Functional trait-based phytoplankton biomass and assemblage analyses in the pre-growing season for comprehensive algal bloom risk assessment.

Algal bloom (AB) risk assessment is critical for maintaining ecosystem health and human sustainability. Previous AB risk assessments have focused on the potential occurrence of ABs and related factors in the growing season, whereas their hazards, especially in the pre-growing season, have attracted less attention. Here, we performed a comprehensive AB risk assessment, including water trophic levels, phytoplankton biomass, functional trait-based assemblages, and related environmental factors, in the pre-growing season in Dongting Lake, China. Although mesotrophic water and low phytoplankton biomass suggested low AB potential, toxic taxa, which constituted 13.28% of the phytoplankton biomass, indicated non-negligible AB hazards. NH4+ and water temperature were key factors affecting phytoplankton motility and toxicity. Our study establishes a new paradigm for quantitative AB risk assessment, including both potential AB occurrence and hazards. We emphasize the importance of phytoplankton functional traits for early AB warning and NH4+ reduction for AB control in the pre-growing season.

Improving prediction model robustness with virtual sample construction for near-infrared spectra analysis.

In a qualitative analysis of near-infrared spectroscopy (NIRS), when the samples to be analyzed are difficult to obtain or there are few counterexamples, the robustness of the models is poor, resulting in the decline of the generalization ability of the models. In this case, the effective method is to construct virtual samples to achieve the balance of categories. In this contribution, three virtual spectrum construction strategies including Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), and Deep Convolutional Generative Adversarial Network (DCGAN) were explored to deal with the problem of insufficient or imbalanced sample numbers in NIRS analysis. The strategies were tested with the melamine and Yali pears two spectral datasets. The PLS-DA and Correct Recognition Rate (CRR) were used for discriminant model construction and accuracy evaluation, respectively. The results show that SMOTE, ADASYN, and DCGAN processing strategies can all improve the global CRR (CRRglob). The SMOTE and ADASYN can improve the CRR for majority class sample (CRRmaj), but the CRR for minority class sample (CRRmin) has decreased. For the DCGAN method, the CRRglob, CRRmaj, and CRRmin were all improved. The standard deviation of the results of the multiple parallel calculations demonstrates the robustness of DCGAN generation method. Therefore, the DCGAN method has good reliability and practicability, and can increase the robustness and generalization ability of the NIRS model.

Online Inspection of Browning in Yali Pears Using Visible-Near Infrared Spectroscopy and Interpretable Spectrogram-Based CNN Modeling.

Browning is the most common physiological disease of Yali pears during storage. At the initial stage, browning only occurs in the tissues near the fruit core and cannot be detected from the appearance. The disease, if not identified and removed in time, will seriously undermine the quality and sale of the whole batch of fruit. Therefore, there is an urgent need to explore a method for early diagnosis of the browning in Yali pears. In order to realize the dynamic and online real-time detection of the browning in Yali pears, this paper conducted online discriminant analysis on healthy Yali pears and those with different degrees of browning using visible-near infrared (Vis-NIR) spectroscopy. The experimental results show that the prediction accuracy of the original spectrum combined with a 1D-CNN deep learning model reached 100% for the test sets of browned pears and healthy pears. Features extracted by the 1D-CNN method were converted into images by Gramian angular field (GAF) for PCA visual analysis, showing that deep learning had good performance in extracting features. In conclusion, Vis-NIR spectroscopy combined with the 1D-CNN discriminant model can realize online detection of browning in Yali pears.

A Magtein®, Magnesium L-Threonate, -Based Formula Improves Brain Cognitive Functions in Healthy Chinese Adults.

