MicroRNAs (miRNAs) are small non-coding RNAs that mediate post-transcriptional gene silencing. Over 700 human miRNAs have currently been identified, many of which are mutated or de-regulated in ...diseases. Here we report the identification of novel miRNAs through deep sequencing the small RNAome (<30 nt) of over 100 tissues or cell lines derived from human female reproductive organs in both normal and disease states. These specimens include ovarian epithelium and ovarian cancer, endometrium and endometriomas, and uterine myometrium and uterine smooth muscle tumors. Sequence reads not aligning with known miRNAs were each mapped to the genome to extract flanking sequences. These extended sequence regions were folded in silico to identify RNA hairpins. Sequences demonstrating the ability to form a stem loop structure with low minimum free energy (<-25 kcal) and predicted Drosha and Dicer cut sites yielding a mature miRNA sequence matching the actual sequence were considered putative novel miRNAs. Additional confidence was achieved when putative novel hairpins assembled a collection of sequences highly similar to the putative mature miRNA but with heterogeneous 3'-ends. A confirmed novel miRNA fulfilled these criteria and had its "star" sequence in our collection. We found 7 distinct confirmed novel miRNAs, and 51 additional novel miRNAs that represented highly confident predictions but without detectable star sequences. Our novel miRNAs were detectable in multiple samples, but expressed at low levels and not specific to any one tissue or cell type. To date, this study represents the largest set of samples analyzed together to identify novel miRNAs.
This study characterizes the impacts of transported peat-forest (PF) burning smoke on an urban environment and evaluates associated source burning conditions based on carbon properties of PM2.5 at ...the receptor site. We developed and validated a three-step classification that enables systematic and more rapid identification of PF smoke impacts on a tropical urban environment with diverse emissions and complex atmospheric processes. This approach was used to characterize over 300 daily PM2.5 data collected during 2011–2013, 2015 and 2019 in Singapore. A levoglucosan concentration of ≥0.1 μg/m3 criterion indicates dominant impacts of transported PF smoke on urban fine aerosols. This approach can be used in other ambient environments for practical and location-dependent applications. Organic carbon (OC) concentrations (as OC indicator) can be an alternate to levoglucosan for assessing smoke impacts on urban environments. Applying the OC concentration indicator identifies smoke impacts on ∼80% of daily samples in 2019 and shows an accuracy of 51–86% for hourly evaluation. Following the systematic identification of urban PM2.5 predominantly affected by PF smoke in 2011–2013, 2015 and 2019, we assessed the concentration ratio of char-EC/soot-EC as an indicator of smoldering- or flaming-dominated burning emissions. When under the influence of transported PF smoke, the mean concentration ratio of char-EC to soot-EC in urban PM2.5 decreased by >70% from 8.2 in 2011 to 2.3 in 2015 but increased to 3.8 in 2019 (p < 0.05). The reversed trend with a 65% increase from 2015 to 2019 shows stronger smoldering relative to flaming, indicating a higher level of soil moisture at smoke origins, possibly associated with rewetting and revegetating peatlands since 2016.
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•Levoglucosan ≥0.1 μg/m3: peat-forest (PF) smoke dominantly affects urban PM2.5.•OC concentration can identify impacts of PF smoke on receptor urban PM2.5.•Char-EC/Soot-EC in receptor PM2.5 infers burning conditions at smoke sources.•Yearly PF burning conditions at sources can reflect changes of peatlands.
• Approaches are developed to timely (hourly) identify impacts of transported biomass burning smoke on urban PM2.5.
• Chemical features of smoke-affected PM2.5 at receptor sites can infer dominant burning conditions at smoke origins.
Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified. To identify further genetic ...risk factors, here we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents (n = 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant, and the overall rate of mutation is only modestly higher than the expected rate. In contrast, the proteins encoded by genes that harboured de novo missense or nonsense mutations showed a higher degree of connectivity among themselves and to previous ASD genes as indexed by protein-protein interaction screens. The small increase in the rate of de novo events, when taken together with the protein interaction results, are consistent with an important but limited role for de novo point mutations in ASD, similar to that documented for de novo copy number variants. Genetic models incorporating these data indicate that most of the observed de novo events are unconnected to ASD; those that do confer risk are distributed across many genes and are incompletely penetrant (that is, not necessarily sufficient for disease). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5- to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case-control study provide strong evidence in favour of CHD8 and KATNAL2 as genuine autism risk factors.
