| 1. |
- Yang, Anning, et al.
(författare)
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Homocysteine accelerates hepatocyte autophagy by upregulating TFEB via DNMT3b-mediated DNA hypomethylation
- 2023
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Ingår i: Acta Biochimica et Biophysica Sinica. - : China Science Publishing & Media Ltd.. - 1672-9145. ; 55:8, s. 1184-1192
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Tidskriftsartikel (refereegranskat)abstract
- Autophagy plays a critical role in the physiology and pathophysiology of hepatocytes. High level of homocysteine (Hcy) promotes autophagy in hepatocytes, but the underlying mechanism is still unknown. Here, we investigate the relationship between Hcy-induced autophagy level and the expression of nuclear transcription factor EB (TFEB). The results show that Hcy-induced autophagy level is mediated by upregulation of TFEB. Silencing of TFEB decreases the level of autophagy-related protein LC3BII/I and increases p62 expression level in hepatocytes after exposure to Hcy. Moreover, the effect of Hcy on the expression of TFEB is regulated by hypomethylation of the TFEB promoter catalyzed by DNA methyltransferase 3b (DNMT3b). In summary, this study shows that Hcy can activate autophagy by inhibiting DNMT3b-mediated DNA methylation and upregulating TFEB expression. These findings provide another new mechanism for Hcy-induced autophagy in hepatocytes.
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| 2. |
- Ai, Chenxiang, et al.
(författare)
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A knitting copolymerization Strategy to Build Porous Polytriazolium Salts for Removal of Anionic Dyes and MnO4−
- 2022
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Ingår i: Macromolecular rapid communications. - : Wiley. - 1022-1336 .- 1521-3927. ; 43:15
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Tidskriftsartikel (refereegranskat)abstract
- Although considerable efforts have been devoted to novel ionic porous networks (IPNs), the development of them in a scalable manner to tackle the issues in pollutant treatment by adsorption remains an imminent challenge. Herein, inspired by natural spider webs, a knitting copolymerization strategy is proposed to construct analogue triazolium salt-based porous networks (IPN-CSUs). It is not only convenient to incorporate the cationic motifs into the network, but easy to control over the contents of ionic pairs. The as-prepared IPN-CSUs displays a high surface area of 924 m2 g−1, a large pore volume of 1.27 cm3 g−1 and abundant ionic sites, thereby exhibiting fast adsorption rate and high adsorption capacity towards organic and inorganic pollutants. The kinetics and thermodynamics study reveal that the adsorption followed a pseudo-second-order kinetic model and Langmuir isotherm model correspondingly. Specifically, the maximum adsorption capacity of the IPN-CSUs is as high as 1.82 mg mg−1 for permanganate ions and up to 0.54 mg mg−1 for methyl orange, which stands out among the previously reported porous adsorbents so far. It is expected that the strategy reported herein can be extended to the development of other potential efficient adsorbents in water purifications.
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| 3. |
- Allahgholi, Aschkan, et al.
(författare)
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The Adaptive Gain Integrating Pixel Detector at the European XFEL
- 2019
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Ingår i: Journal of Synchrotron Radiation. - 0909-0495 .- 1600-5775. ; 26, s. 74-82
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Tidskriftsartikel (refereegranskat)abstract
- The Adaptive Gain Integrating Pixel Detector (AGIPD) is an X-ray imager, custom designed for the European X-ray Free-Electron Laser (XFEL). It is a fast, low-noise integrating detector, with an adaptive gain amplifier per pixel. This has an equivalent noise of less than 1keV when detecting single photons and, when switched into another gain state, a dynamic range of more than 10(4)photons of 12keV. In burst mode the system is able to store 352 images while running at up to 6.5MHz, which is compatible with the 4.5MHz frame rate at the European XFEL. The AGIPD system was installed and commissioned in August 2017, and successfully used for the first experiments at the Single Particles, Clusters and Biomolecules (SPB) experimental station at the European XFEL since September 2017. This paper describes the principal components and performance parameters of the system.
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| 4. |
- Guo, Qingqing, et al.
