Unified flexible total synthesis of chlorofusin and artificial Click mimics as antagonists against p53–HDM2 interactions

A practical HPLC-free total synthesis of chlorofusin has been successfully accomplished in this work. The new synthesis showed great flexibility and convenience in generating artificial natural product-like mimics through Click chemistry, and enabled us to further investigate the biological importance of the chromophore and the spiro-aminal functionality of the natural product. The entire skeleton is believed to be essential for satisfactory biological activities of both natural chlorofusin and unnatural mimics. Two artificial Click hybrids were found to exhibit improved inhibitory activity against p53–HDM2 bindings over the natural product.     

Quantification of fructo-oligosaccharides based on the evaluation of oligomer ratios using an artificial neural network

The application of an internal standard in quantitative analysis is desirable in order to correct for variations in sample preparation and instrumental response. In mass spectrometry of organic compounds, the internal standard is preferably labelled with a stable isotope, such as ¹⁸O, ¹⁵N or ¹³C. In this study, a method for the quantification of fructo-oligosaccharides using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI TOF MS) was proposed and tested on raftilose, a partially hydrolysed inulin with a degree of polymeration 2-7. A tetraoligosaccharide nystose, which is chemically identical to the raftilose tetramer, was used as an internal standard rather than an isotope-labelled analyte. Two mathematical approaches used for data processing, conventional calculations and artificial neural networks (ANN), were compared. The conventional data processing relies on the assumption that a constant oligomer dispersion profile will change after the addition of the internal standard and some simple numerical calculations. On the other hand, ANN was found to compensate for a non-linear MALDI response and variations in the oligomer dispersion profile with raftilose concentration. As a result, the application of ANN led to lower quantification errors and excellent day-to-day repeatability compared to the conventional data analysis. The developed method is feasible for MS quantification of raftilose in the range of 10-750 pg with errors below 7%. The content of raftilose was determined in dietary cream; application can be extended to other similar polymers. It should be stressed that no special optimisation of the MALDI process was carried out. A common MALDI matrix and sample preparation were used and only the basic parameters, such as sampling and laser energy, were optimised prior to quantification.     

Artificial neural network for quantitative determination of total protein in yogurt by infrared spectrometry

A method has been introduced for quantitative determination of protein content in yogurt samples based on the characteristic absorbance of protein in 1800-1500 cm^− 1 spectral region by mid-FTIR spectroscopy and chemometrics. Successive Projection Algorithm (SPA) wavelength selection procedure, coupled with feed forward Back-Propagation Artificial Neural Network (BP-ANN) model was the benefited chemometric technique. Relative Error of Prediction (REP) in BP-ANN and SPA-BP-ANN methods for training set was 7.25 and 3.70 respectively. Considering the complexity of the sample, the ANN model was found to be reliable, while the proposed method is rapid and simple, without any sample preparation step.     

Anion recognition by 1,3-disiloxane-1,1,3,3-tetraols in organic solvents

Anion recognition by 1,3-disiloxane-1,1,3,3-tetraols has been elucidated by ¹H NMR titrations and ESI-MS in organic solvents. The association constants of the receptors for halide anions are larger than those of silanediol and 1,3-disiloxane-1,3-diol due to the cooperative hydrogen bonds by four silanol hydroxy groups of 1,3-disiloxane-1,1,3,3-tetraols.     

THE ELLIPSOID ARTIFICIAL BOUNDARY METHOD FOR THREE-DIMENSIONAL UNBOUNDED DOMAINS

The artificial boundary method is applied to solve three-dimensional exterior problems. Two kind of rotating ellipsoids are chosen as the artificial boundaries and the exact artificial boundary conditions are derived explicitly in terms of an infinite series. Then the well-posedness of the coupled variational problem is obtained. It is found that error estimates derived depend on the mesh size, truncation term and the location of the artificial boundary. Three numerical examples are presented to demonstrate the effectiveness and accuracy of the proposed method.     

Multi-objective Bayesian Artificial Immune System: Empirical Evaluation and Comparative Analyses

Recently, we have proposed a Multi-Objective Bayesian Artificial Immune System (MOBAIS) to deal effectively with building blocks (high-quality partial solutions coded in the solution vector) in combinatorial multi-objective problems. By replacing the mutation and cloning operators with a probabilistic model, more specifically a Bayesian network representing the joint distribution of promising solutions, MOBAIS takes into account the relationships among the variables of the problem, avoiding the disruption of already obtained high-quality partial solutions. The preliminary results have indicated that our proposal is able to properly build the Pareto front. Motivated by this scenario, this paper better formalizes the proposal and investigates its usefulness on more challenging problems. In addition, an important enhancement regarding the Bayesian network learning was incorporated into the algorithm in order to speed up its execution. To conclude, we compare MOBAIS with state-of-the-art algorithms taking into account quantitative aspects of the Pareto front found by the algorithms. MOBAIS outperforms the contenders in terms of the quality of the obtained solutions and requires an amount of computational resource inferior or compatible with the contenders.     

Representation in the (Artificial) Immune System

Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, or, modelling biologically plausible dynamical systems, with little overlap between. We propose that this dichotomy is somewhat to blame for the lack of significant advancement of the field in either direction and demonstrate how a simplistic interpretation of Perelson’s shape-space formalism may have largely contributed to this dichotomy. In this paper, we motivate and derive an alternative representational abstraction. To do so we consider the validity of shape-space from both the biological and machine learning perspectives. We then take steps towards formally integrating these perspectives into a coherent computational model of notions such as life-long learning, degeneracy, constructive representations and contextual recognition—rhetoric that has long inspired work in AIS, while remaining largely devoid of operational definition.     

From manganese complexes to nano-sized manganese oxides as water-oxidizing catalysts for artificial photosynthetic systems: Insights from the Zanjan team

On the occasion of the international year of light and light-based technologies, I provide a short overview of our recent findings and personal view on the Mn-based water-oxidizing catalysts and the related sophisticated strategies toward water oxidation, which are promising in the artificial photosynthetic systems.     

Destabilization of artificial biomembrane induced by the penetration of tryptophan

The effect of tryptophan on the membrane stability was studied by using three artificial biological membranes including liposome, Langmuir monolayer and solid supported bilayer lipid membrane (s-BLM) as models. All the results indicate that the penetration of tryptophan can destabilize different artificial biological membranes. The diameter of liposome and the leakage of calcein from liposome increased with the increase of tryptophan concentration because the penetration of tryptophan was beneficial for dehydrating the polar head groups of lipids and the formation of fusion intermediates. π-A isotherms of lecithin on the subphase of tryptophan solution further confirm that tryptophan can penetrate into lipid monolayer and reduce the stability of lipid monolayer. When the concentration of tryptophan increased from 0 to 2 × 10⁻³ mol L⁻¹, the limiting molecular area of lecithin increased from 110.5 to 138.5 Å², but the collapse pressure of the monolayer decreased from 47.6 to 42.3 mN m⁻¹, indicating the destabilization of lipid monolayer caused by the penetration of tryptophan. The resistance spectra of s-BLM demonstrate that the existence of tryptophan leads to the formation of some defects in s-BLM and the destabilization of s-BLM. The values of electron-transfer resistance and double layer capacitance respectively decreased from 5.765 × 10⁶ Ω and 3.573 × 10⁻⁸ F to 1.391 × 10⁶ Ω and 3.340 × 10⁻⁸ F when the concentration of tryptophan increased from 0 to 2 × 10⁻³ mol L⁻¹. Correspondingly, the breakdown voltage of s-BLM decreased from 2.51 to 1.72 V.