Magnesium is one of the most abundant essential minerals in the body. Magnesium supplements mostly have low bioavailability, except magnesium L-threonate. In 2010, a novel magnesium compound, magnesium L-threonate (Magtein®) was identified and was shown to raise the magnesium levels in the brain and neurons effectively. In this double-blind, placebo-controlled study, Magtein®PS, a magnesium L-threonate (Magtein®)- and phosphatidylserine-based formulation additionally containing vitamins C and D, was tested for its cognitive benefits in 109 healthy Chinese adults aged 18-65 years. Subjects were randomly assigned to receive either Magtein®PS or placebo (starch) capsules, at a dose of 2 g/day. "The Clinical Memory Test", the standard test commonly used in Chinese hospitals and academic institutes for cognitive evaluation, was administered before and 30 days after subjects received the supplement. Subjects receiving Magtein®PS showed significant improvements over the control group in all five subcategories of "The Clinical Memory Test" as well as the overall memory quotient scores. The older participants showed more improvement than younger participants. Results indicated significant benefits of Magtein®PS in improving memory and cognition in healthy Chinese adults.

Establishment of online deep learning model for insect-affected pests in "Yali" pears based on visible-near-infrared spectroscopy.

Insect-affected pests, as an important indicator in inspection and quarantine, must be inspected in the imports and exports of fruits like "Yali" pears (a kind of duck head-shaped pear). Therefore, the insect-affected pests in Yali pears should be previously detected in an online, real-time, and accurate manner during the commercial sorting process, thus improving the import and export trade competitiveness of Yali pears. This paper intends to establish a model of online and real-time discrimination for recessive insect-affected pests in Yali pears during commercial sorting. The visible-near-infrared (Vis-NIR) spectra of Yali samples were pretreated to reduce noise interference and improve the spectral signal-to-noise ratio (SNR). The Competitive Adaptive Reweighted Sampling (CARS) method was adopted for the selection of feature modeling variables, while Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Block Attention Module-Convolutional Neural Networks (CBAM-CNN) were used to establish online discriminant models. T-distributed Stochastic Neighbor Embedding (T-SNE) and Gradient-weighted Class Activation Mapping (Grad-CAM) were used for the clustering and attention distribution display of spectral features of deep learning models. The results show that the online discriminant model obtained by SGS pretreatment combined with the CBAM-CNN deep learning method exhibits the best performance, with 96.88 and 92.71% accuracy on the calibration set and validation set, respectively. The prediction time of a single pear is 0.032 s, which meets the online sorting requirements.

Research on construction method and validity mechanism of robust analysis model in snow peach quality detection based on visible-near infrared spectroscopy.

Visible-near infrared (Vis-NIR) spectra analysis method is widely used in the quality grading of bulk fruits with its rapid, non-destructive, diverse detection modes and flexible modular integration scheme. However, during the online grading of fruits, the random mechanized way of dropping fruit onto the conveyor belt method and the open detection environment led to more spectral abnormal samples, which affect the accuracy of the detection. In this paper, the soluble solids content (SSC) of snow peach is quantitatively analyzed by static and online detection methods. Several spectral preprocessing methods including Norris-Williams Smoothing (NWS), Savitzky-Golay Smoothing (SGS), Continuous Wavelet Derivative (CWD), Multivariate Scattering Correction (MSC), and Variable Sorting for Normalization (VSN) are adopted to eliminate spectral rotation and translation errors and improve the signal-to-noise ratio. Monte Carlo Uninformative Variable Elimination (MCUVE) method is used for the selection of optimal characteristic modeling variables. Partial Least Squares Regression (PLSR) is used to model and analyze the preprocessed spectra and the spectral variables optimized by MCUVE, and the effectiveness of the method is evaluated. Sparse Partial Least Squares Regression (SPLSR) and Sparse Partial Robust M Regression (SPRMR) are used for the construction of robust models. The results showed that the SGS preprocessing method can effectively improve the analysis accuracy of static and online models. The MCUVE method can realize the extraction of stable characteristic variables. The SPRMR model based on SGS preprocessing method and the effective variables has the optimal analysis results. The analysis accuracy of snow peach static model is slightly better than that of online analytical model. Through the test results of the PLSR, SPLSR and SPRMR models by the artificially adding noise test method, it can be seen that the SPRMR method eliminates the influence of abnormal samples on the model during the modeling process, which can effectively improve the anti-noise ability and detection reliability.

The safety of pericardiocentesis in patients under antithrombotic therapy: A single-center experience.

