Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM2.5): Insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations

Geostationary satellite measurements of aerosol optical depth (AOD) over East Asia from the Geostationary Ocean Color Imager (GOCI) and Advanced Himawari Imager (AHI) instruments can augment surface monitoring of fine particulate matter (PM2.5) air quality, but this requires better understanding of the AOD–PM2.5 relationship. Here we use the GEOS-Chem chemical transport model to analyze the critical variables determining the AOD–PM2.5 relationship over East Asia by simulation of observations from satellite, aircraft, and ground-based datasets. This includes the detailed vertical aerosol profiling over South Korea from the KORUS-AQ aircraft campaign (May–June 2016) with concurrent ground-based PM2.5 composition, PM10, and AERONET AOD measurements. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate–nitrate–ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and high hygroscopicity of the PBL aerosols. We updated SNA and organic aerosol size distributions in GEOS-Chem to represent aerosol optical properties over East Asia by using in situ measurements of particle size distributions from KORUS-AQ. We find that SNA and organic aerosols over East Asia have larger size (number median radius of 0.11 µm with geometric standard deviation of 1.4) and 20 % larger mass extinction efficiency as compared to aerosols over North America (default setting in GEOS-Chem). Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM2.5 nitrate. The GOCI–AHI AOD data over East Asia in different seasons show agreement with AERONET AODs and a spatial distribution consistent with surface PM2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM2.5. This is due to low PBL depths compounded by high residential coal emissions in winter and high relative humidity (RH) in summer. Seasonality of AOD and PM2.5 over South Korea is much weaker, reflecting weaker variation in PBL depth and lack of residential coal emissions

South Atlantic Interbasin Exchanges of Mass, Heat, Salt and Anthropogenic Carbon

The exchange of mass, heat, salt and anthropogenic carbon (Cant) between the South Atlantic, south of 24°S, and adjacent ocean basins is estimated from hydrographic data obtained during 2008-2009 using an inverse method. Transports of anthropogenic carbon are calculated across the western (Drake Passage), eastern (30°E) and northern (24°S) boundaries. The freshwater overturning transport of 0.09 Sv is southward, consistent with an overturning circulation that exports freshwater from the North Atlantic, and consistent with a bistable Meridional Overturning Circulation (MOC), under conditions of excess freshwater perturbation. At 30°E, net eastward Antarctic Circumpolar Current (ACC) transport, south of the Subtropical Front, is compensated by a 15.9±2.3 Sv westward flow along the Antarctic boundary. The region as a whole is a substantial sink for atmospheric anthropogenic carbon of 0.51±0.37 PgC yr-1, of which 0.18±0.12 PgC yr-1 accumulates and is stored within the water column. At 24°S, a 20.2 Sv meridional overturning is associated with a 0.11 PgC yr-1 Cant overturning. The remainder is transported into the Atlantic Ocean north of 24°S (0.28±0.16 PgC yr-1) and Indian sector of Southern Ocean (1.12±0.43 PgC yr-1), having been enhanced by inflow through Drake Passage (1.07±0.44 PgC yr-1). This underlines the importance of the South Atlantic as a crucial element of the anthropogenic carbon sink in the global oceans

Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume

The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer’s Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer’s disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes

Genome-Wide Association Study in BRCA1 Mutation Carriers Identifies Novel Loci Associated with Breast and Ovarian Cancer Risk

BRCA1-associated breast and ovarian cancer risks can be modified by common genetic variants. To identify further cancer risk-modifying loci, we performed a multi-stage GWAS of 11,705 BRCA1 carriers (of whom 5,920 were diagnosed with breast and 1,839 were diagnosed with ovarian cancer), with a further replication in an additional sample of 2,646 BRCA1 carriers. We identified a novel breast cancer risk modifier locus at 1q32 for BRCA1 carriers (rs2290854, P = 2.7×10-8, HR = 1.14, 95% CI: 1.09-1.20). In addition, we identified two novel ovarian cancer risk modifier loci: 17q21.31 (rs17631303, P = 1.4×10-8, HR = 1.27, 95% CI: 1.17-1.38) and 4q32.3 (rs4691139, P = 3.4×10-8, HR = 1.20, 95% CI: 1.17-1.38). The 4q32.3 locus was not associated with ovarian cancer risk in the general population or BRCA2 carriers, suggesting a BRCA1-specific associat

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

New insights into the genetic etiology of Alzheimer's disease and related dementias

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine