Fluorescence biosensing strategy based on energy transfer between fluorescently labeled receptors and a metallic surface

A new fluorescence-based biosensor is presented. The biosensing scheme is based on the fact that a fluorophore in close proximity to a metal film (100 Å) experiences strong quenching of fluorescence and a dramatic reduction in the lifetime of the excited state. By immobilizing the analyte of interest (or a structural analog of the analyte) to a metal surface and exposing it to a labeled receptor (e.g. antibody), the fluorescence of the labeled receptor becomes quenched upon binding because of the close proximity to the metal. Upon exposure to free analyte, the labeled receptor dissociates from the surface and diffuses into the bulk of the solution. This increases its separation from the metal and an increase of fluorescence intensity and/or lifetime of the excited state is observed that indicates the presence of the soluble analyte. By enclosing this system within a small volume with a semipermeable membrane, a reversible device is obtained. We demonstrate this scheme using a biotinylated self-assembled monolayer (SAM) on gold as our surface immobilized analyte analog, fluorescently labeled anti-biotin as a receptor, and a solution of biotin in PBS as a model analyte. This scheme could easily be extended to transduce a wide variety of protein–ligand interactions and other biorecognition phenomena (e.g. DNA hybridization) that result in changes in the architecture of surface immobilized biomolecules such that a change in the separation distance between fluorophores and the metal film is obtained.Postprintapplication/pdfPérez-Luna, V. H., Yang S., Rabinovich, E. M., Buranda, T., Sklar, L. A., Hampton, P. D., Lopez, G. P. (2002). Fluorescence biosensing strategy based on energy transfer between fluorescently labeled receptors and a metallic surface. Biosensors and Bioelectronics v. 17 no. 1/2 p. 71-780956-566

Study on the processing of stretch-broken ramie yarns in a cotton spinning system

In this study, conventional long ramie fiber was stretch-broken into short fibers with lengths of 30 mm, 35 mm, 40 mm, 45 mm, and 50 mm. Then, these stretch-broken fibers were processed in a cotton spinning system. The results show that, compared to long ramie fibers processed in a conventional ramie spinning system, the stretch-broken fibers, with reasonable fiber length and high length uniformity, can be processed in a cotton spinning system with high efficiency and generally have better resultant yarn quality. For all of the stretch-broken yarns, the yarn processed from fiber with 40 mm length shows the best comprehensive performance. </jats:p

Research on Dam Deformation Prediction Model Based on Optimized SVM

Although constructing a dam can bring significant economic and social benefits to a region, it can be catastrophic for the population living downstream when it breaks. Given the dynamic and nonlinear characteristics of dam deformation, the traditional dam prediction model has been unable to meet the actual engineering demands. Consequently, this paper advocates for a novel method to solve this issue. The proposed method is based on the optimization of improved chicken swarm (ICSO) and support vector machine (SVM). To begin with, the mean square error is used as the objective function, and then, we apply the improved chicken swarm algorithm to iterate continuously, and finally, the optimal SVM parameters are obtained. Through the modeling and simulation experiments of a nonlinear system, the validity of the improved chicken swarm algorithm to optimize an SVM model has been verified. Based on the horizontal displacement monitoring data of FengMan Dam, this paper analyzed the influencing factors of horizontal displacement. According to the results, three prediction models have been established, respectively: the SVM prediction model optimized by the improved chicken swarm algorithm, the SVM prediction model optimized by the basic chicken swarm algorithm and the BP neural network prediction model optimized by the genetic algorithm. The obtained results from the experiment authenticate the validity and superiority of the proposed method

Research on the Uncertainty of Landslide Susceptibility Prediction Using Various Data-Driven Models and Attribute Interval Division

Two significant uncertainties that are crucial for landslide susceptibility prediction modeling are attribute interval numbers (AIN) division of continuous landslide impact factors in frequency ratio analysis and various susceptibility prediction models. Five continuous landslide impact factor interval attribute classifications (4, 8, 12, 16, 20) and three data-driven models (deep belief networks (DBN), random forest (RF), and neural network (back propagation (BP)) were used for a total of fifteen different scenarios of landslide susceptibility prediction studies in order to investigate the effects of these two factors on modeling and perform a landslide susceptibility index uncertainty analysis (including precision evaluation and statistical law). The findings indicate that: (1) The results demonstrate that for the same model, as the interval attribute value rises from 4 to 8 and finally to 20, the forecast accuracy of landslide susceptibility initially increases gradually, then progressively grows until stable. (2) The DBN model, followed by the RF and BP models, provides the highest prediction accuracy for the same interval attribute value. (3) AIN = 20 and DBN models have the highest prediction accuracy under 15 combined conditions, while AIN = 4 and BP models have the lowest. The accuracy and efficiency of landslide susceptibility modeling are higher when the AIN = 8 and DBN models are combined. (4) The landslide susceptibility index uncertainty predicted by the deeper learning model and the bigger interval attribute value is comparatively low, which is more in line with the real landslide probability distribution features. The conditions that the environmental component attribute interval is divided into eight parts and DBN models are used allow for the efficient and accurate construction of the landslide susceptibility prediction model