PAM: Plaid Atoms Model for Bayesian Nonparametric Analysis of Grouped Data

We consider dependent clustering of observations in groups. The proposed model, called the plaid atoms model (PAM), estimates a set of clusters for each group and allows some clusters to be either shared with other groups or uniquely possessed by the group. PAM is based on an extension to the well-known stick-breaking process by adding zero as a possible value for the cluster weights, resulting in a zero-augmented beta (ZAB) distribution in the model. As a result, ZAB allows some cluster weights to be exactly zero in multiple groups, thereby enabling shared and unique atoms across groups. We explore theoretical properties of PAM and show its connection to known Bayesian nonparametric models. We propose an efficient slice sampler for posterior inference. Minor extensions of the proposed model for multivariate or count data are presented. Simulation studies and applications using real-world datasets illustrate the model's desirable performance

PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data

It is highly desirable to borrow information from external data to augment a control arm in a randomized clinical trial, especially in settings where the sample size for the control arm is limited. However, a main challenge in borrowing information from external data is to accommodate potential heterogeneous subpopulations across the external and trial data. We apply a Bayesian nonparametric model called Plaid Atoms Model (PAM) to identify overlapping and unique subpopulations across datasets, with which we restrict the information borrowing to the common subpopulations. This forms a hybrid control (HC) that leads to more precise estimation of treatment effects Simulation studies demonstrate the robustness of the new method, and an application to an Atopic Dermatitis dataset shows improved treatment effect estimation

Construction of a dense genetic linkage map and mapping quantitative trait loci for economic traits of a doubled haploid population of Pyropia haitanensis (Bangiales, Rhodophyta)

The genotypes of 4550 LP markers that were mapped onto the genetic map. (XLSX 1645 kb

Anti-Allergic Inflammatory Activity of Interleukin-37 Is Mediated by Novel Signaling Cascades in Human Eosinophils

IL-1 family regulatory cytokine IL-37b can suppress innate immunity and inflammatory activity in inflammatory diseases. In this study, IL-37b showed remarkable in vitro suppression of inflammatory tumor necrosis factor-α, IL-1β, IL-6, CCL2, and CXCL8 production in the coculture of human primary eosinophils and human bronchial epithelial BEAS-2B cells with the stimulation of bacterial toll-like receptor-2 ligand peptidoglycan, while antagonizing the activation of intracellular nuclear factor-κB, PI3K–Akt, extracellular signal-regulated kinase 1/2, and suppressing the gene transcription of allergic inflammation-related PYCARD, S100A9, and CAMP as demonstrated by flow cytometry, RNA-sequencing, and bioinformatics. Results therefore elucidated the novel anti-inflammation-related molecular mechanisms mediated by IL-37b. Using the house dust mite (HDM)-induced humanized asthmatic NOD/SCID mice for preclinical study, intravenous administration of IL-37b restored the normal plasma levels of eosinophil activators CCL11 and IL-5, suppressed the elevated concentrations of Th2 and asthma-related cytokines IL-4, IL-6, and IL-13 and inflammatory IL-17, CCL5, and CCL11 in lung homogenate of asthmatic mice. Histopathological results of lung tissue illustrated that IL-37b could mitigate the enhanced mucus, eosinophil infiltration, thickened airway wall, and goblet cells. Together with similar findings using the ovalbumin- and HDM-induced allergic asthmatic mice further validated the therapeutic potential of IL-37b in allergic asthma. The above results illustrate the novel IL-37-mediated regulation of intracellular inflammation mechanism linking bacterial infection and the activation of human eosinophils and confirm the in vivo anti-inflammatory activity of IL-37b on human allergic asthma

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression

Unbalanced power flow algorithm for AC&DC hybrid distribution network with diverse-controlled VSC-MTDC converts

