Plasma NMDAR autoantibody: a new biomarker for the diagnosis of Hirschsprung disease.

Introduction: Hirschsprung Disease (HSCR) is a common congenital intestinal disease in pediatrics. Early diagnosis and treatment after birth alleviate the occurrence of complications. Consequently, we aim to identifiy a biomarker with ease of use, non-invasiveness, and highly accurate for diagnosis.

Magnetic Carbon Porous Polymer Prepared from a New Suspended Emulsion for the Absorption of Heavy Metal Ions.

In this study, magnetic carbon nanopolymers (FeO/C@PM) were synthesized by suspension polymerization using magnetic carbon nanoparticles as the matrix, 2-thiophene formaldehyde and acrylamide as the monomers, and ethylene glycol dimethacrylate (EGDMA) as the crosslinking agent. The obtained material was characterized using multiple techniques, including scanning electron microscopy (SEM), infrared spectroscopy (FTIR), X-ray diffraction (XRD), N adsorption-desorption, and thermogravimetric analysis (TGA). The adsorption effects of Zn, Cd, and Pb in the mixed solution were evaluated using magnetic carbon nanoparticles (FeO/C) and FeO/C@PM as adsorbents.

LR-GNN: a graph neural network based on link representation for predicting molecular associations.

In biomedical networks, molecular associations are important to understand biological processes and functions. Many computational methods, such as link prediction methods based on graph neural networks (GNNs), have been successfully applied in discovering molecular relationships with biological significance. However, it remains a challenge to explore a method that relies on representation learning of links for accurately predicting molecular associations.

HDMC: a novel deep learning-based framework for removing batch effects in single-cell RNA-seq data.

Motivation: With the development of single-cell RNA sequencing (scRNA-seq) techniques, increasingly more large-scale gene expression datasets become available. However, to analyze datasets produced by different experiments, batch effects among different datasets must be considered. Although several methods have been recently published to remove batch effects in scRNA-seq data, two problems remain to be challenging and not completely solved: (i) how to reduce the distribution differences of different batches more accurately; and (ii) how to align samples from different batches to recover the cell type clusters.

P300 and Decision Making under Risk and Ambiguity.

Our study aims to contrast the neural temporal features of early stage of decision making in the context of risk and ambiguity. In monetary gambles under ambiguous or risky conditions, 12 participants were asked to make a decision to bet or not, with the event-related potentials (ERPs) recorded meantime. The proportion of choosing to bet in ambiguous condition was significantly lower than that in risky condition.