Disposable pipette extraction for gas chromatographic determination of codeine, morphine, and 6-monoacetylmorphine in vitreous humor

The availability of a sensitive and rapid analytical method for the determination of opiates, and other substances of forensic interest, in a variety of biological specimens is of utmost importance to forensic laboratories. Solid-phase extraction is very popular in the pre-treatment of forensic samples. Nevertheless, a new approach, disposable pipette extraction (DPX), is gaining increasing interest in sample preparation. DPX has already been applied to the analysis of drugs of abuse in common biological matrices, such as urine and blood, but has not yet been evaluated on alternative biological samples, such as vitreous humor. The objective of this study was to evaluate the applicability of DPX on the analysis of opiates in vitreous humor. The currently developed method is fast, reliable, and easy to perform. The sensitivity, precision, and accuracy are satisfactory. Recoveries obtained are within the range of 72-91%, whereas the sample volume of vitreous humor required is only 100 μL.     

Spectral Exterior Calculus

A spectral approach to building the exterior calculus in manifold learning problems is developed. The spectral approach is shown to converge to the true exterior calculus in the limit of large data. Simultaneously, the spectral approach decouples the memory requirements from the amount of data points and ambient space dimension. To achieve this, the exterior calculus is reformulated entirely in terms of the eigenvalues and eigenfunctions of the Laplacian operator on functions. The exterior derivatives of these eigenfunctions (and their wedge products) are shown to form a frame (a type of spanning set) for appropriate L² spaces of k -forms, as well as higher-order Sobolev spaces. Formulas are derived to express the Laplace-de Rham operators on forms in terms of the eigenfunctions and eigenvalues of the Laplacian on functions. By representing the Laplace-de Rham operators in this frame, spectral convergence results are obtained via Galerkin approximation techniques. Numerical examples demonstrate accurate recovery of eigenvalues and eigenforms of the Laplace-de Rham operator on 1-forms. The correct Betti numbers are obtained from the kernel of this operator approximated from data sampled on several orientable and non-orientable manifolds, and the eigenforms are visualized via their corresponding vector fields. These vector fields form a natural orthonormal basis for the space of square-integrable vector fields, and are ordered by a Dirichlet energy functional which measures oscillatory behavior. The spectral framework also shows promising results on a non-smooth example (the Lorenz 63 attractor), suggesting that a spectral formulation of exterior calculus may be feasible in spaces with no differentiable structure. © 2020 Wiley Periodicals, Inc.     

Development and validation of an HPLC method for the determination of seven penicillin antibiotics in veterinary drugs and bovine blood plasma

Herein a quantitative method for the determination of seven penicillins in bovine plasma and veterinary drugs has been developed. Amoxicillin (AMO), ampicillin (AMP), penicillin G (PENG), penicillin V (PENV), oxacillin (OXA), cloxacillin (CLO) and dicloxacillin (DICLO) were separated on a Perfectsil ODS‐2 (250×4 mm, 5 μm) column, using gradient elution, with a mobile phase of 0.1% v/v TFA and ACN–methanol (90:10 v/v). PDA detection was used at 240 nm. Penicillins were isolated from bovine plasma by SPE on Lichrolut RP‐18 cartridges with mean recoveries from 85.7 to 113.5%. Colchicine (3 ng/μL) was used as an internal standard. The developed method was validated in terms of selectivity, linearity, accuracy, precision, stability and sensitivity. Repeatability (n = 5) and between‐day precision (n = 5) revealed RSD < 12%. The detection limits in the bovine plasma were estimated as 18 ng for AMO and AMP, 25 for PENG, PENV and OXA, 3 ng for CLO and 12 ng for DICLO. Spiked plasma samples were stable for 1 wk, except for AMP and CLO, which were stable for 3 wk and OXA for 4 wk. AMO, PENG and PENV were stable for two freeze–thaw cycles, OXA, CLO and DICLO for four, while AMP only for one.     

Social media and sensemaking patterns in new product development: demystifying the customer sentiment

Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers’ emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers’ appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms.

     

A meta-analysis of the scientific literature on (photo)Fenton and persulfate advanced oxidation processes: Where do we stand and where are we heading to?

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Group sparse Lasso for cognitive network sensing robust to model uncertainties and outliers

To account for variations in the frequency, time, and space dimensions, dynamic re-use of licensed bands under the cognitive radio (CR) paradigm calls for innovative network-level sensing algorithms for multi-dimensional spectrum opportunity awareness. Toward this direction, the present paper develops a collaborative scheme whereby CRs cooperate to localize active primary user (PU) transmitters and reconstruct a power spectral density (PSD) map portraying the spatial distribution of power across the monitored area per frequency band and channel coherence interval. The sensing scheme is based on a parsimonious model that accounts for two forms of sparsity: one due to the narrow-band nature of transmit-PSDs compared to the large portion of spectrum that a CR can sense, and another one emerging when adopting a spatial grid of candidate PU locations. Capitalizing on this dual sparsity, an estimator of the model coefficients is obtained based on the group sparse least-absolute-shrinkage-and-selection operator (GS-Lasso). A novel reduced-complexity GS-Lasso solver is developed by resorting to the alternating direction method of multipliers (ADMoM). Robust versions of this GS-Lasso estimator are also introduced using a GS total least-squares (TLS) approach to cope with both uncertainty in the regression matrices, arising due to inaccurate channel estimation and grid-mismatch effects, and unexpected model outliers. In spite of the non-convexity of the GS-TLS criterion, the novel robust algorithm has guaranteed convergence to (at least) a local optimum. The analytical findings are corroborated by numerical tests.     

Correction to: Spatiotemporal Pattern Extraction by Spectral Analysis of Vector-Valued Observables

Journal of Nonlinear Science - The original version of this article unfortunately contained an error in Acknowledgement section. The authors would like to correct the error with this erratum. The...