bees? To better understand the impact of land-use intensification on communities of trap-nesting solitary bees in managed grasslands, we investigated their pollen foraging, reproductive fitness, and the nutritional quality of larval food along a land-use intensity gradient in Germany. We found bee species diversity to decrease with increasing land-use intensity irrespective of region-specific community compositions and interaction networks. Land use also strongly affected the diversity and composition of pollen collected by bees. Lack of suitable pollen sources likely explains the absence of several bee species at sites of high land-use intensity. The only species present throughout, Osmia bicornis (red mason bee), foraged on largely different pollen sources across sites. In doing so, it maintained a relatively stable, albeit variable nutritional quality of larval diets (i.e., protein to lipid (P:L) ratio). The observed changes in bee–plant pollen interaction patterns indicate that only the flexible generalists, such as O. bicornis, may be able to compensate the strong alterations in floral resource landscapes and to obtain food of sufficient quality through readily shifting to alternative plant sources. In contrast, other, less flexible, bee species disappear.
Osmotic stress can be detrimental to plants, whose survival relies heavily on proteomic plasticity. Protein ubiquitination is a central post-translational modification in osmotic-mediated stress. In this study, we used the K-Ɛ-GG antibody enrichment method integrated with high-resolution mass spectrometry to compile a list of 719 ubiquitinated lysine (K-Ub) residues from 450 Arabidopsis root membrane proteins (58% of which are transmembrane proteins), thereby adding to the database of ubiquitinated substrates in plants. Although no ubiquitin (Ub) motifs could be identified, the presence of acidic residues close to K-Ub was revealed. Our ubiquitinome analysis pointed to a broad role of ubiquitination in the internalization and sorting of cargo proteins. Moreover, the simultaneous proteome and ubiquitinome quantification showed that ubiquitination is mostly not involved in membrane protein degradation in response to short osmotic treatment but that it is putatively involved in protein internalization, as described for the aquaporin PIP2;1. Our in silico analysis of ubiquitinated proteins shows that two E2 Ub-conjugating enzymes, UBC32 and UBC34, putatively target membrane proteins under osmotic stress. Finally, we revealed a positive role for UBC32 and UBC34 in primary root growth under osmotic stress.
Abstract
Introgressive hybridization is a process that enables gene flow across species barriers through the backcrossing of hybrids into a parent population. This may make genetic material, potentially including relevant environmental adaptations, rapidly available in a gene pool. Consequently, it has been postulated to be an important mechanism for enabling evolutionary rescue, that is the recovery of threatened populations through rapid evolutionary adaptation to novel environments. However, predicting the likelihood of such evolutionary rescue for individual species remains challenging. Here, we use the example of Zosterops silvanus, an endangered East African highland bird species suffering from severe habitat loss and fragmentation, to investigate whether hybridization with its congener Zosterops flavilateralis might enable evolutionary rescue of its Taita Hills population. To do so, we employ an empirically parameterized individual‐based model to simulate the species' behaviour, physiology and genetics. We test the population's response to different assumptions of mating behaviour and multiple scenarios of habitat change. We show that as long as hybridization does take place, evolutionary rescue of Z. silvanus is likely. Intermediate hybridization rates enable the greatest long‐term population growth, due to trade‐offs between adaptive and maladaptive introgressed alleles. Habitat change did not have a strong effect on population growth rates, as Z. silvanus is a strong disperser and landscape configuration is therefore not the limiting factor for hybridization. Our results show that targeted gene flow may be a promising avenue to help accelerate the adaptation of endangered species to novel environments, and demonstrate how to combine empirical research and mechanistic modelling to deliver species‐specific predictions for conservation planning.
Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.
After the recent emergence of SARS-CoV-2 infection, unanswered questions remain related to its evolutionary history, path of transmission or divergence and role of recombination. There is emerging evidence on amino acid substitutions occurring in key residues of the receptor-binding domain of the spike glycoprotein in coronavirus isolates from bat and pangolins. In this article, we summarize our current knowledge on the origin of SARS-CoV-2. We also analyze the host ACE2-interacting residues of the receptor-binding domain of spike glycoprotein in SARS-CoV-2 isolates from bats, and compare it to pangolin SARS-CoV-2 isolates collected from Guangdong province (GD Pangolin-CoV) and Guangxi autonomous regions (GX Pangolin-CoV) of South China. Based on our comparative analysis, we support the view that the Guangdong Pangolins are the intermediate hosts that adapted the SARS-CoV-2 and represented a significant evolutionary link in the path of transmission of SARS-CoV-2 virus. We also discuss the role of intermediate hosts in the origin of Omicron.
