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Article: Corrigendum: An initial prediction and fine-tuning model based on improving GCN for 3D human motion prediction.

He, Zhiquan / Zhang, Lujun / Wang, Hengyou

2023 Volume 17, Page(s) 1232765

Abstract: This corrects the article DOI: 10.3389/fncom.2023.1145209.]. ...

Abstract	[This corrects the article DOI: 10.3389/fncom.2023.1145209.].
Language	English
Publishing date	2023-06-13
Publishing country	Switzerland
Document type	Published Erratum
ZDB-ID	2452964-3
ISSN	1662-5188
ISSN	1662-5188
DOI	10.3389/fncom.2023.1232765
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: An initial prediction and fine-tuning model based on improving GCN for 3D human motion prediction.

He, Zhiquan / Zhang, Lujun / Wang, Hengyou

Frontiers in computational neuroscience

2023 Volume 17, Page(s) 1145209

Abstract: Human motion prediction is one of the fundamental studies of computer vision. Much work based on deep learning has shown impressive performance for it in recent years. However, long-term prediction and human skeletal deformation are still challenging ... ...

Abstract	Human motion prediction is one of the fundamental studies of computer vision. Much work based on deep learning has shown impressive performance for it in recent years. However, long-term prediction and human skeletal deformation are still challenging tasks for human motion prediction. For accurate prediction, this paper proposes a GCN-based two-stage prediction method. We train a prediction model in the first stage. Using multiple cascaded spatial attention graph convolution layers (SAGCL) to extract features, the prediction model generates an initial motion sequence of future actions based on the observed pose. Since the initial pose generated in the first stage often deviates from natural human body motion, such as a motion sequence in which the length of a bone is changed. So the task of the second stage is to fine-tune the predicted pose and make it closer to natural motion. We present a fine-tuning model including multiple cascaded causally temporal-graph convolution layers (CT-GCL). We apply the spatial coordinate error of joints and bone length error as loss functions to train the fine-tuning model. We validate our model on Human3.6m and CMU-MoCap datasets. Extensive experiments show that the two-stage prediction method outperforms state-of-the-art methods. The limitations of proposed methods are discussed as well, hoping to make a breakthrough in future exploration.
Language	English
Publishing date	2023-04-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452964-3
ISSN	1662-5188
ISSN	1662-5188
DOI	10.3389/fncom.2023.1145209
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Preparation and properties of citric acid-crosslinked chitosan salt microspheres through radio frequency assisted method

Zhang, Lihui / Zhang, Min / Adhikari, Benu / Zhang, Lujun

Food Hydrocolloids. 2023 May, v. 139 p.108538-

2023

Abstract: High salt intake is one of the reasons causing dietary risk for death from noncommunicable diseases. In this study, citric acid-crosslinked chitosan (CsC) was prepared using chitosan and citric acid under radio frequency (RF) assisted method, which was ... ...

Abstract	High salt intake is one of the reasons causing dietary risk for death from noncommunicable diseases. In this study, citric acid-crosslinked chitosan (CsC) was prepared using chitosan and citric acid under radio frequency (RF) assisted method, which was considered to absorb Cl⁻ in NaCl and further produce high-saltiness microspheres to regard as a low-sodium salt. The characterization and in vitro bioaccessibility of salt microspheres treated with RF (CsC-RF-NaCl) were investigated. The maximum Na⁺ release capacity of CsC-RF-NaCl occurred for 3 h at 30 mm of plate spacing, pH 4 and NaCl-to-CsC-RF mass ratio of 2:1. Electrostatic attraction was responsible for the formation of CsC-RF-NaCl microparticles. CsC-RF-NaCl microparticles exhibited an irregular spherical shape, the surface of microspheres showed a more homogeneous Na⁺ and Cl⁻ dispersion and higher ratio of Na: Cl. CsC-RF-NaCl microparticles were excellent thermal stability, and pleasantly hygroscopic. In addition, the CsC-RF-NaCl microparticles showed the higher release of Na⁺ during oral digestion and lower bioaccessibility in intestinal digestion. The Na⁺ content of CsC-RF-NaCl microparticles was decreased up to 61.80% compared to NaCl at the same saltiness. Therefore, CsC-RF-NaCl microparticles were considered as a low-sodium salt for surface-salted foods.
Keywords	bioavailability ; chitosan ; citric acid ; death ; digestion ; electrostatic interactions ; hydrocolloids ; intestines ; microparticles ; pH ; radio waves ; risk ; saltiness ; thermal stability ; NaCl ; Electrostatic attraction
Language	English
Dates of publication	2023-05
Publishing place	Elsevier Ltd
Document type	Article ; Online
ZDB-ID	742742-6
ISSN	1873-7137 ; 0268-005X
ISSN (online)	1873-7137
ISSN	0268-005X
DOI	10.1016/j.foodhyd.2023.108538
Database	NAL-Catalogue (AGRICOLA)

