Flow Behavior of Nanoparticle Agglomerates in a Fluidized Bed Simulated with Porous-Structure-Based Drag Laws.

Fluidization bed reactor is an attractive method to synthesize and process quantities of functional nanoparticles, due to the large gas-solid contact area and its potential scalability. Nanoparticles fluidize not individually but as a form of porous agglomerates with a typical porosity above 90%. The porous structure has a significant effect on the hydrodynamic behavior of a single nanoparticle agglomerate, but its influence on the flow behavior of nanoparticle agglomerates in a fluidized bed is currently unclear. In the present study, a drag model was developed to consider the porous structure effects of nanoparticle agglomerates by incorporating porous-structure-based drag laws in the Eulerian-Eulerian two-fluid model. Numerical simulations were performed from particulate to bubbling fluidization state to evaluate the applicability of porous-structure-based drag laws. Results obtained for the minimum fluidization and bubbling velocities, bed expansion ratio, and agglomerate dispersion coefficient show that, compared with the drag law of solid sphere, the porous-structure-based drag laws, especially the drag law of fractal porous spheres, provide a closer fit to the experimental data. This indicates that the pore structures have a great impact on gas-solid flow behavior of nanoparticle agglomerates, and the porous-structure-based drag laws are more suitable for describing flows in nanoparticle agglomerate fluidized beds.

Preferential transport activity of DkDTX5/MATE5 affects the formation of different astringency in persimmon.

Proanthocyanidins (PAs) are specialized metabolites that influence persimmon fruit quality. Normal astringent (A)-type and non-astringent (NA)-type mutants show significant variation in PA accumulation, but the influencing mechanism remains unclear. In this study, among the six identified DTXs/MATEs proteins associated with PA accumulation, we observed that allelic variation and preferential transport by DkDTX5/MATE5 induced variation in PA accumulation for A-type and NA-type fruit. The expression pattern of DkDTX5/MATE5 was correlated with PA accumulation in NA-type fruit. Upregulation and downregulation of DkDTX5/MATE5 promoted and inhibited PA accumulation, respectively, in the NA-type fruit. Interestingly, transporter assays of Xenopus laevis oocytes indicated that DkDTX5/MATE5 preferentially transported the PA precursors catechin, epicatechin, and epicatechin gallate, resulting in their increased ratios relative to the total PAs, which was the main source of variation in PA accumulation between the A-type and NA-type. The allele lacking Ser-84 in DkDTX5/MATE5 was identified as a dominantly expressed gene in the A-type and lost its transport function. Site-directed mutagenesis revealed that DkDTX5/MATE5 binds to PA precursors via Ser-84. These findings clarify the association between the transporter function of DkDTX5/MATE5 and PA variation, and can contribute to the breeding of new cultivars with improved fruit quality.

CRBSP:Prediction of CircRNA-RBP Binding Sites Based on Multimodal Intermediate Fusion.

Circular RNA (CircRNA) is widely expressed and has physiological and pathological significance, regulating post-transcriptional processes via its protein-binding activity. However, whereas much work has been done on linear RNA and RNA binding protein (RBP), little is known about the binding sites of CircRNA. The current report is on the development of a medium-term multimodal data fusion strategy, CRBSP, to predict CircRNA-RBP binding sites. CRBSP represents the CircRNA trinucleotide semantic, location, composition and frequency information as the corresponding coding methods of Word to vector (Word2vec), Position-specific trinucleotide propensity (PSTNP), Pseudo trinucleotide composition (PseTNC) and Trinucleotide nucleotide composition (TNC), respectively. CNN (Convolution Neural Networks) was used to extract global information and BiLSTM (bidirectional Long- and Short-Term Memory network) encoder and LSTM (Long- and Short-Term Memory network) decoder for local sequence information. Enhancement of the contributions of key features by the self-attention mechanism was followed by mid-term fusion of the four enhanced features. Logistic Regression (LR) classifier showed that CRBSP gives a mean AUC value of 0.9362 through 5-fold Cross Validation of all 37 datasets, a performance which is superior to five current state-of-the-art models. Similar evaluation of linear RNA-RBP binding sites gave an AUC value of 0.7615 which is also higher than other prediction methods, demonstrating the robustness of CRBSP.

CapsNet-LDA: predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data.

