Modelling of trends in Twitter using retweet graph dynamics

In this paper we model user behaviour in Twitter to capture the emergence of trending topics. For this purpose, we first extensively analyse tweet datasets of several different events. In particular, for these datasets, we construct and investigate the retweet graphs. We find that the retweet graph for a trending topic has a relatively dense largest connected component (LCC). Next, based on the insights obtained from the analyses of the datasets, we design a mathematical model that describes the evolution of a retweet graph by three main parameters. We then quantify, analytically and by simulation, the influence of the model parameters on the basic characteristics of the retweet graph, such as the density of edges and the size and density of the LCC. Finally, we put the model in practice, estimate its parameters and compare the resulting behavior of the model to our datasets.Comment: 16 pages, 5 figures, presented at WAW 201

Single pairs of time-bin-entangled photons

Time-bin-entangled photons are ideal for long-distance quantum communication via optical fibers. Here we present a source where, even at high creation rates, each excitation pulse generates, at most, one time-bin-entangled pair. This is important for the accuracy and security of quantum communication. Our site-controlled quantum dot generates single polarization-entangled photon pairs, which are then converted, without loss of entanglement strength, into single time-bin-entangled photon pairs

Acute cigarette smoke-induced eQTL affects formyl peptide receptor expression and lung function

Background and objective Cigarette smoking is one of the most prevalent causes of preventable deaths worldwide, leading to chronic diseases, including chronic obstructive pulmonary disease (COPD). Cigarette smoke is known to induce significant transcriptional modifications throughout the respiratory tract. However, it is largely unknown how genetic profiles influence the smoking-related transcriptional changes and how changes in gene expression translate into altered alveolar epithelial repair responses. Methods We performed a candidate-based acute cigarette smoke-induced eQTL study, investigating the association between SNP and differential gene expression of FPR family members in bronchial epithelial cells isolated 24 h after smoking and after 48 h without smoking. The effects FPR1 on lung epithelial integrity and repair upon damage in the presence and absence of cigarette smoke were studied in CRISPR-Cas9-generated lung epithelial knockout cells. Results One significant (FDR 2-fold change in gene expression. The minor allele of rs3212855 was associated with significantly higher gene expression of FPR1, FPR2 and FPR3 upon smoking. Importantly, the minor allele of rs3212855 was also associated with lower lung function. Alveolar epithelial FPR1 knockout cells were protected against CSE-induced reduction in repair capacity upon wounding. Conclusion We identified a novel smoking-related inducible eQTL that is associated with a smoke-induced increase in the expression of FPR1, FPR2 and FPR3, and with lowered lung function. in vitro FPR1 down-regulation protects against smoke-induced reduction in lung epithelial repair

Periostin:contributor to abnormal airway epithelial function in asthma?

Periostin may serve as a biomarker for type-2-mediated eosinophilic airway inflammation in asthma. We hypothesised that type-2 cytokine IL-13 induces airway epithelial expression of periostin, which in turn contributes to epithelial changes observed in asthma.We studied the effect of IL-13 on periostin expression in BEAS-2B and air-liquid interface (ALI)-differentiated primary bronchial epithelial cells (PBECs). Additionally, effects of recombinant human periostin on epithelial-to-mesenchymal transition (EMT) markers and mucin genes were assessed. In bronchial biopsies and induced sputum from asthma patients and healthy controls, we analysed periostin single cell gene expression and protein levels.IL-13 increased POSTN expression in both cell types, which was accompanied by EMT-related features in BEAS-2B. In ALI-differentiated PBECs, IL-13 increased periostin basolateral and apical release. Apical administration of periostin increased the expression of MMP9, MUC5B and MUC5AC In bronchial biopsies, POSTN expression was mainly confined to basal epithelial cells, ionocytes, endothelial cells and fibroblasts, showing higher expression in basal epithelial cells from asthma patients versus controls. Higher protein levels of periostin, expressed in epithelial and subepithelial layers, was confirmed in bronchial biopsies from asthma patients compared to healthy controls. Although sputum periostin levels were not higher in asthma, levels correlated with eosinophil numbers and coughing up mucus.Periostin expression is increased by IL-13 in bronchial epithelial cells and higher in bronchial biopsies from asthma patients. This may have important consequences, as administration of periostin increased epithelial expression of mucin genes, supporting the relationship of periostin with type-2 mediated asthma and mucus secretion

SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes.

