Distance-based lateral flow biosensor for the quantitative detection of bacterial endotoxin

Bacterial endotoxin(a type of lipopolysaccharide,LPS)that acts as the strongest immune stimulant exhibits high toxicity to human health.The golden standard detection methods rely heavily on the use of a large amount of tachypleus amebocyte lysate(TAL)reagents,extracted from the unique blue blood of legally protected horseshoe crabs.Herein,a cost-effective distance-based lateral flow(D-LAF)sensor is demonstrated for the first time based on the coagulation cascade process of TAL induced by endotoxin,which causes the generation of gel-state TAL.The gelation process can increase the amount of trapped water molecules and shorten the lateral flow distance of the remaining free water on the pH paper.The water flow distance is directly correlated to the concentration of endotoxin.Noteworthy,the D-LAF sensor allows the detection of endotoxin with the reduced dosage of TAL reagents than the golden standard detection methods.The detection limit of endotoxin is calculated to be 0.0742 EU/mL.This method can be applied to the detection of endotoxin in real samples such as household water and clinical injection solution with excellent performance comparable to the commercial ELISA kit.

Indoor 3D Reconstruction Using Camera, IMU and Ultrasonic Sensors

The recent advances in sensing and display technologies have been transforming our living environments drastically. In this paper, a new technique is introduced to accurately reconstruct indoor environments in three-dimensions using a mobile platform. The system incorporates 4 ultrasonic sensors scanner system, an HD web camera as well as an inertial measurement unit (IMU). The whole platform is mountable on mobile facilities, such as a wheelchair. The proposed mapping approach took advantage of the precision of the 3D point clouds produced by the ultrasonic sensors system despite their scarcity to help build a more definite 3D scene. Using a robust iterative algorithm, it combined the structure from motion generated 3D point clouds with the ultrasonic sensors and IMU generated 3D point clouds to derive a much more precise point cloud using the depth measurements from the ultrasonic sensors. Because of their ability to recognize features of objects in the targeted scene, the ultrasonic generated point clouds performed feature extraction on the consecutive point cloud to ensure a perfect alignment. The range measured by ultrasonic sensors contributed to the depth correction of the generated 3D images (the 3D scenes). Experiments revealed that the system generated not only dense but precise 3D maps of the environments. The results showed that the designed 3D modeling platform is able to help in assistive living environment for self-navigation, obstacle alert, and other driving assisting tasks.