From photon paths to pollution plumes: better radiative transfer calculations to monitor NOx emissions with OMI and TROPOMI

Nitrogen oxides (NOx = NO + NO2) play an important role in atmospheric chemistry, therefore affecting air quality and Earth's radiative forcing, which impact public health, ecosystems and climate. Remote sensing from satellites in the ultraviolet and visible (UV-Vis) spectral range results in measurements of tropospheric NO2 column densities with high spatial and temporal resolution that allow, among many applications, to monitor NO2 concentrations and to estimate NOx emissions. NO2 satellite retrievals have improved extensively in the last decade, together with the increased need of having traceable characterization of the uncertainties associated with the NO2 satellite measurements. The spatial resolution of the satellite instruments is improving such that the observed NO2 pollution can now be traced back to emissions from individual cities, power plants, and transportation sectors. However, the uncertainty of satellite NO2 retrievals is still considerable and mainly related to the adequacy of the assumptions made on the state of the atmosphere. In this thesis we have improved the critical assumptions and our understanding in the radiative transfer modelling for NO2 satellite measurements, and we use the new TROPOMI NO2 measurements to quantify daily NOx emissions from a single urban hot spot. The work presented in this thesis contributes to the satellite remote sensing community (1) because of the improvement of the satellite retrieval and the knowledge of its main uncertainty sources (Chapter 2, 3 and 4), and (2) because of the application of TROPOMI NO2 measurements for the first time to infer daily NOx emissions at urban scales (Chapter 5). </p

Circulating tumor cells criteria (CyCAR) versus standard RECIST criteria for treatment response assessment in metastatic colorectal cancer patients

The use of circulating tumor cells (CTCs) as indicators of treatment response in metastatic colorectal cancer (mCRC) needs to be clarified. The objective of this study is to compare the Response Evaluation Criteria in Solid Tumors (RECIST) with the Cytologic Criteria Assessing Response (CyCAR), based on the presence and phenotypic characterization of CTCs, as indicators of FOLFOX–bevacizumab treatment response. We observed a decrease of CTCs (42.8 vs. 18.2%) and VEGFR positivity (69.7% vs. 41.7%) after treatment. According to RECIST, 6.45% of the patients did not show any clinical benefit, whereas 93.55% patients showed a favorable response at 12 weeks. According to CyCAR, 29% had a non-favorable response and 71% patients did not. No significant differences were found between the response assessment by RECIST and CyCAR at 12 or 24 weeks. However, in the multivariate analysis, RECIST at 12 weeks and CyCAR at 24 weeks were independent prognostic factors for OS (HR: 0.1, 95% CI 0.02–0.58 and HR: 0.35, 95% CI 0.12–0.99 respectively). CyCAR results were comparable to RECIST in evaluating the response in mCRC and can be used as an alternative when the limitation of RECIST requires additional response analysis techniques.This work was supported by Roche Spain and a Ph.D. grant from the University of Granada

From the Ethnic History of Asia – the Dōnghú, Wūhuán and Xiānbēi Proto-Mongolian Tribes