We investigated SARS-CoV-2 potential tropism by surveying expression of viral entry-associated genes in single-cell RNA-sequencing data from multiple tissues from healthy human donors. We co-detected these transcripts in specific respiratory, corneal and intestinal epithelial cells, potentially explaining the high efficiency of SARS-CoV-2 transmission. These genes are co-expressed in nasal epithelial cells with genes involved in innate immunity, highlighting the cells' potential role in initial viral infection, spread and clearance. The study offers a useful resource for further lines of inquiry with valuable clinical samples from COVID-19 patients and we provide our data in a comprehensive, open and user-friendly fashion at www.covid19cellatlas.org.This work was supported by the Wellcome Sanger Institute core funding (WT206194) and the Wellcome Strategic Scientific Support award “Pilot projects for the Human Cell Atlas” (WT211276/Z/18/Z), a Seed Network grant from the Chan Zuckerberg Initiative to P.B., T.D., T.E.D., O.E., P.H., N.H., N.K., M.K., K.B.M., A.M., M.C.N., M.N., D.P., J.R., P.R.T., S.Q., A.R., O.R., M.S., J.S., J.G.S., C.E.S., H.B.S., D.S., A.T., J.W. and K.Z. and by the European Union’s H2020 research and innovation program under grant agreement No 874656 (discovAIR) to P.B., A.B., M.K., S.L., J.L., K.B.M., M.C.N., K.S.P., C.S., H.B.S., J.S., F.J.T. and M.vd.B. W.S. acknowledges funding from the Newton Fund, Medical Research Council (MRC), The Thailand Research Fund (TRF), and Thailand’s National Science and Technology Development Agency (NSTDA). M.C.N acknowledges funding from GSK Ltd, Netherlands Lung Foundation project no. 5.1.14.020 and 4.1.18.226. T.D. acknowledges funding from HubMap consortium and Stanford Child Health Research Institute- Woods Family Faculty Scholarship. T.E.D. acknowledges funding from HubMap. P.H. acknowledges funding from LENDULET-BIOMAG Grant (2018-342) and the European Regional Development Funds (GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-15-2016-00026, GINOP-2.3.2-15-2016-00037). J.L.B. acknowledges funding from Medical Research Council (MRC), and the UK Regenerative Medicine Platform (MR/ 5005579/1). P.B. acknowledges funding from Fondation pour la Recherche Médicale (DEQ20180339158), Agence Nationale de la Recherche (UCAJEDI, ANR-15-IDEX-01; SAHARRA, ANR-19-CE14-0027; France Génomique, ANR-10-INBS-09-03), and Conseil Départemental des Alpes Maritimes (2016-294DGADSH-CV; 2019-390DGADSH-CV). N.E.B. and J.K. acknowledge funding from NIH grant R01HL145372 and DOD grant W81XWH1910416. I.G. acknowledges funding from NIH (5R24HD000836) and the Eunice Kennedy Shriver National Institute of Child Health and Human. N.H., J.G.S. and C.E.S. acknowledge funding by the Leducq foundation. N.H. is recipient of an ERC Advanced Grant. J.K. acknowledges funding from NIH grant K08HL130595 and the Doris Duke Charitable Foundation. N.K. acknowledges funding from NIH grants R01HL127349, U01HL145567 and an unrestricted grant from Three Lakes Foundation. M.K. acknowledges HHMI and Wall Center for Pulmonary Vascular Disease. H.L. acknowledges funding from National Research Foundation of Korea. K.M. acknowledges funding from Wellcome Trust. A.M. acknowledges funding from NIH grants HL135124, AG049665 and AI135964. M.Z.N. acknowledges funding from Rutherford Fund Fellowship allocated by the Medical Research Council and the UK Regenerative Medicine Platform (MR/ 5005579/1 to M.Z.N.). M.Z.N. and M.Y. have been funded by the Rosetrees Grant (Grant number M899). M.N. acknowledges funding from a BHF/DZHK grant and British Heart Foundation (PG/16/47/32156). J.O.-M. acknowledges funding from Richard and Susan Smith Family Foundation. D.P. acknowledges funding from Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center. J.P. acknowledges funding from National Health and Medical Research Council. P.R.T. acknowledges funding from R01HL146557 from NHLBI/NIH. E.L.R. acknowledges funding from MRC MR/P009581/1 and MR/SO35907/1. A.R. and O. R. acknowledge HHMI, the Klarman Cell Observatory, and the Manton Foundation. K.S.-P. acknowledges NIHR Cambridge Biomedical Research Centre. C.S. acknowledges Swedish research Council, Swedish Cancer Society, and CPI. H.B.S. acknowledges German Center for Lung Research and Helmholtz Association. J.S. acknowledges Boehringer Ingelheim, by the German Research Foundation (DFG; EXC2151/1, ImmunoSensation2 - the immune sensory system, project number 390873048), project numbers 329123747, 347286815) and by the HGF grant sparse2big. A.K.S. acknowledges the Beckman Young Investigator Program, a Sloan Fellowship in Chemistry, the NIH (5U24AI118672), and the Bill and Melinda Gates Foundation. F.J.T. acknowledges the German Center for Lung Research. M.vd.B. acknowledges from Ministry of Economic Affairs and Climate Policy by means of the PPP. K.B.W. is funded by the University College London-Birkbeck MRC Doctoral Training Programme. J.W. and Y.Y. acknowledge NIH, U01 HL148856 LungMap Phase II. R.X. acknowledges the NIH (DK043351). H.Z. is supported by the National Key R&D Program (no. 2019YFA0801703) and National Natural Science Foundation of China (no. 31871370

MARS Bulletin Vol.22 No 10 (2014) - High summer crop yields at EU-28 level

Crop Monitoring in EuropeJRC.H.4-Monitoring Agricultural Resource