Regional smoke-induced haze in Southeast Asia, caused by uncontrolled forest and peat fires in Indonesia, is of major environmental and health concern. In this study, we estimated carcinogenic and ...non-carcinogenic health risk due to exposure to fine particles (PM2.5) as emitted from peat fires at Kalimantan, Indonesia. For the health risk analysis, chemical speciation (exchangeable, reducible, oxidizable, and residual fractions) of 12 trace metals (Al, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Ti, V and Zn) in PM2.5 was studied. Results indicate that Al, Fe and Ti together accounted for a major fraction of total metal concentrations (~83%) in PM2.5 emissions in the immediate vicinity of peat fires. Chemical speciation reveals that a major proportion of most of the metals, with the exception of Cr, Mn, Fe, Ni and Cd, was present in the residual fraction. The exchangeable fraction of metals, which represents their bioavailability, could play a major role in inducing human health effects of PM2.5. This fraction contained carcinogenic metals such as Cd (39.2ngm−3) and Ni (249.3ngm−3) that exceeded their WHO guideline values by several factors. Health risk estimates suggest that exposure to PM2.5 emissions in the vicinity of peat fires poses serious health threats.
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a respiratory illness that can result in hospitalization or death. We used exome sequence data ...to investigate associations between rare genetic variants and seven COVID-19 outcomes in 586,157 individuals, including 20,952 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome wide or when specifically focusing on (1) 13 interferon pathway genes in which rare deleterious variants have been reported in individuals with severe COVID-19, (2) 281 genes located in susceptibility loci identified by the COVID-19 Host Genetics Initiative, or (3) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, and results are publicly available through the Regeneron Genetics Center COVID-19 Results Browser.
While stand alone satellite and model aerosol products see wide utilization, there is a significant need in numerous atmospheric and climate applications for a fused product on a regular grid. ...Aerosol data assimilation is an operational reality at numerous centers, and like meteorological reanalyses, aerosol reanalyses will see significant use in the near future. Here we present a standardized 2003–2013 global 1 × 1° and 6-hourly modal aerosol optical thickness (AOT) reanalysis product. This data set can be applied to basic and applied Earth system science studies of significant aerosol events, aerosol impacts on numerical weather prediction, and electro-optical propagation and sensor performance, among other uses. This paper describes the science of how to develop and score an aerosol reanalysis product. This reanalysis utilizes a modified Navy Aerosol Analysis and Prediction System (NAAPS) at its core and assimilates quality controlled retrievals of AOT from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Multi-angle Imaging SpectroRadiometer (MISR) on Terra. The aerosol source functions, including dust and smoke, were regionally tuned to obtain the best match between the model fine- and coarse-mode AOTs and the Aerosol Robotic Network (AERONET) AOTs. Other model processes, including deposition, were tuned to minimize the AOT difference between the model and satellite AOT. Aerosol wet deposition in the tropics is driven with satellite-retrieved precipitation, rather than the model field. The final reanalyzed fine- and coarse-mode AOT at 550 nm is shown to have good agreement with AERONET observations, with global mean root mean square error around 0.1 for both fine- and coarse-mode AOTs. This paper includes a discussion of issues particular to aerosol reanalyses that make them distinct from standard meteorological reanalyses, considerations for extending such a reanalysis outside of the NASA A-Train era, and examples of how the aerosol reanalysis can be applied or fused with other model or remote sensing products. Finally, the reanalysis is evaluated in comparison with other available studies of aerosol trends, and the implications of this comparison are discussed.
A major challenge of biology is understanding the relationship between molecular genetic variation and variation in quantitative traits, including fitness. This relationship determines our ability to ...predict phenotypes from genotypes and to understand how evolutionary forces shape variation within and between species. Previous efforts to dissect the genotype-phenotype map were based on incomplete genotypic information. Here, we describe the Drosophila melanogaster Genetic Reference Panel (DGRP), a community resource for analysis of population genomics and quantitative traits. The DGRP consists of fully sequenced inbred lines derived from a natural population. Population genomic analyses reveal reduced polymorphism in centromeric autosomal regions and the X chromosome, evidence for positive and negative selection, and rapid evolution of the X chromosome. Many variants in novel genes, most at low frequency, are associated with quantitative traits and explain a large fraction of the phenotypic variance. The DGRP facilitates genotype-phenotype mapping using the power of Drosophila genetics.
