Avoimen systeemin magmaattisten prosessien diagnosointi Magmakammiosimulaattorilla. Osa II: hivenalkuaineet ja isotoopit

The Magma Chamber Simulator (MCS) is a thermodynamic model that computes the phase, thermal, and compositional evolution of a multiphase–multicomponent system of a Fractionally Crystallizing resident body of magma (i.e., melt ± solids ± fluid), linked wallrock that may either be assimilated as Anatectic melts or wholesale as Stoped blocks, and multiple Recharge reservoirs (RnASnFC system, where n is the number of user-selected recharge events). MCS calculations occur in two stages; the first utilizes mass and energy balance to produce thermodynamically constrained major element and phase equilibria information for an RnASnFC system; this tool is informally called MCS-PhaseEQ, and is described in a companion paper (Bohrson et al. 2020). The second stage of modeling, called MCS-Traces, calculates the RASFC evolution of up to 48 trace elements and seven radiogenic and one stable isotopic system (Sr, Nd, Hf, 3xPb, Os, and O) for the resident melt. In addition, trace element concentrations are calculated for bulk residual wallrock and each solid (± fluid) phase in the cumulate reservoir and residual wallrock. Input consists of (1) initial trace element concentrations and isotope ratios for the parental melt, wallrock, and recharge magmas/stoped wallrock blocks and (2) solid-melt and solid–fluid partition coefficients (optional temperature-dependence) for stable phases in the resident magma and residual wallrock. Output can be easily read and processed from tabulated worksheets. We provide trace element and isotopic results for the same example cases (FC, R2FC, AFC, S2FC, and R2AFC) presented in the companion paper. These simulations show that recharge processes can be difficult to recognize based on trace element data alone unless there is an independent reference frame of successive recharge events or if serial recharge magmas are sufficiently distinct in composition relative to the parental magma or magmas on the fractionation trend. In contrast, assimilation of wallrock is likely to have a notable effect on incompatible trace element and isotopic compositions of the contaminated resident melt. The magnitude of these effects depends on several factors incorporated into both stages of MCS calculations (e.g., phase equilibria, trace element partitioning, style of assimilation, and geochemistry of the starting materials). Significantly, the effects of assimilation can be counterintuitive and very different from simple scenarios (e.g., bulk mixing of magma and wallrock) that do not take account phase equilibria. Considerable caution should be practiced in ruling out potential assimilation scenarios in natural systems based upon simple geochemical “rules of thumb”. The lack of simplistic responses to open-system processes underscores the need for thermodynamical RASFC models that take into account mass and energy conservation. MCS-Traces provides an unprecedented and detailed framework for utilizing thermodynamic constraints and element partitioning to document trace element and isotopic evolution of igneous systems. Continued development of the Magma Chamber Simulator will focus on easier accessibility and additional capabilities that will allow the tool to better reproduce the documented natural complexities of open-system magmatic processes.Peer reviewe

Avoimen systeemin magmaattisten prosessien diagnosointi Magmakammiosimulaattorilla. Osa I: pääalkuaineet ja faasitasapainot

The Magma Chamber Simulator (MCS) is a thermodynamic tool for modeling the evolution of magmatic systems that are open with respect to assimilation of partial melts or stoped blocks, magma recharge + mixing, and fractional crystallization. MCS is available for both PC and Mac. In the MCS, the thermal, mass, and compositional evolution of a multicomponent-multiphase composite system of resident magma, wallrock, and recharge reservoirs is tracked by rigorous self-consistent thermodynamic modeling. A Recharge-Assimilation (Assimilated partial melt or Stoped blocks)-Fractional Crystallization (R(n)AS(n)FC;n(tot) The trace element and isotope MCS computational tool (MCS-Traces) is described in a separate contribution (part II).Peer reviewe

Activating the knowledge-to-action cycle for geriatric care in India

Despite a rapidly aging population, geriatrics - the branch of medicine that focuses on healthcare of the elderly - is relatively new in India, with many practicing physicians having little knowledge of the clinical and functional implications of aging. Negative attitudes and limited awareness, knowledge or acceptance of geriatrics as a legitimate discipline contribute to inaccessible and poor quality care for India's old. The aim of this paper is to argue that knowledge translation is a potentially effective tool for engaging Indian healthcare providers in the delivery of high quality geriatric care. The paper describes India's context, including demographics, challenges and current policies, summarizes evidence on provider behaviour change, and integrates the two in order to propose an action plan for promoting improvements in geriatric care

Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article

Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study

Background The SARS-CoV-2 variant B.1.1.7 was first identified in December, 2020, in England. We aimed to investigate whether increases in the proportion of infections with this variant are associated with differences in symptoms or disease course, reinfection rates, or transmissibility. Methods We did an ecological study to examine the association between the regional proportion of infections with the SARS-CoV-2 B.1.1.7 variant and reported symptoms, disease course, rates of reinfection, and transmissibility. Data on types and duration of symptoms were obtained from longitudinal reports from users of the COVID Symptom Study app who reported a positive test for COVID-19 between Sept 28 and Dec 27, 2020 (during which the prevalence of B.1.1.7 increased most notably in parts of the UK). From this dataset, we also estimated the frequency of possible reinfection, defined as the presence of two reported positive tests separated by more than 90 days with a period of reporting no symptoms for more than 7 days before the second positive test. The proportion of SARS-CoV-2 infections with the B.1.1.7 variant across the UK was estimated with use of genomic data from the COVID-19 Genomics UK Consortium and data from Public Health England on spike-gene target failure (a non-specific indicator of the B.1.1.7 variant) in community cases in England. We used linear regression to examine the association between reported symptoms and proportion of B.1.1.7. We assessed the Spearman correlation between the proportion of B.1.1.7 cases and number of reinfections over time, and between the number of positive tests and reinfections. We estimated incidence for B.1.1.7 and previous variants, and compared the effective reproduction number, Rt, for the two incidence estimates. Findings From Sept 28 to Dec 27, 2020, positive COVID-19 tests were reported by 36 920 COVID Symptom Study app users whose region was known and who reported as healthy on app sign-up. We found no changes in reported symptoms or disease duration associated with B.1.1.7. For the same period, possible reinfections were identified in 249 (0·7% [95% CI 0·6–0·8]) of 36 509 app users who reported a positive swab test before Oct 1, 2020, but there was no evidence that the frequency of reinfections was higher for the B.1.1.7 variant than for pre-existing variants. Reinfection occurrences were more positively correlated with the overall regional rise in cases (Spearman correlation 0·56–0·69 for South East, London, and East of England) than with the regional increase in the proportion of infections with the B.1.1.7 variant (Spearman correlation 0·38–0·56 in the same regions), suggesting B.1.1.7 does not substantially alter the risk of reinfection. We found a multiplicative increase in the Rt of B.1.1.7 by a factor of 1·35 (95% CI 1·02–1·69) relative to pre-existing variants. However, Rt fell below 1 during regional and national lockdowns, even in regions with high proportions of infections with the B.1.1.7 variant. Interpretation The lack of change in symptoms identified in this study indicates that existing testing and surveillance infrastructure do not need to change specifically for the B.1.1.7 variant. In addition, given that there was no apparent increase in the reinfection rate, vaccines are likely to remain effective against the B.1.1.7 variant. Funding Zoe Global, Department of Health (UK), Wellcome Trust, Engineering and Physical Sciences Research Council (UK), National Institute for Health Research (UK), Medical Research Council (UK), Alzheimer's Society