Characterization of TBP and TAFs in Mungbean (Vigna radiata L.) and Their Potential Involvement in Abiotic Stress Response.

The TATA-box binding protein (TBP) and TBP-associated factors (TAFs) constitute the transcription factor IID (TFIID), a crucial component of RNA polymerase II, essential for transcription initiation and regulation. Several TFIID subunits are shared with the Spt-Ada-Gcn5-acetyltransferase (SAGA) coactivator complex. Recent research has revealed the roles of TBP and TAFs in organogenesis and stress adaptation. In this study, we identified 1 TBP and 21 putative TAFs in the mungbean genome, among which VrTAF5, VrTAF6, VrTAF8, VrTAF9, VrTAF14, and VrTAF15 have paralogous genes. Their potential involvement in abiotic stress responses was also investigated here, including high salinity, water deficit, heat, and cold. The findings indicated that distinct genes exerted predominant influences in the response to different abiotic stresses through potentially unique mechanisms. Specifically, under salt stress, VrTBP, VrTAF2, and VrTAF15-1 were strongly induced, while VrTAF10, VrTAF11, and VrTAF13 acted as negative regulators. In the case of water-deficit stress, it was likely that VrTAF1, VrTAF2, VrTAF5-2, VrTAF9, and VrTAF15-1 were primarily involved. Additionally, in response to changes in ambient temperature, it was possible that genes such as VrTAF5-1, VrTAF6-1, VrTAF9-2, VrTAF10, VrTAF13, VrTAF14b-2, and VrTAF15-1 might play a dominant role. This comprehensive exploration of VrTBP and VrTAFs can offer a new perspective on understanding plant stress responses and provide valuable insights into breeding improvement.

A predicted three-dimensional dose sequence based treatment planning optimization method for gynecologic IMRT.

In knowledge-based treatment planning (KBTP) for intensity-modulated radiation therapy (IMRT), the quality of the plan is dependent on the sophistication of the predicted dosimetric information and its application. In this paper, we propose a KBTP method that based on the effective and reasonable utilization of a three-dimensional (3D) dose prediction on planning optimization. We used an organs-at-risk (OARs) dose distribution prediction model to create a voxel-based dose sequence based optimization objective for OARs doses. This objective was used to reformulate a traditional fluence map optimization model, which involves a tolerable spatial re-assignment of the predicted dose distribution to the OAR voxels based on their current doses' positions at a sorted dose sequencing. The feasibility of this method was evaluated with ten gynecology (GYN) cancer IMRT cases by comparing its generated plan quality with the original clinical plan. Results showed feasible plan by proposed method, with comparable planning target volume (PTV) dose coverage and greater dose sparing of the OARs. Among ten GYN cases, the average V30 and V45 of rectum were decreased by 4%±4% (p = 0.02) and 4%±3% (p<0.01), respectively. V30 and V45 of bladder were decreased by 8%±2% (p<0.01) and 3%±2% (p<0.01), respectively. Our predicted dose sequence-based planning optimization method for GYN IMRT offered a flexible use of predicted 3D doses while ensuring the output plan consistency.

Genome-Wide Identification, Expression Analysis, and Potential Roles under Abiotic Stress of the YUCCA Gene Family in Mungbean (Vigna radiata L.).

YUCCA, belonging to the class B flavin-dependent monooxygenases, catalyzes the rate-limiting step for endogenous auxin synthesis and is implicated in plant-growth regulation and stress response. Systematic analysis of the YUCCA gene family and its stress response benefits the dissection of regulation mechanisms and breeding applications. In this study, 12 YUCCA genes were identified from the mungbean (Vigna radiata L.) genome and were named based on their similarity to AtYUCCAs. Phylogenetic analysis revealed that the 12 VrYUCCAs could be divided into 4 subfamilies. The evidence from enzymatic assays in vitro and transgenetic Arabidopsis in vivo indicated that all the isolated VrYUCCAs had biological activity in response to IAA synthesis. Expression pattern analysis showed that functional redundancy and divergence existed in the VrYUCCA gene family. Four VrYUCCAs were expressed in most tissues, and five VrYUCCAs were specifically highly expressed in the floral organs. The response toward five stresses, namely, auxin (indole-3-acetic acid, IAA), salinity, drought, high temperatures, and cold, was also investigated here. Five VrYUCCAs responded to IAA in the root, while only VrYUCCA8a was induced in the leaf. VrYUCCA2a, VrYUCCA6a, VrYUCCA8a, VrYUCCA8b, and VrYUCCA10 seemed to dominate under abiotic stresses, due to their sensitivity to the other four treatments. However, the response modes of the VrYUCCAs varied, indicating that they may regulate different stresses in distinct ways to finely adjust IAA content. The comprehensive analysis of the VrYUCCAs in this study lays a solid foundation for further investigation of VrYUCCA genes' mechanisms and applications in breeding.

