@article{MrestaniPauliKollmannsbergeretal.2021, author = {Mrestani, Achmed and Pauli, Martin and Kollmannsberger, Philip and Repp, Felix and Kittel, Robert J. and Eilers, Jens and Doose, S{\"o}ren and Sauer, Markus and Sir{\´e}n, Anna-Leena and Heckmann, Manfred and Paul, Mila M.}, title = {Active zone compaction correlates with presynaptic homeostatic potentiation}, series = {Cell Reports}, volume = {37}, journal = {Cell Reports}, number = {1}, doi = {10.1016/j.celrep.2021.109770}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-265497}, pages = {109770}, year = {2021}, abstract = {Neurotransmitter release is stabilized by homeostatic plasticity. Presynaptic homeostatic potentiation (PHP) operates on timescales ranging from minute- to life-long adaptations and likely involves reorganization of presynaptic active zones (AZs). At Drosophila melanogaster neuromuscular junctions, earlier work ascribed AZ enlargement by incorporating more Bruchpilot (Brp) scaffold protein a role in PHP. We use localization microscopy (direct stochastic optical reconstruction microscopy [dSTORM]) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) to study AZ plasticity during PHP at the synaptic mesoscale. We find compaction of individual AZs in acute philanthotoxin-induced and chronic genetically induced PHP but unchanged copy numbers of AZ proteins. Compaction even occurs at the level of Brp subclusters, which move toward AZ centers, and in Rab3 interacting molecule (RIM)-binding protein (RBP) subclusters. Furthermore, correlative confocal and dSTORM imaging reveals how AZ compaction in PHP translates into apparent increases in AZ area and Brp protein content, as implied earlier.}, language = {en} } @article{VedderAnkenbrandSarmentoCabral2021, author = {Vedder, Daniel and Ankenbrand, Markus and Sarmento Cabral, Juliano}, title = {Dealing with software complexity in individual-based models}, series = {Methods in Ecology and Evolution}, volume = {12}, journal = {Methods in Ecology and Evolution}, number = {12}, doi = {10.1111/2041-210X.13716}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-258214}, pages = {2324-2333}, year = {2021}, abstract = {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.}, language = {en} } @article{LewerentzHoffmannSarmentoCabral2021, author = {Lewerentz, Anne and Hoffmann, Markus and Sarmento Cabral, Juliano}, title = {Depth diversity gradients of macrophytes: Shape, drivers, and recent shifts}, series = {Ecology and Evolution}, volume = {11}, journal = {Ecology and Evolution}, number = {20}, doi = {10.1002/ece3.8089}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-260280}, pages = {13830-13845}, year = {2021}, abstract = {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.}, language = {en} } @article{BerberichKurzReinhardetal.2021, author = {Berberich, Andreas and Kurz, Andreas and Reinhard, Sebastian and Paul, Torsten Johann and Burd, Paul Ray and Sauer, Markus and Kollmannsberger, Philip}, title = {Fourier Ring Correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules}, series = {Frontiers in Bioinformatics}, volume = {1}, journal = {Frontiers in Bioinformatics}, doi = {10.3389/fbinf.2021.752788}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-261686}, year = {2021}, abstract = {Single-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the localization precision of individual fluorescent molecules, but also by their density, which becomes a limiting factor e.g., in expansion microscopy. Artificial deep neural networks can learn to reconstruct dense super-resolved structures such as microtubules from a sparse, noisy set of data points. This approach requires a robust method to assess the quality of a predicted density image and to quantitatively compare it to a ground truth image. Such a quality measure needs to be differentiable to be applied as loss function in deep learning. We developed a new trainable quality measure based on Fourier Ring Correlation (FRC) and used it to train deep neural networks to map a small number of sampling points to an underlying density. Smooth ground truth images of microtubules were generated from localization coordinates using an anisotropic Gaussian kernel density estimator. We show that the FRC criterion ideally complements the existing state-of-the-art multiscale structural similarity index, since both are interpretable and there is no trade-off between them during optimization. The TensorFlow implementation of our FRC metric can easily be integrated into existing deep learning workflows.}, language = {en} } @article{LopezArboledaReinertNordborgetal.2021, author = {Lopez-Arboleda, William Andres and Reinert, Stephan and Nordborg, Magnus and Korte, Arthur}, title = {Global genetic heterogeneity in adaptive traits}, series = {Molecular Biology and Evolution}, volume = {38}, journal = {Molecular Biology and Evolution}, number = {11}, doi = {10.1093/molbev/msab208}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-270410}, pages = {4822-4831}, year = {2021}, abstract = {Understanding the genetic architecture of complex traits is a major objective in biology. The standard approach for doing so is genome-wide association studies (GWAS), which aim to identify genetic polymorphisms responsible for variation in traits of interest. In human genetics, consistency across studies is commonly used as an indicator of reliability. However, if traits are involved in adaptation to the local environment, we do not necessarily expect reproducibility. On the contrary, results may depend on where you sample, and sampling across a wide range of environments may decrease the power of GWAS because of increased genetic heterogeneity. In this study, we examine how sampling affects GWAS in the model plant species Arabidopsis thaliana. We show that traits like flowering time are indeed influenced by distinct genetic effects in local populations. Furthermore, using gene expression as a molecular phenotype, we show that some genes are globally affected by shared variants, whereas others are affected by variants specific to subpopulations. Remarkably, the former are essentially all cis-regulated, whereas the latter are predominately affected by trans-acting variants. Our result illustrate that conclusions about genetic architecture can be extremely sensitive to sampling and population structure.