High-Resolution Tandem Mass Spectrometry-Based Analysis of Model Lignin-Iron Complexes: Novel Pipeline and Complex Structures.

Understanding the chemical nature of soil organic carbon (SOC) with great potential to bind iron (Fe) minerals is critical for predicting the stability of SOC. Organic ligands of Fe are among the top candidates for SOCs able to strongly sorb on Fe minerals, but most of them are still molecularly uncharacterized. To shed insights into the chemical nature of organic ligands in soil and their fate, this study developed a protocol for identifying organic ligands using ultrahigh-performance liquid chromatography-high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) and metabolomic tools.

Deep-KEDI: Deep learning-based zigzag generative adversarial network for encryption and decryption of medical images.

Background: Medical imaging techniques have improved to the point where security has become a basic requirement for all applications to ensure data security and data transmission over the internet. However, clinical images hold personal and sensitive data related to the patients and their disclosure has a negative impact on their right to privacy as well as legal ramifications for hospitals.

Objective: In this research, a novel deep learning-based key generation network (Deep-KEDI) is designed to produce the secure key used for decrypting and encrypting medical images.

In silico studies and development of a protein-based electrochemical sensor for selective and sensitive detection of aflatoxin B1.

Proteins from different species have been docked with aflatoxin B1 (AFB1) and identified 3 proteins (prostaglandin-E(2)9-reductase from Oryctolagus uniculus, proto-oncogene serine/threonine-protein kinase Pim-1 and human immunoglobulin G (hIgG)) as potential candidates to develop an electrochemical sensor. Fluorescence spectroscopy experiments have confirmed the interaction of hIgG with AFB1 with an affinity constant of 4.6 × 10 M.