Physiological mechanism of the response to Cr(VI) in the aerobic denitrifying ectomycorrhizal fungus Pisolithus sp.1.

Pisolithus sp. 1 (P sp. 1) is an ectomycorrhizal fungus (EMF) with a strong Cr(VI) tolerance and reduction ability. The noninvasive microttest technique (NMT), real-time quantitative PCR (qRT-PCR), and the three-dimensional excitation-emission matrix (3D-EEM) were used to deeply explore the physiological mechanism of the P sp. 1 response to Cr(VI) and investigate the relationship between Cr(VI) reduction and denitrification in P sp. Cr(VI) induced the strongest elevations in nitrate reductase (NR) activity and NO production in the mycelia after treatment with Cr(VI) for 48 h under aerobic conditions. The NR inhibitor tungstate significantly inhibited Cr(VI) reduction, proton efflux and the expression of the NR gene (niaD) and NiR gene (niiA). In addition, NO was generated via NR-regulated denitrification. Combined treatments with Cr(VI) and the NO scavenger carboxy-PTIO (cPTIO) significantly increased O2-, H2O2 and MDA contents and reduced SDH, CAT, GSH, GR and GSNOR activity. Therefore, the NR-driven aerobic denitrifying process requires protons, and the generated NO reduces the oxidative stress effect of Cr(VI) on mycelia by reducing ROS accumulation and lipid peroxidation, enhancing mycelial and CAT activity, and promoting GSH recycling and regeneration. Psp.1 can also secrete humic acid-like and protein-like substances to combine with Cr(III) in a culture system.

Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases.

OBJECTIVES: To investigate whether dosiomics can benefit to IMRT treated patient's locoregional recurrences (LR) prediction through a comparative study on prediction performance inspection between radiomics methods and that integrating dosiomics in head and neck cancer cases. MATERIALS AND METHODS: A cohort of 237 patients with head and neck cancer from four different institutions was obtained from The Cancer Imaging Archive and utilized to train and validate the radiomics-only prognostic model and integrate the dosiomics prognostic model. For radiomics, the radiomics features were initially extracted from images, including CTs and PETs, and selected on the basis of their concordance index (CI) values, then condensed via principle component analysis. Lastly, multivariate Cox proportional hazards regression models were constructed with class-imbalance adjustment as the LR prediction models by inputting those condensed features. For dosiomics integration model establishment, the initial features were similar, but with additional 3-dimensional dose distribution from radiation treatment plans. The CI and the Kaplan-Meier curves with log-rank analysis were used to assess and compare these models. RESULTS: Observed from the independent validation dataset, the CI of the model for dosiomics integration (0.66) was significantly different from that for radiomics (0.59) (Wilcoxon test, p=5.9×10-31). The integrated model successfully classified the patients into high- and low-risk groups (log-rank test, p=2.5×10-02), whereas the radiomics model was not able to provide such classification (log-rank test, p=0.37). CONCLUSION: Dosiomics can benefit in predicting the LR in IMRT-treated patients and should not be neglected for related investigations.

Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy.

PURPOSE: The purpose of this study is to investigate the effect of different magnetic resonance (MR) sequences on the accuracy of deep learning-based synthetic computed tomography (sCT) generation in the complex head and neck region. METHODS: Four sequences of MR images (T1, T2, T1C, and T1DixonC-water) were collected from 45 patients with nasopharyngeal carcinoma. Seven conditional generative adversarial network (cGAN) models were trained with different sequences (single channel) and different combinations (multi-channel) as inputs. To further verify the cGAN performance, we also used a U-net network as a comparison. Mean absolute error, structural similarity index, peak signal-to-noise ratio, dice similarity coefficient, and dose distribution were evaluated between the actual CTs and sCTs generated from different models. RESULTS: The results show that the cGAN model with multi-channel (i.e., T1 + T2 + T1C + T1DixonC-water) as input to predict sCT achieves higher accuracy than any single MR sequence model. The T1-weighted MR model achieves better results than T2, T1C, and T1DixonC-water models. The comparison between cGAN and U-net shows that the sCTs predicted by cGAN retains additional image details are less blurred and more similar to the actual CT. CONCLUSIONS: Conditional generative adversarial network with multiple MR sequences as model input shows the best accuracy. The T1-weighted MR images provide sufficient image information and are suitable for sCT prediction in clinical scenarios with limited acquisition sequences or limited acquisition time.

