TY - JOUR A1 - Schilcher, Felix A1 - Hilsmann, Lioba A1 - Ankenbrand, Markus J. A1 - Krischke, Markus A1 - Mueller, Martin J. A1 - Steffan-Dewenter, Ingolf A1 - Scheiner, Ricarda T1 - Honeybees are buffered against undernourishment during larval stages JF - Frontiers in Insect Science N2 - 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. KW - nutrition KW - juvenile hormone KW - nurse bees KW - foragers KW - triglycerides KW - undernourishment KW - task allocation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-304646 SN - 2673-8600 VL - 2 ER - TY - JOUR A1 - Schilcher, Felix A1 - Hilsmann, Lioba A1 - Rauscher, Lisa A1 - Değirmenci, Laura A1 - Krischke, Markus A1 - Krischke, Beate A1 - Ankenbrand, Markus A1 - Rutschmann, Benjamin A1 - Mueller, Martin J. A1 - Steffan-Dewenter, Ingolf A1 - Scheiner, Ricarda T1 - In vitro rearing changes social task performance and physiology in honeybees JF - Insects N2 - 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. KW - honeybee KW - artificial rearing KW - behavior KW - in vitro KW - juvenile hormone KW - triglycerides KW - PER KW - foraging KW - nursing Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-252305 SN - 2075-4450 VL - 13 IS - 1 ER - TY - JOUR A1 - Ankenbrand, Markus J. A1 - Weber, Lorenz A1 - Becker, Dirk A1 - Förster, Frank A1 - Bemm, Felix T1 - TBro: visualization and management of de novo transcriptomes JF - Database N2 - RNA sequencing (RNA-seq) has become a powerful tool to understand molecular mechanisms and/or developmental programs. It provides a fast, reliable and cost-effective method to access sets of expressed elements in a qualitative and quantitative manner. Especially for non-model organisms and in absence of a reference genome, RNA-seq data is used to reconstruct and quantify transcriptomes at the same time. Even SNPs, InDels, and alternative splicing events are predicted directly from the data without having a reference genome at hand. A key challenge, especially for non-computational personnal, is the management of the resulting datasets, consisting of different data types and formats. Here, we present TBro, a flexible de novo transcriptome browser, tackling this challenge. TBro aggregates sequences, their annotation, expression levels as well as differential testing results. It provides an easy-to-use interface to mine the aggregated data and generate publication-ready visualizations. Additionally, it supports users with an intuitive cart system, that helps collecting and analysing biological meaningful sets of transcripts. TBro’s modular architecture allows easy extension of its functionalities in the future. Especially, the integration of new data types such as proteomic quantifications or array-based gene expression data is straightforward. Thus, TBro is a fully featured yet flexible transcriptome browser that supports approaching complex biological questions and enhances collaboration of numerous researchers. KW - database Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-147954 VL - 2016 ER - TY - JOUR A1 - Sickel, Wiebke A1 - Ankenbrand, Markus J. A1 - Grimmer, Gudrun A1 - Holzschuh, Andrea A1 - Härtel, Stephan A1 - Lanzen, Jonathan A1 - Steffan-Dewenter, Ingolf A1 - Keller, Alexander T1 - Increased efficiency in identifying mixed pollen samples by meta-barcoding with a dual-indexing approach JF - BMC Ecology N2 - Background Meta-barcoding of mixed pollen samples constitutes a suitable alternative to conventional pollen identification via light microscopy. Current approaches however have limitations in practicability due to low sample throughput and/or inefficient processing methods, e.g. separate steps for amplification and sample indexing. Results We thus developed a new primer-adapter design for high throughput sequencing with the Illumina technology that remedies these issues. It uses a dual-indexing strategy, where sample-specific combinations of forward and reverse identifiers attached to the barcode marker allow high sample throughput with a single sequencing run. It does not require further adapter ligation steps after amplification. We applied this protocol to 384 pollen samples collected by solitary bees and sequenced all samples together on a single Illumina MiSeq v2 flow cell. According to rarefaction curves, 2,000–3,000 high quality reads per sample were sufficient to assess the complete diversity of 95% of the samples. We were able to detect 650 different plant taxa in total, of which 95% were classified at the species level. Together with the laboratory protocol, we also