The paper aims to propose an algorithm to calculate the power flow of an AC&DC hybrid distribution system. AC&DC distribution networks have recently attracted increasing attention, for the distributed generations (DGs) and DC loads can be integrated in DC networks in more simple and flexible ways than AC networks. Many efforts have been made to deal with the power flow problem of hybrid networks, however, the DC-side power flow's effect on three-phase unbalanced AC side and the influence to entire distribution system which DG directly connected in the DC side are both not considered. Therefore, this paper discusses the grid architecture with multiple AC&DC feeders. Then, models of VSC-MTDC, DC/DC converters, DGs, and other elements are formulated for power flow calculation. Furthermore, power flow equations of DC distribution and VSC converters are deduced in detail. As for converters under different control strategies and diverse forms of linking combinations between AC&DC grids, calculating approaches are considered to be of partial differences. Considering these distinct cases, a specific and improved sequential method is employed to compute distribution network's power flow. Simulation results on a modified IEEE 13 Node Test Feeder demonstrate the rapidity, accuracy, and easy-convergence of the algorithm

Cells tile a flat plane by controlling geometries during morphogenesis of Pyropia thalli

Background Pyropia haitanensis thalli, which are made of a single layer of polygonal cells, are a perfect model for studying the morphogenesis of multi-celled organisms because their cell proliferation process is an excellent example of the manner in which cells control their geometry to create a two-dimensional plane. Methods Cellular geometries of thalli at different stages of growth revealed by light microscope analysis. Results This study showed the cell division transect the middle of the selected paired-sides to divide the cell into two equal portions, thus resulting in cell sides ≥4 and keeping the average number of cell sides at approximately six even as the thallus continued to grow, such that more than 90% of the cells in thalli longer than 0.08 cm had 5–7 sides. However, cell division could not fully explain the distributions of intracellular angles. Results showed that cell-division-associated fast reorientation of cell sides and cell divisions together caused 60% of the inner angles of cells from longer thalli to range from 100–140°. These results indicate that cells prefer to form regular polygons. Conclusions This study suggests that appropriate cell-packing geometries maintained by cell division and reorientation of cell walls can keep the cells bordering each other closely, without gaps

Transcriptome Dynamic Analysis Reveals New Candidate Genes Associated with Resistance to Fusarium Head Blight in Two Chinese Contrasting Wheat Genotypes

In recent years, Fusarium head blight (FHB) has developed into a global disease that seriously affects the yield and quality of wheat. Effective measures to solve this problem include exploring disease-resistant genes and breeding disease-resistant varieties. In this study, we conducted a comparative transcriptome analysis to identify the important genes that are differentially expressed in FHB medium-resistant (Nankang 1) and FHB medium-susceptible (Shannong 102) wheat varieties for various periods after Fusarium graminearum infection using RNA-seq technology. In total, 96,628 differentially expressed genes (DEGs) were identified, 42,767 from Shannong 102 and 53,861 from Nankang 1 (FDR 1). Of these, 5754 and 6841 genes were found to be shared among the three time points in Shannong 102 and Nankang 1, respectively. After inoculation for 48 h, the number of upregulated genes in Nankang 1 was significantly lower than that of Shannong 102, but at 96 h, the number of DEGs in Nankang 1 was higher than that in Shannong 102. This indicated that Shannong 102 and Nankang 1 had different defensive responses to F. graminearum in the early stages of infection. By comparing the DEGs, there were 2282 genes shared at the three time points between the two strains. GO and KEGG analyses of these DEGs showed that the following pathways were associated with disease resistance genes: response to stimulus pathway in GO, glutathione metabolism, phenylpropanoid biosynthesis, plant hormone signal transduction, and plant–pathogen interaction in KEGG. Among them, 16 upregulated genes were identified in the plant–pathogen interaction pathway. There were five upregulated genes, TraesCS5A02G439700, TraesCS5B02G442900, TraesCS5B02G443300, TraesCS5B02G443400, and TraesCS5D02G446900, with significantly higher expression levels in Nankang 1 than in Shannong 102, and these genes may have an important role in regulating the resistance of Nankang 1 to F. graminearum infection. The PR proteins they encode are PR protein 1-9, PR protein 1-6, PR protein 1-7, PR protein 1-7, and PR protein 1-like. In addition, the number of DEGs in Nankang 1 was higher than that in Shannong 102 on almost all chromosomes, except chromosomes 1A and 3D, but especially on chromosomes 6B, 4B, 3B, and 5A. These results indicate that gene expression and the genetic background must be considered for FHB resistance in wheat breeding