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Book ; Online: Meta Semantics

Hu, Xiaolin

Towards better natural language understanding and reasoning

2023

Abstract: Natural language understanding is one of the most challenging topics in artificial intelligence. Deep neural network methods, particularly large language module (LLM) methods such as ChatGPT and GPT-3, have powerful flexibility to adopt informal text but ...

Abstract	Natural language understanding is one of the most challenging topics in artificial intelligence. Deep neural network methods, particularly large language module (LLM) methods such as ChatGPT and GPT-3, have powerful flexibility to adopt informal text but are weak on logical deduction and suffer from the out-of-vocabulary (OOV) problem. On the other hand, rule-based methods such as Mathematica, Semantic web, and Lean, are excellent in reasoning but cannot handle the complex and changeable informal text. Inspired by pragmatics and structuralism, we propose two strategies to solve the OOV problem and a semantic model for better natural language understanding and reasoning. Comment: 10 pages, 8 figures, 2 tables
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; 03B65(Primary) 68T50(Secondary) ; I.2.4 ; I.2.7
Publishing date	2023-04-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Recurrent Neural Networks

Hu, Xiaolin / Balasubramaniam, P.

2008

Keywords	Artificial intelligence ; Neural networks & fuzzy systems
Size	1 electronic resource (402 pages)
Publisher	IntechOpen
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT021045953
ISBN	9789535157953 ; 9535157957
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article: Construction of ceRNA network based on RNA-seq for identifying prognostic lncRNA biomarkers in Perthes disease.

Zhang, Tianjiu / Hu, Xiaolin / Yu, Song / Wei, Chunyan

Frontiers in genetics

2023 Volume 14, Page(s) 1105893

Abstract: Introduction: ...

Abstract	Introduction:
Language	English
Publishing date	2023-05-26
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2023.1105893
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Integration of Transcriptomics and Non-Targeted Metabolomics Reveals the Underlying Mechanism of Skeletal Muscle Development in Duck during Embryonic Stage

Zhigang Hu / Xiaolin Liu

International Journal of Molecular Sciences, Vol 24, Iss 5214, p

2023 Volume 5214

Abstract: Skeletal muscle is an important economic trait in duck breeding; however, little is known about the molecular mechanisms of its embryonic development. Here, the transcriptomes and metabolomes of breast muscle of Pekin duck from 15 (E15_BM), 21 (E21_BM), ... ...

Abstract	Skeletal muscle is an important economic trait in duck breeding; however, little is known about the molecular mechanisms of its embryonic development. Here, the transcriptomes and metabolomes of breast muscle of Pekin duck from 15 (E15_BM), 21 (E21_BM), and 27 (E27_BM) days of incubation were compared and analyzed. The metabolome results showed that the differentially accumulated metabolites (DAMs), including the up-regulated metabolites, l-glutamic acid, n-acetyl-1-aspartylglutamic acid, l-2-aminoadipic acid, 3-hydroxybutyric acid, bilirubin, and the significantly down-regulated metabolites, palmitic acid, 4-guanidinobutanoate, myristic acid, 3-dehydroxycarnitine, and s-adenosylmethioninamine, were mainly enriched in metabolic pathways, biosynthesis of secondary metabolites, biosynthesis of cofactors, protein digestion and absorption, and histidine metabolism, suggesting that these pathways may play important roles in the muscle development of duck during the embryonic stage. Moreover, a total of 2142 (1552 up-regulated and 590 down-regulated), 4873 (3810 up-regulated and 1063 down-regulated), and 2401 (1606 up-regulated and 795 down-regulated) DEGs were identified from E15_BM vs. E21_BM, E15_BM vs. E27_BM and E21_BM vs. E27_BM in the transcriptome, respectively. The significantly enriched GO terms from biological processes were positive regulation of cell proliferation, regulation of cell cycle, actin filament organization, and regulation of actin cytoskeleton organization, which were associated with muscle or cell growth and development. Seven significant pathways, highly enriched by FYN , PTK2 , PXN , CRK , CRKL , PAK , RHOA , ROCK , INSR , PDPK1 , and ARHGEF , were focal adhesion, regulation of actin cytoskeleton, wnt signaling pathway, insulin signaling pathway, extracellular matrix (ECM)-receptor interaction, cell cycle, and adherens junction, which participated in regulating the development of skeletal muscle in Pekin duck during the embryonic stage. KEGG pathway analysis of the integrated transcriptome ...
Keywords	Pekin duck ; skeletal muscle ; transcriptome ; metabolome ; pathway ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Subject code	571
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Integration of Transcriptomics and Non-Targeted Metabolomics Reveals the Underlying Mechanism of Skeletal Muscle Development in Duck during Embryonic Stage.

