Patterns and source analysis for atmospheric mercury at Auchencorth Moss, Scotland

Gaseous elemental (GEM), particulate bound (PBM) and gaseous oxidised (GOM) mercury species were monitored between 2009-2011 at the rural monitoring site, Auchencorth Moss, Scotland using the Tekran speciation monitoring system. GEM average for the three year period was 1.40 ± 0.19 ng m-3 which is comparable with other northern hemisphere studies. PBM and GOM concentrations are very low in 2009 and 2010 with geometric mean (x/÷ Standard Deviation) PBM values of 2.56 (x/÷ 3.44) and 0.03 (x/÷ 17.72) pg m-3 and geometric mean (x/÷ Standard Deviation) GOM values of 0.11 (x/÷ 4.94) and 0.09 (x/÷ 8.88) pg m-3 respectively. Using wind sector analysis and air mass back trajectories, the importance of local and regional sources on speciated mercury are investigated and we show the long range contribution to GEM from continental Europe, and that the lowest levels are associated with polar and marine air masses from the north west sector

Management of Anticoagulant and Thrombolytic Agents in Deep Venous Thrombosis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68445/2/10.1177_153857448201600101.pd

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

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 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

Effects of organism preparation in metallothionein and metal analysis in marine invertebrates for biomonitoring marine pollution

Metallothionein (MT) is established as a potentially useful biomarker for monitoring aquatic pollution. This paper addresses widespread inconsistencies in storage conditions, tissue type selection and pre-treatment of samples before MT and metal analysis in biomarker studies. This variation hampers comparability and so the widespread implementation of this monitoring approach. Actively sampled Mytilus edulis in Southampton Water, UK were exposed to different storage temperatures, a variety tissue types were analysed, and various pre-treatments of transportation on ice, transportation in seawater, depuration, and rapid dissection in the field were examined. Storage temperatures of -20?C were found to be adequate for periods of at least ten weeks, as MT was not reduced by protein degradation compared with samples kept at -80?C. Whole tissue and digestive gland concentrations of MT and metals were significantly positively correlated and directly relatable. MT in the digestive gland appeared to be more responsive to metals than in whole tissue, where it may be diluted, masking MT responses. However, longer study periods may suffer the effects of mass changes to the digestive gland, which alters MT concentration, and it may therefore be advisable to measure whole tissue. Depuration and transportation in seawater reduced both MT and metal concentrations in the digestive gland, and few correlations between MT and metals were identified for these treatments. It is therefore recommended that: i) samples are transported to the laboratory on ice and dissected as soon as possible thereafter, ii) depuration should not be used when examining MT response to metal exposure until further research clarifying its utility is reported, iii) either whole tissue or the digestive gland can be used to measure MT, though whole tissue may be preferable on long-term studies, and iv) organisms can be stored at -20?C before analysis for up to ten weeks. These practices can be applied to future biomonitoring studies and will improve the comparability and repeatability of using MT as a biomarker

The matrix of group analysis. An historical perspective

In 1939 a German—Jewish psychoanalyst who had left Germany in 1933 and who had, in 1938 moved to Exeter, a small city in the south west of England, began to practise group analysis. Soon caught up in military psychiatry, where he had ample opportunity to put his ideas and experience into practice, S.H. Foulkes elaborated his theoretical ideas in his first book in 1948. Thus the practice of group analysis began in England, geographically far from Frankfurt and from Vienna, where Foulkes had trained and worked, and in relative professional isolation. This is often a necessary condition for original work; compare the example of Ronald Fairbairn, contemporaneous in Edinburgh. But no man is an island and Foulkes' work has to be set in the context of the European ideas of his intellectual and social inheritance. We must situate him in history, figure against ground, as he himself insisted was a basic component of group-analytic theory

STORM Climate Change synthetic tropical cyclone tracks

UPDATE 22/06/2023: Tom Russell (Oxford University) and colleagues have created global .tiff maps for the return period datasets. You can find them here: https://zenodo.org/record/7438145Datasets consisting of 10,000 years of synthetic tropical cyclone tracks, generated using the Synthetic Tropical cyclOne geneRation Model (STORM) algorithm (see Bloemendaal et al (2020)). The dataset is generated by extracting the climate change signal from each of the four general circulation models listed below, and adding this signal to the historical data from IBTrACS. This new dataset is then used as input for STORM, and resembles future-climate (2015-2050; RCP8.5/SSP5) conditions. The data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones.Climate change information from the following models is used in this study (each model has its own 10.000 years of STORM data):1) CMCC-CM2-VHR42) CNRM-CM6-1-HR3) EC-Earth3P-HR4) HAdGEM3-GC31-HMSee Roberts et al (2020) for more information on these models.</p

STORM Climate Change synthetic tropical cyclone tracks

UPDATE 22/06/2023: Tom Russell (Oxford University) and colleagues have created global .tiff maps for the return period datasets. You can find them here: https://zenodo.org/record/7438145Datasets consisting of 10,000 years of synthetic tropical cyclone tracks, generated using the Synthetic Tropical cyclOne geneRation Model (STORM) algorithm (see Bloemendaal et al (2020)). The dataset is generated by extracting the climate change signal from each of the four general circulation models listed below, and adding this signal to the historical data from IBTrACS. This new dataset is then used as input for STORM, and resembles future-climate (2015-2050; RCP8.5/SSP5) conditions. The data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones.Climate change information from the following models is used in this study (each model has its own 10.000 years of STORM data):1) CMCC-CM2-VHR42) CNRM-CM6-1-HR3) EC-Earth3P-HR4) HAdGEM3-GC31-HMSee Roberts et al (2020) for more information on these models.</p