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Artikel ; Online: Circular RNA circ_0000467 regulates colorectal cancer development via miR-382-5p/EN2 axis.

Xie, Lu / Pan, Zhihong

2021 Band 12, Heft 1, Seite(n) 886–897

Abstract: Circular RNAs (CircRNAs), belonging to non-coding RNAs, exert a crucial modulatory role in cancer progression. In this study, circRNA microarray analysis was utilized to screen differentially expressed circRNA in colorectal cancer (CRC) and circ_0000467 ... ...

Abstract	Circular RNAs (CircRNAs), belonging to non-coding RNAs, exert a crucial modulatory role in cancer progression. In this study, circRNA microarray analysis was utilized to screen differentially expressed circRNA in colorectal cancer (CRC) and circ_0000467 was identified as one circRNA whose expression was significantly upregulated in CRC. Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) indicated that circ_0000467 and engrailed-2 (EN2) expression levels were up-modulated, while the expression level of miR-382-5p was down-modulated in CRC tissues. The depletion of circ_0000467 expression was found to impede the multiplication, migration, invasion, and epithelial-mesenchymal transition (EMT) processes in CRC cells, which were examined by 3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) and Transwell experiments. Dual-luciferase reporter assay was used to verify the targeting relationship between circ_0000467 and miR-382-5p. It was also revealed that circ_0000467 could up-regulate EN2 expression via repressing miR-382-5p in CRC cells. Furthermore, EN2 overexpression counteracted the suppressing effects of circ_0000467 knockdown on the malignant behaviors of CRC cells. To sum up, circ_0000467 facilitates CRC development by modulating the miR-382-5p/EN2 axis, and circ_0000467 is a promising target for CRC therapy.
Mesh-Begriff(e)	Cell Line, Tumor ; Colorectal Neoplasms/genetics ; Colorectal Neoplasms/metabolism ; Colorectal Neoplasms/pathology ; Female ; Homeodomain Proteins/genetics ; Homeodomain Proteins/metabolism ; Humans ; Male ; MicroRNAs/genetics ; MicroRNAs/metabolism ; Middle Aged ; Nerve Tissue Proteins/genetics ; Nerve Tissue Proteins/metabolism ; RNA, Circular/genetics ; RNA, Circular/metabolism
Chemische Substanzen	Homeodomain Proteins ; MIRN382 microRNA, human ; MicroRNAs ; Nerve Tissue Proteins ; RNA, Circular ; engrailed 2 protein
Sprache	Englisch
Erscheinungsdatum	2021-02-18
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	2737830-5
ISSN	2165-5987 ; 2165-5979
ISSN (online)	2165-5987
ISSN	2165-5979
DOI	10.1080/21655979.2021.1889130
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Microbial-induced synthesis of calcite based on carbon dioxide capture and its cementing mechanism

Zhan, Qiwei / Yu, Xiaoniu / Pan, Zhihong / Qian, Chunxiang

Journal of cleaner production. 2021 Jan. 01, v. 278

2021

Abstract: As a new type of green cementitious materials, inorganic minerals synthesized by microbial-induced mineralization could cement loose sand particles. Their advantages included efficient preparation process, easy control and environmental friendliness, and ...

Abstract	As a new type of green cementitious materials, inorganic minerals synthesized by microbial-induced mineralization could cement loose sand particles. Their advantages included efficient preparation process, easy control and environmental friendliness, and accordingly they could be used in desert treatment, fugitive dust control, foundation reinforcement, and slope stability. This study identified microbial growth under different conditions and obtained effective methods for the promotion of microbial growth were obtained. The enzyme protein expression was identified using an electrophoretic and gel imaging system, and the results indicated that the main enzyme protein in the bacterial solution was carbonic anhydrase. By means of X ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) analysis, it was concluded that mineralization products were near-spherical calcite with particle sizes of approximately 5 μm. The microstructure between the mineralization products and loose sand particles was analyzed by scanning electron microscopy (SEM) and transmission electron microscopy (TEM), and the difference of cementing effect between different methods was determined. The feasibility of cementing sand by microbial-induced mineralization was demonstrated. Based on the analysis of interaction between the mineralization products and loose sand particles by fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC) and nuclear magnetic resonance (NMR), it was found that the essential reason for cementing loose sand particles was the formation of intermolecular hydrogen bonds. Therefore, this study identified the mechanism of microbial-induced mineralization as the basis for the optimization and regulation of the whole process of mineralization and cementation. In this study, characteristics of the mineralization product and hydrogen bonding mechanism were investigated systematically. Carbon dioxide was used as a carbon source to synthesize mineralization products, and greenhouse gas was effectively utilized without toxic and harmful by-products. The research provided new ecological materials and technologies for environmental governance, which was expected to attract considerable attention.
Schlagwörter	Fourier transform infrared spectroscopy ; X-ray diffraction ; byproducts ; calcite ; carbon ; carbon dioxide ; carbonate dehydratase ; cement ; differential scanning calorimetry ; dust control ; electrophoresis ; energy-dispersive X-ray analysis ; environmental governance ; gels ; greenhouse gases ; hydrogen bonding ; image analysis ; microbial growth ; microstructure ; mineralization ; nuclear magnetic resonance spectroscopy ; particle size ; protein synthesis ; sand ; scanning electron microscopy ; toxicity ; transmission electron microscopy
Sprache	Englisch
Erscheinungsverlauf	2021-0101
Erscheinungsort	Elsevier Ltd
Dokumenttyp	Artikel
ISSN	0959-6526
DOI	10.1016/j.jclepro.2020.123398
Datenquelle	NAL Katalog (AGRICOLA)

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Artikel ; Online: An Adaptive Learning Image Denoising Algorithm Based on Eigenvalue Extraction and the GAN Model.

Wang, Feng / Xu, Zhiming / Ni, Weichuan / Chen, Jinhuang / Pan, Zhihong

Computational intelligence and neuroscience

2022 Band 2022, Seite(n) 5792767

Abstract: This paper proposes a self-adjusting generative confrontation network image denoising algorithm. The algorithm combines noise reduction and the adaptive learning GAN model. First, the algorithm uses image features to preprocess the image and extract the ... ...

Abstract	This paper proposes a self-adjusting generative confrontation network image denoising algorithm. The algorithm combines noise reduction and the adaptive learning GAN model. First, the algorithm uses image features to preprocess the image and extract the effective information of the image. Then, the edge signal is classified according to the threshold value to suppress the problem of "excessive strangulation," and then the edge signal of the image is extracted to enhance the effective signal in the high-frequency signal. Finally, the algorithm uses an adaptive learning GAN model to further train the image. Each iteration of the generator network is composed of three stages. And then, we get the best value. Through experiments, it can be seen from the data that the article algorithm is compared with the traditional algorithm and the literature algorithm. Under the same conditions, the algorithm can ensure the operating efficiency while having better fidelity, and it can still denoise at the same time. The edge signal of the image is preserved and has a better visual effect.
Mesh-Begriff(e)	Algorithms ; Image Processing, Computer-Assisted/methods ; Signal-To-Noise Ratio
Sprache	Englisch
Erscheinungsdatum	2022-02-09
Erscheinungsland	United States
Dokumenttyp	Journal Article ; Review
ZDB-ID	2388208-6
ISSN	1687-5273 ; 1687-5273
ISSN (online)	1687-5273
ISSN	1687-5273
DOI	10.1155/2022/5792767
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Buch ; Online: Raising The Limit Of Image Rescaling Using Auxiliary Encoding

Yin, Chenzhong / Pan, Zhihong / Zhou, Xin / Kang, Le / Bogdan, Paul

2023

Abstract: Normalizing flow models using invertible neural networks (INN) have been widely investigated for successful generative image super-resolution (SR) by learning the transformation between the normal distribution of latent variable $z$ and the conditional ... ...