Cumulative studies have shown that many long non-coding RNAs (lncRNAs) are crucial in a number of diseases. Predicting potential lncRNA-disease associations (LDAs) can facilitate disease prevention, diagnosis and treatment. Therefore, it is vital to develop practical computational methods for LDA prediction. In this study, we propose a novel predictor named capsule network (CapsNet)-LDA for LDA prediction. CapsNet-LDA first uses a stacked autoencoder for acquiring the informative low-dimensional representations of the lncRNA-disease pairs under multiple views, then the attention mechanism is leveraged to implement an adaptive allocation of importance weights to them, and they are subsequently processed using a CapsNet-based architecture for predicting LDAs. Different from the conventional convolutional neural networks (CNNs) that have some restrictions with the usage of scalar neurons and pooling operations. the CapsNets use vector neurons instead of scalar neurons that have better robustness for the complex combination of features and they use dynamic routing processes for updating parameters. CapsNet-LDA is superior to other five state-of-the-art models on four benchmark datasets, four perturbed datasets and an independent test set in the comparison experiments, demonstrating that CapsNet-LDA has excellent performance and robustness against perturbation, as well as good generalization ability. The ablation studies verify the effectiveness of some modules of CapsNet-LDA. Moreover, the ability of multi-view data to improve performance is proven. Case studies further indicate that CapsNet-LDA can accurately predict novel LDAs for specific diseases.

Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction.

BACKGROUND: DNA N4-methylcytosine is part of the restrictive modification system, which works by regulating some biological processes, for example, the initiation of DNA replication, mismatch repair and inactivation of transposon. However, using experimental methods to detect 4mC sites is time-consuming and expensive. Besides, considering the huge differences in the number of 4mC samples among different species, it is challenging to achieve a robust multi-species 4mC site prediction performance. Hence, it is of great significance to develop effective computational tools to identify 4mC sites. RESULTS: This work proposes a flexible deep learning-based framework to predict 4mC sites, called Hyb4mC. Hyb4mC adopts the DNA2vec method for sequence embedding, which captures more efficient and comprehensive information compared with the sequence-based feature method. Then, two different subnets are used for further analysis: Hyb_Caps and Hyb_Conv. Hyb_Caps is composed of a capsule neural network and can generalize from fewer samples. Hyb_Conv combines the attention mechanism with a text convolutional neural network for further feature learning. CONCLUSIONS: Extensive benchmark tests have shown that Hyb4mC can significantly enhance the performance of predicting 4mC sites compared with the recently proposed methods.

MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model.

BACKGROUND: Many long non-coding RNAs (lncRNAs) have key roles in different human biologic processes and are closely linked to numerous human diseases, according to cumulative evidence. Predicting potential lncRNA-disease associations can help to detect disease biomarkers and perform disease analysis and prevention. Establishing effective computational methods for lncRNA-disease association prediction is critical. RESULTS: In this paper, we propose a novel model named MAGCNSE to predict underlying lncRNA-disease associations. We first obtain multiple feature matrices from the multi-view similarity graphs of lncRNAs and diseases utilizing graph convolutional network. Then, the weights are adaptively assigned to different feature matrices of lncRNAs and diseases using the attention mechanism. Next, the final representations of lncRNAs and diseases is acquired by further extracting features from the multi-channel feature matrices of lncRNAs and diseases using convolutional neural network. Finally, we employ a stacking ensemble classifier, consisting of multiple traditional machine learning classifiers, to make the final prediction. The results of ablation studies in both representation learning methods and classification methods demonstrate the validity of each module. Furthermore, we compare the overall performance of MAGCNSE with that of six other state-of-the-art models, the results show that it outperforms the other methods. Moreover, we verify the effectiveness of using multi-view data of lncRNAs and diseases. Case studies further reveal the outstanding ability of MAGCNSE in the identification of potential lncRNA-disease associations. CONCLUSIONS: The experimental results indicate that MAGCNSE is a useful approach for predicting potential lncRNA-disease associations.

Cardiac fibroblasts secrete exosome microRNA to suppress cardiomyocyte pyroptosis in myocardial ischemia/reperfusion injury.

Molecular mechanisms underlying myocardial ischemia/reperfusion (MI/R) injury and effective strategies to treat MI/R injury are both in shortage. Although pyroptosis of cardiomyocytes and the protective role of cardiac fibroblasts (CFs) have been well recognized as targets to reduce MI/R injury and sudden cardiac death (SCD), the connection has not yet been established. Here, we showed that CFs protected cardiomyocytes against MI/R-induced injury through suppression of pyroptosis. A novel molecular mechanism underpinning this effect was further identified. Under hypoxia/reoxygenation condition, CFs were found to secrete exosomes, which contain increased level of microRNA-133a (miR-133a). These exosomes then delivered miR-133a into cardiomyocytes to target ELAVL1 and repressed cardiomyocyte pyroptosis. Based on this finding, we successfully developed a new strategy that used exosomes derived from CFs with overexpressed miR-133a to enhance the therapeutic outcomes for the MI/R injury. Overall, our results provide a novel molecular basis for understanding and treating MI/R injury, and our study also provides novel insight for the postmortem diagnosis of MI/R injury induced SCD by using exosome biomarker in forensic.