(författare)
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LGANet : Local-Global Augmentation Network for Skin Lesion Segmentation
- 2023
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Ingår i: 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023. - 1945-8452 .- 1945-7928. - 9781665473583 ; 2023-April
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Konferensbidrag (refereegranskat)abstract
- Automatic segmentation of skin lesion is still challenging due to ambiguous boundary and noise interference of lesion regions. Recent exiting Transformer-based methods often directly apply Transformer to obtain long-range dependency to overcome these problems. However, they generally do not consider that patch partitioning strategy of Transformer could lead to the loss of local details around boundaries. Furthermore, dependencies across local windows only represent global information at a coarse level. Therefore, to overcome the limitations, two novel modules, Local Focus Module (LFM) and Global Augmentation Module (GAM) are proposed in this paper. LFM learns the local context around boundary regions to strengthen the discrimination between classes. And GAM learns the global context at a finer level to enhance global feature representation. Integrating LFM and GAM, a new Transformer encoder based framework, Local-Global Augmentation Network (LGANet), is proposed. LGANet is efficient in segmenting lesions with ambiguous boundary and with noise interference and its performances are demonstrated with extensive experiments on two public skin lesion segmentation datasets.
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| 5. |
- Guo, Qingqing, et al.
(författare)
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Parallel matters : Efficient polyp segmentation with parallel structured feature augmentation modules
- 2023
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Ingår i: IET Image Processing. - 1751-9659. ; 17:8, s. 2503-2515
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Tidskriftsartikel (refereegranskat)abstract
- The large variations of polyp sizes and shapes and the close resemblances of polyps to their surroundings call for features with long-range information in rich scales and strong discrimination. This article proposes two parallel structured modules for building those features. One is the Transformer Inception module (TI) which applies Transformers with different reception fields in parallel to input features and thus enriches them with more long-range information in more scales. The other is the Local-Detail Augmentation module (LDA) which applies the spatial and channel attentions in parallel to each block and thus locally augments the features from two complementary dimensions for more object details. Integrating TI and LDA, a new Transformer encoder based framework, Parallel-Enhanced Network (PENet), is proposed, where LDA is specifically adopted twice in a coarse-to-fine way for accurate prediction. PENet is efficient in segmenting polyps with different sizes and shapes without the interference from the background tissues. Experimental comparisons with state-of-the-arts methods show its merits.
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| 6. |
- Guo, Qingqing, et al.
(författare)
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Polyp Segmentation of Colonoscopy Images by Exploring the Uncertain Areas
- 2022
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Ingår i: IEEE Access. - 2169-3536. ; 10, s. 52971-52981
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Tidskriftsartikel (refereegranskat)abstract
- Colorectal cancer is one of the leading causes of death worldwide. Polyps are early symptoms of colorectal cancer and prone to malignant transformation. Polyp segmentation of colonoscopy images can help diagnosis. However, existing studies on polyp segmentation of colonoscopy images face two main difficulties: blurry polyp boundaries, close resemblances between polyps and surrounding tissues. The former may lead to partial segmentations, while the latter can result in false positive segmentations. This paper proposes a new polyp segmentation framework to tackle the two challenges. In this method, an uncertainty region based module called Uncertainty eXploration (UnX) is introduced to get the complete polyp region while eliminating the interferences from the backgrounds. Specifically, it refines the feature maps with ternary guidance masks by dividing the initial guidance maps into three types: foreground, background and uncertain region, so that the uncertain areas are highlighted for more foreground objects while the backgrounds are forcefully suppressed to avoid interferences of tissues in background. Taking UnX as side supervision to the transformer encoder based backbone stages, the proposed method can mine the boundary areas from the uncertainty regions gradually and obtain robust polyp segmentation finally. Moreover, a new module called Feature Enhancement (FeE) is also incorporated in the framework to enhance the discrimination for images with significant variation of sizes and shapes of polyps. FeE can supply multi-scale features to the global oriented transformer features. Experiments on five polyp segmentation benchmark datasets of colonoscopy images, Kvasir, CVC-ClinicDB, ETIS, CVC-ColonDB and CVC-300, show the superior performances of our proposed method. Especially, for ETIS, the most challenging among the five datasets, our method achieves 7.7% and 5.6% improvements in mDSC (mean Dice Similarity Coefficient) and mIoU (mean Intersection over Union) respectively in comparison with the state-of-the-arts methods.
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| 7. |
- Guo, Qingqing, et al.
