Why do models overestimate surface ozone in the Southeast United States

Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NOx  ≡  NO + NO2) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC4RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25°  ×  0.3125° horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NOx from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC4RS observations of NOx and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30–60 %, dependent on the assumption of the contribution by soil NOx emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NOx emissions. We find that only half of isoprene oxidation proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. GEOS-Chem with reduced NOx emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NOx oxidation products. However, the model is still biased high by 6 ± 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer

Learning from the COVID-19 Pandemic: How Faculty Experiences Can Prepare Us for Future System-Wide Disruption

The COVID-19 pandemic provided education researchers with a natural experiment: an opportunity to investigate the impacts of a system-wide, involuntary move to online teaching and to assess the characteristics of individuals who adapted more readily. To capture the impacts in real time, our team recruited college-level geoscience instructors through the National Association of Geoscience Teachers (NAGT) and American Geophysical Union (AGU) communities to participate in our study in the spring of 2020. Each weekday for three successive weeks, participants (n = 262) were asked to rate their experienced disruption in four domains: teaching, research, ability to communicate with their professional community, and work-life balance. The rating system (a scale of 1–5, with 5 as severely disrupted) was designed to assess (a) where support needs were greatest, (b) how those needs evolved over time, and (c) respondents’ capacity to adapt. In addition, participants were asked two open-response questions, designed to provide preliminary insights into how individuals were adapting—what was their most important task that day and what was their greatest insight from the previous day. Participants also provided information on their institution type, position, discipline, gender, race, dependents, and online teaching experience (see supplemental material)

Bioinformatic and Genetic Association Analysis of MicroRNA Target Sites in One-Carbon Metabolism Genes

One-carbon metabolism (OCM) is linked to DNA synthesis and methylation, amino acid metabolism and cell proliferation. OCM dysfunction has been associated with increased risk for various diseases, including cancer and neural tube defects. MicroRNAs (miRNAs) are ∼22 nt RNA regulators that have been implicated in a wide array of basic cellular processes, such as differentiation and metabolism. Accordingly, mis-regulation of miRNA expression and/or activity can underlie complex disease etiology. We examined the possibility of OCM regulation by miRNAs. Using computational miRNA target prediction methods and Monte-Carlo based statistical analyses, we identified two candidate miRNA “master regulators” (miR-22 and miR-125) and one candidate pair of “master co-regulators” (miR-344-5p/484 and miR-488) that may influence the expression of a significant number of genes involved in OCM. Interestingly, miR-22 and miR-125 are significantly up-regulated in cells grown under low-folate conditions. In a complementary analysis, we identified 15 single nucleotide polymorphisms (SNPs) that are located within predicted miRNA target sites in OCM genes. We genotyped these 15 SNPs in a population of healthy individuals (age 18–28, n = 2,506) that was previously phenotyped for various serum metabolites related to OCM. Prior to correction for multiple testing, we detected significant associations between TCblR rs9426 and methylmalonic acid (p  =  0.045), total homocysteine levels (tHcy) (p  =  0.033), serum B12 (p < 0.0001), holo transcobalamin (p < 0.0001) and total transcobalamin (p < 0.0001); and between MTHFR rs1537514 and red blood cell folate (p < 0.0001). However, upon further genetic analysis, we determined that in each case, a linked missense SNP is the more likely causative variant. Nonetheless, our Monte-Carlo based in silico simulations suggest that miRNAs could play an important role in the regulation of OCM

Investigating the genetic architecture of dementia with Lewy bodies: a two-stage genome-wide association study

Background Dementia with Lewy bodies is the second most common form of dementia in elderly people but has been overshadowed in the research field, partly because of similarities between dementia with Lewy bodies, Parkinson’s disease, and Alzheimer’s disease. So far, to our knowledge, no large-scale genetic study of dementia with Lewy bodies has been done. To better understand the genetic basis of dementia with Lewy bodies, we have done a genome-wide association study with the aim of identifying genetic risk factors for this disorder. Methods In this two-stage genome-wide association study, we collected samples from white participants of European ancestry who had been diagnosed with dementia with Lewy bodies according to established clinical or pathological criteria. In the discovery stage (with the case cohort recruited from 22 centres in ten countries and the controls derived from two publicly available database of Genotypes and Phenotypes studies [phs000404.v1.p1 and phs000982.v1.p1] in the USA), we performed genotyping and exploited the recently established Haplotype Reference Consortium panel as the basis for imputation. Pathological samples were ascertained following autopsy in each individual brain bank, whereas clinical samples were collected by clinical teams after clinical examination. There was no specific timeframe for collection of samples. We did association analyses in all participants with dementia with Lewy bodies, and also in only participants with pathological diagnosis. In the replication stage, we performed genotyping of significant and suggestive results from the discovery stage. Lastly, we did a meta-analysis of both stages under a fixed-effects model and used logistic regression to test for association in each stage. Findings This study included 1743 patients with dementia with Lewy bodies (1324 with pathological diagnosis) and 4454 controls (1216 patients with dementia with Lewy bodies vs 3791 controls in the discovery stage; 527 vs 663 in the replication stage). Results confirm previously reported associations: APOE (rs429358; odds ratio [OR] 2·40, 95% CI 2·14–2·70; p=1·05 × 10–⁴⁸), SNCA (rs7681440; OR 0·73, 0·66–0·81; p=6·39 × 10–¹⁰), and GBA (rs35749011; OR 2·55, 1·88–3·46; p=1·78 × 10–⁹). They also provide some evidence for a novel candidate locus, namely CNTN1 (rs7314908; OR 1·51, 1·27–1·79; p=2·21 × 10–⁶); further replication will be important. Additionally, we estimate the heritable component of dementia with Lewy bodies to be about 36%. Interpretation Despite the small sample size for a genome-wide association study, and acknowledging the potential biases from ascertaining samples from multiple locations, we present the most comprehensive and well powered genetic study in dementia with Lewy bodies so far. These data show that common genetic variability has a role in the disease

