Tackling MARCKS-PIP3 circuit attenuates fibroblast activation and fibrosis progression.

Targeting activated fibroblasts, including myofibroblast differentiation, has emerged as a key therapeutic strategy in patients with idiopathic pulmonary fibrosis (IPF). However, there is no available therapy capable of selectively eradicating myofibroblasts or limiting their genesis. Through an integrative analysis of the regulator genes that are responsible for the activation of IPF fibroblasts, we noticed the phosphatidylinositol 4,5-bisphosphate (PIP2)-binding protein, myristoylated alanine-rich C-kinase substrate (MARCKS), as a potential target molecule for IPF. Herein, we have employed a 25-mer novel peptide, MARCKS phosphorylation site domain sequence (MPS), to determine if MARCKS inhibition reduces pulmonary fibrosis through the inactivation of PI3K/protein kinase B (AKT) signaling in fibroblast cells. We first observed that higher levels of MARCKS phosphorylation and the myofibroblast marker α-smooth muscle actin (α-SMA) were notably overexpressed in all tested IPF lung tissues and fibroblast cells. Treatment with the MPS peptide suppressed levels of MARCKS phosphorylation in primary IPF fibroblasts. A kinetic assay confirmed that this peptide binds to phospholipids, particularly PIP2, with a dissociation constant of 17.64 nM. As expected, a decrease of phosphatidylinositol (3,4,5)-trisphosphate pools and AKT activity occurred in MPS-treated IPF fibroblast cells. MPS peptide was demonstrated to impair cell proliferation, invasion, and migration in multiple IPF fibroblast cells in vitro as well as to reduce pulmonary fibrosis in bleomycin-treated mice in vivo. Surprisingly, we found that MPS peptide decreases α-SMA expression and synergistically interacts with nintedanib treatment in IPF fibroblasts. Our data suggest MARCKS as a druggable target in pulmonary fibrosis and also provide a promising antifibrotic agent that may lead to effective IPF treatments.-Yang, D. C., Li, J.-M., Xu, J., Oldham, J., Phan, S. H., Last, J. A., Wu, R., Chen, C.-H. Tackling MARCKS-PIP3 circuit attenuates fibroblast activation and fibrosis progression

DocStormer: Revitalizing Multi-Degraded Colored Document Images to Pristine PDF

For capturing colored document images, e.g. posters and magazines, it is common that multiple degradations such as shadows, wrinkles, etc., are simultaneously introduced due to external factors. Restoring multi-degraded colored document images is a great challenge, yet overlooked, as most existing algorithms focus on enhancing color-ignored document images via binarization. Thus, we propose DocStormer, a novel algorithm designed to restore multi-degraded colored documents to their potential pristine PDF. The contributions are: firstly, we propose a "Perceive-then-Restore" paradigm with a reinforced transformer block, which more effectively encodes and utilizes the distribution of degradations. Secondly, we are the first to utilize GAN and pristine PDF magazine images to narrow the distribution gap between the enhanced results and PDF images, in pursuit of less degradation and better visual quality. Thirdly, we propose a non-parametric strategy, PFILI, which enables a smaller training scale and larger testing resolutions with acceptable detail trade-off, while saving memory and inference time. Fourthly, we are the first to propose a novel Multi-Degraded Colored Document image Enhancing dataset, named MD-CDE, for both training and evaluation. Experimental results show that the DocStormer exhibits superior performance, capable of revitalizing multi-degraded colored documents into their potential pristine digital versions, which fills the current academic gap from the perspective of method, data, and task

Futuristic 6G Pervasive On-Demand Services:Integrating Space Edge Computing With Terrestrial Networks

Futuristic 6G technologies will integrate emerging low-Earth orbit (LEO) megaconstellations into terrestrial networks, promising to provide ubiquitous, low-latency and high-throughput network services on-demand. However, several unique characteristics of satellites (e.g., high dynamics and error-prone operational environments) make it very challenging to unleash the potential of megacons-tellations and accomplish these aforementioned promises

Deep-Learning-Enabled Fast Optical Identification and Characterization of Two-Dimensional Materials

Advanced microscopy and/or spectroscopy tools play indispensable role in nanoscience and nanotechnology research, as it provides rich information about the growth mechanism, chemical compositions, crystallography, and other important physical and chemical properties. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural-network-based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, segment sizes and their distributions, based on which we develop an ensemble approach topredict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other applications such as identifying layer numbers of a new 2D material, or materials produced by a different synthetic approach. Our artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials and potentially accelerate new material discoveries

