12-Eth­oxy-2,3,8,9-tetra­methoxy­benzo[c]phenanthridine dichloro­methane solvate

The title compound, C23H23NO5·CH2Cl2, was obtained via the alkyl­ation of the 12-hydr­oxy-2,3,8,9-tetra­methoxy­benzo[c]phenanthridine salt. The benzo[c]phenanthridine ring system is essentially planar, with a mean out-of-plane deviation of 0.026 Å. A dicloromethane mol­ecule of solvation is present and located between the sheets of phenanthridine mol­ecules, preventing any significant inter­molecular hydrogen-bonding or π–π inter­actions

1,3-Bis(2,4,6-trimethylphenyl)imidazolium perchlorate

The title compound, C21H25N2+·ClO4−, arose as an unexpected oxidation product of the carbene 1,3-bis(2,4,6-trimethylphenyl)-1,3-dihydro-2H-imidazol-2-ylidine in methanol. It crystallizes with two unique cations and anions in the P-type monoclinic unit cell. The five-membered cationic imidazolium rings are essentially planar and in each imidazolium cation the phenyl rings of the 2,4,6-trimethylphenyl groups are staggered with respect to the imidazolium ring [dihedral angles ranging from 60.9 (3) to 86.3 (3)°]. In the crystal, a hydrogen-bonding network is created via C—H...O interactions between the imidazolium ring H atoms and the perchlorate-anion oxygen atoms. The crystal studied was refined as an inversion twin

Synthesis, structure and electrochemistry of nitrogen base adducts of tetraacetatodiruthenium(II,III): dependence of redox potential and Ru-Ru bond length on axial ligand donor strength

Diadduct complexes of the mixed-valent form of diruthenium tetraacetate, [Ru-2(mu-O2CCH3)(4)L-2](PF6), with L = N-heterocyclic axial ligands quinuclidine (quin) (1), 4-methylpyridine (4-Mepy) (2), pyridine (py) (3), 4-cyanopyridine (4-CNpy) (4), 3-cyanopyridine (3-CNpy) (5) and 4-phenylpyridine (4-Phpy) (6) have been synthesized and all but 5 were characterized by X-ray crystallography to study the effect of the variation of the donor number (DN) of L on the Ru-Ru and Ru-L-ax bond lengths, the magnetic moment, the electronic spectral properties and the redox potential. When data from previous studies on O-donor adducts was also included a DN range of 18-61 could be established. Over this range the Ru-Ru bond length increases slightly from 2.265(1) to 2.2917(6) Angstrom as the donor number is increased from 18 (in [Ru-2(mu-O2CCH3)(4)(H2O)(2)](PF6)) to 61 in 1. UV - Vis measurements show a very slight increase in energy of the pi(Ru-O, Ru-2)-->pi*(Ru-2) transition, however, room temperature magnetic susceptibility measurements show no change in the magnetic moment over the same range of donor numbers. Electrochemical measurements in 1,2-dichloroethane of the Ru-2(4+/5+) redox couple show a decrease in the E-1/2 of 292 mV on going from complex 5 (weakest N-donor) to complex 1 (strongest N-donor). The E-1/2 range is over 400 mV when the unligated [Ru-2(mu-O2C(CH2)(6)CH3)(4)] complex is included (DN = 1 for dichloromethane). The variation of axial ligand base strength does not effect, the near-degeneracy of the (pi*delta*)(3) HOMO or the pi-->pi* energy gap, however, the actual (pi*delta*)(3) HOMO energy varies significantly and increases as the basicity of the axial ligand increases allowing selective tuning of the redox potential. (C) 2000 Elsevier Science S.A. All rights reserved

A pentaammineruthenium(III) dimer with the novel bridging ligand 4,4′-dicyanamidobiphenyl dianion

The novel ligand 4,4′-dicyanamidobiphenyl dianion (bp2-) has been synthesized and characterized by 13C NMR spectroscopy, cyclic voltammetry, and crystallography. The crystal structure of [Ph4As]2[bp]·H2O showed that bp2- is approximately planar with a dihedral angle of 8.2° between phenyl ring planes and the cyanamide groups in an ami conformation. The water of crystallization is asymmetrically hydrogen bonded between cyanamide groups of adjacent bp2- ions. The crystal data for C62H48N4As2+H2O are monoclinic crystal system and space group P21/c with a = 12.998(5) Å, b = 13.465(4) Å, c = 28.703(13) Å, β = 98.94(3)°, V = 4963(3) Å3, and Z = 4. The structure was refined by using 4555 reflections with I > 2.5σ(I) to an R factor of 0.058. The complex, [{(NH3}5Ru}2(μ-bp)][X]4, where X = tosylate or PF6 - ions, was also synthesized and characterized by 1H NMR spectroscopy, cyclic voltammetry, spectroelectrochemistry, and temperature-dependent magnetic susceptibility measurements. From cyclic voltammetry measurements, the comproportionation constants to form the mixed-valence complex [{(NH3}5Ru)2(μ-bp)]3+ were estimated to be 4.1, 16, and 22 in water, acetonitrile, and nitromethane, respectively. The trend and magnitude of Kc suggests solvent valence trapping of a weakly coupled Class II ion. The MMCT band of the mixed-valence complex had to be deconvoluted from the low-energy LMCT band and had the following properties in acetonitrile, νmax = 8400 cm-1, εmax = 3300 M-1 cm-1, and Δν1/2 = 3300 cm-1. The weak superexchange mediating properties of bp2- compared to 1,4-dicyanamidobenzene dianion were suggested to arise from the

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.

Whole-genome sequencing reveals host factors underlying critical COVID-19

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease