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Current Amplification Driven by Reversible Redox Cycling in a Thin-Layer Reactor Using Boron-Doped Diamond Electrodes
A neuromorphic electronic artist for robotic painting
Abstract Recent advances in deep learning have sparked interest in AI-generated art, including robot-assisted painting. Traditional painting machines use static images and offline processing without considering the dynamic nature of painting. Neuromorphic cameras, which capture light intensity changes through asynchronous events, and mixed-signal neuromorphic processors, which implement biologically plausible spiking neural networks, offer a promising alternative. In this work, we present a robotic painting system comprising a 6-DOF robotic arm, event-based input from a Dynamic Vision Sensor (DVS) camera and a neuromorphic processor to produce dynamic brushstrokes, and tactile feedback from a force-torque sensor to compensate for brush deformation. The system receives DVS events representing the desired brushstroke trajectory and maps these events onto the processor’s neurons to compute joint velocities in close-loop. The variability in the input’s noisy event streams and the processor’s analog circuits reproduces the heterogeneity of human brushstrokes. Tested in a real-world setting, the system successfully generated diverse physical brushstrokes. This network marks a first step towards a fully spiking robotic controller with ultra-low latency responsiveness, applicable to any robotic task requiring real-time closed-loop adaptive control.
A Supramolecular Nanosheet Assembled from Carpyridines and Water
Location-specific analysis of clinicopathological characteristics and long-term prognosis of primary gastrointestinal diffuse large B-cell lymphoma
Structure Flexibility Enabled by Surface High-Concentration Titanium Doping for Durable Lithium-Ion Battery Cathodes
Urban tourism management based on artificial neural networks analysis and data mining
Modulating Adsorption Kinetics in a 3D-Interconnected Nanocavity Framework with Narrow Apertures for Enhanced Propylene Separation
Development of a microarray based telomerase binding assay reveals unusual binding of a cytochalasin derivative
Abstract Telomerase reverse transcriptase is crucial for cellular development, regeneration, and disease processes. Strategies for both telomerase activation and inhibition have been intensively explored in the past decades. In this study, we present a highly miniaturized, microarray-based assay designed to identify compounds that target telomerase. The active protein was either recombinantly derived from E. coli or obtained from cell lysates of human cancer cell lines and mouse cells expressing telomerase. Using non-contact spotter technology, these lysates or purified telomerase proteins were transferred onto nitrocellulose pads on a microarray. A telomerase binding assay, incorporating fluorescent labelled primer, the template RNA telomerase RNA component, and fluorescent labelled nucleotide as a primer cocktail, was conducted in incubation chambers. Binding of this primer cocktail to spotted telomerase from cell lysates, and from purified recombinant telomerase resulted in an increase in bound fluorescence. Epigallocatechin gallate, a known telomerase inhibitor, reduced this fluorescence in a dose-dependent manner with micromolar affinity. The inhibitory effect on telomerase was validated by thermophoresis and its impact on activity was shown in a Telomerase Repeated Amplification Protocol (TRAP) assay. Additional screening identified that 4’-iodo cytochalasin H inhibits primer cocktail binding to cell lysate in the low micromolar range. Molecular modeling and docking pinpointed a putative binding site for epigallocatechin gallate in a human telomerase homologue, and a putative binding site for 4’-iodo cytochalasin H. In summary, we developed an assay that can be employed to discover new telomerase inhibitors and that will serve as a valuable tool for screening of activators.
Dual-State Ambipolar Charge Transport in Antiaromatic [4]cyclodibenzopentalene Single-Molecule Nanohoops
Comparative analysis of occlusal stresses on bone around natural teeth, platform matched and switched implants abutments: a finite element study
Nanoscale Origin of the Soft-to-Hard Short-Circuit Transition in Inorganic Solid-State Electrolytes
Mutation adaptive cuckoo search hybridized naked mole rat algorithm for industrial engineering problems
Abstract Cuckoo Search (CS) is a popular algorithm used to solve numerous challenging problems. In the present work, a novel variant of CS is presented to eliminate its shortcomings. The proposed algorithm is hybridized with the naked mole rat algorithm (NMRA) to enhance the exploitative behavior of CS, and is called Mutated Adaptive Cuckoo Search Algorithm (MaCN). This new algorithm has self-adaptive properties and its key feature is to divide the solutions into multiple sections, which are often called sub-swarms. In addition, a bare-bones search mechanism is also added to enhance exploration. The use of adaptive inertia weights helps optimize the switching probability, an important CS parameter that helps to achieve a balanced operation. The proposed MaCN algorithm is tested on CEC 2005 and CEC 2014 benchmark problems. Comparative studies showed that MaCN delivers promising results in solving CEC competition benchmark problems compared to JADE, success history-based adaptive DE (SHADE), LSHADE-SPACMA and self-adaptive DE (SaDE), among others. In addition to numerical benchmarks, MaCN is used to solve the industrial engineering frame structure and a comparison with hybridization of particle swarm with passive congregation (PSOPC), shuffled frog leaping algorithm hybrid with invasive weed optimization (SFLAIWO), particle swarm ant colony optimization (PSOACO), early strategy with DE (ES-DE), and others show its superiority. In addition, the Wilcoxon rankum and the Freidmann test statistically prove the significance of the proposed MaCN algorithm. MaCN was found to score first rank for the benchmarks. The application of the MaCN algorithm to solve the design problems of the suggests that the best new results are obtained for all test cases.
Artificial intelligence enhanced electrochemical immunoassay for staphylococcal enterotoxin B
One-Step Radical-Induced Synthesis of Graft Copolymers for Effective Compatibilization of Polyethylene and Polypropylene
Radiosensitizing effects of Withaferin A in gastric cancer cells via autophagy Inhibition and mitochondrial disruption
Algorithm and ninhydrin method allow for measurement of the postprandial appearance of peptides in blood
Abstract The recognition of amine groups by ninhydrin, along with a simple mathematical algorithm, showed that di- and tripeptides derived from dietary protein are the major end products of protein digestion entering the blood postprandially. There are thousands of oligopeptides appearing in the gut during protein digestion. However, the presented study on a pig model clearly shows that peptides longer than tri-amino acid peptides do not appear postprandially in the blood in nutritional amounts. We hypothesize that the measurement of postprandial free amino acids, di- and tripeptides and proportions between these components could be a useful, desirable tool both in laboratory and clinical practice, which could be used to determine the metabolic importance of protein digestion end products in health and disease.