Simultaneous determination of wild-type and total p53 proteins (wild-type and mutant combined) present in cancer cell lysates has been performed with a dual-channel surface plasmon resonance (SPR) instrument. To achieve specificity, each channel of the SPR chip was modified with a consensus double-stranded (ds-) DNA and a monoclonal antibody. The high affinity of the consensus ds-DNA to the wild-type p53 and the antibody to total p53 results in remarkably low detection levels (10.6 and 1.06 pM for the wild-type and total p53, respectively). The difference between the SPR signals reveals the extent of p53 mutation, which is indicative of cancer development. The SPR signals increase with the p53 concentration across a wide range (from low picomolar to nanomolar levels) that amply encompasses the typical cellular p53 concentrations. The applicability of the method to real sample analysis has been demonstrated with the comparative analyses of normal and cancer cell lysates. The normal cell samples all displayed significantly higher levels of wild-type p53. In contrast, elevated levels of mutant p53 were observed from the cancer cell lysates. In comparison with enzyme-linked immunosorbant assay (ELISA), SPR obviates the need of a second antibody labeled with an enzyme in the "sandwich enzyme immunoassay" format and is capable of real-time monitoring of the binding events. Thus, SPR could potentially serve as an attractive technique for rapid, sensitive, reliable, and label-free cancer diagnoses.

The choice of ZR relationship is an essential source of error in radar Quantitative Precipitation Estimation (QPE). A QPE algorithm combining the Optimization process of precipitation Classification and Dynamical adjustments (OCD) is proposed to improve the accuracy of QPE in Yinchuan city, China. A detailed evaluation and study of Z = 300R1.4 (fixed Z-R), Optimization Processing (OP), Optimization processing of Dynamical Adjustments (ODA), and OCD were performed using various evaluation metrics. The results show that ODA and OCD can significantly reduce the error of QPE, with OCD being the best estimator, reaching a correlation coefficient (CC) of 0.7 and reducing mean absolute error (MAE) and root mean square error (RMSE) by 31% and 34%, respectively. OCD outperforms other algorithms in terms of MAE and RMSE for different rain rates (RR), and the various assessment metrics at hourly scales are also more concentrated in reasonable intervals. OP gives fair results at weaker rain rates (0.2 ≤ RR < 8 mm/h) but underestimates rainfall more incorrectly at stronger rain rates (8 mm/h ≤ RR). Both the OCD and ODA provide a more significant improvement in the estimation of the area and magnitude of strong rainfall, with the OCD providing a better description of the local characteristics of the rainfall distribution, further demonstrating the advantages of the ODA.