Phenotyping progenies for complex architectural traits: a strategy for 1-year-old apple trees (Malus x domestica Borkh.)

International audienceThe aim of this study was to define a methodology for describing architectural traits in a quantitative way on tree descendants. Our strategy was to collect traits related to both tree structural organization, resulting from growth and branching, and tree form and then to select among these traits relevant descriptors on the basis of their genetic parameters. Because the complexity of tree architecture increases with tree age, we chose to describe the trees in the early stages of development. The study was carried out on a 1-year-old apple progeny derived from two parent cultivars with contrasted architecture. A large number of variables were collected at different positions and scales within the trees. Broad-sense heritability and genetic correlations were estimated and the within tree variability was analyzed for variables measured on long sylleptic axillary shoots (LSAS). These results were combined to select heritable and not correlated variables. Finally, the selection of variables proposed combines topological with geometric traits measured on both trunks and LSAS: (1) on the trunk, mean internode length, and number of sylleptic axillary shoots; (2) on axillary shoots, conicity, bending, and number of sylleptic axillary shoots born at order 3. The trees of the progeny were partitioned on the basis of these variables. The putative agronomic interest of the selected variables with respect to the subsequent tree development is discussed

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO

Biomineralisation by earthworms: an investigation into the stability and distribution of amorphous calcium carbonate

Background Many biominerals form from amorphous calcium carbonate (ACC), but this phase is highly unstable when synthesised in its pure form inorganically. Several species of earthworm secrete calcium carbonate granules which contain highly stable ACC. We analysed the milky fluid from which granules form and solid granules for amino acid (by liquid chromatography) and functional group (by Fourier transform infrared (FTIR) spectroscopy) compositions. Granule elemental composition was determined using inductively coupled plasma-optical emission spectroscopy (ICP-OES) and electron microprobe analysis (EMPA). Mass of ACC present in solid granules was quantified using FTIR and compared to granule elemental and amino acid compositions. Bulk analysis of granules was of powdered bulk material. Spatially resolved analysis was of thin sections of granules using synchrotron-based μ-FTIR and EMPA electron microprobe analysis. Results The milky fluid from which granules form is amino acid-rich (≤ 136 ± 3 nmol mg−1 (n = 3; ± std dev) per individual amino acid); the CaCO3 phase present is ACC. Even four years after production, granules contain ACC. No correlation exists between mass of ACC present and granule elemental composition. Granule amino acid concentrations correlate well with ACC content (r ≥ 0.7, p ≤ 0.05) consistent with a role for amino acids (or the proteins they make up) in ACC stabilisation. Intra-granule variation in ACC (RSD = 16%) and amino acid concentration (RSD = 22–35%) was high for granules produced by the same earthworm. Maps of ACC distribution produced using synchrotron-based μ-FTIR mapping of granule thin sections and the relative intensity of the ν2: ν4 peak ratio, cluster analysis and component regression using ACC and calcite standards showed similar spatial distributions of likely ACC-rich and calcite-rich areas. We could not identify organic peaks in the μ-FTIR spectra and thus could not determine whether ACC-rich domains also had relatively high amino acid concentrations. No correlation exists between ACC distribution and elemental concentrations determined by EMPA. Conclusions ACC present in earthworm CaCO3 granules is highly stable. Our results suggest a role for amino acids (or proteins) in this stability. We see no evidence for stabilisation of ACC by incorporation of inorganic components

Ovarian cancer

Ovarian cancer is not a single disease and can be subdivided into at least five different histological subtypes that have different identifiable risk factors, cells of origin, molecular compositions, clinical features and treatments. Ovarian cancer is a global problem, is typically diagnosed at a late stage and has no effective screening strategy. Standard treatments for newly diagnosed cancer consist of cytoreductive surgery and platinum-based chemotherapy. In recurrent cancer, chemotherapy, anti-angiogenic agents and poly(ADP-ribose) polymerase inhibitors are used, and immunological therapies are currently being tested. High-grade serous carcinoma (HGSC) is the most commonly diagnosed form of ovarian cancer and at diagnosis is typically very responsive to platinum-based chemotherapy. However, in addition to the other histologies, HGSCs frequently relapse and become increasingly resistant to chemotherapy. Consequently, understanding the mechanisms underlying platinum resistance and finding ways to overcome them are active areas of study in ovarian cancer. Substantial progress has been made in identifying genes that are associated with a high risk of ovarian cancer (such as BRCA1 and BRCA2), as well as a precursor lesion of HGSC called serous tubal intraepithelial carcinoma, which holds promise for identifying individuals at high risk of developing the disease and for developing prevention strategies

Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method.

The use of subgroups based on biological-clinical and socio-demographic variables to deal with population heterogeneity is well-established in public policy. The use of subgroups based on preferences is rare, except when religion based, and controversial. If it were decided to treat subgroup preferences as valid determinants of public policy, a transparent analytical procedure is needed. In this proof of method study we show how public preferences could be incorporated into policy decisions in a way that respects both the multi-criterial nature of those decisions, and the heterogeneity of the population in relation to the importance assigned to relevant criteria. It involves combining Cluster Analysis (CA), to generate the subgroup sets of preferences, with Multi-Criteria Decision Analysis (MCDA), to provide the policy framework into which the clustered preferences are entered. We employ three techniques of CA to demonstrate that not only do different techniques produce different clusters, but that choosing among techniques (as well as developing the MCDA structure) is an important task to be undertaken in implementing the approach outlined in any specific policy context. Data for the illustrative, not substantive, application are from a Randomized Controlled Trial of online decision aids for Australian men aged 40-69 years considering Prostate-specific Antigen testing for prostate cancer. We show that such analyses can provide policy-makers with insights into the criterion-specific needs of different subgroups. Implementing CA and MCDA in combination to assist in the development of policies on important health and community issues such as drug coverage, reimbursement, and screening programs, poses major challenges -conceptual, methodological, ethical-political, and practical - but most are exposed by the techniques, not created by them

