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153. Artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low‐grade squamous intraepithelial lesion as diagnostic threshold

Author

Yao, Bin, primary, Feng, Yadong, additional, Zhao, Kai, additional, Liang, Yan, additional, Huang, Peilin, additional, Zang, Juncai, additional, Song, Jie, additional, Li, Mengjie, additional, Wang, Xiaofen, additional, Shu, Huazhong, additional, and Shi, Ruihua, additional

Published
2022
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161. Additional file 1 of Topical and intravenous administration of human umbilical cord mesenchymal stem cells in patients with diabetic foot ulcer and peripheral arterial disease: a phase I pilot study with a 3-year follow-up

Author

Zhang, Che, Huang, Li, Wang, Xiaofen, Zhou, Xiaoya, Zhang, Xiaoxian, Li, Ling, Wu, Jieying, Kou, Meng, Cai, Cheguo, Lian, Qizhou, and Zhou, Xihui

Abstract

Additional file 1: Table S1. Wagner grades: a classification system for diabetic foot ulcer. Table S2. Rutherford category: Clinical categories of chronic limb ischemia. Table S3. Basic treatments. Table S4. The empirical regimens of antibiotics for infected ulcers. Table S5 hUC-MSC treatments. Text S1. Measurement methods of ulcer area. Text S2. Visual analogue scale (VAS).

Published
2022
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169. Artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low‐grade squamous intraepithelial lesion as diagnostic threshold.

Author

Yao, Bin, Feng, Yadong, Zhao, Kai, Liang, Yan, Huang, Peilin, Zang, Juncai, Song, Jie, Li, Mengjie, Wang, Xiaofen, Shu, Huazhong, and Shi, Ruihua

Subjects
ARTIFICIAL intelligence, ESOPHAGEAL cancer, CERVICAL intraepithelial neoplasia, MEDICAL screening, SQUAMOUS cell carcinoma
Abstract

Background: Manual cytological diagnosis for early esophageal squamous cell carcinoma (early ESCC) and high‐grade intraepithelial neoplasia (HGIN) is unsatisfactory. Herein, we have introduced an artificial intelligence (AI)‐assisted cytological diagnosis for such lesions. Methods: Low‐grade squamous intraepithelial lesion or worse was set as the diagnostic threshold for AI‐assisted diagnosis. The performance of AI‐assisted diagnosis was evaluated and compared to that of manual diagnosis. Feasibility in large‐scale screening was also assessed. Results: AI‐assisted diagnosis for abnormal cells was superior to manual reading by presenting a higher efficiency for each slide (50.9 ± 0.8 s vs 236.8 ± 3.9 s, p = 1.52 × 10−76) and a better interobserver agreement (93.27% [95% CI, 92.76%–93.74%] vs 65.29% [95% CI, 64.35%–66.22%], p = 1.03 × 10−84). AI‐assisted detection showed a higher diagnostic accuracy (96.89% [92.38%–98.57%] vs 72.54% [65.85%–78.35%], p = 1.42 × 10−14), sensitivity (99.35% [95.92%–99.97%] vs 68.39% [60.36%–75.48%], p = 7.11 × 10−15), and negative predictive value (NPV) (97.06% [82.95%–99.85%] vs 40.96% [30.46%–52.31%], p = 1.42 × 10−14). Specificity and positive predictive value (PPV) were not significantly differed. AI‐assisted diagnosis demonstrated a smaller proportion of participants of interest (3.73%, [79/2117] vs.12.84% [272/2117], p = 1.59 × 10−58), a higher consistence between cytology and endoscopy (40.51% [32/79] vs. 12.13% [33/272], p = 1.54 × 10−8), specificity (97.74% [96.98%–98.32%] vs 88.52% [87.05%–89.84%], p = 3.19 × 10−58), and PPV (40.51% [29.79%–52.15%] vs 12.13% [8.61%–16.75%], p = 1.54 × 10−8) in community‐based screening. Sensitivity and NPV were not significantly differed. AI‐assisted diagnosis as primary screening significantly reduced average cost for detecting positive cases. Conclusion: Our study provides a novel cytological method for detecting and screening early ESCC and HGIN. [ABSTRACT FROM AUTHOR]

Published
2023
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177. FGF signaling regulates otic placode induction and refinement by controlling both ectodermal target genes and hindbrain Wnt8a

Author

Urness, Lisa D., Paxton, Christian N., Wang, Xiaofen, Schoenwolf, Gary C., and Mansour, Suzanne L.

