Dynamic functional connections analysis with spectral learning for brain disorder detection.

Dynamic functional connections (dFCs), can reveal neural activities, which provides an insightful way of mining the temporal patterns within the human brain and further detecting brain disorders. However, most existing studies focus on the dFCs estimation to identify brain disorders by shallow temporal features and methods, which cannot capture the inherent temporal patterns of dFCs effectively. To address this problem, this study proposes a novel method, named dynamic functional connections analysis with spectral learning (dCSL), to explore inherently temporal patterns of dFCs and further detect the brain disorders. Concretely, dCSL includes two components, dFCs estimation module and dFCs analysis module. In the former, dFCs are estimated via the sliding window technique. In the latter, the spectral kernel mapping is first constructed by combining the Fourier transform with the non-stationary kernel. Subsequently, the spectral kernel mapping is stacked into a deep kernel network to explore higher-order temporal patterns of dFCs through spectral learning. The proposed dCSL, sharing the benefits of deep architecture and non-stationary kernel, can not only calculate the long-range relationship but also explore the higher-order temporal patterns of dFCs. To evaluate the proposed method, a set of brain disorder classification tasks are conducted on several public datasets. As a result, the proposed dCSL achieves 5% accuracy improvement compared with the widely used approaches for analyzing sequence data, 1.3% accuracy improvement compared with the state-of-the-art methods for dFCs. In addition, the discriminative brain regions are explored in the ASD detection task. The findings in this study are consistent with the clinical performance in ASD.

Chemokines: Function and therapeutic potential in bone metastasis of lung cancer.

Lung cancer is a rapidly progressing disease with a poor prognosis. Bone metastasis is commonly found in 40.6% of advanced-stage patients. The mortality rate of lung cancer patients with bone metastasis can be significantly decreased by implementing novel diagnostic techniques, improved staging and classification systems, precise surgical interventions, and advanced treatment modalities. However, it is important to note that there is currently a lack of radical procedures available for these patients due to the development of drug resistance. Consequently, palliative care approaches are commonly employed in clinical practice. Therefore, new understandings of the process of bone metastasis of lung cancer are critical for developing better treatment strategies to improve patient's clinical cure rate and quality of life. Chemokines are cell-secreted small signaling proteins in cancer occurrence, proliferation, invasion, and metastasis. In this study, we review the development of bone metastasis in lung cancer and discuss the mechanisms of specific chemokine families (CC, CXC, CX3C, and XC) in regulating the biological activities of tumors and promoting bone metastasis. We also highlight some preclinical studies and clinical trials on chemokines for lung cancer and bone metastasis.

Biofortification of different maize cultivars with zinc, iron and selenium by foliar fertilizer applications.

Fertilizer-based biofortification is a strategy for combating worldwide malnutrition of zinc (Zn), iron (Fe) and selenium (Se). Field experiments were conducted to investigate the effects of foliar treatments on concentrations of Zn, Fe, Se, N and bioavailability of Zn and Fe in grains of three maize cultivars grown at three locations. We compared the efficacy of ZnO nanoparticles (ZnO-NPs), Zn complexed chitosan nanoparticles (Zn-CNPs), conventional ZnSO4 and a cocktail solution (containing Zn, Fe and Se). All treatments were foliar-applied at rate of 452 mg Zn L-1, plus urea. Applying ten-fold less Zn (at rate of 45.2 mg Zn L-1) plus urea in the form of ZnO-NPs, Zn-CNPs, or ZnSO4 resulted in no increase, or a negligible increase, in grain Zn concentration compared with deionized water. By contrast, among the different Zn sources plus urea applied by foliar sprays, conventional ZnSO4 was the most efficient in improving grain Zn concentration. Furthermore, foliar application of a cocktail solution effectively improved grain concentrations of Zn, Fe, Se and N simultaneously, without a grain yield trade-off. For example, the average grain concentrations were simultaneously increased from 13.8 to 22.1 mg kg-1 for Zn, from 17.2 to 22.1 mg kg-1for Fe, from 21.4 to 413.5 ug kg-1 for Se and from 13.8 to 14.7 g kg-1 for N by foliar application of a cocktail solution. Because grain yield was significantly negatively correlated with grain nutrient concentrations, the magnitude of increase in grain concentrations of Zn and Fe was most pronounced in the maize cultivar with the lowest grain yield (Zhengdan958 grown in Linyi). Foliar application of a cocktail solution also significantly decreased the phytic acid (PA) concentration, ratios of PA/Fe and PA/Zn in grains, indicating an increased bioavailability of Fe and Zn for human health. In conclusion, we found that a foliar application of a cocktail solution including Zn, Fe, Se and N was most effective for biofortification, but that the grains with the lowest yield contained the greatest concentration of these elements. This finding highlights the need to breed maize varieties that are capable of achieving both high grain yield and high grain nutritional quality to address food security and human health challenges.

