Synthesis of bifunctional chelating agents based on mono and diphosphonic derivatives of diethylenetriaminepentaacetic acid

Bifunctional chelating agents (BFCAs) are small molecules containing a chelating unit, able to strongly coordinate a metal ion, and a reactive functional group, devised to form a stable covalent bond with another molecule. BFCAs are widely employed since their conjugation to a suitable biomolecule (e.g., a peptide or an antibody) allows the synthesis of diagnostic or therapeutic agents that specifically target diseased tissue with metals or radiometals. For this reason, BFCAs find application in diagnostic imaging, molecular imaging, and radiotherapy of cancer. The synthesis of new BFCAs based on a diethylenetriaminepentaacetic acid (DTPA) structure in which one or two carboxylic groups are replaced with phosphonic units is described. The phosphonic group, aside from being a classical isostere of the carboxylic acid in coordination chemistry, allows to modulate the physico-chemical properties of the ligands and of the corresponding complexes.     

Dynamical analysis and anti-oscillation-based adaptive control of the FO arch MEMS with optimality

Nonlinear Dynamics - This paper mainly investigates dynamical analysis and anti-oscillation-based adaptive control issues of the fractional-order (FO) arch microelectromechanical system (MEMS) with...     

Dietary Nutritional Information Autonomous Perception Method Based on Machine Vision in Smart Homes

In order to automatically perceive the user’s dietary nutritional information in the smart home environment, this paper proposes a dietary nutritional information autonomous perception method based on machine vision in smart homes. Firstly, we proposed a food-recognition algorithm based on YOLOv5 to monitor the user’s dietary intake using the social robot. Secondly, in order to obtain the nutritional composition of the user’s dietary intake, we calibrated the weight of food ingredients and designed the method for the calculation of food nutritional composition; then, we proposed a dietary nutritional information autonomous perception method based on machine vision (DNPM) that supports the quantitative analysis of nutritional composition. Finally, the proposed algorithm was tested on the self-expanded dataset CFNet-34 based on the Chinese food dataset ChineseFoodNet. The test results show that the average recognition accuracy of the food-recognition algorithm based on YOLOv5 is 89.7%, showing good accuracy and robustness. According to the performance test results of the dietary nutritional information autonomous perception system in smart homes, the average nutritional composition perception accuracy of the system was 90.1%, the response time was less than 6 ms, and the speed was higher than 18 fps, showing excellent robustness and nutritional composition perception performance.