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

Vibration Control for the Flexible Rotor with Piezoelectric Bearings Based on the Mixed Sensitivity Robust Controller

Active control of flexible rotors is a challenging issue in modern industries. This paper focuses on the synthesis of a mixed sensitivity robust controller for a linear parameter-varying (LPV) system. The objective is to control rotor vibration, especially when the rotor is passing the first two bending critical speeds. In the formulation of the problem for the controller, weighting functions are proposed based on the relationship between the desired shape of the open-loop transfer function and sensitivity functions of the closed-loop system. Recent research has highlighted the efficiency of mixed sensitivity robust controllers in stabilizing a wide range of magnetic bearing systems. Here, the method is extended to control the vibration of a piezoelectric bearing system. The experimental rotor features two unbalance-exited resonances within its operating range. Experimental results demonstrate good performance of the vibration reduction and the effectiveness of the design method

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

Research on Grinding Force Prediction of Flexible Abrasive Disc Grinding Process of TC17 Titanium Alloy

Abrasive disc grinding is currently a key manufacturing process to achieve better accuracy and high-quality surfaces of TC17 components. Grinding force, which results from the friction and elastic–plastic deformation during the contact and interaction between the abrasive grains and the workpiece, is a critical parameter that represents the grinding accuracy and efficiency. In order to understand the influence factors of grinding force, the characteristics of the flexible abrasive disc grinding process were studied. Considering the contact state between the abrasive tool and the workpiece, the theoretical model of normal grinding force was established in detail, from macro- and micro-perspectives. By conducting single-factor and orthogonal grinding experiments of TC17 components, the influence of different process parameters on the normal grinding force was revealed. The normal grinding force prediction models of the abrasive disc grinding process were developed based on the Box–Behnken design (BBD) and particle swarm optimization–back propagation (PSO-BP) neural networks, respectively. The results showed that the normal grinding force was negatively correlated with the disc rotational speed, and positively correlated with the contact angle, grinding depth, and feed rate, and the interaction of the factor feed rate and grinding depth was the more influential factor. Both the BBD and PSO-BP force models had good reliability and accuracy, and the mean absolute error (MAE) and mean relative error (MRE) of the above two prediction models were 0.22 N and 0.16 N, and 13.3% and 10.9%, respectively

Vibration control for the flexible rotor with piezoelectric bearings based on the mixed sensitivity robust controller

Active control of flexible rotors is a challenging issue in modern industries. This paper focuses on the synthesis of a mixed sensitivity robust controller for a linear parameter-varying (LPV) system. The objective is to control rotor vibration, especially when the rotor is passing the first two bending critical speeds. In the formulation of the problem for the controller, weighting functions are proposed based on the relationship between the desired shape of the open-loop transfer function and sensitivity functions of the closed-loop system. Recent research has highlighted the efficiency of mixed sensitivity robust controllers in stabilizing a wide range of magnetic bearing systems. Here, the method is extended to control the vibration of a piezoelectric bearing system. The experimental rotor features two unbalance-exited resonances within its operating range. Experimental results demonstrate good performance of the vibration reduction and the effectiveness of the design method

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