Adaptive Responses of Common and Hybrid Bermudagrasses to Shade Stress Associated With Changes in Morphology, Photosynthesis, and Secondary Metabolites.

Alteration of ploidy in one particular plant species often influences their environmental adaptation. Warm-season bermudagrass is widely used as forage, turfgrass, and ground-cover plant for ecological remediation, but exhibits low shade tolerance. Adaptive responses to shade stress between triploid hybrid bermudagrass cultivars ["Tifdwarf" (TD), "Tifsport" (TS), and "Tifway" (TW)] and tetraploid common bermudagrass cultivar "Chuanxi" (CX) were studied based on changes in phenotype, photosynthesis, and secondary metabolites in leaves and stems. Shade stress (250 luminance, 30 days) significantly decreased stem diameter and stem internode length, but did not affect the leaf width of four cultivars. Leaf length of CX, TD, or TW showed no change in response to shade stress, whereas shade stress significantly elongated the leaf length of TS. The CX and the TS exhibited significantly higher total chlorophyll (Chl), Chl a, carotenoid contents, photosynthetic parameters [PSII photochemical efficiency (Fv/Fm), transpiration rate, and stomatal conductance] in leaves than the TW and the TD under shade stress. The CX also showed a significantly higher performance index on absorption basis (PIABS) in leaf and net photosynthetic rate (Pn) in leaf and stem than the other three cultivars under shade stress. In addition, the TS maintained higher proantho cyanidims content than the TW and the TD after 30 days of shade stress. Current results showed that tetraploid CX exhibited significantly higher shade tolerance than triploid TD, TS, and TW mainly by maintaining higher effective photosynthetic leaf area, photosynthetic performance of PSI and PSII (Pn and Fv/Fm), and photosynthetic pigments as well as lower Chl a/b ratio for absorption, transformation, and efficient use of light energy under shade stress. For differential responses to shade stress among three triploid cultivars, an increase in leaf length and maintenance of higher Fv/Fm, gas exchange, water use efficiency, carotenoid, and proanthocyanidin contents in leaves could be better morphological and physiological adaptations of TS to shade than other hybrid cultivars (TD and TW).

Joint affine and deformable three-dimensional networks for brain MRI registration.

PURPOSE: Volumetric medical image registration has important clinical significance. Traditional registration methods may be time-consuming when processing large volumetric data due to their iterative optimizations. In contrast, existing deep learning-based networks can obtain the registration quickly. However, most of them require independent rigid alignment before deformable registration; these two steps are often performed separately and cannot be end-to-end. METHODS: We propose an end-to-end joint affine and deformable network for three-dimensional (3D) medical image registration. The proposed network combines two deformation methods; the first one is for obtaining affine alignment and the second one is a deformable subnetwork for achieving the nonrigid registration. The parameters of the two subnetworks are shared. The global and local similarity measures are used as loss functions for the two subnetworks, respectively. Moreover, an anatomical similarity loss is devised to weakly supervise the training of the whole registration network. Finally, the trained network can perform deformable registration in one forward pass. RESULTS: The efficacy of our network was extensively evaluated on three public brain MRI datasets including Mindboggle101, LPBA40, and IXI. Experimental results demonstrate our network consistently outperformed several state-of-the-art methods with respect to the metrics of Dice index (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD). CONCLUSIONS: The proposed network provides accurate and robust volumetric registration without any pre-alignment requirement, which facilitates the end-to-end deformable registration.

Unsupervised 3D End-to-end Deformable Network for Brain MRI Registration.

Volumetric medical image registration has important clinical significance. Traditional registration methods may be time-consuming when processing large volumetric data due to their iterative optimizations. In contrast, existing deep learning-based networks can obtain the registration quickly. However, most of them require independent rigid alignment before deformable registration; these two steps are often performed separately and cannot be end-to-end. Moreover, registration ground-truth is difficult to obtain for supervised learning methods. To tackle the above issues, we propose an unsupervised 3D end-to-end deformable registration network. The proposed network cascades two subnetworks; the first one is for obtaining affine alignment, and the second one is a deformable subnetwork for achieving the non-rigid registration. The parameters of the two subnetworks are shared. The global and local similarity measures are used as loss functions for the two subnetworks, respectively. The trained network can perform end-to-end deformable registration. We conducted experiments on brain MRI datasets (LPBA40, Mindboggle101, and IXI) and experimental results demonstrate the efficacy of the proposed registration network.

Edge-Aware Pyramidal Deformable Network for Unsupervised Registration of Brain MR Images.

Deformable image registration is of essential important for clinical diagnosis, treatment planning, and surgical navigation. However, most existing registration solutions require separate rigid alignment before deformable registration, and may not well handle the large deformation circumstances. We propose a novel edge-aware pyramidal deformable network (referred as EPReg) for unsupervised volumetric registration. Specifically, we propose to fully exploit the useful complementary information from the multi-level feature pyramids to predict multi-scale displacement fields. Such coarse-to-fine estimation facilitates the progressive refinement of the predicted registration field, which enables our network to handle large deformations between volumetric data. In addition, we integrate edge information with the original images as dual-inputs, which enhances the texture structures of image content, to impel the proposed network pay extra attention to the edge-aware information for structure alignment. The efficacy of our EPReg was extensively evaluated on three public brain MRI datasets including Mindboggle101, LPBA40, and IXI30. Experiments demonstrate our EPReg consistently outperformed several cutting-edge methods with respect to the metrics of Dice index (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD). The proposed EPReg is a general solution for the problem of deformable volumetric registration.

