Ni(II)-Schiff base/SBA-15: a nanostructure and reusable catalyst for one-pot three-component green synthesis of 3,4-dihydropyrano[3,2-c]chromene derivatives

Research on Chemical Intermediates - An efficient, rapid, and environmentally benign Ni(II)-Schiff base/SBA-15-catalyzed one-pot three-component cyclocondensation process was described via the...     

Glycoconjugated Metallohelices have Improved Nuclear Delivery and Suppress Tumour Growth In Vivo

Monosaccharides are added to the hydrophilic face of a self‐assembled asymmetric Fe^II metallohelix, using CuAAC chemistry. The sixteen resulting architectures are water‐stable and optically pure, and exhibit improved antiproliferative selectivity against colon cancer cells (HCT116 p53^+/+) with respect to the non‐cancerous ARPE‐19 cell line. While the most selective compound is a glucose‐appended enantiomer, its cellular entry is not mainly glucose transporter‐mediated. Glucose conjugation nevertheless increases nuclear delivery ca 2.5‐fold, and a non‐destructive interaction with DNA is indicated. Addition of the glucose units affects the binding orientation of the metallohelix to naked DNA, but does not substantially alter the overall affinity. In a mouse model, the glucose conjugated compound was far better tolerated, and tumour growth delays for the parent compound (2.6 d) were improved to 4.3 d; performance as good as cisplatin but with the advantage of no weight loss in the subjects.     

Fast simultaneous LC/MS/MS determination of 10 active compounds in human serum for therapeutic drug monitoring in psychiatric medication

A UPLC/MS/MS method with simple protein precipitation has been validated for the fast simultaneous analysis of agomelatine, asenapine, amisulpride, iloperidone, zotepine, melperone, ziprasidone, vilazodone, aripiprazole and its metabolite dehydro‐aripiprazole in human serum. Alprenolol was applied as an internal standard. A BEH C₁₈ (2.1 × 50 mm, 1.7 µm) column provided chromatographic separation of analytes using a binary mobile phase gradient (A, 2 mmol/L ammonium acetate, 0.1% formic acid in 5% acetonitrile, v/v/v; B, 2 mmol/L ammonium acetate, 0.1% formic acid in 95% acetonitrile, v/v/v). Mass spectrometric detection was performed in the positive electrospray ionization mode and ion suppression owing to matrix effects was evaluated. The validation criteria were determined: linearity, precision, accuracy, recovery, limit of detection, limit of quantification, reproducibility and matrix effect. The concentration range was as follows: 0.25–1000 ng/mL for agomelatine; 0.25–100 ng/mL for asenapine and iloperidone; 2.5–1000 ng/mL for amisulpride, aripiprazole, vilazodone and zotepine; 2.3–924.6 ng/mL for dehydroaripiprazole; 2.2–878.4 ng/mL for melperone; and 2.2–883.5 ng/mL for ziprasidone. Limits of quantitation below a therapeutic reference range were achieved for all analytes. Intra‐run precision of 0.4–5.5 %, inter‐run precision of 0.6–8.2% and overall recovery of 87.9–114.1% were obtained. The validated method was successfully implemented into routine practice for therapeutic drug monitoring in our hospital. Copyright © 2015 John Wiley & Sons, Ltd.     

Riordan group involutions

We study involutions in the Riordan group, especially those with combinatorial meaning. We give a new determinantal criterion for a matrix to be a Riordan involution and examine several classes of examples. A complete characterization of involutions in the Appell subgroup is developed. In another direction we find several examples that generalize the RNA matrix but are of independent interest.     

Dimension Gaps between Representability and Collapsibility

A simplicial complex K\mathsf{K} is called d -representable if it is the nerve of a collection of convex sets in ℝ ^d ; K\mathsf{K} is d -collapsible if it can be reduced to an empty complex by repeatedly removing a face of dimension at most d−1 that is contained in a unique maximal face; and K\mathsf{K} is d -Leray if every induced subcomplex of K\mathsf{K} has vanishing homology of dimension d and larger. It is known that d-representable implies d-collapsible implies d-Leray, and no two of these notions coincide for d≥2. The famous Helly theorem and other important results in discrete geometry can be regarded as results about d-representable complexes, and in many of these results, “d-representable” in the assumption can be replaced by “d-collapsible” or even “d-Leray.”     

Simple proofs of open problems about the structure of involutions in the Riordan group

We prove that if D=(g(x),f(x)) is an element of order 2 in the Riordan group then g(x)=±exp[Φ(x,xf(x)] for some antisymmetric function Φ(x,z). Also we prove that every element of order 2 in the Riordan group can be written as BMB^-1 for some element B and M=(1,-1) in the Riordan group. These proofs provide solutions to two open problems presented by L. Shapiro [L.W. Shapiro, Some open questions about random walks, involutions, limiting distributions and generating functions, Adv. Appl. Math. 27 (2001) 585-596].     

A Bootstrap Framework for Aggregating within and between Feature Selection Methods

In the past decade, big data has become increasingly prevalent in a large number of applications. As a result, datasets suffering from noise and redundancy issues have necessitated the use of feature selection across multiple domains. However, a common concern in feature selection is that different approaches can give very different results when applied to similar datasets. Aggregating the results of different selection methods helps to resolve this concern and control the diversity of selected feature subsets. In this work, we implemented a general framework for the ensemble of multiple feature selection methods. Based on diversified datasets generated from the original set of observations, we aggregated the importance scores generated by multiple feature selection techniques using two methods: the Within Aggregation Method (WAM), which refers to aggregating importance scores within a single feature selection; and the Between Aggregation Method (BAM), which refers to aggregating importance scores between multiple feature selection methods. We applied the proposed framework on 13 real datasets with diverse performances and characteristics. The experimental evaluation showed that WAM provides an effective tool for determining the best feature selection method for a given dataset. WAM has also shown greater stability than BAM in terms of identifying important features. The computational demands of the two methods appeared to be comparable. The results of this work suggest that by applying both WAM and BAM, practitioners can gain a deeper understanding of the feature selection process.