Multi-omics integration reveals molecular networks and regulators of psoriasis.

BackgroundPsoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis.MethodsTo achieve a comprehensive mechanistic understanding of psoriasis, we conducted a systems biology study, integrating multi-omics datasets including GWAS, EWAS, tissue-specific transcriptome, expression quantitative trait loci (eQTLs), gene networks, and biological pathways to identify the key genes, processes, and networks that are genetically and epigenetically associated with psoriasis risk.ResultsThis integrative genomics study identified both well-characterized (e.g., the IL17 pathway in both GWAS and EWAS) and novel biological processes (e.g., the branched chain amino acid catabolism process in GWAS and the platelet and coagulation pathway in EWAS) involved in psoriasis. Finally, by utilizing tissue-specific gene regulatory networks, we unraveled the interactions among the psoriasis-associated genes and pathways in a tissue-specific manner and detected potential key regulatory genes in the psoriasis networks.ConclusionsThe integration and convergence of multi-omics signals provide deeper and comprehensive insights into the biological mechanisms associated with psoriasis susceptibility

Enhancing Learning Engagement and Academic Performance: A Systematic Review among Private College and University Students in Xi'an, China

This systematic review aims to explore the factors influencing learning engagement and its impact on learning performance among private college and university students in Xi'an, China. Learning engagement is a crucial element in students' educational experiences, and understanding the factors that contribute to it can significantly enhance learning outcomes. By systematically reviewing and synthesizing existing literature, this study identifies the key factors influencing learning engagement and investigates the relationship between learning engagement and learning performance. The findings of this review can inform educational institutions and policymakers in designing effective strategies to enhance student engagement and improve academic performance

Deep Homography Estimation for Dynamic Scenes

Homography estimation is an important step in many computer vision problems. Recently, deep neural network methods have shown to be favorable for this problem when compared to traditional methods. However, these new methods do not consider dynamic content in input images. They train neural networks with only image pairs that can be perfectly aligned using homographies. This paper investigates and discusses how to design and train a deep neural network that handles dynamic scenes. We first collect a large video dataset with dynamic content. We then develop a multi-scale neural network and show that when properly trained using our new dataset, this neural network can already handle dynamic scenes to some extent. To estimate a homography of a dynamic scene in a more principled way, we need to identify the dynamic content. Since dynamic content detection and homography estimation are two tightly coupled tasks, we follow the multi-task learning principles and augment our multi-scale network such that it jointly estimates the dynamics masks and homographies. Our experiments show that our method can robustly estimate homography for challenging scenarios with dynamic scenes, blur artifacts, or lack of textures.Comment: CVPR 2020, https://github.com/lcmhoang/hmg-dynamic

Programmable folding of fusion RNA \u3cem\u3ein vivo\u3c/em\u3e and \u3cem\u3ein vitro\u3c/em\u3e driven by pRNA 3WJ motif of phi29 DNA packaging motor

Misfolding and associated loss of function are common problems in constructing fusion RNA complexes due to changes in energy landscape and the nearest-neighbor principle. Here we report the incorporation and application of the pRNA-3WJ motif of the phi29 DNA packaging motor into fusion RNA with controllable and predictable folding. The motif included three discontinuous ∼18 nucleotide (nt) fragments, displayed a distinct low folding energy (Shu D et al., Nature Nanotechnology, 2011, 6:658–667), and folded spontaneously into a leading core that enabled the correct folding of other functionalities fused to the RNA complex. Three individual fragments dispersed at any location within the sequence allowed the other RNA functional modules to fold into their original structures with authentic functions, as tested by Hepatitis B virus ribozyme, siRNA, and aptamers for malachite green (MG), spinach, and streptavidin (STV). Only nine complementary nucleotides were present for any two of the three ∼18-nt fragments, but the three 9 bp branches were so powerful that they disrupted other double strands with more than 15 bp within the fusion RNA. This system enabled the production of fusion complexes harboring multiple RNA functionalities with correct folding for potential applications in biotechnology, nanomedicine and nanotechnology. We also applied this system to investigate the principles governing the folding of RNA in vivo and in vitro. Temporal production of RNA sequences during in vivo transcription caused RNA to fold into different conformations that could not be predicted with routine principles derived from in vitro studies

The Atomic-to-Molecular Transition in Galaxies. III. A New Method for Determining the Molecular Content of Primordial and Dusty Clouds

Understanding the molecular content of galaxies is a critical problem in star formation and galactic evolution. Here we present a new method, based on a Stromgren-type analysis, to calculate the amount of HI that surrounds a molecular cloud irradiated by an isotropic radiation field. We consider both planar and spherical clouds, and H_2 formation either in the gas phase or catalyzed by dust grains. Under the assumption that the transition from atomic to molecular gas is sharp, our method gives the solution without any reference to the photodissociation cross section. We test our results for the planar case against those of a PDR code, and find typical accuracies of about 10%. Our results are also consistent with the scaling relations found in Paper I of this series, but they apply to a wider range of physical conditions. We present simple, accurate analytic fits to our results that are suitable for comparison to observations and to implementation in numerical and semi-analytic models.Comment: 14 pages, 5 figures, accepted to Ap