TrG2P: A transfer learning-based tool integrating multi-trait data for accurate prediction of crop yield.

Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. As yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain, considered as a great potential method for improving yield prediction by integrating multi-trait data. However, it has not been applied to genotype-to-phenotype prediction before due to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield trait phenotypic and genotypic data, thus obtaining the pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining the fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum), and compared model precision to that of seven other popular GS tools: rrBLUP, Random Forest, Support Vector Regression, LightGBM, CNN, DeepGS, and DNNGP. TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared to predictions generated by the best performing comparison model. Our work therefore demonstrated that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield trait data. We attribute the enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.

A chimeric adenovirus-vectored vaccine based on Beta spike and Delta RBD confers a broad-spectrum neutralization against Omicron-included SARS-CoV-2 variants.

Urgent research into innovative severe acute respiratory coronavirus-2 (SARS-CoV-2) vaccines that may successfully prevent various emerging emerged variants, particularly the Omicron variant and its subvariants, is necessary. Here, we designed a chimeric adenovirus-vectored vaccine named Ad5-Beta/Delta. This vaccine was created by incorporating the receptor-binding domain from the Delta variant, which has the L452R and T478K mutations, into the complete spike protein of the Beta variant. Both intramuscular (IM) and intranasal (IN) vaccination with Ad5-Beta/Deta vaccine induced robust broad-spectrum neutralization against Omicron BA.5-included variants. IN immunization with Ad5-Beta/Delta vaccine exhibited superior mucosal immunity, manifested by higher secretory IgA antibodies and more tissue-resident memory T cells (TRM) in respiratory tract. The combination of IM and IN delivery of the Ad5-Beta/Delta vaccine was capable of synergically eliciting stronger systemic and mucosal immune responses. Furthermore, the Ad5-Beta/Delta vaccination demonstrated more effective boosting implications after two dosages of mRNA or subunit recombinant protein vaccine, indicating its capacity for utilization as a booster shot in the heterologous vaccination. These outcomes quantified Ad5-Beta/Delta vaccine as a favorable vaccine can provide protective immunity versus SARS-CoV-2 pre-Omicron variants of concern and BA.5-included Omicron subvariants.

Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms.

Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement.

Heritable microbiome variation is correlated with source environment in locally adapted maize varieties.

Beneficial interactions with microorganisms are pivotal for crop performance and resilience. However, it remains unclear how heritable the microbiome is with respect to the host plant genotype and to what extent host genetic mechanisms can modulate plant-microbiota interactions in the face of environmental stresses. Here we surveyed 3,168 root and rhizosphere microbiome samples from 129 accessions of locally adapted Zea, sourced from diverse habitats and grown under control and different stress conditions. We quantified stress treatment and host genotype effects on the microbiome. Plant genotype and source environment were predictive of microbiome abundance. Genome-wide association analysis identified host genetic variants linked to both rhizosphere microbiome abundance and source environment. We identified transposon insertions in a candidate gene linked to both the abundance of a keystone bacterium Massilia in our controlled experiments and total soil nitrogen in the source environment. Isolation and controlled inoculation of Massilia alone can contribute to root development, whole-plant biomass production and adaptation to low nitrogen availability. We conclude that locally adapted maize varieties exert patterns of genetic control on their root and rhizosphere microbiomes that follow variation in their home environments, consistent with a role in tolerance to prevailing stress.

Generation and characterization of mAb 61H9 against junctional adhesion molecule-a with potent antitumor activity.

Junctional adhesion molecule-A (JAM-A) is an adhesion molecule that exists on the surface of certain types of cells, including white blood cells, endothelial cells, and dendritic cells. In this study, the cDNA sequences of JAM-A-Fc were chemically synthesized with optimization for mammalian expression. Afterward, we analyzed JAM-A protein expression through transient transfection in HEK293 cell lines. Mice were immunized with JAM-A-Fc protein, and hybridoma was prepared by fusing myeloma cells and mouse spleen cells. Antibodies were purified from the hybridoma supernatant and four monoclonal strains were obtained and numbered 61H9, 70E5, 71A8, and 74H3 via enzyme-linked immunosorbent assay screening. Immunofluorescence staining assay showed 61H9 was the most suitable cell line for mAb production due to its fluorescence signal being the strongest. Flow cytometric analysis proved that 61H9 possessed high affinity. Moreover, antagonism of JAM-A mAb could attenuate the proliferative, migrative, and invasive abilities of ESCC cells and significantly inhibit tumor growth in mice. By examining hematoxylin-eosin staining mice tumor tissues, we found inflammatory cells infiltrated lightly in the anti-JAM-A group. The expression of BCL-2 and IκBα in the anti-JAM-A group were decreased in mice tumor tissues compared to the control group. Ultimately, a method for preparing high-yield JAM-A-Fc protein was created and a high affinity mAb against JAM-A with an antitumor effect was prepared.

MCT1-governed pyruvate metabolism is essential for antibody class-switch recombination through H3K27 acetylation.

Monocarboxylate transporter 1 (MCT1) exhibits essential roles in cellular metabolism and energy supply. Although MCT1 is highly expressed in activated B cells, it is not clear how MCT1-governed monocarboxylates transportation is functionally coupled to antibody production during the glucose metabolism. Here, we report that B cell-lineage deficiency of MCT1 significantly influences the class-switch recombination (CSR), rendering impaired IgG antibody responses in Mct1f/fMb1Cre mice after immunization. Metabolic flux reveals that glucose metabolism is significantly reprogrammed from glycolysis to oxidative phosphorylation in Mct1-deficient B cells upon activation. Consistently, activation-induced cytidine deaminase (AID), is severely suppressed in Mct1-deficient B cells due to the decreased level of pyruvate metabolite. Mechanistically, MCT1 is required to maintain the optimal concentration of pyruvate to secure the sufficient acetylation of H3K27 for the elevated transcription of AID in activated B cells. Clinically, we found that MCT1 expression levels are significantly upregulated in systemic lupus erythematosus patients, and Mct1 deficiency can alleviate the symptoms of bm12-induced murine lupus model. Collectively, these results demonstrate that MCT1-mediated pyruvate metabolism is required for IgG antibody CSR through an epigenetic dependent AID transcription, revealing MCT1 as a potential target for vaccine development and SLE disease treatment.

Non-IL-2-blocking anti-CD25 antibody inhibits tumor growth by depleting Tregs and has synergistic effects with anti-CTLA-4 therapy.

CD25, also known as the interleukin-2 receptor α chain (IL-2Rα), is highly expressed on regulatory T cells (Tregs), but relatively lower on effector T cells (Teffs). This makes it a potential target for Treg depletion, which can be used in tumor immunotherapy. However, marketed anti-CD25 antibodies (Basiliximab and Daclizumab) were originally developed as immunosuppressive drugs to prevent graft rejection, because these antibodies can block IL-2 binding to CD25 on Teffs, which in turn destroys the function of Teffs. Recent studies have shown that non-IL-2-blocking anti-CD25 antibodies have displayed exciting antitumor effects. Here, we screened out a non-IL-2-blocking anti-CD25 monoclonal antibody (mAb) 7B7 by hybridoma technology, and confirmed its antitumor activity via depleting Tregs in a CD25 humanized mouse model. Subsequently, we verified that the humanized 7B7, named as h7B7-15S, has comparable activities to 7B7, and that its Treg depletion is further increased when combined with anti-CTLA-4, leading to enhanced remodeling of the tumor immune microenvironment. Moreover, our findings reveal that the Fab form of h7B7-15S has the ability to deplete Tregs, independent of the Fc region. Taken together, our studies expand the application of anti-CD25 in tumor immunotherapy and provide insight into the underlying mechanism.

Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize.

