Real-time source apportionment of organic aerosols in three European cities.

97% of the urban population in the EU in 2019 were exposed to an annual fine particulate matter level higher than the World Health Organization (WHO) guidelines (5 μg/m3). Organic aerosol (OA) is one of the major air pollutants, and the knowledge of its sources is crucial for designing cost-effective mitigation strategies. Positive matrix factorization (PMF) on aerosol mass spectrometer (AMS) or aerosol chemical speciation monitor (ACSM) data is the most common method for source apportionment (SA) analysis on ambient OA. However, conventional PMF requires extensive human labor, preventing the implementation of SA for routine monitoring applications. This study proposes the source finder real-time (SoFi RT, Datalystica Ltd.) approach for efficient retrieval of OA sources. The results generated by SoFi RT agree remarkably well with the conventional rolling PMF results regarding factor profiles, time series, diurnal patterns, and yearly relative contributions of OA factor on three year-long ACSM data sets collected in Athens, Paris, and Zurich. Although the initialization of SoFi RT requires a priori knowledge of OA sources (i.e., the approximate number of factors and relevant factor profiles) for the sampling site, this technique minimizes user interactions. Eventually, it could provide up-to-date trustable information on timescales useful to policymakers and air quality modelers

Body segment parameters of Paralympic athletes from dual-energy X-ray absorptiometry

The final publication is available at Springer via http://dx.doi.org/10.1007/s12283-016-0200-3This research represents the first documented investigation into the body segment parameters of Paralympic athletes (e.g., individuals with spinal cord injuries and lower extremity amputations). Two-dimensional body segment parameters (i.e., mass, length, position vector of the center of mass, and principal mass moment of inertia about the center of mass) were quantified from dual-energy X-ray absorptiometry (DXA). In addition to establishing a body segment parameter database of Paralympic athletes for prospective biomechanists and engineers, the mass of each body segment as experimentally measured via the DXA imaging was compared with that reported by previous research of able-bodied cadavers. In general, there were significant differences in the body segment masses between the different methods. These findings support the implementation of the proposed database for developing valid multibody biomechanical models of Paralympic athletes with distinct physical disabilities.This research was funded by Dr. John McPhee’s Tier I Canada Research Chair in Biomechatronic System Dynamics

Two-stroke scooters are a dominant source of air pollution in many cities.

Fossil fuel-powered vehicles emit significant particulate matter, for example, black carbon and primary organic aerosol, and produce secondary organic aerosol. Here we quantify secondary organic aerosol production from two-stroke scooters. Cars and trucks, particularly diesel vehicles, are thought to be the main vehicular pollution sources. This needs re-thinking, as we show that elevated particulate matter levels can be a consequence of 'asymmetric pollution' from two-stroke scooters, vehicles that constitute a small fraction of the fleet, but can dominate urban vehicular pollution through organic aerosol and aromatic emission factors up to thousands of times higher than from other vehicle classes. Further, we demonstrate that oxidation processes producing secondary organic aerosol from vehicle exhaust also form potentially toxic 'reactive oxygen species'.This work was supported by the Swiss Federal Office for the Environment (FOEN), the Federal Roads Office (FEDRO), the Swiss National Science Foundation (Ambizione PZ00P2_131673, SAPMAV 200021_13016), the EU commission (FP7, COFUND: PSI-Fellow, grant agreement n.° 290605), the UK Natural Environment Research Council (NERC), the French Environment and Energy Management Agency (ADEME, Grant number 1162C00O2) and the Velux Foundation.This is the accepted manuscript version. The final version is available from http://www.nature.com/ncomms/2014/140513/ncomms4749/full/ncomms4749.html

Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window

We collected 1 year of aerosol chemical speciation monitor (ACSM) data in Magadino, a village located in the south of the Swiss Alpine region, one of Switzerland's most polluted areas. We analysed the mass spectra of organic aerosol (OA) by positive matrix factorisation (PMF) using Source Finder Professional (SoFi Pro) to retrieve the origins of OA. Therein, we deployed a rolling algorithm, which is closer to the measurement, to account for the temporal changes in the source profiles. As the first-ever application of rolling PMF with multilinear engine (ME-2) analysis on a yearlong dataset that was collected from a rural site, we resolved two primary OA factors (traffic-related hydrocarbon-like OA (HOA) and biomass burning OA (BBOA)), one mass-to-charge ratio ( m/z) 58-related OA (58-OA) factor, a less oxidised oxygenated OA (LO-OOA) factor, and a more oxidised oxygenated OA (MO-OOA) factor. HOA showed stable contributions to the total OA through the whole year ranging from 8.1 % to 10.1 %, while the contribution of BBOA showed an apparent seasonal variation with a range of 8.3 %–27.4 % (highest during winter, lowest during summer) and a yearly average of 17.1 %. OOA (sum of LO-OOA and MO-OOA) contributed 71.6 % of the OA mass, varying from 62.5 % (in winter) to 78 % (in spring and summer). The 58-OA factor mainly contained nitrogen-related variables which appeared to be pronounced only after the filament switched. However, since the contribution of this factor was insignificant (2.1 %), we did not attempt to interpolate its potential source in this work. The uncertainties (σ) for the modelled OA factors (i.e. rotational uncertainty and statistical variability in the sources) varied from ±4 % (58-OA) to a maximum of ±40 % (LO-OOA). Considering that BBOA and LO-OOA (showing influences of biomass burning in winter) had significant contributions to the total OA mass, we suggest reducing and controlling biomass-burning-related residential heating as a mitigation strategy for better air quality and lower PM levels in this region or similar locations. In Appendix A, we conduct a head-to-head comparison between the conventional seasonal PMF analysis and the rolling mechanism. We find similar or slightly improved results in terms of mass concentrations, correlations with external tracers, and factor profiles of the constrained POA factors. The rolling results show smaller scaled residuals and enhanced correlations between OOA factors and corresponding inorganic salts compared to those of the seasonal solutions, which was most likely because the rolling PMF analysis can capture the temporal variations in the oxidation processes for OOA components. Specifically, the time-dependent factor profiles of MO-OOA and LO-OOA can well explain the temporal viabilities of two main ions for OOA factors, m/z 44 (CO⁺₂) and m/z 43 (mostly C2H3O+). Therefore, this rolling PMF analysis provides a more realistic source apportionment (SA) solution with time-dependent OA sources. The rolling results also show good agreement with offline Aerodyne aerosol mass spectrometer (AMS) SA results from filter samples, except for in winter. The latter discrepancy is likely because the online measurement can capture the fast oxidation processes of biomass burning sources, in contrast to the 24 h filter samples. This study demonstrates the strengths of the rolling mechanism, provides a comprehensive criterion list for ACSM users to obtain reproducible SA results, and is a role model for similar analyses of such worldwide available data

