与单独供硝（NO₃^-）或者单独供铵（NH₄⁺）相比，混合供氮能够促进苗期玉米的生长。前期研究表明，混合供氮不仅可以提高玉米的光合效率，还可以促进地上部生长素的合成来增强叶片生长，进而为碳和氮的利用构建一个较大的库。然而，该过程是否依赖于氮的吸收还尚不清楚。在此，将玉米幼苗在具有三种供氮形态（单独供硝，75/25硝铵比和单独供铵）的水培实验中进行生长。结果表明，在0-3天，混合供氮下玉米生长速率和地上部含氮量与单独供硝处理间无显著差异，在6-9天，混合供氮下玉米生长速率和地上部含氮量显著高于单独供硝处理。于此同时，虽然混合供氮条件下15NO₃^-与15NH₄⁺的瞬时吸收速率较单独供硝和单独供铵相比皆有所下降，但混合供氮在6-9天具有最高的总氮吸收速率。QRT-PCR结果表明，长期混合供氮条件下根系N吸收的增加可能与长期处理下NO₃^-转运蛋白基因（例如ZmNRT1.1A， ZmNRT1.1B，ZmNRT1.1C， ZmNRT1.2和ZmNRT1.3）的高表达或铵转运蛋白基因（例如ZmAMT1.1A）的高表达有关，尤其是后者。此外，与单独供硝处理相比，混合供氮处理下植株地上部与根系中具有较高的谷氨酰胺合酶（GS）活性以及氨基酸含量。硝酸还原酶酶（NR）和GS酶抑制剂实验进一步证明了混合供氮情况下氮的同化能力对于玉米生长促进是至关重要的。该研究证明了混合供氮能够促进氮素吸收并进一步促进了氮的同化，而该过程可能是促进玉米上生长的主要驱动力。

Maize (Zea mays L.), also known as corn, is the third most cultivated crop in the world.  Northern corn leaf blight (NCLB) is a globally devastating maize foliar disease caused by Setosphaeria turcica (Luttrell) Leonard and Suggs.  Early intelligent diagnosis and warning is an effective and economical strategy to control this disease.  Today, deep learning is beginning to play an essential role in agriculture.  Notably, deep convolutional neural networks (DCNN) are amongst the most successful machine learning techniques in plant disease detection and diagnosis.  Our study aims to identify NCLB in the maize-producing area in Jilin Province based on several DCNN models.  We established a database of 985 leaf images of healthy and infected maize and applied data augmentation techniques including image segmentation, image resizing, image cropping, and image transformation, to expand to 30 655 images.  Several proven convolutional neural networks, such as AlexNet, GoogleNet, VGG16, and VGG19, were then used to identify diseases.  Based on the best performance of the DCNN pre-trained model GoogleNet, some of the recent loss functions developed for deep facial recognition tasks such as ArcFace, CosFace, and A-Softmax were applied to detect NCLB.  We found that a pre-trained GoogleNet architecture with the Softmax loss function can achieve an excellent accuracy of 99.94% on NCLB diagnosis.  The analysis was implemented in Python with two deep learning frameworks, Pytorch and Keras.  The techniques, training, validation, and test results are presented in this paper.  Overall, our study explores intelligent identification technology for NCLB and effectively diagnoses NCLB from images of maize.

To assess levels of contamination and human health risk, we analyzed the concentrations of the heavy metals lead (Pb), cadmium (Cd), chromium (Cr), and nickel (Ni) in China’s main deciduous fruits - apple, pear, peach, grape, and jujube. The concentration order of the heavy metals was Ni>Cr>Pb>Cd. In 97.5% of the samples, heavy metal concentrations were within the maximum permissible limits. Among the fruits studied, the heavy metal concentrations in jujube and peach proved to be the highest, and those in grape proved to be the lowest. Only 2.2% of the samples were polluted by Ni, only 0.4% of the samples were polluted by Pb, and no samples were polluted by Cd or Cr. Compared with the other fruits, the combined heavy metal pollution was significantly higher (P<0.05) in peach and significantly lower (P<0.05) in grape. For the combined heavy metal pollution, 96.9% of the samples were at safe level, 2.32% at warning level, 0.65% at light level, and 0.13% at moderate level. In the fruits studied, the contribution of heavy metals to the daily intake rates (DIR) followed the order of Ni>Cr>Pb>Cd. The highest DIR came from apple, while the lowest DIR came from grape. For each of the heavy metals, the total DIR from five studied fruits corresponded to no more than 1.1% of the tolerable daily intake, indicating that no significant adverse health effects are expected from the heavy metals and the fruits studied. The target hazard quotients and the total target hazard quotients demonstrated that none of the analyzed heavy metals may pose risk to consumers through the fruits studied. The highest risk was posed by apple, followed in decreasing order by peach and pear, jujube, and grape. We suggest that the main deciduous fruits (apple, pear, peach, grape, and jujube) of China’s main producing areas are safe to eat.