Objective: This study aimed to analyze the characteristics of patients with pericardial effusion requiring pericardiocentesis and to evaluate the safety of pericardiocentesis without discontinuation of anticoagulant or antiplatelet drugs. Methods: We performed a retrospective study of patients undergoing pericardiocentesis in our hospital between 2012 and 2022. Patients were categorized into the Antithrombotic Group if they had used any antiplatelet or anticoagulant drugs on the day of pericardiocentesis; otherwise they were categorized into the Non-antithrombotic Group. All procedures were performed by experienced cardiologists with echocardiographic guidance. Bleeding events were defined using the National Institutes of Health scale of adverse events. Results: A total of 501 consecutive patients were identified and 70 cases were under antithrombotic drugs (Antithrombotic Group). Patients in Antithrombotic Group were older, had more comorbidities, presented with lower platelet counts and prolonged activated partial thromboplastin time (all p < 0.05). Malignancy was the most common etiology for pericardial effusion in both groups (28.6% in Antithrombotic Group and 54.7% in Non-antithrombotic Group) and tuberculosis was the second etiology in the Non-antithrombotic Group (21.9%), while procedure-related effusion (17.1%) accounted for the second cause in the Antithrombotic Group. Two patients in the Antithrombotic Group had mild oozing at the puncture site that resolved without interventions (2.9 vs. 0%, p = 0.019), and no bleeding events higher than Grade 1 occurred in either group. Conclusion: Although antiplatelet or anticoagulant drugs may put patients undergoing pericardiocentesis at theoretically higher risk of bleeding, our study demonstrated that they are not associated with increased major bleeding complications.

Indirect herbivore biomanipulation may halt regime shift from clear to turbid after macrophyte restoration.

Eutrophication transforms clear water into turbid water in shallow lakes. Current restoration techniques focus on re-establishing the clear-water state rather than on its maintenance. We investigated the response of submerged macrophytes to temporary grass carp (Ctenopharyngodon idella) and scraping snail (Bellamya aeruginosa) introductions. We also explored the impacts of herbivores on underwater light conditions to identify their long- and short-term potential to halt regime shift from clear to turbid after clear-water state reestablishment. Herbivores reduced both the biomass of submerged macrophytes and accumulated nutrients in the tissue of submerged macrophytes. This potentially avoided the pulse of endogenous nutrient release which would have exceeded the threshold required for the regime shift from clear to turbid. However, herbivores had a non-significant impact on submerged macrophyte-reduced light attenuation coefficient, which has a positive linear relationship with water chlorophyll a. Further, grass carp and snails enhanced the inhibition ratio of submerged macrophytes to phytoplankton by 3.96 and 2.13 times, respectively. Our study provides novel findings on the potential of herbivore introduction as an indirect biomanipulation tool for halting the regime shift of shallow lakes from clear to turbid after the restoration of submerged macrophytes.

Nanomaterials-Mediated Co-Stimulation of Toll-Like Receptors and CD40 for Antitumor Immunity.

Toll-like receptors (TLRs) and CD40-related signaling pathways represent critical bridges between innate and adaptive immune responses. Here, an immunotherapy regimen that enables co-stimulation of TLR7/8- and CD40-mediated pathways is developed. TLR7/8 agonist resiquimod (R848) derived amino lipids, RAL1 and RAL2, are synthesized and formulated into RAL-derived lipid nanoparticles (RAL-LNPs). The RAL2-LNPs show efficient CD40 mRNA delivery to DCs both in vitro (90.8 ± 2.7%) and in vivo (61.3 ± 16.4%). When combined with agonistic anti-CD40 antibody, this approach can produce effective antitumor activities in mouse melanoma tumor models, thereby suppressing tumor growth, prolonging mouse survival, and establishing antitumor memory immunity. Overall, RAL2-LNPs provide a novel platform toward cancer immunotherapy by integrating innate and adaptive immunity.

Rice Paddies Reduce Subsequent Yields of Wheat Due to Physical and Chemical Soil Constraints.

