Root System Architecture from Coupling Cell Shape to Auxin Transport

Lateral organ position along roots and shoots largely determines plant architecture, and depends on auxin distribution patterns. Determination of the underlying patterning mechanisms has hitherto been complicated because they operate during growth and division. Here, we show by experiments and computational modeling that curvature of the Arabidopsis root influences cell sizes, which, together with tissue properties that determine auxin transport, induces higher auxin levels in the pericycle cells on the outside of the curve. The abundance and position of the auxin transporters restricts this response to the zone competent for lateral root formation. The auxin import facilitator, AUX1, is up-regulated by auxin, resulting in additional local auxin import, thus creating a new auxin maximum that triggers organ formation. Longitudinal spacing of lateral roots is modulated by PIN proteins that promote auxin efflux, and pin2,3,7 triple mutants show impaired lateral inhibition. Thus, lateral root patterning combines a trigger, such as cell size difference due to bending, with a self-organizing system that mediates alterations in auxin transport

Hyperlactataemia in HIV-infected patients: the role of NRTI-treatment

BACKGROUND: Long-term treatment with nucleoside reverse transcriptase inhibitors (NRTIs) can induce mitochondrial dysfunction, most severely represented by lactic acidosis. Diagnostic tests for mitochondrial dysfunction are lacking, although persistently elevated serum lactate might be a surrogate marker. OBJECTIVES: To determine the occurrence of hyperlactataemia in HIV-infected patients on NRTI-treatment and to evaluate the possible risk factors. METHODS: Cross-sectional analysis of lactic-acid levels in asymptomatic HIV-infected patients. Hyperlactactaemia was considered mild if between 2.0-5 mmol/l, serious if >5 mmol/l and lactic acidosis was defined as lactic acid levels >5 mmol/l with bicarbonate <20 mmol/l. Possible risk factors, such as current and preceding NRTI-treatment as well as treatment with non-nucleoside reverse transcriptase inhibitors or protease inhibitors and concurrent liver disease, were analysed. RESULTS: Two hundred and twenty three asymptomatic HIV-infected patients were studied, including 174 patients (78%) on NRTI treatment, 12 patients (5%) treated without NRTIs and 37 patients (17%) not treated. Mild hyperlactataemia was found in 42 patients (19%), from whom 38/42 (90%) were NRTI-treated and the remaining patients (4/42, 10%) received no treatment (chi2, P <0.05). The significant risk factors for hyperlactataemia in the univariate analysis were NRTI-treatment as a group (P=0.03) and elevated ALT (P=0.008). In multivariate analysis NRTI use (P=0.05) and ALT level (P=0.03) remained a significant determinant of hyperlactataemia. Among the different individual NRTIs, a stavudine-containing (P=0.004) and a zalcitabine-containing (P=0.07) regimen were most notably associated with the development of hyperlactataemia, whereas for the combinations of NRTIs, such association was only found for stavudine/lamivudine (P=0.05). CONCLUSIONS: A correlation between hyperlactataemia and NRTI treatment was found, but the value of routine lactate measurement for individual treatment monitoring remains uncertai

L

International audienceThe L(2)-Boosting algorithm is one of the most promising machine-learning techniques that has appeared in recent decades. It may be applied to high-dimensional problems such as whole-genome studies, and it is relatively simple from a computational point of view. In this study, we used this algorithm in a genomic selection context to make predictions of yet to be observed outcomes. Two data sets were used: (1) productive lifetime predicted transmitting abilities from 4702 Holstein sires genotyped for 32 611 single nucleotide polymorphisms (SNPs) derived from the Illumina BovineSNP50 BeadChip, and (2) progeny averages of food conversion rate, pre-corrected by environmental and mate effects, in 394 broilers genotyped for 3481 SNPs. Each of these data sets was split into training and testing sets, the latter comprising dairy or broiler sires whose ancestors were in the training set. Two weak learners, ordinary least squares (OLS) and non-parametric (NP) regression were used for the L2-Boosting algorithm, to provide a stringent evaluation of the procedure. This algorithm was compared with BL [Bayesian LASSO (least absolute shrinkage and selection operator)] and BayesA regression. Learning tasks were carried out in the training set, whereas validation of the models was performed in the testing set. Pearson correlations between predicted and observed responses in the dairy cattle (broiler) data set were 0.65 (0.33), 0.53 (0.37), 0.66 (0.26) and 0.63 (0.27) for OLS-Boosting, NP-Boosting, BL and BayesA, respectively. The smallest bias and mean-squared errors (MSEs) were obtained with OLS-Boosting in both the dairy cattle (0.08 and 1.08, respectively) and broiler (-0.011 and 0.006) data sets, respectively. In the dairy cattle data set, the BL was more accurate (bias=0.10 and MSE=1.10) than BayesA (bias=1.26 and MSE=2.81), whereas no differences between these two methods were found in the broiler data set. L2-Boosting with a suitable learner was found to be a competitive alternative for genomic selection applications, providing high accuracy and low bias in genomic-assisted evaluations with a relatively short computational time