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- Thermography (8)
- Catastrophic optical damage (4)
- Fluorescence (4)
- High-power diode lasers (4)
- Non-destructive testing (4)
- Semiconductor laser (4)
- Laser thermography (3)
- Nitrotyrosine (3)
- ABC transporter (2)
- Active thermal imaging (2)
Organisationseinheit der BAM
Probleme der Sprengtechnik in Ghana, dargestellt am Beispiel einer Gold- und einer Manganmine
(1983)
The allergenic and inflammatory potential of proteins can be enhanced by chemical modification upon exposure to atmospheric or physiological oxidants. The molecular mechanisms and kinetics of such modifications, however, have not yet been fully resolved. We investigated the oligomerization and nitration of the grass pollen allergen Phl p 5 by ozone (O3), nitrogen dioxide (NO2), and peroxynitrite (ONOO–). Within several hours of exposure to atmospherically relevant concentration levels of O3 and NO2, up to 50% of Phl p 5 were converted into protein oligomers, likely by formation of dityrosine cross-links. Assuming that tyrosine residues are the preferential site of nitration, up to 10% of the 12 tyrosine residues per protein monomer were nitrated. For the reaction with peroxynitrite, the largest oligomer mass fractions (up to 50%) were found for equimolar concentrations of peroxynitrite over tyrosine residues. With excess peroxynitrite, the nitration degrees increased up to 40% whereas the oligomer mass fractions decreased to 20%. Our results suggest that protein oligomerization and nitration are competing processes, which is consistent with a two-step mechanism involving a reactive oxygen intermediate (ROI), as observed for other proteins. The modified proteins can promote pro-inflammatory cellular signaling that may contribute to chronic inflammation and allergies in response to air pollution.
Environmental pollutants like fine particulate matter can cause adverse health effects through oxidative stress and inflammation. Reactive oxygen and nitrogen species (ROS/RNS) such as peroxynitrite can chemically modify proteins, but the effects of such modifications on the immune system and human health are not well understood. In the course of inflammatory processes, the Toll-like receptor 4 (TLR4) can sense damage-associated molecular patterns (DAMPs). Here, we investigate how the TLR4 response and pro-inflammatory potential of the proteinous DAMPs α-Synuclein (α-Syn), heat shock protein 60 (HSP60), and high-mobility-group box 1 protein (HMGB1), which are relevant in neurodegenerative and cardiovascular diseases, changes upon chemical modification with peroxynitrite.
For the peroxynitrite-modified proteins, we found a strongly enhanced activation of TLR4 and the pro-inflammatory transcription factor NF-κB in stable reporter cell lines as well as increased mRNA expression and secretion of the pro-inflammatory cytokines TNF-α, IL-1β, and IL-8 in human monocytes (THP-1). This enhanced activation of innate immunity via TLR4 is mediated by covalent chemical modifications of the studied DAMPs.
Our results show that proteinous DAMPs modified by peroxynitrite more potently amplify inflammation via TLR4 activation than the native DAMPs, and provide first evidence that such modifications can directly enhance innate immune responses via a defined receptor. These findings suggest that environmental pollutants and related ROS/RNS may play a role in promoting acute and chronic inflammatory disorders by structurally modifying the body's own DAMPs. This may have important consequences for chronic neurodegenerative, cardiovascular or gastrointestinal diseases that are prevalent in modern societies, and calls for action, to improve air quality and climate in the Anthropocene.