The Cost of Simplifying Air Travel When Modeling Disease Spread

BACKGROUND: Air travel plays a key role in the spread of many pathogens. Modeling the long distance spread of infectious disease in these cases requires an air travel model. Highly detailed air transportation models can be over determined and computationally problematic. We compared the predictions of a simplified air transport model with those of a model of all routes and assessed the impact of differences on models of infectious disease. METHODOLOGY/PRINCIPAL FINDINGS: Using U.S. ticket data from 2007, we compared a simplified "pipe" model, in which individuals flow in and out of the air transport system based on the number of arrivals and departures from a given airport, to a fully saturated model where all routes are modeled individually. We also compared the pipe model to a "gravity" model where the probability of travel is scaled by physical distance; the gravity model did not differ significantly from the pipe model. The pipe model roughly approximated actual air travel, but tended to overestimate the number of trips between small airports and underestimate travel between major east and west coast airports. For most routes, the maximum number of false (or missed) introductions of disease is small (<1 per day) but for a few routes this rate is greatly underestimated by the pipe model. CONCLUSIONS/SIGNIFICANCE: If our interest is in large scale regional and national effects of disease, the simplified pipe model may be adequate. If we are interested in specific effects of interventions on particular air routes or the time for the disease to reach a particular location, a more complex point-to-point model will be more accurate. For many problems a hybrid model that independently models some frequently traveled routes may be the best choice. Regardless of the model used, the effect of simplifications and sensitivity to errors in parameter estimation should be analyzed

A Phase 2a active-comparator-controlled study to evaluate the efficacy and safety of efinopegdutide in patients with nonalcoholic fatty liver disease

Background and aimsThis study assessed the effects of the GLP-1/glucagon receptor co-agonist efinopegdutide relative to the selective GLP-1 receptor agonist semaglutide on liver fat content (LFC) in patients with nonalcoholic fatty liver disease (NAFLD).MethodsThis was a Phase 2a, randomized, active-comparator-controlled, parallel-group, open-label study. A magnetic resonance imaging-estimated proton density fat fraction assessment was performed to determine LFC at screening and Week 24. Participants with an LFC of ≥10% at screening were randomized 1:1 to open-label efinopegdutide 10 mg subcutaneous (SC) once weekly or semaglutide 1 mg SC once weekly for 24 weeks, stratified according to concurrent diagnosis of type 2 diabetes. Both drugs were titrated to the target dose over an 8-week time period. The primary efficacy endpoint was relative reduction from baseline in LFC (%) after 24 weeks of treatment.ResultsAmong 145 randomized participants (efinopegdutide N=72, semaglutide N=73), 33.1% had T2DM. At baseline, mean body mass index was 34.3 kg/m2and mean LFC was 20.3%. The least squares (LS) mean relative reduction from baseline in LFC at Week 24 was significantly (p&lt;0.001) greater with efinopegdutide (72.7% [90% CI: 66.8, 78.7]) than with semaglutide (42.3% [90% CI: 36.5, 48.1]). Both treatment groups had an LS mean percent reduction from baseline in body weight at Week 24 (efinopegdutide 8.5% vs semaglutide 7.1%; p=0.085). Slightly higher incidences of adverse events and drug-related adverse events were observed in the efinopegdutide group compared with the semaglutide group, primarily related to an imbalance in gastrointestinal adverse events.ConclusionsIn patients with NAFLD, treatment with efinopegdutide 10 mg weekly led to a significantly greater reduction in LFC than semaglutide 1 mg weekly. (EudraCT: 2020-005136-30; NCT: 04944992) IMPACT AND IMPLICATIONS: Currently, there are no approved therapies for nonalcoholic steatohepatitis (NASH). The weight loss associated with glucagon-like peptide-1 (GLP-1) receptor agonists has been shown to decrease hepatic inflammation in patients with NASH. In addition to reducing liver fat content (LFC) indirectly through weight loss, glucagon receptor agonism may also reduce LFC by acting on the liver directly to stimulate fatty acid oxidation and reduce lipogenesis. This study demonstrated that treatment of nonalcoholic fatty liver disease (NAFLD) patients with the GLP-1/glucagon receptor co-agonist efinopegdutide at 10 mg weekly led to a significantly greater reduction in LFC compared with the GLP-1 receptor agonist semaglutide at 1 mg weekly, suggesting that efinopegdutide may be an effective treatment for NASH.Clinical trial numberEudraCT: 2020-005136-30; NCT: 04944992

Search for new physics in the multijet and missing transverse momentum final state in proton-proton collisions at √s=8 Tev

Peer reviewe

The disruption of proteostasis in neurodegenerative diseases

Cells count on surveillance systems to monitor and protect the cellular proteome which, besides being highly heterogeneous, is constantly being challenged by intrinsic and environmental factors. In this context, the proteostasis network (PN) is essential to achieve a stable and functional proteome. Disruption of the PN is associated with aging and can lead to and/or potentiate the occurrence of many neurodegenerative diseases (ND). This not only emphasizes the importance of the PN in health span and aging but also how its modulation can be a potential target for intervention and treatment of human diseases.info:eu-repo/semantics/publishedVersio