Plant R2R3-MYBs comprise one of the largest transcription factor families; however, few R2R3-MYB genes in pecan have been functionally analyzed due to the limited genome information and potential functional redundancy caused by gene duplication. In this study, 153 R2R3-MYB genes were identified and subjected to comparative phylogenetic analysis with four other plant species. Then, the pecan R2R3-MYB gene family was divided into different clades, which were also supported by gene structure and motif composition results. Fifty-two duplication events including 77 R2R3-MYB genes were identified in this gene family, and Ka/Ks values showed that all of the duplication events were under the influence of negative selection. Expression levels of pecan R2R3-MYB genes during the graft union formation process were further investigated using RNA-seq with four different timepoints after grafting, namely, 0, 8, 15 and 30 d. Sixty-four differentially expressed R2R3-MYB genes were identified and showed different expression patterns after grafting. Co-expression networks were further constructed to discover the relationships between these genes. The co-expression networks contained 57 nodes (R2R3-MYB genes) and 219 edges (co-expression gene pairs) and CIL1528S0032 contained the maximum number of edges. Fifteen genes contained more than 10 edges; the majority of these were up-regulated during graft union formation and verified by qRT-PCR. This study provides a foundation for functional analysis to investigate the roles that R2R3-MYBs play in graft union formation in pecan and identify the key candidate genes.

Pecan [Carya illinoinensis (Wangenh.) K. Koch] is an economically important nut tree and grafting is often used for clonal propagation of cultivars. However, there is a lack of research on the effects of rootstocks on scions, which are meaningful targets for directed breeding of pecan grafts. MicroRNAs (miRNAs) play an important role in many biological processes, but the mechanism underlying the involvement of miRNAs in grafting-conferred physiological changes is unclear. To identify the grafting-responsive miRNAs that may be involved in the regulation of growth in grafted pecan, six small RNA libraries were constructed from the phloem of two groups of grafts with significantly different growth performance on short and tall rootstocks. A total of 441 conserved miRNAs belonging to 42 miRNA families and 603 novel miRNAs were identified. Among the identified miRNAs, 24 (seven conserved and 17 novel) were significantly differentially expressed by the different grafts, implying that they might be responsive to grafting and potentially involved in the regulation of graft growth. Ninety-five target genes were predicted for the differentially expressed miRNAs; gene annotation was available for 33 of these. Analysis of their targets suggested that the miRNAs may regulate auxin transport, cell activity, and inorganic phosphate (Pi) acquisition, and thereby, mediate pecan graft growth. Use of the recently-published pecan genome enabled identification of a substantial population of miRNAs, which are now available for further research. We also identified the grafting-responsive miRNAs and their potential roles in pecan graft growth, providing a basis for research on long-distance regulation in grafted pecan.

Due to high production costs, the popularization and application of microbial flocculants in the field of water treatment have been limited. In this study, the capture of lead ions by the fermentation broth of a novel Paenibacillus sp. strain A9 and cultured with food wastewater was further investigated. The results revealed that the production of MBFA9 could be increased significantly by adding a small amount of carbon and nitrogen to food wastewater. Under the best experimental conditions (pH 8.5, culture temperature 30°C, 150 r/min), adding 1% (m/v) carbon and 0.1% (m/v) nitrogen to 1% (v/v) wastewater resulted in a yield of MBFA9 of 6.29 g/l. At a temperature of 30°C, pH of 5, contact time of 35 min, and FBA9 dosage of 5%, the removal rate and removal capacity of Pb(II) reached the highest values of 95.1% and 317 mg/g, respectively. Field emission scanning electron microscopy analysis indicated that bacterial cells, metabolite small molecule acids, and MBFA9 in FBA9 all contributed to the removal of Pb(II). Fourier-transform infrared spectrometry analysis indicated that functional groups such as –OH, –COOH, –CO, and –NH2 existed in MBFA9 and on the cell surface. Various mechanisms involved in Pb(II) removal can occur simultaneously, including cell surface adsorption, microcrystallization, and biological flocculation.

BackgroundIntracerebral hemorrhage (ICH) is one of the most serious complications in patients with chronic kidney disease undergoing long-term hemodialysis. It has high mortality and disability rates and imposes a serious economic burden on the patient's family and society. An early prediction of ICH is essential for timely intervention and improving prognosis. This study aims to build an interpretable machine learning-based model to predict the risk of ICH in patients undergoing hemodialysis.MethodsThe clinical data of 393 patients with end-stage kidney disease undergoing hemodialysis at three different centers between August 2014 and August 2022 were retrospectively analyzed. A total of 70% of the samples were randomly selected as the training set, and the remaining 30% were used as the validation set. Five machine learning (ML) algorithms, namely, support vector machine (SVM), extreme gradient boosting (XGB), complement Naïve Bayes (CNB), K-nearest neighbor (KNN), and logistic regression (LR), were used to develop a model to predict the risk of ICH in patients with uremia undergoing long-term hemodialysis. In addition, the area under the curve (AUC) values were evaluated to compare the performance of each algorithmic model. Global and individual interpretive analyses of the model were performed using importance ranking and Shapley additive explanations (SHAP) in the training set.ResultsA total of 73 patients undergoing hemodialysis developed spontaneous ICH among the 393 patients included in the study. The AUC of SVM, CNB, KNN, LR, and XGB models in the validation dataset were 0.725 (95% CI: 0.610 ~ 0.841), 0.797 (95% CI: 0.690 ~ 0.905), 0.675 (95% CI: 0.560 ~ 0.789), 0.922 (95% CI: 0.862 ~ 0.981), and 0.979 (95% CI: 0.953 ~ 1.000), respectively. Therefore, the XGBoost model had the best performance among the five algorithms. SHAP analysis revealed that the levels of LDL, HDL, CRP, and HGB and pre-hemodialysis blood pressure were the most important factors.ConclusionThe XGB model developed in this study can efficiently predict the risk of a cerebral hemorrhage in patients with uremia undergoing long-term hemodialysis and can help clinicians to make more individualized and rational clinical decisions. ICH events in patients undergoing maintenance hemodialysis (MHD) are associated with serum LDL, HDL, CRP, HGB, and pre-hemodialysis SBP levels.