RESUMO
Excessive nitrogen fertilizer application is harmful to the environment and reduces the quality of cereal crops. Maintaining crop yields under low nitrogen (LN) conditions and improving quality are important goals for cereal crop breeding. Although the effects of nitrogen assimilation on crop nitrogen-use efficiency (NUE) have been intensively studied, natural variations of the key assimilation genes underlying grain development and quality are largely unclear. Here, we identified an NUE-associated gene, OsGS1;1, encoding glutamine synthase, through genome-wide association analysis, followed by validation experiments and functional analysis. Fifteen single-nucleotide polymorphisms in the OsGS1;1 region led to alternative splicing that generated two functional transcripts: OsGS1;1a and OsGS1;1b. The elite haplotype of OsGS1;1 showed high OsGS1;1b activity, which improved NUE, affected grain development, and reduced amylose content. The results show that OsGS1;1, which is induced under LN conditions, affects grain formation by regulating sugar metabolism and may provide a new avenue for the breeding of high-yield and high-quality rice (Oryza sativa).
Assuntos
Oryza , Processamento Alternativo/genética , Amilose/metabolismo , Grão Comestível/metabolismo , Estudo de Associação Genômica Ampla , Nitrogênio/metabolismo , Oryza/metabolismo , Melhoramento VegetalRESUMO
BACKGROUND: To integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT). METHODS: Total of 143 EC patients in the authors' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated. RESULTS: Models with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively. CONCLUSION: CT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT.
Assuntos
Neoplasias Esofágicas , Nomogramas , Pneumonite por Radiação , Humanos , Neoplasias Esofágicas/radioterapia , Pneumonite por Radiação/etiologia , Feminino , Masculino , Estudos Retrospectivos , Pessoa de Meia-Idade , Idoso , Dosagem Radioterapêutica , Prognóstico , Idoso de 80 Anos ou mais , Tomografia Computadorizada por Raios X , RadiômicaRESUMO
Excessive nitrogen promotes the formation of nonproductive tillers in rice, which decreases nitrogen use efficiency (NUE). Developing high-NUE rice cultivars through balancing nitrogen uptake and the formation of productive tillers remains a long-standing challenge, yet how these two processes are coordinated in rice remains elusive. Here we identify the transcription factor OsGATA8 as a key coordinator of nitrogen uptake and tiller formation in rice. OsGATA8 negatively regulates nitrogen uptake by repressing transcription of the ammonium transporter gene OsAMT3.2. Meanwhile, it promotes tiller formation by repressing the transcription of OsTCP19, a negative modulator of tillering. We identify OsGATA8-H as a high-NUE haplotype with enhanced nitrogen uptake and a higher proportion of productive tillers. The geographical distribution of OsGATA8-H and its frequency change in historical accessions suggest its adaption to the fertile soil. Overall, this study provides molecular and evolutionary insights into the regulation of NUE and facilitates the breeding of rice cultivars with higher NUE.
Assuntos
Regulação da Expressão Gênica de Plantas , Haplótipos , Nitrogênio , Oryza , Proteínas de Plantas , Oryza/genética , Oryza/crescimento & desenvolvimento , Oryza/metabolismo , Nitrogênio/metabolismo , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Fatores de Transcrição/metabolismo , Fatores de Transcrição/genética , Proteínas de Transporte de Cátions/genética , Proteínas de Transporte de Cátions/metabolismoRESUMO
PURPOSE: To investigate the feasibility and performance of deep learning (DL) models combined with plan complexity (PC) and dosiomics features in the patient-specific quality assurance (PSQA) for patients underwent volumetric modulated arc therapy (VMAT). METHODS: Total of 201 VMAT plans with measured PSQA results were retrospectively enrolled and divided into training and testing sets randomly at 7:3. PC metrics were calculated using house-built algorithm based on Matlab. Dosiomics features were extracted and selected using Random Forest (RF) from planning target volume (PTV) and overlap regions with 3D dose distributions. The top 50 dosiomics and 5 PC features were selected based on feature importance screening. A DL DenseNet was adapted and trained for the PSQA prediction. RESULTS: The measured average gamma passing rate (GPR) of these VMAT plans was 97.94% ± 1.87%, 94.33% ± 3.22%, and 87.27% ± 4.81% at the criteria of 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. Models with PC features alone demonstrated the lowest area under curve (AUC). The AUC and sensitivity of PC and dosiomics (D) combined model at 2%/2 mm were 0.915 and 0.833, respectively. The AUCs of DL models were improved from 0.943, 0.849, 0.841 to 0.948, 0.890, 0.942 in the combined models (PC + D + DL) at 3%/3 mm, 3%/2 mm and 2%/2 mm, respectively. A best AUC of 0.942 with a sensitivity, specificity and accuracy of 100%, 81.8%, and 83.6% was achieved with combined model (PC + D + DL) at 2%/2 mm. CONCLUSIONS: Integrating DL with dosiomics and PC metrics is promising in the prediction of GPRs in PSQA for patients underwent VMAT.