WITHDRAWN: Application of Chemotherapeutic Drugs and Convectionenhanced Delivery Technology in Glioma

Since the authors are not responding to the editor's requests to fulfill the editorial requirement, therefore, the article has been withdrawn from the website of the journal Anti-Cancer Agents in Medicinal Chemistry.Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused.The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorialpolicies-main.php. BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Supramolecule-Controlled Enantioselectivity for Electrochemical Asymmetric Hydrogenation of Coumarins with a Chiral Macrocyclic Compound.

The supramolecular strategy was subjected to the asymmetric hydrogenation of 4-methylumbelliferone by electrochemical reduction in the presence of a chiral macrocyclic multifarane[3,3], which offered a l-7-hydroxy-4-methylchroman-2-one product with a chemical yield of 65% and enantioselectivity up to >99% ee. The high stability of the developed chiral supramolecular electrode guaranteed the recyclability and repeatability in the electrolysis, and therefore, the application was extended to more coumarin derivatives to provide satisfactory chemical yields and enantioselectivities.

Cropland nitrous oxide emissions exceed the emissions of RCP 2.6: A global spatial analysis.

Nitrous oxide (N2O), as a potent greenhouse gas, must be limited to prevent the global temperature increasing by >2 °C. Cropland is the largest source of anthropogenic N2O emissions; however, earlier estimates for emissions and their exceedances still remain uncertainties. Here, we used a spatially explicit model to estimate cropland N2O emission in 2014 by refined grid-level crop-specific EFs and considered the background emission. We also sought to determine where N2O emissions exceed the "boundary" through analysis of spatial data from representative concentration pathway (RCP) 2.6. The global cropland N2O emission was 2.92 ± 0.59 Tg N yr-1, which far exceeds the 0.82 Tg N yr-1 boundary, over 90 % of cropland areas exceeded the boundary. Western Europe, Southeastern China, Pakistan, and the Ganges Plain exceeded the boundary by >2 kg N ha-1 yr-1. The boundary exceedances showed a positive linear response with respect to total cropland emission and a quadratic response to GDP per capita at the country level. Our study highlights the necessity of accurate estimations of spatial variations in cropland N2O emissions and evaluation of exceedances, to facilitate the development of more effective mitigation measures in different regions.

Selecting Multiple Node Statistics Jointly from Functional Connectivity Networks for Brain Disorders Identification.

Functional connectivity networks (FCN) analysis is instructive for the diagnosis of brain diseases, such as mild cognitive impairment (MCI) and major depressive disorder (MDD) at their early stages. As the critical step of FCN analysis, feature representation provides the basis for finding potential biomarkers of brain diseases. In previous studies, different node statistics (e.g. local efficiency and local clustering coefficients) are usually extracted from FCNs as features for the diagnosis/classification task, which can specifically locate disease-related regions on the node level, so as to help us understand the neurodevelopmental roots of brain disorders. However, each node statistic is proposed only considering a kind of specific network property, which has one-sidedness and limitations. As a result, it is incomplete to represent a node with only one statistic. To resolve this issue, we put forward a novel scheme to select multiple node statistics jointly from the estimated FCNs for automated classification, called multiple node statistics feature selection (MNSFS). Specifically, we first extract multiple statistics from the same nodes and assign each kind of statistic into a group. Then, sparse group least absolute shrinkage and selection operator (sgLASSO) is used to select groups (nodes) and statistics in the groups towards a better classification performance. Such a technique enables us to simultaneously locate the discriminative brain regions, as well as the specific statistics associated with these brain regions, making the classification results more interpretable. We conducted our scheme on two public databases for identifying subjects with MCI and MDD from normal controls. Experimental results show that the proposed scheme achieves superior classification accuracy and features interpreted on the benchmark datasets.

Feasibility Analysis of 3D Printing With Prenatal Ultrasound for the Diagnosis of Fetal Abnormalities.

