Novel and multifaceted regulations of photoperiodic flowering by phytochrome A in soybean.

Photoperiod is an important environmental cue. Plants can distinguish the seasons and flower at the right time through sensing the photoperiod. Soybean is a sensitive short-day crop, and the timing of flowering varies greatly at different latitudes, thus affecting yields. Soybean cultivars in high latitudes adapt to the long day by the impairment of two phytochrome genes, PHYA3 and PHYA2, and the legume-specific flowering suppressor, E1. However, the regulating mechanism underlying phyA and E1 in soybean remains largely unknown. Here, we classified the regulation of the E1 family by phyA2 and phyA3 at the transcriptional and posttranscriptional levels, revealing that phyA2 and phyA3 regulate E1 by directly binding to LUX proteins, the critical component of the evening complex, to regulate the stability of LUX proteins. In addition, phyA2 and phyA3 can also directly associate with E1 and its homologs to stabilize the E1 proteins. Therefore, phyA homologs control the core flowering suppressor E1 at both the transcriptional and posttranscriptional levels, to double ensure the E1 activity. Thus, our results disclose a photoperiod flowering mechanism in plants by which the phytochrome A regulates LUX and E1 activity.

An in vivo and in vitro model on the protective effect of corilagin on doxorubicin-induced cardiotoxicity via regulation of apoptosis and PI3-K/AKT signaling pathways.

Globally, doxorubicin (DOX)-induced cardio dysfunction is a serious cause of morbidity and mortality in cancerous patients. An adverse event of cardiotoxicity is the main deem to restrict in the clinical application by oncologists. Corilagin (CN) is well known for its antioxidative, anti-fibrosis, and anticancer effects. Herein, we aimed to evaluate the action of CN on DOX-induced experimental animals and H9c2 cells. The myocardium-specific marker, CK-MB, and the influx of mitochondrial calcium levels were measured by using commercial kits. Biochemical indices reflecting oxidative stress and antioxidant attributes such as malondialdehyde, glutathione peroxidase, reduced glutathione, superoxide dismutase, and catalase were also analyzed in DOX-induced cardiotoxic animals. In addition, mitochondrial ROS were measured by DCFH-DA in H9c2 cells under fluorescence microscopy. DOX induction significantly increased oxidative stress levels and also modulated apoptosis/survival protein expressions in myocardial tissues. Western blots were used to measure the expressional levels of Bax/Bcl-2, caspase-3, PI3-K/AKT, and PPARγ signaling pathways. Histological studies were executed to observe morphological changes in myocardial tissues. All of these DOX-induced effects were attenuated by CN (100 mg/kg bw). These in vitro and in vivo results point towards the fact that CN might be a novel cardioprotective agent against DOX-induced cardiotoxicity through modulating cardio apoptosis and oxidative stress.

Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms.

Objective: The objective of this study is to develop a model to predicts the postoperative Hunt-Hess grade in patients with intracranial aneurysms by integrating radiomics and deep learning technologies, using preoperative CTA imaging data. Thereby assisting clinical decision-making and improving the assessment and prognosis of postoperative neurological function. Methods: This retrospective study encompassed 101 patients who underwent aneurysm embolization surgery. 851 radiomic features were extracted from CTA images. 512 deep learning features are extracted from last layer of ResNet50 deep convolutional neural network model. The feature screening process pipeline encompassed intraclass correlation coefficient analysis, principal component analysis, U test, spearman correlation analysis, minimum redundancy maximum relevance algorithm and Lasso regression, to identify features most correlated with postoperative Hunt-Hess grading. In the model construction phase, three distinct models were constructed: radiomics feature-based model (RSM), deep learning feature-based model (DLM), and deep learning-radiomics feature fusion model (DLRSCM). The study also calculated the radiomics score and combined it with clinical data to construct a Nomogram for predictive modeling. DLM, RSM and DLRSCM model was constructed by 9 base algorithms and 1 ensemble learning algorithm - Stacking ensemble model. Model performance was evaluated based on the area under the Receiver Operating Characteristic (ROC) curve (AUC), Matthews Correlation Coefficient (MCC), calibration curves, and decision curves analysis. Results: 5 significant radiomic feature and 4 significant deep learning features were obtained through the feature selection process. These features were utilized for model construction. Bootstrap resampling method was used for internal validation of the models. In terms of model evaluation, the DLM model, the stacking ensemble algorithm results achieved an AUC of 0.959 and MCC of 0.815. In the RSM model, the stacking ensemble model AUC was 0.935 and MCC was 0.793. The stacking ensemble model in DLRSCM outperformed others, with an AUC of 0.968 and MCC of 0.820. Results indicated that the ANN performed optimally among all base models, while the stacked ensemble learning model exhibited the highest predictive performance. Conclusion: This study demonstrates that the combination of radiomics and deep learning is an effective approach to predict the postoperative Hunt-Hess grade in patients with intracranial aneurysms. This holds significant value in the early identification of postoperative neurological complications and in enhancing clinical decision-making.