A Cr(VI)-tolerant strain, Pisolithus sp1, with a high accumulation capacity of Cr in mycelium and highly efficient assisting Pinus thunbergii for phytoremediation.

Ectomycorrhizal (ECM) fungi can improve the growth of pine trees and enhance their tolerance to heavy metal stress, and may also be useful during the afforestation and phytoremediation of polluted regions with pine trees. Hebeloma vinosophyllum (Cr(VI)-sensitive strain) and Pisolithus sp1 ((Cr(VI)-tolerant strain) were selected through liquid culture experiment, and were used in symbiosis with Japanese black pine (Pinus thunbergii) in pot experiments, to determine their potential for improving phytoremediation of Cr(VI)-contaminated soils. Our results indicated that Pisolithus sp1 also had a significantly higher accumulation of Cr than H. vinosophyllum in mycelium under the same Cr(VI) treatments in liquid culture experiment. The tolerance index of Pisolithus sp1 ECM seedlings' shoots and roots to Cr(VI) were significantly higher than that of H. vinosophyllum ECM and non-ectomycorrhizal (NM) seedlings while the total accumulated Cr per seedling in Pisolithus sp1 ECM seedlings were 1.50-1.96 and 2.83-27.75 fold higher that of H. vinosophyllum ECM and NM seedlings, respectively, within 0-800 mg kg-1 Cr(VI) treatments in pot experiments. In addition, the significant differences ratios of photosynthetic rate, stomatal conductance, transpiration rate and intercellular CO2 concentration between Pisolithus sp1 ECM and NM seedlings were significantly higher than those between H. vinosophyllum ECM and NM seedlings under 400 and 800 mg kg-1 Cr(VI) treatments. Compared with the control (no plant), and planting NM or H. vinosophyllum ECM seedlings, the planting of Pisolithus sp1 ECM seedlings significantly reduced the percentage content of exchangeable Cr in the soil.

[Prediction of three-dimensional dose distribution in intensity-modulated radiation therapy based on neural network learning].

OBJECTIVE: To establish the association between the geometric anatomical characteristics of the patients and the corresponding three-dimensional (3D) dose distribution of radiotherapy plan via feed-forward back-propagation neural network for clinical prediction of the plan dosimetric features. METHODS: A total of 25 fixed 13-field clinical prostate cancer intensity-modulated radiation therapy (IMRT)/stereotactic body radiation therapy (SBRT) plans were collected with a prescribed dose of 50 Gy. With the distance from each voxel to the planned target volume (PTV) boundary, the distance from each voxel to each organ-at-risk (OAR), and the volume of PTV as the geometric anatomical characteristics of the patients, the voxel deposition dose was used as the plan dosimetric feature. A neural network was used to construct the correlation model between the selected input features and output dose distribution, and the model was trained with 20 randomly selected cases and verified in 5 cases. RESULTS: The constructed model showed a small model training error, small dose differences among the verification samples, and produced accurate prediction results. In the model training, the point-to-point mean dose difference (hereinafter dose difference) of the 3D dose distribution was no greater than 0.0919∓3.6726 Gy, and the average of the relative volume values corresponding to the fixed dose sequence in the DVH (hereinafter DVH difference) did not exceed 1.7%. The dose differences among the 5 samples for validation was 0.1634∓10.5246 Gy with percent dose differences within 2.5% and DVH differences within 3%. The 3D dose distribution showed that the dose difference was small with reasonable predicted dose distribution. This model showed better performances for dose distribution prediction for bladder and rectum than for the femoral heads. CONCLUSION: We established the relationships between the geometric anatomical characteristics of the patients and the corresponding planning 3D dose distribution via feed-forward back-propagation neural network in patients receiving IMRT/SBRT for the same tumor site. The proposed model provides individualized quality standards for automatic plan quality control.