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Background Sheath blight is an important disease caused by Rhizoctonia cerealis that affects wheat yields worldwide. No wheat varieties have been identified with high resistance or immunity to sheath blight. Understanding the sheath blight resistance mechanism is essential for controlling this disease. In this study, we investigated the response of wheat to Rhizoctonia cerealis infection by analyzing the cytological changes and transcriptomes of common wheat 7182 with moderate sensitivity to sheath blight and H83 with moderate resistance. Results The cytological observation showed that the growth of Rhizoctonia cerealis on the surface and its expansion inside the leaf sheath tissue were more rapid in the susceptible material. According to the transcriptome sequencing results, a total of 88685 genes were identified in both materials, including 20156 differentially expressed genes (DEGs) of which 12087 was upregulated genes and 8069 was downregulated genes. At 36 h post-inoculation, compared with the uninfected control, 11498 DEGs were identified in resistant materials, with 5064 downregulated genes and 6434 upregulated genes, and 13058 genes were detected in susceptible materials, with 6759 downregulated genes and 6299 upregulated genes. At 72 h post-inoculation, compared with the uninfected control, 6578 DEGs were detected in resistant materials, with 2991 downregulated genes and 3587 upregulated genes, and 7324 genes were detected in susceptible materials, with 4119 downregulated genes and 3205 upregulated genes. Functional annotation and enrichment analysis showed that the main pathways enriched for the DEGs included biosynthesis of secondary metabolites, carbon metabolism, plant hormone signal transduction, and plant–pathogen interaction. In particular, phenylpropane biosynthesis pathway is specifically activated in resistant variety H83 after infection. Many DEGs also belonged to the MYB, AP2, NAC, and WRKY transcription factor families. Conclusions Thus, we suggest that the normal functioning of plant signaling pathways and differences in the expression of key genes and transcription factors in some important metabolic pathways may be important for defending wheat against sheath blight. These findings may facilitate further exploration of the sheath blight resistance mechanism in wheat and the cloning of related genes.

Pore hierarchy facilitates the mass transportation/exchange between the interior surface and bulk solution, which is critical for the enhancement of capacitive performance. Herein, by applying in situ foamed Mg chelates as precursors, we managed the scalable fabrication of hierarchically porous carbon (HPC) materials and explored their capacitive applications. Particularly, citric acid first reacted with magnesium nitrate to form Mg chelate while the generated gaseous HNO3 molecules bubbled the intermediate carbon framework to produce abundant open pores. The as-made precursors were then submitted to potassium hydroxide activation for a high carbonization degree and rich meso-/micropores. The optimized sample (HPC-2) exhibited very high specific capacitance of 213.5 F g−1 in neutral NaCl solution and a high rate capability of ~ 67.5% at 10.0 A g−1. Furthermore, it showed impressive capacitive deionization performance regarding high removal efficiency (67.1%), large capacity of 1810.1 mg g−1 (in 2200 mg L−1 NaCl solution), and robust cycling stability.

The hot compressive tests of 6082 aluminum alloy were conducted on a Gleeble-3500 thermomechanical simulator at temperature ranges of 380-530 °C and strain rate range of 0.01-10 s−1. The constitutive analysis and microstructural evolution of the alloy were investigated. It was indicated that the peak stress increased with increasing strain rate and decreasing temperature. Dynamic recovery and dynamic recrystallization lead to the softening behavior of the alloy. In order to characterize the flow behavior of this alloy, some models were established based on the experimental data including the phenomenological Arrhenius-type model, the physically based Estrin and Mecking (EM) model for work hardening and dynamic recovery, and the EM model, which was combined with the Avrami equation for dynamic recrystallization. An artificial neural network model was also established to predict the flow stress. The results indicate that the Arrhenius-type model is more simple and more efficient than the EM + Avrami model. Moreover, the well-trained ANN model has the best predicting performance.

To characterize the hot deformation behavior of commonly used aluminum alloy, a homogeneous Al–Mg–Si–Mn–Cr alloy was analyzed by thermal simulation test at deformation temperature range of 653–803 K and strain rate range of 0.01–10 s−1. The flow stresses were predicted by modified Johnson–Cook model, modified Zerilli–Armstrong model and strain-compensated Arrhenius model. The results show that the three models are able to predict the flow behavior of the alloy. Strain-compensated Arrhenius model has the best simulation ability in predicting flow stresses, while the modified Johnson–Cook model has lower prediction accuracy and the modified Zerilli–Armstrong model has poorer predictive ability at low strain rates. Microstructure evolution shows that subgrain boundaries form at original grain boundaries at first, moving toward to the center of the deformed grains. The dislocation density decreases, while the number and the size of subgrains increase with the decreasing Zener–Hollomon (Z) parameter. Both dynamic recovery (DRV) and dynamic recrystallization take place in hot deformation process. DRV is considered to be the primary dynamic softening mechanism throughout the entire hot deformation range. Continuous dynamic recrystallization and discontinuous dynamic recrystallization operate concurrently at low strain rates and high temperatures. The relationship of subgrain size and predicted flow stress is presented. Moreover, activation volume is introduced to reveal the thermal activation mechanism during hot deformation.

