Benedikt K. Kaufmann
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Benedikt K. Kaufmann has a total of 31 co-authors. Here are the top co-authors by number of shared publications:
- Stefanie Sudhop
Center for Applied Tissue Engineering and Regenerative Medicine, Department of Applied Sciences and Mechatronics, Munich University of Applied Sciences, Lothstr. 34, 80335 Munich, Germany
4 shared publications - Hauke Clausen-Schaumann
Munich University of Applied Sciences
3 shared publications - Bastian Hartmann
Munich University of Applied Sciences
2 shared publications - Oliver Hayden
Technical University of Munich
2 shared publications - Yasemin A. Geiger
Heinz-Nixdorf-Chair of Biomedical Electronics, School of Computation, Information and Technology, Technical University of Munich, TranslaTUM, Einsteinstr. 25, 81675 Munich, Germany
2 shared publications - Andreia Silva1 shared publication
Latest Publications
- Open Access
- 27 October 2026
- Published by MDPI AG in Micromachines
- Vol. 17 (11) , 1231
Synthetic-to-Real 5D Pose Estimation for Acoustically Levitated Micro-Calibrators in Lab-on-a-Chip Devices
Established acoustofluidic lab-on-a-chip devices enable contactless, label-free trapping of cells, but do not provide a quantitative readout of a levitated sample’s orientation, restricting their analytical reach. We present a synthetic-to-real deep learning framework that addresses this gap by estimating the five-dimensional (5D) pose of a trident-shaped micro-calibrator co-trapped with the cells. Here, 5D pose denotes the two-dimensional (2D) bounding-box center in the image plane and the calibrator’s three-dimensional (3D) orientation. Printed by two-photon polymerization with 10, 15, and 20 m arms, the calibrator provides the rigid, asymmetric geometry that a translucent, deformable spheroid lacks. The framework cascades detection, foreground segmentation, and rotation regression, with the normalized bounding-box center as a positional auxiliary input, and optionally applies temporal smoothing; it is trained on domain-randomized renderings, eliminating manual 3D pose annotation. On 15,000 synthetic test images, it reached a mean geodesic rotation error of 0.82°. The ablation study showed that the positional input substantially improves rotation estimation and background removal further lowers the error, and identified segmentation, not detection, as the dominant upstream error source. Pseudo-label adaptation raised the expert-assessed correctness rate from 34.71% to 83.62% on 5281 real microscopy frames from which the pseudo-labels were drawn (adaptation performance) and from 2.48% to 29.34% on 242 held-out fluorescence frames with adherent cells (generalization performance). This demonstrates the feasibility of a calibrator-based orientation readout on an established acoustofluidic platform without hardware change.- 27 October 2026
- Published by MDPI AG in Micromachines
- Vol. 17 (11) , 1231
Open AccessSynthetic-to-Real 5D Pose Estimation for Acoustically Levitated Micro-Calibrators in Lab-on-a-Chip Devices
Established acoustofluidic lab-on-a-chip devices enable contactless, label-free trapping of cells, but do not provide a quantitative readout of a levitated sample’s orientation, restricting their analytical reach. We present a synthetic-to-real deep learning framework that addresses this gap by estimating the five-dimensional (5D) pose of a trident-shaped micro-calibrator co-trapped with the cells. Here, 5D pose denotes the two-dimensional (2D) bounding-box center in the image plane and the calibrator’s three-dimensional (3D) orientation. Printed by two-photon polymerization with 10, 15, and 20 m arms, the calibrator provides the rigid, asymmetric geometry that a translucent, deformable spheroid lacks. The framework cascades detection, foreground segmentation, and rotation regression, with the normalized bounding-box center as a positional auxiliary input, and optionally applies temporal smoothing; it is trained on domain-randomized renderings, eliminating manual 3D pose annotation. On 15,000 synthetic test images, it reached a mean geodesic rotation error of 0.82°. The ablation study showed that the positional input substantially improves rotation estimation and background removal further lowers the error, and identified segmentation, not detection, as the dominant upstream error source. Pseudo-label adaptation raised the expert-assessed correctness rate from 34.71% to 83.62% on 5281 real microscopy frames from which the pseudo-labels were drawn (adaptation performance) and from 2.48% to 29.34% on 242 held-out fluorescence frames with adherent cells (generalization performance). This demonstrates the feasibility of a calibrator-based orientation readout on an established acoustofluidic platform without hardware change. - Open Access
- 16 November 2025
- Published by Elsevier in Bioprinting
- Vol. 52
Tuning the Young’s modulus of gelatin methacryloyl hydrogels with masked stereolithography bioprinting
- 16 November 2025
- Published by Elsevier in Bioprinting
- Vol. 52
Open AccessTuning the Young’s modulus of gelatin methacryloyl hydrogels with masked stereolithography bioprinting
- Open Access
- 8 April 2022
- Published by American Association for the Advancement of Science (AAAS) in Science Advances
- Vol. 8 (14) , eabl9228
Synthetic cell–based materials extract positional information from morphogen gradients
Biomaterials composed of synthetic cells have the potential to adapt and differentiate guided by physicochemical environmental cues. Inspired by biological systems in development, which extract positional information (PI) from morphogen gradients in the presence of uncertainties, we here investigate how well synthetic cells can determine their position within a multicellular structure. To calculate PI, we created and analyzed a large number of synthetic cellular assemblies composed of emulsion droplets connected via lipid bilayer membranes. These droplets contained cell-free feedback gene circuits that responded to gradients of a genetic inducer acting as a morphogen. PI is found to be limited by gene expression noise and affected by the temporal evolution of the morphogen gradient and the cell-free expression system itself. The generation of PI can be rationalized by computational modeling of the system. We scale our approach using three-dimensional printing and demonstrate morphogen-based differentiation in larger tissue-like assemblies.
