Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest.

Nanomaterial-based aptasensors serve as useful instruments for detecting small biological entities. This work utilizes data gathered from three electrochemical aptamer-based sensors varying in receptors, analytes of interest, and lengths of signals. Our ultimate objective was the automatic detection and quantification of target analytes from a segment of the signal recorded by these sensors.

Photocaging of Pyridinylimidazole-Based Covalent JNK3 Inhibitors Affords Spatiotemporal Control of the Binding Affinity in Live Cells.

The concept of photocaging represents a promising approach to acquire spatiotemporal control over molecular bioactivity. To apply this strategy to pyridinylimidazole-based covalent JNK3 inhibitors, we used acrylamido--(4-((4-(4-(4-fluorophenyl)-1-methyl-2-(methylthio)-1-imidazol-5-yl)pyridin-2-yl)amino)phenyl)benzamide () as a lead compound to design novel covalent inhibitors of JNK3 by modifying the amide bond moiety in the linker. The newly synthesized inhibitors demonstrated IC values in the low double-digit nanomolar range in a radiometric kinase assay.

Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks.

Anomaly detection is a significant task in sensors' signal processing since interpreting an abnormal signal can lead to making a high-risk decision in terms of sensors' applications. Deep learning algorithms are effective tools for anomaly detection due to their capability to address imbalanced datasets. In this study, we took a semi-supervised learning approach, utilizing normal data for training the deep learning neural networks, in order to address the diverse and unknown features of anomalies.

Fiber-based food packaging materials in view of bacterial growth and survival capacities.

Understanding interactions of bacteria with fiber-based packaging materials is fundamental for appropriate food packaging. We propose a laboratory model to evaluate microbial growth and survival in liquid media solely consisting of packaging materials with different fiber types. We evaluated food contaminating species (, , ), two packaging material isolates and bacterial endospores for their growth abilities.

Predicting Analyte Concentrations from Electrochemical Aptasensor Signals Using LSTM Recurrent Networks.

Nanomaterial-based aptasensors are useful devices capable of detecting small biological species. Determining suitable signal processing methods can improve the identification and quantification of target analytes detected by the biosensor and consequently improve the biosensor's performance. In this work, we propose a data augmentation method to overcome the insufficient amount of available original data and long short-term memory (LSTM) to automatically predict the analyte concentration from part of a signal registered by three electrochemical aptasensors, with differences in bioreceptors, analytes, and the signals' lengths for specific concentrations.