OBJECTIVE: To assess the feasibility and accuracy of 3D printing with prenatal three-dimensional ultrasound (3DUS) in the diagnosis of fetal abnormalities. METHODS: Fetuses initially diagnosed with various abnormalities were included in this retrospective study. The fetuses were examined by 3DUS, modeled, and 3D printed, and the dimensional accuracy of the 3D prints was analyzed. The effectiveness, demand, necessity of 3D printing, and the diagnostic accuracy of different methods were analyzed based on questionnaire responses from 40 senior ultrasound doctors and 40 postgraduate students. RESULTS: A total of 12 fetuses with cleft lip and palate, spinal, heart, or brain abnormalities were included for detailed assessment. All deviations (mean deviation: 0.1 mm) between the original images and the final 3D prints lay within the consistency boundary (-1.12, 1.31 mm) (P > .05). In the subsequent analyses, 90.8% of the doctors and 94.2% of the students strongly agreed that 3D printing could precisely represent and depict fetal abnormalities. The average misdiagnosis rate of the doctors decreased from 5% to 0.4% after the application of 3D printing combined with 3DUS in comparison with 3DUS alone, and the corresponding value for the students dropped from 17.9% to 5.2%. CONCLUSIONS: The errors in modeling and 3D printing based on 3DUS were within acceptable limits, and 3D printing improved the diagnosis of various fetal abnormalities.

Integrating crop redistribution and improved management towards meeting China's food demand with lower environmental costs.

China feeds 19.1% of the world's population with 8.6% of the arable land. Here we propose an integrated approach combining crop redistribution and improved management to meet China's food demand in 2030. We simulated the food demand, estimated the national crop production through the productivity of the top 10% of producers in each county, and optimized the spatial distribution of 11 groups of crop types among counties using the data of the top producers. Integrating crop redistribution and improved management increased crop production and can meet the food demand in 2030, while the agricultural inputs (N and P fertilizers and irrigation water) and environmental impacts (reactive N loss and greenhouse gas emissions) were reduced. Although there are significant socio-economic and cultural barriers to implementing such redistribution, these results suggest that integrated measures can achieve food security and decrease negative environmental impacts. County-specific policies and advisory support will be needed to achieve the promises of combining optimization strategies.

Learning Brain Functional Networks With Latent Temporal Dependency for MCI Identification.

Resting-state functional magnetic resonance imaging (rs-fMRI) has become a popular non-invasive way of diagnosing neurological disorders or their early stages by probing functional connectivity between different brain regions of interest (ROIs) across subjects. In the past decades, researchers have proposed many methods to estimate brain functional networks (BFNs) based on blood-oxygen-level-dependent (BOLD) signals captured by rs-fMRI. However, most of the existing methods estimate BFNs under the assumption that signals are independently sampled, which ignores the temporal dependency and sequential order of different time points (or volumes). To address this problem, in this paper, we first propose a novel BFN estimation model by introducing a latent variable to control the sequence of volumes for encoding the temporal dependency and sequential information of signals into the estimated BFNs. Then, we develop an efficient learning algorithm to solve the proposed model by the alternating optimization scheme. To verify the effectiveness of the proposed method, the estimated BFNs are used to identify subjects with mild cognitive impairment (MCIs) from normal controls (NCs). Experimental results show that our method outperforms the baseline methods in the terms of classification performance.

Interaction Effects of Nitrogen Rates and Forms Combined With and Without Zinc Supply on Plant Growth and Nutrient Uptake in Maize Seedlings.

Previous studies have shown that zinc (Zn) accumulation in shoot and grain increased as applied nitrogen (N) rate increased only when Zn supply was not limiting, suggesting a synergistic effect of N on plant Zn accumulation. However, little information is available about the effects of different mineral N sources combined with the presence or absence of Zn on the growth of both shoot and root and nutrient uptake. Maize plants were grown under sand-cultured conditions at three N forms as follows: NO3 - nutrition alone, mixture of NO3 -/NH4 + with molar ratio of 1:1 (recorded as mixed-N), and NH4 + nutrition alone including zero N supply as the control. These treatments were applied together without or with Zn supply. Results showed that N forms, Zn supply, and their interactions exerted a significant effect on the growth of maize seedlings. Under Zn-sufficient conditions, the dry weight (DW) of shoot, root, and whole plant tended to increase in the order of NH4 + < NO3 - < mixed-N nutrition. Compared with NH4 + nutrition alone, mixed-N supply resulted in a 27.4 and 28.1% increase in leaf photosynthetic rate and stomatal conductance, which further resulted in 35.7 and 33.5% of increase in shoot carbon (C) accumulation and shoot DW, respectively. Furthermore, mixed-N supply resulted in a 19.7% of higher shoot C/N ratio vs. NH4 + nutrition alone, which means a higher shoot biomass accumulation, because of a significant positive correlation between shoot C/N ratio and shoot DW (R 2 = 0.682***). Additionally, mixed-N supply promoted the greatest root DW, total root length, and total root surface area and synchronously improved the root absorption capacity of N, iron, copper, manganese, magnesium, and calcium. However, the above nutrient uptake and the growth of maize seedlings supplied with NH4 + were superior to either NO3 - or mixed-N nutrition under Zn-deficient conditions. These results suggested that combined applications of mixed-N nutrition and Zn fertilizer can maximize plant growth. This information may be useful for enabling integrated N management of Zn-deficient and Zn-sufficient soils and increasing plant and grain production in the future.