Abstract	Normalizing flow models using invertible neural networks (INN) have been widely investigated for successful generative image super-resolution (SR) by learning the transformation between the normal distribution of latent variable $z$ and the conditional distribution of high-resolution (HR) images gave a low-resolution (LR) input. Recently, image rescaling models like IRN utilize the bidirectional nature of INN to push the performance limit of image upscaling by optimizing the downscaling and upscaling steps jointly. While the random sampling of latent variable $z$ is useful in generating diverse photo-realistic images, it is not desirable for image rescaling when accurate restoration of the HR image is more important. Hence, in places of random sampling of $z$, we propose auxiliary encoding modules to further push the limit of image rescaling performance. Two options to store the encoded latent variables in downscaled LR images, both readily supported in existing image file format, are proposed. One is saved as the alpha-channel, the other is saved as meta-data in the image header, and the corresponding modules are denoted as suffixes -A and -M respectively. Optimal network architectural changes are investigated for both options to demonstrate their effectiveness in raising the rescaling performance limit on different baseline models including IRN and DLV-IRN.
Schlagwörter	Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing
Thema/Rubrik (Code)	006
Erscheinungsdatum	2023-03-12
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Buch ; Online: Smooth and Stepwise Self-Distillation for Object Detection

Deng, Jieren / Zhou, Xin / Tian, Hao / Pan, Zhihong / Aguiar, Derek

2023

Abstract: Distilling the structured information captured in feature maps has contributed to improved results for object detection tasks, but requires careful selection of baseline architectures and substantial pre-training. Self-distillation addresses these ... ...

Abstract	Distilling the structured information captured in feature maps has contributed to improved results for object detection tasks, but requires careful selection of baseline architectures and substantial pre-training. Self-distillation addresses these limitations and has recently achieved state-of-the-art performance for object detection despite making several simplifying architectural assumptions. Building on this work, we propose Smooth and Stepwise Self-Distillation (SSSD) for object detection. Our SSSD architecture forms an implicit teacher from object labels and a feature pyramid network backbone to distill label-annotated feature maps using Jensen-Shannon distance, which is smoother than distillation losses used in prior work. We additionally add a distillation coefficient that is adaptively configured based on the learning rate. We extensively benchmark SSSD against a baseline and two state-of-the-art object detector architectures on the COCO dataset by varying the coefficients and backbone and detector networks. We demonstrate that SSSD achieves higher average precision in most experimental settings, is robust to a wide range of coefficients, and benefits from our stepwise distillation procedure.
Schlagwörter	Computer Science - Computer Vision and Pattern Recognition
Thema/Rubrik (Code)	004
Erscheinungsdatum	2023-03-08
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Buch ; Online: GBSD

Deng, Jieren / Zhou, Xin / Tian, Hao / Pan, Zhihong / Aguiar, Derek

Generative Bokeh with Stage Diffusion

2023

Abstract: The bokeh effect is an artistic technique that blurs out-of-focus areas in a photograph and has gained interest due to recent developments in text-to-image synthesis and the ubiquity of smart-phone cameras and photo-sharing apps. Prior work on rendering ... ...

Abstract	The bokeh effect is an artistic technique that blurs out-of-focus areas in a photograph and has gained interest due to recent developments in text-to-image synthesis and the ubiquity of smart-phone cameras and photo-sharing apps. Prior work on rendering bokeh effects have focused on post hoc image manipulation to produce similar blurring effects in existing photographs using classical computer graphics or neural rendering techniques, but have either depth discontinuity artifacts or are restricted to reproducing bokeh effects that are present in the training data. More recent diffusion based models can synthesize images with an artistic style, but either require the generation of high-dimensional masks, expensive fine-tuning, or affect global image characteristics. In this paper, we present GBSD, the first generative text-to-image model that synthesizes photorealistic images with a bokeh style. Motivated by how image synthesis occurs progressively in diffusion models, our approach combines latent diffusion models with a 2-stage conditioning algorithm to render bokeh effects on semantically defined objects. Since we can focus the effect on objects, this semantic bokeh effect is more versatile than classical rendering techniques. We evaluate GBSD both quantitatively and qualitatively and demonstrate its ability to be applied in both text-to-image and image-to-image settings.
Schlagwörter	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence
Thema/Rubrik (Code)	004
Erscheinungsdatum	2023-06-14
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Buch ; Online: Fast Diffusion Probabilistic Model Sampling through the lens of Backward Error Analysis

