Dynamic Changes in Subgraph Preference Profiles of Crucial Transcription Factors

Transcription factors with a large number of target genes—transcription hub(s), or THub(s)—are usually crucial components of the regulatory system of a cell, and the different patterns through which they transfer the transcriptional signal to downstream cascades are of great interest. By profiling normalized abundances (A(N)) of basic regulatory patterns of individual THubs in the yeast Saccharomyces cerevisiae transcriptional regulation network under five different cellular states and environmental conditions, we have investigated their preferences for different basic regulatory patterns. Subgraph-normalized abundances downstream of individual THubs often differ significantly from that of the network as a whole, and conversely, certain over-represented subgraphs are not preferred by any THub. The THub preferences changed substantially when the cellular or environmental conditions changed. This switching of regulatory pattern preferences suggests that a change in conditions does not only elicit a change in response by the regulatory network, but also a change in the mechanisms by which the response is mediated. The THub subgraph preference profile thus provides a novel tool for description of the structure and organization between the large-scale exponents and local regulatory patterns

NPInter: the noncoding RNAs and protein related biomacromolecules interaction database

The noncoding RNAs and protein related biomacromolecules interaction database (NPInter; or ) is a database that documents experimentally determined functional interactions between noncoding RNAs (ncRNAs) and protein related biomacromolecules (PRMs) (proteins, mRNAs or genomic DNAs). NPInter intends to provide the scientific community with a comprehensive and integrated tool for efficient browsing and extraction of information on interactions between ncRNAs and PRMs. Beyond cataloguing details of these interactions, the NPInter will be useful for understanding ncRNA function, as it adds a very important functional element, ncRNAs, to the biomolecule interaction network and sets up a bridge between the coding and the noncoding kingdoms

BMP-driven NRF2 activation in esophageal basal cell differentiation and eosinophilic esophagitis

Tissue homeostasis requires balanced self-renewal and differentiation of stem/progenitor cells, especially in tissues that are constantly replenished like the esophagus. Disruption of this balance is associated with pathological conditions, including eosinophilic esophagitis (EoE), in which basal progenitor cells become hyperplastic upon proinflammatory stimulation. However, how basal cells respond to the inflammatory environment at the molecular level remains undetermined. We previously reported that the bone morphogenetic protein (BMP) signaling pathway is critical for epithelial morphogenesis in the embryonic esophagus. Here, we address how this pathway regulates tissue homeostasis and EoE development in the adult esophagus. BMP signaling was specifically activated in differentiated squamous epithelium, but not in basal progenitor cells, which express the BMP antagonist follistatin. Previous reports indicate that increased BMP activity promotes Barrett’s intestinal differentiation; however, in mice, basal progenitor cell–specific expression of constitutively active BMP promoted squamous differentiation. Moreover, BMP activation increased intracellular ROS levels, initiating an NRF2-mediated oxidative response during basal progenitor cell differentiation. In both a mouse EoE model and human biopsies, reduced squamous differentiation was associated with high levels of follistatin and disrupted BMP/NRF2 pathways. We therefore propose a model in which normal squamous differentiation of basal progenitor cells is mediated by BMP-driven NRF2 activation and basal cell hyperplasia is promoted by disruption of BMP signaling in EoE

Healthy cities initiative in China: Progress, challenges, and the way forward

Article discusses how China implemented the first phase of its National Healthy Cities pilot program from 2016-20. Authors recommend aligning the Healthy Cities initiative in China with strategic national and global level agendas such as Healthy China 2030 and the Sustainable Development Goals (SDGs) by providing an integrative governance framework to facilitate a coherent intersectoral program to systemically improve population health

Fourier-Domain Optical Coherence Tomography for Monitoring the Lower Tear Meniscus in Dry Eye after Acupuncture Treatment

