Simultaneous determination of iron, cobalt, nickel and copper by UV-visible spectrophotometry with multivariate calibration

A method for the simultaneous spectrophotometric determination of the divalent ions of iron, cobalt, nickel and copper based on the formation of their complexes with 1,5-bis(di-2-pyridylmethylene) thiocarbonohydrazide (DPTH) is proposed. The resolution of quaternary mixtures of these metallic ions was accomplished by several chemometric approaches. A comparative study of the results obtained for simultaneous determinations in mixture by using principal component regression (PCR) and partial least-squares regression (PLS-1 and PLS-2) for absorbance, first-derivative and second-derivative data is presented. In general, the best recovery values are obtained by the PLS-2 method for absorbance data. This procedure allows the simultaneous determination of the cited ions in alloys and biological materials Good reliability of the determination was proved.     

Simultaneous spectrophotometric determination of cadmium, copper and zinc

A method for the simultaneous spectrophotometric determination of cadmium, copper and zinc based on the formation of their complexes with 1,5-bis(di-2-pyridylmethylene)thiocarbonohydrazide is proposed. The absorption curves of these complexes overlap severely in the scanning range 380–480 nm. The analyte concentrations are calculated by a least squares fit of the pure spectra to the mixture spectra. A linear determination range of 0.1–1.7 μg/ml for cadmium, 0.1–1.3 μg/ml for copper and 0.2–1.2 μg/ml for zinc were obtained. The effect of interference was studied. The method has been applied to the determination of these metal ions in various type of materials.     

Stochastic Control for Bayesian Neural Network Training

In this paper, we propose to leverage the Bayesian uncertainty information encoded in parameter distributions to inform the learning procedure for Bayesian models. We derive a first principle stochastic differential equation for the training dynamics of the mean and uncertainty parameter in the variational distributions. On the basis of the derived Bayesian stochastic differential equation, we apply the methodology of stochastic optimal control on the variational parameters to obtain individually controlled learning rates. We show that the resulting optimizer, StochControlSGD, is significantly more robust to large learning rates and can adaptively and individually control the learning rates of the variational parameters. The evolution of the control suggests separate and distinct dynamical behaviours in the training regimes for the mean and uncertainty parameters in Bayesian neural networks.     

Controlled Click‐Assembly of Well‐Defined Hetero‐Bifunctional Cubic Silsesquioxanes and Their Application in Targeted Bioimaging

A general procedure for the assembly of hetero‐bifunctional cubic silsesquioxanes with diverse functionality and a perfectly controlled distribution of functional groups on the inorganic framework has been developed. The method is based on a two‐step sequence of mono‐ and hepta‐functionalization through the ligand‐accelerated copper(I)‐catalyzed azide–alkyne cycloaddition of a readily available octaazido cubic silsesquioxane. The stoichiometry of the reactants and the law of binomial distribution essentially determine the selectivity of the key monofunctionalization reaction when a copper catalyst with strong donor ligands is used. The methodology has been applied to the preparation of a set of bifunctional nano‐building‐blocks with orthogonal reactivity for the controlled assembly of precisely defined hybrid nanomaterials and a fluorescent multivalent probe for application in targeted cell‐imaging. The inorganic cage provides an improved photostability to the covalently attached dye as well as a convenient framework for the 3D multivalent display of the pendant epitopes. Thus, fluorescent bioprobes based on well‐defined cubic silsesquioxanes offer interesting advantages over more conventional fully organic analogues and ill‐defined hybrid nanoparticles and promise to become powerful tools for the study of cell biology and for biomedical applications.     

Racemic and Quasi‐Racemic X‐ray Structures of Cyclic Disulfide‐Rich Peptide Drug Scaffolds

Cyclic disulfide‐rich peptides have exceptional stability and are promising frameworks for drug design. We were interested in obtaining X‐ray structures of these peptides to assist in drug design applications, but disulfide‐rich peptides can be notoriously difficult to crystallize. To overcome this limitation, we chemically synthesized the L ‐ and D ‐forms of three prototypic cyclic disulfide‐rich peptides: SFTI‐1 (14‐mer with one disulfide bond), cVc1.1 (22‐mer with two disulfide bonds), and kB1 (29‐mer with three disulfide bonds) for racemic crystallization studies. Facile crystal formation occurred from a racemic mixture of each peptide, giving structures solved at resolutions from 1.25 Å to 1.9 Å. Additionally, we obtained the quasi‐racemic structures of two mutants of kB1, [G6A]kB1, and [V25A]kB1, which were solved at a resolution of 1.25 Å and 2.3 Å, respectively. The racemic crystallography approach appears to have broad utility in the structural biology of cyclic peptides.     

Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects

Traditionally, Hawkes processes are used to model time-continuous point processes with history dependence. Here, we propose an extended model where the self-effects are of both excitatory and inhibitory types and follow a Gaussian Process. Whereas previous work either relies on a less flexible parameterization of the model, or requires a large amount of data, our formulation allows for both a flexible model and learning when data are scarce. We continue the line of work of Bayesian inference for Hawkes processes, and derive an inference algorithm by performing inference on an aggregated sum of Gaussian Processes. Approximate Bayesian inference is achieved via data augmentation, and we describe a mean-field variational inference approach to learn the model parameters. To demonstrate the flexibility of the model we apply our methodology on data from different domains and compare it to previously reported results.     

Separation and Preconcentration by Dispersive Liquid�CLiquid Microextraction Procedure: Recent Applications

Dispersive liquid?Cliquid microextraction (DLLME) is a relatively novel miniaturized sample pre-treatment technique. DLLME has been applied for the analysis of a large variety of organic compounds and metal ions in different samples. This paper reviews the more recent applications of this procedure for sample preparation.     

Even G-liaison classes of some unions of curves

After establishing bounds on the Rao function and on the genus of projective curves that generalize the ones in [5] and in [12], we describe the even G-liaison classes of some unions of curves attaining the bounds, and of more general unions with analogous geometric properties. In particular, we prove that their Hartshorne-Rao module identifies the even G-liaison class.     

Experimental design in the optimization of a microwave acid digestion procedure for the determination of metals in biomorphic ceramic samples by inductively coupled plasma mass spectrometry and atomic absorption spectrometry

In the present paper, we have synthesized a biomorphic ceramic material from oak wood as biological template structure and infiltration with zirconia-sol. After the material characterization, we have optimized the sample dissolution by acid attack in an oven under microwave irradiation. Experimental designs were used as a multivariate strategy for the effect's evaluation of varying several variables. This article describes the development by response surface methodology (RSM) of a procedure for zirconium determination, and other ions, such as copper and nickel by inductively coupled plasma mass spectrometry (ICP-MS) and others, such as iron, calcium and magnesium determination by flame atomic absorption spectrometry (FAAS) in the synthesized sample after digestion. A full factorial design (3³) was used to find optimal conditions for the procedure through response surface study. Three variables (time, HNO₃ volume and HF volume) were regarded as factors and as response to the concentration of different metal ions in the optimization study.