Controllable Image Captioning via Prompting

Despite the remarkable progress of image captioning, existing captioners typically lack the controllable capability to generate desired image captions, e.g., describing the image in a rough or detailed manner, in a factual or emotional view, etc. In this paper, we show that a unified model is qualified to perform well in diverse domains and freely switch among multiple styles. Such a controllable capability is achieved by embedding the prompt learning into the image captioning framework. To be specific, we design a set of prompts to fine-tune the pre-trained image captioner. These prompts allow the model to absorb stylized data from different domains for joint training, without performance degradation in each domain. Furthermore, we optimize the prompts with learnable vectors in the continuous word embedding space, avoiding the heuristic prompt engineering and meanwhile exhibiting superior performance. In the inference stage, our model is able to generate desired stylized captions by choosing the corresponding prompts. Extensive experiments verify the controllable capability of the proposed method. Notably, we achieve outstanding performance on two diverse image captioning benchmarks including COCO Karpathy split and TextCaps using a unified model

Discussion of the Segregation and Low Hardness of Large-Diameter M3 High-Speed Steel Produced by Spray Forming

As an advanced near-net-shape processing method in which directly preformed, semi-finished products are created from liquid metals, spray forming has become popular in the development and application of new materials and is supporting industrialization. However, as investigated in this work, the problems of segregation and low hardness exist in the actual industrialization process, particularly for large-diameter M3 high-speed steel. It was here found that the annual ring segregation morphologies were mostly distributed from the edge to 1/2R, with a large number of stripes primarily enriched in C, Mo, and Cr elements, and the degree of segregation was mild. The ring segregation was located at the 1/2R position, where the main elemental enrichments were C, W, Mo, Cr, and V, and the segregation degree was severe. The formation of segregation during deposition is described based on an equilibrium solidification model. A slow cooling rate and heat dissipation from the surface to the inside were judged to be the main factors causing segregation and changes in the carbide morphology. In terms of hardness, with the increase in the quenching temperature to 1230 °C, the tempering hardness increased significantly. The analysis shows that a faster cooling rate in the atomization stage caused the solidified droplets to exhibit rapid solidification characteristics, and there was a higher proportion of MC carbide in the deposited billet. MC carbides cannot be fully dissolved using the conventional heat treatment process, which decreases the C, Cr, Mo, and V contents in the solution and, thus, reduces the secondary hardening capability. The findings show that, when the spray forming process is used to prepare large-diameter materials, it should not be considered a rapid solidification technology simply because of its atomization stage. Moreover, more attention should be paid to the influence of microstructure transformation during atomization and deposition

Long non-coding RNA HIF1A-AS2 facilitates adipose-derived stem cells (ASCs) osteogenic differentiation through miR-665/IL6 axis via PI3K/Akt signaling pathway

Abstract Background This study was aimed to investigate the role and specific molecular mechanism of HIF1A-AS2/miR-665/IL6 axis in regulating osteogenic differentiation of adipose-derived stem cells (ASCs) via the PI3K/Akt signaling pathway. Methods RNAs’ expression profile in normal/osteogenic differentiation-induced ASCs (osteogenic group) was from the Gene Expression Omnibus database. The analysis was carried out using Bioconductor of R. Gene Set Enrichment Analysis and Kyoto Encyclopedia of Genes and Genomes dataset were applied to identify up- and downregulated signaling pathways. Co-expression network of specific lncRNAs and mRNAs was structured by Cytoscape, while binding sites amongst lncRNA, mRNA, and miRNA were predicted by TargetScan and miRanda. ASCs were derived from human adipose tissue and were authenticated by flow cytometry. ASC cell function was surveyed by alizarin red and alkaline phosphatase (ALP) staining. Molecular mechanism of HIF1A-AS2/miR-665/IL6 axis was investigated by RNAi, cell transfection, western blot, and qRT-PCR. RNA target relationships were validated by dual-luciferase assay. Results HIF1A-AS2 and IL6 were highly expressed while miR-665 was lowly expressed in induced ASCs. HIF1A-AS2 and IL6 improved the expression level of osteoblast markers Runx2, Osterix, and Osteocalcin and also accelerated the formation of calcium nodule and ALP activity, yet miR-665 had opposite effects. HIF1A-AS2 directly targeted miR-665, whereas miR-665 repressed IL6 expression. Moreover, the HIF1A-AS2/miR-665/IL6 regulating axis activated the PI3K/Akt signaling pathway. Conclusions LncRNA HIF1A-AS2 could sponge miR-665 and hence upregulate IL6, activate the PI3K/Akt signaling pathway, and ultimately promote ASC osteogenic differentiation