Transcription factor OsSNAC1 positively regulates nitrate transporter gene expression in rice.

Nitrogen (N) is a critical factor for crop growth and yield. Improving N use efficiency (NUE) in agricultural systems is crucial for sustainable food production. However, the underlying regulation of N uptake and utilization in crops is not well known. Here, we identified OsSNAC1 (stress-responsive NAC 1) as an upstream regulator of OsNRT2.1 (nitrate transporter 2.1) in rice (Oryza sativa) by yeast 1-hybridization screening. OsSNAC1 was mainly expressed in roots and shoots and induced by N deficiency. We observed similar expression patterns of OsSNAC1, OsNRT2.1/2.2, and OsNRT1.1A/B in response to NO3- supply. Overexpression of OsSNAC1 resulted in increased concentrations of free NO3- in roots and shoots, as well as higher N uptake, higher NUE, and N use index (NUI) in rice plants, which conferred increased plant biomass and grain yield. On the contrary, mutations in OsSNAC1 resulted in decreased N uptake and lower NUI, which inhibited plant growth and yield. OsSNAC1 overexpression significantly upregulated OsNRT2.1/2.2 and OsNRT1.1A/B expression, while the mutation in OsSNAC1 significantly downregulated OsNRT2.1/2.2 and OsNRT1.1A/B expression. Y1H, transient co-expression, and ChIP assays showed OsSNAC1 directly binds to the upstream promoter regions of OsNRT2.1/2.2 and OsNRT1.1A/1.1B. In conclusion, we identified a NAC transcription factor in rice, OsSNAC1, with a positive role in regulating NO3- uptake through direct binding to the upstream promoter regions of OsNRT2.1/2.2 and OsNRT1.1A/1.1B and activating their expression. Our results provide a potential genetic approach for improving crop NUE in agriculture.

Backpropagation of pseudo-errors: neural networks that are adaptive to heterogeneous noise.

Neural networks are used for prediction model in many applications. The backpropagation algorithm used in most cases corresponds to a statistical nonlinear regression model assuming the constant noise level. Many proposed prediction intervals in the literature so far also assume the constant noise level. There are no prediction intervals in the literature that are accurate under varying noise level and skewed noises. We propose prediction intervals that can automatically adjust to varying noise levels by applying the regression transformation model of Carroll and Rupert (1988). The parameter estimation under the transformation model with power transformations is shown to be equivalent to the backpropagation of pseudo-errors. This new backpropagation algorithm preserves the ability of online training for neural networks.

Assessing antiviral potency of anti-HIV therapies in vivo by comparing viral decay rates in viral dynamic models.

A virologic marker, the number of HIV RNA copies or viral load, is currently used to evaluate anti-HIV therapies in AIDS clinical trials. This marker can be used to assess the antiviral potency of therapies, but is easily affected by noncompliance, drug resistance, toxicities, and other factors during the long-term treatment evaluation process. Recently it has been suggested to use viral dynamics to assess the potency of antiviral drugs and therapies, since viral decay rates in viral dynamic models have been shown to be related to the antiviral drug potency directly, and they need a shorter evaluation time. In this paper we first review the two statistical approaches for characterizing HIV dynamics and estimating viral decay rates: the individual nonlinear least squares regression (INLS) method and the population nonlinear mixed-effect model (PMEM) approach. To compare the viral decay rates between two treatment arms, parametric and nonparametric tests, based on the estimates of viral decay rates (the derived variables) from both the INLS and PMEM methods, are proposed and studied. We show, using the concept of exchangeability, that the test based on the empirical Bayes' estimates from the PMEM is valid, powerful and robust. This proposed method is very useful in most practical cases where the INLS-based tests and the general likelihood ratio test may not apply. We validate and compare various tests for finite samples using Monte Carlo simulations. Finally, we apply the proposed tests to an AIDS clinical trial to compare the antiviral potency between a 3-drug combination regimen and a 4-drug combination regimen. The proposed tests provide some significant evidence that the 4-drug regimen is more potent than the 3-drug regimen, while the naive methods fail to give a significant result.*To whom correspondence should be addressed.

A comparison study of models and fitting procedures for biphasic viral dynamics in HIV-1 infected patients treated with antiviral therapies.