Pretreatment with NaCl Promotes the Seed Germination of White Clover by Affecting Endogenous Phytohormones, Metabolic Regulation, and Dehydrin-Encoded Genes Expression under Water Stress.

This study was designed to examine the effects of NaCl pretreatment on the seed germination of white clover (Trifolium repens cv. Ladino) under water stress induced by 19% polyethylene glycol (PEG) 6000. Lower concentrations of NaCl (0.5, 1, and 2.5 mM) pretreatment significantly alleviated stress-induced decreases in germination percentage, germination vigor, germination index, and radicle length of seedlings after seven days of germination under water stress. The soaking with 1 mM of NaCl exhibited most the pronounced effects on improving seed germination and alleviating stress damage. NaCl-induced seeds germination and growth could be associated with the increases in endogenous gibberellic acid (GA) and indole-3-acetic acid (IAA) levels through activating amylases leading to improved amylolysis under water stress. Seedlings pretreated with NaCl had a significantly lower osmotic potential than untreated seedlings during seed germination, which could be related to significantly higher soluble sugars and free proline content in NaCl-treated seedlings under water stress. For antioxidant metabolism, NaCl pretreatment mainly improved superoxide dismutase, peroxidase, ascorbate peroxidase, and glutathione reductase activities, transcript levels of FeSOD, APX, and DHAR, and the content of ascorbic acid, reduced glutathione, and oxidized glutathione during seed germination under water stress. The results indicated that seeds soaking with NaCl could remarkably enhance antioxidant metabolism, thereby decreasing the accumulation of reactive oxygen species and membrane lipid peroxidation during germination under water stress. In addition, NaCl-upregulated dehydrin-encoded genes SK2 expression could be another important mechanism of drought tolerance during seeds germination of white clover in response to water stress.

The γ-Aminobutyric Acid (GABA) Alleviates Salt Stress Damage during Seeds Germination of White Clover Associated with Na⁺/K⁺ Transportation, Dehydrins Accumulation, and Stress-Related Genes Expression in White Clover.

The objective of this study was to determine the effect of soaking with γ-aminobutyric acid (GABA) on white clover (Trifolium repens cv. Haifa) seed germination under salt stress induced by 100 mM NaCl. Seeds soaking with GABA (1 µM) significantly alleviated salt-induced decreases in endogenous GABA content, germination percentage, germination vigor, germination index, shoot and root length, fresh and dry weight, and root activity of seedling during seven days of germination. Exogenous application of GABA accelerated starch catabolism via the activation of amylase and also significantly reduced water-soluble carbohydrate, free amino acid, and free proline content in seedlings under salt stress. In addition, improved antioxidant enzyme activities (SOD, GPOX, CAT, APX, DHAR, GR and MDHR) and gene transcript levels (Cu/ZnSOD, FeSOD, MnSOD, CAT, GPOX, APX, MDHR, GPX and GST) was induced by seeds soaking with GABA, followed by decreases in O2∙-, H2O2, and MDA accumulation during germination under salt stress. Seeds soaking with GABA could also significantly improve Na⁺/K⁺ content and transcript levels of genes encoding Na⁺/K⁺ transportation (HKT1, HKT8, HAL2, H⁺-ATPase and SOS1) in seedlings of white clover. Moreover, exogenous GABA significantly induced the accumulation of dehydrins and expression of genes encoding dehydrins (SK2, Y2K, Y2SK, and dehydrin b) in seedlings under salt stress. These results indicate that GABA mitigates the salt damage during seeds germination through enhancing starch catabolism and the utilization of sugar and amino acids for the maintenance of growth, improving the antioxidant defense for the alleviation of oxidative damage, increasing Na⁺/K⁺ transportation for the osmotic adjustment, and promoting dehydrins accumulation for antioxidant and osmotic adjustment under salt stress.

Network-based inference methods for drug repositioning.

Mining potential drug-disease associations can speed up drug repositioning for pharmaceutical companies. Previous computational strategies focused on prior biological information for association inference. However, such information may not be comprehensively available and may contain errors. Different from previous research, two inference methods, ProbS and HeatS, were introduced in this paper to predict direct drug-disease associations based only on the basic network topology measure. Bipartite network topology was used to prioritize the potentially indicated diseases for a drug. Experimental results showed that both methods can receive reliable prediction performance and achieve AUC values of 0.9192 and 0.9079, respectively. Case studies on real drugs indicated that some of the strongly predicted associations were confirmed by results in the Comparative Toxicogenomics Database (CTD). Finally, a comprehensive prediction of drug-disease associations enables us to suggest many new drug indications for further studies.