Ingenious fabrication of bamboo leaf-like ferric vanadate intertwined multi-walled carbon nanotubes nanocomposite as a sensitive sensor for determination of uric acid in fetal bovine serum.

A novel nanocomposite material, ferric vanadate intertwined multi-walled carbon nanotubes (FeV/MWCNTs), has been designed which was drop-coated onto a glassy carbon electrode (GCE). The constructed sensor was used for the sensitive determination of uric acid (UA) in fetal bovine serum (FBS) and human serum (HS). A series of characterization and electrochemical tests showed that the ultrasound-assisted assembly of FeV with MWCNTs not only overcame the disadvantages of low conductivity and easy (unwanted) aggregation, but also avoided the decrease in effective surface area due to the severe aggregation of each individual raw material. The fabricated FeV/MWCNTs nanocomposites exhibited higher conductivity, larger effective surface area, and better electrocatalytic activity. In addition, under optimized conditions, the developed electrochemical sensor FeV/MWCNTs/GCE has a lower limit of detection (LOD, 0.05 µM; Ep = 0.268 V vs. Ag/AgCl) and wider linear range (0.20-100 µM), which can satisfy the criteria of trace UA detection. The results of UA determination in FBS (recovery = 95.5-103%; RSD ≤ 3.1%) and HS (recovery = 95.5-103%; RSD ≤ 4.3%) further validated the feasibility of FeV/MWCNTs-based electrochemical sensors for the determination of UA in biological fluids.

Cooperative learning for multiview analysis.

We propose a method for supervised learning with multiple sets of features ("views"). The multiview problem is especially important in biology and medicine, where "-omics" data, such as genomics, proteomics, and radiomics, are measured on a common set of samples. "Cooperative learning" combines the usual squared-error loss of predictions with an "agreement" penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. One version of our fitting procedure is modular, where one can choose different fitting mechanisms (e.g., lasso, random forests, boosting, or neural networks) appropriate for different data views. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty, yielding feature sparsity. The method can be especially powerful when the different data views share some underlying relationship in their signals that can be exploited to boost the signals. We show that cooperative learning achieves higher predictive accuracy on simulated data and real multiomics examples of labor-onset prediction. By leveraging aligned signals and allowing flexible fitting mechanisms for different modalities, cooperative learning offers a powerful approach to multiomics data fusion.