Hu, Zhigang / Liu, Xiaolin

International journal of molecular sciences

2023 Volume 24, Issue 6

Abstract	Skeletal muscle is an important economic trait in duck breeding; however, little is known about the molecular mechanisms of its embryonic development. Here, the transcriptomes and metabolomes of breast muscle of Pekin duck from 15 (E15_BM), 21 (E21_BM), and 27 (E27_BM) days of incubation were compared and analyzed. The metabolome results showed that the differentially accumulated metabolites (DAMs), including the up-regulated metabolites, l-glutamic acid, n-acetyl-1-aspartylglutamic acid, l-2-aminoadipic acid, 3-hydroxybutyric acid, bilirubin, and the significantly down-regulated metabolites, palmitic acid, 4-guanidinobutanoate, myristic acid, 3-dehydroxycarnitine, and s-adenosylmethioninamine, were mainly enriched in metabolic pathways, biosynthesis of secondary metabolites, biosynthesis of cofactors, protein digestion and absorption, and histidine metabolism, suggesting that these pathways may play important roles in the muscle development of duck during the embryonic stage. Moreover, a total of 2142 (1552 up-regulated and 590 down-regulated), 4873 (3810 up-regulated and 1063 down-regulated), and 2401 (1606 up-regulated and 795 down-regulated) DEGs were identified from E15_BM vs. E21_BM, E15_BM vs. E27_BM and E21_BM vs. E27_BM in the transcriptome, respectively. The significantly enriched GO terms from biological processes were positive regulation of cell proliferation, regulation of cell cycle, actin filament organization, and regulation of actin cytoskeleton organization, which were associated with muscle or cell growth and development. Seven significant pathways, highly enriched by
MeSH term(s)	Animals ; Transcriptome ; Ducks/genetics ; Histidine/metabolism ; Gene Expression Profiling ; Muscle, Skeletal/metabolism ; Muscle Development
Chemical Substances	Histidine (4QD397987E)
Language	English
Publishing date	2023-03-08
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms24065214
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Quantifying the Impact of Environment Loads on Displacements in a Suspension Bridge with a Data-Driven Approach.

Li, Jiaojiao / Meng, Xiaolin / Hu, Liangliang / Bao, Yan

Sensors (Basel, Switzerland)

2024 Volume 24, Issue 6

Abstract: Long-span bridges are susceptible to damage, aging, and deformation in harsh environments for a long time. Therefore, structural health monitoring (SHM) systems need to be used for reasonable monitoring and maintenance. Among various indicators, bridge ... ...

Abstract	Long-span bridges are susceptible to damage, aging, and deformation in harsh environments for a long time. Therefore, structural health monitoring (SHM) systems need to be used for reasonable monitoring and maintenance. Among various indicators, bridge displacement is a crucial parameter reflecting the bridge's health condition. Due to the simultaneous bearing of multiple environmental loads on suspension bridges, determining the impact of different loads on displacement is beneficial for the better understanding of the health conditions of the bridges. Considering the fact that extreme gradient boosting (XGBoost) has higher prediction performance and robustness, the authors of this paper have developed a data-driven approach based on the XGBoost model to quantify the impact between different environmental loads and the displacement of a suspension bridge. Simultaneously, this study combined wavelet threshold (WT) denoising and the variational mode decomposition (VMD) method to conduct a modal decomposition of three-dimensional (3D) displacement, further investigating the interrelationships between different loads and bridge displacements. This model links wind speed, temperature, air pressure, and humidity with the 3D displacement response of the span using the bridge monitoring data provided by the GNSS and Earth Observation for Structural Health Monitoring (GeoSHM) system of the Forth Road Bridge (FRB) in the United Kingdom (UK), thus eliminating the temperature time-lag effect on displacement data. The effects of the different loads on the displacement are quantified individually with partial dependence plots (PDPs). Employing testing, it was found that the XGBoost model has a high predictive effect on the target variable of displacement. The analysis of quantification and correlation reveals that lateral displacement is primarily affected by same-direction wind, showing a clear positive correlation, and vertical displacement is mainly influenced by temperature and exhibits a negative correlation. Longitudinal displacement is jointly influenced by various environmental loads, showing a positive correlation with atmospheric pressure, temperature, and vertical wind and a negative correlation with longitudinal wind, lateral wind, and humidity. The results can guide bridge structural health monitoring in extreme weather to avoid accidents.
Language	English
Publishing date	2024-03-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s24061877
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Psychometric evaluation of the Chinese version of the stressors in breast cancer scale: a translation and validation study.

