Examining the relationship between Supplemental Instruction (SI) and student retention at a doctoral extensive institution

This study tracked 3,286 students over a five-year period who were enrolled in entry-level biology, chemistry, mathematics, and physics courses offering SI in the fall 1999 to see if they were retained or graduated at a Midwestern doctoral extensive institution and identified which predictor variables (demographic, achievement, and level of SI participation) most significantly predicted student retention or graduation. Chi-square analysis, based on two-way contingency tables indicated that SI participants are retained at higher rates than non-SI participants while having lower mean ACT composite scores and fewer semesters of high school preparation in calculus, chemistry, and physics. Backward stepwise multiple logistic regression analysis was used to determine that the most significant predictor of student retention/graduation was high school rank. Positive predictors for the various disciplines across the five year period included the number of SI sessions attended the number of transfer credits earned, and the number of semesters of high school calculus, chemistry, or physics. Negative predictors of student retention or graduation included Pell Grant eligibility and being a member of ethnic minority group.;The results of this study, in addition to making a significant contribution to literature on retention and SI, also have implications for institutional practice. Specifically, this study provides a model for evaluating SI programs or other academic support programs to demonstrate how the program helps retain students. The findings also may be used to inform institutional leaders, policymakers, and the public about how SI is a useful tool to retain students and encourage the expansion of SI programs to meet the needs of additional learners

Bifunctional Pt-Re Catalysts in Hydrodeoxygenation of Isoeugenol as a Model Compound for Renewable Jet Fuel Production

Bimetallic platinum–rhenium catalysts supported on activated carbon were tested for the hydrodeoxygenation (HDO) of isoeugenol at 250 °C and 30 bar of H2 in a batch reactor. The catalysts were characterized by inductively coupled plasma atomic emission spectrometry (ICP-IES), N2 physisorption, electron microscopy (high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), transmission electron microscopy (TEM)), temperature-programmed reduction, X-ray absorption spectroscopy (X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS)), and temperature-programmed desorption of ammonia. Bimetallic catalysts containing Pt and Re were much more active than monometallic Pt/C and Re/C. Complete isoeugenol conversion, high propylcyclohexane yield (99%), and a high liquid-phase mass balance (77%) were obtained for the catalyst with the highest Re loading, Pt–Re(1:5)/C. Such high activity is attributed to a synergistic effect between the reduced Pt and the Re-oxide species, as both metal active sites and oxygen vacancies are required for HDO. The apparent activation energy for the HDO of isoeugenol with Pt–Re(1:5)/C was 44 kJ/mol

The fingerprint of the summer 2018 drought in Europe on ground-based atmospheric CO2 measurements

During the summer of 2018, a widespread drought developed over Northern and Central Europe. The increase in temperature and the reduction of soil moisture have influenced carbon dioxide (CO2) exchange between the atmosphere and terrestrial ecosystems in various ways, such as a reduction of photosynthesis, changes in ecosystem respiration, or allowing more frequent fires. In this study, we characterize the resulting perturbation of the atmospheric CO2 seasonal cycles. 2018 has a good coverage of European regions affected by drought, allowing the investigation of how ecosystem flux anomalies impacted spatial CO2 gradients between stations. This density of stations is unprecedented compared to previous drought events in 2003 and 2015, particularly thanks to the deployment of the Integrated Carbon Observation System (ICOS) network of atmospheric greenhouse gas monitoring stations in recent years. Seasonal CO2 cycles from 48 European stations were available for 2017 and 2018.The UK sites were funded by the UK Department of Business, Energy and Industrial Strategy (formerly the Department of Energy and Climate Change) through contracts TRN1028/06/2015 and TRN1537/06/2018. The stations at the ClimaDat Network in Spain have received funding from the ‘la Caixa’ Foundation, under agreement 2010-002624

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

Multi-laboratory compilation of atmospheric carbon dioxide data for the period 1957-2020 [Dataset]

This product is constructed using the Observation Package (ObsPack) framework [Masarie et al., 2014; www.earth-syst-sci-data.net/6/375/2014/]. The framework is designed to bring together atmospheric greenhouse gas (GHG) observations from a variety of sampling platforms, prepare them with specific applications in mind, and package and distribute them in a self-consistent and well-documented product. ObsPack products are intended to support GHG budget studies and represent a new generation of cooperative value-added GHG data products. This product includes 524 atmospheric carbon dioxide datasets derived from observations made by 63 laboratories from 21 countries. Data for the period 1957-2020 (where available) are included

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