present an update of the reference database used by the classifier software, which increases the total number of covered global plant species included in the database from 37,403 to 72,325 (93% increase). Conclusions This study thus offers improvements for the laboratory and bioinformatical workflow to existing approaches regarding data quantity and quality as well as processing effort and cost-effectiveness. Although only tested for pollen samples, it is furthermore applicable to other research questions requiring plant identification in mixed and challenging samples. KW - pollination ecology KW - next generation sequencing KW - ITS2 KW - illumina MiSeq platform KW - high throughput sequencing KW - DNA barcoding KW - NGS KW - osmia KW - palynolog Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-125730 VL - 15 IS - 20 ER - TY - JOUR A1 - Wolf, Matthias A1 - Chen, Shilin A1 - Song, Jingyuan A1 - Ankenbrand, Markus A1 - Müller, Tobias T1 - Compensatory Base Changes in ITS2 Secondary Structures Correlate with the Biological Species Concept Despite Intragenomic Variability in ITS2 Sequences – A Proof of Concept JF - PLoS ONE N2 - Compensatory base changes (CBCs) in internal transcribed spacer 2 (ITS2) rDNA secondary structures correlate with Ernst Mayr’s biological species concept. This hypothesis also referred to as the CBC species concept recently was subjected to large-scale testing, indicating two distinct probabilities. (1) If there is a CBC then there are two different species with a probability of ~0.93. (2) If there is no CBC then there is the same species with a probability of ~0.76. In ITS2 research, however, the main problem is the multicopy nature of ITS2 sequences. Most recently, 454 pyrosequencing data have been used to characterize more than 5000 intragenomic variations of ITS2 regions from 178 plant species, demonstrating that mutation of ITS2 is frequent, with a mean of 35 variants per species, respectively per individual organism. In this study, using those 454 data, the CBC criterion is reconsidered in the light of intragenomic variability, a proof of concept, a necessary criterion, expecting no intragenomic CBCs in variant ITS2 copies. In accordance with the CBC species concept, we could demonstrate that the probability that there is no intragenomic CBC is ~0.99. KW - citrus KW - concerted evolution KW - DNA sequences KW - Genome evolution KW - Phylogenetics KW - plant evolution KW - sequence alignment KW - sequence databases Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-96450 ER - TY - JOUR A1 - Ankenbrand, Markus Johannes A1 - Lohr, David A1 - Schlötelburg, Wiebke A1 - Reiter, Theresa A1 - Wech, Tobias A1 - Schreiber, Laura Maria T1 - Deep learning-based cardiac cine segmentation: Transfer learning application to 7T ultrahigh-field MRI JF - Magnetic Resonance in Medicine N2 - Purpose Artificial neural networks show promising performance in automatic segmentation of cardiac MRI. However, training requires large amounts of annotated data and generalization to different vendors, field strengths, sequence parameters, and pathologies is limited. Transfer learning addresses this challenge, but specific recommendations regarding type and amount of data required is lacking. In this study, we assess data requirements for transfer learning to experimental cardiac MRI at 7T where the segmentation task can be challenging. In addition, we provide guidelines, tools, and annotated data to enable transfer learning approaches by other researchers and clinicians. Methods A publicly available segmentation model was used to annotate a publicly available data set. This labeled data set was subsequently used to train a neural network for segmentation of left ventricle and myocardium in cardiac cine MRI. The network is used as starting point for transfer learning to 7T cine data of healthy volunteers (n = 22; 7873 images) by updating the pre-trained weights. Structured and random data subsets of different sizes were used to systematically assess data requirements for successful transfer learning. Results Inconsistencies in the publically available data set were corrected, labels created, and a neural network trained. On 7T cardiac cine images the model pre-trained on public imaging data, acquired at 1.5T and 3T, achieved DICE\(_{LV}\) = 0.835 and DICE\(_{MY}\) = 0.670. Transfer learning using 7T cine data and ImageNet weight initialization improved model performance to DICE\(_{LV}\) = 0.900 and DICE\(_{MY}\) = 0.791. Using only end-systolic and end-diastolic images reduced