Changes in the rhizosphere and root-associated bacteria community of white Guinea yam (Dioscorea rotundata Poir.) impacted by genotype and nitrogen fertilization.

The bacterial diversity and composition of water yam (Dioscorea alata L. cv. A-19), which can grow without chemical fertilization, have recently been characterized with no significant differences compared with the use of chemical fertilization. However, the diversity and community structure of bacteria associated with the white Guinea yam (Dioscorea rotundata), the most cultivated and economically important yam in West Africa, have not yet been investigated. This study characterized the bacterial diversity and composition associated with bulk soil, rhizosphere, and plant roots in six white Guinea yam genotypes (S004, S020, S032, S042, S058, and S074) in field experiments in Ibadan, Nigeria under N-based chemical fertilizer application. The largest diversity of bacteria was found in the bulk soil, followed by the rhizosphere and roots. Based on the alpha diversity analysis, the bacterial diversity in both S020 and S042 increased with fertilizer application among the bulk soil samples. S058 grown under no-fertilizer conditions had the highest bacterial diversity among the rhizosphere samples. Beta diversity analysis highlighted the significant difference in the composition of bacteria associated with the genotypes and fertilizer treatments, and S032 had a unique bacterial composition compared to the other genotypes. The dominant phylum across all sample types was Proteobacteria. Actinobacteriota was the dominant phylum among bulk soil samples. At the genus level, Bacillus was the most abundant bacterial genus across both the control and treated samples. Pseudomonas was predominant across all rhizosphere samples. Chryseobacterium, Sphingobium, Delftia and Klebsiella associated with the rhizosphere were shown the altered relative abundance between the control and treated samples depending on genotypes. A genus related to symbiotic nitrogen-fixing bacteria, the Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium clade, showed higher relative abundance among all root samples, indicating that it is a core bacterial genus. Furthermore, the field application of chemical fertilizer had a significant impact on the relative abundances of two genera related to symbiotic nitrogen-fixers, Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium clade and Bradyrhizobium in the rhizosphere and root. These results suggest that N-based chemical fertilizers and plant genotypes would influence the compositional arrangement of associated bacterial communities, including symbiotic nitrogen-fixing bacteria.

Bacterial Community of Water Yam (Dioscorea alata L.) cv. A-19.

The bacterial community of water yam (Dioscorea alata L.) cv. A-19 is vital because it may promote plant growth without the need for fertilization. However, the influence of fertilization practices on the composition and proportion of the bacterial community of water yam cv. A-19 has not yet been extensively examined. Therefore, we herein investigated the diversity and composition of the bacterial community of water yam cv. A-19 cultivated with and without chemical fertilization using amplicon community profiling based on 16S rRNA gene sequences. No significant difference was detected in the growth of plants cultivated with or without chemical fertilization. Alpha diversity indices were significantly dependent on each compartment, and a decrease was observed in indices from the belowground (rhizosphere and root) to aboveground compartments (stem and leaf). The bacterial composition of each compartment was clustered into three groups: bulk soil, rhizosphere and root, and stem and leaf. Chemical fertilization did not significantly influence the diversity or composition of the water yam cv. A-19 bacterial community. It remained robust in plants cultivated with chemical fertilization. The amplicon community profiling of bacterial communities also revealed the dominance of two bacterial clades, the Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium clade and Burkholderia-Caballeronia-Paraburkholderia clade, with and without chemical fertilization. This is the first study to characterize the bacterial community of water yam cv. A-19 cultivated with and without chemical fertilization.

Isolation of phenolic acids and tannin acids from Mangifera indica L. kernels as inhibitors of lipid accumulation in 3T3-L1 cells.

Mango (Mangifera indica L.) kernels are usually discarded as waste, but they contain many pharmacological properties and bioactivities. In this study, we isolated antiobesity agents from mango kernels that inhibit intracellular lipid formation in 3T3-L1 adipocytes. Two phenolic acids, ethyl gallate and ethyl digallate, and 2 tannin acids, 1,2,3,4,6-penta-O-galloyl-ß-d-glucose (PGG) and 3-O-digalloyl-1,2,4,6-tetra-O-ß-d-glucose (HGG), were identified from mango kernels and were found to be suppressed lipid accumulation as evidenced by Oil Red O staining. Furthermore, ethyl digallate, PGG, and HGG significantly downregulated the mRNA expression of adipogenic transcription factors such as C/EBPα and PPARγ. However, ethyl gallate did not affect the expression of these transcription factors. Our findings reveal the presence of antiobesity compounds in mango kernels, implying its therapeutic role against obesity.

Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [18F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer.