Root-associated microbiomes in the rhizosphere (rhizobiomes) are increasingly known to play an important role in nutrient acquisition, stress tolerance, and disease resistance of plants. However, it remains largely unclear to what extent these rhizobiomes contribute to trait variation for different genotypes and if their inclusion in the genomic selection protocol can enhance prediction accuracy. To address these questions, we developed a microbiome-enabled genomic selection method that incorporated host SNPs and amplicon sequence variants from plant rhizobiomes in a maize diversity panel under high and low nitrogen (N) field conditions. Our cross-validation results showed that the microbiome-enabled genomic selection model significantly outperformed the conventional genomic selection model for nearly all time-series traits related to plant growth and N responses, with an average relative improvement of 3.7%. The improvement was more pronounced under low N conditions (8.4-40.2% of relative improvement), consistent with the view that some beneficial microbes can enhance N nutrient uptake, particularly in low N fields. However, our study could not definitively rule out the possibility that the observed improvement is partially due to the amplicon sequence variants being influenced by microenvironments. Using a high-dimensional mediation analysis method, our study has also identified microbial mediators that establish a link between plant genotype and phenotype. Some of the detected mediator microbes were previously reported to promote plant growth. The enhanced prediction accuracy of the microbiome-enabled genomic selection models, demonstrated in a single environment, serves as a proof-of-concept for the potential application of microbiome-enabled plant breeding for sustainable agriculture.

Multi-integrated genomic data for Passiflora foetida provides insights into genome size evolution and floral development in Passiflora.

Passiflora is a plant genus known for its extremely distinctive and colorful flowers and a wide range of genome size variation. However, how genome characteristics are related to flower traits among Passiflora species remains poorly understood. Here, we assembled a chromosome-scale genome of P. foetida, which belongs to the same subgenus as the commercial passionfruit P. edulis. The genome of P. foetida is smaller (424.16 Mb) and contains fewer copies of long terminal repeat retrotransposons (LTR-RTs). The disparity in LTR-RTs is one of the main contributors to the differences in genome sizes between these two species and possibly in floral traits. Additionally, we observed variation in insertion times and copy numbers of LTR-RTs across different transposable element (TE) lineages. Then, by integrating transcriptomic data from 33 samples (eight floral organs and flower buds at three developmental stages) with phylogenomic and metabolomic data, we conducted an in-depth analysis of the expression, phylogeny, and copy number of MIKC-type MADS-box genes and identified essential biosynthetic genes responsible for flower color and scent from glandular bracts and other floral organs. Our study pinpoints LRT-RTs as an important player in genome size variation in Passiflora species and provides insights into future genetic improvement.

A patient with a pulmonary hernia and chance fracture of the thoracic vertebra following a fall: A case report and literature review.

INTRODUCTION AND IMPORTANCE: Pulmonary hernia is a rare disease caused mostly by chest trauma. Patients often present with non-specific signs and symptoms. Currently, there is no unified approach to treating it. For asymptomatic pulmonary hernias, conservative treatment has been chosen in the past. However, the increasing number of cases has shown that surgery has a more positive effect on some patients with asymptomatic pulmonary hernias. CASE PRESENTATION: A 63-year-old female patient who accidentally fell from an agricultural vehicle while doing farm work presented with back pain and lower limb paralysis. Her vital signs were stable. A chest computed tomography (CT) scan showed the patient had a pulmonary hernia, thoracic vertebra Chance fracture, rib fractures, and right hydropneumothorax. The patient received an open reduction internal fixation (ORIF) of the thoracic vertebra at the trauma emergency center. The postoperative chest CT scan showed that the pulmonary hernia had reset to the chest cavity. Subsequently, the patient got a pulmonary hernia repair at the thoracic surgery department. The patient was discharged on the 19th day after the injury. Long-term follow-up showed good recovery from the thoracic trauma. CLINICAL DISCUSSION: The patient had a pulmonary hernia combined with thoracic Chance fracture and other injuries. Surgical repair achieved satisfactory results. CONCLUSION: We lack guidelines on whether to manage pulmonary hernias surgically. Patients with asymptomatic pulmonary hernias particularly should have their long-term prognoses fully evaluated. Surgery is needed if the patient has high-risk factors and severe intercostal muscle defects.

New hope for tumor immunotherapy: the macrophage-related "do not eat me" signaling pathway.