training data by 90%, with no negative impact on segmentation performance (DICE\(_{LV}\) = 0.908, DICE\(_{MY}\) = 0.805). Conclusions This work demonstrates and quantifies the benefits of transfer learning for cardiac cine image segmentation. We provide practical guidelines for researchers planning transfer learning projects in cardiac MRI and make data, models, and code publicly available. KW - 7T KW - ultrahigh-field KW - transfer learning KW - segmentation KW - neural networks KW - deep learning KW - cardiac magnetic resonance KW - cardiac function Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-257604 VL - 86 IS - 4 ER - TY - INPR A1 - Heidenreich, Julius F. A1 - Gassenmaier, Tobias A1 - Ankenbrand, Markus J. A1 - Bley, Thorsten A. A1 - Wech, Tobias T1 - Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction N2 - 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. KW - Deep learning KW - CMR KW - Segmentation KW - Myocardial infarction KW - Scar KW - nnU-net Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323418 UR - https://doi.org/10.1016/j.ejrad.2021.109817 ET - accepted version ER - TY - JOUR A1 - Bemm, Felix A1 - Becker, Dirk A1 - Larisch, Christina A1 - Kreuzer, Ines A1 - Escalante-Perez, Maria A1 - Schulze, Waltraud X. A1 - Ankenbrand, Markus A1 - Van de Weyer, Anna-Lena A1 - Krol, Elzbieta A1 - Al-Rasheid, Khaled A. A1 - Mithöfer, Axel A1 - Weber, Andreas P. A1 - Schultz, Jörg A1 - Hedrich, Rainer T1 - Venus flytrap carnivorous lifestyle builds on herbivore defense strategies JF - Genome Research N2 - Although the concept of botanical carnivory has been known since Darwin's time, the molecular mechanisms that allow animal feeding remain unknown, primarily due to a complete lack of genomic information. Here, we show that the transcriptomic landscape of the Dionaea trap is dramatically shifted toward signal transduction and nutrient transport upon insect feeding, with touch hormone signaling and protein secretion prevailing. At the same time, a massive induction of general defense responses is accompanied by the repression of cell death-related genes/processes. We hypothesize that the carnivory syndrome of Dionaea evolved by exaptation of ancient defense pathways, replacing cell death with nutrient acquisition. KW - Dionaea-muscipula ellis KW - Plant utricularia-gibba KW - Programmed cell-death KW - Genomics data sets KW - RNA-SEQ data KW - Arabidopsis-thaliana KW - Jasmonate perception KW - Action potentials KW - Stress responses KW - Wonderful plants Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-188799 VL - 26 IS - 6 ER - TY - JOUR A1 - Keller, Alexander A1 - Brandel, Annette A1 - Becker, Mira C. A1 - Balles, Rebecca A1 - Abdelmohsen, Usama Ramadan A1 - Ankenbrand, Markus J. A1 - Sickel, Wiebke T1 - Wild bees and their nests host Paenibacillus bacteria with functional potential of avail JF - Microbiome N2 - Background: In previous studies, the gram-positive firmicute genus Paenibacillus was found with significant abundances in nests of wild solitary bees. Paenibacillus larvae is well-known for beekeepers as a severe pathogen causing the fatal honey bee disease American foulbrood, and other members of the genus are either secondary invaders of European foulbrood or considered a threat to honey bees. We thus investigated whether Paenibacillus is a common bacterium associated with various wild bees and hence poses a latent threat to honey bees visiting the same flowers. Results: We collected 202 samples from 82 individuals or nests of 13 bee species at the same location and screened each for Paenibacillus using high-throughput sequencing-based 16S metabarcoding. We then isolated the identified strain Paenibacillus MBD-MB06 from a solitary bee nest and sequenced its genome. We did find conserved toxin genes and such encoding for chitin-binding proteins, yet none specifically related to foulbrood virulence or chitinases. Phylogenomic analysis revealed a closer relationship to strains of root-associated Paenibacillus rather than strains causing foulbrood or other accompanying diseases. We found anti-microbial evidence within the genome, confirmed by experimental bioassays with strong growth inhibition of selected fungi as well as gram-positive and gram-negative bacteria. Conclusions: The isolated wild bee associate Paenibacillus MBD-MB06 is a common, but irregularly occurring part of wild bee microbiomes, present on adult body surfaces and guts and within nests especially in megachilids. It was phylogenetically and functionally distinct from harmful