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Article: Salt reducing and saltiness perception enhancing strategy for shiitake (Lentinus edodes) bud using novel combined treatment of yeast extract and radio frequency

Zhang, Lihui / Zhang, Min / Adhikari, Benu / Zhang, Lujun

Food chemistry. 2023 Feb. 15, v. 402

2023

Abstract: This study investigated the potential of yeast extract and radio frequency (RF) treatment as a strategy of reducing salt and enhancing saltiness perception for Lentinus edodes bud. The results of E-nose demonstrated yeast extract and RF treatment ... ...

Abstract	This study investigated the potential of yeast extract and radio frequency (RF) treatment as a strategy of reducing salt and enhancing saltiness perception for Lentinus edodes bud. The results of E-nose demonstrated yeast extract and RF treatment improved the saltiness of Lentinus edodes bud. Meanwhile, yeast extract and RF treatment significantly decreased the addition of salt (P < 0.05), and led to the formation of special flavor substances, whereas amino acid nitrogen content decreased. On the other hand, sensory attribute, hardness, total flavonoid and phenolic content, antioxidant capacity of L. edodes buds significantly (P < 0.05) increased after the yeast extract combined with RF treatment. In addition, the modification of water distribution, the formation of dense structure, uniform microstructure and Na⁺ distribution were observed in treated sample, causing the enhancement of saltiness perception. Accordingly, the alteration of properties contributed to higher sensory properties of texture, taste, flavor, and overall acceptability.
Keywords	Lentinula edodes ; amino acids ; antioxidant activity ; electronic nose ; flavonoids ; food chemistry ; hardness ; microstructure ; mushrooms ; nitrogen content ; radio waves ; saltiness ; taste ; texture ; water distribution ; yeast extract
Language	English
Dates of publication	2023-0215
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	243123-3
ISSN	1873-7072 ; 0308-8146
ISSN (online)	1873-7072
ISSN	0308-8146
DOI	10.1016/j.foodchem.2022.134149
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Determination of polysaccharide content in shiitake mushroom beverage by NIR spectroscopy combined with machine learning: A comparative analysis

Wang, Dayuan / Zhang, Min / Adhikari, Benu / Zhang, Lujun

Journal of Food Composition and Analysis. 2023 Sept., v. 122 p.105460-

2023

Abstract: A comprehensive comparative analysis of the performance of near-infrared (NIR) spectroscopy combined with various chemometrics and machine learning methods was performed to quantitatively determine polysaccharide content in shiitake mushroom beverages. ... ...

Abstract	A comprehensive comparative analysis of the performance of near-infrared (NIR) spectroscopy combined with various chemometrics and machine learning methods was performed to quantitatively determine polysaccharide content in shiitake mushroom beverages. First, Savitzky-Golay (SG) and orthogonal signal correction (OSC) methods were used for spectral pretreatment. Then, to simplify the model and enhance the generalization performance, variable selection (VS) methods including selectivity ratio (sRatio), variable importance in projection (VIP), recursive partial least square (rPLS), synergy interval partial least square (si-PLS), genetic algorithm (GA), and successive projection algorithm (SPA) were used to select the feature wavelengths. Finally, multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR), back propagation neural network (BPNN) and extreme gradient boosting (XGBoost) regression algorithms were used for the multivariate quantitative analysis of NIR data. The results show that different combinations of pretreatment, VS and modelling methods lead to different prediction performances. Except for XGBoost, almost all linear and non-linear models showed satisfying prediction performance with their Rₚ² > 0.95. Of these, SG-VS-BPNN models exhibited the best performance with their Rₚ² ≥ 0.98. Therefore, NIR combined with machine learning can provide an intelligent, nondestructive and rapid quantitation of polysaccharide content in beverages.
Keywords	algorithms ; beverages ; chemometrics ; food composition ; least squares ; models ; mushrooms ; near-infrared spectroscopy ; polysaccharides ; prediction ; quantitative analysis ; Intelligent nondestructive detection ; Polysaccharide content ; Shiitake mushroom beverage ; Variables selection ; Machine learning
Language	English
Dates of publication	2023-09
Publishing place	Elsevier Inc.
Document type	Article ; Online
ZDB-ID	743572-1
ISSN	0889-1575 ; 1096-0481
ISSN	0889-1575 ; 1096-0481
DOI	10.1016/j.jfca.2023.105460
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Natural deep eutectic solvents for the extraction of lentinan from shiitake mushroom: COSMO-RS screening and ANN-GA optimizing conditions.

