Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Mycobacterium tuberculosis Lipolytic Enzymes as Potential Biomarkers for the Diagnosis of Active Tuberculosis

BACKGROUND: New diagnosis tests are urgently needed to address the global tuberculosis (TB) burden and to improve control programs especially in resource-limited settings. An effective in vitro diagnostic of TB based on serological methods would be regarded as an attractive progress because immunoassays are simple, rapid, inexpensive, and may offer the possibility to detect cases missed by standard sputum smear microscopy. However, currently available serology tests for TB are highly variable in sensitivity and specificity. Lipolytic enzymes have recently emerged as key factors in lipid metabolization during dormancy and/or exit of the non-replicating growth phase, a prerequisite step of TB reactivation. The focus of this study was to analyze and compare the potential of four Mycobacterium tuberculosis lipolytic enzymes (LipY, Rv0183, Rv1984c and Rv3452) as new markers in the serodiagnosis of active TB. METHODS: Recombinant proteins were produced and used in optimized ELISA aimed to detect IgG and IgM serum antibodies against the four lipolytic enzymes. The capacity of the assays to identify infection was evaluated in patients with either active TB or latent TB and compared with two distinct control groups consisting of BCG-vaccinated blood donors and hospitalized non-TB individuals. RESULTS: A robust humoral response was detected in patients with active TB whereas antibodies against lipolytic enzymes were infrequently detected in either uninfected groups or in subjects with latent infection. High specifity levels, ranging from 93.9% to 97.5%, were obtained for all four antigens with sensitivity values ranging from 73.4% to 90.5%, with Rv3452 displaying the highest performances. Patients with active TB usually exhibited strong IgG responses but poor IgM responses. CONCLUSION: These results clearly indicate that the lipolytic enzymes tested are strongly immunogenic allowing to distinguish active from latent TB infections. They appear as potent biomarkers providing high sensitivity and specificity levels for the immunodiagnosis of active TB

The Arabidopsis thaliana F-box gene HAWAIIAN SKIRT is a new player in the microRNA pathway

In Arabidopsis, the F-box HAWAIIAN SKIRT (HWS) protein is important for organ growth. Loss of function of HWS exhibits pleiotropic phenotypes including sepal fusion. To dissect the HWS role, we EMS-mutagenized hws-1 seeds and screened for mutations that suppress hws-1 associated phenotypes. We identified shs-2 and shs-3 (suppressor of hws-2 and 3) mutants in which the sepal fusion phenotype of hws-1 was suppressed. shs-2 and shs-3 (renamed hst-23/hws-1 and hst-24/hws-1) carry transition mutations that result in premature terminations in the plant homolog of Exportin-5 HASTY (HST), known to be important in miRNA biogenesis, function and transport. Genetic crosses between hws-1 and mutant lines for genes in the miRNA pathway, also suppress the phenotypes associated with HWS loss of function, corroborating epistatic relations between the miRNA pathway genes and HWS. In agreement with these data, accumulation of miRNA is modified in HWS loss or gain of function mutants. Our data propose HWS as a new player in the miRNA pathway, important for plant growth

Alternative processing of its precursor is related to miR319 decreasing in melon plants exposed to cold

