Trace metal transformation in gasification

The Energy & Environmental Research Center (EERC) is carrying out an investigation that will provide methods to predict the fate of selected trace elements in integrated gasification combined cycle (IGCC) and integrated gasification fuel cell (IGFC) systems to aid in the development of methods to control the emission of trace elements determined to be air toxics. The goal of this project is to identify the effects of critical chemical and physical transformations associated with trace element behavior in IGCC and IGFC systems. The trace elements included in this project are arsenic, chromium, cadmium, mercury, nickel, selenium, and lead. The research seeks to identify and fill, experimentally and/or theoretically, data gaps that currently exist on the fate and composition of trace elements. The specific objectives are to 1) review the existing literature to identify the type and quantity of trace elements from coal gasification systems, 2) perform laboratory-scale experimentation and computer modeling to enable prediction of trace element emissions, and 3) identify methods to control trace element emissions

The dynamics and neural correlates of audio-visual integration capacity as determined by temporal unpredictability, proactive interference, and SOA

Over 5 experiments, we challenge the idea that the capacity of audio-visual integration need be fixed at 1 item. We observe that the conditions under which audio-visual integration is most likely to exceed 1 occur when stimulus change operates at a slow rather than fast rate of presentation and when the task is of intermediate difficulty such as when low levels of proactive interference (3 rather than 8 interfering visual presentations) are combined with the temporal unpredictability of the critical frame (Experiment 2), or, high levels of proactive interference are combined with the temporal predictability of the critical frame (Experiment 4). Neural data suggest that capacity might also be determined by the quality of perceptual information entering working memory. Experiment 5 supported the proposition that audio-visual integration was at play during the previous experiments. The data are consistent with the dynamic nature usually associated with cross-modal binding, and while audio-visual integration capacity likely cannot exceed uni-modal capacity estimates, performance may be better than being able to associate only one visual stimulus with one auditory stimulus

Speed has an effect on multiple-object tracking independently of the number of close encounters between targets and distractors

Multiple-object tracking (MOT) studies have shown that tracking ability declines as object speed increases. However, this might be attributed solely to the increased number of times that target and distractor objects usually pass close to each other (“close encounters”) when speed is increased, resulting in more target–distractor confusions. The present study investigates whether speed itself affects MOT ability by using displays in which the number of close encounters is held constant across speeds. Observers viewed several pairs of disks, and each pair rotated about the pair’s midpoint and, also, about the center of the display at varying speeds. Results showed that even with the number of close encounters held constant across speeds, increased speed impairs tracking performance, and the effect of speed is greater when the number of targets to be tracked is large. Moreover, neither the effect of number of distractors nor the effect of target–distractor distance was dependent on speed, when speed was isolated from the typical concomitant increase in close encounters. These results imply that increased speed does not impair tracking solely by increasing close encounters. Rather, they support the view that speed affects MOT capacity by requiring more attentional resources to track at higher speeds

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 hospitalization2–4 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

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

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

HEAT TRANSFER CHARACTERISTICS OF TWO PHASE FLOW THROUGH PACKED BEDS

A number of experiments have been performed to obtain the thermal characteristics of two phase flow through packed beds. This data has been analyzed in terms of two parameters: the effective thermal conductivity (K//e), and the wall heat transfer coefficient (H//w). The experimental program involved the cocurrent vertical upflow of heated nitrogen and water through a packed bed of 1/4-inch cylinders

Thermal dispersion and heat transfer in nonisothermal bubble columns

Local axial and radial temperatures were measured at steady-state conditions in a 0.078-m-I.D. bubble column heat exchanger. Nitrogen and water superficial velocity ranges were 0-0.6 m/s and 0-0.02 m/s, respectively. Average column pressures were 3.0, 5.1, and 7.1 atm. The axial temperature profile varied significantly with all conditions encountered. Radial temperature profiles were found to be nearly constant, indicating very good radial mixing. An axial thermal dispersion heat transfer model, capable of representing nonisothermal systems, was employed to characterize the measured bubble column temperature profiles. Thermal dispersion was apparent from large temperature changes in the entrance of the bubble column. Heat transfer coefficients depended on the gas and liquid flow rates. However, the thermal dispersion coefficients depended on linear gas velocity and were a weak function of liquid flow rates. The thermal dispersion coefficients obtained in this study were found to be consistent with other investigations. In addition, they were compared to the mass dispersion coefficients obtained by other studies and found to be in good agreement. © 1983, Taylor & Francis Group, LLC. All rights reserved

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3–7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease