TiO2 assisted photocatalytic degradation of macrolide antibiotics

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Base-catalyzed reactions of environmentally relevant N-chloro-piperidines. A quantum-chemical study

Electronic structure methods have been applied to calculate the gas and aqueous phase reaction energies for base-induced rearrangements of N-chloropiperidine, N-chloro-3-(hydroxymethyl)piperidine, and N-chloro-4-(4-fluorophenyl)-3-(hydroxymethyl)piperidine. These derivatives have been selected as representative models for studying the chemical fate of environmentally relevant chloramines. The performance of different computational methods (MP2, MP4, QCISD, B3LYP and B2PLYP) for calculating the thermochemistry of rearrangement reactions was assessed. The latter method produces energies similar to those obtained at G3B3(+) level, which themselves have been tested against experimental results. Experimental energy barriers and enthalpies for ring inversion, nitrogen inversion and dehydrochlorination reactions in N-chloropiperidine have been accurately reproduced when solvent effects have been included. It was also found that the combined use of continuum solvation models (e.g. CPCM) and explicit consideration of a single water molecule is sufficient to properly describe the water-assisted rearrangement of N-chlorinated compounds in basic media. In the case of N-chloro-4-(4-fluorophenyl)-3-(hydroxymethyl)piperidine, which represents the chlorinated metabolite of the antidepressant paroxetine, several different reactions (intramolecular addition, substitution, and elimination reactions) have been investigated. Transition state structures for these processes have been located together with minimum energy structures of conceivable products. Imine 4A is predicted to be the most stable reaction product, closely followed by imine 4B and oxazinane 8, while formation of isoxazolidine 7 is much less favourable. Calculated reaction barriers in aqueous solution are quite similar for all four processes, the lowest barrier being predicted for the formation of imine 4A.     

Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes

We investigate how simultaneously recorded long-range power-law correlated multivariate signals cross-correlate. To this end we introduce a two-component ARFIMA stochastic process and a two-component FIARCH process to generate coupled fractal signals with long-range power-law correlations which are at the same time long-range cross-correlated. We study how the degree of cross-correlations between these signals depends on the scaling exponents characterizing the fractal correlations in each signal and on the coupling between the signals. Our findings have relevance when studying parallel outputs of multiple component of physical, physiological and social systems.     

Macroscopic Time Evolution and MaxEnt Inference for Closed Systems with Hamiltonian Dynamics

MaxEnt inference algorithm and information theory are relevant for the time evolution of macroscopic systems considered as problem of incomplete information. Two different MaxEnt approaches are introduced in this work, both applied to prediction of time evolution for closed Hamiltonian systems. The first one is based on Liouville equation for the conditional probability distribution, introduced as a strict microscopic constraint on time evolution in phase space. The conditional probability distribution is defined for the set of microstates associated with the set of phase space paths determined by solutions of Hamilton’s equations. The MaxEnt inference algorithm with Shannon’s concept of the conditional information entropy is then applied to prediction, consistently with this strict microscopic constraint on time evolution in phase space. The second approach is based on the same concepts, with a difference that Liouville equation for the conditional probability distribution is introduced as a macroscopic constraint given by a phase space average. We consider the incomplete nature of our information about microscopic dynamics in a rational way that is consistent with Jaynes’ formulation of predictive statistical mechanics, and the concept of macroscopic reproducibility for time dependent processes. Maximization of the conditional information entropy subject to this macroscopic constraint leads to a loss of correlation between the initial phase space paths and final microstates. Information entropy is the theoretic upper bound on the conditional information entropy, with the upper bound attained only in case of the complete loss of correlation. In this alternative approach to prediction of macroscopic time evolution, maximization of the conditional information entropy is equivalent to the loss of statistical correlation, and leads to corresponding loss of information. In accordance with the original idea of Jaynes, irreversibility appears as a consequence of gradual loss of information about possible microstates of the system.     

Benchmarking Attention-Based Interpretability of Deep Learning in Multivariate Time Series Predictions

The adaptation of deep learning models within safety-critical systems cannot rely only on good prediction performance but needs to provide interpretable and robust explanations for their decisions. When modeling complex sequences, attention mechanisms are regarded as the established approach to support deep neural networks with intrinsic interpretability. This paper focuses on the emerging trend of specifically designing diagnostic datasets for understanding the inner workings of attention mechanism based deep learning models for multivariate forecasting tasks. We design a novel benchmark of synthetically designed datasets with the transparent underlying generating process of multiple time series interactions with increasing complexity. The benchmark enables empirical evaluation of the performance of attention based deep neural networks in three different aspects: (i) prediction performance score, (ii) interpretability correctness, (iii) sensitivity analysis. Our analysis shows that although most models have satisfying and stable prediction performance results, they often fail to give correct interpretability. The only model with both a satisfying performance score and correct interpretability is IMV-LSTM, capturing both autocorrelations and crosscorrelations between multiple time series. Interestingly, while evaluating IMV-LSTM on simulated data from statistical and mechanistic models, the correctness of interpretability increases with more complex datasets.     

