@article{SchilcherHilsmannRauscheretal.2021,
  author    = {Schilcher, Felix and Hilsmann, Lioba and Rauscher, Lisa and Değirmenci, Laura and Krischke, Markus and Krischke, Beate and Ankenbrand, Markus and Rutschmann, Benjamin and Mueller, Martin J. and Steffan-Dewenter, Ingolf and Scheiner, Ricarda},
  title     = {In vitro rearing changes social task performance and physiology in honeybees},
  series = {Insects},
  volume    = {13},
  journal   = {Insects},
  number    = {1},
  issn      = {2075-4450},
  doi       = {10.3390/insects13010004},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-252305},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Schmid2024,
  author    = {Schmid, Kerstin},
  title     = {Integrative, three-dimensional \(in\) \(silico\) modeling of gas exchange in the human alveolus},
  doi       = {10.25972/OPUS-35182},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-351823},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2024},
  abstract  = {Die Lunge erf{\"u}llt durch den Austausch von Atemgasen eine {\"u}berlebenswichtige Aufgabe. Der Gasaustausch erfolgt durch einen einfachen, aber entscheidenden passiven Diffusionsprozess. Dieser findet in den Alveolen statt, ballonartigen Strukturen, die an die peripheren Atemwege grenzen. Alveolen sind von einem dichten Netz aus kleinen Kapillaren umgeben. Hier kommt die eingeatmete Luft in unmittelbare N{\"a}he zu dem vom Herzen kommenden sauerstoffarmen Blut und erm{\"o}glicht den Austausch von Sauerstoff und Kohlenstoffdioxid {\"u}ber deren Konzentrationsgradienten. Die Effizienz des Gasaustauschs kann anhand von Indikatoren wie der Sauerstoffdiffusionskapazit{\"a}t der Lunge und der Reaktionshalbzeit gemessen werden. Beim Menschen besteht eine betr{\"a}chtliche Diskrepanz zwischen physiologischen Sch{\"a}tzungen der Diffusionskapazit{\"a}t und der theoretischen Maximalkapazit{\"a}t unter optimalen strukturellen Bedingungen (der morphologischen Sch{\"a}tzung). Diese Diskrepanz wird durch eine Reihe ineinandergreifender Faktoren beeinflusst, darunter strukturelle Elemente wie die Oberfl{\"a}che und die Dicke der Diffusionsbarriere sowie physiologische Faktoren wie die Blutflussdynamik. Um die verschiedenen Rollen dieser Faktoren zu entschl{\"u}sseln, untersuchten wir, wie die morphologischen und physiologischen Eigenschaften der menschlichen alveol{\"a}ren Mikroumgebung kollektiv und individuell den Prozess des Gasaustauschs beeinflussen. Zu diesem Zweck entwickelten wir einen integrativen in silico Ansatz, der 3D morphologische Modellierung und Simulation von Blutfluss und Sauerstofftransport kombiniert. Im Mittelpunkt unseres Ansatzes steht die Simulationssoftware Alvin, die als interaktive Plattform f{\"u}r das zugrundeliegende mathematische Modell des Sauerstofftransports in der Alveole dient. Unser r{\"a}umlich-zeitliches Modell wurde durch die Integration und Erweiterung bestehender mathematischer Modelle entwickelt und liefert Ergebnisse, die mit experimentellen Daten im Einklang stehen. Alvin erm{\"o}glicht eine immersive Auseinandersetzung mit dem simulierten Gasaustausch, indem sie Parameter{\"a}nderungen in Echtzeit und die Ausf{\"u}hrung mehrerer Simulationsinstanzen gleichzeitig erm{\"o}glicht w{\"a}hrend sie ein detailliertes quantitatives Feedback liefert. Die beteiligten morphologischen und physiologischen Parameter wurden mit einem Fokus auf der Mikrovaskulatur weiter untersucht. Durch die Zusammenstellung stereologischer Daten aus der Literatur und geometrischer 3D-Modellierung erstellten wir ein "sheet-flow" Modell als realistische Darstellung des menschlichen alveol{\"a}ren Kapillarnetzwerks. Blutfluss wurde mit Hilfe numerischer Str{\"o}mungsdynamik simuliert. Unsere Ergebnisse stimmen mit fr{\"u}heren Sch{\"a}tzungen {\"u}berein und unterstreichen die entscheidende Rolle von