Fast and accurate determination of the protein content of a sample is an important and non-trivial task of many biochemical, biomedical, food chemical, pharmaceutical, and environmental research activities. Different methods of total protein determination are used for a wide range of proteins with highly variable properties in complex matrices. These methods usually work reasonably well for proteins under controlled conditions, but the results for non-standard and complex samples are often questionable. Here, we compare new and well-established methods, including traditional amino acid analysis (AAA), aromatic amino acid analysis (AAAA) based on the amino acids phenylalanine and tyrosine, reversed-phase liquid chromatography of intact proteins with UV absorbance measurements at 220 and 280 nm (LC-220, LC-280), and colorimetric assays like Coomassie Blue G-250 dye-binding assay (Bradford) and bicinchoninic acid (BCA) assay. We investigated different samples, including proteins with challenging properties, chemical modifications, mixtures, and complex matrices like air particulate matter and pollen extracts. All methods yielded accurate and precise results for the protein and matrix used for calibration. AAA, AAAA with fluorescence detection, and the LC-220 method yielded robust results even under more challenging conditions (variable analytes and matrices). These methods turned out to be well-suited for reliable determination of the protein content in a wide range of samples, such as air particulate matter and pollen.
Acoustic levitation is used as a newtool to study concentration-dependent processes influorescence
spectroscopy. With this technique, small amounts of liquid and solid samples can be measured
without the need for sample supports or containers, which often limits signal acquisition and
can even alter sample properties due to interactions with the support material. We demonstrate
that, because of the small sample volume, fluorescence measurements at high concentrations
of an organic dye are possible without the limitation of inner-filter effects, which hamper such
experiments in conventional, cuvette-based measurements. Furthermore, we show that acoustic
levitation of liquid samples provides an experimentally simple way to study distance-dependent
fluorescence modulations in semiconductor nanocrystals. The evaporation of the solvent during
levitation leads to a continuous increase of solute concentration and can easily be monitored by
laser-induced fluorescence.
Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2022)
Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. We show the benefits of using a learned block iterative shrinkage thresholding algorithm (LBISTA) that is able to learn the choice of regularization parameters, without the need to manually select them. In addition, LBISTA enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present LBISTA and compare it with state-of-the-art block iterative shrinkage thresholding using synthetically generated and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations. Thus, this allows us to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super-resolution imaging.
The early stages of catastrophic optical damage (COD) in 808 nm emitting diode lasers are mapped by simultaneously monitoring the optical emission with a 1 ns time resolution and deriving the device temperature from thermal images. COD occurs in highly localized damage regions on a 30 to 400 ns time scale which is determined by the accumulation of excess energy absorbed from the optical output. We identify regimes in which COD is avoided by the proper choice of operation parameters.
Photothermal radiometry with an infrared camera allows the contactless temperature measurement of multiple surface pixels simultaneously. A short light pulse heats the sample. The heat propagates through the sample by diffusion and the corresponding temperature evolution is measured at the sample’s surface by an infrared camera. The main drawback in radiometric imaging is the loss of the spatial resolution with increasing depth due to heat diffusion, which results in blurred images for deeper lying structures. We circumvent this information loss due to the diffusion process by using blind structured illumination, combined with a non-linear joint sparsity reconstruction algorithm. The structured illumination is realized by parallel laser lines from a vertical-cavity surface-emitting laser (VCSEL) array controlled by a random binary pattern generator. By using 150 different patterns of structured illumination and our iterative joint sparsity algorithm, it was possible to resolve 1 mm thick lines at a distance down to 0.5 mm, which results in a resolution enhancement of approximately a factor of four compared to the resolution of 5.9 mm for homogenous illuminated thermographic reconstruction.
Classification of Spot-Welded Joints in Laser Thermography Data Using Convolutional Neural Networks
(2021)
Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of the test material, which drain conventional approaches to its limits. In this article, we propose an approach for quality inspection of spot weldings using images from laser thermography data. We propose data preparation approaches based on the underlying physics of spot-welded joints, heated with pulsed laser thermography by analyzing the intensity over time and derive dedicated data filters to generate training datasets. Subsequently, we utilize convolutional neural networks to classify weld quality and compare the performance of different models against each other. We achieve competitive results in terms of classifying the different welding quality classes compared to traditional approaches, reaching an accuracy of more than 95 percent. Finally, we explore the effect of different augmentation methods.