members causing honey bee colony diseases, although it shared few conserved proteins putatively toxic to insects that might indicate ancestral predisposition for the evolution of insect pathogens within the group. By contrast, our strain showed anti-microbial capabilities and the genome further indicates abilities for chitin-binding and biofilm-forming, suggesting it is likely a useful associate to avoid fungal penetration of the bee cuticula and a beneficial inhabitant of nests to repress fungal threats in humid and nutrient-rich environments of wild bee nests. KW - 16S metabarcoding KW - American foulbrood KW - anti-microbial activit KW - bacterial genomics KW - bioassays KW - European foulbrood KW - Paenibacterin KW - phylogenomics KW - bee disease KW - pathogen vector Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-177554 VL - 6 IS - 229 ER - TY - JOUR A1 - Wech, Tobias A1 - Ankenbrand, Markus Johannes A1 - Bley, Thorsten Alexander A1 - Heidenreich, Julius Frederik T1 - A data-driven semantic segmentation model for direct cardiac functional analysis based on undersampled radial MR cine series JF - Magnetic Resonance in Medicine N2 - Purpose Image acquisition and subsequent manual analysis of cardiac cine MRI is time-consuming. The purpose of this study was to train and evaluate a 3D artificial neural network for semantic segmentation of radially undersampled cardiac MRI to accelerate both scan time and postprocessing. Methods A database of Cartesian short-axis MR images of the heart (148,500 images, 484 examinations) was assembled from an openly accessible database and radial undersampling was simulated. A 3D U-Net architecture was pretrained for segmentation of undersampled spatiotemporal cine MRI. Transfer learning was then performed using samples from a second database, comprising 108 non-Cartesian radial cine series of the midventricular myocardium to optimize the performance for authentic data. The performance was evaluated for different levels of undersampling by the Dice similarity coefficient (DSC) with respect to reference labels, as well as by deriving ventricular volumes and myocardial masses. Results Without transfer learning, the pretrained model performed moderately on true radial data [maximum number of projections tested, P = 196; DSC = 0.87 (left ventricle), DSC = 0.76 (myocardium), and DSC =0.64 (right ventricle)]. After transfer learning with authentic data, the predictions achieved human level even for high undersampling rates (P = 33, DSC = 0.95, 0.87, and 0.93) without significant difference compared with segmentations derived from fully sampled data. Conclusion A 3D U-Net architecture can be used for semantic segmentation of radially undersampled cine acquisitions, achieving a performance comparable with human experts in fully sampled data. This approach can jointly accelerate time-consuming cine image acquisition and cumbersome manual image analysis. KW - undersampling KW - cardiovascular magnetic resonance (CMR) KW - deep learning KW - radial KW - semantic segmentation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-257616 VL - 87 IS - 2 ER - TY - THES A1 - Ankenbrand, Markus Johannes T1 - Squeezing more information out of biological data - development and application of bioinformatic tools for ecology, evolution and genomics T1 - Mehr aus biologischen Daten herausholen - Entwicklung und Anwendung bioinformatischer Programme für Ökologie, Evolution und Genomik N2 - New experimental methods have drastically accelerated the pace and quantity at which biological data is generated. High-throughput DNA sequencing is one of the pivotal new technologies. It offers a number of novel applications in various fields of biology, including ecology, evolution, and genomics. However, together with those opportunities many new challenges arise. Specialized algorithms and software are required to cope with the amount of data, often requiring substantial training in bioinformatic methods. Another way to make those data accessible to non-bioinformaticians is the development of programs with intuitive user interfaces. In my thesis I developed analyses and programs to tackle current problems with high-throughput data in biology. In the field of ecology this covers the establishment of the bioinformatic workflow for pollen DNA meta-barcoding. Furthermore, I developed an application that facilitates the analysis of ecological communities in the context of their traits. Information from multiple public databases have been aggregated and can now be mapped automatically