The teleconnection of the tropical Atlantic to Indo-Pacific sea surface temperatures on inter-annual to centennial time scales: a review of recent findings

In this paper, the teleconnections from the tropical Atlantic to the Indo-Pacific region from inter-annual to centennial time scales will be reviewed. Identified teleconnections and hypotheses on mechanisms at work are reviewed and further explored in a century-long pacemaker coupled ocean-atmosphere simulation ensemble. There is a substantial impact of the tropical Atlantic on the Pacific region at inter-annual time scales. An Atlantic Niño (Niña) event leads to rising (sinking) motion in the Atlantic region, which is compensated by sinking (rising) motion in the central-western Pacific. The sinking (rising) motion in the central-western Pacific induces easterly (westerly) surface wind anomalies just to the west, which alter the thermocline. These perturbations propagate eastward as upwelling (downwelling) Kelvin-waves, where they increase the probability for a La Niña (El Niño) event. Moreover, tropical North Atlantic sea surface temperature anomalies are also able to lead La Niña/El Niño development. At multidecadal time scales, a positive (negative) Atlantic Multidecadal Oscillation leads to a cooling (warming) of the eastern Pacific and a warming (cooling) of the western Pacific and Indian Ocean regions. The physical mechanism for this impact is similar to that at inter-annual time scales. At centennial time scales, the Atlantic warming induces a substantial reduction of the eastern Pacific warming even under CO2 increase and to a strong subsurface cooling

Jet energy measurement with the ATLAS detector in proton-proton collisions at sqrt(s) = 7 TeV

The jet energy scale (JES) and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of sqrt(s) = 7 TeV corresponding to an integrated luminosity of 38 inverse pb. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0.4 or R=0.6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pt > 20 GeV and pseudorapidities eta 50 GeV after a dedicated correction for this effect. The JES is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pt, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pt jets recoiling against a high-pt jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, providing an improved jet energy resolution and a reduced flavour dependence of the jet response. The JES systematic uncertainty determined from a combination of in situ techniques are consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pt jets

The systemic lupus erythematosus IRF5 risk haplotype is associated with systemic sclerosis

Systemic sclerosis (SSc) is a fibrotic autoimmune disease in which the genetic component plays an important role. One of the strongest SSc association signals outside the human leukocyte antigen (HLA) region corresponds to interferon (IFN) regulatory factor 5 (IRF5), a major regulator of the type I IFN pathway. In this study we aimed to evaluate whether three different haplotypic blocks within this locus, which have been shown to alter the protein function influencing systemic lupus erythematosus (SLE) susceptibility, are involved in SSc susceptibility and clinical phenotypes. For that purpose, we genotyped one representative single-nucleotide polymorphism (SNP) of each block (rs10488631, rs2004640, and rs4728142) in a total of 3,361 SSc patients and 4,012 unaffected controls of Caucasian origin from Spain, Germany, The Netherlands, Italy and United Kingdom. A meta-analysis of the allele frequencies was performed to analyse the overall effect of these IRF5 genetic variants on SSc. Allelic combination and dependency tests were also carried out. The three SNPs showed strong associations with the global disease (rs4728142: P = 1.34×10<sup>−8</sup>, OR = 1.22, CI 95% = 1.14–1.30; rs2004640: P = 4.60×10<sup>−7</sup>, OR = 0.84, CI 95% = 0.78–0.90; rs10488631: P = 7.53×10<sup>−20</sup>, OR = 1.63, CI 95% = 1.47–1.81). However, the association of rs2004640 with SSc was not independent of rs4728142 (conditioned P = 0.598). The haplotype containing the risk alleles (rs4728142*A-rs2004640*T-rs10488631*C: P = 9.04×10<sup>−22</sup>, OR = 1.75, CI 95% = 1.56–1.97) better explained the observed association (likelihood P-value = 1.48×10<sup>−4</sup>), suggesting an additive effect of the three haplotypic blocks. No statistical significance was observed in the comparisons amongst SSc patients with and without the main clinical characteristics. Our data clearly indicate that the SLE risk haplotype also influences SSc predisposition, and that this association is not sub-phenotype-specific

A genome-wide association study follow-up suggests a possible role for PPARG in systemic sclerosis susceptibility