Hu, Wenqi / Bao, Jiahui / Yang, Xiaolin / Ye, Mao

BMC public health

2024 Volume 24, Issue 1, Page(s) 425

Abstract: Objective: To translate the Stressors in Breast Cancer Scale (SBCS) from English to Chinese and assess its psychometric properties.: Methods: The Brislin's translation model was applied to perform forward translation, back translation, cross-cultural ...

Abstract	Objective: To translate the Stressors in Breast Cancer Scale (SBCS) from English to Chinese and assess its psychometric properties. Methods: The Brislin's translation model was applied to perform forward translation, back translation, cross-cultural adaptation, Whereas the Chinese version of the SBCS was formed by conducting pre-testing. A cohort of 878 breast cancer patients participated in this methodological study. Content validity, construct validity, convergent validity, discriminant validity, and criterion-related validity were used to establish validity. Internal consistency reliability, split-half reliability, and test-retest reliability were used to establish reliability. Results: The final scale contained five dimensions and 24 items, including interpersonal relationship and healthcare strains, worries and concerns about the future, physical appearance and sex strains, daily difficulties and health. The average content validity index of the scale was 0.975. The goodness-of-fit index (χ2/DF = 2.416, RMSEA = 0.057, GFI = 0.896, CFI = 0.947, IFI = 0.947, and TLI = 0.939) indicated that the model was well-fitted. The composite reliability (CR) of the dimensions ranged from 0.825 to 0.934, the average variance extracted (AVE) ranged from 0.539 to 0.712, and the correlation coefficients of each dimension with the other dimensions were less than the square root of the AVE for that dimension. The Criterion-related validity was 0.511. The Cronbach's alpha was 0.938, and the dimensions ranged from 0.779 to 0.900. Split-half reliability was 0.853, with dimensions ranging from 0.761 to 0.892. Test-retest reliability was 0.855. Conclusions: The Chinese version of the SBCS has good reliability and validity, which can be applied to the assessment of stressors in breast cancer patients in China.
MeSH term(s)	Female ; Humans ; Asian People/psychology ; Breast Neoplasms/diagnosis ; Breast Neoplasms/psychology ; China ; Psychometrics ; Reproducibility of Results ; Surveys and Questionnaires ; Translating ; Stress, Psychological/diagnosis
Language	English
Publishing date	2024-02-09
Publishing country	England
Document type	Journal Article ; Validation Study
ZDB-ID	2041338-5
ISSN	1471-2458 ; 1471-2458
ISSN (online)	1471-2458
ISSN	1471-2458
DOI	10.1186/s12889-024-18000-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm.

Hu, Xiaoqi / Hu, Xiaolin / Yu, Ya / Wang, Jia

Frontiers in endocrinology

2023 Volume 14, Page(s) 1105062

Abstract: Objective: To develop the extreme gradient boosting (XG Boost) machine learning (ML) model for predicting gestational diabetes mellitus (GDM) compared with a model using the traditional logistic regression (LR) method.: Methods: A case-control study ... ...

Abstract	Objective: To develop the extreme gradient boosting (XG Boost) machine learning (ML) model for predicting gestational diabetes mellitus (GDM) compared with a model using the traditional logistic regression (LR) method. Methods: A case-control study was carried out among pregnant women, who were assigned to either the training set (these women were recruited from August 2019 to November 2019) or the testing set (these women were recruited in August 2020). We applied the XG Boost ML model approach to identify the best set of predictors out of a set of 33 variables. The performance of the prediction model was determined by using the area under the receiver operating characteristic (ROC) curve (AUC) to assess discrimination, and the Hosmer-Lemeshow (HL) test and calibration plots to assess calibration. Decision curve analysis (DCA) was introduced to evaluate the clinical use of each of the models. Results: A total of 735 and 190 pregnant women were included in the training and testing sets, respectively. The XG Boost ML model, which included 20 predictors, resulted in an AUC of 0.946 and yielded a predictive accuracy of 0.875, whereas the model using a traditional LR included four predictors and presented an AUC of 0.752 and yielded a predictive accuracy of 0.786. The HL test and calibration plots show that the two models have good calibration. DCA indicated that treating only those women whom the XG Boost ML model predicts are at risk of GDM confers a net benefit compared with treating all women or treating none. Conclusions: The established model using XG Boost ML showed better predictive ability than the traditional LR model in terms of discrimination. The calibration performance of both models was good.
MeSH term(s)	Humans ; Female ; Pregnancy ; Diabetes, Gestational/diagnosis ; Case-Control Studies ; Algorithms ; Machine Learning ; Logistic Models
Language	English
Publishing date	2023-03-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2592084-4
ISSN	1664-2392
ISSN	1664-2392
DOI	10.3389/fendo.2023.1105062
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Convolutional Neural Networks With Gated Recurrent Connections.

Wang, Jianfeng / Hu, Xiaolin

IEEE transactions on pattern analysis and machine intelligence

2022 Volume 44, Issue 7, Page(s) 3421–3435

Abstract: The convolutional neural network (CNN) has become a basic model for solving many computer vision problems. In recent years, a new class of CNNs, recurrent convolution neural network (RCNN), inspired by abundant recurrent connections in the visual systems ...

Abstract	The convolutional neural network (CNN) has become a basic model for solving many computer vision problems. In recent years, a new class of CNNs, recurrent convolution neural network (RCNN), inspired by abundant recurrent connections in the visual systems of animals, was proposed. The critical element of RCNN is the recurrent convolutional layer (RCL), which incorporates recurrent connections between neurons in the standard convolutional layer. With increasing number of recurrent computations, the receptive fields (RFs) of neurons in RCL expand unboundedly, which is inconsistent with biological facts. We propose to modulate the RFs of neurons by introducing gates to the recurrent connections. The gates control the amount of context information inputting to the neurons and the neurons' RFs therefore become adaptive. The resulting layer is called gated recurrent convolution layer (GRCL). Multiple GRCLs constitute a deep model called gated RCNN (GRCNN). The GRCNN was evaluated on several computer vision tasks including object recognition, scene text recognition and object detection, and obtained much better results than the RCNN. In addition, when combined with other adaptive RF techniques, the GRCNN demonstrated competitive performance to the state-of-the-art models on benchmark datasets for these tasks.
MeSH term(s)	Algorithms ; Neural Networks, Computer ; Visual Perception
Language	English
Publishing date	2022-06-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2021.3054614
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits.

Li, Kai / Xie, Fenghua / Chen, Hang / Yuan, Kexin / Hu, Xiaolin

IEEE transactions on pattern analysis and machine intelligence

2024 Volume PP

Abstract: Audio-visual approaches involving visual inputs have laid the foundation for recent progress in speech separation. However, the optimization of the concurrent usage of auditory and visual inputs is still an active research area. Inspired by the cortico- ... ...

Abstract	Audio-visual approaches involving visual inputs have laid the foundation for recent progress in speech separation. However, the optimization of the concurrent usage of auditory and visual inputs is still an active research area. Inspired by the cortico-thalamo-cortical circuit, in which the sensory processing mechanisms of different modalities modulate one another via the non-lemniscal sensory thalamus, we propose a novel cortico-thalamo-cortical neural network (CTCNet) for audio-visual speech separation (AVSS). First, the CTCNet learns hierarchical auditory and visual representations in a bottom-up manner in separate auditory and visual subnetworks, mimicking the functions of the auditory and visual cortical areas. Then, inspired by the large number of connections between cortical regions and the thalamus, the model fuses the auditory and visual information in a thalamic subnetwork through top-down connections. Finally, the model transmits this fused information back to the auditory and visual subnetworks, and the above process is repeated several times. The results of experiments on three speech separation benchmark datasets show that CTCNet remarkably outperforms existing AVSS methods with considerably fewer parameters. These results suggest that mimicking the anatomical connectome of the mammalian brain has great potential for advancing the development of deep neural networks.
Language	English
Publishing date	2024-04-02
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2024.3384034
Database	MEDical Literature Analysis and Retrieval System OnLINE

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