[EN] miRNAs are fundamental endogenous regulators of gene expression in higher organisms. miRNAs modulate multiple biological processes in plants. Consequently, miRNA accumulation is strictly controlled through miRNA precursor accumulation and processing. Members of the miRNA319 family are ancient ribo-regulators that are essential for plant development and stress responses and exhibit an unusual biogenesis that is characterized by multiple processing of their precursors. The significance of the high conservation of these non-canonical biogenesis pathways remains unknown. Here, we analyze data obtained by massive sRNA sequencing and 5 ' - RACE to explore the accumulation and infer the processing of members of the miR319 family in melon plants exposed to adverse environmental conditions. Sequence data showed that miR319c was down regulated in response to low temperature. However, the level of its precursor was increased by cold, indicating that miR319c accumulation is not related to the stem loop levels. Furthermore, we found that a decrease in miR319c was inversely correlated with the stable accumulation of an alternative miRNA (#miR319c) derived from multiple processing of the miR319c precursor. Interestingly, the alternative accumulation of miR319c and #miR319c was associated with an additional and non-canonical partial cleavage of the miR319c precursor during its loop-to-base-processing. Analysis of the transcriptional activity showed that miR319c negatively regulated the accumulation of HY5 via TCP2 in melon plants exposed to cold, supporting its involvement in the low temperature signaling pathway associated with anthocyanin biosynthesis. Our results provide new insights regarding the versatility of plant miRNA processing and the mechanisms regulating them as well as the hypothetical mechanism for the response to cold-induced stress in melon, which is based on the alternative regulation of miRNA biogenesis.Bustamante-González, AJ.; Marques Romero, MC.; Sanz-Carbonell, A.; Mulet, JM.; Gomez, GG. (2018). Alternative processing of its precursor is related to miR319 decreasing in melon plants exposed to cold. Scientific Reports. 8:1-13. https://doi.org/10.1038/s41598-018-34012-7S1138Borges, F. & Martienssen, R. A. The expanding world of small RNAs in plants. Nat Rev Mol Cell Biol 16, 727–741 (2015).Shriram, V., Kumar, V., Devarumath, R. M., Khare, T. S. & Wani, S. H. MicroRNAs As Potential Targets for Abiotic Stress Tolerance in Plants. Front Plant Sci 7, 817 (2016).Xie, M., Zhang, S. & Yu, B. microRNA biogenesis, degradation and activity in plants. Cell Mol Life Sci 72, 87–99 (2015).Bologna, N. G., Schapire, A. L. & Palatnik, J. F. Processing of plant microRNA precursors. Brief Funct Genomics 12, 37–45 (2012).Achkar, N. P., Cambiagno, D. A. & Manavella, P. A. miRNA Biogenesis: A Dynamic Pathway. Trends Plant Sci 21, 1034–1044 (2016).Dong, Z., Han, M. H. & Fedoroff, N. The RNA-binding proteins HYL1 and SE promote accurate in vitro processing of pri-miRNA by DCL1. Proc Natl Acad Sci USA 105, 9970–9975 (2008).Bologna, N. G. et al. Multiple RNA recognition patterns during microRNA biogenesis in plants. Genome Research 23, 1675–1689 (2013).Baranauskė, S. et al. Functional mapping of the plant small RNA methyltransferase: HEN1 physically interacts with HYL1 and DICER-LIKE 1 proteins. Nucleic Acids Res 43, 2802–2812 (2015).Zhang, S., Liu, Y. & Yu, B. New insights into pri-miRNA processing and accumulation in plants. WIREs. RNA 6, 533–545 (2015).Ren, G. et al. Regulation of miRNA abundance by RNA binding protein TOUGH in Arabidopsis. Proc Natl Acad Sci USA 109, 12817–12821 (2012).Cuperus, J. T., Fahlgren, N. & Carrington, J. C. Evolution and functional diversification of MIRNA genes. Plant Cell 23, 431–442 (2011).Zhang, W. et al. Multiple distinct small RNAs originate from the same microRNA precursors. Genome Biol 11(8), r81 (2010).Addo-Quaye, C. et al. Sliced microRNA targets and precise loop-first processing of MIR319 hairpins revealed by analysis of the Physcomitrella patens degradome. RNA 15, 2112–2121 (2009).Axtell, M. J., Snyder, J. A. & Bartel, D. P. Common functions for diverse small RNAs of land plants. Plant Cell 19, 1750–1769 (2007).Bologna, N. G., Mateos, J. L., Bresso, E. G. & Palatnik, J. F. A loop-to-base processing mechanism underlies the biogenesis of plant microRNAs miR319 and miR159. EMBO J 28, 3646–3656 (2009).Li, Y., Li, C., Ding, G. & Jin, Y. Evolution of MIR159/319 microRNA genes and their post-transcriptional regulatory link to siRNA pathways. BMC Evol Biol 11, 122 (2011).Sobkowiak, L., Karlowski, W., Jarmolowski, A. & Szweykowska-Kulinska, Z. Non-Canonical Processing of Arabidopsis pri-miR319a/b/c Generates Additional microRNAs to Target One RAP2.12 mRNA Isoform. Front Plant Sci 3, 46 (2012).Achard, P., Herr, A., Baulcombe, D. C. & Harberd, N. P. Modulation of floral development by a gibberellin-regulated microRNA. Development 131, 3357–3365 (2004).Allen, R. S. et al. Genetic analysis reveals functional redundancy and the major target genes of the Arabidopsis miR159 family. Proc Natl Acad Sci USA 104, 16371–16376 (2007).Jones-Rhoades, M. W. & Bartel, D. P. Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell 14, 787–799 (2004).Palatnik, J. F. et al. Control of leaf morphogenesis by microRNAs. Nature 425, 257–263 (2003).Wang, S. T. et al. MicroRNA319 positively regulates cold tolerance by targeting OsPCF6 and OsTCP21 in rice (Oryza sativa). PLoS One 9(3), e91357 (2014).Thiebaut, F. et al. Regulation of miR319 during cold stress in sugarcane. Plant Cell Environ 35, 502–512 (2012).Sunkar, R. & Zhu, J. K. Novel and stress-regulated microRNAs and other small RNAs from Arabidopsis. Plant Cell 16, 2001–2019 (2004).Chen, H. et al. A comparison of the low temperature transcriptomes of two tomato genotypes that differ in freezing tolerance: Solanum lycopersicum and Solanum habrochaites. BMC Plant Biol 15, 132 (2015).Garcia-Mas, J. et al. The genome of melon (Cucumis melo L.). Proc Natl Acad Sci USA 109, 11872–11877 (2012).Nuñez-Palenius, H. G. et al. Melon fruits: genetic diversity, physiology, and biotechnology features. Crit Rev Biotechnol 28, 13–55 (2008).Gonzalez-Ibeas, D. et al. Analysis of the melon (Cucumis melo) small RNAome by high-throughput pyrosequencing. BMC Genomics 12, 393 (2011).Herranz, M. C., Navarro, J. A., Sommen, E. & Pallas, V. Comparative analysis among the small RNA populations of source, sink and conductive tissues in two different plant-virus pathosystems. BMC Genomics 16, 117 (2015).Sattar, S. et al. Expression of small RNA in Aphis gossypii and its potential role in the resistance interaction with melon. PLoS One 7(11), e48579 (2012).Dai, X. & Zhao, P. X. psRNATarget: a plant small RNA target analysis server. Nucleic Acids Res. 39, W155–9 (2011).Palatnik, J. F. et al. Sequence and expression differences underlie functional specialization of Arabidopsis microRNAs miR159 and miR319. Dev Cell 13, 115–125 (2007).He, Z., Zhao, X., Kong, F., Zuo, Z. & Liu, X. TCP2 positively regulates HY5/HYH and photomorphogenesis in Arabidopsis. J Exp Bot 67, 775–785 (2016).Lau, O. S. & Deng, X. W. Plant hormone signaling lightens up: integrators of light and hormones. Curr Opin Plant Biol 13, 571–577 (2010).Oyama, T., Shimura, Y. & Okada, K. The Arabidopsis HY5 gene encodes a bZIP protein that regulates stimulus-induced development of root and hypocotyl. Genes Dev 11, 2983–2995 (1997).Ahmed, N. U., Park, J. I., Jung, H. J., Hur, Y. & Nou, I. S. Anthocyanin biosynthesis for cold and freezing stress tolerance and desirable color in Brassica rapa. Funct Integr Genomics 15, 383–394 (2015).Catalá, R., Medina, J. & Salinas, J. Integration of low temperature and light signaling during cold acclimation response in Arabidopsis. Proc Natl Acad Sci USA 108, 16475–16480 (2011).Schulz, E., Tohge, T., Zuther, E., Fernie, A. R. & Hincha, D. K. Natural variation in flavonol and anthocyanin metabolism during cold acclimation in Arabidopsis thaliana accessions. Plant Cell Environ 38, 1658–1672 (2015).Perea-Resa, C., Rodríguez-Milla, M. A., Iniesto, E., Rubio, V. & Salinas, J. Prefoldins Negatively Regulate Cold Acclimation in Arabidopsis thaliana by Promoting Nuclear Proteasome-Mediated HY5 Degradation. Mol Plant 10, 791–804 (2017).Solfanelli, C., Poggi, A., Loreti, E., Alpi, A. & Perata, P. Sucrose-Specific Induction of the Anthocyanin Biosynthetic Pathway in Arabidopsis. Plant Physiol 140, 637–646 (2006).Reis, R. S., Eamens, A. L. & Waterhouse, P. M. Missing Pieces in the Puzzle of Plant MicroRNAs. Trends Plant Sci 20, 721–728 (2015).Kumar, R. Role of microRNAs in biotic and abiotic stress responses in crop plants. Appl Biochem Biotech 174, 93–115 (2014).Ma, C., Burd, S. & Lers, A. miR408 is involved in abiotic stress responses in Arabidopsis. Plant J 84, 169–187 (2015).Song, L., Axtell, M. J. & Fedoroff, N. V. RNA secondary structural determinants of miRNA precursor processing in Arabidopsis. Curr Biol 20, 37–41 (2010).Bracken, C. P. et al. Global analysis of the mammalian RNA degradome reveals widespread miRNA-dependent and miRNA-independent endonucleolytic cleavage. Nucleic Acids Res 39, 5658–5668 (2011).Gurtan, A. M., Lu, V., Bhutkar, A. & Sharp, P. A. In vivo structure-function analysis of human Dicer reveals directional processing of precursor miRNAs. RNA 18, 1116–1122 (2012).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Journal 17, 10–12 (2011).Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq-2. Genome Biol 15, 550 (2014).Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11, 3–r25 (2010).Griffiths-Jones, S. miRBase: microRNA sequences and annotation. Current protocols in bioinformatics 12, 9 (2010).Li, H. et al. 1000 Genome Project Data Processing Subgroup The sequence alignment/map format & SAMtools. Bioinformatics 25, 2078–2079 (2009).Quinlan, A. & Hall, I. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25, 402–408 (2001).Llave, C., Xie, Z., Kasschau, K. D. & Carrington, J. C. Cleavage of Scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science 297, 2053–2056 (2002).Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10, 3–r25 (2009)