A Brief Review of circRNA Biogenesis, Detection, and Function.

Circular RNA (circRNA) is a non-coding molecule produced through alternative splicing of one or more exons of a gene in the presence of an RNA-induced silencing complex (RISC). Its formation depends on complementary intron sequences on both sides of the circularized sequence. CircRNA functions as a sponge for miRNA, playing the role of the transcriptional regulator or potential biomarker. It has an impact on fetal growth and on synaptic facilitation in the brain. In this review, we illustrate biogenesis mechanisms, characteristics, and functions of cirRNAs. We also summarize methods using sequence feature and RNA next-generation sequencing data for circRNA prediction. Finally, we discuss the state of the research on circRNA in diseases, which will bring new contributions to future disease treatments.

The association between soluble suppression of tumorigenicity-2 and long-term prognosis in patients with coronary artery disease: A meta-analysis.

OBJECTIVE: Findings regarding the prognostic value of soluble suppression of tumorigenecity-2 (sST2) in patients with coronary artery disease (CAD) remain inconsistent. Therefore, we conducted this meta-analysis to investigate the long-term prognostic value of sST2 in patients with CAD. METHODS: A comprehensive literature search was conducted across the PubMed, Embase, and Cochrane Library databases up to June 3, 2020. The primary outcome was major adverse cardiac events (MACEs). The secondary outcomes were all-cause mortality, cardiovascular (CV) death, heart failure (HF), and myocardial infarction (MI). Pooled estimations and 95% confidence intervals (CIs) were assessed using a random-effects model. RESULTS: Twenty-two articles that enrolled a total of 17,432 patients with CAD were included in the final analysis. CAD patients in the highest categories of baseline sST2 had a significantly higher risk of MACEs (HR: 1.42, 95% CI: 1.09-1.76), all-cause mortality (HR: 2.00, 95% CI: 1.54-2.46), and CV death (HR: 1.42, 95% CI: 1.15-1.68), HF (HR: 2.41, 95% CI: 1.87-2.94), but not that of MI (HR: 1.15, 95% CI: -0.73-3.04), than those in the lowest categories. These results were consistent when baseline sST2 was presented as continuous values in one unit increments. Moreover, subgroup analysis showed that elevated baseline sST2 levels increased the long-term risk of MACEs in the acute coronary syndrome (ACS) population (HR: 1.74, 95% CI: 1.39-2.09) but only showed a trend toward higher risk of MACEs in the non-ACS population (HR: 1.09, 95% CI: 0.87-1.30). CONCLUSIONS: The findings suggest that a higher concentration of baseline sST2 is associated with a higher risk of MACEs, all-cause mortality, CV death, and HF in patients with CAD. Elevated sST2 levels could significantly predict future MACEs in the ACS population but not in the non-ACS population.

Bmi1 and BRG1 drive myocardial repair by regulating cardiac stem cell function in acute rheumatic heart disease.

Rheumatic heart disease (RHD) occurs due to the accumulation of complications associated with rheumatic fever, and it results in high morbidity and mortality. The majority of cases of RHD are diagnosed in the chronic stages, when treatment options are limited. A small reservoir of cardiac stem cells is responsible for maintaining cardiac homeostasis and repairing tissue damage. Understanding the role of cardiac stem cells and the various proteins responsible for their functions in different pathological stages of RHD is an important area of investigation. Polycomb complex protein BMI-1 (Bmi1) and transcription activator BRG1 (BRG1) are associated with the maintenance of stemness in various types of stem cells. The present study investigated the role served by Bmi1 and BRG1 in cardiac stem cells during various pathological stages of RHD through immunohistochemistry and western blotting. A rat model of RHD was established via immunization with the Group A Streptococcus M5 protein. The rat was demonstrated to develop acute RHD 2 months after the final immunization, characterized by cardiac inflammation and tissue damage. Chronic RHD was identified 4 months after the final immunization, revealed by cardiac tissue compression and shrinkage. Expression of the cardiac stem cell marker mast/stem cell growth factor receptor kit was identified to be elevated during acute RHD, but downregulated in the chronic stages of RHD. A similar pattern of expression was revealed for Bmi1 and BRG1, indicating that they serve a role in regulating cardiac stem cell proliferation during acute RHD. These results suggest that cardiac stem cells serve a supportive role in the acute, but not chronic, stages of RHD via expression of Bmi1 and BRG1.