Viskosit{\"a}tsmodellen bei der Vorhersage des Druckabfalls in der Mikrovaskulatur. Dar{\"u}ber hinaus zeigten wir, wie unser Ansatz genutzt werden kann, um strukturelle Details wie die Konnektivit{\"a}t des alveol{\"a}ren Kapillarnetzes mit dem Gef{\"a}ßbaum anhand von Blutflussindizes zu untersuchen. Es ist wichtig zu betonen, dass wir uns bislang auf verschiedene Datenquellen st{\"u}tzten und dass f{\"u}r weitere Fortschritte eine experimentelle Vailidierung erforderlich ist. Die Integration unserer Ergebnisse in Alvin erm{\"o}glichte die Quantifizierung des simulierten Gasaustauschprozesses {\"u}ber die Sauerstoffdiffusionskapazit{\"a}t und die Reaktionshalbzeit. Neben der Bewertung der kollektiven Einfl{\"u}sse der morphologischen und physiologischen Eigenschaften erleichterte unsere interaktive Software auch die Bewertung einzelner Parameter{\"a}nderungen. Die Betrachtung des Blutvolumens und der f{\"u}r den Gasaustausch zur Verf{\"u}gung stehenden Oberfl{\"a}che ergab lineare Korrelationen mit der Diffusionskapazit{\"a}t. Die Blutflussgeschwindigkeit hatte einen positiven, nichtlinearen Effekt auf die Diffusionskapazit{\"a}t. Die Reaktionshalbzeit best{\"a}tigte, dass der Gasaustauschprozess in der Regel nicht diffusionslimitiert ist. Insgesamt lieferte unser Alveolenmodell einen Wert f{\"u}r die Diffusionskapazit{\"a}t, der in der Mitte der fr{\"u}heren physiologischen und morphologischen Sch{\"a}tzung lag. Daraus l{\"a}sst sich schließen, dass Ph{\"a}nomene auf Alveolarebene zu 50\% der Limitierung der Diffusionskapazit{\"a}t beitragen, die in vivo eintreten. Zusammenfassend l{\"a}sst sich sagen, dass unser integrativer in silico Ansatz verschiedene strukturelle und funktionelle Einfl{\"u}sse auf den alveol{\"a}ren Gasaustausch aufschl{\"u}sselt und damit die traditionelle Forschung in der Atemwegsforschung erg{\"a}nzt. Zus{\"a}tzlich zeigen wir seinen Nutzen in der Lehre oder bei der Interpretation ver{\"o}ffentlichter Daten auf. Um unser Verst{\"a}ndnis zu verbessern, sollten k{\"u}nftige Arbeiten vorrangig darauf ausgerichtet sein, einen zusammenh{\"a}ngenden experimentellen Datensatz zu erhalten und ein geeignetes Viskosit{\"a}tsmodell f{\"u}r Blutflusssimulationen zu finden.},
  subject      = {Gasaustausch},
  language  = {en}
}
@article{TooKellerSickeletal.2018,
  author    = {Too, Chin Chin and Keller, Alexander and Sickel, Wiebke and Lee, Sui Mae and Yule, Catherine M.},
  title     = {Microbial Community Structure in a Malaysian Tropical Peat Swamp Forest: The Influence of Tree Species and Depth},
  series = {Frontiers in Microbiology},
  volume    = {9},
  journal   = {Frontiers in Microbiology},
  doi       = {10.3389/fmicb.2018.02859},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229000},
  year      = {2018},
  abstract  = {Tropical peat swamp forests sequester globally significant stores of carbon in deep layers of waterlogged, anoxic, acidic and nutrient-depleted peat. The roles of microbes in supporting these forests through the formation of peat, carbon sequestration and nutrient cycling are virtually unknown. This study investigated physicochemical peat properties and microbial diversity between three dominant tree species: Shorea uliginosa (Dipterocarpaceae), Koompassia malaccensis (legumes associated with nitrogen-fixing bacteria), Eleiodoxa conferta (palm) and depths (surface, 45 and 90 cm) using microbial 16S rRNA gene amplicon sequencing. Water pH, oxygen, nitrogen, phosphorus, total phenolic contents and C/N ratio differed significantly between depths, but not tree species. Depth also strongly influenced microbial diversity and composition, while both depth and tree species exhibited significant impact on the archaeal communities. Microbial diversity was highest at the surface, where fresh leaf litter accumulates, and nutrient supply is