Search for heavy neutral Higgs bosons produced in association with b-quarks and decaying into b-quarks at root s=13 TeV with the ATLAS detector

A search for heavy neutral Higgs bosons produced in association with one or two b -quarks and decaying to b -quark pairs is presented using 27.8  fb − 1 of √ s = 13  TeV proton-proton collision data recorded by the ATLAS detector at the Large Hadron Collider during 2015 and 2016. No evidence of a signal is found. Upper limits on the heavy neutral Higgs boson production cross section times its branching ratio to b ¯ b are set, ranging from 4.0 to 0.6 pb at 95% confidence level over a Higgs boson mass range of 450 to 1400 GeV. Results are interpreted within the two-Higgs-doublet model and the minimal supersymmetric Standard Model

Measurement of single top-quark production in association with a W boson in the single-lepton channel at \sqrt{s} = 8\,\text {TeV} with the ATLAS detector

The production cross-section of a top quark in association with a W boson is measured using proton–proton collisions at \sqrt{s} = 8\,\text {TeV}. The dataset corresponds to an integrated luminosity of 20.2\,\text {fb}^{-1}, and was collected in 2012 by the ATLAS detector at the Large Hadron Collider at CERN. The analysis is performed in the single-lepton channel. Events are selected by requiring one isolated lepton (electron or muon) and at least three jets. A neural network is trained to separate the tW signal from the dominant t{\bar{t}} background. The cross-section is extracted from a binned profile maximum-likelihood fit to a two-dimensional discriminant built from the neural-network output and the invariant mass of the hadronically decaying W boson. The measured cross-section is \sigma _{tW} = 26 \pm 7\,\text {pb}, in good agreement with the Standard Model expectation

Identification of boosted Higgs bosons decaying into b-quark pairs with the ATLAS detector at 13 TeV

This paper describes a study of techniques for identifying Higgs bosons at high transverse momenta decaying into bottom-quark pairs, H→bb¯ , for proton–proton collision data collected by the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy s√=13 TeV . These decays are reconstructed from calorimeter jets found with the anti- kt R=1.0 jet algorithm. To tag Higgs bosons, a combination of requirements is used: b-tagging of R=0.2 track-jets matched to the large-R calorimeter jet, and requirements on the jet mass and other jet substructure variables. The Higgs boson tagging efficiency and corresponding multijet and hadronic top-quark background rejections are evaluated using Monte Carlo simulation. Several benchmark tagging selections are defined for different signal efficiency targets. The modelling of the relevant input distributions used to tag Higgs bosons is studied in 36 fb −1 of data collected in 2015 and 2016 using g→bb¯ and Z(→bb¯)γ event selections in data. Both processes are found to be well modelled within the statistical and systematic uncertainties

Measurement of the inclusive cross-section for the production of jets in association with a Z boson in proton-proton collisions at 8 TeV using the ATLAS detector

The inclusive cross-section for jet production in association with a Z boson decaying into an electron–positron pair is measured as a function of the transverse momentum and the absolute rapidity of jets using 19.9 fb −1 of s√=8 TeV proton–proton collision data collected with the ATLAS detector at the Large Hadron Collider. The measured Z + jets cross-section is unfolded to the particle level. The cross-section is compared with state-of-the-art Standard Model calculations, including the next-to-leading-order and next-to-next-to-leading-order perturbative QCD calculations, corrected for non-perturbative and QED radiation effects. The results of the measurements cover final-state jets with transverse momenta up to 1 TeV, and show good agreement with fixed-order calculations

Search for long-lived, massive particles in events with a displaced vertex and a muon with large impact parameter in pp collisions at root s=13 TeV with the ATLAS detector

A search for long-lived particles decaying into hadrons and at least one muon is presented. The analysis selects events that pass a muon or missing-transverse-momentum trigger and contain a displaced muon track and a displaced vertex. The analyzed dataset of proton-proton collisions at √ s = 13 TeV was collected with the ATLAS detector and corresponds to 136 fb − 1. The search employs dedicated reconstruction techniques that significantly increase the sensitivity to long-lived particle decays that occur in the ATLAS inner detector. Background estimates for Standard Model processes and instrumental effects are extracted from data. The observed event yields are compatible with those expected from background processes. The results are presented as limits at 95% confidence level on model-independent cross sections for processes beyond the Standard Model, and interpreted as exclusion limits in scenarios with pair production of long-lived top squarks that decay via a small R -parity-violating coupling into a quark and a muon. Top squarks with masses up to 1.7 TeV are excluded for a lifetime of 0.1 ns, and masses below 1.3 TeV are excluded for lifetimes between 0.01 ns and 30 ns