Brain Function Network: Higher Order vs. More Discrimination.

Brain functional network (BFN) has become an increasingly important tool to explore individual differences and identify neurological/mental diseases. For estimating a "good" BFN (with more discriminative information for example), researchers have developed various methods, in which the most popular and simplest is Pearson's correlation (PC). Despite its empirical effectiveness, PC only encodes the low-order (second-order) statistics between brain regions. To model high-order statistics, researchers recently proposed to estimate BFN by conducting two sequential PCs (denoted as PC 2 in this paper), and found that PC 2-based BFN can provide additional information for group difference analysis. This inspires us to think about (1) what will happen if continuing the correlation operation to construct much higher-order BFN by PC n (n>2), and (2) whether the higher-order correlation will result in stronger discriminative ability. To answer these questions, we use PC n -based BFNs to predict individual differences (Female vs. Male) as well as identify subjects with mild cognitive impairment (MCI) from healthy controls (HCs). Through experiments, we have the following findings: (1) with the increase of n, the discriminative ability of PC n -based BFNs tends to decrease; (2) fusing the PC n -based BFNs (n>1) with the PC 1-based BFN can generally improve the sensitivity for MCI identification, but fail to help the classification accuracy. In addition, we empirically find that the sequence of BFN adjacency matrices estimated by PC n (n = 1,2,3,⋯ ) will converge to a binary matrix with elements of ± 1.

Source-Sink Manipulation Affects Accumulation of Zinc and Other Nutrient Elements in Wheat Grains.

To better understand the source-sink flow and its relationships with zinc (Zn) and other nutrients in wheat (Triticum aestivum L.) plants for biofortification and improving grain nutritional quality, the effects of reducing the photoassimilate source (through the flag leaf removal and spike shading) or sink (through the removal of all spikelets from one side of the spike, i.e., 50% spikelets removal) in the field of the accumulation of Zn and other nutrients in grains of two wheat cultivars (Jimai 22 and Jimai 44) were investigated at two soil Zn application levels. The kernel number per spike (KNPS), single panicle weight (SPW), thousand kernel weight (TKW), total grain weight (TGW) sampled, concentrations and yields of various nutrient elements including Zn, iron (Fe), manganese (Mn), copper (Cu), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg), phytate phosphorus (phytate-P), phytic acid (PA) and phytohormones (ABA: abscisic acid, and the ethylene precursor ACC: 1-aminocylopropane-1-carboxylic acid), and carbon/N ratios were determined. Soil Zn application significantly increased the concentrations of grain Zn, N and K. Cultivars showing higher grain yields had lower grain protein and micronutrient nutritional quality. SPW, KNPS, TKW (with the exception of TKW in the removal of half of the spikelets), TGW, and nutrient yields in wheat grains were most severely reduced by half spikelet removal, secondly by spike shading, and slightly by flag leaf removal. Grain concentrations of Zn, N and Mg consistently showed negative correlations with SPW, KNPS and TGW, but positive correlations with TKW. There were general positive correlations among grain concentrations of Zn, Fe, Mn, Cu, N and Mg, and the bioavailability of Zn and Fe (estimated by molar ratios of PA/Zn, PA/Fe, PA × Ca/Zn, or PA × Ca/Fe). Although Zn and Fe concentrations were increased and Ca was decreased in treatments of half spikelet removal and spike shading, the treatments simultaneously increased PA and limited the increase in bioavailability of Zn and Fe. In general, different nutrient elements interact with each other and are affected to different degrees by source-sink manipulation. Elevated endogenous ABA levels and ABA/ACC ratios were associated with increased TKW and grain-filling of Zn, Mn, Ca and Mg, and inhibited K in wheat grains. However, the effects of ACC were diametrically opposite. These results provide a basis for wheat grain biofortification to alleviate human malnutrition.