Yields of wheat crops that succeed rice paddy crops are generally low. To date, it has been unclear whether such low yields were due to rice paddies altering soil physical or mineral characteristics, or both. To investigate this quandary, we conducted field experiments in the Jianghan Plain to analyze differences in the spatial distribution of wheat roots between rice-wheat rotation (RW) and dryland-wheat rotations (DW) using a range of nitrogen treatments. Dryland wheat crops were preceded by either dryland soybean or corn in the prior summer. Biomass of wheat crops in RW systems was significantly lower than that of DW for all N fertilizer treatments, although optimal nitrogen management resulted in comparable wheat yields in both DW and RW. Soil saturated water capacity and non-capillary porosity were higher in DW than RW, whereas soil bulk density was higher in RW. Soil available nitrogen and organic matter were higher in DW than RW irrespective of N application, while soil available P and K were higher under RW both at anthesis and post-harvest stages. At anthesis, root length percentage (RLP) was more concentrated in surface layers (0-20 cm) in RW, whereas at 20-40 cm and 40-60 cm, RLP was higher in DW than RW for all N treatments. At maturity, RLP were ranked 0-20 > 20-40 > 40-60 cm under both cropping systems irrespective of N fertilization. Root length percentage and soil chemical properties at 0-20 cm were positively correlated (r = 0.79 at anthesis, r = 0.68 at post-harvest) with soil available P, while available N (r = -0.59) and soil organic matter (r = -0.39) were negatively correlated with RLP at anthesis. Nitrogen applied at 180 kg ha-1 in three unform amounts of 60 kg N ha-1 at sowing, wintering and jointing resulted in higher yields than other treatments for both cropping systems. Overall, our results suggest that flooding of rice paddies increased bulk density and reduced available nitrogen, inhibiting the growth and yield of subsequent wheat crops relative to rainfed corn or soybean crops.

Di-n-butyl phthalate regulates vascular smooth muscle cells phenotypic switching by MiR-139-5p-MYOCD pathways.

Di-n-butyl phthalate (DBP) is ubiquitous in environment and has been detected in almost all human bodies. Few data could be found about the effects of DBP on cardiovascular system, though its reproductive toxicities have been studied extensively. This study aimed to explore effects of DBP on phenotypic switching of vascular smooth muscle cells (VSMCs), an essential step during the formation of atherosclerosis (AS). A7r5 cells were employed and exposed to various levels of DBP (10-9, 10-8, 10-7, 10-6, and 10-5 M) or DMSO as control. CCK-8 assay was used to detect the effects of DBP on cell viability. Expressions of mRNA/miRNAs and proteins were measured by qRT-PCR and western blotting, respectively. Bioinformatic analysis and dual-luciferase reporter assay were used to analyze the combination between miR-139-5p and Myocardin (MYOCD). Results revealed that DBP at 10-7 M prompted phenotypic switching from contractile to synthetic of VSMCs by inhibiting contractile VSMCs marker genes via suppressing the expression of MYOCD. Moreover, miR-139c-5p directly targeted MYOCD 3'UTR and modulated MYOCD expression. Besides, DBP inhibited the expression of MYOCD and VSMCs marker genes by upregulating miR-139-5p. Collectively, these data suggested that DBP could promote the phenotypic switching from contractile to synthetic of VSMCs in A7r5 cells through miR-139-5p-MYOCD.

Di-n-butyl phthalate promotes monocyte recruitment via miR-137-3p-SP1-MCP-1 pathway.

Since non-covalent bound character and widespread application in numerous products, people are exposed to di-n-butyl phthalate (DBP) at low levels through various ways. Epidemiological studies suggested an association between DBP exposure and atherosclerosis (AS). Still, molecular mechanisms remain unclear. This study aimed to explore the effects of DBP on monocyte recruitment, a key and initial step of AS. EA.hy926 cells were treated with DBP (10-9-10-5 M) or DMSO as control. Chemotaxis assay was applied to investigate THP-1 recruitment. Expression of mRNA /miRNAs and proteins were measured by qRT-PCR and Western blotting, respectively. Levels of monocyte chemotactic protein 1 (MCP-1) in supernatant were detected by ELISA assay. Receptor internalization assay was performed to confirm C-C chemokine receptor type 2 (CCR2) subcellular localization in THP-1 cells and the binding between CCR2 and MCP-1. Dual-luciferase reporter assay was used to analyze the combination between miR-137-3p and specificity protein 1 (SP1), as well as SP1 and MCP-1. Results showed that number of recruited THP-1 cells after EA.hy926 cells treated by DBP was significantly higher than that in the control group due to promoted MCP-1 expression. In addition, expression of MCP-1 was regulated through miR-137-3p-SP1 cascade. Besides, overexpression of miR-137-3p reversed the increased number of recruited THP-1 cells. Our results implied that DBP might promote THP-1 recruitment by targeting miR-137-3p-SP1-MCP-1 in EA.hy926 cells.