The study of HIV dynamics is one of the most important developments in recent AIDS research. It has led to a new understanding of the pathogenesis of HIV infection. But, although important findings in HIV dynamics have been published in prestigious scientific journals in the last 5 years, the model-fitting procedures used in these publications have not been studied in any detail. In this paper, we evaluate the performance of four model-fitting procedures proposed and used in biphasic HIV dynamic data analysis via extensive Monte Carlo simulations. We propose some guidelines for practitioners to select an appropriate method for their own data analysis. Real data examples from an AIDS clinical trial are provided as illustrations.

Relationships between antiviral treatment effects and biphasic viral decay rates in modeling HIV dynamics.

Recently, potent combination antiviral therapies consisting of reverse transcriptase inhibitor (RTI) drugs and protease inhibitor (PI) drugs, have been developed which can rapidly suppress HIV below the limit of detection. Two phases of plasma viral decay after initiation of treatment have been observed in clinical studies. Some researchers have suggested that the viral decay rates may reflect the potency (efficacy) of antiviral therapies. In this paper we model the effect of RTI drugs and PI drugs as inhibition rates of cell infection and infectious virus production, respectively, based on the biological mechanisms of these two different types of drugs. Through rigorous mathematical derivation, we show that the two viral decay rates are monotone functions of the treatment effects of these antiviral therapies. We derive approximation formulas for the relationships between viral decay rates and treatment effects. Computer simulations show that the approximation formulas approximate the true values very well. These formulas may be used to study what factors really affect the viral decay rates. The results in this paper provide a theoretical justification for using both viral decay rates for evaluation of the treatment efficacy of antiviral therapies.

Population HIV-1 dynamics in vivo: applicable models and inferential tools for virological data from AIDS clinical trials.

In this paper, we introduce a novel application of hierarchical nonlinear mixed-effect models to HIV dynamics. We show that a simple model with a sum of exponentials can give a good fit to the observed clinical data of HIV-1 dynamics (HIV-1 RNA copies) after initiation of potent antiviral treatments and can also be justified by a biological compartment model for the interaction between HIV and its host cells. This kind of model enjoys both biological interpretability and mathematical simplicity after reparameterization and simplification. A model simplification procedure is proposed and illustrated through examples. We interpret and justify various simplified models based on clinical data taken during different phases of viral dynamics during antiviral treatments. We suggest the hierarchical nonlinear mixed-effect model approach for parameter estimation and other statistical inferences. In the context of an AIDS clinical trial involving patients treated with a combination of potent antiviral agents, we show how the models may be used to draw biologically relevant interpretations from repeated HIV-1 RNA measurements and demonstrate the potential use of the models in clinical decision-making.

Neural-network prediction with noisy predictors.

Very often the input variables for neural-network predictions contain measurement errors. In particular, this may happen because the original input variables are often not available at the time of prediction and have to be replaced by predicted values themselves. This issue is usually ignored and results in nonoptimal predictions. This paper shows that under some general conditions, the optimal prediction using noisy input variables can be represented by a neural network with the same structure and the same weights as the optimal prediction using exact input variables. Only the activation functions have to be adjusted. Therefore we can achieve optimal prediction without costly retraining of the neural network. We explicitly provide an exact formula for adjusting the activation functions in a logistic network with Gaussian measurement errors in input variables. This approach is illustrated by an application to short-term load forecasting.

Estimation of HIV dynamic parameters.

Investigation of HIV viral dynamics is important for understanding the HIV pathogenesis and for development of treatment strategies. Perelson et al. demonstrated that simple viral dynamic models fit to data on viral load as measured by plasma HIV-RNA could produce estimates of rates of clearance of virus and of infected CD4+ T-lymphocytes. In this paper we extend the work of Perelson et al. by proposing models with less restrictive assumptions about drug activity. Our models take into account the fact that infectious and non-infectious virions are produced by infected T-cells both before and after the treatment. We also show that direct measurement of infectious virus load provides sufficient information for estimation of antiretroviral drug efficacy parameter. For characterizing viral dynamics of populations and estimation of dynamic parameters, we propose a hierarchical non-linear model. Compared to other methods such as the non-linear least square method used by Perelson et al., we show that the proposed approach has the following advantages: (i) it is more appropriate for modelling within-patient and between-patient variation and to characterize the population dynamics; (ii) it is flexible enough to deal with both rich and sparse individual data; (iii) it has more power to detect model misspecification; (iv) it allows incorporation of covariates for viral dynamic parameters; (v) it makes more efficient use of between-subject information to get better parameter estimates. We give two simulation examples to illustrate the proposed approach and its advantages. Finally, we discuss practical issues regarding the clinical trial design for viral dynamic studies.