The "do not eat me" signaling pathway is extremely active in tumor cells, providing a means for these cells to elude macrophage phagocytosis and escape immune surveillance. Representative markers of this pathway, such as CD47 and CD24, are highly expressed in numerous tumors. The interaction of SIRPα with CD47 reduces the accumulation of non-myosin ⅡA on the cell membrane. The combination of CD24 and Siglec10 ultimately leads to the recruitment of SHP-1 or SHP-2 to reduce signal transduction. Both of them weaken the ability of macrophages to engulf tumor cells. Blocking the mutual recognition between CD47-SIRPα or CD24-Siglec10 using large molecular proteins or small molecular drugs represents a promising avenue for tumor immunotherapy. Doing so can inhibit signal transduction and enhance macrophage clearance rates of cancer cells. In this paper, we summarize the characteristics of the drugs that affect the "do not eat me" signaling pathway via classical large molecular proteins and small molecule drugs, which target the CD47-SIRPα and CD24-Siglec10 signaling pathways, which target the CD47-SIRPα and CD24-Siglec10 signaling pathways. We expect it will offer insight into the development of new drugs centered on blocking the "do not eat me" signaling pathway.

Unraveling Prevalence and Effects of Deleterious Mutations in Maize Elite Lines across Decades of Modern Breeding.

Future breeding is likely to involve the detection and removal of deleterious alleles, which are mutations that negatively affect crop fitness. However, little is known about the prevalence of such mutations and their effects on phenotypic traits in the context of modern crop breeding. To address this, we examined the number and frequency of deleterious mutations in 350 elite maize inbred lines developed over the past few decades in China and the United States. Our findings reveal an accumulation of weakly deleterious mutations and a decrease in strongly deleterious mutations, indicating the dominant effects of genetic drift and purifying selection for the two types of mutations, respectively. We also discovered that slightly deleterious mutations, when at lower frequencies, were more likely to be heterozygous in the developed hybrids. This is consistent with complementation as a potential explanation for heterosis. Subsequently, we found that deleterious mutations accounted for more of the variation in phenotypic traits than nondeleterious mutations with matched minor allele frequencies, especially for traits related to leaf angle and flowering time. Moreover, we detected fewer deleterious mutations in the promoter and gene body regions of differentially expressed genes across breeding eras than in nondifferentially expressed genes. Overall, our results provide a comprehensive assessment of the prevalence and impact of deleterious mutations in modern maize breeding and establish a useful baseline for future maize improvement efforts.

Hybrid allele-specific ChIP-seq analysis identifies variation in brassinosteroid-responsive transcription factor binding linked to traits in maize.

BACKGROUND: Genetic variation in regulatory sequences that alter transcription factor (TF) binding is a major cause of phenotypic diversity. Brassinosteroid is a growth hormone that has major effects on plant phenotypes. Genetic variation in brassinosteroid-responsive cis-elements likely contributes to trait variation. Pinpointing such regulatory variations and quantitative genomic analysis of the variation in TF-target binding, however, remains challenging. How variation in transcriptional targets of signaling pathways such as the brassinosteroid pathway contributes to phenotypic variation is an important question to be investigated with innovative approaches. RESULTS: Here, we use a hybrid allele-specific chromatin binding sequencing (HASCh-seq) approach and identify variations in target binding of the brassinosteroid-responsive TF ZmBZR1 in maize. HASCh-seq in the B73xMo17 F1s identifies thousands of target genes of ZmBZR1. Allele-specific ZmBZR1 binding (ASB) has been observed for 18.3% of target genes and is enriched in promoter and enhancer regions. About a quarter of the ASB sites correlate with sequence variation in BZR1-binding motifs and another quarter correlate with haplotype-specific DNA methylation, suggesting that both genetic and epigenetic variations contribute to the high level of variation in ZmBZR1 occupancy. Comparison with GWAS data shows linkage of hundreds of ASB loci to important yield and disease-related traits. CONCLUSION: Our study provides a robust method for analyzing genome-wide variations of TF occupancy and identifies genetic and epigenetic variations of the brassinosteroid response transcription network in maize.

Preclinical Evaluation of 9MW2821, a Site-Specific Monomethyl Auristatin E-based Antibody-Drug Conjugate for Treatment of Nectin-4-expressing Cancers.