to existing community tables for interactive inspection. In evolution the new data are used to reconstruct phylogenetic trees from multiple genes. I developed the tool bcgTree to automate this process for bacteria. Many plant genomes have been sequenced in current years. Sequencing reads of those projects also contain data from the chloroplasts. The tool chloroExtractor supports the targeted extraction and analysis of the chloroplast genome. To compare the structure of multiple genomes specialized software is required for calculation and visualization of the relationships. I developed AliTV to address this. In contrast to existing programs for this task it allows interactive adjustments of produced graphics. Thus, facilitating the discovery of biologically relevant information. Another application I developed helps to analyze transcriptomes even if no reference genome is present. This is achieved by aggregating the different pieces of information, like functional annotation and expression level, for each transcript in a web platform. Scientists can then search, filter, subset, and visualize the transcriptome. Together the methods and tools expedite insights into biological systems that were not possible before. N2 - Neue experimentelle Methoden haben die Geschwindigkeit und Masse, in der biologische Daten generiert werden, in den letzten Jahren enorm gesteigert. Eine zentrale neue Technologie ist die Hochdurchsatzsequenzierung von DNA. Diese Technik eröffnet eine ganze Reihe Anwendungsmöglichkeiten in vielen Bereichen der Biologie, einschließlich der Ökologie, Evolution und Genomik. Neben den neuen Möglichkeiten treten jedoch auch neue Herausforderungen auf. So bedarf es spezialisierter Algorithmen und Computerprogramme, um mit der Masse an Daten umgehen zu können. Diese erfordern in der Regel ein fundiertes Training in bioinformatischen Methoden. Ein Weg, die Daten auch Wissenschaftlern ohne diesen Hintergrund zugänglich zu machen ist die Entwicklung von Programmen, die sich intuitiv bedienen lassen. In meiner Doktorarbeit habe ich Analysen und Programme entwickelt, um einige aktuelle Probleme mit Hochdurchsatzdaten in der Biologie zu lösen. Im Bereich der Ökologie umfasst das die Etablierung der bioinformatischen Methode, um Pollen DNA Metabarcoding durchzuführen. Darüberhinaus habe ich eine Anwendung entwickelt, die es ermöglicht Artgemeinschaften im Kontext ihrer Eigenschaften zu erforschen. Dazu wurden Informationen aus diversen öffentlichen Datenbanken zusammen getragen. Diese können nun automatisch auf bestehende Projekte übertragen und interaktiv analysiert werden. Im Bereich der Evolution ermöglichen die neuen Daten phylogenetische Berechnungen mit multiplen Genen durchzuführen. Um dies für Bakterien zu automatisieren habe ich das Programm bcgTree entwickelt. In den letzten Jahren wurden viele pflanzliche Genome sequenziert. Die Sequenzdaten des pflanzlichen Genoms enthalten auch die des Chloroplasten. Das Programm chloroExtractor unterstützt die gezielte Analyse des Chloroplasten Genoms. Um jedoch die Struktur mehrerer Genome miteinander vergleichen zu können, wird spezielle Software benötigt, die den Vergleich berechnen und visuell darstellen kann. Daher habe ich das Programm AliTV entwickelt. Im Gegensatz zu bestehenden Programmen erlaubt AliTV interaktive Anpassungen der erzeugten Grafik. Das erleichtert es die relevanten Informationen zu finden. Ein weiteres von mir entwickeltes Programm hilft dabei Transkriptom Daten zu analysieren, auch wenn kein Referenzgenom vorliegt. Dazu werden Informationen zu jedem Transkript, z.B. Funktion und Expressionslevel, in einer Webanwendung aggregiert. Forscher können diese durchsuchen, filtern und graphisch darstellen. Zusammen eröffnen die entwickelten Methoden und Programme die Möglichkeit, Erkenntnisse über biologische Systeme zu erlangen, die bislang nicht möglich waren. KW - bioinformatics KW - research software KW - ecology KW - evolution KW - genomics Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-156344 ER - TY - JOUR A1 - Voulgari‐Kokota, Anna A1 - Ankenbrand, Markus J. A1 - Grimmer, Gudrun A1 - Steffan‐Dewenter, Ingolf A1 - Keller, Alexander T1 - Linking pollen foraging of megachilid bees to their nest bacterial microbiota JF - Ecology and Evolution N2 - Solitary bees build their nests by modifying the interior of natural cavities, and they provision them with food by importing collected pollen. As a result, the microbiota of the solitary bee nests may be highly dependent on introduced materials. In order to investigate