Controls on atmospheric chloroiodomethane (CH2ClI) in marine environments

Mixing ratios of chloroiodomethane (CH2ClI) in ambient air were quantified in the coastal North Atlantic region (Thompson Farm, Durham, New Hampshire, and Appledore Island, Maine) and two remote Pacific areas (Christmas Island, Kiribati, and Oahu, Hawaii). Average mixing ratios were 0.15 ± 0.18 and 0.68 ± 0.66 parts per trillion by volume (pptv) at Thompson Farm and Appledore Island, respectively, compared to 0.10 ± 0.05 pptv at Christmas Island and 0.04 ± 0.02 pptv in Hawaii. Photolysis constrained the daytime mixing ratios of CH2ClI at all locations with the minimum occurring at 1600 local time. Daily average fluxes to the atmosphere were estimated from mixing ratios and loss due to photolysis at Appledore Island, Christmas Island and Hawaii, and were 58 ± 9, 19 ± 3, and 5.8 ± 1.0 nmol CH2ClI m−2 d−1, respectively. The measured sea‐to‐air flux from seawater equilibrator samples obtained near Appledore Island was 6.4 ± 2.9 nmol CH2ClI m−2 d−1. Mixing ratios of CH2ClI at Appledore Island increased with increasing wind speed. The maximum mixing ratios observed at Thompson Farm (1.6 pptv) and Appledore Island (3.4 pptv) are the highest reported values to date, and coincided with high winds associated with the passage of Tropical Storm Bonnie. We estimate that high winds during the 2004 hurricane season increased the flux of CH2ClI from the North Atlantic Ocean by 8 ± 2%

The University of Washington Ice-Liquid Discriminator (UWILD) improves single-particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning

Mixed-phase Southern Ocean clouds are challenging to simulate, and their representation in climate models is an important control on climate sensitivity. In particular, the amount of supercooled water and frozen mass that they contain in the present climate is a predictor of their planetary feedback in a warming climate. The recent Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) vastly increased the amount of in situ data available from mixed-phase Southern Ocean clouds useful for model evaluation. Bulk measurements distinguishing liquid and ice water content are not available from SOCRATES, so single-particle phase classifications from the Two-Dimensional Stereo (2D-S) probe are invaluable for quantifying mixed-phase cloud properties. Motivated by the presence of large biases in existing phase discrimination algorithms, we develop a novel technique for single-particle phase classification of binary 2D-S images using a random forest algorithm, which we refer to as the University of Washington Ice-Liquid Discriminator (UWILD). UWILD uses 14 parameters computed from binary image data, as well as particle inter-arrival time, to predict phase. We use liquid-only and ice-dominated time periods within the SOCRATES dataset as training and testing data. This novel approach to model training avoids major pitfalls associated with using manually labeled data, including reduced model generalizability and high labor costs. We find that UWILD is well calibrated and has an overall accuracy of 95 % compared to 72 % and 79 % for two existing phase classification algorithms that we compare it with. UWILD improves classifications of small ice crystals and large liquid drops in particular and has more flexibility than the other algorithms to identify both liquid-dominated and ice-dominated regions within the SOCRATES dataset. UWILD misclassifies a small percentage of large liquid drops as ice. Such misclassified particles are typically associated with model confidence below 75 % and can easily be filtered out of the dataset. UWILD phase classifications show that particles with area-equivalent diameter (Deq) \u3c 0.17 mm are mostly liquid at all temperatures sampled, down to -40 °. Larger particles (Deq\u3e0.17 mm) are predominantly frozen at all temperatures below 0 °. Between 0 and 5 °, there are roughly equal numbers of frozen and liquid mid-sized particles (0.170.33 mm) are mostly frozen. We also use UWILD\u27s phase classifications to estimate sub-1 Hz phase heterogeneity, and we show examples of meter-scale cloud phase heterogeneity in the SOCRATES dataset

Observations of APAN during TexAQS 2000

Measurements of peroxycarboxylic nitric anhydrides (PANs) made in Houston, Texas during TexAQS (Texas Air Quality Study) 2000 showed a relatively abundant PAN compound that had not been identified in previous studies in North America [cf. Williams et al., 2000]. This compound was hypothesized to be peroxyacrylic nitric anhydride { CH2=CHC(O)OONO2, APAN} based on the work of Tanimoto and Akimoto, [2001]. APAN was synthesized and characterized on one of the two GC systems used to make those measurements, subsequent to the TexAQS 2000 field study, confirming that APAN was observed during TexAQS 2000, both on the ground and in airborne measurements. Mixing ratios of APAN were estimated from the response of the system to PAN and PPN and ranged up to 502 pptv, which was 30% of PAN. High APAN values were associated with the precursor species 1,3-butadiene and acrolein, which had local petrochemical sources. The presence of APAN at these unprecedented levels demonstrates the impact of these reactive VOC species, and may have associated health effects

Fine-mapping of the HNF1B multicancer locus identifies candidate variants that mediate endometrial cancer risk.