Fiber Bragg grating is inscribed on microfiber with femtosecond laser pulses irradiation. The microfiber is fabricated by stretching a section of single mode fiber over a flame. Periodic grooves are carved on the microfiber by the laser as have been observed experimentally. The microfiber Bragg grating is demonstrated for temperature and strain sensing, and the strain sensitivity is improved with decreased diameters of the microfibers.

Seventeen PM2.5 samples were collected at Xinxiang during winter in 2014. Nine water-soluble ions, 19 trace elements and eight fractions of carbonaceous species in PM2.5 were analyzed. PM2.5 concentrations and elements species during different periods with different pollution situations were compared. The threat of heavy metals in PM2.5 was assessed using incremental lifetime cancer risk. During the whole period, serious regional haze pollution persisted, and the averaged concentration of PM2.5 was 168.5 μg m−3, with 88.2 % of the daily samples exhibiting higher PM2.5 concentrations than the national air quality standard II. The high NO3 −/SO4 2− ratio suggested that vehicular exhaust made an important contribution to atmospheric pollution. All of organic carbon and elemental carbon ratios in this study were above 2.0 for PM2.5, which might reflect the combined contributions from coal combustion, motor vehicle exhaust and biomass burning. Mean 96-h backward trajectory clusters indicated that more serious air pollution occurred when air masses transported from the Hebei, Shanxi and Zhengzhou. The concentrations of the water-soluble ions and trace elements on haze days were 2 and 1.8 times of those on clear days. The heavy metals in PM2.5 might not cause non-cancerous health issues by exposure through the human respiratory system. However, lifetime cancer risks of heavy metals obviously exceeded the threshold (10−6) and might have a cancer risk for residents in Xinxiang. This study provided detailed composition data and comprehensive analysis of PM2.5 during the serious haze pollution period and their potential impact on human health in Xinxiang.

Background The classification of cancer subtypes is of great importance to cancer disease diagnosis and therapy. Many supervised learning approaches have been applied to cancer subtype classification in the past few years, especially of deep learning based approaches. Recently, the deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble decision trees. It has been proved that the deep forest model has competitive or even better performance than deep neural networks in some extent. However, the standard deep forest model may face overfitting and ensemble diversity challenges when dealing with small sample size and high-dimensional biology data. Results In this paper, we propose a deep learning model, so-called BCDForest, to address cancer subtype classification on small-scale biology datasets, which can be viewed as a modification of the standard deep forest model. The BCDForest distinguishes from the standard deep forest model with the following two main contributions: First, a named multi-class-grained scanning method is proposed to train multiple binary classifiers to encourage diversity of ensemble. Meanwhile, the fitting quality of each classifier is considered in representation learning. Second, we propose a boosting strategy to emphasize more important features in cascade forests, thus to propagate the benefits of discriminative features among cascade layers to improve the classification performance. Systematic comparison experiments on both microarray and RNA-Seq gene expression datasets demonstrate that our method consistently outperforms the state-of-the-art methods in application of cancer subtype classification. Conclusions The multi-class-grained scanning and boosting strategy in our model provide an effective solution to ease the overfitting challenge and improve the robustness of deep forest model working on small-scale data. Our model provides a useful approach to the classification of cancer subtypes by using deep learning on high-dimensional and small-scale biology data.

Background Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based gene semantic similarity has been effectively used to model gene-to-gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and gene annotations. Results We present NETSIM2, a new network-based method that allows researchers to measure GO-based gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based gene functional similarity. Conclusions Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global gene-to-gene interactions in the co-functional network, especially on the species that gene annotations in GO are far from complete.

In this work, principal component analysis/multiple linear regression (PCA/MLR), positive matrix factorization (PMF), and UNMIX model were employed to apportion potential sources of polycyclic aromatic hydrocarbons (PAHs) in surface sediments from middle and lower reaches of the Yellow River, based on the measured PAHs concentrations in sediments collected from 22 sites in November 2005. The results suggested that pyrogenic sources were major sources of PAHs. Further analysis indicated that source contributions of PAHs compared well among PCA/MLR, PMF, and UNMIX. Vehicles contributed 25.1-36.7 %, coal 34.0-41.6 %, and biomass burning and coke oven 29.2-33.2 % of the total PAHs, respectively. Coal combustion and traffic-related pollution contributed approximately 70 % of anthropogenic PAHs to sediments, which demonstrated that energy consumption was a predominant factor of PAH pollution in middle and lower reaches of the Yellow River. In addition, the distributions of contribution for each identified source category were studied, which showed similar distributed patterns for each source category among the sampling sites.

