Development and evaluation of a DAS-ELISA for rapid detection of Tembusu virus using monoclonal antibodies against the envelope protein.

Since April 2010, Tembusu virus (TMUV) which is a contagious pathogen of waterfowls, causing symptoms of high fever, loss of appetite and fall in egg production, has been reported in east of China. A double antibody sandwich enzyme-linked immunosorbent assay (DAS-ELISA) which detects for TMUV was developed, using two monoclonal antibodies (mAbs) against the TMUV envelope (E) protein. BALB/c mice were immunized with purified recombinant E protein expressed in E. coli. Three hybridoma cell lines designated as 12B1, 10C6 and 2D2, were screened by cell fusion and indirect ELISA for their ability to recognize different linear epitopes on the E protein, and were characterized subsequently. High-affinity mAbs 12B1 and 2D2 were used as capture and detection antibodies, respectively. The reaction conditions for the DAS-ELISA were optimized for TMUV detection. The cross-reactivity of the DAS-ELISA was determined using TMUV, duck plague virus, avian influenza virus subtype H9, Newcastle disease virus, duck hepatitis A virus type 1 and duck reovirus samples. A total of 191 homogenized tissues of field samples were simultaneously detected by DAS-ELISA and by RT-PCR. The former was found to have a high specificity of 99.1% and a sensitivity of 93.1%. These results reveal a positive coincidence between DAS-ELISA and RT-PCR at a coincidence rate of 95.8%. The method developed in this study can be used for the diagnosis of TMUV infection of duck origin

First-Principles Computation of Microscopic Mechanical Properties and Atomic Migration Behavior for Al₄Si Aluminum Alloy

In this paper, the interfacial behavior and the atom diffusion behavior of an Al4Si alloy were systematically investigated by means of first-principles calculations. The K-points and cutoff energy of the computational system were determined by convergence tests, and the surface energies for five different surfaces of Al4Si alloys were investigated. Among the five surfaces investigated for Al4Si, it was found that the (111) surface was the surface with the lowest surface energy. Subsequently, we investigated the interfacial stability of the (111) surface and found that there were two types of interfaces, the Al/Al interface and the Al/Si interface. The fracture energies and theoretical strengths of the two interfaces were calculated; the results show that the Al/Al interface had the highest interfacial strength, and the calculation of their electronic results explained the above phenomenon. Subsequently, we investigated the diffusion and migration behavior of Si atoms in the alloy system, mainly in the form of vacancies. We considered the diffusion of Si atoms in vacancies of Al and Si atoms, respectively; the results showed that Si atoms are more susceptible to diffusive migration to Al atomic vacancies than to Si atomic vacancies. The results of the calculations on the micromechanics of aluminum alloys, as well as the diffusion migration behavior, provide a theoretical basis for the further development of new aluminum alloys

A Study of the Adsorption Properties of Individual Atoms on the Graphene Surface: Density Functional Theory Calculations Assisted by Machine Learning Techniques

In this research, the adsorption performance of individual atoms on the surface of monolayer graphene surface was systematically investigated using machine learning methods to accelerate density functional theory. The adsorption behaviors of over thirty different atoms on the graphene surface were computationally analyzed. The adsorption energy and distance were extracted as the research targets, and the basic information of atoms (such as atomic radius, ionic radius, etc.) were used as the feature values to establish the dataset. Through feature engineering selection, the corresponding input feature values for the input-output relationship were determined. By comparing different models on the dataset using five-fold cross-validation, the mathematical model that best fits the dataset was identified. The optimal model was further fine-tuned by adjusting of the best mathematical ML model. Subsequently, we verified the accuracy of the established machine learning model. Finally, the precision of the machine learning model forecasts was verified by the method of comparing and contrasting machine learning results with density functional theory. The results suggest that elements such as Zr, Ti, Sc, and Si possess some potential in controlling the interfacial reaction of graphene/aluminum composites. By using machine learning to accelerate first-principles calculations, we have further expanded our choice of research methods and accelerated the pace of studying element–graphene interactions

Cross-reaction of DAS-ELISA.

a<p>Each sample was tested in two parallel repetitions by established DAS-ELISA. Samples except TMUV positive sera (OD450 values<0.2) were determined as negative.</p

Field samples collected from different duck farms in Shandong, China.

a<p>Samples were collected from dead ducks with symptoms of reduction in egg production (laying ducks), loss of appetite and encephalitis (ducklings).</p>b<p>These samples were collected from house sparrow euthanized with CO₂ near the TMUV-infected duck farms.</p

Properties of monoclonal antibodies against TMUV E protein.

a<p>MAb titer was the last dilution that yielded an absorption value above 0.3 at 30 min after adding the substrate at room temperature.</p