Identifying differentially expressed genes in cancer patients using a non-parameter Ising model.

Identification of genes and pathways involved in diseases and physiological conditions is a major task in systems biology. In this study, we developed a novel non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. We also proposed a simulated annealing algorithm to find the optimal configuration of the Ising model. The Ising model was applied to two breast cancer microarray data sets. The results showed that more cancer-related DE sub-networks and genes were identified by the Ising model than those by the Markov random field model. Furthermore, cross-validation experiments showed that DE genes identified by Ising model can improve classification performance compared with DE genes identified by Markov random field model.

Estimating and evaluating the rice cluster distribution uniformity with UAV-based images.

The uniformity of the rice cluster distribution in the field affects population quality and the precise management of pesticides and fertilizers. However, there is no appropriate technical system for estimating and evaluating the uniformity at present. For that reason, a method based on unmanned aerial vehicle (UAV images) is proposed to estimate and evaluate the uniformity in this present study. This method includes rice cluster recognition and location determination based on the RGB color characteristics of the seedlings of aerial images, region segmentation considering the rice clusters based on Voronoi Diagram, and uniformity index definition for evaluating the rice cluster distribution based on the variation coefficient. The results indicate the rice cluster recognition attains a high precision, with the precision, accuracy, recall, and F1-score of rice cluster recognition reaching > 95%, 97%, 97%, 95%, and 96%, respectively. The rice cluster location error is small and obeys the gamma (3.00, 0.54) distribution (mean error, 1.62 cm). The uniformity index is reasonable for evaluating the rice cluster distribution verified via simulation. As a whole process, the estimating method is sufficiently high accuracy with relative error less than 0.01% over the manual labeling method. Therefore, this method based on UAV images is feasible, convenient, technologically advanced, inexpensive, and highly precision for the estimation and evaluation of the rice cluster distribution uniformity. However, the evaluation application indicates that there is much room for improvement in terms of the uniformity of mechanized paddy field transplanting in South China.

Quantitative descriptions of rice plant architecture and their application.

Plant architecture is an important agronomic trait, and improving plant architecture has attracted the attention of scientists for decades, particularly studies to create desirable plant architecture for high grain yields through breeding and culture practices. However, many important structural phenotypic traits still lack quantitative description and modeling on structural-functional relativity. This study defined new architecture indices (AIs) derived from the digitalized plant architecture using the virtual blade method. The influences of varieties and crop management on these indices and the influences of these indices on biomass accumulation were analyzed using field experiment data at two crop growth stages: early and late panicle initiation. The results indicated that the vertical architecture indices (LAI, PH, 90%-DRI, MDI, 90%-LI) were significantly influenced by variety, water, nitrogen management and the interaction of water and nitrogen, and compact architecture indices (H-CI, Q-CI, 90%-LI, 50%-LI) were significantly influenced by nitrogen management and the interaction of variety and water. Furthermore, there were certain trends in the influence of variety, water, and nitrogen management on AIs. Biomass accumulation has a positive linear correlation with vertical architecture indices and has a quadratic correlation with compact architecture indices, respectively. Furthermore, the combination of vertical and compact architecture indices is the indicator for evaluating the effects of plant architecture on biomass accumulation.

A technique system for the measurement, reconstruction and character extraction of rice plant architecture.

This study developed a technique system for the measurement, reconstruction, and trait extraction of rice canopy architectures, which have challenged functional-structural plant modeling for decades and have become the foundation of the design of ideo-plant architectures. The system uses the location-separation-measurement method (LSMM) for the collection of data on the canopy architecture and the analytic geometry method for the reconstruction and visualization of the three-dimensional (3D) digital architecture of the rice plant. It also uses the virtual clipping method for extracting the key traits of the canopy architecture such as the leaf area, inclination, and azimuth distribution in spatial coordinates. To establish the technique system, we developed (i) simple tools to measure the spatial position of the stem axis and azimuth of the leaf midrib and to capture images of tillers and leaves; (ii) computer software programs for extracting data on stem diameter, leaf nodes, and leaf midrib curves from the tiller images and data on leaf length, width, and shape from the leaf images; (iii) a database of digital architectures that stores the measured data and facilitates the reconstruction of the 3D visual architecture and the extraction of architectural traits; and (iv) computation algorithms for virtual clipping to stratify the rice canopy, to extend the stratified surface from the horizontal plane to a general curved surface (including a cylindrical surface), and to implement in silico. Each component of the technique system was quantitatively validated and visually compared to images, and the sensitivity of the virtual clipping algorithms was analyzed. This technique is inexpensive and accurate and provides high throughput for the measurement, reconstruction, and trait extraction of rice canopy architectures. The technique provides a more practical method of data collection to serve functional-structural plant models of rice and for the optimization of rice canopy types. Moreover, the technique can be easily adapted for other cereal crops such as wheat, which has numerous stems and leaves sheltering each other.