(författare)
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Reduced volume of diabetic pancreatic islets in rodents detected by synchrotron X-ray phase-contrast microtomography and deep learning network
- 2023
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Ingår i: Heliyon. - : Elsevier BV. - 2405-8440. ; 9:2
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Tidskriftsartikel (refereegranskat)abstract
- The pancreatic islet is a highly structured micro-organ that produces insulin in response to rising blood glucose. Here we develop a label-free and automatic imaging approach to visualize the islets in situ in diabetic rodents by the synchrotron radiation X-ray phase-contrast microtomography (SRμCT) at the ID17 station of the European Synchrotron Radiation Facility. The large-size images (3.2 mm × 15.97 mm) were acquired in the pancreas in STZ-treated mice and diabetic GK rats. Each pancreas was dissected by 3000 reconstructed images. The image datasets were further analysed by a self-developed deep learning method, AA-Net. All islets in the pancreas were segmented and visualized by the three-dimension (3D) reconstruction. After quantifying the volumes of the islets, we found that the number of larger islets (=>1500 μm3) was reduced by 2-fold (wt 1004 ± 94 vs GK 419 ± 122, P < 0.001) in chronically developed diabetic GK rat, while in STZ-treated diabetic mouse the large islets were decreased by half (189 ± 33 vs 90 ± 29, P < 0.001) compared to the untreated mice. Our study provides a label-free tool for detecting and quantifying pancreatic islets in situ. It implies the possibility of monitoring the state of pancreatic islets in vivo diabetes without labelling.
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| 8. |
- Guo, Qingqing, et al.
(författare)
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Robust fusion for skin lesion segmentation of dermoscopic images
- 2023
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Ingår i: Frontiers in Bioengineering and Biotechnology. - : Frontiers Media SA. - 2296-4185. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Robust skin lesion segmentation of dermoscopic images is still very difficult. Recent methods often take the combinations of CNN and Transformer for feature abstraction and multi-scale features for further classification. Both types of combination in general rely on some forms of feature fusion. This paper considers these fusions from two novel points of view. For abstraction, Transformer is viewed as the affinity exploration of different patch tokens and can be applied to attend CNN features in multiple scales. Consequently, a new fusion module, the Attention-based Transformer-And-CNN fusion module (ATAC), is proposed. ATAC augments the CNN features with more global contexts. For further classification, adaptively combining the information from multiple scales according to their contributions to object recognition is expected. Accordingly, a new fusion module, the GAting-based Multi-Scale fusion module (GAMS), is also introduced, which adaptively weights the information from multiple scales by the light-weighted gating mechanism. Combining ATAC and GAMS leads to a new encoder-decoder-based framework. In this method, ATAC acts as an encoder block to progressively abstract strong CNN features with rich global contexts attended by long-range relations, while GAMS works as an enhancement of the decoder to generate the discriminative features through adaptive fusion of multi-scale ones. This framework is especially good at lesions of varying sizes and shapes and of low contrasts and its performances are demonstrated with extensive experiments on public skin lesion segmentation datasets.
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| 9. |
- Kuang, Chaoyang, et al.
(författare)
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Switchable Broadband Terahertz Absorbers Based on Conducting Polymer-Cellulose Aerogels
- 2023
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Ingår i: Advanced Science. - : WILEY. - 2198-3844.
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Tidskriftsartikel (refereegranskat)abstract
- Terahertz (THz) technologies provide opportunities ranging from calibration targets for satellites and telescopes to communication devices and biomedical imaging systems. A main component will be broadband THz absorbers with switchability. However, optically switchable materials in THz are scarce and their modulation is mostly available at narrow bandwidths. Realizing materials with large and broadband modulation in absorption or transmission forms a critical challenge. This study demonstrates that conducting polymer-cellulose aerogels can provide modulation of broadband THz light with large modulation range from ≈ 13% to 91% absolute transmission, while maintaining specular reflection loss < −30 dB. The exceptional THz modulation is associated with the anomalous optical conductivity peak of conducting polymers, which enhances the absorption in its oxidized state. The study also demonstrates the possibility to reduce the surface hydrophilicity by simple chemical modifications, and shows that broadband absorption of the aerogels at optical frequencies enables de-frosting by solar-induced heating. These low-cost, aqueous solution-processable, sustainable, and bio-friendly aerogels may find use in next-generation intelligent THz devices.
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| 10. |
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