Discovering Rehabilitation trends in Spain: A bibliometric analysis

The main purpose of this study is to offer an overview of the rehabilitation research area in Spain from 1970 to 2018 through a bibliometric analysis. Analysis of performance and a co-word science mapping analysis were conducted to highlight the topics covered. The software tool SciMAT was used to analyse the themes concerning their performance and impact measures. A total of 3,564 documents from the Web of Science were retrieved. Univ Deusto, Univ Rey Juan Carlos and Basque Foundation for Science are the institutions with highest relative priority. The most important research themes are IntellectualDisability, Neck-Pain and Pain

Determination of trace levels of nickel(II) by adsorptive stripping voltammetry using a disposable and low-cost carbon screen-printed electrode

A commercial and disposable screen-printed carbon electrode (SPCE) has been proposed for a fast, simple and low-cost determination of Ni(II) at very low concentration levels by differential pulse adsorptive stripping voltammetry (DPAdSV) in the presence of dimethylglyoxime (DMG) as complexing agent. In contrast with previously proposed methods, the Ni(II)-DMG complex adsorbs directly on the screen-printed carbon surface, with no need of mercury, bismuth or antimony coatings. Well-defined stripping peaks and a linear dependence of the peak area on the concentration of Ni(II) was achieved in the range from 1.7 to 150 microg/L, with a limit of detection of 0.5 microg/L using a deposition time of 120 s. An excellent reproducibility and repeatability with 0.3% (n = 3) and 1.5% (n = 15) relative standard deviation, respectively, were obtained. In addition, the suitability of the SPCE as sensing unit has been successfully assessed in a wastewater certificated reference material with remarkable trueness and very good reproducibility

A hybrid sensing system combining simultaneous optical and electrochemical measurements: application to beer discriminations

A hybrid sensing system, which combines simultaneous cyclic voltammetric (CV) and UV-vis absorbance measurements using a commercial carbon screen-printed electrode and a set of optical fibres in disposable cuvettes, is proposed. The hybrid system approach was applied to 27 samples of recognized beer brands, improving the classification power as compared to only voltammetric or only spectrophotometric measurements. The developed partial least squares discriminant analysis (PLS-DA) model was able to discriminate between five types of beer (lager, marzen, black/stout, alcohol-free and white/ale). The model was also successfully applied to 28 beer samples of white-label brands sold in local supermarkets, demonstrating their similarity to recognized brand beers

Authentication of soothing herbs by UV-vis spectroscopic and chromatographic data fusion strategy

A data fusion approach combining chromatographic and spectroscopic profiles is proposed for the discrimination and classification of soothing herbs in different types of herbal preparations. Particularly, chamomile, lavender, passionflower, and valerian were considered. The proposed data fusion approach revealed a higher clusterization ability than each analytical technique in a separate way, which was assessed through an exploratory analysis based on Principal Component Analysis (PCA) coupled to Silhouette analysis: percentage of samples with a negative Silhouette width were 19, 15 and 10 for chromatography, spectroscopy and data fusion, respectively. Furthermore, a Partial Least Squares - Discriminant Analysis (PLS-DA) model developed based on data fusion was able to perfectly discriminate samples of chamomile, passionflower, and valerian in a set of 20 samples, overcoming the difficulties related to dealing with different types of herbal preparations including pure herbs, infusions, tablets, capsules and herbal drops

On the Effect of Radical Character, Substitution and Atom Encapsulation on the Volume of Icosahedral (Car) boranes1

By means of quantum-mechanical calculations, we study the influence of the charge, spin, substituents, and atom encapsulation on the volume of the cages in icosahedral boranes and carboranes B12H12 2–, CB11H12 –, o-C2B10H12, m- C2B10H12, p-C2B10H12 and 1,2-disubstituted o-C2B10H12.Monoradicals derived from hydrogen abstraction in o-C2B10H12, m-C2B10H12, p-C2B10H12 lead to slight cage contractions (|ΔV| < 0.1 Å3 ). On the other hand, 1,2-disubstitution in o-C2B10H12 and their dianions derived from double proton abstraction on the susbtituent, and {Li+, Be2+} atom encapsulation in B12H12 2–, CB11H12–, o-C2B10H12, m-C2B10H12, p-C2B10H12 always leads to a cage expansion, to a larger extent for endohedral compounds (ΔV ≈ 2 Å3 ) as compared to dianions derived from 1,2-disubstituted o-C2B10H12 (ΔV ≈ 1 Å3 ) and 1,2-disubstituted o-C2B10H12 (ΔV < 0.14 Å3 )

