A set up of a modern analytical laboratory for wastewaters from pulp and paper industry

The introduction of analytical techniques allowing rapid, selective, sensitive, and reliable determination of aqueous pollutants is of crucial importance for the protection of the environment. This critical review summarizes the advanced analytical techniques suggested over the last ten years together with already established methods, and evaluates whether they are fit for wastewater quality assessment considering the area of application, interferences, limit of detection, calibration function, and precision. The key parameters of wastewater quality assessment are: total organic carbon (TOC), chemical oxygen demand (COD), biochemical oxygen demand (BOD), organochlorines (AOX), nitrogen, phosphorus, sulfur, and toxicity. Chromatography and capillary electrophoresis, photocatalytic oxidation with semiconductor nanofilms and atomic emission spectrometry, optical fibre sensors and chemiluminescence, amperometric mediated biosensors and microbial fuel cells, respirometry and bioluminescence measurements are just part of the proposed wastewater analyst's toolkit. The diversity of fundamental phenomena and the captivating elegance of interdisciplinary applications involved in the development of wastewater analytical techniques should attract the interest of a wide scientific audience including analytical chemists, chemical physicists, microbiologists and environmentalists. To conclude, we suggest a laboratory set up for the analysis of wastewaters from the pulp and paper industry.     

Hollow nanoparticle nanotubes with a nanoscale brick wall structure of clay mineral platelets

Long hollow inorganic nanoparticle nanotubes have been synthesized by templating of block copolymer electrospun fibers with clay mineral platelets followed by interlinking of the platelets using condensation reactions.     

Hybrid Supramolecular and Colloidal Hydrogels that Bridge Multiple Length Scales

Hybrid nanocomposites were constructed based on colloidal nanofibrillar hydrogels with interpenetrating supramolecular hydrogels, displaying enhanced rheological yield strain and a synergistic improvement in storage modulus. The supramolecular hydrogel consists of naphthyl‐functionalized hydroxyethyl cellulose and a cationic polystyrene derivative decorated with methylviologen moieties, physically cross‐linked with cucurbit[8]uril macrocyclic hosts. Fast exchange kinetics within the supramolecular system are enabled by reversible cross‐linking through the binding of the naphthyl and viologen guests. The colloidal hydrogel consists of nanofibrillated cellulose that combines a mechanically strong nanofiber skeleton with a lateral fibrillar diameter of a few nanometers. The two networks interact through hydroxyethyl cellulose adsorption to the nanofibrillated cellulose surfaces. This work shows methods to bridge the length scales of molecular and colloidal hybrid hydrogels, resulting in synergy between reinforcement and dynamics.     

Comparison of virtual high-throughput screening methods for the identification of phosphodiesterase-5 inhibitors

Reliable and effective virtual high-throughput screening (vHTS) methods are desperately needed to minimize the expenses involved in drug discovery projects. Here, we present an improvement to the negative image-based (NIB) screening: the shape, the electrostatics, and the solvation state of the target protein's ligand-binding site are included into the vHTS. Additionally, the initial vHTS results are postprocessed with molecular mechanics/generalized Born surface area (MMGBSA) calculations to estimate the favorability of ligand-protein interactions. The results show that docking produces very good early enrichment for phosphodiesterase-5 (PDE-5); however, in general, the NIB and the ligand-based screening performed better with or without the added electrostatics. Furthermore, the postprocessing of the NIB screening results using MMGBSA calculations improved the early enrichment for the PDE-5 considerably, thus, making hit discovery affordable.     

Hydrogen-bond strengths by magnetically induced currents

We present here a "non-invasive" computational method to estimate the strength of individual hydrogen bonds using magnetically induced currents. The method is calibrated using H-bonding dimers, and applied on Watson-Crick DNA base pairs and proton wires in carbonic anhydrase.     

Microwave assisted acid cleavage for denaturation and proteolysis of intact human adenovirus

Microwave assisted acid cleavage was applied directly to intact adenovirus type 5 to achieve denaturation and proteolysis in a single reaction. The speed of the digestion, coupled with the simplicity of MALDI analysis, allowed peptide products to be profiled in less than 5 min. Identification of peptides from a range of proteins by MALDI-TOF confirms that both denaturation and proteolysis were achieved using low concentrations of acetic acid (12.5%) and short incubations (1.5 to 2 min) at high temperatures (140° C). These conditions favor production of peptides that carry Asp on their C-termini. When this cleavage reaction is carried out in highly enriched H(2) (18)O, a single atom of (18)O is introduced site-specifically into the carboxyl terminal. The labeling reaction is applied to label reporter peptides from human adenovirus type 5 harvested from HeLa cells. Small peptide products of endogenous processing were also observed.     

The Essential Norm of Weighted Composition Operators on Weighted Banach Spaces of Analytic Functions

For a wide class of radial weights we calculate the essential norm of a weighted composition operator uC_j{uC_\varphi} on the weighted Banach spaces of analytic functions in terms of the analytic function u \colon \mathbb D ? \mathbb C{u \colon \mathbb D \to \mathbb C} and the nth power of the analytic selfmap j{\varphi} of the open unit disc \mathbb D{\mathbb D} . We also apply our result to calculate the essential norm of composition operators acting on Bloch type spaces with general radial weights.     

Normalized Augmented Inverse Probability Weighting with Neural Network Predictions

The estimation of average treatment effect (ATE) as a causal parameter is carried out in two steps, where in the first step, the treatment and outcome are modeled to incorporate the potential confounders, and in the second step, the predictions are inserted into the ATE estimators such as the augmented inverse probability weighting (AIPW) estimator. Due to the concerns regarding the non-linear or unknown relationships between confounders and the treatment and outcome, there has been interest in applying non-parametric methods such as machine learning (ML) algorithms instead. Some of the literature proposes to use two separate neural networks (NNs) where there is no regularization on the network’s parameters except the stochastic gradient descent (SGD) in the NN’s optimization. Our simulations indicate that the AIPW estimator suffers extensively if no regularization is utilized. We propose the normalization of AIPW (referred to as nAIPW) which can be helpful in some scenarios. nAIPW, provably, has the same properties as AIPW, that is, the double-robustness and orthogonality properties. Further, if the first-step algorithms converge fast enough, under regulatory conditions, nAIPW will be asymptotically normal. We also compare the performance of AIPW and nAIPW in terms of the bias and variance when small to moderate L1 regularization is imposed on the NNs.