A selective and low organic-solvent-consuming method of sample preparation combined with high-performance liquid chromatography with diode-array detection is introduced for analysis of phthalic acid esters in edible oils. Sample treatment involves initial liquid–liquid partitioning with acetonitrile, then QuEChERS cleanup by dispersive solid-phase extraction with primary secondary amine as sorbent. Preconcentration of the analytes is performed by ionic-liquid-based dispersive liquid–liquid microextraction, with the cleaned-up extract as disperser solvent and 1-hexyl-3-methylimidazolium hexafluorophosphate as extraction solvent. Under the optimized conditions, correlation coefficients (r) were 0.998–0.999 and standard errors (S y/x ) were 2.67–3.37 × 103 for calibration curves in the range 50–1000 ng g−1. Detection limits, at a signal-to-noise ratio of 3, ranged from 6 to 9 ng g−1. Intra-day and inter-day repeatability, expressed as relative standard deviation, were in the ranges 1.0–6.9 % and 2.4–9.4 %, respectively. Recovery varied between 84 % and 106 %. The developed method was successfully used for analysis of the analytes in 28 edible oils. The dibutyl phthalate content of four of the 28 samples (14 %) exceeded the specific migration limit established by domestic and international regulations.

A dispersive liquid–liquid microextraction (DLLME) method coupled to high-performance liquid chromatography was developed for the analysis of α-tocopherol in grain samples. The DLLME parameters including the type and volume of extractants, the volume of disperser and the addition of salt were examined. The optimized DLLME procedure consisted in the formation of a cloudy solution promoted by the fast addition to the sample (5 mL of saponified sample solution diluted with 5 mL of water) of a mixture of carbon tetrachloride (extraction solvent, 80 μL) and ethanol (dispersive solvent, 200 μL) without the addition of salt, followed by shaking for 5 min and centrifuging for 3 min at 5,000 rpm. Intra- and inter-day repeatability expressed as % RSD were 3.5 and 7.6 %, respectively. The limit of detection and the limit of quantification were 1.9 and 6.3 μg L−1. The comparison of this method with the national standardized extraction method, supercritical carbon dioxide extraction, accelerated solvent extraction, and conventional heat-reflux extraction indicates that the DLLME was accurate (no significant differences at the 0.05 % probability level), high efficient, low organic solvent-consuming, and low cost. This procedure was successfully applied to 42 samples of 14 types of purple wheat, for which the content of α-tocopherol exhibited a significantly negative correlation with the pigment content measured by a spectrophotometer. The recovery rates ranged from 90.5 to 103.7 %.

A field experiment was carried out to research the changes and spatial distributions of soil nutrients in saline–sodic soil for different number of cultivated years under drip irrigation. The distributions of available potassium (AK), available phosphorus (AP), nitrate nitrogen (NO3−–N), ammonium nitrogen (NH4+–N), as well as the amount of total nitrogen (TN), total phosphorus (TP) and organic carbon (OC) in the 0–40 cm soil layers in saline–sodic soils planted with Leymus chinensis for 1, 2, and 3 years were studied. The results showed that the distance from the emitter had an obvious effect on soil nutrients. Drip irrigation had substantial effects on levels of AK, AP, and NO3−–N. The contents of AK, AP, and NO3−–N were very high in the area near the emitter in the horizontal direction. In the vertical direction, levels of all of the available and total soil nutrients decreased with increased soil depth. Levels of AK, AP, NO3−–N, NH4+–N, TN, TP, and OC all increased with continued cultivation of crops on saline–sodic soil using drip irrigation. Compared to the nutrients found in soils from the natural L. chinensis grasslands, the contents of AK and TP were higher in the drip-irrigated soils, although the contents of AP, NO3−–N, and NH4+–N were broadly comparable. Given the rate of improvements in nutrient levels, we forecast that the nutrients in drip-irrigated saline–sodic soils should match those of the natural L. chinensis grasslands after 3–6 years of cultivation.

A field experiment was carried out to research the changes and spatial distributions of soil enzyme activities in saline-sodic soil for a different number of cultivated years under drip irrigation. The distributions of alkaline phosphatase, urease, and sucrase activities within 40 cm in both horizontal and vertical directions of the emitter in saline-sodic soils planted with Leymus chinensis for 1st, 2nd, and 3rd year were studied. A mathematical method was used to determine the relationships between soil enzyme activities and soil environmental factors contain the electrical conductivity of saturated-soil extract, pH value, available nutrient, and organic carbon. Alkaline phosphatase, urease, and sucrase activities all increased with cultivated years in saline-sodic soil under drip irrigation: from 4.5, 1.39 and 19.39 to 20.25, 3.17, and 61.33 mu g g(-1) h(-1), respectively, after planting L. chinensis for 3 year. Alkaline phosphatase, urease, and sucrase activities all decreased with increased horizontal and vertical distance from the emitter. After 3 year of drip irrigation, the correlations between soil enzyme activities and soil environment factors had stronger correlations than in the unreclaimed land. After 4-6 years, the soil enzyme activities should attain the level of the natural L. chinensis grassland.