how the collected pollen is associated with the nest microbiota, we used metabarcoding of the ITS2 rDNA and the 16S rDNA to simultaneously characterize the pollen composition and the bacterial communities of 100 solitary bee nest chambers belonging to seven megachilid species. We found a weak correlation between bacterial and pollen alpha diversity and significant associations between the composition of pollen and that of the nest microbiota, contributing to the understanding of the link between foraging and bacteria acquisition for solitary bees. Since solitary bees cannot establish bacterial transmission routes through eusociality, this link could be essential for obtaining bacterial symbionts for this group of valuable pollinators. KW - foraging patterns KW - nest microbiota KW - plant–microbe–pollinator triangle KW - pollination network KW - solitary bees KW - wild bees Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-201749 SN - 00 VL - 2019 IS - 9 ER - TY - JOUR A1 - Hesselbach, Hannah A1 - Seeger, Johannes A1 - Schilcher, Felix A1 - Ankenbrand, Markus A1 - Scheiner, Ricarda T1 - Chronic exposure to the pesticide flupyradifurone can lead to premature onset of foraging in honeybees Apis mellifera JF - Journal of Applied Ecology N2 - 1.Honeybees Apis mellifera and other pollinating insects suffer from pesticides in agricultural landscapes. Flupyradifurone is the active ingredient of a novel pesticide by the name of ‘Sivanto’, introduced by Bayer AG (Crop Science Division, Monheim am Rhein, Germany). It is recommended against sucking insects and marketed as ‘harmless’ to honeybees. Flupyradifurone binds to nicotinergic acetylcholine receptors like neonicotinoids, but it has a different mode of action. So far, little is known on how sublethal flupyradifurone doses affect honeybees. 2. We chronically applied a sublethal and field‐realistic concentration of flupyradifurone to test for long‐term effects on flight behaviour using radio‐frequency identification. We examined haematoxylin/eosin‐stained brains of flupyradifurone‐treated bees to investigate possible changes in brain morphology and brain damage. 3. A field‐realistic flupyradifurone dose of approximately 1.0 μg/bee/day significantly increased mortality. Pesticide‐treated bees initiated foraging earlier than control bees. No morphological damage in the brain was observed. 4. Synthesis and applications. The early onset of foraging induced by a chronical application of flupyradifurone could be disadvantageous for honeybee colonies, reducing the period of in‐hive tasks and life expectancy of individuals. Radio‐frequency identification technology is a valuable tool for studying pesticide effects on lifetime foraging behaviour of insects. KW - radiofrequency identification KW - flight behaviour KW - flupyradifurone KW - foraging KW - histology KW - honeybee KW - insecticide KW - mortality Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-212769 VL - 57 IS - 3 ER - TY - JOUR A1 - Hock, Michael A1 - Terekhov, Maxim A1 - Stefanescu, Maria Roxana A1 - Lohr, David A1 - Herz, Stefan A1 - Reiter, Theresa A1 - Ankenbrand, Markus A1 - Kosmala, Aleksander A1 - Gassenmaier, Tobias A1 - Juchem, Christoph A1 - Schreiber, Laura Maria T1 - B\(_{0}\) shimming of the human heart at 7T JF - Magnetic Resonance in Medicine N2 - Purpose Inhomogeneities of the static magnetic B\(_{0}\) field are a major limiting factor in cardiac MRI at ultrahigh field (≥ 7T), as they result in signal loss and image distortions. Different magnetic susceptibilities of the myocardium and surrounding tissue in combination with cardiac motion lead to strong spatio‐temporal B\(_{0}\)‐field inhomogeneities, and their homogenization (B0 shimming) is a prerequisite. Limitations of state‐of‐the‐art shimming are described, regional B\(_{0}\) variations are measured, and a methodology for spherical harmonics shimming of the B\(_{0}\) field within the human myocardium is proposed. Methods The spatial B\(_{0}\)‐field distribution in the heart was analyzed as well as temporal B\(_{0}\)‐field variations in the myocardium over the cardiac cycle. Different shim region‐of‐interest selections were compared, and hardware limitations of spherical harmonics B\(_{0}\) shimming were evaluated by calibration‐based B0‐field modeling. The role of third‐order spherical harmonics terms was analyzed as well as potential benefits from cardiac phase–specific shimming. Results The strongest B\(_{0}\)‐field inhomogeneities were observed in localized spots within the left‐ventricular and