Intratumoral delivery of IL-12 and IL-27 mRNA using lipid nanoparticles for cancer immunotherapy.

Cytokines are important immunotherapeutics with approved drugs for the treatment of human cancers. However, systemic administration of cytokines often fails to achieve adequate concentrations to immune cells in tumors due to dose-limiting toxicity. Thus, developing localized therapy that directly delivers immune-stimulatory cytokines to tumors may improve the therapeutic efficacy. In this study, we generated novel lipid nanoparticles (LNPs) encapsulated with mRNAs encoding cytokines including IL-12, IL-27 and GM-CSF, and tested their anti-tumor activity. We first synthesized ionizable lipid materials containing di-amino groups with various head groups (DALs). The novel DAL4-LNP effectively delivered different mRNAs in vitro to tumor cells and in vivo to tumors. Intratumoral injection of DAL4-LNP loaded with IL-12 mRNA was most potent in inhibiting B16F10 melanoma tumor growth compared to IL-27 or GM-CSF mRNAs in monotherapy. Furthermore, intratumoral injection of dual DAL4-LNP-IL-12 mRNA and IL-27 mRNA showed a synergistic effect in suppressing tumor growth without causing systematic toxicity. Most importantly, intratumoral delivery of IL-12 and IL-27 mRNAs induced robust infiltration of immune effector cells, including IFN-γ and TNF-α producing NK and CD8+ T cells into tumors. Thus, intratumoral administration of DAL-LNP loaded with IL-12 and IL-27 mRNA provides a new treatment strategy for cancer.

Formulation and Delivery Technologies for mRNA Vaccines.

mRNA vaccines have become a versatile technology for the prevention of infectious diseases and the treatment of cancers. In the vaccination process, mRNA formulation and delivery strategies facilitate effective expression and presentation of antigens, and immune stimulation. mRNA vaccines have been delivered in various formats: encapsulation by delivery carriers, such as lipid nanoparticles, polymers, peptides, free mRNA in solution, and ex vivo through dendritic cells. Appropriate delivery materials and formulation methods often boost the vaccine efficacy which is also influenced by the selection of a proper administration route. Co-delivery of multiple mRNAs enables synergistic effects and further enhances immunity in some cases. In this chapter, we overview the recent progress and existing challenges in the formulation and delivery technologies of mRNA vaccines with perspectives for future development.

Biomimetic nanoparticles deliver mRNAs encoding costimulatory receptors and enhance T cell mediated cancer immunotherapy.

Antibodies targeting costimulatory receptors of T cells have been developed for the activation of T cell immunity in cancer immunotherapy. However, costimulatory molecule expression is often lacking in tumor-infiltrating immune cells, which can impede antibody-mediated immunotherapy. Here, we hypothesize that delivery of costimulatory receptor mRNA to tumor-infiltrating T cells will enhance the antitumor effects of antibodies. We first design a library of biomimetic nanoparticles and find that phospholipid nanoparticles (PL1) effectively deliver costimulatory receptor mRNA (CD137 or OX40) to T cells. Then, we demonstrate that the combination of PL1-OX40 mRNA and anti-OX40 antibody exhibits significantly improved antitumor activity compared to anti-OX40 antibody alone in multiple tumor models. This treatment regimen results in a 60% complete response rate in the A20 tumor model, with these mice being resistant to rechallenge by A20 tumor cells. Additionally, the combination of PL1-OX40 mRNA and anti-OX40 antibody significantly boosts the antitumor immune response to anti-PD-1 + anti-CTLA-4 antibodies in the B16F10 tumor model. This study supports the concept of delivering mRNA encoding costimulatory receptors in combination with the corresponding agonistic antibody as a strategy to enhance cancer immunotherapy.

