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- ItemQuantification of osseointegration of plasma-polymer coated titanium alloyed implants by means of microcomputed tomography versus histomorphometry(New York [u.a.] : Hindawi, 2015) Gabler, Carolin; Zietz, Carmen; Bieck, Richard; Göhler, Rebecca; Lindner, Tobias; Haenle, Maximilian; Finke, Birgit; Meichsner, Jürgen; Testrich, Holger; Nowottnick, Mathias; Frerich, Bernhard; Bader, RainerA common method to derive both qualitative and quantitative data to evaluate osseointegration of implants is histomorphometry. The present study describes a new image reconstruction algorithm comparing the results of bone-to-implant contact (BIC) evaluated by means of µCT with histomorphometry data. Custom-made conical titanium alloyed (Ti6Al4V) implants were inserted in the distal tibial bone of female Sprague-Dawley rats. Different surface configurations were examined: Ti6Al4V implants with plasma-polymerized allylamine (PPAAm) coating and plasma-polymerized ethylenediamine (PPEDA) coating as well as implants without surface coating. After six weeks postoperatively, tibiae were explanted and BIC was determined by µCT (3D) and afterwards by histomorphometry (2D). In comparison to uncoated Ti6Al4V implants demonstrating low BIC of 32.4% (histomorphometry) and 51.3% (µCT), PPAAm and PPEDA coated implants showed a nonsignificant increase in BIC (histomorphometry: 45.7% and 53.5% and µCT: 51.8% and 62.0%, resp.). Mean BIC calculated by µCT was higher for all surface configurations compared to BIC detected by histomorphometry. Overall, a high correlation coefficient of 0.70 () was found between 3D and 2D quantification of BIC. The μCT analysis seems to be suitable as a nondestructive and accurate 3D imaging method for the evaluation of the bone-implant interface.
- ItemHigh-throughput screening Raman microspectroscopy for assessment of drug-induced changes in diatom cells(Cambridge : Royal Society of Chemistry, 2019) Rüger J.; Mondol A.S.; Schie I.W.; Popp J.; Krafft C.High-throughput screening Raman spectroscopy (HTS-RS) with automated localization algorithms offers unsurpassed speed and sensitivity to investigate the effect of dithiothreitol on the diatom Phaedactylum tricornutum. The HTS-RS capability that was demonstrated for this model system can be transferred to unmet analytical applications such as kinetic in vivo studies of microalgal assemblages. © 2019 The Royal Society of Chemistry.
- ItemInvestigation of horizontal structures at mesospheric altitudes using coherent radar imaging(Göttingen : Copernicus, 2013) Sommer, S.; Stober, G.; Schult, C.; Zecha, M.; Latteck, R.The Middle Atmosphere Alomar Radar System (MAARSY) in Northern Norway (69.30°N, 16.04°E) was used to perform interferometric observations of Polar Mesosperic Summer Echoes (PMSE) in June 2012. Coherent Radar Imaging (CRI) using Capon's method was applied allowing a high spatial resolution. The algorithm was validated by simulation and trajectories of meteor head echoes. Both data sets show a good correspondence with the algorithm. Using this algorithm, the aspect sensitivity of PMSE was analysed in a case study, making use of the capability of CRI to resolve the pattern within the beam volume. No correction of the beam pattern was made yet. It was found in this case study, that no large variations in the scattering width and the scattering center occured apart from a very short period of time at the upper edge of the PMSE.
- ItemImproving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis(San Francisco, California, US : PLOS, 2016) Deliano, Matthias; Tabelow, Karsten; König, Reinhard; Polzehl, JörgEstimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors in the analysis of single-subject data as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning.
- ItemUsing Bayesian Networks to Investigate the Influence of Subseasonal Arctic Variability on Midlatitude North Atlantic Circulation(Boston, Mass. [u.a.] : AMS, 2021) Harwood, Nathanael; Hall, Richard; Di Capua, Giorgia; Russell, Andrew; Tucker, AllanRecent enhanced warming and sea ice depletion in the Arctic have been put forward as potential drivers of severe weather in the midlatitudes. Evidence of a link between Arctic warming and midlatitude atmospheric circulation is growing, but the role of Arctic processes relative to other drivers remains unknown. Arctic–midlatitude connections in the North Atlantic region are particularly complex but important due to the frequent occurrence of severe winters in recent decades. Here, dynamic Bayesian networks with hidden variables are introduced to the field to assess their suitability for teleconnection analyses. Climate networks are constructed to analyze North Atlantic circulation variability at 5-day to monthly time scales during the winter months of the years 1981–2018. The inclusion of a number of Arctic, midlatitude, and tropical variables allows for an investigation into the relative role of Arctic influence compared to internal atmospheric variability and other remote drivers. A robust covariability between regions of amplified Arctic warming and two definitions of midlatitude circulation is found to occur entirely within winter at submonthly time scales. Hidden variables incorporated in networks represent two distinct modes of stratospheric polar vortex variability, capturing a periodic shift between average conditions and slower anomalous flow. The influence of the Barents–Kara Seas region on the North Atlantic Oscillation is found to be the strongest link at 5- and 10-day averages, while the stratospheric polar vortex strongly influences jet variability on monthly time scales.