PURPOSE: To examine the prognostic significance of pretreatment 2-deoxy-2-[18F]fluoro-D-glucose ([18F]-FDG) positron emission tomography (PET)-based radiomic features using a machine learning approach in patients with endometrial cancers. PROCEDURES: Included in this retrospective study were 53 patients with endometrial cancers who underwent [18F]-FDG PET/X-ray computed tomography (CT) before treatment. Since two different PET scanners were used, post-reconstruction harmonization was performed for all PET parameters using the ComBat harmonization method. Four clinical (age, histological type, stage, and treatment method) and 40 [18F]-FDG PET-based radiomic features were ranked, and a subset of useful features was selected based on the decrease in the Gini impurity in terms of associations with disease progression. The machine learning algorithms (random forest, neural network, k-nearest neighbors (kNN), naive Bayes, logistic regression, and support vector machine) were compared using the areas under the receiver operating characteristic curve (AUC) and validated by the random sampling method. Progression-free survival (PFS) and overall survival (OS) were assessed by the Cox regression analysis. RESULTS: The five best predictors of disease progression were coarseness, gray-level run length nonuniformity, stage, treatment method, and gray-level zone length nonuniformity. The kNN model obtained the best performance classifier for predicting the disease progression (AUC =0.890, accuracy =0.849, F1 score =0.848, precision =0.857, and recall =0.849). Coarseness which was the first ranked radiomic feature was selected for survival analyses, and only coarseness remained as a significant and independent factor for both PFS (hazard ratios (HR), 0.65; 95 % confidence interval [CI], 0.49-0.86; p=0.003) and OS (HR, 0.52; 95 % CI, 0.36-0.76; p<0.001) at multivariate Cox regression analysis. CONCLUSIONS: [18F]-FDG PET-based radiomic analysis using a machine learning approach may be useful for predicting tumor progression and prognosis in patients with endometrial cancers.

Differentiating malignant from benign salivary gland lesions: a multiparametric non-contrast MR imaging approach.

The purpose of this study is to determine whether multiparametric non-contrast MR imaging including diffusion-weighted imaging (DWI), arterial spin labeling (ASL), and amide proton transfer (APT) weighted imaging can help differentiate malignant from benign salivary gland lesions. The study population consisted of 42 patients, with 31 benign and 11 malignant salivary gland lesions. All patients were evaluated using DWI, three-dimensional pseudo-continuous ASL, and APT-weighted imaging on 3 T MR imaging before treatment. Apparent diffusion coefficient (ADC), tumor blood flow (TBF), and APT-related signal intensity (APTSI) values within the lesion were compared between the malignant and benign lesions by Mann-Whitney U test. For each parameter, optimal cutoff values were chosen using a threshold criterion that maximized the Youden index for predicting malignant lesions. The performance of ADC, TBF, APTSI, individually and combined, was evaluated in terms of diagnostic ability for malignant lesions. Diagnostic performance was compared by McNemar test. APTSI was significantly higher in malignant lesions (2.18 ± 0.89%) than in benign lesions (1.57 ± 1.09%, p = 0.047). There was no significant difference in ADC or TBF between benign and malignant lesions (p = 0.155 and 0.498, respectively). The accuracy of ADC, TBF, and APTSI for diagnosing malignant lesions was 47.6%, 50.0%, and 66.7%, respectively; whereas the accuracy of the three parameters combined was 85.7%, which was significantly higher than that of each parameter alone (p = 0.001, 0.001, and 0.008, respectively). Therefore, the combination of ADC, TBF, and APTSI can help differentiate malignant from benign salivary gland lesions.

Rhizobium dioscoreae sp. nov., a plant growth-promoting bacterium isolated from yam (Dioscorea species).

This study investigated endophytic nitrogen-fixing bacteria isolated from two species of yam (water yam, Dioscorea alata L.; lesser yam, Dioscorea esculenta L.) grown in nutrient-poor alkaline soil conditions on Miyako Island, Okinawa, Japan. Two bacterial strains of the genus Rhizobium, S-93T and S-62, were isolated. The phylogenetic tree, based on the almost-complete 16S rRNA gene sequences (1476 bp for each strain), placed them in a distinct clade, with Rhizobium miluonense CCBAU 41251T, Rhizobium hainanense I66T, Rhizobium multihospitium HAMBI 2975T, Rhizobium freirei PRF 81T and Rhizobium tropici CIAT 899T being their closest species. Their bacterial fatty acid profile, with major components of C19 : 0 cyclo ω8c and summed feature 8, as well as other phenotypic characteristics and DNA G+C content (59.65 mol%) indicated that the novel strains belong to the genus Rhizobium. Pairwise average nucleotide identity analyses separated the novel strains from their most closely related species with similarity values of 90.5, 88.9, 88.5, 84.5 and 84.4 % for R. multihospitium HAMBI 2975T, R. tropici CIAT 899T, R. hainanense CCBAU 57015T, R. miluonense HAMBI 2971T and R. freirei PRF 81T, respectively; digital DNA-DNA hybridization values were in the range of 26-42 %. Considering the phenotypic characteristics as well as the genomic data, it is suggested that strains S-93T and S-62 represent a new species, for which the name Rhizobium dioscoreae is proposed. The type strain is S-93T (=NRIC 0988T=NBRC 114257T=DSM 110498T).