guaranteed. Nitrogen was the core parameter correlating to microbial communities, but the interactive effects from various environmental variables displayed significant correlation to relative abundance of major microbial groups. Proteobacteria was the dominant phylum and the most abundant genus, Rhodoplanes, might be involved in nitrogen fixation. The most abundant methanogens and methanotrophs affiliated, respectively, to families Methanomassiliicoccaceae and Methylocystaceae. Our results demonstrated diverse microbial communities and provide valuable insights on microbial ecology in these extreme ecosystems.},
  language  = {en}
}
@phdthesis{Anwar2022,
  author    = {Anwar, Ammarah},
  title     = {Natural variation of gene regulatory networks in \(Arabidopsis\) \(thaliana\)},
  doi       = {10.25972/OPUS-29154},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-291549},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {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.},
  subject      = {Arabidopsis thaliana},
  language  = {en}
}
@article{FaistAnkenbrandSickeletal.2023,
  author    = {Faist, Hanna and Ankenbrand, Markus J. and Sickel, Wiebke and Hentschel, Ute and Keller, Alexander and Deeken, Rosalia},
  title     = {Opportunistic bacteria of grapevine crown galls are equipped with the genomic repertoire for opine utilization},
  series = {Genome Biology and Evolution},
  volume    = {15},
  journal   = {Genome Biology and Evolution},
  number    = {12},
  doi       = {10.1093/gbe/evad228},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350172},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@article{ImhoffRahnKuenzeletal.2020,
  author    = {Imhoff, Johannes F. and Rahn, Tanja and K{\"u}nzel, Sven and Keller, Alexander and Neulinger, Sven C.},
  title     = {Osmotic adaptation and compatible solute biosynthesis of phototrophic bacteria as revealed from genome analyses},
  series = {Microorganisms},
  volume    = {9},
  journal   = {Microorganisms},
  number    = {1},
  issn      = {2076-2607},
  doi       = {10.3390/microorganisms9010046},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-220161},
  year      = {2020},
  abstract  = {Osmotic adaptation and accumulation of compatible solutes is a key process for life at high osmotic pressure and elevated salt concentrations. Most important solutes that can protect cell structures and metabolic processes at high salt concentrations are glycine betaine and ectoine. The genome analysis of more than 130 phototrophic bacteria shows that biosynthesis of glycine betaine is common among marine and halophilic phototrophic Proteobacteria and their chemotrophic relatives, as well as in representatives of Pirellulaceae and Actinobacteria, but are also found in halophilic Cyanobacteria and Chloroherpeton thalassium. This ability correlates well with the successful toleration of extreme salt concentrations. Freshwater bacteria in general lack the possibilities to synthesize and often also to take up these compounds. The biosynthesis of ectoine is found in the phylogenetic lines of phototrophic Alpha- and Gammaproteobacteria, most prominent in the Halorhodospira species and a number of Rhodobacteraceae. It is also common among Streptomycetes and Bacilli. The phylogeny of glycine-sarcosine methyltransferase (GMT) and diaminobutyrate-pyruvate aminotransferase (EctB) sequences correlate well with otherwise established phylogenetic groups. Most significantly, GMT sequences of cyanobacteria form two major phylogenetic branches and the branch of Halorhodospira species is distinct from all other Ectothiorhodospiraceae. A variety of transport systems for osmolytes are present in the studied bacteria.},
  language  = {en}
}
@article{MarquardtHartrampfKollmannsbergeretal.2023,
