Proposal of a new method to measure FRET quantitatively in living or fixed biomedical specimens on a laser microscope

Förster Resonance Energy Transfer , abbreviated FRET , is a fluorescence phenomenon, which can be used to study and map co-localizations and dynamics of co-localizations at nanometer precision on a light microscope. FRET has been described as a spectroscopic ruler . The efficiency of the radiationless energy transfer from an excited chromophore, the donor , to another chromophore, the acceptor , the excitation energy of which approximately matches the energy to be released by the donor, is dependent on the sixth power of the mutual distance between the two molecules in space. We propose a new, non-destructive technique for measuring FRET quantitatively and at high spatial and temporal resolution on a laser scanning microscope: Two laser beams of wavelengths suitable for the mutually exclusive excitation of the donor and the acceptor, the donor beam and the acceptor beam , respectively, are intensity modulated by means of two electro optical modulators (EOM). The modulation patterns are rectangular at duty cycle ½. The modulation frequencies differ slightly. The acceptor beam is saturating the acceptor so that it cannot accept energy from the donor. The saturation is modulated in the same way as the acceptor beam. Since the donor beam also is modulated, though at a frequency slightly different from that of the acceptor beam, the intensity of the released donor fluorescence is modulated with the beat frequency of the frequencies of the two laser beam modulations and can be detected and interpreted in quantitative terms by means of a lock in amplifier. Copyright 2011 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited

Neural Networks for Constitutive Modeling -- From Universal Function Approximators to Advanced Models and the Integration of Physics

Analyzing and modeling the constitutive behavior of materials is a core area in materials sciences and a prerequisite for conducting numerical simulations in which the material behavior plays a central role. Constitutive models have been developed since the beginning of the 19th century and are still under constant development. Besides physics-motivated and phenomenological models, during the last decades, the field of constitutive modeling was enriched by the development of machine learning-based constitutive models, especially by using neural networks. The latter is the focus of the present review, which aims to give an overview of neural networks-based constitutive models from a methodical perspective. The review summarizes and compares numerous conceptually different neural networks-based approaches for constitutive modeling including neural networks used as universal function approximators, advanced neural network models and neural network approaches with integrated physical knowledge. The upcoming of these methods is in-turn closely related to advances in the area of computer sciences, what further adds a chronological aspect to this review. We conclude this review paper with important challenges in the field of learning constitutive relations that need to be tackled in the near future

Effects of Purified Recombinant Neural and Muscle Agrin on Skeletal Muscle Fibers in Vivo

Aggregation of acetylcholine receptors (AChRs) in muscle fibers by nerve-derived agrin plays a key role in the formation of neuromuscular junctions. So far, the effects of agrin on muscle fibers have been studied in culture systems, transgenic animals, and in animals injected with agrin–cDNA constructs. We have applied purified recombinant chick neural and muscle agrin to rat soleus muscle in vivo and obtained the following results. Both neural and muscle agrin bind uniformly to the surface of innervated and denervated muscle fibers along their entire length. Neural agrin causes a dose-dependent appearance of AChR aggregates, which persist ≥7 wk after a single application. Muscle agrin does not cluster AChRs and at 10 times the concentration of neural agrin does not reduce binding or AChR-aggregating activity of neural agrin. Electrical muscle activity affects the stability of agrin binding and the number, size, and spatial distribution of the neural agrin–induced AChR aggregates. Injected agrin is recovered from the muscles together with laminin and both proteins coimmunoprecipitate, indicating that agrin binds to laminin in vivo. Thus, the present approach provides a novel, simple, and efficient method for studying the effects of agrin on muscle under controlled conditions in vivo

A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization

The optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed to manufacture workpieces with targeted material microstructures. These microstructures are defined by the material properties of interest and identifying them is a question of materials design. In the present paper, we addresse this issue and introduce a generic multi-task learning-based optimization approach. The approach enables the identification of sets of highly diverse microstructures for given desired properties and corresponding tolerances. Basically, the approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task learning with siamese neural networks. The resulting model (1) relates microstructures and properties, (2) estimates the likelihood of a microstructure of being producible, and (3) performs a distance preserving microstructure feature extraction in order to generate a lower dimensional latent feature space to enable efficient optimization. The proposed approach is applied on a crystallographic texture optimization problem for rolled steel sheets given desired properties

Institute of Ion Beam Physics and Materials Research: Annual Report 2002

Summary of the scientific activities of the institute in 2002 including selected highlight reports, short research contributions and an extended statistics overview

Deep Reinforcement Learning Methods for Structure-Guided Processing Path Optimization

A major goal of materials design is to find material structures with desired properties and in a second step to find a processing path to reach one of these structures. In this paper, we propose and investigate a deep reinforcement learning approach for the optimization of processing paths. The goal is to find optimal processing paths in the material structure space that lead to target-structures, which have been identified beforehand to result in desired material properties. There exists a target set containing one or multiple different structures. Our proposed methods can find an optimal path from a start structure to a single target structure, or optimize the processing paths to one of the equivalent target-structures in the set. In the latter case, the algorithm learns during processing to simultaneously identify the best reachable target structure and the optimal path to it. The proposed methods belong to the family of model-free deep reinforcement learning algorithms. They are guided by structure representations as features of the process state and by a reward signal, which is formulated based on a distance function in the structure space. Model-free reinforcement learning algorithms learn through trial and error while interacting with the process. Thereby, they are not restricted to information from a priori sampled processing data and are able to adapt to the specific process. The optimization itself is model-free and does not require any prior knowledge about the process itself. We instantiate and evaluate the proposed methods by optimizing paths of a generic metal forming process. We show the ability of both methods to find processing paths leading close to target structures and the ability of the extended method to identify target-structures that can be reached effectively and efficiently and to focus on these targets for sample efficient processing path optimization