Development and validation of a cellular biosensor detecting pesticide residues in tomatoes

Two of the most important categories of pesticides used in agricultural practice are organophosphates and dithiocarbamates. Their extensive and inappropriate use has rendered their reliable monitoring at trace levels more and more necessary. This study presents the construction of a rapid and sensitive cellular biosensor test based on the measurement of changes of the cell membrane potential of immobilized cells, according to the working principle of the Bioelectric Recognition Assay (BERA). The cells were immobilized by entrapment in a sodium alginate bead and directly applied in different pesticide dilutions and agricultural samples. The pesticides used were the organophosphate insecticide diazinon and the dithiocarbamate fungicide propineb. Two different cell types, N2a (neuroblastoma) and Vero (fibroblast) were used as the biosensory elements in order to investigate their differential response against the pesticides. In this way, we hoped to increase the selectivity of the assay. Based on the observed patterns of response, we demonstrate that the sensor can be used for the qualitative and, in some concentrations, quantitative detection of the pesticides with a high degree of reproducibility. The lowest detected concentration was 3 nM. Finally, for the investigation of the effects of different pesticides on the accumulation of cytosolic Ca²⁺, we conducted a fluorescent assay on N2a cells treated with tomato sample extracts, which were replicates of the E.U. proficiency test sample. The tomato samples were either organically grown or contained 14 different pesticides. The experimental results showed a higher increase of the intracellular Ca²⁺ concentration in cells treated with non-organic samples compared to the cells treated with organic samples.     

C1‐Symmetric Aminosulfoximines in Copper‐Catalyzed Asymmetric Vinylogous Mukaiyama Aldol Reactions

Vinylogous Mukaiyama‐type aldol reactions have been catalyzed by a combination of Cu(OTf)₂ and readily available C₁‐symmetric aminosulfoximines. After a fine‐tuning of the reaction conditions and an optimization of the modularly assembled ligand structure, high stereoselectivities and excellent yields have been achieved in catalyzed reactions involving various electrophile/nucleophile combinations. The relative and absolute configurations of two products were assigned by X‐ray single crystal structure analysis and a comparison of calculated and experimental CD spectra.     

Modulating the acidity: highly acidic Brønsted acids in asymmetric catalysis

Recently, chiral highly acidic Brønsted acids have emerged as powerful catalysts for enantioselective C C and C X bond‐forming reactions. Their strong acidity renders them valuable tools for the activation of imines, carbonyl compounds, and other weakly basic substrates. As a result, new perspectives are opened and highly stereoselective transformations based on the concept of chiral contact‐ion‐pair catalysis can be realized. This Minireview gives an overview of the design and application of these new organocatalysts and presents recent results in this rapidly growing field.     

AutoPPI: An Ensemble of Deep Autoencoders for Protein–Protein Interaction Prediction

Proteins are essential molecules, that must correctly perform their roles for the good health of living organisms. The majority of proteins operate in complexes and the way they interact has pivotal influence on the proper functioning of such organisms. In this study we address the problem of protein–protein interaction and we propose and investigate a method based on the use of an ensemble of autoencoders. Our approach, entitled AutoPPI, adopts a strategy based on two autoencoders, one for each type of interactions (positive and negative) and we advance three types of neural network architectures for the autoencoders. Experiments were performed on several data sets comprising proteins from four different species. The results indicate good performances of our proposed model, with accuracy and AUC values of over 0.97 in all cases. The best performing model relies on a Siamese architecture in both the encoder and the decoder, which advantageously captures common features in protein pairs. Comparisons with other machine learning techniques applied for the same problem prove that AutoPPI outperforms most of its contenders, for the considered data sets.     

Microwave-assisted extraction of trichloroethylene from clay samples

A new method for volatile organic compounds (VOCs) extraction from low-permeability media, such as clay, has been developed and tested using trichloroethylene (TCE) as a model compound. The method is based on microwave-assisted extraction (MAE), which uses microwave energy to heat the extracting solvent and the sample. MAE allows the extraction process to be carried out at elevated temperatures and pressures, which dramatically reduces the time required to complete the process. A custom-made PTFE vessel was used for extraction investigations. TCE analysis was performed using gas chromatography with electron capture detection (GC-ECD). Three different solvents were tested: methanol, 1?:?1 hexane?:?acetone mixture, and 10?:?1 hexane?:?acetone mixture. A comparison of TCE recoveries from clay samples using the new method and the standard methanol extraction method was carried out. The newly developed method and the method currently in use were found to recover similar amounts of TCE. The major advantage of the MAE technique is the very short time needed to obtain complete analyte recovery (6–10?min), which makes it possible to analyse a large number of samples without the need for sample preservation or prolonged storage. Thus, the new method is much more efficient than the existing methods. The technique has a good potential for field application.     

A numerical approximation of parabolic stochastic partial differential equations driven by a Poisson random measure

The paper deals with SPDEs driven by Poisson random measures in Banach spaces and its numerical approximation. We investigate the accuracy of space and time approximation. As the space approximation we consider spectral methods and as time approximation the implicit Euler scheme and the explicit Euler scheme. AMS subject classification (2000) 60H15, 35R30     

Determination of the anchoring constant for a compensated binary cholesteric mixture

The optical transmission and the electric power was simultaneously recorded for the binary compensated cholesteric mixture cholesteryl chloride + cholesteryl nonanoate (70:30 molar). The values of the critical voltages U₁ and U₂ (at which the planar texture changes into a conic focal one, and the cholesteric-nematic transition take place) at different temperatures were obtained. The temperature dependence of the anchoring constant is determined.     

Biomarker Candidates of <Emphasis Type="Italic">Chlamydophila pneumoniae</Emphasis> Proteins and Protein Fragments Identified by Affinity-Proteomics Using FTICR-MS and LC-MS/MS

We report here an affinity-proteomics approach that combines 2D-gel electrophoresis and immunoblotting with high performance mass spectrometry to the identification of both full length protein antigens and antigenic fragments of Chlamydophila pneumoniae (C. pneumoniae). The present affinity-mass spectrometry approach effectively utilized high resolution FTICR mass spectrometry and LC-tandem-MS for protein identification, and enabled the identification of several new highly antigenic C. pneumoniae proteins that were not hitherto reported or previously detected only in other Chlamydia species, such as Chlamydia trachomatis. Moreover, high resolution affinity-MS provided the identification of several neo-antigenic protein fragments containing N- and C-terminal, and central domains such as fragments of the membrane protein Pmp21 and the secreted chlamydial proteasome-like factor (Cpaf), representing specific biomarker candidates.