518 research outputs found

    Glutaredoxin GRXS17 associates with the cytosolic iron-sulfur cluster assembly pathway

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    Cytosolic monothiol glutaredoxins (GRXs) are required in iron-sulfur (Fe-S) cluster delivery and iron sensing in yeast and mammals. In plants, it is unclear whether they have similar functions. Arabidopsis (Arabidopsis thaliana) has a sole class II cytosolic monothiol GRX encoded by GRXS17. Here, we used tandem affinity purification to establish that Arabidopsis GRXS17 associates with most known cytosolic Fe-S assembly (CIA) components. Similar to mutant plants with defective CIA components, grxs17 loss-of-function mutants showed some degree of hypersensitivity to DNA damage and elevated expression of DNA damage marker genes. We also found that several putative Fe-S client proteins directly bind to GRXS17, such as XANTHINE DEHYDROGENASE1 (XDH1), involved in the purine salvage pathway, and CYTOSOLIC THIOURIDYLASE SUBUNIT1 and CYTOSOLIC THIOURIDYLASE SUBUNIT2, both essential for the 2-thiolation step of 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U) modification of tRNAs. Correspondingly, profiling of the grxs17-1 mutant pointed to a perturbed flux through the purine degradation pathway and revealed that it phenocopied mutants in the elongator subunit ELO3, essential for the mcm5 tRNA modification step, although we did not find XDH1 activity or tRNA thiolation to be markedly reduced in the grxs17-1 mutant. Taken together, our data suggest that plant cytosolic monothiol GRXs associate with the CIA complex, as in other eukaryotes, and contribute to, but are not essential for, the correct functioning of client Fe-S proteins in unchallenged conditions

    Nonlinear superchiral meta-surfaces: tuning chirality and disentangling non-reciprocity at the nanoscale.

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    Circularly polarized light is incident on a nanostructured chiral meta-surface. In the nanostructured unit cells whose chirality matches that of light, superchiral light is forming and strong optical second harmonic generation can be observed

    Entrepreneurs’ age, institutions, and social value creation goals: a multi-country study

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    This study explores the relationship between an entrepreneur's age and his/her social value creation goals. Building on the lifespan developmental psychology literature and institutional theory, we hypothesize a U-shaped relationship between entrepreneurs’ age and their choice to create social value through their ventures, such that younger and older entrepreneurs create more social value with their businesses while middle age entrepreneurs are relatively more economically and less socially oriented with their ventures. We further hypothesize that the quality of a country’s formal institutions in terms of economic, social, and political freedom steepen the U-shaped relationship between entrepreneurs’ age and their choice to pursue social value creation as supportive institutional environments allow entrepreneurs to follow their age-based preferences. We confirm our predictions using multilevel mixed-effects linear regressions on a sample of over 15,000 entrepreneurs (aged between 18 and 64 years) in 45 countries from Global Entrepreneurship Monitor data. The findings are robust to several alternative specifications. Based on our findings, we discuss implications for theory and practice, and we propose future research directions

    Using data mining techniques to explore physicians' therapeutic decisions when clinical guidelines do not provide recommendations: methods and example for type 2 diabetes

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    <p>Abstract</p> <p>Background</p> <p>Clinical guidelines carry medical evidence to the point of practice. As evidence is not always available, many guidelines do not provide recommendations for all clinical situations encountered in practice. We propose an approach for identifying knowledge gaps in guidelines and for exploring physicians' therapeutic decisions with data mining techniques to fill these knowledge gaps. We demonstrate our method by an example in the domain of type 2 diabetes.</p> <p>Methods</p> <p>We analyzed the French national guidelines for the management of type 2 diabetes to identify clinical conditions that are not covered or those for which the guidelines do not provide recommendations. We extracted patient records corresponding to each clinical condition from a database of type 2 diabetic patients treated at Avicenne University Hospital of Bobigny, France. We explored physicians' prescriptions for each of these profiles using C5.0 decision-tree learning algorithm. We developed decision-trees for different levels of detail of the therapeutic decision, namely the type of treatment, the pharmaco-therapeutic class, the international non proprietary name, and the dose of each medication. We compared the rules generated with those added to the guidelines in a newer version, to examine their similarity.</p> <p>Results</p> <p>We extracted 27 rules from the analysis of a database of 463 patient records. Eleven rules were about the choice of the type of treatment and thirteen rules about the choice of the pharmaco-therapeutic class of each drug. For the choice of the international non proprietary name and the dose, we could extract only a few rules because the number of patient records was too low for these factors. The extracted rules showed similarities with those added to the newer version of the guidelines.</p> <p>Conclusion</p> <p>Our method showed its usefulness for completing guidelines recommendations with rules learnt automatically from physicians' prescriptions. It could be used during the development of guidelines as a complementary source from practice-based knowledge. It can also be used as an evaluation tool for comparing a physician's therapeutic decisions with those recommended by a given set of clinical guidelines. The example we described showed that physician practice was in some ways ahead of the guideline.</p

    Spatial heterogeneity of habitat suitability for Rift Valley fever occurrence in Tanzania: an ecological niche modelling approach

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    Despite the long history of Rift Valley fever (RVF) in Tanzania, extent of its suitable habitat in the country remains unclear. In this study we investigated potential effects of temperature, precipitation, elevation, soil type, livestock density, rainfall pattern, proximity to wild animals, protected areas and forest on the habitat suitability for RVF occurrence in Tanzania. Presence-only records of 193 RVF outbreak locations from 1930 to 2007 together with potential predictor variables were used to model and map the suitable habitats for RVF occurrence using ecological niche modelling. Ground-truthing of the model outputs was conducted by comparing the levels of RVF virus specific antibodies in cattle, sheep and goats sampled from locations in Tanzania that presented different predicted habitat suitability values. Habitat suitability values for RVF occurrence were higher in the northern and central-eastern regions of Tanzania than the rest of the regions in the country. Soil type and precipitation of the wettest quarter contributed equally to habitat suitability (32.4% each), followed by livestock density (25.9%) and rainfall pattern (9.3%). Ground-truthing of model outputs revealed that the odds of an animal being seropositive for RVFV when sampled from areas predicted to be most suitable for RVF occurrence were twice the odds of an animal sampled from areas least suitable for RVF occurrence (95% CI: 1.43, 2.76, p < 0.001). The regions in the northern and central-eastern Tanzania were more suitable for RVF occurrence than the rest of the regions in the country. The modelled suitable habitat is characterised by impermeable soils, moderate precipitation in the wettest quarter, high livestock density and a bimodal rainfall pattern. The findings of this study should provide guidance for the design of appropriate RVF surveillance, prevention and control strategies which target areas with these characteristics
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