Overexpression of nectin cell adhesion protein 4 correlates with cancer progression and poor prognosis in many human malignancies. Enfortumab vedotin (EV) is the first nectin-4-targeting antibody-drug conjugate (ADC) approved by the FDA for the treatment of urothelial cancer. However, inadequate efficacy has limited progress in the treatment of other solid tumors with EV. Furthermore, ocular, pulmonary, and hematologic toxic side effects are common in nectin-4-targeted therapy, which frequently results in dose reduction and/or treatment termination. Thus, we designed a second generation nectin-4-specific drug, 9MW2821, based on interchain-disulfide drug conjugate technology. This novel drug contained a site specifically conjugated humanized antibody and the cytotoxic moiety monomethyl auristatin E. The homogenous drug-antibody ratio and novel linker chemistry of 9MW2821 increased the stability of conjugate in the systemic circulation, enabling highly efficient drug delivery and avoiding off-target toxicity. In preclinical evaluation, 9MW2821 exhibited nectin-4-specific cell binding, efficient internalization, bystander killing, and equivalent or superior antitumor activity compared with EV in both cell line-derived xenograft and patient-derived xenograft (PDX) models. In addition, 9MW2821 demonstrated a favorable safety profile; the highest nonseverely toxic dose in monkey toxicologic studies was 6 mg/kg, with milder adverse events compared with EV. Overall, 9MW2821 is a nectin-4-directed, investigational ADC based on innovative technology that endowed the drug with compelling preclinical antitumor activity and a favorable therapeutic index. The 9MW2821 ADC is being investigated in a phase I/II clinical trial (NCT05216965 and NCT05773937) in patients with advanced solid tumors.

A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.

Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (mean R2=0.688), with partial least-squares regression outperforming deep neural network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (mean R2=0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (mean R2=0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping, whilst extra-weight spiking improves model transferability and extends its utility.

Filamin A is overexpressed in non-alcoholic steatohepatitis and contributes to the progression of inflammation and fibrosis.

Non-alcoholic steatohepatitis (NASH) is a chronic and progressive liver disease characterized by steatosis, inflammation, and fibrosis. Filamin A (FLNA), an actin-binding protein, is involved in various cell functions, including the regulation of immune cells and fibroblasts. However, its role in the development of NASH through inflammation and fibrogenesis is not fully understood. In this study, we found that FLNA expression was increased in liver tissues of patients with cirrhosis and mice with non-alcoholic fatty liver disease (NAFLD)/NASH and fibrosis. Immunofluorescence analysis showed that FLNA was primarily expressed in macrophages and hepatic stellate cells (HSCs). Knocking down of FLNA by specific shRNA in phorbol-12-myristate-13-acetate (PMA)-derived THP-1 macrophages reduced lipopolysaccharide (LPS)-stimulated inflammatory response. The decreased mRNA levels of inflammatory cytokines and chemokines and suppression of the STAT3 signaling were observed in FLNA-downregulated macrophages. In addition, knockdown of FLNA in immortalized human hepatic stellate cells (LX-2 cells) resulted in decreased mRNA levels of fibrotic cytokines and enzymes involved in collagen synthesis, as well as increased levels of metalloproteinases and pro-apoptotic proteins. Overall, these results suggest that FLNA may contribute to the pathogenesis of NASH through its role in the regulation of inflammatory and fibrotic mediators.

Heterologous vaccination with subunit protein vaccine induces a superior neutralizing capacity against BA.4/5-included SARS-CoV-2 variants than homologous vaccination of mRNA vaccine.

BA.4 and BA.5 (BA.4/5), the subvariants of Omicron, are more transmissible than BA.1 with more robust immune evasion capability because of its unique spike protein mutations. In light of such situation, the vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is in desperate need of the third booster. It has been reported that heterologous boosters might produce more effective immunity against wild-type SARS-CoV-2 and the variants. Additionally, the third heterologous protein subunit booster should be considered potentially. In the present study, we prepared a Delta full-length spike protein sequence-based mRNA vaccine as the "priming" shot and developed a recombinant trimeric receptor-binding domain (RBD) protein vaccine referred to as RBD-HR/trimer as a third heterologous booster. Compared to the homologous mRNA group, the heterologous group (RBD-HR/trimer vaccine primed with two mRNA vaccines) induced higher neutralizing antibody titers against BA.4/5-included SARS-CoV-2 variants. In addition, heterologous vaccination exhibited stronger cellular immune response and long-lasting memory response than the homologous mRNA vaccine. In conclusion, a third heterologous boosting with RBD-HR/trimer following two-dose mRNA priming vaccination should be a superior strategy than a third homologous mRNA vaccine. The RBD-HR/trimer vaccine becomes an appropriate candidate for a booster immune injection.

On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies.

Studying the canopy spectral reflection characteristics of different N-efficient maize varieties and analyzing the relationship between their growth indicators and spectral vegetation indices can help the breeding and application of N-efficient maize varieties. To achieve the optimal management of N fertilizer resources, developing N-efficient maize varieties is necessary. In this research, maize varieties, i.e., the low-N-efficient (Zhengdan 958, ZD958), the high-N efficient (Xianyu 335, XY335), the double-high varieties (Qiule 368, QL368), and the double inefficient-type varieties (Yudan 606 YD606), were used as materials. Results indicate that nitrogen fertilization significantly increased the vegetation indices NDVI, GNDVI, GOSAVI, and RVI of maize varieties with different nitrogen efficiencies. These findings were consistent with the performance of yield, dry matter mass, and leaf nitrogen content and were also found highest under both medium and high nitrogen conditions in the double-high variety QL368. The correlations of dry matter quality, leaf nitrogen content, yield, and vegetation indices (NDVI, GNDVI, RVI, and GOSAVI) at the filling stage of different N-efficient maize varieties were all highly significant and positive. In this relationship, the best effect was found at the filling stages, with correlation coefficients reaching 0.772-0.942, 0.774-0.970, 0754-0.960, and 0.800-0.960. The results showed that the yield, dry matter weight, and leaf nitrogen content of maize varieties with different nitrogen efficiencies increased first and then stabilized with the increase in the nitrogen application level in different periods, and the highest nitrogen application level of maize yield should be between 270 and 360 kg/hm2. At the filling stage, canopy vegetation index of maize varieties with different nitrogen efficiencies was positively correlated with yield, dry matter weight, and leaf nitrogen content, especially GNDVI and GOSAVI on the leaf nitrogen content. It can be used as a means to predict its growth index.

Preparation of Multicolor Biomass Carbon Dots Based on Solvent Control and Their Application in Cr(VI) Detection and Advanced Anti-Counterfeiting.

Multicolor fluorescent carbon dots (CDs) have received widespread attention due to their excellent fluorescence performance and promising prospects in anti-counterfeiting and sensing detection. To date, most of the multicolor CDs synthesized are derived from chemical reagents; however, the overuse of chemical reagents during the synthesis process will pollute the environment and limit their application. Herein, multicolor fluorescent biomass CDs (BCDs) were prepared by a one-pot ecofriendly solvothermal method, with spinach as the raw material based on solvent control. The as-obtained BCDs can emit blue, crimson, grayish white, and red luminescence, and their quantum yields (QYs) are 8.9, 12.3, 10.8, and 14.4%, respectively. The results of the characterization of BCDs reveal that the regulating mechanism for multicolor luminescence is mainly ascribed to the change of the boiling point and polarity of solvents, which changes the carbonization process of polysaccharides and chlorophyll in spinach, resulting in the altered particle size, surface functional groups, and porphyrin luminescence properties. Further research reveals that blue BCDs (BCD1) show an excellent sensitive and selective response to Cr(VI) in a concentration scale of 0-220 µM with a detection limit (LOD) of 0.242 µM. More importantly, the intraday and interday relative standard deviation (RSD) values were less than 2.99%. The recovery rate of the Cr(VI) sensor for tap water and river water is 101.52-107.51%, which indicates that the sensor has the advantages of high sensitivity, selectivity, rapidity, and reproducibility. Consequently, different multicolor patterns are obtained by using the obtained four BCDs as fluorescent inks, which exhibit beautiful landscape and advanced anti-counterfeiting effects. This study provides a low-cost and facile green synthesis strategy for multicolor luminescent BCDs and proves that BCDs have broad application prospects in ion detection and advanced anti-counterfeiting.