Research on WNN Greenhouse Temperature Prediction Method Based on GA

Temperature in agricultural production has a direct impact on the growth of crops. The emergence of greenhouses has improved the impact of the original unpredictable changes in temperature, but the temperature modeling of greenhouses is still the main direction at present. Neural network modeling relies on sufficient actual data to model greenhouses, but there is a widening gap in the application of different neural networks. This paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm (GA-WNN). With the simple network structure and the nonlinear adaptability of the wavelet basis function, wavelet neural network (WNN) improved model training speed and accuracy of prediction results compared with back propagation neural networks (BPNN), which was conducive to the prediction and control of short-term greenhouse temperature fluctuations. At the same time, the genetic algorithm (GA) was introduced to globally optimize the initial weights of the original model, which improved the insensitivity of the model to the initial weights and thresholds, and improved the training speed and stability of the model. Finally, simulation results for the greenhouse showed that the model training speed, prediction results accuracy and model stability of the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN in the greenhouse.     

植物适应酸铝胁迫机理的研究进展

酸铝胁迫是限制植物正常生长发育的重要非生物胁迫因子,严重制约了我国酸性土壤地区的农业生产水平。植物抵御酸铝胁迫的形式复杂多样,如分泌有机酸、提高根际pH、分泌黏液、细胞壁对Al³⁺的固定、有机酸对细胞溶质中Al³⁺的螯合与液泡区隔化等。现有研究多集中于常规生理特征分析,缺乏深入的分子生物学解析。基于此,本文对国内外植物适应酸铝胁迫机理的相关研究进行了归纳和总结,从酸铝胁迫对植物生长与生理代谢的影响、植物适应酸铝胁迫最主要的两种生理机制(Al排除机制、Al耐受机制)以及分子水平上调控相关耐铝基因进行了综述。最后针对现有研究的不足提出了展望,以期为深入揭示植物适应酸铝胁迫的机理以及挖掘适于酸土生长的优质作物资源提供理论依据。     

Hepatic overexpression of methionine sulfoxide reductase A reduces atherosclerosis in apolipoprotein E-deficient mice

Methionine sulfoxide reductase A (MsrA), a specific enzyme that converts methionine-S-sulfoxide to methionine, plays an important role in the regulation of protein function and the maintenance of redox homeostasis. In this study, we examined the impact of hepatic MsrA overexpression on lipid metabolism and atherosclerosis in apoE-deficient (apoE^−/−) mice. In vitro study showed that in HepG2 cells, lentivirus-mediated human MsrA (hMsrA) overexpression upregulated the expression levels of several key lipoprotein-metabolism-related genes such as liver X receptor α, scavenger receptor class B type I, and ABCA1. ApoE^−/− mice were intravenously injected with lentivirus to achieve high-level hMsrA expression predominantly in the liver. We found that hepatic hMsrA expression significantly reduced plasma VLDL/LDL levels, improved plasma superoxide dismutase, and paraoxonase-1 activities, and decreased plasma serum amyloid A level in apoE^−/− mice fed a Western diet, by significantly altering the expression of several genes in the liver involving cholesterol selective uptake, conversion and excretion into bile, TG biosynthesis, and inflammation. Moreover, overexpression of hMsrA resulted in reduced hepatic steatosis and aortic atherosclerosis. These results suggest that hepatic MsrA may be an effective therapeutic target for ameliorating dyslipidemia and reducing atherosclerosis-related cardiovascular diseases.     

Genetic variants in root architecture-related genes in a Glycine soja accession,a potential resource to improve cultivated soybean

Background

Root system architecture is important for water acquisition and nutrient acquisition for all crops. In soybean breeding programs, wild soybean alleles have been used successfully to enhance yield and seed composition traits, but have never been investigated to improve root system architecture. Therefore, in this study, high-density single-feature polymorphic markers and simple sequence repeats were used to map quantitative trait loci (QTLs) governing root system architecture in an inter-specific soybean mapping population developed from a cross between Glycine max and Glycine soja.

Results

Wild and cultivated soybean both contributed alleles towards significant additive large effect QTLs on chromosome 6 and 7 for a longer total root length and root distribution, respectively. Epistatic effect QTLs were also identified for taproot length, average diameter, and root distribution. These root traits will influence the water and nutrient uptake in soybean. Two cell division-related genes (D type cyclin and auxin efflux carrier protein) with insertion/deletion variations might contribute to the shorter root phenotypes observed in G. soja compared with cultivated soybean. Based on the location of the QTLs and sequence information from a second G. soja accession, three genes (slow anion channel associated 1 like, Auxin responsive NEDD8-activating complex and peroxidase), each with a non-synonymous single nucleotide polymorphism mutation were identified, which may also contribute to changes in root architecture in the cultivated soybean. In addition, Apoptosis inhibitor 5-like on chromosome 7 and slow anion channel associated 1-like on chromosome 15 had epistatic interactions for taproot length QTLs in soybean.