Dry eye is highly prevalent and has a significant impact on quality of life. Acupuncture was found to be effective to treat dry eye. However, little was known about the effect of acupuncture on different subtypes of dry eye. The objective of this study was to investigate the applicability of tear meniscus assessment by Fourier-domain optical coherence tomography in the evaluation of acupuncture treatment response in dry eye patients and to explore the effect of acupuncture on different subtypes of dry eye compared with artificial tear treatment. A total of 108 dry eye patients were randomized into acupuncture or artificial tear group. Each group was divided into three subgroups including lipid tear deficiency (LTD), Sjögren syndrome dry eye (SSDE), and non-Sjögren syndrome dry eye (Non-SSDE) for data analysis. After 4-week treatment, the low tear meniscus parameters including tear meniscus height (TMH), tear meniscus depth (TMD), and tear meniscus area (TMA) in the acupuncture group increased significantly for the LTD and Non-SSDE subgroups compared with both the baseline and the control groups (all P values < 0.05), but not for the SSDE. Acupuncture provided a measurable improvement of the tear meniscus dimensions for the Non-SSDE and LTD patients, but not for the SSDE patients

Volumetric next best view by 3D occupancy mapping using Markov Chain Gibbs sampler for precise manufacturing

In this paper, we propose a model-free volumetric Next Best View (NBV) algorithm for accurate 3D reconstruction using a Markov Chain Monte Carlo method for high-mix-low-volume objects in manufacturing. The volumetric information gain based Next Best View algorithm can in real-time select the next optimal view that reveals the maximum uncertainty of the scanning environment with respect to a partially reconstructed 3D Occupancy map, without any priori knowledge of the target. Traditional Occupancy grid maps make two independence assumptions for computational tractability but suffer from the overconfident estimation of the occupancy probability for each voxel leading to less precise surface reconstructions. This paper proposes a special case of the Markov Chain Monte Carlo (MCMC) method, the Gibbs sampler, to accurately estimate the posterior occupancy probability of a voxel by randomly sampling from its high-dimensional full posterior occupancy probability given the entire volumetric map with respect to the forward sensor model with a Gaussian distribution. Numerical experiments validate the performance of the MCMC Gibbs sampler algorithm under the ROS-Industry framework to prove the accuracy of the reconstructed Occupancy map and the completeness of the registered point cloud. The proposed MCMC Occupancy mapping could be used to optimise the tuning parameters of the online NBV algorithms via the inverse sensor model to realise industry automation.</p

C4-HSL aptamers for blocking qurom sensing and inhibiting biofilm formation in Pseudomonas aeruginosa and its structure prediction and analysis.

This study aimed to screen DNA aptamers against the signal molecule C4-HSL of the rhl system for the inhibition of biofilm formation of Pseudomonas aeruginosa using an improved systematic evolution of ligand by exponential enrichment (SELEX) method based on a structure-switching fluorescent activating bead. The aptamers against the C4-HSL with a high affinity and specifity were successfully obtained and evaluated in real-time by this method. Results of biofilm inhibition experiments in vitro showed that the biofilm formation of P. aeruginosa was efficiently reduced to about 1/3 by the aptamers compared with that of the groups without the aptamers. Independent secondary structure simulation and computer-aided tertiary structure prediction (3dRNA) showed that the aptamers contained a highly conserved Y-shaped structural unit. Therefore, this study benefits the search for new methods for the detection and treatment of P. aeruginosa biofilm formation

Subspace Projection Attention Network for GPR Heterogeneous Clutter Removal

Clutter removal in ground-penetrating radar (GPR) based on deep learning has been studied in recent years. However, existing methods are primarily designed for homogeneous background conditions and utilize only local spatial information via the convolution operation. In order to solve these issues, a subspace projection attention (SPA) network is proposed for GPR heterogeneous clutter removal in this article. First, a heterogeneous concrete dataset based on a numerical model with randomly placed aggregates is constructed, which incorporates the complex electromagnetic propagation process accurately to improve the effectiveness for heterogeneous clutter removal. In addition, the clutter basis learning neural network is designed by integrating the SPA module into the skip connection paths of U-Net architecture. By learning the subspace basis vectors adaptively, the SPA exploits both local and global spatial information to extract target features precisely. At the same time, the feature maps are projected to the target subspace to remove heterogeneous clutter features. Finally, the performance and effectiveness of proposed method are validated by simulations and experiments