Synthesis of 1-aryl-3H-[1,2,5]triazepino[5,4-a]benzimidazol-4(5H)-ones and quantum chemical investigation of the rotamers of the Boc-protected hydrazide key intermediate

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PASE synthesis of 1-azolyl-1H-1,2,4-triazoles by the reaction of diazoazoles with ethyl isocyanoacetate

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Modeling of the glycine tripeptide cyclization in the Ser65Gly/Tyr66Gly mutant of green fluorescent protein

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Sorbents based on transition metal salts for the retention of low molecular weight N-, O- and S-containing organic compounds from the air flow

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Solvent-free silica gel mediated decarboxylation of C–O coupling products of β-diketones and β-oxo esters with malonyl peroxides

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Automatic hierarchy classification in venation networks using directional morphological filtering for hierarchical structure traits extraction

The extraction of vein traits from venation networks is of great significance to the development of a variety of research fields, such as evolutionary biology. However, traditional studies normally target to the extraction of reticulate structure traits (ReSTs), which is not sufficient enough to distinguish the difference between vein orders. For hierarchical structure traits (HiSTs), only a few tools have made attempts with human assistance, and obviously are not practical for large-scale traits extraction. Thus, there is a necessity to develop the method of automated vein hierarchy classification, raising a new challenge yet to be addressed. We propose a novel vein hierarchy classification method based on directional morphological filtering to automatically classify vein orders. Different from traditional methods, our method classify vein orders from highly dense venation networks for the extraction of traits with ecological significance. To the best of our knowledge, this is the first attempt to automatically classify vein hierarchy. To evaluate the performance of our method, we prepare a soybean transmission image dataset (STID) composed of 1200 soybean leaf images and the vein orders of these leaves are manually coarsely annotated by experts as ground truth. We apply our method to classify vein orders of each leaf in the dataset. Compared with ground truth, the proposed method achieves great performance, while the average deviation on major vein is less than 5 pixels and the average completeness on second-order veins reaches 54.28%.     

The double deflating technique for irreducible singular M-matrix algebraic Riccati equations in the critical case

As is known, Alternating-Directional Doubling Algorithm (ADDA) is quadratically convergent for computing the minimal nonnegative solution of an irreducible singular M-matrix algebraic Riccati equation (MARE) in the noncritical case or a nonsingular MARE, but ADDA is only linearly convergent in the critical case. The drawback can be overcome by deflating techniques for an irreducible singular MARE so that the speed of quadratic convergence is still preserved in the critical case and accelerated in the noncritical case. In this paper, we proposed an improved deflating technique to accelerate further the convergence speed – the double deflating technique for an irreducible singular MARE in the critical case. We proved that ADDA is quadratically convergent instead of linearly when it is applied to the deflated algebraic Riccati equation (ARE) obtained by a double deflating technique. We also showed that the double deflating technique is better than the deflating technique from the perspective of dimension of the deflated ARE. Numerical experiments are provided to illustrate that our double deflating technique is effective.     

Green synthesis and chemical characterization of silver nanoparticles obtained using Allium saralicum aqueous extract and survey of in vitro antioxidant,cytotoxic, antibacterial and antifungal properties

Allium saralicum R.M. Fritsch has been used in Iranian traditional medicine as a remedial supplement for microbial diseases. This paper reports the green synthesis, chemical characterization and antioxidant, cytotoxic, antibacterial and antifungal properties of silver nanoparticles obtained using aqueous extract of A. saralicum leaves. In this synthesis, no surfactants or stabilizers were used. For characterization, UV–visible spectroscopy, transmission electron microscopy, X‐ray diffraction, energy‐dispersive X‐ray spectroscopy and field emission scanning electron microscopy were used. 2,2‐Diphenyl‐1‐picrylhydrazyl was used in experiments to assess the antioxidant potential of the silver nanoparticles, which revealed an impressive prevention in comparison with butylated hydroxytoluene. The synthesized silver nanoparticles at low doses (1–250 μg dl^?1) did not show marked cytotoxic activity (against cervical cancer cells (Hela), breast cancer cells (MCF‐7) and human embryonic kidney cells (HEK‐293)). Agar diffusion tests were applied to determine the antibacterial and antifungal characteristics. Compared with all standard antimicrobials, the silver nanoparticles showed higher antibacterial and antifungal activities (p ≤ 0.01). Also, the silver nanoparticles inhibited the growth of all bacteria and fungi at concentrations of 31–250 μg ml^?1, and destroyed them at concentrations of 31–500 μg ml^?1 (p ≤ 0.01). Because the silver nanoparticles obtained using aqueous extract of A. saralicum leaves have antioxidant, non‐cytotoxic, antifungal and antibacterial potentials, they can be used as a medical supplement or drug.