Conclusion

Rare alleles from a G. soja accession are expected to enhance our understanding of the genetic components involved in root architecture traits, and could be combined to improve root system and drought adaptation in soybean.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1334-6) contains supplementary material, which is available to authorized users.     

Structural and thermal characterization of hemicelluloses isolated by organic solvents and alkaline solutions from Tamarix austromongolica

Three organosolv and three alkaline hemicellulosic fractions were prepared from lignocellulosic biomass of the fast-growing shrub Tamarix austromongolica (Tamarix Linn.). Sugar analysis revealed that the organosolv-soluble fractions contained a higher content of glucose (33.7-6.5%) and arabinose (14.8-5.6%), and a lower content of xylose (62.2-54.8%) than the hemicellulosic fractions isolated with aqueous alkali solutions. A relatively high concentration of alkali resulted in a decreasing trend of the xylose/4-O-methyl-d-glucuronic acid ratio in the alkali-soluble fractions. The results of NMR analysis supported a major substituted structure based on a linear polymer of β-(1 → 4)-linked d-xylopyranosyl residues, having ramifications of α-l-arabinofuranose and 4-O-methyl-d-glucuronic acid residues monosubstituted at O-3 and O-2, respectively. Thermogravimetric analysis revealed that one step of major mass loss occurred between 200-400 °C, as hemicelluloses devolatilized with total volatile yield of about 55%. It was found that organosolv-soluble fractions are more highly ramified, and showed a higher thermal stability than the alkali-soluble fractions.     

An accurate feature-based method for identifying DNA-binding residues on protein surfaces

Proteins that interact with DNA play vital roles in all mechanisms of gene expression and regulation. In order to understand these activities, it is crucial to analyze and identify DNA-binding residues on DNA-binding protein surfaces. Here, we proposed two novel features B-factor and packing density in combination with several conventional features to characterize the DNA-binding residues in a well-constructed representative dataset of 119 protein-DNA complexes from the Protein Data Bank (PDB). Based on the selected features, a prediction model for DNA-binding residues was constructed using support vector machine (SVM). The predictor was evaluated using a 5-fold cross validation on above dataset of 123 DNA-binding proteins. Moreover, two independent datasets of 83 DNA-bound protein structures and their corresponding DNA-free forms were compiled. The B-factor and packing density features were statistically analyzed on these 83 pairs of holo-apo proteins structures. Finally, we developed the SVM model to accurately predict DNA-binding residues on protein surface, given the DNA-free structure of a protein. Results showed here indicate that our method represents a significant improvement of previously existing approaches such as DISPLAR. The observation suggests that our method will be useful in studying protein-DNA interactions to guide consequent works such as site-directed mutagenesis and protein-DNA docking.     

Design and synthesis of 3-pyridylacetamide derivatives as dipeptidyl peptidase IV (DPP-4) inhibitors targeting a bidentate interaction with Arg125

We have previously discovered nicotinic acid derivative 1 as a structurally novel dipeptidyl peptidase IV (DPP-4) inhibitor. In this study, we obtained the X-ray co-crystal structure between nicotinic acid derivative 1 and DPP-4. From these X-ray co-crystallography results, to achieve more potent inhibitory activity, we targeted Arg125 as a potential amino acid residue because it was located near the pyridine core, and some known DPP-4 inhibitors were reported to interact with this residue. We hypothesized that the guanidino group of Arg125 could interact with two hydrogen-bond acceptors in a bidentate manner. Therefore, we designed a series of 3-pyridylacetamide derivatives possessing an additional hydrogen-bond acceptor that could have the desired bidentate interaction with Arg125. We discovered the dihydrochloride of 1-{[5-(aminomethyl)-2-methyl-4-(4-methylphenyl)-6-(2-methylpropyl)pyridin-3-yl]acetyl}-l-prolinamide (13j) to be a potent and selective DPP-4 inhibitor that could interact with the guanidino group of Arg125 in a unique bidentate manner.     

EGID: an ensemble algorithm for improved genomic island detection in genomic sequences

Genomic islands (GIs) are genomic regions that are originally transferred from other organisms. The detection of genomic islands in genomes can lead to many applications in industrial, medical and environmental contexts. Existing computational tools for GI detection suffer either low recall or low precision, thus leaving the room for improvement. In this paper, we report the development of our Ensemble algorithm for Genomic Island Detection (EGID). EGID utilizes the prediction results of existing computational tools, filters and generates consensus prediction results. Performance comparisons between our ensemble algorithm and existing programs have shown that our ensemble algorithm is better than any other program. EGID was implemented in Java, and was compiled and executed on Linux operating systems. EGID is freely available at http://www5.esu.edu/cpsc/bioinfo/software/EGID.