Due to an increase in the consumption of food, feed, fuel and to meet global food security needs for the rapidly growing human population, there is a necessity to breed high yielding crops that can adapt to the future climate changes, particularly in developing countries. To solve these global challenges, novel approaches are required to identify quantitative phenotypes and to explain the genetic basis of agriculturally important traits. These advances will facilitate the screening of germplasm with high performance characteristics in resource-limited environments. Recently, plant phenomics has offered and integrated a suite of new technologies, and we are on a path to improve the description of complex plant phenotypes. High-throughput phenotyping platforms have also been developed that capture phenotype data from plants in a non-destructive manner. In this review, we discuss recent developments of high-throughput plant phenotyping infrastructure including imaging techniques and corresponding principles for phenotype data analysis.

BackgroundPredication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size.ResultsWe developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network.ConclusionsFor network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0395-x) contains supplementary material, which is available to authorized users.

Abstract:Biomass is an important phenotypic trait in functional ecology and growth analysis. The typical methods for measuring biomass are destructive, and they require numerous individuals to be cultivated for repeated measurements. With the advent of image-based high-throughput plant phenotyping facilities, non-destructive biomass measuring methods have attempted to overcome this problem. Thus, the estimation of plant biomass of individual plants from their digital images is becoming more important. In this paper, we propose an approach to biomass estimation based on image derived phenotypic traits. Several image-based biomass studies state that the estimation of plant biomass is only a linear function of the projected plant area in images. However, we modeled the plant volume as a function of plant area, plant compactness, and plant age to generalize the linear biomass model. The obtained results confirm the proposed model and can explain most of the observed variance during image-derived biomass estimation. Moreover, a small difference was observed between actual and estimated digital biomass, which indicates that our proposed approach can be used to estimate digital biomass accurately.

This study has been undertaken to explore the therapeutic effects of deguelin and specific siRNAs in HeLa cells. The data provided clearly show the silencing of ERK 1/2 with siRNAs and inhibition of ERK1/2 with deguelin treatment in HeLa cells. Additionally, we are providing information that deguelin binds directly to anti-apoptotic Bcl-2, Bcl-xl and Mcl-1 in the hydrophobic grooves, thereby releasing BAD and BAX from dimerization with these proteins. This results in increased apoptotic activity through the intrinsic pathway involved in rupture of mitochondrial membrane and release of cytochrome C. Evidence for inhibition of ERK1/2 by deguelin and escape of BAD phosphorylation at serine 112 through ERK/RSK pathway has been further fortified by obtaining similar results by silencing ERK 1/2 each with specific siRNAs. Increase in BAD after treatment with deguelin or siRNAs has been interpreted to mean that deguelin acts through several alternative pathways and therefore can be used as effective therapeutic agent.