The presence of pesticide residues in pears is a serious health concern. This study presents the results from a 2-year investigation (2013–2014) that used gas chromatography, GS/MS and UPLC/MS-MS to measure the levels of 104 pesticides in 310 pear samples. In 93.2% of the samples, 43 pesticides were detected, of which the maximum residue levels (MRLs) were exceeded in 2.6% of the samples. Multiple residues (two to eight compounds) were present in 69.7% of the samples; one sample contained nine pesticides and one sample contained 10. Only 6.8% of the samples did not contain residues. To assess the health risks, the pesticide residue data have been combined with daily pear consumption data for children and adult populations. A deterministic model was used to assess the chronic and acute exposures based on the Joint Meeting on Pesticide Residues (JMPR) method. A potential acute risk was demonstrated for children in the case of bifenthrin, which was found to be present at 105.36% of the acute reference dose (ARfD) value. The longterm exposure of the Chinese consumer to pesticide residues through the consumption of raw pears was far below the acceptable daily intake (ADI) criterion. Additionally, the matrix ranking scheme was used to classify risk subgroups of pesticides and pear samples. In general, 95.5% of samples were deemed to be safe and nine pesticides were classified as being of a relatively high risk. The findings indicated that the occurrence of pesticide residues in pears should not be considered a serious public health problem. Nevertheless, a more detailed study is required for vulnerable consumer groups, especially children. Continuous monitoring of pesticides in pears and tighter regulation of pesticide residue standards are recommended.

The exocyst is a conserved protein complex, and required for vesicles tethering, fusion and polarized exocytosis. Exo70A1, the exocyst subunit, is essential for assembly of the exocyst complex. To better understand potential roles of Exo70A1 in non-heading Chinese cabbage (Brassica campestris ssp. chinensis), we obtained the full-length cDNA of Exo70A1 gene, which consisted of 1 917 bp and encoded a protein of 638 amino acids. BlastX showed BcExo70A1 shared 94.9% identity with Brassica oleracea var. acephala (AEI26267.1), and clustered into a same group with other homologues in B. oleracea var. acephala and Brassica napus. Subcellular localization analysis showed BcExo70A1 was localized to punctate structures in cytosol of onion epithelial cells. Results showed that BcExo70A1 was widely presented in stamens, young stems, petals, unpollinated pistils, roots and leaves of self compatible and incompatible plants. The transcripts of BcExo70A1 in non- heading Chinese cabbage declined during initial 1.5 h after incompatible pollination, while an opposite trend was presented after compatible pollination. Our study reveals that BcExo70A1 could play essential roles in plant growth and development, and is related to the rejection of self pollen in non-heading Chinese cabbage.

The German wheat cultivar Ibis has excellent adult plant resistance (APR) to stripe rust in Gansu, a hotspot for stripe rust in China. To elucidate the genetic basis of APR to stripe rust in Ibis, 237 F3 lines derived from the cross Ibis/Huixianhong were evaluated at Tianshui, Gansu, in the 2008-2009 and 2009-2010 cropping seasons, and at Chengdu, Sichuan Province, China, in the 2009-2010 cropping season. Inoculations were conducted with a mixture of several prevalent Pst races in both locations. Maximum disease severity (MDS) data showed a continuous distribution of response, indicating quantitative nature of resistance to stripe rust in Ibis. The broad-sense heritability of MDS was 0.75 based on the mean values averaged across three environments. A total of 723 simple sequence repeat (SSR) markers were used to map the QTL for APR by inclusive composite interval mapping (ICIM). QTLs mapping to chromosomes 2BS and 6BS, designated as QYr.caas-2BS.1 and QYr.caas-6BS.1, respectively, explained 4.1-40.7% of the phenotypic variance in MDS across environments. The major effect QTL QYr.caas-2BS.1, flanked by Xgwm148 and Xwmc360, was consistently detected at all three sites as well as the averaged data over three environments, accounting for 40.7, 24.2, 5.2 and 29.9% of phenotypic variance, respectively. The molecular markers closely linked to this QTL have potential for use in marker-assisted selection and gene pyramiding to improve the durability of stripe rust resistance in wheat breeding.