Polynuclear Metallic Architectures Based on Fluorinated Functionalized Diketonates

K.Y.S. is thankful to the Council for grants of the President of Russian Federation (grant no. 1453.2019.3)

A Rare Example of Discrete Lanthanide–Lithium Tetrakis-β-Diketonates: Synthesis, Structures, and Luminescence Properties

Abstract: The reactions of functionalized lithium CF3-β-diketonate (LiL) with europium(III) and terbium(III) chlorides in acetonitrile afford heterobimetallic complexes with the general formula [LiL-nL4(H2O)](CH3CN) (Ln = Eu (IIIa) and Tb (IIIb)). It is found that Ln–Li tetrakis-β-diketonates can also be prepared by the crystallization from acetonitrile of the previously synthesized complexes [(LnL3)(LiL)(MeOH)] (I) and [(LnL3)(LiL)(H2O)] (II). For the first time, the single crystals of the compounds in a series of Ln–Li tetrakis-β-diketonates are characterized by X-ray diffraction analysis (CIF files CCDC nos. 1973632 (IIIa) and 1973633 (IIIb)). The photoluminescence properties of the synthesized heterometallic complexes in the solid state are studied. © 2020, Pleiades Publishing, Ltd.This work was supported by the Russian Foundation for Basic Research (project no. 18-33-20124), Council for Grants of President of the Russian Federation (grant no. MK-1453.2019.3), and basic themes of the Russian Academy of Sciences (state registration nos. AAAA-A19-119011790132-7 and AAAA-A19-119012490006-1)

Breast cancer risk genes: association analysis in more than 113,000 women

BACKGROUNDGenetic testing for breast cancer susceptibility is widely used, but for many genes, evidence of an association with breast cancer is weak, underlying risk estimates are imprecise, and reliable subtype-specific risk estimates are lacking.METHODSWe used a panel of 34 putative susceptibility genes to perform sequencing on samples from 60,466 women with breast cancer and 53,461 controls. In separate analyses for protein-truncating variants and rare missense variants in these genes, we estimated odds ratios for breast cancer overall and tumor subtypes. We evaluated missense-variant associations according to domain and classification of pathogenicity.RESULTSProtein-truncating variants in 5 genes (ATM, BRCA1, BRCA2, CHEK2, and PALB2) were associated with a risk of breast cancer overall with a P value of less than 0.0001. Protein-truncating variants in 4 other genes (BARD1, RAD51C, RAD51D, and TP53) were associated with a risk of breast cancer overall with a P value of less than 0.05 and a Bayesian false-discovery probability of less than 0.05. For protein-truncating variants in 19 of the remaining 25 genes, the upper limit of the 95% confidence interval of the odds ratio for breast cancer overall was less than 2.0. For protein-truncating variants in ATM and CHEK2, odds ratios were higher for estrogen receptor (ER)-positive disease than for ER-negative disease; for protein-truncating variants in BARD1, BRCA1, BRCA2, PALB2, RAD51C, and RAD51D, odds ratios were higher for ER-negative disease than for ER-positive disease. Rare missense variants (in aggregate) in ATM, CHEK2, and TP53 were associated with a risk of breast cancer overall with a P value of less than 0.001. For BRCA1, BRCA2, and TP53, missense variants (in aggregate) that would be classified as pathogenic according to standard criteria were associated with a risk of breast cancer overall, with the risk being similar to that of protein-truncating variants.CONCLUSIONSThe results of this study define the genes that are most clinically useful for inclusion on panels for the prediction of breast cancer risk, as well as provide estimates of the risks associated with protein-truncating variants, to guide genetic counseling. (Funded by European Union Horizon 2020 programs and others.)Molecular tumour pathology - and tumour geneticsMTG1 - Moleculaire genetica en pathologie van borstkanke

Electroless deposition of Ni-Sn-P and Ni-Sn-Cu-P coatings

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Dimerization of CtIP, a BRCA1- and CtBP-interacting protein, is mediated by an N-terminal coiled-coil motif

This article is hosted on a website external to the CBCRA Open Access Archive. Selecting “View/Open” below will launch the full-text article in another browser window.CtIP is a transcriptional co-regulator that binds a number of proteins involved in cell cycle control and cell development, such as CtBP (C terminus-binding protein), BRCA1 (breast cancer-associated protein-1), and LMO4 (LIM-only protein-4). The only recognizable structural motifs within CtIP are two putative coiled-coil domains located near the N and C termini of the protein. We now show that the N-terminal coiled coil (residues 45-160), but not the C-terminal coiled coil, mediates homodimerization of CtIP in mammalian 293T cells. The N-terminal coiled coil did not facilitate binding to LMO4 and BRCA1 proteins in these cells. A protease-resistant domain (residues 27-168) that minimally encompasses the putative N-terminal coiled coil was identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. This region is predicted to contain two smaller coiled-coil regions. The CtIP-(45-160) dimerization domain is helical and dimeric, indicating that the domain does form a coiled coil. The two smaller domains, CtIP-(45-92) and CtIP-(93-160), also formed dimers of lower binding affinity, but with less helical content than the longer peptide. The hydrodynamic radius of CtIP-(45-160) is the same as those of CtIP-(45-92) and CtIP-(93-160), implying that CtIP-(45-160) does not form a single long coiled coil, but a more compact structure involving homodimerization of the two smaller coiled coils, which fold back as a four-helix bundle or other compact structure. These results suggest a specific model for CtIP homodimerization via its N terminus and contribute to an improved understanding of how this protein might assemble other factors required for its role as a transcriptional corepressor

Simulated annealing technique for fast learning of SOM networks

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensional data. Several experiments are used to compare the proposed approach with the original algorithm and some of its modification and speed-up techniques