}, language = {en} } @article{MarquardtLandwehrRonchietal.2021, author = {Marquardt, Andr{\´e} and Landwehr, Laura-Sophie and Ronchi, Cristina L. and di Dalmazi, Guido and Riester, Anna and Kollmannsberger, Philip and Altieri, Barbara and Fassnacht, Martin and Sbiera, Silviu}, title = {Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning}, series = {Cancers}, volume = {13}, journal = {Cancers}, number = {18}, issn = {2072-6694}, doi = {10.3390/cancers13184671}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-246245}, year = {2021}, abstract = {Simple Summary Using a visual-based clustering method on the TCGA RNA sequencing data of a large adrenocortical carcinoma (ACC) cohort, we were able to classify these tumors in two distinct clusters largely overlapping with previously identified ones. As previously shown, the identified clusters also correlated with patient survival. Applying the visual clustering method to a second dataset also including benign adrenocortical samples additionally revealed that one of the ACC clusters is more closely located to the benign samples, providing a possible explanation for the better survival of this ACC cluster. Furthermore, the subsequent use of machine learning identified new possible biomarker genes with prognostic potential for this rare disease, that are significantly differentially expressed in the different survival clusters and should be further evaluated. Abstract Adrenocortical carcinoma (ACC) is a rare disease, associated with poor survival. Several "multiple-omics" studies characterizing ACC on a molecular level identified two different clusters correlating with patient survival (C1A and C1B). We here used the publicly available transcriptome data from the TCGA-ACC dataset (n = 79), applying machine learning (ML) methods to classify the ACC based on expression pattern in an unbiased manner. UMAP (uniform manifold approximation and projection)-based clustering resulted in two distinct groups, ACC-UMAP1 and ACC-UMAP2, that largely overlap with clusters C1B and C1A, respectively. However, subsequent use of random-forest-based learning revealed a set of new possible marker genes showing significant differential expression in the described clusters (e.g., SOAT1, EIF2A1). For validation purposes, we used a secondary dataset based on a previous study from our group, consisting of 4 normal adrenal glands and 52 benign and 7 malignant tumor samples. The results largely confirmed those obtained for the TCGA-ACC cohort. In addition, the ENSAT dataset showed a correlation between benign adrenocortical tumors and the good prognosis ACC cluster ACC-UMAP1/C1B. In conclusion, the use of ML approaches re-identified and redefined known prognostic ACC subgroups. On the other hand, the subsequent use of random-forest-based learning identified new possible prognostic marker genes for ACC.}, language = {en} } @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} } @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} } @unpublished{HeidenreichGassenmaierAnkenbrandetal.2021, author = {Heidenreich, Julius F. and Gassenmaier, Tobias and Ankenbrand, Markus J. and Bley, Thorsten A. and Wech, Tobias}, title = {Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction}, edition = {accepted version}, doi = {10.1016/j.ejrad.2021.109817}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-323418}, year = {2021}, abstract = {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 S{\o}rensen-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.}, language = {en} } @article{BritzMarkertWitvlietetal.2021, author = {Britz, Sebastian and Markert, Sebastian Matthias and Witvliet, Daniel and Steyer, Anna Maria and Tr{\"o}ger, Sarah and Mulcahy, Ben and Kollmannsberger, Philip and Schwab, Yannick and Zhen, Mei and Stigloher, Christian}, title = {Structural Analysis of the Caenorhabditis elegans Dauer Larval Anterior Sensilla by Focused Ion Beam-Scanning Electron Microscopy}, series = {Frontiers in Neuroanatomy}, volume = {15}, journal = {Frontiers in Neuroanatomy}, issn = {1662-5129}, doi = {10.3389/fnana.2021.732520}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-249622}, year = {2021}, abstract = {At the end of the first larval stage, the nematode Caenorhabditis elegans developing in harsh environmental conditions is able to choose an alternative developmental path called the dauer diapause. Dauer larvae exhibit different physiology and behaviors from non-dauer larvae. Using focused ion beam-scanning electron microscopy (FIB-SEM), we volumetrically reconstructed the anterior sensory apparatus of C. elegans dauer larvae with unprecedented precision. We provide a detailed description of some neurons, focusing on structural details that were unknown or unresolved by previously published studies. They include the following: (1) dauer-specific branches of the IL2 sensory neurons project into the periphery of anterior sensilla and motor or putative sensory neurons at the sub-lateral cords; (2) ciliated endings of URX sensory neurons are supported by both ILso and AMso socket cells near the amphid openings; (3) variability in amphid sensory dendrites among dauers; and (4) somatic RIP interneurons maintain their projection into the pharyngeal nervous system. Our results support the notion that dauer larvae structurally expand their sensory system to facilitate searching for more favorable environments.}, language = {en} }