Correction: Estimating sparse functional brain networks with spatial constraints for MCI identification.

[This corrects the article DOI: 10.1371/journal.pone.0235039.].

Estimating sparse functional connectivity networks via hyperparameter-free learning model.

Functional connectivity networks (FCNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Currently, researchers have proposed many methods for FCN construction, among which the most classic example is Pearson's correlation (PC). Despite its simplicity and popularity, PC always results in dense FCNs, and thus a thresholding strategy is usually needed in practice to sparsify the estimated FCNs prior to the network analysis, which undoubtedly causes the problem of threshold parameter selection. As an alternative to PC, sparse representation (SR) can directly generate sparse FCNs due to the l1 regularizer in the estimation model. However, similar to the thresholding scheme used in PC, it is also challenging to determine suitable values for the regularization parameter in SR. To circumvent the difficulty of parameter selection involved in these traditional methods, we propose a hyperparameter-free method for FCN construction based on the global representation among fMRI time courses. Interestingly, the proposed method can automatically generate sparse FCNs, without any thresholding or regularization parameters. To verify the effectiveness of the proposed method, we conduct experiments to identify subjects with mild cognitive impairment (MCI) and Autism spectrum disorder (ASD) from normal controls (NCs) based on the estimated FCNs. Experimental results on two benchmark databases demonstrate that the achieved classification performance of our proposed scheme is comparable to four conventional methods.

Optimization of China's maize and soy production can ensure feed sufficiency at lower nitrogen and carbon footprints.

China purchases around 66% of the soy that is traded internationally. This strains the global food supply and contributes to greenhouse gas emissions. Here we show that optimizing the maize and soy production of China can improve its self-sufficiency and also alleviate adverse environmental effects. Using data from more than 1,800 counties in China, we estimate the area-weighted yield potential (Ypot) and yield gaps, setting the attainable yield (Yatt) as the yield achieved by the top 10% of producers per county. We also map out county-by-county acreage allocation and calculate the attainable production capacity according to a set of sustainability criteria. Under optimized conditions, China would be able to produce all the maize and 45% of the soy needed by 2035-while reducing nitrogen fertilizer use by 26%, reactive nitrogen loss by 28% and greenhouse gas emissions by 19%-with the same acreage as 2017, our reference year.

Safeguarding Food Supply and Groundwater Safety for Maize Production in China.

Quantifying sustainable nitrogen (N) management at the national scale is critical for developing targeted policies and strategies to simultaneously achieve food security and groundwater protection. In this study, we report county-scale optimization scenarios for Chinese maize production and evaluate their outcomes for safeguarding food supply and groundwater safety. First, we performed random forest regression modeling to simulate in situ NO3- leaching based on a meta-analysis that integrates climate, soil, water, and N balance parameters. The NO3- leaching was then mapped for 1406 counties based on data compiled from 2.89 million farmer surveys. Average NO3- leaching during the maize growth season was estimated to be 27.6 kg N ha-1, and 56% of counties had groundwater whose nitrate concentrations exceeded drinking water safety levels during 2005-2014. The top 5% farmers in each county produced not only more grain but also greater NO3- leaching. Scenario analysis of potential management changes found that when these top producers combined optimal N management practices, national N use in Chinese maize system was reduced by 25%, from 9.1 to 6.9 Mt, while maize production increased by 6.1%. Modeled NO3- leaching was 0.58 Mt, which was 31% lower than groundwater safety levels and 53% lower than the current leaching amount. This study provides evidence that integrated crop and N management practices implemented at the county level safeguard both maize crop food security and enhance environment sustainability.

Estimating sparse functional brain networks with spatial constraints for MCI identification.