Construction of Messenger RNA (mRNA) Probes Delivered By Lipid Nanoparticles to Visualize Intracellular Protein Expression and Localization at Organelles.

Organelles are specialized compartments, where various proteins reside and play crucial roles to maintain essential cellular structures and functions in mammalian cells. A comprehensive understanding of protein expressions and subsequent localizations at each organelle is of great benefit to the development of organelle-based therapies. Herein, a set of single or dual organelle labeling messenger RNAs (SOLAR or DOLAR) is designed as novel imaging probes, which encode fluorescent proteins with various organelle localization signals. These mRNA probes enable to visualize the protein localizations at different organelles and investigate their trafficking from ribosomal machinery to specific organelles. According to the in vitro results, SOLAR probes show organelle targeting capabilities consistent with the design. Moreover, DOLAR probes with different linkers display distinct targeting properties depending on different organelle localization signals. Additionally, these mRNA probes also exhibit organelle labeling ability in vivo when delivered by lipid nanoparticles (LNPs). Therefore, these mRNA-based probes provide a unique tool to study cell organelles and may facilitate the design of organelle-based therapies.

Monitoring air quality can help for lakes excessive proliferation of phytoplankton control.

Previous studies assessing excessive proliferation of phytoplankton (EPP) in lakes are generally based on single investigation and focused on limited environmental factors; meanwhile, less attention has been paid to lakes susceptibility to EPP. Here, we identify the priority of lakes for EPP control in a basin by assessing EPP in multiple lakes and identify the key factors related to lakes' vulnerability to EPP. Field measurements, as well as multi-source survey data acquisition were conducted for 63 shallow lakes in the middle-lower Yangtze River basin. Resource-use efficiency by phytoplankton (RUE) was then used to represent lake susceptibility to EPP. Generalized linear models were used to assess the relative importance of environmental factors for RUE. We found that most lakes (76.19 %) were not suitable for recreation, due to health concern attributed to irritative or allergenic risk caused by EPP. Phosphorus was the primary limiting nutrient for EPP (74.60 % of lakes) which should be limited to < 0.09 mg/L. The linear model that included latitude, particulate matter 10, and precipitation explained 27.60 % of the variation of RUETP among lakes. In contrast, the linear model that included ozone, Secchi depth, and wind speed explained 19.41 % of the variation of RUETN among lakes. The key factor related to RUETP and RUETN was particulate matter 10 and ozone, respectively, both of which potentially increase RUE or reflect it. Our results suggest that integrating multiple survey datasets is critical for lakes EPP assessment in a basin, while lakes impacted by air pollution are a high priority for EPP control.

Effects of air quality and vegetation on algal bloom early warning systems in large lakes in the middle-lower Yangtze River basin.

Studies of algal bloom early warning systems have rarely paid attention to the dynamics of excessive proliferation of phytoplankton (EPP), which occurs prior to algal blooms, or to the sensitivity of a lake to EPP based on multiple environmental factors. In this study, we investigated EPP dynamics in large lakes and identified major factors that influenced the lake's vulnerability to EPP, to improve algal bloom early warning systems. High temporal moderate resolution imaging spectroradiometer (MODIS) images and multi-source daily site monitoring data of large lakes in the middle-lower Yangtze River basin were analyzed. Then, the floating algal index (FAI) and resource use efficiency (RUE) by phytoplankton were used to investigate the EPP dynamics and lake's vulnerability to EPP, respectively. Moreover, generalized linear models were used to assess the relative importance of environmental factors on RUE. The results indicate that the lakes freely connected (FC) to the Yangtze River (Dongting Lake and Poyang Lake) had lower FAIs but higher RUEs than the non-connected lakes (NC; Chaohu Lake and Taihu Lake). The key factors affecting RUE-FC were standard deviation of water level within 30 days(WL30), particulate matter <10 µm(PM10), and relative humidity(Hum), which explained 15.91% of the variations in RUE. The key factors affecting RUE-NC were ozone(O3), basin normalized difference vegetation index standard deviation(BNDVISD), and dissolved oxygen(DO), which explained 35.28% of the variations in RUE. These results emphasize the importance of air quality in influencing or reflecting EPP risks in large lakes. In addition, basin vegetation and hydrological rhythms can influence NH4+ through non-point source loading. Algal bloom early warning systems can be improved by routine monitoring and forecasting of potential environmental factors such as air quality and basin vegetation.