Simultaneous CXCL12 and ESR1 CpG island hypermethylation correlates with poor prognosis in sporadic breast cancer

<p>Abstract</p> <p>Background</p> <p>CXCL12 is a chemokine that is constitutively expressed in many organs and tissues. <it>CXCL12 </it>promoter hypermethylation has been detected in primary breast tumours and contributes to their metastatic potential. It has been shown that the oestrogen receptor α (<it>ESR1</it>) gene can also be silenced by DNA methylation. In this study, we used methylation-specific PCR (MSP) to analyse the methylation status in two regions of the <it>CXCL12 </it>promoter and <it>ESR1 </it>in tumour cell lines and in primary breast tumour samples, and correlated our results with clinicopathological data.</p> <p>Methods</p> <p>First, we analysed <it>CXCL12 </it>expression in breast tumour cell lines by RT-PCR. We also used 5-aza-2'-deoxycytidine (5-aza-CdR) treatment and DNA bisulphite sequencing to study the promoter methylation for a specific region of <it>CXCL12 </it>in breast tumour cell lines. We evaluated <it>CXCL12 </it>and <it>ESR1 </it>methylation in primary tumour samples by methylation-specific PCR (MSP). Finally, promoter hypermethylation of these genes was analysed using Fisher's exact test and correlated with clinicopathological data using the Chi square test, Kaplan-Meier survival analysis and Cox regression analysis.</p> <p>Results</p> <p><it>CXCL12 </it>promoter hypermethylation in the first region (island 2) and second region (island 4) was correlated with lack of expression of the gene in tumour cell lines. In the primary tumours, island 2 was hypermethylated in 14.5% of the samples and island 4 was hypermethylated in 54% of the samples. The <it>ESR1 </it>promoter was hypermethylated in 41% of breast tumour samples. In addition, the levels of ERα protein expression diminished with increased frequency of <it>ESR1 </it>methylation (p < 0.0001). This study also demonstrated that <it>CXCL12 </it>island 4 and <it>ESR1 </it>methylation occur simultaneously at a high frequency (p = 0.0220).</p> <p>Conclusions</p> <p>This is the first study showing a simultaneous involvement of epigenetic regulation for both <it>CXCL12 </it>and <it>ESR1 </it>genes in Brazilian women. The methylation status of both genes was significantly correlated with histologically advanced disease, the presence of metastases and death. Therefore, the methylation pattern of these genes could be used as a molecular marker for the prediction of breast cancer outcome.</p

Understanding atmospheric organic aerosols via factor analysis of aerosol mass spectrometry: a review

Organic species are an important but poorly characterized constituent of airborne particulate matter. A quantitative understanding of the organic fraction of particles (organic aerosol, OA) is necessary to reduce some of the largest uncertainties that confound the assessment of the radiative forcing of climate and air quality management policies. In recent years, aerosol mass spectrometry has been increasingly relied upon for highly time-resolved characterization of OA chemistry and for elucidation of aerosol sources and lifecycle processes. Aerodyne aerosol mass spectrometers (AMS) are particularly widely used, because of their ability to quantitatively characterize the size-resolved composition of submicron particles (PM1). AMS report the bulk composition and temporal variations of OA in the form of ensemble mass spectra (MS) acquired over short time intervals. Because each MS represents the linear superposition of the spectra of individual components weighed by their concentrations, multivariate factor analysis of the MS matrix has proved effective at retrieving OA factors that offer a quantitative and simplified description of the thousands of individual organic species. The sum of the factors accounts for nearly 100% of the OA mass and each individual factor typically corresponds to a large group of OA constituents with similar chemical composition and temporal behavior that are characteristic of different sources and/or atmospheric processes. The application of this technique in aerosol mass spectrometry has grown rapidly in the last six years. Here we review multivariate factor analysis techniques applied to AMS and other aerosol mass spectrometers, and summarize key findings from field observations. Results that provide valuable information about aerosol sources and, in particular, secondary OA evolution on regional and global scales are highlighted. Advanced methods, for example a-priori constraints on factor mass spectra and the application of factor analysis to combined aerosol and gas phase data are discussed. Integrated analysis of worldwide OA factors is used to present a holistic regional and global description of OA. Finally, different ways in which OA factors can constrain global and regional models are discussed

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries