Maize yield response to water supply and fertilizer input in a semi-arid environment of Northeast China.

Maize grain yield varies highly with water availability as well as with fertilization and relevant agricultural management practices. With a 311-A optimized saturation design, field experiments were conducted between 2006 and 2009 to examine the yield response of spring maize (Zhengdan 958, Zea mays L) to irrigation (I), nitrogen fertilization (total nitrogen, urea-46% nitrogen,) and phosphorus fertilization (P2O5, calcium superphosphate-13% P2O5) in a semi-arid area environment of Northeast China. According to our estimated yield function, the results showed that N is the dominant factor in determining maize grain yield followed by I, while P plays a relatively minor role. The strength of interaction effects among I, N and P on maize grain yield follows the sequence N+I >P+I>N+P. Individually, the interaction effects of N+I and N+P on maize grain yield are positive, whereas that of P+I is negative. To achieve maximum grain yield (10506.0 kg · ha(-1)) for spring maize in the study area, the optimum application rates of I, N and P are 930.4 m(3) · ha(-1), 304.9 kg · ha(-1) and 133.2 kg · ha(-1) respectively that leads to a possible economic profit (EP) of 10548.4 CNY · ha(-1) (CNY, Chinese Yuan). Alternately, to obtain the best EP (10827.3 CNY · ha(-1)), the optimum application rates of I, N and P are 682.4 m(3) · ha(-1), 241.0 kg · ha(-1) and 111.7 kg · ha(-1) respectively that produces a potential grain yield of 10289.5 kg · ha(-1).

Quantifying spatial variability of selected soil trace elements and their scaling relationships using multifractal techniques.

Multifractal techniques were utilized to quantify the spatial variability of selected soil trace elements and their scaling relationships in a 10.24-ha agricultural field in northeast China. 1024 soil samples were collected from the field and available Fe, Mn, Cu and Zn were measured in each sample. Descriptive results showed that Mn deficiencies were widespread throughout the field while Fe and Zn deficiencies tended to occur in patches. By estimating single multifractal spectra, we found that available Fe, Cu and Zn in the study soils exhibited high spatial variability and the existence of anomalies ([α(q)max-α(q)min]≥0.54), whereas available Mn had a relatively uniform distribution ([α(q)max-α(q)min]≈0.10). The joint multifractal spectra revealed that the strong positive relationships (r≥0.86, P<0.001) among available Fe, Cu and Zn were all valid across a wider range of scales and over the full range of data values, whereas available Mn was weakly related to available Fe and Zn (r≥0.18, P<0.01) but not related to available Cu (r = -0.03, P = 0.40). These results show that the variability and singularities of selected soil trace elements as well as their scaling relationships can be characterized by single and joint multifractal parameters. The findings presented in this study could be extended to predict selected soil trace elements at larger regional scales with the aid of geographic information systems.

[Fractal theory and its application in the analysis of soil spatial variability: a review].

Soil has spatial variability in its attributes. The analysis of soil spatial variability is of significance for soil management. This paper summarized the fractal theory and its application in spatial analysis of soil variability, with the focus on the utilization of moment method in calculating the fractal dimension of soil attributes, the multi-fractal analysis of soil spatial variability, and the scaling up of soil attributes based on multi-fractal parameters. The studies on the application of fractal theory and multi-fractal method in the analysis of soil spatial variability were also reviewed. Fractal theory could be an important tool in quantifying the spatial variability and scaling up of soil attributes.

[Fractal characteristics of visible spectra across a hilly area].

The spectral characteristic of remotely sensed image is mainly the results of integrative effects on spectrum from heterogeneous ground reflectors. Investigating its spatial distribution characteristics may be helpful for image interpreting and modeling based on remote sensing technique. In the present study, spatial heterogeneity of remotely sensed multispectral TM image across a hilly area in late October was studied by the combination of statistical method and multifractal analysis. The results showed that distribution of digital number (DN) values of visible spectra (0.45-0.69 microm) had statistical scale-invariance. The generalized fractal dimension function D(q) suggested that distribution of TM 2 (0.52-0.60 microm) DN values was monofractal type, whereas DN values of TM 1 (0.45-0.52 microm) and TM 3 (0.63-0.69 microm) had multifractal distribution characteristics. The parameters (alpha(max)-alpha(min)) and [f(a(max))-f(alpha(min))] of multifractal spectra further indicated that TM 3 DN values had the high est spatial heterogeneity and most abundant information, followed by TM 1, while the extremely narrow spectrum of TM 2 DN values showed its relatively low spatial heterogeneity and information capacity.

Predicting the solvent accessibility of transmembrane residues from protein sequence.

In this study, we propose a novel method to predict the solvent accessible surface areas of transmembrane residues. For both transmembrane alpha-helix and beta-barrel residues, the correlation coefficients between the predicted and observed accessible surface areas are around 0.65. On the basis of predicted accessible surface areas, residues exposed to the lipid environment or buried inside a protein can be identified by using certain cutoff thresholds. We have extensively examined our approach based on different definitions of accessible surface areas and a variety of sets of control parameters. Given that experimentally determining the structures of membrane proteins is very difficult and membrane proteins are actually abundant in nature, our approach is useful for theoretically modeling membrane protein tertiary structures, particularly for modeling the assembly of transmembrane domains. This approach can be used to annotate the membrane proteins in proteomes to provide extra structural and functional information.

Differential use of signal peptides and membrane domains is a common occurrence in the protein output of transcriptional units.

Membrane organization describes the orientation of a protein with respect to the membrane and can be determined by the presence, or absence, and organization within the protein sequence of two features: endoplasmic reticulum signal peptides and alpha-helical transmembrane domains. These features allow protein sequences to be classified into one of five membrane organization categories: soluble intracellular proteins, soluble secreted proteins, type I membrane proteins, type II membrane proteins, and multi-spanning membrane proteins. Generation of protein isoforms with variable membrane organizations can change a protein's subcellular localization or association with the membrane. Application of MemO, a membrane organization annotation pipeline, to the FANTOM3 Isoform Protein Sequence mouse protein set revealed that within the 8,032 transcriptional units (TUs) with multiple protein isoforms, 573 had variation in their use of signal peptides, 1,527 had variation in their use of transmembrane domains, and 615 generated protein isoforms from distinct membrane organization classes. The mechanisms underlying these transcript variations were analyzed. While TUs were identified encoding all pairwise combinations of membrane organization categories, the most common was conversion of membrane proteins to soluble proteins. Observed within our high-confidence set were 156 TUs predicted to generate both extracellular soluble and membrane proteins, and 217 TUs generating both intracellular soluble and membrane proteins. The differential use of endoplasmic reticulum signal peptides and transmembrane domains is a common occurrence within the variable protein output of TUs. The generation of protein isoforms that are targeted to multiple subcellular locations represents a major functional consequence of transcript variation within the mouse transcriptome.

LOCATE: a mouse protein subcellular localization database.

We present here LOCATE, a curated, web-accessible database that houses data describing the membrane organization and subcellular localization of proteins from the FANTOM3 Isoform Protein Sequence set. Membrane organization is predicted by the high-throughput, computational pipeline MemO. The subcellular locations of selected proteins from this set were determined by a high-throughput, immunofluorescence-based assay and by manually reviewing >1700 peer-reviewed publications. LOCATE represents the first effort to catalogue the experimentally verified subcellular location and membrane organization of mammalian proteins using a high-throughput approach and provides localization data for approximately 40% of the mouse proteome. It is available at http://locate.imb.uq.edu.au.

MemO: a consensus approach to the annotation of a protein's membrane organization.

Membrane organization describes the relationship of proteins to the membrane, that is, whether the protein crosses the membrane or is integral to the membrane and its orientation with respect to the membrane. Membrane organization is determined primarily by the presence of two features which target proteins to the secretory pathway: the endoplasmic reticulum signal peptide and the ?-helical transmembrane domain. In order to generate membrane organization annotation of high quality, confidence and throughput, the Membrane Organization (MemO) pipeline was developed, incorporating consensus feature prediction modules with integration and annotation rules derived from biological observations. The pipeline classifies proteins into six categories based on the presence or absence of predicted features: Soluble, intracellular proteins; Soluble, secreted proteins; Type I membrane proteins; Type II membrane proteins; Multi-span membrane proteins and Glycosylphosphatidylinositol anchored membrane proteins. The MemO pipeline represents an integrated strategy for the application of state-of-the-art bioinformatics tools to the annotation of protein membrane organization, a property which adds biological context to the large quantities of protein sequence information available.

Computational differentiation of N-terminal signal peptides and transmembrane helices.

Signal peptides and transmembrane helices both contain a stretch of hydrophobic amino acids. This common feature makes it difficult for signal peptide and transmembrane helix predictors to correctly assign identity to stretches of hydrophobic residues near the N-terminal methionine of a protein sequence. The inability to reliably distinguish between N-terminal transmembrane helix and signal peptide is an error with serious consequences for the prediction of protein secretory status or transmembrane topology. In this study, we report a new method for differentiating protein N-terminal signal peptides and transmembrane helices. Based on the sequence features extracted from hydrophobic regions (amino acid frequency, hydrophobicity, and the start position), we set up discriminant functions and examined them on non-redundant datasets with jackknife tests. This method can incorporate other signal peptide prediction methods and achieve higher prediction accuracy. For Gram-negative bacterial proteins, 95.7% of N-terminal signal peptides and transmembrane helices can be correctly predicted (coefficient 0.90). Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 99% (coefficient 0.92). For eukaryotic proteins, 94.2% of N-terminal signal peptides and transmembrane helices can be correctly predicted with coefficient 0.83. Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 87% (coefficient 0.85). The method can be used to complement current transmembrane protein prediction and signal peptide prediction methods to improve their prediction accuracies.