Comparison of early season crop types for wheat production and nitrogen use efficiency in the Jianghan Plain in China.

The rice-wheat (RW) cropping system is one of the most prevalent double-cropping systems used to farm the Jianghan Plain in China. However, it can lead to low wheat yields and reduced nitrogen use efficiency compared with dryland wheat (DW). We evaluated wheat yield and nitrogen use efficiency for two rotations (summer rice-winter wheat and summer soybean-winter wheat) from 2017 to 2019 and applied the results to improve nitrogen management for planting wheat after rice in the Jianghan Plain. Field experiments were conducted over two years with two nitrogen treatments: traditional nitrogen management (M1: 90 kg N ha-1 was applied at sowing and jointing, respectively ) and optimized nitrogen management (M2: 60 kg N ha-1 was applied at sowing, wintering and jointing, respectively). The highest total wheat production was achieved under M2 for both cropping systems and the two-year average yield was 6,128 kg ha-1 in DW and 6,166 kg ha-1 in RW. The spike number in DW was 15% higher than RW in M1 and 13% higher in M2, but the kernel per spike and 1,000-grain weight was lower than RW. The nitrogen accumulation of DW was 24% higher than RW in M1 and 33% in M2. Compared with RW, DW had higher NO3 - content in the soil surface layer (0-20 cm) and a higher root length density (RLD) in the deeper layer (40-60 cm), which may account for the higher N uptake in DW. Our results show that the grain yield of RW was comparable to that of DW by optimum nitrogen management. The rice-wheat cropping system combined with optimum nitrogen management may be of economic and agronomic benefit to the wheatbelt in the Jianghan Plain in China.

The effect of different sowing dates on dry matter and nitrogen dynamics for winter wheat: an experimental simulation study.

BACKGROUND: Timely sowing is an important agronomic measure to ensure the normal germination, stable seedling establishment, and yield formation for winter wheat (Triticum aestivum L.). Delayed sowing frequently occurs in the current multi-cropping system and mechanized production of this crop. However, the ways in which different sowing dates affect yield and its potential mechanism is still unknown in the middle-lower Yangtze River Basin. We sought to provide a theoretical basis for these mechanisms to improve regional wheat production. METHODS: We investigated the wheat's yield differences in a two-year field study under different sowing dates and took into account related growth characteristics including meteorological conditions, growth period, tillers, dry matter accumulation (DMA), and nitrogen accumulation (NA). We used the logistic curve model to simulate DMA and NA dynamics of single stem wheat under different sowing dates. We then analyzed and compared wheat accumulation for different sowing dates. RESULTS: Our results showed that grain yield declined by 0.97 ± 0.22% with each one-day change (either early or delayed) in sowing beyond the normal sowing date. The yield loss could be explained by the inhibition of crop growth, yield components, biomass and nitrogen (N) production. The negative effects of delayed sowing were caused by environmental limitations including adverse weather factors such as low temperature during vegetative growth, shortened duration of various phases of crop development, and increased temperature during the grain-filling period. The grain yield gap decreased between the late and normal sowing periods owing to a compensatory effect between the highest average rates (Vt ) and the rapid accumulation period (T) of DMA and NA for single stem wheat. The grain yield was maintained at 6,000 kg ha-1 or more when the ratio of DMA at the mature-to-jointing stage (MD/JD) and the ratio of NA at the mature-to-jointing stage (MN/JN) was 4.06 (P < 0.01) and 2.49 (P < 0.05), respectively. The compensatory effect did not prevent the impact caused by delayed sowing, which caused biomass and N production to decrease. Physiological development reached a maximal accumulation rate (Tm ) of NA earlier than DMA.