A Bendable Biofuel Cell-Based Fully Integrated Biomedical Nanodevice for Point-of-Care Diagnosis of Scurvy.

Fully integrated nanodevices that allow the complete functional implementation without an external accessory or equipment are deemed to be one of the most ideal and ultimate goals for modern nanodevice design and construction. In this work, we demonstrate the first example of a bendable biofuel cell (BFC)-based fully integrated biomedical nanodevice with simple, palm-sized, easy-to-carry, pump-free, cost-saving, and easy-to-use features for the point-of-care (POC) diagnosis of scurvy from a single drop of untreated human serum (down to 0.2 µL) by integrating a bendable and disposable vitamin C/air microfluidic BFC (micro-BFC) (named iezCard) for self-powered vitamin C biosensing with a custom mini digital LED voltmeter (named iezBox) for signal processing and transmission, along with a ″built-in″ biocomputing BUFFER gate for intelligent diagnosis. Under the simplicity- and practicability-oriented idea, a cost-effective strategy (e.g., biomass-derived hierarchical micro-mesoporous carbon aerogels, screen-printed technique, a single piece of Kimwipes paper, LED display, and universal components) was implemented for nanodevice design rather than any top-end or pricey method (e.g., photolithography/electron-beam evaporation, peristaltic pump, wireless system, and 3D printing technique), which enormously reduces the cost of feedstock down to ∼USD 2.55 per integrated kit including a disposal iezCard (∼USD 0.08 per test) and a reusable iezBox (∼USD 2.47 for large-scale tests). These distinctive and attractive features allow such a fully integrated biomedical nanodevice to fully satisfy the basic requirements for POC diagnosis of scurvy from a single drop of raw human serum and make it particularly appropriate for resource-poor settings, where there is a lack of medical facilities, funds, and qualified personnel.

Prediction of Talc Content in Wheat Flour Based on a Near-Infrared Spectroscopy Technique.

A procedure for the prediction of talc content in wheat flour based on radial basis function (RBF) neural network and near-infrared spectroscopy (NIRS) data is described. In this study, 41 wheat flour samples adulterated with different concentrations of talc were used. The diffuse reflectance spectra of all samples were collected by NIRS analyzer in the spectral range of 400 to 2,500 nm. A sample of outliers was eliminated by Mahalanobis distance based on near-infrared spectral scanning, and the remaining 40 wheat flour samples were used for spectral characteristic analysis. A calibration set of 26 samples and a prediction set of 14 samples of wheat flour were built as a result of sample set partitioning based on joint x-y distances division. A comparison of Savitzky-Golay smoothing, multiplicative scatter correction (MSC), first derivation, second derivation, and standard normal variation in the modeling showed that MSC has the best preprocessing effect. To develop a simpler, more efficient prediction model, the correlation coefficient method (CCM) was used to reduce spectral redundancy and determine the maximum correlation informative wavelength (MIW). From the full 1,050 wavelengths, 59 individual MIWs were finally selected. The optimal combined detection model was CCM-MSC-RBF based on the selected MIWs, with a determination of prediction coefficients of prediction (Rp) of 0.9999, root-mean-square error of prediction of 0.0765, and residual predictive deviation of 65.0909. The study serves as a proof of concept that NIRS technology combined with multivariate analysis has the potential to provide a fast, nondestructive and reliable assay for the prediction of talc content in wheat flour.

Parameter Identifiability in Statistical Machine Learning: A Review.

This review examines the relevance of parameter identifiability for statistical models used in machine learning. In addition to defining main concepts, we address several issues of identifiability closely related to machine learning, showing the advantages and disadvantages of state-of-the-art research and demonstrating recent progress. First, we review criteria for determining the parameter structure of models from the literature. This has three related issues: parameter identifiability, parameter redundancy, and reparameterization. Second, we review the deep influence of identifiability on various aspects of machine learning from theoretical and application viewpoints. In addition to illustrating the utility and influence of identifiability, we emphasize the interplay among identifiability theory, machine learning, mathematical statistics, information theory, optimization theory, information geometry, Riemann geometry, symbolic computation, Bayesian inference, algebraic geometry, and others. Finally, we present a new perspective together with the associated challenges.