FRENCH SCIENTIFIC AND CULTURAL DIPLOMACY

International audienc

The mechanisms leading to a stratospheric hydration by overshooting convection

AbstractOvershoots are convective air parcels that rise beyond their level of neutral buoyancy. A giga-large-eddy simulation (100-m cubic resolution) of “Hector the Convector,” a deep convective system that regularly forms in northern Australia, is analyzed to identify overshoots and quantify the effect of hydration of the stratosphere. In the simulation, 1507 individual overshoots were identified, and 46 of them were tracked over more than 10 min. Hydration of the stratosphere occurs through a sequence of mechanisms: overshoot penetration into the stratosphere, followed by entrainment of stratospheric air and then by efficient turbulent mixing between the air in the overshoot and the entrained warmer air, leaving the subsequent mixed air at about the maximum overshooting altitude. The time scale of these mechanisms is about 1 min. Two categories of overshoots are distinguished: those that significantly hydrate the stratosphere and those that have little direct hydration effect. The former reach higher altitudes and hence entrain and mix with air that has higher potential temperatures. The resulting mixed air has higher temperatures and higher saturation mixing ratios. Therefore, a greater amount of the hydrometeors carried by the original overshoot sublimates to form a persistent vapor-enriched layer. This makes the maximum overshooting altitude the key prognostic for the parameterization of deep convection to represent the correct overshoot transport. One common convection parameterization is tested, and the results suggest that the overshoot downward acceleration due to negative buoyancy is too large relative to that predicted by the numerical simulations and needs to be reduced.This research was supported by the StratoClim project funded by the European Union Seventh Framework Programme under Grant Agree- ment 603557 and the Idex Teasao project. Todd Lane is supported by the Australian Research Council’s Centres of Excellence scheme (CE170100023). Computer re- sources were allocated by GENCI through Projects 90569 and 100231 (Grand Challenge Turing)

Quark propagator and vertex: systematic corrections of hypercubic artifacts from lattice simulations

This is the first part of a study of the quark propagator and the vertex function of the vector current on the lattice in the Landau gauge and using both Wilson-clover and overlap actions. In order to be able to identify lattice artifacts and to reach large momenta we use a range of lattice spacings. The lattice artifacts turn out to be exceedingly large in this study. We present a new and very efficient method to eliminate the hypercubic (anisotropy) artifacts based on a systematic expansion on hypercubic invariants which are not SO(4) invariant. A simpler version of this method has been used in previous works. This method is shown to be significantly more efficient than the popular ``democratic'' methods. It can of course be applied to the lattice simulations of many other physical quantities. The analysis indicates a hierarchy in the size of hypercubic artifacts: overlap larger than clover and propagator larger than vertex function. This pleads for the combined study of propagators and vertex functions via Ward identities.Comment: 14 pags., 9 fig

LHC Predictions from a Tevatron Anomaly in the Top Quark Forward-Backward Asymmetry

We examine the implications of the recent CDF measurement of the top-quark forward-backward asymmetry, focusing on a scenario with a new color octet vector boson at 1-3 TeV. We study several models, as well as a general effective field theory, and determine the parameter space which provides the best simultaneous fit to the CDF asymmetry, the Tevatron top pair production cross section, and the exclusion regions from LHC dijet resonance and contact interaction searches. Flavor constraints on these models are more subtle and less severe than the literature indicates. We find a large region of allowed parameter space at high axigluon mass and a smaller region at low mass; we match the latter to an SU(3)xSU(3)/SU(3) coset model with a heavy vector-like fermion. Our scenario produces discoverable effects at the LHC with only 1-2 inverse femtobarns of luminosity at 7-8 TeV. Lastly, we point out that a Tevatron measurement of the b-quark forward-backward asymmetry would be very helpful in characterizing the physics underlying the top-quark asymmetry.Comment: 35 pages, 10 figures, 4 table

From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions

©2009 Gao, Skolnick. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.doi:10.1371/journal.pcbi.1000341DNA–protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA–protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA–protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA–protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA–protein interaction modes exhibit some similarity to specific DNA–protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Ca deviation from native is up to 5 Å from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA–protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein

Top quark forward-backward asymmetry in R-parity violating supersymmetry

The interaction of bottom squark-mediated top quark pair production, occurring in the R-parity violating minimal supersymmetric standard model (MSSM), is proposed as an explanation of the anomalously large $t\bar{t}$ forward-backward asymmetry (FBA) observed at the Tevatron. We find that this model can give a good fit to top quark data, both the inclusive and invariant mass-dependent asymmetries, while remaining consistent (at the 2-$\sigma$ level) with the total and differential production cross-sections. The scenario is challenged by strong constraints from atomic parity violation (APV), but we point out an extra diagram for the effective down quark-Z vertex, involving the same coupling constant as required for the FBA, which tends to weaken the APV constraint, and which can nullify it for reasonable values of the top squark masses and mixing angle. Large contributions to flavor-changing neutral currents can be avoided if only the third generation of sparticles is light.Comment: 24 pages, 7 figures. v3: included LHC top production cross section data; model still consistent at 2 sigma leve

Pathogen survival trajectories: an eco-environmental approach to the modeling of human campylobacteriosis ecology.

Campylobacteriosis, like many human diseases, has its own ecology in which the propagation of human infection and disease depends on pathogen survival and finding new hosts in order to replicate and sustain the pathogen population. The complexity of this process, a process common to other enteric pathogens, has hampered control efforts. Many unknowns remain, resulting in a poorly understood disease ecology. To provide structure to these unknowns and help direct further research and intervention, we propose an eco-environmental modeling approach for campylobacteriosis. This modeling approach follows the pathogen population as it moves through the environments that define the physical structure of its ecology. In this paper, we term the ecologic processes and environments through which these populations move "pathogen survival trajectories." Although such a modeling approach could have veterinary applications, our emphasis is on human campylobacteriosis and focuses on human exposures to Campylobacter through feces, food, and aquatic environments. The pathogen survival trajectories that lead to human exposure include ecologic filters that limit population size, e.g., cooking food to kill Campylobacter. Environmental factors that influence the size of the pathogen reservoirs include temperature, nutrient availability, and moisture availability during the period of time the pathogen population is moving through the environment between infected and susceptible hosts. We anticipate that the modeling approach proposed here will work symbiotically with traditional epidemiologic and microbiologic research to help guide and evaluate the acquisition of new knowledge about the ecology, eventual intervention, and control of campylobacteriosis

Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Overexpression of P70 S6 kinase protein is associated with increased risk of locoregional recurrence in node-negative premenopausal early breast cancer patients

The RPS6KB1 gene is amplified and overexpressed in approximately 10% of breast carcinomas and has been found associated with poor prognosis. We studied the prognostic significance of P70 S6 kinase protein (PS6K) overexpression in a series of 452 node-negative premenopausal early-stage breast cancer patients (median follow-up: 10.8 years). Immunohistochemistry was used to assess PS6K expression in the primary tumour, which had previously been analysed for a panel of established prognostic factors in breast cancer. In a univariate analysis, PS6K overexpression was associated with worse distant disease-free survival as well as impaired locoregional control (HR 1.80, P 0.025 and HR 2.50, P 0.006, respectively). In a multivariate analysis including other prognostic factors, PS6K overexpression remained an independent predictor for poor locoregional control (RR 2.67, P 0.003). To our knowledge, P70 S6 kinase protein is the first oncogenic marker that has prognostic impact on locoregional control and therefore may have clinical implications in determining the local treatment strategy in early-stage breast cancer patients

Histone deacetylases as new therapy targets for platinum-resistant epithelial ovarian cancer

Introduction: In developed countries, ovarian cancer is the fourth most common cancer in women. Due to the nonspecific symptomatology associated with the disease many patients with ovarian cancer are diagnosed late, which leads to significantly poorer prognosis. Apart from surgery and radiotherapy, a substantial number of ovarian cancer patients will undergo chemotherapy and platinum based agents are the mainstream first-line therapy for this disease. Despite the initial efficacy of these therapies, many women relapse; therefore, strategies for second-line therapies are required. Regulation of DNA transcription is crucial for tumour progression, metastasis and chemoresistance which offers potential for novel drug targets. Methods: We have reviewed the existing literature on the role of histone deacetylases, nuclear enzymes regulating gene transcription. Results and conclusion: Analysis of available data suggests that a signifant proportion of drug resistance stems from abberant gene expression, therefore HDAC inhibitors are amongst the most promising therapeutic targets for cancer treatment. Together with genetic testing, they may have a potential to serve as base for patient-adapted therapies