Subjects
Genetic research -- Analysis, Gene expression -- Analysis, Brain -- Analysis, Fibroblast growth factors -- Analysis, Biological sciences
Abstract

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ydbio.2010.02.016 Byline: Lisa D. Urness (a), Christian N. Paxton (b), Xiaofen Wang (a), Gary C. Schoenwolf (b), Suzanne L. Mansour (a)(b) Keywords: FGF signaling; Otic placode induction; WNT signaling; Microarray analysis Abstract: The inner ear epithelium, with its complex array of sensory, non-sensory, and neuronal cell types necessary for hearing and balance, is derived from a thickened patch of head ectoderm called the otic placode. Mouse embryos lacking both Fgf3 and Fgf10 fail to initiate inner ear development because appropriate patterns of gene expression fail to be specified within the pre-otic field. To understand the transcriptional 'blueprint' initiating inner ear development, we used microarray analysis to identify prospective placode genes that were differentially expressed in control and Fgf3.sup.- .sup./ .sup.- ;Fgf10.sup.- .sup./ .sup.- embryos. Several genes in the down-regulated class, including Hmx3, Hmx2, Foxg1, Sox9, Has2, and Slc26a9 were validated by in situ hybridization. We also assayed candidate target genes suggested by other studies of otic induction. Two placode markers, Fgf4 and Foxi3, were down-regulated in Fgf3.sup.- .sup./ .sup.- ;Fgf10.sup.- .sup./ .sup.- embryos, whereas Foxi2, a cranial epidermis marker, was expanded in double mutants, similar to its behavior when WNT responses are blocked in the otic placode. Assays of hindbrain Wnt genes revealed that only Wnt8a was reduced or absent in FGF-deficient embryos, and that even some Fgf3.sup.- .sup./ .sup.- ;Fgf10.sup.- .sup./+ and Fgf3.sup.- .sup./ .sup.- embryos failed to express Wnt8a, suggesting a key role for Fgf3, and a secondary role for Fgf10, in Wnt8a expression. Chick explant assays showed that FGF3 or FGF4, but not FGF10, were sufficient to induce Wnt8a. Collectively, our results suggest that Wnt8a provides the link between FGF-induced formation of the pre-otic field and restriction of the otic placode to ectoderm adjacent to the hindbrain. Author Affiliation: (a) Department of Human Genetics, University of Utah, 15 N 2030 E, RM 2100, Salt Lake City, UT 84112-5330, USA (b) Department of Neurobiology and Anatomy, University of Utah, 30 N 1900 E, RM 2R066 SOM, Salt Lake City, UT 84132-3401, USA Article History: Received 13 January 2010; Revised 10 February 2010; Accepted 10 February 2010

Published
2010

182. A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine

Author

Ke Zhang, Keyi Guo, Wang Xiaofen, Hu Zhi, and Yufei Zhan

Subjects
Control and Optimization, Computer science, intrusion detection system (IDS), Energy Engineering and Power Technology, 02 engineering and technology, Intrusion detection system, lcsh:Technology, Field (computer science), Chromosome (genetic algorithm), Genetic algorithm, 0202 electrical engineering, electronic engineering, information engineering, smart grid, advanced metering infrastructure (AMI), extreme learning machine (ELM), Electrical and Electronic Engineering, Engineering (miscellaneous), Extreme learning machine, Fitness function, Renewable Energy, Sustainability and the Environment, business.industry, lcsh:T, 020206 networking & telecommunications, Pattern recognition, Smart grid, 020201 artificial intelligence & image processing, Artificial intelligence, False positive rate, business, Energy (miscellaneous)
Abstract

The smart grid is vulnerable to network attacks, thus requiring a high detection rate and fast detection speed for intrusion detection systems. With a fast training speed and a strong model generalization ability, the extreme learning machine (ELM) perfectly meets the needs of intrusion detection of the smart grid. In this paper, the ELM is applied to the field of smart grid intrusion detection. Aiming at the problem that the randomness of input weights and hidden layer bias in the ELM cannot guarantee the optimal performance of the ELM intrusion detection model, a genetic algorithm (GA)-ELM algorithm based on a genetic algorithm (GA) is proposed. GA is used to optimize the input weight and hidden layer bias of the ELM. Firstly, the input weight and hidden layer bias of the ELM are mapped to the chromosome vector of a GA, and the test error of the ELM model is set as the fitness function of the GA. Then, the parameters of the ELM intrusion detection model are optimized by genetic operation; the input weight and bias, corresponding to the minimum test error, are selected to improve the performance of the ELM model. Compared with the ELM and online sequential extreme learning machine (OS-ELM), the GA-ELM effectively improves the accuracy, detection rate and precision of intrusion detection and reduces the false positive rate and missing report rate.

Published
2020
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186. Educational attainment could be a protective factor against obstructive sleep apnea: a study based on Mendelian randomization

Author

Song, Lijuan, primary, Li, Hao, additional, Wang, Jia, additional, Xie, Junyang, additional, Chen, Gui, additional, Liang, Tianhao, additional, Wang, Yiyan, additional, Ye, Liyao, additional, Wang, Xiaofen, additional, Kuang, Xiaoxuan, additional, Ren, Mei, additional, Ye, Jinfeng, additional, Tang, Yaqian, additional, Ji, Ketong, additional, Liao, Wenjing, additional, and Zhang, Xiaowen, additional

Published
2021
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188. Effects of Expanded Perlite on the Composting of Sheep Manure and Jujube Branch: Nitrogen Conservation and Organic Matter Degradation

Author

Hu, Kai, Xu, Shulin, Chen, Daitao, Gao, Youhui, Zhao, Yubin, Hu, Yuegao, and Wang, Xiaofen

Abstract

AbstractCo-composting of sheep manure and jujube branch, a potential technology of waste management in Xinjiang, China, is currently undermined by the low degradation rate of jujube branch waste. This study investigated the potential to improve the co-composting process by the addition of expanded perlite, a potential additive more economical than biochar and zeolite. Expanded perlite was added into the co-compost system in the ratios 0%, 1%, 5% and 10% based on the initial compost volume. Addition of expanded perlite extended the thermophilic phase up to 8 days and helped to increase the speed of compost maturation. Expanded perlite enhanced the degradation of organic matter by accelerating the decomposition of cell wall hemicelluloses during the very early stages and cellulose during the thermophilic and cooling stages. Moreover, total nitrogen was improved by 10.2% and 11.8% through adding 5% and 10% expanded perlite, respectively. From both economic and functionality perspectives, expanded perlite is a potential additive to compost, and 5% EP is a suitable ratio to co-composting mixes of sheep manure and jujube branch.

Published
2022
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196. HUCDO

Author

Huang, Ke, primary, Zhang, Xiaosong, additional, Wang, Xiaofen, additional, Mu, Yi, additional, Rezaeibagha, Fatemeh, additional, Xu, Guangquan, additional, Wang, Hao, additional, Zheng, Xi, additional, Yang, Guomin, additional, Xia, Qi, additional, and Du, Xiaojiang, additional

Published
2020
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200. Rain Streak Removal via Multi-scale Mixture Exponential Power Model

Author

Jun Chen, Xiong Mingfu, Qi Zheng, Zhen Han, Chao Liang, Zhongyuan Wang, and Wang Xiaofen

Subjects
Laplace transform, Computer science, Gaussian, Streak, 02 engineering and technology, Mixture model, 01 natural sciences, Exponential function, Matrix decomposition, 010104 statistics & probability, symbols.namesake, Distortion, 0202 electrical engineering, electronic engineering, information engineering, symbols, 020201 artificial intelligence & image processing, 0101 mathematics, Algorithm
Abstract

Rain streaks severely hamper the visible performance of the outdoor surveillance videos, which becomes an attractive issue in recent computer vision research. Existing methods usually encode rain streaks into Gaussian Mixture Model (GM-M). However, the limited number of Gaussian components in the GMM compromises the ability of the model in fitting real noise, such as sparse noise, which is exactly the characteristic of the rain streaks. In this paper, a novel model named Mixture Exponential Power Model (MEPM) is exploited. It sets multiple Laplace noise components and expands the representation capability for the sparse noise. Moreover, considering that the rain streaks in a video occur in different distances from the camera, we encode rain streaks into Multi-scale Mixture Exponential Power Model. The model is opti-mized by expectation-maximization (EM) algorithm and La-grange multiplier strategy. Experiments are implemented on synthetic and real rain videos and verify the superiority of the proposed method, compared with state-of-the-art methods.

Published
2019

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