Gao, Yansong / Pan, Zhihong / Zhou, Xin / Kang, Le / Chaudhari, Pratik

2023

Abstract: Denoising diffusion probabilistic models (DDPMs) are a class of powerful generative models. The past few years have witnessed the great success of DDPMs in generating high-fidelity samples. A significant limitation of the DDPMs is the slow sampling ... ...

Abstract	Denoising diffusion probabilistic models (DDPMs) are a class of powerful generative models. The past few years have witnessed the great success of DDPMs in generating high-fidelity samples. A significant limitation of the DDPMs is the slow sampling procedure. DDPMs generally need hundreds or thousands of sequential function evaluations (steps) of neural networks to generate a sample. This paper aims to develop a fast sampling method for DDPMs requiring much fewer steps while retaining high sample quality. The inference process of DDPMs approximates solving the corresponding diffusion ordinary differential equations (diffusion ODEs) in the continuous limit. This work analyzes how the backward error affects the diffusion ODEs and the sample quality in DDPMs. We propose fast sampling through the \textbf{Restricting Backward Error schedule (RBE schedule)} based on dynamically moderating the long-time backward error. Our method accelerates DDPMs without any further training. Our experiments show that sampling with an RBE schedule generates high-quality samples within only 8 to 20 function evaluations on various benchmark datasets. We achieved 12.01 FID in 8 function evaluations on the ImageNet $128\times128$, and a $20\times$ speedup compared with previous baseline samplers. Comment: arXiv admin note: text overlap with arXiv:2101.12176 by other authors
Schlagwörter	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence
Thema/Rubrik (Code)	519
Erscheinungsdatum	2023-04-22
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Buch ; Online: Diffusion Motion

Ren, Zhiyuan / Pan, Zhihong / Zhou, Xin / Kang, Le

Generate Text-Guided 3D Human Motion by Diffusion Model

2022

Abstract: We propose a simple and novel method for generating 3D human motion from complex natural language sentences, which describe different velocity, direction and composition of all kinds of actions. Different from existing methods that use classical ... ...

Abstract	We propose a simple and novel method for generating 3D human motion from complex natural language sentences, which describe different velocity, direction and composition of all kinds of actions. Different from existing methods that use classical generative architecture, we apply the Denoising Diffusion Probabilistic Model to this task, synthesizing diverse motion results under the guidance of texts. The diffusion model converts white noise into structured 3D motion by a Markov process with a series of denoising steps and is efficiently trained by optimizing a variational lower bound. To achieve the goal of text-conditioned image synthesis, we use the classifier-free guidance strategy to fuse text embedding into the model during training. Our experiments demonstrate that our model achieves competitive results on HumanML3D test set quantitatively and can generate more visually natural and diverse examples. We also show with experiments that our model is capable of zero-shot generation of motions for unseen text guidance. Comment: Accepted by ICASSP 2023
Schlagwörter	Computer Science - Computer Vision and Pattern Recognition
Thema/Rubrik (Code)	004
Erscheinungsdatum	2022-10-21
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel: Influence of Recycled Fine Aggregate Content on Properties of Soft Soil Solidified by Industrial Waste Residue.

Wang, Anhui / Zhan, Qiwei / Dong, Wanying / Gu, Weiyang / Zhou, Juanlan / Pan, Zhihong

Materials (Basel, Switzerland)

2022 Band 15, Heft 21

Abstract: The influence of recycled fine aggregate content on the properties of soft soil solidified by industrial waste residue was systematically studied. First, the addition of recycled fine aggregate may provide skeleton support, which was conducive to ... ...

