Trends in single-cell analysis by use of ICP-MS

The analysis of single cells is a growing research field in many disciplines such as toxicology, medical diagnosis, drug and cancer research or metallomics, and different methods based on microscopic, mass spectrometric, and spectroscopic techniques are under investigation. This review focuses on the most recent trends in which inductively coupled plasma mass spectrometry (ICP-MS) and ICP optical emission spectrometry (ICP-OES) are applied for single-cell analysis using metal atoms being intrinsically present in cells, taken up by cells (e.g., nanoparticles), or which are artificially bound to a cell. For the latter, especially element tagged antibodies are of high interest and are discussed in the review. The application of different sample introduction systems for liquid analysis (pneumatic nebulization, droplet generation) and elemental imaging by laser ablation ICP-MS (LA-ICP-MS) of single cells are highlighted. Because of the high complexity of biological systems and for a better understanding of processes and dynamics of biologically or medically relevant cells, the authors discuss the idea of “multimodal spectroscopies.”     

An Exploratory Study on the Complexity and Machine Learning Predictability of Stock Market Data

This paper shows if and how the predictability and complexity of stock market data changed over the last half-century and what influence the M1 money supply has. We use three different machine learning algorithms, i.e., a stochastic gradient descent linear regression, a lasso regression, and an XGBoost tree regression, to test the predictability of two stock market indices, the Dow Jones Industrial Average and the NASDAQ (National Association of Securities Dealers Automated Quotations) Composite. In addition, all data under study are discussed in the context of a variety of measures of signal complexity. The results of this complexity analysis are then linked with the machine learning results to discover trends and correlations between predictability and complexity. Our results show a decrease in predictability and an increase in complexity for more recent years. We find a correlation between approximate entropy, sample entropy, and the predictability of the employed machine learning algorithms on the data under study. This link between the predictability of machine learning algorithms and the mentioned entropy measures has not been shown before. It should be considered when analyzing and predicting complex time series data, e.g., stock market data, to e.g., identify regions of increased predictability.     

Peels of buckyballs

The polymer analogous aromatization of double-stranded Diels-Alder polymers to give fully unsaturated, all-carbon ladder polymers by means of dehydration and dehydrogenation is described. The polymers obtained were characterized by solid state ¹³C NMR spectroscopy.     

The catalysis of the stereospecific butadiene polymerization by Allyl Nickel and Allyl Lanthanide complexes — A mechanistic comparison

A short overview on the general mechanistic principles of the allyl metal complex catalyzed stereo-specific butadiene polymerization is given. The present state of knowledge on the mechanism of stereoregulation and in the understanding of the catalytic structure reactivity relationships is discussed comparatively for allyl nickel and allyl lanthanide catalysts.     

Notes on D-optimal designs

The purpose of this paper is to exhibit new infinite families of D-optimal (0, 1)-matrices. We show that Hadamard designs lead to D-optimal matrices of size (j, mj) and (j − 1, mj), for certain integers j ≡ 3 (mod 4) and all positive integers m. For j a power of a prime and j ≡ 1 (mod 4), supplementary difference sets lead to D-optimal matrices of size (j, 2mj) and (j − 1, 2mj), for all positive integers m. We also show that for a given j and d sufficiently large, about half of the entries in each column of a D-optimal matrix are ones. This leads to a new relationship between D-optimality for (0, 1)-matrices and for (±1)-matrices. Known results about D-optimal (±1)-matrices are then used to obtain new D-optimal (0, 1)-matrices.