Purpose
To fully automatically derive quantitative parameters from late gadolinium enhancement (LGE) cardiac MR (CMR) in patients with myocardial infarction and to investigate if phase sensitive or magnitude reconstructions or a combination of both results in best segmentation accuracy.
Methods
In this retrospective single center study, a convolutional neural network with a U-Net architecture with a self-configuring framework (“nnU-net”) was trained for segmentation of left ventricular myocardium and infarct zone in LGE-CMR. A database of 170 examinations from 78 patients with history of myocardial infarction was assembled. Separate fitting of the model was performed, using phase sensitive inversion recovery, the magnitude reconstruction or both contrasts as input channels.
Manual labelling served as ground truth. In a subset of 10 patients, the performance of the trained models was evaluated and quantitatively compared by determination of the Sørensen-Dice similarity coefficient (DSC) and volumes of the infarct zone compared with the manual ground truth using Pearson’s r correlation and Bland-Altman analysis.
Results
The model achieved high similarity coefficients for myocardium and scar tissue. No significant difference was observed between using PSIR, magnitude reconstruction or both contrasts as input (PSIR and MAG; mean DSC: 0.83 ± 0.03 for myocardium and 0.72 ± 0.08 for scars). A strong correlation for volumes of infarct zone was observed between manual and model-based approach (r = 0.96), with a significant underestimation of the volumes obtained from the neural network.
Conclusion
The self-configuring nnU-net achieves predictions with strong agreement compared to manual segmentation, proving the potential as a promising tool to provide fully automatic quantitative evaluation of LGE-CMR.
In vitro rearing of honeybee larvae is an established method that enables exact control and monitoring of developmental factors and allows controlled application of pesticides or pathogens. However, only a few studies have investigated how the rearing method itself affects the behavior of the resulting adult honeybees. We raised honeybees in vitro according to a standardized protocol: marking the emerging honeybees individually and inserting them into established colonies. Subsequently, we investigated the behavioral performance of nurse bees and foragers and quantified the physiological factors underlying the social organization. Adult honeybees raised in vitro differed from naturally reared honeybees in their probability of performing social tasks. Further, in vitro-reared bees foraged for a shorter duration in their life and performed fewer foraging trips. Nursing behavior appeared to be unaffected by rearing condition. Weight was also unaffected by rearing condition. Interestingly, juvenile hormone titers, which normally increase strongly around the time when a honeybee becomes a forager, were significantly lower in three- and four-week-old in vitro bees. The effects of the rearing environment on individual sucrose responsiveness and lipid levels were rather minor. These data suggest that larval rearing conditions can affect the task performance and physiology of adult bees despite equal weight, pointing to an important role of the colony environment for these factors. Our observations of behavior and metabolic pathways offer important novel insight into how the rearing environment affects adult honeybees.
Investigating diversity gradients helps to understand biodiversity drivers and threats. However, one diversity gradient is rarely assessed, namely how plant species distribute along the depth gradient of lakes. Here, we provide the first comprehensive characterization of depth diversity gradient (DDG) of alpha, beta, and gamma species richness of submerged macrophytes across multiple lakes. We characterize the DDG for additive richness components (alpha, beta, gamma), assess environmental drivers, and address temporal change over recent years. We take advantage of yet the largest dataset of macrophyte occurrence along lake depth (274 depth transects across 28 deep lakes) as well as of physiochemical measurements (12 deep lakes from 2006 to 2017 across Bavaria), provided publicly online by the Bavarian State Office for the Environment. We found a high variability in DDG shapes across the study lakes. The DDGs for alpha and gamma richness are predominantly hump-shaped, while beta richness shows a decreasing DDG. Generalized additive mixed-effect models indicate that the depth of the maximum richness (Dmax) is influenced by light quality, light quantity, and layering depth, whereas the respective maximum alpha richness within the depth gradient (Rmax) is significantly influenced by lake area only. Most observed DDGs seem generally stable over recent years. However, for single lakes we found significant linear trends for Rmax and Dmax going into different directions. The observed hump-shaped DDGs agree with three competing hypotheses: the mid-domain effect, the mean–disturbance hypothesis, and the mean–productivity hypothesis. The DDG amplitude seems driven by lake area (thus following known species–area relationships), whereas skewness depends on physiochemical factors, mainly water transparency and layering depth. Our results provide insights for conservation strategies and for mechanistic frameworks to disentangle competing explanatory hypotheses for the DDG.