Cilj je ovog članka prikazati povijest protomongolskih plemena Dōnghú, Wūhuán i Xiānbēi od 4. stoljeća pr. Kr. do kraja 3. stoljeća po. Kr. Povijest drevnih nomadskih naroda koji su živjeli sjeverno od Kine zapisana je u kineskim dinastijskim kronikama. Protomongolska plemena 1. tisućljeća pr. Kr. u kineskim se izvorima nazivaju Dōnghú. Najstarije vijesti o njima potječu iz razdoblja Zaraćenih država (4. – 3. st. pr. Kr.), a govore o sukobu sa sjevernim kineskim državama. Druga vrsta izvora za povijest protomongolskih plemena arheološki su nalazi, koji mongolsku etnogenezu povezuju s kulturama pločastih grobova, i Mlađi Xiàjiādiàn. Lingvisti građu za istraživanje mongolske etnogeneze pronalaze u altajskoj jezičnoj porodici, kojoj pripada i mongolski jezik. U radu se na temelju navedenih izvora opisuje promjena političke situacije u stepi krajem 3. stoljeća pr. Kr., kada narod Xiōngnú stvara moćnu državu i pokorava Dōnghúe. Ostatke razbijenih Dōnghúa, koji su najvećim dijelom migrirali na sjever, kineske kronike bilježe pod novim topoetnonimima – Xiānbēi i Wūhuán. Slabljenje i pad države Xiōngnúa omogućili su protomongolskim plemenima ponovni izlazak na povijesnu scenu. Kinesko carstvo Hàn uspostavilo je krajem 1. stoljeća pr. Kr. najprije odnose s plemenima Wūhuán, a sredinom 1. stoljeća po. Kr. i s plemenima Xiānbēi. Oba plemenska saveza u početku su priznavala vrhovnu vlast Kine i obavljala graničarsku službu. Pod vodstvom plemenskih starješina u 2. stoljeću po. Kr. počela su voditi samostalnu politiku i napadati pogranična kineska područja. U zaključnom dijelu rada govori se o vremenu kada su plemena Wūhuán i Xiānbēi bila na vrhuncu moći. No već početkom 3. stoljeća Wūhuáni su potpali pod vlast Kineza i Xiānbēija; plemenski savez Xiānbēi raspao se u drugoj polovini 3. stoljeća.The aim of this paper is to present the history of the Dōnghú, Wūhuán and Xiānbēi Proto-Mongolian tribes in the period from the 4th century B.C. to the end of the 3rd century A.D. The history of the ancient nomadic peoples who lived north of China is written in Chinese dynasty chronicles. Proto-Mongolian tribes from the 1st century B.C. are called Dōnghú in Chinese sources. The earliest news on them originates from the Warring States Period (4th – 3rd century B.C.), and tells of a conflict with the northern Chinese states. Other types of sources on the history of the Proto-Mongolian tribes are archaeological findings, which associate Mongolian ethnogenesis with slab grave cultures and the Lower Xiàjiādiàn. Linguists find the materials for the research on Mongolian ethnogenesis in the Altaic linguistic family, which the Mongolian language belongs to as well. Based on the mentioned sources, the change in the political situation in the steppes at the end of the 3rd century B.C., when the people of Xiōngnú created a powerful state and conquered the Dōnghúes, is described in the paper. The remains of the shattered Dōnghúes, who had mostly migrated to the north, have been recorded in Chinese chronicles under new topoethnonyms: Xiānbēi and Wūhuán. The weakening and fall of the Xiōngnúes’ state enabled the Proto-Mongolian tribes to re-enter the historical scene. At the end of the 1st century B.C. the Chinese Hàn Empire firstly established relations with the Wūhuán tribes and in the middle of the 1st century A.D. with the Xiānbēi tribes, too. In the beginning both tribal alliances acknowledged the supreme authority of China and carried out frontier service. Under the guidance of tribal chiefs the tribes started to run an independent policy and attack China’s border areas during the 2nd century A.D. In the conclusion, the author describes the period when the Wūhuán and Xiānbēi tribes were at the peak of their power. However, already at the beginning of the 3rd century, the Wūhuáns fell under the authorities of China and Xiānbēi, but the Xiānbēi tribal alliance fell apart in the second half of the 3rd century

CARB-ES-19 Multicenter Study of Carbapenemase-Producing Klebsiella pneumoniae and Escherichia coli From All Spanish Provinces Reveals Interregional Spread of High-Risk Clones Such as ST307/OXA-48 and ST512/KPC-3