Introduction: A recent genome-wide association study (GWAS) comprising a French cohort of systemic sclerosis (SSc) reported several non-HLA single-nucleotide polymorphisms (SNPs) showing a nominal association in the discovery phase. We aimed to identify previously overlooked susceptibility variants by using a follow-up strategy.<p></p> Methods: Sixty-six non-HLA SNPs showing a P value <10-4 in the discovery phase of the French SSc GWAS were analyzed in the first step of this study, performing a meta-analysis that combined data from the two published SSc GWASs. A total of 2,921 SSc patients and 6,963 healthy controls were included in this first phase. Two SNPs, PPARG rs310746 and CHRNA9 rs6832151, were selected for genotyping in the replication cohort (1,068 SSc patients and 6,762 healthy controls) based on the results of the first step. Genotyping was performed by using TaqMan SNP genotyping assays. Results: We observed nominal associations for both PPARG rs310746 (PMH = 1.90 × 10-6, OR, 1.28) and CHRNA9 rs6832151 (PMH = 4.30 × 10-6, OR, 1.17) genetic variants with SSc in the first step of our study. In the replication phase, we observed a trend of association for PPARG rs310746 (P value = 0.066; OR, 1.17). The combined overall Mantel-Haenszel meta-analysis of all the cohorts included in the present study revealed that PPARG rs310746 remained associated with SSc with a nominal non-genome-wide significant P value (PMH = 5.00 × 10-7; OR, 1.25). No evidence of association was observed for CHRNA9 rs6832151 either in the replication phase or in the overall pooled analysis.<p></p> Conclusion: Our results suggest a role of PPARG gene in the development of SSc

Single hadron response measurement and calorimeter jet energy scale uncertainty with the ATLAS detector at the LHC

The uncertainty on the calorimeter energy response to jets of particles is derived for the ATLAS experiment at the Large Hadron Collider (LHC). First, the calorimeter response to single isolated charged hadrons is measured and compared to the Monte Carlo simulation using proton-proton collisions at centre-of-mass energies of root s = 900 GeV and 7 TeV collected during 2009 and 2010. Then, using the decay of K-s and Lambda particles, the calorimeter response to specific types of particles (positively and negatively charged pions, protons, and anti-protons) is measured and compared to the Monte Carlo predictions. Finally, the jet energy scale uncertainty is determined by propagating the response uncertainty for single charged and neutral particles to jets. The response uncertainty is 2-5 % for central isolated hadrons and 1-3 % for the final calorimeter jet energy scale

The InBIO barcoding initiative database: DNA barcodes of Iberian Trichoptera, documenting biodiversity for freshwater biomonitoring in a Mediterranean hotspot

The Trichoptera are an important component of freshwater ecosystems. In the Iberian Peninsula, 380 taxa of caddisflies are known, with nearly 1/3 of the total species being endemic in the region. A reference collection of morphologically identified Trichoptera specimens, representing 142 Iberian taxa, was constructed. The InBIO Barcoding Initiative (IBI) Trichoptera 01 dataset contains records of 438 sequenced specimens. The species of this dataset correspond to about 37% of Iberian Trichoptera species diversity. Specimens were collected between 1975 and 2018 and are deposited in the IBI collection at the CIBIO (Research Center in Biodiversity and Genetic Resources, Portugal) or in the collection Marcos A. González at the University of Santiago de Compostela (Spain).Twenty-nine species, from nine different families, were new additions to the Barcode of Life Data System (BOLD). A success identification rate of over 80% was achieved when comparing morphological identifications and DNA barcodes for the species analysed. This encouraging step advances incorporation of informed Environmental DNA tools in biomonitoring schemes, given the shortcomings of morphological identifications of larvae and adult Caddisflies in such studies. DNA barcoding was not successful in identifying species in six Trichoptera genera: Hydropsyche (Hydropsychidae), Athripsodes (Leptoceridae), Wormaldia (Philopotamidae), Polycentropus (Polycentropodidae) Rhyacophila (Rhyacophilidae) and Sericostoma (Sericostomatidae). The high levels of intraspecific genetic variability found, combined with a lack of a barcode gap and a challenging morphological identification, rendered these species as needing additional studies to resolve their taxonomy

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes