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2,009 results on '"Guo Xiaohui"'

1. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study

Author

Yuan Xiaoyong, Lu Difei, Meng Shuyou, Yang Lihui, Yuan Geheng, Qi Xin, Wen Bing, Zhang Junqing, and Guo Xiaohui

Subjects
region differences, diabetic foot, infection, amputation, death rate, Biology (General), QH301-705.5
Abstract

The objective of this study was to explore the clinical characteristics and prognosis of diabetic foot in hospitalized patients with diabetes in Tibet. To achieve that, patients hospitalized in People’s Hospital of Tibet Autonomous Region and diagnosed with diabetic foot ulcer (DFU) from January 1, 2016 to December 31, 2020 were enrolled in the study, and DFU cases of Peking University First Hospital were collected as control group. Analysis and comparison of clinical characteristics of DFU in plateau and plain areas were conducted. Normal distribution data or non-normal distribution data between groups were analyzed by t-test analysis or the nonparametric Mann–Whitney U test, and categorical variants were compared by Chi-square of Pearson. A total of 54 DFU cases were enrolled in the study in the People’s Hospital of Tibet Autonomous Region (Tibet group for short). Males accounted for 83.3% (45 cases) in Tibet group, which was higher than that of Peking University First Hospital (Beijing group for short), which accounted for 67.0%. Compared with the DFU patients in the Beijing group, the Tibet group was younger (58.11 ± 12.25 years vs 64.18 ± 11.37 years, P < 0.05), with a shorter disease duration (7.00 years vs 12.00 years, P < 0.05). In contrast, alcohol consumption was higher in the Tibet group (44.4 vs 27.4%, P < 0.05), and the number of patients with smoking habit was higher in the Beijing group (29.6 vs 43.7%, P < 0.05). The Tibet group had higher HbA1c (10.2 vs 8.7%, P < 0.05) and lower DFU proportion (22.2 vs 44.2%, P < 0.05). There was no statistically significant difference in the proportion of moderate to severe infections between the two groups (58.5 vs 59.6%, P = 0.887). Leukocytes (6.75 × 109/L vs 8.72 × 109/L, P < 0.05) and neutrophils (4.07 × 109/L vs 6.26 × 109/L, P < 0.05) in Tibet group were lower. Although the DFU amputation rate in the Tibet group was lower than that in the Beijing group (9.3 vs 29.8%, P < 0.05), there was no statistically significant difference between the two groups in terms of treatment cost, hospital stay, and mortality. In conclusion, patients with DFU in Tibet had a smaller age, shorter duration of diabetes, and more male predominance. The proportions of gangrene and amputation were lower in Tibet, with gangrene accounting for 80% of all amputees.

Published
2023
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2. GABPA protects against gastric cancer deterioration via negatively regulating GPX1

Author

Yin Binghua, Dong Bing, Guo Xiaohui, Wang Can, and Huo Huazhi

Subjects
gabpa, gpx1, gastric cancer, migration, Biochemistry, QD415-436
Abstract

Background: To explore the anti-cancer role of GABPA in the progression of gastric cancer (GC), and the underlying mechanism. Methods: Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to detect the expression pattern of GABPA in 45 pairs of GC and non-tumoral tissues. The relationship between GABPA expression and clinic pathological indicators of GC patients was analyzed. In AGS and SGC-7901 cells overexpressing GABPA, their migratory ability was determined by trans well and wound healing assay. The interaction between GABPA and its downstream target GPX1 was explored by dual-luciferase reporter assay, and their synergistical regulation on GC cell migration was finally elucidated. Results: GABPA was downregulated in GC tissues in comparison to normal ones. Low level of GABPA predicted high incidences of lymphatic and distant metastasis in GC. Overexpression of GABPA blocked AGS and SGC-7901 cells to migrate. GABPA could target GPX1 via the predicted binding site. GPX1 was upregulated in clinical samples of GC, and negatively correlated to GABPA level. The anticancer effect of GABPA on GC relied on the involvement of GPX1. Conclusions: GABPA is downregulated in GC samples, which can be utilized to predict GC metastasis. Serving as a tumor suppressor, GABPA blocks GC cells to migrate by targeting GPX1.

Published
2022
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3. Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching

Author

Li, Zhuoran, Hu, Chunming, Chen, Junfan, Chen, Zhijun, Guo, Xiaohui, and Zhang, Richong

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Code-switching is a data augmentation scheme mixing words from multiple languages into source lingual text. It has achieved considerable generalization performance of cross-lingual transfer tasks by aligning cross-lingual contextual word representations. However, uncontrolled and over-replaced code-switching would augment dirty samples to model training. In other words, the excessive code-switching text samples will negatively hurt the models' cross-lingual transferability. To this end, we propose a Progressive Code-Switching (PCS) method to gradually generate moderately difficult code-switching examples for the model to discriminate from easy to hard. The idea is to incorporate progressively the preceding learned multilingual knowledge using easier code-switching data to guide model optimization on succeeding harder code-switching data. Specifically, we first design a difficulty measurer to measure the impact of replacing each word in a sentence based on the word relevance score. Then a code-switcher generates the code-switching data of increasing difficulty via a controllable temperature variable. In addition, a training scheduler decides when to sample harder code-switching data for model training. Experiments show our model achieves state-of-the-art results on three different zero-shot cross-lingual transfer tasks across ten languages., Comment: 9 pages, 5 figures, 6 tables. Accepted by International Joint Conference on Artificial Intelligence (IJCAI 2024)

Published
2024

13. A novel Multi to Single Module for small object detection

Author

Guo, Xiaohui

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Small object detection presents a significant challenge in computer vision and object detection. The performance of small object detectors is often compromised by a lack of pixels and less significant features. This issue stems from information misalignment caused by variations in feature scale and information loss during feature processing. In response to this challenge, this paper proposes a novel the Multi to Single Module (M2S), which enhances a specific layer through improving feature extraction and refining features. Specifically, M2S includes the proposed Cross-scale Aggregation Module (CAM) and explored Dual Relationship Module (DRM) to improve information extraction capabilities and feature refinement effects. Moreover, this paper enhances the accuracy of small object detection by utilizing M2S to generate an additional detection head. The effectiveness of the proposed method is evaluated on two datasets, VisDrone2021-DET and SeaDronesSeeV2. The experimental results demonstrate its improved performance compared with existing methods. Compared to the baseline model (YOLOv5s), M2S improves the accuracy by about 1.1\% on the VisDrone2021-DET testing dataset and 15.68\% on the SeaDronesSeeV2 validation set.

Published
2023

14. A Review of Security Assessment Methods for 5G Industrial Internet

Author

Liu, Guang, Chen, Xingchi, Guo, Xiaohui, He, Yuanwen, Huang, Xun, Lu, Hui, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gu, Zhaoquan, editor, Zhou, Wanlei, editor, Zhang, Jiawei, editor, Xu, Guandong, editor, and Jia, Yan, editor

Published
2024
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16. Deep Contrastive One-Class Time Series Anomaly Detection

Author

Wang, Rui, Liu, Chongwei, Mou, Xudong, Gao, Kai, Guo, Xiaohui, Liu, Pin, Wo, Tianyu, and Liu, Xudong

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

The accumulation of time-series data and the absence of labels make time-series Anomaly Detection (AD) a self-supervised deep learning task. Single-normality-assumption-based methods, which reveal only a certain aspect of the whole normality, are incapable of tasks involved with a large number of anomalies. Specifically, Contrastive Learning (CL) methods distance negative pairs, many of which consist of both normal samples, thus reducing the AD performance. Existing multi-normality-assumption-based methods are usually two-staged, firstly pre-training through certain tasks whose target may differ from AD, limiting their performance. To overcome the shortcomings, a deep Contrastive One-Class Anomaly detection method of time series (COCA) is proposed by authors, following the normality assumptions of CL and one-class classification. It treats the original and reconstructed representations as the positive pair of negative-sample-free CL, namely "sequence contrast". Next, invariance terms and variance terms compose a contrastive one-class loss function in which the loss of the assumptions is optimized by invariance terms simultaneously and the "hypersphere collapse" is prevented by variance terms. In addition, extensive experiments on two real-world time-series datasets show the superior performance of the proposed method achieves state-of-the-art.

Published
2022

19. Photocatalytic Removal of Dye and Reaction Mechanism Analysis over Y2O3 Composite Nanomaterials

Author

Guo Xiaohui, Liu Jinyan, and Guo Guibao

Subjects
Engineering (General). Civil engineering (General), TA1-2040
Abstract

Y2O3 supported photocatalysts MOx (M = Fe, Ti and Bi)/Y2O3 were synthesized by hydrothermal and deposition-precipitation method and used in photocatalytic degradation of xylenol orange and rhodamine B under UV light irradiation. The crystalline structure and optical properties were well characterized by XRD and UV-vis DRS. The results of XRD revealed that the MOx/Y2O3 were composed of Fe2O3, TiO2, Bi0.8Y1.2O3 and Y2O3. The UV-vis DRS showed that MOx/Y2O3 photocatalysts exhibited stronger absorption in ultraviolet, and the absorption edge shifted to visible light region significantly. The photocatalytic experiments indicated that MOx/Y2O3 photocatalysts showed better activity for photodegradation of xylenol orange than rhodamine B. Moreover, the role of Y2O3 and the mechanism of photocatalysis is proposed.

Published
2017
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