Abstract	The influence of recycled fine aggregate content on the properties of soft soil solidified by industrial waste residue was systematically studied. First, the addition of recycled fine aggregate may provide skeleton support, which was conducive to improving the solidification properties. Comparing the addition of recycled fine aggregate content and a composite solidification agent separately, the compressive strength increased 48.01 times and 1.32 times, respectively. Second, the composition and quantity of the hydration products were analyzed by X-ray diffraction (XRD) and thermal gravity analysis (TG/DTG). In addition to silicon dioxide and aluminum oxide, a number of new minerals, including hydrated calcium silicate, calcium hydroxide and ettringite, were produced under different recycled fine aggregate contents. The diffraction peak of hydrated calcium hydroxide was weak, which indicated that the crystallinity and relative content was low. The main reason for this was that it was consumed as the activator of the secondary hydration reaction of blast furnace slag. With the increase in recycled fine aggregate content, the total weight loss (hydration products, crystal water, impurities) increased significantly, at rates of 6.9%, 7.0%, 7.2%, 8.8% and 9.7%. The addition of recycled fine aggregate does not change the composition and quantity of the hydration products, and the increased weight loss in this part might be caused by the cement paste attached to the surface of the recycled fine aggregate. Finally, their microstructure was analyzed by scanning electron microscopy (SEM). Larger and more pores appeared in the solidification system with the increase in recycled fine aggregate, and a large amount of ettringite was prepared. An excess in recycled fine aggregate caused more pores, and the negative impact of too many pores exceeded the lifting effect of the aggregate, resulting in the decline of its mechanical properties. Therefore, there was a suitable range for the use of recycled fine aggregate, which was not more than 40%. In conclusion, recycled fine aggregate not only acts as a skeleton to improve solidification strength, but could also realize the comprehensive utilization of waste, which provided a new scheme for solid waste utilization and soft soil solidification.
Sprache	Englisch
Erscheinungsdatum	2022-10-28
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2487261-1
ISSN	1996-1944
ISSN	1996-1944
DOI	10.3390/ma15217580
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Buch ; Online: Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network

Zhang, Min / Pan, Zhihong / Zhou, Xin / Kuo, C. -C. Jay

2022

Abstract: Normalizing flow models have been used successfully for generative image super-resolution (SR) by approximating complex distribution of natural images to simple tractable distribution in latent space through Invertible Neural Networks (INN). These models ...

Abstract	Normalizing flow models have been used successfully for generative image super-resolution (SR) by approximating complex distribution of natural images to simple tractable distribution in latent space through Invertible Neural Networks (INN). These models can generate multiple realistic SR images from one low-resolution (LR) input using randomly sampled points in the latent space, simulating the ill-posed nature of image upscaling where multiple high-resolution (HR) images correspond to the same LR. Lately, the invertible process in INN has also been used successfully by bidirectional image rescaling models like IRN and HCFlow for joint optimization of downscaling and inverse upscaling, resulting in significant improvements in upscaled image quality. While they are optimized for image downscaling too, the ill-posed nature of image downscaling, where one HR image could be downsized to multiple LR images depending on different interpolation kernels and resampling methods, is not considered. A new downscaling latent variable, in addition to the original one representing uncertainties in image upscaling, is introduced to model variations in the image downscaling process. This dual latent variable enhancement is applicable to different image rescaling models and it is shown in extensive experiments that it can improve image upscaling accuracy consistently without sacrificing image quality in downscaled LR images. It is also shown to be effective in enhancing other INN-based models for image restoration applications like image hiding. Comment: Accepted by ACM Multimedia 2022
Schlagwörter	Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing ; I.4.5
Thema/Rubrik (Code)	006
Erscheinungsdatum	2022-07-24
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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