Improved brain metastases segmentation using generative adversarial network and conditional random field optimization mask R-CNN.

BACKGROUND: In radiotherapy, the delineation of the gross tumor volume (GTV) in brain metastases using computed tomography (CT) simulation localization is very important. However, despite the criticality of this process, a pronounced gap exists in the availability of tools tailored for the automatic segmentation of the GTV based on CT simulation localization images. PURPOSE: This study aims to fill this gap by devising an effective tool specifically for the automatic segmentation of the GTV using CT simulation localization images. METHODS: A dual-network generative adversarial network (GAN) architecture was developed, wherein the generator focused on refining CT images for more precise delineation, and the discriminator differentiated between real and augmented images. This architecture was coupled with the Mask R-CNN model to achieve meticulous GTV segmentation. An end-to-end training process facilitated the integration between the GAN and Mask R-CNN functionalities. Furthermore, a conditional random field (CRF) was incorporated to refine the initial masks generated by the Mask R-CNN model to ensure optimal segmentation accuracy. The performance was assessed using key metrics, namely, the Dice coefficient (DSC), intersection over union (IoU), accuracy, specificity, and sensitivity. RESULTS: The GAN+Mask R-CNN+CRF integration method in this study performs well in GTV segmentation. In particular, the model has an overall average DSC of 0.819 ± 0.102 and an IoU of 0.712 ± 0.111 in the internal validation. The overall average DSC in the external validation data is 0.726 ± 0.128 and the IoU is 0.640 ± 0.136. It demonstrates favorable generalization ability. CONCLUSION: The integration of the GAN, Mask R-CNN, and CRF optimization provides a pioneering tool for the sophisticated segmentation of the GTV in brain metastases using CT simulation localization images. The method proposed in this study can provide a robust automatic segmentation approach for brain metastases in the absence of MRI.

Wavelength demultiplexing with layered multiple Bragg gratings in LiNbO3:Fe crystal.

Dense wavelength division multiplexing (DWDM) is an important technology for expanding the capacity of optical network. The optical component based on the superimposed Bragg gratings shows that it can be used as one of advantageous multichannel components because of its excellent angle and wavelength selectivities. An optimized method for recording multiple Bragg gratings for wavelength demultiplexing in optical telecommunication band is proposed to achieve gratings with equal diffraction efficiency. A structure of three layers with twenty four gratings is demonstrated in a LiNbO(3):Fe crystal by employing the optimized recording method. Then an initial wavelength demultiplexing experiment based on the formed gratings is carried out in optical telecommunication C-band. The results obtained by measuring and analyzing the transmitted spectra of the fabricated gratings show that the diffraction efficiencies of the gratings are uniform. It is suggested that this kind of multiple gratings can be used for increasing the number of the demultiplexed wavelengths in recording medium with unit volume for WDM.

Polarization-dependent diffraction efficiency of a photorefractive volume grating and suppression of this efficiency.

In terms of refractive-index ellipsoid of a uniaxial crystal, the relationship between the diffraction efficiency of a volume grating and the polarization state of a readout beam is theoretically analyzed. The direction of a refractive light beam and the corresponding refractive-index modulation will both be changed by a variation of the polarization state. In the polarization state of the readout beam, which may lead to a strong variation in the diffraction efficiency of the volume grating. This kind of polarization-dependent diffraction efficiency of a volume grating in an anisotropic crystal is extremely disadvantageous for some applications. A method to suppress the polarization-dependent diffraction efficiency by use of double volume gratings is presented, and experiments with LiNbO3:Fe crystal are also demonstrated. The experimental results indicate that this method can well suppress the polarization-dependent diffraction efficiency of a volume grating. Furthermore, the diffraction properties of the double volume gratings are almost independent of the polarization state of the readout beam. The relative values of the diffraction peaks are calculated on the basis of the relationship between index modulation and the state of polarization. The experimental values are in good agreement with the theoretical analyses.