Functional brain network (FBN), estimated with functional magnetic resonance imaging (fMRI), has become a potentially useful way of diagnosing neurological disorders in their early stages by comparing the connectivity patterns between different brain regions across subjects. However, this depends, to a great extent, on the quality of the estimated FBNs, indicating that FBN estimation is a key step for the subsequent task of disorder identification. In the past decades, researchers have developed many methods to estimate FBNs, including Pearson's correlation and (regularized) partial correlation, etc. Despite their widespread applications in current studies, most of the existing methods estimate FBNs only based on the dependency between the measured blood oxygen level dependent (BOLD) signals, which ignores spatial relationship of signals associated with different brain regions. Due to the space and material parsimony principle of our brain, we believe that the spatial distance between brain regions has an important influence on FBN topology. Therefore, in this paper, we assume that spatially neighboring brain regions tend to have stronger connections and/or share similar connections with others; based on this assumption, we propose two novel methods to estimate FBNs by incorporating the information of brain region distance into the estimation model. To validate the effectiveness of the proposed methods, we use the estimated FBNs to identify subjects with mild cognitive impairment (MCI) from normal controls (NCs). Experimental results show that the proposed methods are better than the baseline methods in the sense of MCI identification accuracy.

Calculating socially optimal nitrogen (N) fertilization rates for sustainable N management in China.

Current nitrogen (N) fertilization rates in China have incurred high social costs in the drive to achieve higher yields and economic returns. We conducted an intensive nation-wide investigation to estimate the socially optimal N rate (SOR) for Chinese maize, rice and wheat as a balance between crop productivity, farm income, ecological health and human health. The social cost of N impacts (SCN) was calculated based on 2210 field observations reported in 264 publications. The estimated SCN for three cereal crops grown in China was in the range $142-218 ha-1 at medium N fertilization rates (173-204 kg N ha-1). The net benefits of N use were calculated as the differences between private profitability and the SCN. The minimum N application rate with maximized net benefit was estimated as the SOR calculated from data compiled from 27,476 on-farm year-site trials. The average SOR was in the range 149-160 kg ha-1; values in this range were 18.1-23.7% lower than the privately optimal N rate (POR). The yield losses associated with implementation of the SOR were not significant (p < 0.01) compared with the yield of POR implementation. The POR calculates the minimum N application required to maximize private profitability, i.e., traditional N recommended practice. Compared with the POR, implementation of SOR reduced reactive N losses by 17.8-39.0%, and the SCN by 18.8-30.9%. Finally, we simulated the SOR at the county level for each soil type based on data collected from no-N control plots yields and maximum achieved yields (p < 0.01). Thus, we estimated the SOR at the Chinese county level for three cereal crops using direct on-farm measurements. This study provide updated estimates of optimizing N management to simultaneously address production and pollution problems in China and other similar regions of the world.

Dynamic Zinc Accumulation and Contributions of Pre- and/or Post-Silking Zinc Uptake to Grain Zinc of Maize as Affected by Nitrogen Supply.

Nitrogen (N) supply could improve the grain yield of maize, which is of great importance to provide calories and nutrients in the diets of both humans and animals. Field experiments were conducted in 2009 and 2010 to investigate dynamic zinc (Zn) accumulation and the pre-silking and post-silking Zn uptake and their contributions to grain Zn accumulation of maize with different N supply under field conditions. Results showed that only 1.2% to 39.4% of grain Zn accumulation derived from pre-silking Zn uptake, with Zn remobilization being negatively affected by increasing N supply. However, post-silking Zn uptake (0.8-2.3 mg plant-1) and its substantial contribution to grain Zn accumulation (60.6%-98.8%) were progressively enhanced with the increasing N supply. Furthermore, grain Zn concentration was positively associated with grain N concentration (r = 0.752***), post-silking N uptake (r = 0.695***), and post-silking Zn uptake (r = 738***). A significant positive relationship was also found between post-silking uptake of N and Zn (r = 0.775***). These results suggest that N nutrition is a critical factor for shoot Zn uptake and its allocation to maize grain. Dry weight, and N and Zn concentration of grain and straw were significantly enhanced with the increasing N from "no N" to "optimal N" supply (150 kg N ha-1 in 2009 and 105 kg N ha-1 in 2010), but further increasing N supply (250 kg N ha-1) generally resulted in a non-significant increase in both cropping seasons. During the grain development, N supply also generally tended to improve grain N and Zn concentrations, but decrease phosphorus (P) concentration and the molar ratio of P to Zn compared with null N application. These results suggest that grain Zn accumulation mainly originates from post-silking Zn uptake. Applying N at optimal rates ensures better shoot Zn nutrition and contributes to post-silking Zn uptake, maintaining higher grain Zn availability by decreasing the molar ratio of P to Zn, and resulting in benefits to human nutrition.