Electron-Rich EDOT Linkers in Tetracationic bis-Triarylborane Chromophores: Influence on Water Stability,Biomacromolecule Sensing,and Photoinduced Cytotoxicity

Three novel tetracationic bis-triarylboranes with 3,4-ethylenedioxythiophene (EDOT) linkers, and their neutral precursors, showed significant red-shifted absorption and emission compared to their thiophene-containing analogues, with one of the EDOT-derivatives emitting in the NIR region. Only the EDOT-linked trixylylborane tetracation was stable in aqueous solution, indicating that direct attachment of a thiophene or even 3-methylthiophene to the boron atom is insufficient to provide hydrolytic stability in aqueous solution. Further comparative analysis of the EDOT-linked trixylylborane tetracation and its bis-thiophene analogue revealed efficient photo-induced singlet oxygen production, with the consequent biological implications. Thus, both analogues bind strongly to ds-DNA and BSA, very efficiently enter living human cells, accumulate in several different cytoplasmic organelles with no toxic effect but, under intense visible light irradiation, they exhibit almost instantaneous and very strong cytotoxic effects, presumably attributed to singlet oxygen production. Thus, both compounds are intriguing theranostic agents, whose intracellular and probably intra-tissue location can be monitored by strong fluorescence, allowing switching on of the strong bioactivity by well-focused visible light.     

Admissibility on the half line for evolution families

For an arbitrary evolution family, we consider the notion of an exponential dichotomy with respect to a family of norms and characterize it completely in terms of the admissibility of bounded solutions, that is, the existence of a unique bounded solution for each bounded perturbation. In particular, by considering a family of Lyapunov norms, we recover the notion of a nonuniform exponential dichotomy. As a nontrivial application of the characterization, we establish the robustness of the notion of an exponential dichotomy with respect to a family of norms under sufficiently small Lipschitz and C ¹ parameterized perturbations. Moreover, we establish the optimal regularity of the dependence on the parameter of the projections onto the stable spaces of the perturbation.     

Forecasting human exposure to PM10 at the national level using an artificial neural network approach

A neural network model for predicting country‐level concentrations of the fraction of particulates in the air with sizes less than 10 µm (PM10) has been developed using widely available sustainability and economical/industrial parameters as inputs. The model was trained and validated with the data for 23 European Union (EU) countries plus the EU27 as a group for the period from 2000 to 2008. The inputs for the model were selected using correlation analyses. Country‐level PM10 concentration data that were used as a model output were obtained from the World Bank. The artificial neural network (ANN) model, created with inputs chosen by correlation analyses, has shown very good performance in the forecast of country‐level PM10 concentrations. The mean absolute error for the ANN model prediction, in the case of most of the EU countries, was less than 13%, indicating stable and accurate predictions. The predictions obtained from the principal component regression model, which was trained and tested using the same datasets and input variables, had mean absolute errors from 20% to 150% for most of the countries. The wide availability of input parameters used in this model can overcome the problem of lack and scarcity of data in many countries, which can in turn prevent the determination of human exposure to PM10 at the national level. Copyright © 2013 John Wiley & Sons, Ltd.     

Desymmetrisation of aromatic diamines and synthesis of non-symmetrical thiourea derivatives by click-mechanochemistry

ortho- and para-Phenylenediamines were desymmetrised and quantitatively transformed into mono- and bis-(thio)ureas or mixed thiourea-ureas through a one-pot mechanochemical click reaction sequence; mechanochemical desymmetrisation proceeds quantitatively without excess reagents and allows the controlled extension of a molecular structure by combining normally competing reactions.     

The nano-structural properties of hydrogenated a-Si and Si-C thin films alloys by GISAXS and vibrational spectroscopy

Amorphous hydrogenated silicon (a-Si:H) with high hydrogen content (10-40 at.%), and non-stehiometric silicon-carbon (Si_1−xC_x) thin films with a variation of the carbon to silicon ratio up to 0.3, were deposited by using a magnetron sputtering source. The Si_1−xC_x thin films were partially crystallised after deposition by thermal annealing up to 1050 °C.The GISAXS (Grazing Incidence Small Angle X-ray Scattering) spectra of all of the prepared specimens indicate the presence of “particles” in the “bulk” of the films. For the a-Si:H samples, “particles” are most probably voids agglomerates with a variation in size between 3 and 6 nm. The mean value of the size distribution of the “particles” increases while its width slightly decreases with the hydrogen content in the film. This indicates a better structural ordering which is consistent with the results of Raman spectroscopy that show a decrease of the ratio between intensities of transversal acoustic (TA) and transversal optic (TO) phonon peaks, I_TA/I_TO, and a narrowing of the TO peak with increasing hydrogen content. These results are discussed as a consequence of the beneficial influence of hydrogen bombardment during the film growth.For Si_1−xC_x thin films, the “particles” are assumed to be SiC nano-crystals with a size between 2 and 14 nm and they are larger in films with a higher carbon concentration. Inside each of the films, the crystals are larger closer to surface and they grow faster in the direction parallel to the surface than in that which is perpendicular to it.