  author    = {Marquardt, Andr{\´e} and Hartrampf, Philipp and Kollmannsberger, Philip and Solimando, Antonio G. and Meierjohann, Svenja and K{\"u}bler, Hubert and Bargou, Ralf and Schilling, Bastian and Serfling, Sebastian E. and Buck, Andreas and Werner, Rudolf A. and Lapa, Constantin and Krebs, Markus},
  title     = {Predicting microenvironment in CXCR4- and FAP-positive solid tumors — a pan-cancer machine learning workflow for theranostic target structures},
  series = {Cancers},
  volume    = {15},
  journal   = {Cancers},
  number    = {2},
  issn      = {2072-6694},
  doi       = {10.3390/cancers15020392},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-305036},
  year      = {2023},
  abstract  = {(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.},
  language  = {en}
}
@article{VedderLeidingerSarmentoCabral2021,
  author    = {Vedder, Daniel and Leidinger, Ludwig and Sarmento Cabral, Juliano},
  title     = {Propagule pressure and an invasion syndrome determine invasion success in a plant community model},
  series = {Ecology and Evolution},
  volume    = {11},
  journal   = {Ecology and Evolution},
  number    = {23},
  doi       = {10.1002/ece3.8348},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259107},
  pages     = {17106-17116},
  year      = {2021},
  abstract  = {The success of species invasions depends on multiple factors, including propagule pressure, disturbance, productivity, and the traits of native and non-native species. While the importance of many of these determinants has already been investigated in relative isolation, they are rarely studied in combination. Here, we address this shortcoming by exploring the effect of the above-listed factors on the success of invasions using an individual-based mechanistic model. This approach enables us to explicitly control environmental factors (temperature as surrogate for productivity, disturbance, and propagule pressure) as well as to monitor whole-community trait distributions of environmental adaptation, mass, and dispersal abilities. We simulated introductions of plant individuals to an oceanic island to assess which factors and species traits contribute to invasion success. We found that the most influential factors were higher propagule pressure and a particular set of traits. This invasion trait syndrome was characterized by a relative similarity in functional traits of invasive to native species, while invasive species had on average higher environmental adaptation, higher body mass, and increased dispersal distances, that is, had greater competitive and dispersive abilities. Our results highlight the importance in management practice of reducing the import of alien species, especially those that display this trait syndrome and come from similar habitats as those being managed.},
  language  = {en}
}
@article{DirkFischerSchardtetal.2023,
  author    = {Dirk, Robin and Fischer, Jonas L. and Schardt, Simon and Ankenbrand, Markus J. and Fischer, Sabine C.},
  title     = {Recognition and reconstruction of cell differentiation patterns with deep learning},
  series = {PLoS Computational Biology},
  volume    = {19},
  journal   = {PLoS Computational Biology},
  number    = {10},
  doi       = {10.1371/journal.pcbi.1011582},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350167},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@article{ReinhardHelmerichBorasetal.2022,
  author    = {Reinhard, Sebastian and Helmerich, Dominic A. and Boras, Dominik and Sauer, Markus and Kollmannsberger, Philip},
  title     = {ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy},
  series = {BMC Bioinformatics},
  volume    = {23},
  journal   = {BMC Bioinformatics},
  number    = {1},
  doi       = {10.1186/s12859-022-05071-5},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-299768},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}