- ItemEvolutionary design of explainable algorithms for biomedical image segmentation([London] : Nature Publishing Group UK, 2023) Cortacero, Kévin; McKenzie, Brienne; Müller, Sabina; Khazen, Roxana; Lafouresse, Fanny; Corsaut, Gaëlle; Van Acker, Nathalie; Frenois, François-Xavier; Lamant, Laurence; Meyer, Nicolas; Vergier, Béatrice; Wilson, Dennis G.; Luga, Hervé; Staufer, Oskar; Dustin, Michael L.; Valitutti, Salvatore; Cussat-Blanc, SylvainAn unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.
- ItemComputational design and optimization of electro-physiological sensors([London] : Nature Publishing Group UK, 2021) Nittala, Aditya Shekhar; Karrenbauer, Andreas; Khan, Arshad; Kraus, Tobias; Steimle, JürgenElectro-physiological sensing devices are becoming increasingly common in diverse applications. However, designing such sensors in compact form factors and for high-quality signal acquisition is a challenging task even for experts, is typically done using heuristics, and requires extensive training. Our work proposes a computational approach for designing multi-modal electro-physiological sensors. By employing an optimization-based approach alongside an integrated predictive model for multiple modalities, compact sensors can be created which offer an optimal trade-off between high signal quality and small device size. The task is assisted by a graphical tool that allows to easily specify design preferences and to visually analyze the generated designs in real-time, enabling designer-in-the-loop optimization. Experimental results show high quantitative agreement between the prediction of the optimizer and experimentally collected physiological data. They demonstrate that generated designs can achieve an optimal balance between the size of the sensor and its signal acquisition capability, outperforming expert generated solutions.
- ItemDigital IIR filters design using differential evolution algorithm with a controllable probabilistic population size(San Francisco, CA : Public Library of Science (PLoS), 2012) Zhu, W.; Fang, J.-A.; Tang, Y.; Zhang, W.; Du, W.Design of a digital infinite-impulse-response (IIR) filter is the process of synthesizing and implementing a recursive filter network so that a set of prescribed excitations results a set of desired responses. However, the error surface of IIR filters is usually non-linear and multi-modal. In order to find the global minimum indeed, an improved differential evolution (DE) is proposed for digital IIR filter design in this paper. The suggested algorithm is a kind of DE variants with a controllable probabilistic (CPDE) population size. It considers the convergence speed and the computational cost simultaneously by nonperiodic partial increasing or declining individuals according to fitness diversities. In addition, we discuss as well some important aspects for IIR filter design, such as the cost function value, the influence of (noise) perturbations, the convergence rate and successful percentage, the parameter measurement, etc. As to the simulation result, it shows that the presented algorithm is viable and comparable. Compared with six existing State-of-the-Art algorithms-based digital IIR filter design methods obtained by numerical experiments, CPDE is relatively more promising and competitive.
- ItemBenchmarking successional progress in a quantitative food web(San Francisco, CA : Public Library of Science (PLoS), 2014) Boit, A.; Gaedke, U.Central to ecology and ecosystem management, succession theory aims to mechanistically explain and predict the assembly and development of ecological communities. Yet processes at lower hierarchical levels, e.g. at the species and functional group level, are rarely mechanistically linked to the under-investigated system-level processes which drive changes in ecosystem properties and functioning and are comparable across ecosystems. As a model system for secondary succession, seasonal plankton succession during the growing season is readily observable and largely driven autogenically. We used a long-term dataset from large, deep Lake Constance comprising biomasses, auto- and heterotrophic production, food quality, functional diversity, and mass-balanced food webs of the energy and nutrient flows between functional guilds of plankton and partly fish. Extracting population- and system-level indices from this dataset, we tested current hypotheses about the directionality of successional progress which are rooted in ecosystem theory, the metabolic theory of ecology, quantitative food web theory, thermodynamics, and information theory. Our results indicate that successional progress in Lake Constance is quantifiable, passing through predictable stages. Mean body mass, functional diversity, predator-prey weight ratios, trophic positions, system residence times of carbon and nutrients, and the complexity of the energy flow patterns increased during succession. In contrast, both the mass-specific metabolic activity and the system export decreased, while the succession rate exhibited a bimodal pattern. The weighted connectance introduced here represents a suitable index for assessing the evenness and interconnectedness of energy flows during succession. Diverging from earlier predictions, ascendency and eco-exergy did not increase during succession. Linking aspects of functional diversity to metabolic theory and food web complexity, we reconcile previously disjoint bodies of ecological theory to form a complete picture of successional progress within a pelagic food web. This comprehensive synthesis may be used as a benchmark for quantifying successional progress in other ecosystems.
- ItemPolymer Brushes under High Load(San Francisco, CA : Public Library of Science, 2013) Balko, S.M.; Kreer, T.; Costanzo, P.J.; Patten, T.E.; Johner, A.; Kuhl, T.L.; Marques, C.M.Polymer coatings are frequently used to provide repulsive forces between surfaces in solution. After 25 years of design and study, a quantitative model to explain and predict repulsion under strong compression is still lacking. Here, we combine experiments, simulations, and theory to study polymer coatings under high loads and demonstrate a validated model for the repulsive forces, proposing that this universal behavior can be predicted from the polymer solution properties.