Ionic self-assembly of pillar[5]arenes: proton-conductive liquid crystals and aqueous nanoobjects with encapsulation properties

Liquid crystal (LC) pillar[n]arenes have been barely explored due to their time-consuming and complicated synthesis, despite their promising properties for metal-ion separation, drug delivery, or surface functionalization. Herein, we report an easy and reliable method to functionalize pillar[n]arene macrocycles through electrostatic interactions. These ionic materials were prepared by ionically functionalizing a pillar[n]arene containing ten amine terminal groups with six different carboxylic acids. This supramolecular approach results in ionic pillar[n]arenes which self-organize into LC phases with good proton-conducting properties. Moreover, ionic functionalization provides a new amphiphilic character to the pillar[n]arenes, which self-assemble in water to produce a variety of nanoobjects (i.e., spherical or cylindrical micelles, vesicles, solid nanospheres, or nanotubes) that are capable of encapsulating a model hydrophobic drug. Interestingly, the presence of coumarin moieties in the chemical structure of the ionic pillar[n]arenes results in self-organized materials with light-responsive properties due to the ability of coumarins to undergo photo-induced [2+2] cycloaddition. In particular, we demonstrate that coumarin pohotodimerization can be employed to fabricate mechanically stable proton-conductive LC materials, as well as to obtain photo-responsive nanocarriers with light-induced release of encapsulated molecules

New discrimination tools for harvest year and varieties of white wines based on hydrophilic interaction liquid chromatography with amperometric detection

A simple HPLC-EC method based on hydrophilic interaction liquid chromatography with amperometric detection through gold screen-printed electrodes has been developed and applied for the first time to the determination of aminothiols in white wines. Moreover, the coupling of the method with partial least squares discriminant analysis (PLS-DA) using the analysed aminothiols as biomarkers provides wine discrimination in terms of harvest year. White wine samples were directly injected and chromatographic areas, together with pH and redox potential values, allowed a successful discrimination of wines from different harvest years with a global classification rate of 97.8%. The developed HPLC-EC method also generated characteristic fingerprints that were combined with PLS-DA to classify wines according to three wine varieties, with a global classification rate of 95.3%

Building uncertainty models on top of black-box predictive APIs

With the commoditization of machine learning, more and more off-the-shelf models are available as part of code libraries or cloud services. Typically, data scientists and other users apply these models as ''black boxes'' within larger projects. In the case of regressing a scalar quantity, such APIs typically offer a predict() function, which outputs the estimated target variable (often referred to as y¿ or, in code, y_hat). However, many real-world problems may require some sort of deviation interval or uncertainty score rather than a single point-wise estimate. In other words, a mechanism is needed with which to answer the question ''How confident is the system about that prediction?'' Motivated by the lack of this characteristic in most predictive APIs designed for regression purposes, we propose a method that adds an uncertainty score to every black-box prediction. Since the underlying model is not accessible, and therefore standard Bayesian approaches are not applicable, we adopt an empirical approach and fit an uncertainty model using a labelled dataset (x, y) and the outputs y¿ of the black box. In order to be able to use any predictive system as a black box and adapt to its complex behaviours, we propose three variants of an uncertainty model based on deep networks. The first adds a heteroscedastic noise component to the black-box output, the second predicts the residuals of the black box, and the third performs quantile regression using deep networks. Experiments using real financial data that contain an in-production black-box system and two public datasets (energy forecasting and biology responses) illustrate and quantify how uncertainty scores can be added to black-box outputs