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Your search keyword '"Frederic Schulz"' showing total 3 results
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3 results on '"Frederic Schulz"'

1. Branch point strength controls species-specific CAMK2B alternative splicing and regulates LTP

Author

Andreas Franz, A Ioana Weber, Marco Preußner, Nicole Dimos, Alexander Stumpf, Yanlong Ji, Laura Moreno-Velasquez, Anne Voigt, Frederic Schulz, Alexander Neumann, Benno Kuropka, Ralf Kühn, Henning Urlaub, Dietmar Schmitz, Markus C Wahl, and Florian Heyd

Subjects
Cancer Research, Ecology, Health, Toxicology and Mutagenesis, species-specific CAMK2B, 500 Naturwissenschaften und Mathematik::570 Biowissenschaften, Biologie::570 Biowissenschaften, Biologie, Plant Science, Technology Platforms, Function and Dysfunction of the Nervous System, Biochemistry, Genetics and Molecular Biology (miscellaneous)
Abstract

Regulation and functionality of species-specific alternative splicing has remained enigmatic to the present date. Calcium/calmodulin-dependent protein kinase IIβ (CaMKIIβ) is expressed in several splice variants and plays a key role in learning and memory. Here, we identify and characterize several primate-specificCAMK2Bsplice isoforms, which show altered kinetic properties and changes in substrate specificity. Furthermore, we demonstrate that primate-specificCAMK2Balternative splicing is achieved through branch point weakening during evolution. We show that reducing branch point and splice site strengths during evolution globally renders constitutive exons alternative, thus providing novel mechanistic insight intocis-directed species-specific alternative splicing regulation. Using CRISPR/Cas9, we introduce a weaker, human branch point sequence into the mouse genome, resulting in strongly alteredCamk2bsplicing in the brains of mutant mice. We observe a strong impairment of long-term potentiation in CA3-CA1 synapses of mutant mice, thus connecting branch point–controlledCAMK2Balternative splicing with a fundamental function in learning and memory.

Published
2023

2. Branch point evolution controls species-specific alternative splicing and regulates long term potentiation

Author

Andreas Franz, A. Ioana Weber, Marco Preußner, Nicole Dimos, Alexander Stumpf, Yanlong Ji, Laura Moreno-Velasquez, Anne Voigt, Frederic Schulz, Alexander Neumann, Benno Kuropka, Ralf Kühn, Henning Urlaub, Dietmar Schmitz, Markus C. Wahl, and Florian Heyd

Subjects
Cancer Research, Technology Platforms, Function and Dysfunction of the Nervous System
Abstract

Regulation and functionality of species-specific alternative splicing has remained enigmatic to the present date. Calcium/calmodulin-dependent protein kinase IIβ (CaMKIIβ) is expressed in several splice variants and plays a key role in learning and memory. Here, we identify and characterize several primate-specific CAMK2B splice isoforms, which show altered kinetic properties and changes in substrate specificity. Furthermore, we demonstrate that primate-specific Camk2β alternative splicing is achieved through branch point weakening during evolution. We show that reducing branch point and splice site strengths during evolution globally renders constitutive exons alternative, thus providing a paradigm for cis-directed species-specific alternative splicing regulation. Using CRISPR/Cas9 we introduced a weaker human branch point into the mouse genome, resulting in human-like CAMK2B splicing in the brain of mutant mice. We observe a strong impairment of long-term potentiation in CA3-CA1 synapses of mutant mice, thus connecting branch point-controlled, species-specific alternative splicing with a fundamental function in learning and memory.

Published
2022
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3. A Spatio-Temporal Ensemble Deep Learning Architecture for Real-Time Defect Detection during Laser Welding on Low Power Embedded Computing Boards

Author

Christian Knaak, Jakob von Eßen, Moritz Kröger, Frederic Schulze, Peter Abels, and Arnold Gillner

Subjects
real-time process monitoring, recurrent neural network, high-speed infrared imaging, convolutional neural network, lack of fusion (false friends), feature importance, Chemical technology, TP1-1185
Abstract

In modern production environments, advanced and intelligent process monitoring strategies are required to enable an unambiguous diagnosis of the process situation and thus of the final component quality. In addition, the ability to recognize the current state of product quality in real-time is an important prerequisite for autonomous and self-improving manufacturing systems. To address these needs, this study investigates a novel ensemble deep learning architecture based on convolutional neural networks (CNN), gated recurrent units (GRU) combined with high-performance classification algorithms such as k-nearest neighbors (kNN) and support vector machines (SVM). The architecture uses spatio-temporal features extracted from infrared image sequences to locate critical welding defects including lack of fusion (false friends), sagging, lack of penetration, and geometric deviations of the weld seam. In order to evaluate the proposed architecture, this study investigates a comprehensive scheme based on classical machine learning methods using manual feature extraction and state-of-the-art deep learning algorithms. Optimal hyperparameters for each algorithm are determined by an extensive grid search. Additional work is conducted to investigate the significance of various geometrical, statistical and spatio-temporal features extracted from the keyhole and weld pool regions. The proposed method is finally validated on previously unknown welding trials, achieving the highest detection rates and the most robust weld defect recognition among all classification methods investigated in this work. Ultimately, the ensemble deep neural network is implemented and optimized to operate on low-power embedded computing devices with low latency (1.1 ms), demonstrating sufficient performance for real-time applications.

Published
2021
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