BackgroundmiR-146a has been demonstrated to be involved in normal hematopoiesis and the pathogenesis of many hematological malignancies by inhibiting the expression of its targets. Rs2910164(G>C) may modify the expression of the miR-146a gene, which might influence an individual's predisposition to childhood acute lymphoblastic leukemia (ALL). However, inconsistent findings have been reported on the association between the rs2910164(G>C) polymorphism and the risk of childhood ALL. Methods A comprehensive meta-analysis was performed to accurately estimate the association between the miR-146a rs2910164 polymorphism and childhood ALL among four different genetic models. Results This meta-analysis included Asian studies with a total of 1,543 patients and 1,816 controls. We observed a significant difference between patients and controls for the additive model (CC vs. GG OR = 1.598, 95% CI 1.003-2.545, P = 0.049) using a random effects model. Meanwhile, there was a trend of increased childhood ALL risk in the dominant model (CC + CG vs. GG OR = 1.501, 95% CI 0.976-2.307, P = 0.065), recessive model (CC vs. GG + CG OR = 1.142, 95% CI 0.946-1.380, P = 0.168) and allele model (C vs. G OR = 1.217, 95% CI 0.987-1.500, P = 0.066) between patients and controls. Conclusions Our findings suggest that the miR-146a rs2910164 CC genotype was significantly associated with childhood ALL susceptibility.Epigenetic gene regulation is a major control mechanism of gene expression. Most existing methods for modeling control mechanisms of gene expression use only a single epigenetic marker and very few methods are successful in modeling complex mechanisms of gene regulations using multiple epigenetic markers on transcriptional regulation. In this paper, we propose a multi-attention based deep learning model that integrates multiple markers to characterize complex gene regulation mechanisms. In experiments with 18 cell line multi-omics data, our proposed model predicted the gene expression level more accurately than the state-of-the-art model. Moreover, the model successfully revealed cell-type-specific gene expression control mechanisms. Finally, the model was used to identify genes enriched for specific cell types in terms of their functions and epigenetic regulation.Orphan genes are associated with regulatory patterns, but experimental methods for identifying orphan genes are both time-consuming and expensive. Designing an accurate and robust classification model to detect orphan and non-orphan genes in unbalanced distribution datasets poses a particularly huge challenge. Synthetic minority over-sampling algorithms (SMOTE) are selected in a preliminary step to deal with unbalanced gene datasets. To identify orphan genes in balanced and unbalanced Arabidopsis thaliana gene datasets, SMOTE algorithms were then combined with traditional and advanced ensemble classified algorithms respectively, using Support Vector Machine, Random Forest (RF), AdaBoost (adaptive boosting), GBDT (gradient boosting decision tree), and XGBoost (extreme gradient boosting). After comparing the performance of these ensemble models, SMOTE algorithms with XGBoost achieved an F1 score of 0.94 with the balanced A. thaliana gene datasets, but a lower score with the unbalanced datasets. The proposed ensemble method combines different balanced data algorithms including Borderline SMOTE (BSMOTE), Adaptive Synthetic Sampling (ADSYN), SMOTE-Tomek, and SMOTE-ENN with the XGBoost model separately. The performances of the SMOTE-ENN-XGBoost model, which combined over-sampling and under-sampling algorithms with XGBoost, achieved higher predictive accuracy than the other balanced algorithms with XGBoost models. Thus, SMOTE-ENN-XGBoost provides a theoretical basis for developing evaluation criteria for identifying orphan genes in unbalanced and biological datasets.Genes encoding 45S ribosomal RNA (rDNA) are known for their abundance within eukaryotic genomes and for their unstable copy numbers in response to changes in various genetic and epigenetic factors. Commonly, we understand as epigenetic factors (affecting gene expression without a change in DNA sequence), namely DNA methylation, histone posttranslational modifications, histone variants, RNA interference, nucleosome remodeling and assembly, and chromosome position effect. All these were actually shown to affect activity and stability of rDNA. Here, we focus on another phenomenon - the potential of DNA containing shortly spaced oligo-guanine tracts to form quadruplex structures (G4). Interestingly, sites with a high propensity to form G4 were described in yeast, animal, and plant rDNAs, in addition to G4 at telomeres, some gene promoters, and transposons, suggesting the evolutionary ancient origin of G4 as a regulatory module. Here, we present examples of rDNA promoter regions with extremely high potential to form G4 in two model plants, Arabidopsis thaliana and Physcomitrella patens. The high G4 potential is balanced by the activity of G4-resolving enzymes. The ability of rDNA to undergo these "structural gymnastics" thus represents another layer of the rich repertoire of epigenetic regulations, which is pronounced in rDNA due to its highly repetitive character.Brassinosteroids (BRs) are known as one of the major classes of phytohormones essential for various processes during normal plant growth, development, and adaptations to biotic and abiotic stresses. Significant progress has been achieved on revealing mechanisms regulating BR biosynthesis, catabolism, and signaling in many crops and in model plant Arabidopsis. It is known that BRs control plant growth and development in a dosage-dependent manner. Maintenance of BR homeostasis is therefore critical for optimal functions of BRs. In this review, updated discoveries on mechanisms controlling BR homeostasis in higher plants in response to internal and external cues are discussed.Plants are continuously exposed to environmental stressors. They have thus evolved complex signaling pathways to govern responses to a variety of stimuli. The hormone abscisic acid (ABA) has been implicated in modulating both abiotic and biotic stress responses in plants.  https://www.selleckchem.com/products/Sunitinib-Malate-(Sutent).html  ABI5 Binding Proteins (AFPs) are a family of negative regulators of bZIP transcription factors of the AREB/ABF family, which promote ABA responses. AFP2 interacts with Snf1-Related protein Kinase 1 (SnRK1), which belongs to a highly conserved heterotrimeric kinase complex that is activated to re-establish energy homeostasis following stress. However, the role of this interaction is currently unknown. Here, we show that transient overexpression of Arabidopsis thaliana AFP2 in Nicotiana benthamiana leaves induces cell death (CD). Using truncated AFP2 constructs, we demonstrate that CD induction by AFP2 is dependent on the EAR domain. Co-expression of the catalytic subunit SnRK1α1, but not SnRK1α2, rescues AFP2-induced CD. Overexpression of SnRK1α1 has little effect on AFP2 protein level and does not affect AFP2 subcellular localization.