ObjectivesCARB-ES-19 is a comprehensive, multicenter, nationwide study integrating whole-genome sequencing (WGS) in the surveillance of carbapenemase-producing K. pneumoniae (CP-Kpn) and E. coli (CP-Eco) to determine their incidence, geographical distribution, phylogeny, and resistance mechanisms in Spain.MethodsIn total, 71 hospitals, representing all 50 Spanish provinces, collected the first 10 isolates per hospital (February to May 2019); CPE isolates were first identified according to EUCAST (meropenem MIC &gt; 0.12 mg/L with immunochromatography, colorimetric tests, carbapenem inactivation, or carbapenem hydrolysis with MALDI-TOF). Prevalence and incidence were calculated according to population denominators. Antibiotic susceptibility testing was performed using the microdilution method (EUCAST). All 403 isolates collected were sequenced for high-resolution single-nucleotide polymorphism (SNP) typing, core genome multilocus sequence typing (cgMLST), and resistome analysis.ResultsIn total, 377 (93.5%) CP-Kpn and 26 (6.5%) CP-Eco isolates were collected from 62 (87.3%) hospitals in 46 (92%) provinces. CP-Kpn was more prevalent in the blood (5.8%, 50/853) than in the urine (1.4%, 201/14,464). The cumulative incidence for both CP-Kpn and CP-Eco was 0.05 per 100 admitted patients. The main carbapenemase genes identified in CP-Kpn were blaOXA–48 (263/377), blaKPC–3 (62/377), blaVIM–1 (28/377), and blaNDM–1 (12/377). All isolates were susceptible to at least two antibiotics. Interregional dissemination of eight high-risk CP-Kpn clones was detected, mainly ST307/OXA-48 (16.4%), ST11/OXA-48 (16.4%), and ST512-ST258/KPC (13.8%). ST512/KPC and ST15/OXA-48 were the most frequent bacteremia-causative clones. The average number of acquired resistance genes was higher in CP-Kpn (7.9) than in CP-Eco (5.5).ConclusionThis study serves as a first step toward WGS integration in the surveillance of carbapenemase-producing Enterobacterales in Spain. We detected important epidemiological changes, including increased CP-Kpn and CP-Eco prevalence and incidence compared to previous studies, wide interregional dissemination, and increased dissemination of high-risk clones, such as ST307/OXA-48 and ST512/KPC-3

Extracellular vesicle-miRNAs as liquid biopsy biomarkers for disease identification and prognosis in metastatic colorectal cancer patients

We would like to extend our gratitude to the all the patients and the healthy volunteers who participated in the study, as well as the University of Granada, Biomedicine PhD program. This work was supported by Roche Spain, the PhD grant from the University of Granada (DdMP) (2014) and the PhD grant from the Spanish Government (ARM) (FPU) 2014, REF FPU14/05461.Disseminated disease is present in ≈50% of colorectal cancer patients upon diagnosis, being responsible for most of cancer deaths. Addition of biological drugs, as Bevacizumab, to chemotherapy, has increased progression free survival and overall survival of metastatic colorectal cancer (mCRC) patients. However, these benefits have been only reported in a small proportion of patients. To date, there are not biomarkers that could explain the heterogeneity of this disease and would help in treatment selection. Recent findings demonstrated that microRNAs (miRNAs) play an important role in cancer and they can be encapsulated with high stability into extracellular vesicles (EVs) that are released in biological fluids. EVs can act as cell-to-cell communicators, transferring genetic information, such as miRNAs. In this context, we aimed to investigate serum EV associated miRNAs (EV-miRNAs) as novel non-invasive biomarkers for the diagnosis and prognosis of Bevacizumab-treated mCRC patients. We observed that baseline miRNA-21 and 92a outperformed carcinoembryonic antigen levels in the diagnosis of our 44 mCRC patients, compared to 17 healthy volunteers. In addition, patients who died presented higher levels of miRNA-92a and 222 at 24 weeks. However, in the multivariate Cox analysis, higher levels of miRNA-222 at 24 weeks were associated with lower overall survival. Altogether, these data indicate that EV-miRNAs have a strong potential as liquid biopsy biomarkers for the identification and prognosis of mCRC.Roche SpainUniversity of Granada (DdMP)Spanish Government REF FPU14/0546

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)