right‐ventricular myocardium and varied between systolic and diastolic cardiac phases. An anatomy‐driven shim region‐of‐interest selection allowed for improved B\(_{0}\)‐field homogeneity compared with a standard shim region‐of‐interest cuboid. Third‐order spherical harmonics terms were demonstrated to be beneficial for shimming of these myocardial B\(_{0}\)‐field inhomogeneities. Initial results from the in vivo implementation of a potential shim strategy were obtained. Simulated cardiac phase–specific shimming was performed, and a shim term‐by‐term analysis revealed periodic variations of required currents. Conclusion Challenges in state‐of‐the‐art B\(_{0}\) shimming of the human heart at 7 T were described. Cardiac phase–specific shimming strategies were found to be superior to vendor‐supplied shimming. KW - 7 T KW - B KW - cardiac MRI KW - shimming KW - ultrahigh field Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-218096 VL - 85 IS - 1 SP - 182 EP - 196 ER - TY - JOUR A1 - Ankenbrand, Markus J. A1 - Shainberg, Liliia A1 - Hock, Michael A1 - Lohr, David A1 - Schreiber, Laura M. T1 - Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI JF - BMC Medical Imaging N2 - Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable. KW - deep learning KW - neural networks KW - cardiac magnetic resonance KW - sensitivity analysis KW - transformations KW - augmentation KW - segmentation Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-259169 VL - 21 IS - 1 ER - TY - JOUR A1 - Vedder, Daniel A1 - Ankenbrand, Markus A1 - Sarmento Cabral, Juliano T1 - Dealing with software complexity in individual‐based models JF - Methods in Ecology and Evolution N2 - Individual-based models are doubly complex: as well as representing complex ecological systems, the software that implements them is complex in itself. Both forms of complexity must be managed to create reliable models. However, the ecological modelling literature to date has focussed almost exclusively on the biological complexity. Here, we discuss methods for containing software complexity. Strategies for containing complexity include avoiding, subdividing, documenting and reviewing it. Computer science has long-established techniques for all of these strategies. We present some of these techniques and set them in the context of IBM development, giving examples from published models. Techniques for avoiding software complexity are following best practices for coding style, choosing suitable programming languages and file formats and setting up an automated workflow. Complex software systems can be made more tractable by encapsulating individual subsystems. Good documentation needs to take into account the perspectives of scientists, users and developers. Code reviews are an effective way to check for errors, and can be used together with manual or automated unit and integration tests. Ecological modellers can learn from computer scientists how to deal with complex software systems. Many techniques are readily available, but must be disseminated among modellers. There is a need for further work to adapt software development techniques to the requirements of academic research groups and individual-based modelling. KW - software development KW - ecological modelling KW - individual-based models KW - model complexity KW - research software engineering KW - software complexity Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-258214 VL - 12 IS - 12 ER - TY - JOUR A1 - Faist, Hanna A1 - Ankenbrand, Markus J. A1 - Sickel, Wiebke A1 - Hentschel, Ute A1 - Keller, Alexander A1 - Deeken, Rosalia T1 - Opportunistic bacteria of grapevine crown galls are equipped with the genomic repertoire for opine utilization JF - Genome Biology and Evolution N2 - 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. KW - Vitis vinifera KW - bacterial community KW - Agrobacterium KW - Allorhizobium vitis KW - Ti plasmids KW - de novo sequenced genomes Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-350172 VL - 15 IS - 12 ER - TY - JOUR A1 - Dirk, Robin A1 - Fischer, Jonas L. A1 - Schardt, Simon A1 - Ankenbrand, Markus J. A1 - Fischer, Sabine C. T1 - Recognition and reconstruction of cell differentiation patterns with deep learning JF - PLoS Computational Biology N2 - 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. KW - recognition KW - reconstruction KW - cell differentiation patterns KW - deep learning KW - mouse embryonic stem cells KW - multilayer perceptron model Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-350167 VL - 19 IS - 10 ER -