- 8 April 2022
- Published by American Association for the Advancement of Science (AAAS) in Science Advances
- Vol. 8 (14) , eabl9228
Open AccessSynthetic cell–based materials extract positional information from morphogen gradients
Biomaterials composed of synthetic cells have the potential to adapt and differentiate guided by physicochemical environmental cues. Inspired by biological systems in development, which extract positional information (PI) from morphogen gradients in the presence of uncertainties, we here investigate how well synthetic cells can determine their position within a multicellular structure. To calculate PI, we created and analyzed a large number of synthetic cellular assemblies composed of emulsion droplets connected via lipid bilayer membranes. These droplets contained cell-free feedback gene circuits that responded to gradients of a genetic inducer acting as a morphogen. PI is found to be limited by gene expression noise and affected by the temporal evolution of the morphogen gradient and the cell-free expression system itself. The generation of PI can be rationalized by computational modeling of the system. We scale our approach using three-dimensional printing and demonstrate morphogen-based differentiation in larger tissue-like assemblies.
- 5 June 2019
- Published by American Chemical Society (ACS) in ACS Nano
- Vol. 13 (6) , 7155-7165
Pilus-1 Backbone Protein RrgB of Streptococcus pneumoniae Binds Collagen I in a Force-Dependent Way
Attachment to host tissue is a prerequisite for successful host colonization and invasion of pathogens. Many pathogenic bacteria use surface appendices, called pili, to bind and firmly attach to host tissue surfaces. Although it has been speculated that the laterally positioned D3 domain of the pilus-1 backbone protein RrgB of Streptococcus Pneumoniae, may promote bacterial-host interaction, via adhesion to extracellular matrix molecules, such as collagen, earlier studies showed no affinity of RrgB to collagen I. Using atomic force microscopy based single molecule force spectroscopy combined with lateral force microscopy, we show that under mechanical load, RrgB in fact binds to human collagen I in a force dependent manner. We observe exceptionally strong interactions, with interaction forces reaching as much as 1500 pN, and we show that high force loading and shearing rates enhance and further strengthen the interaction. In addition, the affinity of RrgB to collagen I under mechanical load not only depends on the orientation of the D3 domain, but also on the orientation of the collagen fibrils, relative to the pulling direction. Both exceptionally high binding forces and force induced bond strengthening resemble the behavior of so-called catch bonds, which have recently been observed in bacterial adhesins, but have not been reported for multimeric backbone subunits of virulence related pili.
- 5 June 2019
- Published by American Chemical Society (ACS) in ACS Nano
- Vol. 13 (6) , 7155-7165
Pilus-1 Backbone Protein RrgB of Streptococcus pneumoniae Binds Collagen I in a Force-Dependent Way
Attachment to host tissue is a prerequisite for successful host colonization and invasion of pathogens. Many pathogenic bacteria use surface appendices, called pili, to bind and firmly attach to host tissue surfaces. Although it has been speculated that the laterally positioned D3 domain of the pilus-1 backbone protein RrgB of Streptococcus Pneumoniae, may promote bacterial-host interaction, via adhesion to extracellular matrix molecules, such as collagen, earlier studies showed no affinity of RrgB to collagen I. Using atomic force microscopy based single molecule force spectroscopy combined with lateral force microscopy, we show that under mechanical load, RrgB in fact binds to human collagen I in a force dependent manner. We observe exceptionally strong interactions, with interaction forces reaching as much as 1500 pN, and we show that high force loading and shearing rates enhance and further strengthen the interaction. In addition, the affinity of RrgB to collagen I under mechanical load not only depends on the orientation of the D3 domain, but also on the orientation of the collagen fibrils, relative to the pulling direction. Both exceptionally high binding forces and force induced bond strengthening resemble the behavior of so-called catch bonds, which have recently been observed in bacterial adhesins, but have not been reported for multimeric backbone subunits of virulence related pili.