Analysis of C9orf72 repeat expansions in a large international cohort of dementia with Lewy bodies

C9orf72 repeat expansions are a common cause of amyotrophic lateral sclerosis and frontotemporal dementia. To date, no large-scale study of dementia with Lewy bodies (DLB) has been undertaken to assess the role of C9orf72 repeat expansions in the disease. Here, we investigated the prevalence of C9orf72 repeat expansions in a large cohort of DLB cases and identified no pathogenic repeat expansions in neuropathologically or clinically defined cases, showing that C9orf72 repeat expansions are not causally associated with DLB. (C) 2016 Elsevier Inc. All rights reserved.Peer reviewe

A comprehensive screening of copy number variability in dementia with Lewy bodies

The role of genetic variability in dementia with Lewy bodies (DLB) is now indisputable; however, data regarding copy number variation (CNV) in this disease has been lacking. Here, we used whole-genome genotyping of 1454 DLB cases and 1525 controls to assess copy number variability. We used 2 algorithms to confidently detect CNVs, performed a case-control association analysis, screened for candidate CNVs previously associated with DLB-related diseases, and performed a candidate gene approach to fully explore the data. We identified 5 CNV regions with a significant genome-wide association to DLB; 2 of these were only present in cases and absent from publicly available databases: one of the regions overlapped LAPTM4B, a known lysosomal protein, whereas the other overlapped the NME1 locus and SPAG9. We also identified DLB cases presenting rare CNVs in genes previously associated with DLB or related neurodegenerative diseases, such as SNCA, APP, and MAPT. To our knowledge, this is the first study reporting genome-wide CNVs in a large DLB cohort. These results provide preliminary evidence for the contribution of CNVs in DLB risk. (C) 2019 Elsevier Inc. All rights reserved.Peer reviewe

Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group

Brain-behaviour modes of covariation in healthy and clinically depressed young people

Abstract: Understanding how variations in dimensions of psychometrics, IQ and demographics relate to changes in brain connectivity during the critical developmental period of adolescence and early adulthood is a major challenge. This has particular relevance for mental health disorders where a failure to understand these links might hinder the development of better diagnostic approaches and therapeutics. Here, we investigated this question in 306 adolescents and young adults (14–24 y, 25 clinically depressed) using a multivariate statistical framework, based on canonical correlation analysis (CCA). By linking individual functional brain connectivity profiles to self-report questionnaires, IQ and demographic data we identified two distinct modes of covariation. The first mode mapped onto an externalization/internalization axis and showed a strong association with sex. The second mode mapped onto a well-being/distress axis independent of sex. Interestingly, both modes showed an association with age. Crucially, the changes in functional brain connectivity associated with changes in these phenotypes showed marked developmental effects. The findings point to a role for the default mode, frontoparietal and limbic networks in psychopathology and depression

Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes

OBJECTIVES: A novel longitudinal clustering technique was applied to comprehensive autoantibody data from a large, well-characterised, multinational inception systemic lupus erythematosus (SLE) cohort to determine profiles predictive of clinical outcomes. METHODS: Demographic, clinical and serological data from 805 patients with SLE obtained within 15 months of diagnosis and at 3-year and 5-year follow-up were included. For each visit, sera were assessed for 29 antinuclear antibodies (ANA) immunofluorescence patterns and 20 autoantibodies. K-means clustering on principal component analysis-transformed longitudinal autoantibody profiles identified discrete phenotypic clusters. One-way analysis of variance compared cluster enrolment demographics and clinical outcomes at 10-year follow-up. Cox proportional hazards model estimated the HR for survival adjusting for age of disease onset. RESULTS: Cluster 1 (n=137, high frequency of anti-Smith, anti-U1RNP, AC-5 (large nuclear speckled pattern) and high ANA titres) had the highest cumulative disease activity and immunosuppressants/biologics use at year 10. Cluster 2 (n=376, low anti-double stranded DNA (dsDNA) and ANA titres) had the lowest disease activity, frequency of lupus nephritis and immunosuppressants/biologics use. Cluster 3 (n=80, highest frequency of all five antiphospholipid antibodies) had the highest frequency of seizures and hypocomplementaemia. Cluster 4 (n=212) also had high disease activity and was characterised by multiple autoantibody reactivity including to antihistone, anti-dsDNA, antiribosomal P, anti-Sjögren syndrome antigen A or Ro60, anti-Sjögren syndrome antigen B or La, anti-Ro52/Tripartite Motif Protein 21, antiproliferating cell nuclear antigen and anticentromere B). Clusters 1 (adjusted HR 2.60 (95% CI 1.12 to 6.05), p=0.03) and 3 (adjusted HR 2.87 (95% CI 1.22 to 6.74), p=0.02) had lower survival compared with cluster 2. CONCLUSION: Four discrete SLE patient longitudinal autoantibody clusters were predictive of long-term disease activity, organ involvement, treatment requirements and mortality risk