Label-Free Phenotypic Profiling Identified D-Luciferin as a GPR35 Agonist

Fluorescent and luminescent probes are essential to both in vitro molecular assays and in vivo imaging techniques, and have been extensively used to measure biological function. However, little is known about the biological activity, thus potential interferences with the assay results, of these probe molecules. Here we show that D-luciferin, one of the most widely used bioluminescence substrates, is a partial agonist for G protein-coupled receptor-35 (GPR35). Label-free phenotypic profiling using dynamic mass redistribution (DMR) assays showed that D-luciferin led to a DMR signal in native HT-29 cells, whose characteristics are similar to those induced by known GPR35 agonists including zaprinast and pamoic acid. DMR assays further showed that D-luciferin is a partial agonist competitive to several known GPR35 agonists and antagonists. D-luciferin was found to cause the phosphorylation of ERK that was suppressed by known GPR35 antagonists, and also result in β-arrestin translocation signal but with low efficacy. These results not only suggest that D-luciferin is a partial agonist of GPR35, but also will evoke careful interpretation of biological data obtained using molecular and in vivo imaging assays when these probe molecules are used

A Novel Modification Method of Stainless-steel Electrode for Sulfur Preparation by Reduction of Sulfur Dioxide

A novel modification method of stainless-steel electrode for reduction of sulfur dioxide to prepare sulfur was reported. Sulfur dioxide can spontaneously react with modified stainless-steel electrode at a rate of 12.77 mA/cm2 to produce sulfur under acidic conditions without additional energy. Evidence was found for the destruction of passive film on the stainless-steel surface in the process of modification which leads to the open circuit potential of stainless-steel electrode shift negatively to the reduction potential range of sulfur dioxide. When modified stainless steel was used as cathode in electrolysis, the contributions of impressed current and redox reaction made the sulfur yield up to 88% within 3 hours

Aberration Correction and Speckle Noise Reduction in Image Minified Lensless Holographic Projection Based on Digital Micro-Mirror Device

Lensless holographic projection technology allows for removing of the projection lens and simplifies the optical projection system. It has great potential to be applied in the fields of three-dimensional printing, integrated circuit fabrication, and display. In this article, for image minified lensless holographic projection based on digital micro-mirror device (DMD), we examine the aberration that arises from the use of the DMD in the presence of oblique converging spherical wave illumination. To correct this aberration, we employ a diagonal compression technique on the target pattern. Additionally, we address the issue of speckle noise by relaxing the amplitude constraint in the non-signal domain, which is generated by our proposed aberration correction method. Importantly, this relaxation is achieved without reducing the size of the valid image. Our experimental results demonstrate the successful reconstruction of high-quality images in a lensless holographic projection system

Correction: Wu et al. Full-Color See-Through Three-Dimensional Display Method Based on Volume Holography. Sensors 2021, 21, 2698

The authors make the following corrections to the published paper [...

Optical Encryption Based on Computer Generated Holograms in Photopolymer

An optical encryption method based on computer generated holograms printing of photopolymer is presented. Fraunhofer diffraction is performed based on the Gerchberg-Saxton algorithm, and a hologram of the Advanced Encryption Standard encrypted Quick Response code is generated to record the ciphertext. The holograms of the key and the three-dimensional image are generated by the angular spectrum diffraction algorithm. The experimental results show that large-size encrypted Quick Response (QR) code and miniature keys can be printed in photopolymers, which has good application prospects in optical encryption. This method has the advantages of high-density storage, high speed, large fault tolerance, and anti-peeping

EEG-Based Evaluation of Aesthetic Experience Using BiLSTM Network

Evaluation of aesthetic design fulfills a pivotal function in product development, which urges for an efficacious objective method to measure customers&#39; experience. The stability and effectiveness of electroencephalography (EEG) make it a suitable tool for aesthetic experience measurement. Nevertheless, existing studies have several limitations, especially regarding the stimuli and the algorithm. The potential of an EEG-based deep learning model has not been verified in pinpointing subtle differences in physical product aesthetics. To fill the research gap in this issue, we recorded EEG signals in real-life scenarios when participants were presented with different types of physical smartphones, and asked participants to rate them from four dimensions of aesthetic experience (arousal, valence, likeness, and aesthetic evaluation). Then, the time-frequency data were fed into a spatial feature extraction network and an attention-based bidirectional long short-term memory (BiLSTM) optimized by the cross-entropy loss function. The result showed that at 16s window size, the four outcome models yielded the best joint recognition performance of aesthetic experience with an average accuracy of over 85% (arousal: 88.10%, valence: 87.97%, likeness: 85.99%, and aesthetic evaluation: 87.23%). It provides an objective cross-subject recognition method with multi-faceted evaluation results of aesthetic experience. Additionally, we verified the ability of EEG as a reliable and informative resource in terms of aesthetic experience evaluation, even with subtle differences. More practically, a future direction of incorporating EEG signals into subjective product aesthetics measurement could be given more credit.</p

EEG-Based Evaluation of Aesthetic Experience Using BiLSTM Network

Evaluation of aesthetic design fulfills a pivotal function in product development, which urges for an efficacious objective method to measure customers’ experience. The stability and effectiveness of electroencephalography (EEG) make it a suitable tool for aesthetic experience measurement. Nevertheless, existing studies have several limitations, especially regarding the stimuli and the algorithm. The potential of an EEG-based deep learning model has not been verified in pinpointing subtle differences in physical product aesthetics. To fill the research gap in this issue, we recorded EEG signals in real-life scenarios when participants were presented with different types of physical smartphones, and asked participants to rate them from four dimensions of aesthetic experience (arousal, valence, likeness, and aesthetic evaluation). Then, the time–frequency data were fed into a spatial feature extraction network and an attention-based bidirectional long short-term memory (BiLSTM) optimized by the cross-entropy loss function. The result showed that at 16s window size, the four outcome models yielded the best joint recognition performance of aesthetic experience with an average accuracy of over 85% (arousal: 88.10%, valence: 87.97%, likeness: 85.99%, and aesthetic evaluation: 87.23%). It provides an objective cross-subject recognition method with multi-faceted evaluation results of aesthetic experience. Additionally, we verified the ability of EEG as a reliable and informative resource in terms of aesthetic experience evaluation, even with subtle differences. More practically, a future direction of incorporating EEG signals into subjective product aesthetics measurement could be given more credit.</p