The iron-oxide content of dust in the atmosphere and most notably its apportionment between hematite (α-Fe2O3) and goethite (α-FeOOH) are key determinants in quantifying dust’s light absorption, its ...top of atmosphere UV radiances used for dust monitoring, and ultimately shortwave dust direct radiative effects (DRE). Hematite and goethite column mass concentrations and iron-oxide mass fractions of total dust mass concentration were retrieved from the DeepSpace Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) measurements in the ultraviolet–visible (UV–Vis) channels. The retrievals were performed for dust-identified aerosol plumes over land using aerosol optical depth (AOD) and spectral imaginary refractive index provided by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm over six continental regions (North America, North Africa, West Asia, Central Asia, EastAsia, and Australia). The dust particles are represented as an internal mixture of non-absorbing host and absorbing hematite and goethite. We use the Maxwell–Garnett effective medium approximation with carefully selected complex refractive indices of hematite and goethite that produce mass fractions of iron oxides species consistent with in situ values found in the literature to derive the hematite and goethite volumetric/mass concentrations from MAIAC EPIC products. We compared the retrieved hematite and goethite concentrations with in situ dust aerosol mineralogical content measurements, as well as with published data. Our data display variations within the published range of hematite, goethite, and iron-oxide mass fractions for pure mineral dust cases. A specific analysis is presented for 15 sites over the main dust source regions. Sites in the central Sahara, Sahel, and Middle East exhibit a greater temporal variability of iron oxides relative to other sites. Niger site(13.52°N, 2.63°E) is dominated by goethite over Harmattan season with median of ~2 weight percentage (wt.%) of iron-oxide. Saudi Arabia site (27.49°N, 41.98°E) over Middle East also exhibited surge of goethite content with the beginning of Shamal season. The Sahel dust is richer in iron-oxide than Saharan and northern China dust except in Summer. The Bodélé Depression area shows a distinctively lower iron-oxide concentration (~1 wt.%) throughout the year. Finally, we show that EPIC data allow to constrain the hematite refractive index. Specifically, we select 5 out of 13 different number of hematite refractive indices widely variable in published laboratory studies by constraining the iron-oxide mass ratio to the known measured values. Provided climatology of hematite and goethite mass fractions across main dust regions of the Earth will be useful for dust shortwave DRE studies and climate modeling.
We report on results from whole-exome sequencing (WES) of 1,039 subjects diagnosed with autism spectrum disorders (ASD) and 870 controls selected from the NIMH repository to be of similar ancestry to ...cases. The WES data came from two centers using different methods to produce sequence and to call variants from it. Therefore, an initial goal was to ensure the distribution of rare variation was similar for data from different centers. This proved straightforward by filtering called variants by fraction of missing data, read depth, and balance of alternative to reference reads. Results were evaluated using seven samples sequenced at both centers and by results from the association study. Next we addressed how the data and/or results from the centers should be combined. Gene-based analyses of association was an obvious choice, but should statistics for association be combined across centers (meta-analysis) or should data be combined and then analyzed (mega-analysis)? Because of the nature of many gene-based tests, we showed by theory and simulations that mega-analysis has better power than meta-analysis. Finally, before analyzing the data for association, we explored the impact of population structure on rare variant analysis in these data. Like other recent studies, we found evidence that population structure can confound case-control studies by the clustering of rare variants in ancestry space; yet, unlike some recent studies, for these data we found that principal component-based analyses were sufficient to control for ancestry and produce test statistics with appropriate distributions. After using a variety of gene-based tests and both meta- and mega-analysis, we found no new risk genes for ASD in this sample. Our results suggest that standard gene-based tests will require much larger samples of cases and controls before being effective for gene discovery, even for a disorder like ASD.
Due to a lack of high-latitude ground-based and
satellite-based data from traditional passive- and active-based
measurements, the impact of aerosol particles on the Arctic region is one of
the least ...understood factors contributing to recent Arctic sea ice changes.
In this study, we investigated the feasibility of using the ultraviolet (UV)
aerosol index (AI) parameter from the Ozone Monitoring Instrument (OMI), a
semi-quantitative aerosol parameter, for quantifying spatiotemporal changes
in UV-absorbing aerosols over the Arctic region. We found that OMI AI data
are affected by an additional row anomaly that is unflagged by the OMI quality
control flag and are systematically biased as functions of observing
conditions, such as azimuth angle, and certain surface types over the Arctic
region, resulting in an anomalous “ring” of climatologically high AI
centered at about 70∘ N, surrounding an area of low AI over the pole.
Two methods were developed in this study for quality-assuring the Arctic AI
data. Using quality-controlled OMI AI data from 2005 through 2020, we found
decreases in UV-absorbing aerosols in the spring months (April and May) over
much of the Arctic region and increases in UV-absorbing aerosols in the
summer months (June, July, and August) over northern Russia and northern
Canada. Additionally, we found significant increases in the frequency and
size of UV-absorbing aerosol events across the Arctic and high-Arctic (north
of 80∘ N) regions for the latter half of the study period
(2014–2020), driven primarily by a significant increase in boreal
biomass-burning plume coverage.
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