Common variants in the hepatocyte nuclear factor 1 homeobox B (HNF1B) gene are associated with the risk of Type II diabetes and multiple cancers. Evidence to date indicates that cancer risk may be mediated via genetic or epigenetic effects on HNF1B gene expression. We previously found single-nucleotide polymorphisms (SNPs) at the HNF1B locus to be associated with endometrial cancer, and now report extensive fine-mapping and in silico and laboratory analyses of this locus. Analysis of 1184 genotyped and imputed SNPs in 6608 Caucasian cases and 37 925 controls, and 895 Asian cases and 1968 controls, revealed the best signal of association for SNP rs11263763 (P = 8.4 × 10(-14), odds ratio = 0.86, 95% confidence interval = 0.82-0.89), located within HNF1B intron 1. Haplotype analysis and conditional analyses provide no evidence of further independent endometrial cancer risk variants at this locus. SNP rs11263763 genotype was associated with HNF1B mRNA expression but not with HNF1B methylation in endometrial tumor samples from The Cancer Genome Atlas. Genetic analyses prioritized rs11263763 and four other SNPs in high-to-moderate linkage disequilibrium as the most likely causal SNPs. Three of these SNPs map to the extended HNF1B promoter based on chromatin marks extending from the minimal promoter region. Reporter assays demonstrated that this extended region reduces activity in combination with the minimal HNF1B promoter, and that the minor alleles of rs11263763 or rs8064454 are associated with decreased HNF1B promoter activity. Our findings provide evidence for a single signal associated with endometrial cancer risk at the HNF1B locus, and that risk is likely mediated via altered HNF1B gene expression

TGFβ pathway limits dedifferentiation following WNT and MAPK pathway activation to suppress intestinal tumourigenesis

Recent studies have suggested increased plasticity of differentiated cells within the intestine to act both as intestinal stem cells (ISCs) and tumour-initiating cells. However, little is known of the processes that regulate this plasticity. Our previous work has shown that activating mutations of Kras or the NF-κB pathway can drive dedifferentiation of intestinal cells lacking Apc. To investigate this process further, we profiled both cells undergoing dedifferentiation in vitro and tumours generated from these cells in vivo by gene expression analysis. Remarkably, no clear differences were observed in the tumours; however, during dedifferentiation in vitro we found a marked upregulation of TGFβ signalling, a pathway commonly mutated in colorectal cancer (CRC). Genetic inactivation of TGFβ type 1 receptor (Tgfbr1/Alk5) enhanced the ability of KrasG12D/+ mutation to drive dedifferentiation and markedly accelerated tumourigenesis. Mechanistically this is associated with a marked activation of MAPK signalling. Tumourigenesis from differentiated compartments is potently inhibited by MEK inhibition. Taken together, we show that tumours arising in differentiated compartments will be exposed to different suppressive signals, for example, TGFβ and blockade of these makes tumourigenesis more efficient from this compartment

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-19(1,2), 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 across19 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 diseases(3-7). 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.Peer reviewe

The Confidence Database

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Identification of highly penetrant Rb-related synthetic lethal interactions in triple negative breast cancer.

Although defects in the RB1 tumour suppressor are one of the more common driver alterations found in triple-negative breast cancer (TNBC), therapeutic approaches that exploit this have not been identified. By integrating molecular profiling data with data from multiple genetic perturbation screens, we identified candidate synthetic lethal (SL) interactions associated with RB1 defects in TNBC. We refined this analysis by identifying the highly penetrant effects, reasoning that these would be more robust in the face of molecular heterogeneity and would represent more promising therapeutic targets. A significant proportion of the highly penetrant RB1 SL effects involved proteins closely associated with RB1 function, suggesting that this might be a defining characteristic. These included nuclear pore complex components associated with the MAD2 spindle checkpoint protein, the kinase and bromodomain containing transcription factor TAF1, and multiple components of the SCFSKP Cullin F box containing complex. Small-molecule inhibition of SCFSKP elicited an increase in p27Kip levels, providing a mechanistic rationale for RB1 SL. Transcript expression of SKP2, a SCFSKP component, was elevated in RB1-defective TNBCs, suggesting that in these tumours, SKP2 activity might buffer the effects of RB1 dysfunction