A Quick and Parallel Analytical Method Based on Quantum Dots Labeling for ToRCH-Related Antibodies

Quantum dot is a special kind of nanomaterial composed of periodic groups of II–VI, III–V or IV–VI materials. Their high quantum yield, broad absorption with narrow photoluminescence spectra and high resistance to photobleaching, make them become a promising labeling substance in biological analysis. Here, we report a quick and parallel analytical method based on quantum dots for ToRCH-related antibodies including Toxoplasma gondii, Rubella virus, Cytomegalovirus and Herpes simplex virus type 1 (HSV1) and 2 (HSV2). Firstly, we fabricated the microarrays with the five kinds of ToRCH-related antigens and used CdTe quantum dots to label secondary antibody and then analyzed 100 specimens of randomly selected clinical sera from obstetric outpatients. The currently prevalent enzyme-linked immunosorbent assay (ELISA) kits were considered as “golden standard” for comparison. The results show that the quantum dots labeling-based ToRCH microarrays have comparable sensitivity and specificity with ELISA. Besides, the microarrays hold distinct advantages over ELISA test format in detection time, cost, operation and signal stability. Validated by the clinical assay, our quantum dots-based ToRCH microarrays have great potential in the detection of ToRCH-related pathogens

A Sparse Optimization Method for Distributed Hydrology Information Monitoring System

The sparse optimization method is an effective technological approach to solve information detection and spectrum sensing in hydrology information monitoring sensor network and it is at the frontier domain of water data collection and transmission field in home and abroad. By combining sparse optimization with distributed hydrology information monitoring and based on the theoretical framework of sparse optimization of distributed water level monitoring, the paper puts forward the distributed sparse optimization monitoring method of water environment information, which expands the new application of sparse optimization in distributed network on one hand. On the other hand, it points out the reconstruction method of joint sparse signals based on the block coordinate descent method. Simulation experiment shows that the proposed method can converge quickly to the approximate optimal solution and has a good robustness for calculation error caused by inaccurate average and other uncertain factors in the network