Code2vect: An efficient heterogenous data classifier and nonlinear regression technique

The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm.     

Data assimilation and pollution forecasting in Burgers' equation with model error function

This article presents a correction method for a better resolution of the problem of estimating and predicting pollution, governed by Burgers' equations. The originality of the method consists in the introduction of an error function into the system's equations of state to model uncertainty in the model. The initial conditions and diffusion coefficients, present in the equations for pollution and concentration, and also those in the model error equations, are estimated by solving a data assimilation problem. The efficiency of the correction method is compared with that produced by the traditional method without introduction of an error function.Three test cases are presented in this study in order to compare the performances of the proposed methods. In the first two tests, the reference is the analytical solution and the last test is formulated as part of the “twin experiment”.The numerical results obtained confirm the important role of the model error equation for improving the prediction capability of the system, in terms of both accuracy and speed of convergence.     

A microfluidics-dispensing-printing strategy for Janus photonic crystal microspheres towards smart patterned displays

From the implementation point of view, the printable magnetic Janus colloidal photonic crystals (CPCs) microspheres are highly desirable. Herein, we developed a dispensing-printing strategy for magnetic Janus CPCs display via a microfluidics-automatic printing system. Monodisperse core/shell colloidal particles and magnetic Fe₃O₄ nanoparticles precursor serve as inks. Based on the equilibrium of three-phase interfacial tensions, Janus structure is successfully formed, followed by UV irradiation and self-assembly of colloid particle to generate magnetic Janus CPCs microspheres. Notably, this method shows distinct superiority with highly uniform Janus CPCs structure, where the TMPTA/Fe₃O₄ hemisphere is in the bottom side while CPCs hemisphere is in the top side. Thus, by using Janus CPCs microspheres with two different structural colors as pixel points, a pattern with red flower and green leaf is achieved. Moreover, 1D linear Janus CPCs pattern encapsulated by hydrogel is also fabricated. Both the color and the shape can be changed under the traction of magnets, showing great potentials in flexible smart displays. We believe this work not only offers a new feasible pathway to construct magnetic Janus CPCs patterns by a dispensing-printable fashion, but also provides new opportunities for flexible and smart displays.     

Selection of phage-displayed antibodies with high affinity and specificity by electrophoresis in microfluidic devices

A method development aimed for high-throughput and automated antibody screening holds great potential for areas ranging from fundamental molecular interactions to the discovery of novel disease markers, therapeutic targets, and monoclonal antibody engineering. Surface display techniques enable efficient manipulation of large molecular libraries in small volumes. Specifically, phage display appeared as a powerful technology for selecting peptides and proteins with enhanced, target-specific binding affinities. Here, we present a phage-selection microfluidic device wherein electrophoresis was performed under two orthogonal electric fields through an agarose gel functionalized with the respective antigen. This microdevice was capable of screening and sorting in a single round high-affinity phage-displayed antibodies against virus glycoproteins, including human immunodeficiency virus-1 glycoprotein 120 or the Ebola virus glycoprotein (EBOV-GP). Phages were differentially and laterally swept depending on their antigen affinity; the high-affinity phages were recovered at channels proximal to the application site, whereas low-affinity phages migrated distal after electrophoresis. These experiments proved that the microfluidic device specifically designed for phage-selection is rapid, sensitive, and effective. Therefore, this is an efficient and cost-effective method that allowed highly controlled assay conditions for isolating and sorting high-affinity ligands displayed in phages.     

Amperometric l-glutamate biosensor based on bacterial cell-surface displayed glutamate dehydrogenase

A novel l-glutamate biosensor was fabricated using bacteria surface-displayed glutamate dehydrogenase (Gldh-bacteria). Here the cofactor NADP⁺-specific dependent Gldh was expressed on the surface of Escherichia coli using N-terminal region of ice nucleation protein (INP) as the anchoring motif. The cell fractionation assay and SDS-PAGE analysis indicated that the majority of INP-Gldh fusion proteins were located on the surface of cells. The biosensor was fabricated by successively casting polyethyleneimine (PEI)-dispersed multi-walled carbon nanotubes (MWNTs), Gldh-bacteria and Nafion onto the glassy carbon electrode (Nafion/Gldh-bacteria/PEI-MWNTs/GCE). The MWNTs could not only significantly lower the oxidation overpotential towards NAPDH, which was the product of NADP⁺ involving in the oxidation of glutamate by Gldh, but also enhanced the current response. Under the optimized experimental conditions, the current–time curve of the Nafion/Gldh-bacteria/PEI-MWNTs/GCE was performed at +0.52 V (vs. SCE) by amperometry varying glutamate concentration. The current response was linear with glutamate concentration in two ranges (10 μM–1 mM and 2–10 mM). The low limit of detection was estimated to be 2 μM glutamate (S/N = 3). Moreover, the proposed biosensor is stable, specific, reproducible and simple, which can be applied to real samples detection.     

Data fusion methodologies for food and beverage authentication and quality assessment – A review

The ever increasing interest of consumers for safety, authenticity and quality of food commodities has driven the attention towards the analytical techniques used for analyzing these commodities. In recent years, rapid and reliable sensor, spectroscopic and chromatographic techniques have emerged that, together with multivariate and multiway chemometrics, have improved the whole control process by reducing the time of analysis and providing more informative results. In this progression of more and better information, the combination (fusion) of outputs of different instrumental techniques has emerged as a means for increasing the reliability of classification or prediction of foodstuff specifications as compared to using a single analytical technique. Although promising results have been obtained in food and beverage authentication and quality assessment, the combination of data from several techniques is not straightforward and represents an important challenge for chemometricians. This review provides a general overview of data fusion strategies that have been used in the field of food and beverage authentication and quality assessment.     

Missing value imputation strategies for metabolomics data

The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k‐means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a “gray area” and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k‐means nearest neighbor and the best approximation of positioning real zeros.