Wang, Dayuan / Zhang, Min / Law, Chung Lim / Zhang, Lujun

Food chemistry

2023 Volume 430, Page(s) 136990

Abstract: Using natural deep eutectic solvents (NDES) for green extraction of lentinan from shiitake mushroom is a high-efficiency method. However, empirical and trial-and-error methods commonly used to select suitable NDES are unconvincing and time-consuming. ... ...

Abstract	Using natural deep eutectic solvents (NDES) for green extraction of lentinan from shiitake mushroom is a high-efficiency method. However, empirical and trial-and-error methods commonly used to select suitable NDES are unconvincing and time-consuming. Conductor-like screening model for realistic solvation (COSMO-RS) is helpful for the priori design of NDES by predicting the solubility of biomolecules. In this study, 372 NDES were used to evaluate lentinan dissolution capability via COSMO-RS. The results showed that the solvent formed by carnitine (15 wt%), urea (40.8 wt%), and water (44.2 wt%) exhibited the best performance for the extraction of lentinan. In the extraction stage, an artificial neural network coupled with genetic algorithm (ANN-GA) was developed to optimize the extraction conditions and to analyze their interaction effects on lentinan content. Therefore, COSMO-RS and ANN-GA can be used as powerful tools for solvent screening and extraction process optimization, which can be extended to various bioactive substance extraction.
MeSH term(s)	Deep Eutectic Solvents ; Lentinan ; Shiitake Mushrooms ; Solvents ; Water
Chemical Substances	Deep Eutectic Solvents ; Lentinan (37339-90-5) ; Solvents ; Water (059QF0KO0R)
Language	English
Publishing date	2023-07-29
Publishing country	England
Document type	Journal Article
ZDB-ID	243123-3
ISSN	1873-7072 ; 0308-8146
ISSN (online)	1873-7072
ISSN	0308-8146
DOI	10.1016/j.foodchem.2023.136990
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: RFtest: A Robust and Flexible Community-Level Test for Microbiome Data Powerfully Detects Phylogenetically Clustered Signals.

Zhang, Lujun / Wang, Yanshan / Chen, Jingwen / Chen, Jun

Frontiers in genetics

2022 Volume 12, Page(s) 749573

Abstract: Random forest is considered as one of the most successful machine learning algorithms, which has been widely used to construct microbiome-based predictive models. However, its use as a statistical testing method has not been explored. In this study, we ... ...

Abstract	Random forest is considered as one of the most successful machine learning algorithms, which has been widely used to construct microbiome-based predictive models. However, its use as a statistical testing method has not been explored. In this study, we propose "Random Forest Test" (RFtest), a global (community-level) test based on random forest for high-dimensional and phylogenetically structured microbiome data. RFtest is a permutation test using the generalization error of random forest as the test statistic. Our simulations demonstrate that RFtest has controlled type I error rates, that its power is superior to competing methods for phylogenetically clustered signals, and that it is robust to outliers and adaptive to interaction effects and non-linear associations. Finally, we apply RFtest to two real microbiome datasets to ascertain whether microbial communities are associated or not with the outcome variables.
Language	English
Publishing date	2022-01-24
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2021.749573
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Article ; Online: Salt reducing and saltiness perception enhancing strategy for shiitake (Lentinus edodes) bud using novel combined treatment of yeast extract and radio frequency.

Zhang, Lihui / Zhang, Min / Adhikari, Benu / Zhang, Lujun

Food chemistry

2022 Volume 402, Page(s) 134149

Abstract	This study investigated the potential of yeast extract and radio frequency (RF) treatment as a strategy of reducing salt and enhancing saltiness perception for Lentinus edodes bud. The results of E-nose demonstrated yeast extract and RF treatment improved the saltiness of Lentinus edodes bud. Meanwhile, yeast extract and RF treatment significantly decreased the addition of salt (P < 0.05), and led to the formation of special flavor substances, whereas amino acid nitrogen content decreased. On the other hand, sensory attribute, hardness, total flavonoid and phenolic content, antioxidant capacity of L. edodes buds significantly (P < 0.05) increased after the yeast extract combined with RF treatment. In addition, the modification of water distribution, the formation of dense structure, uniform microstructure and Na
MeSH term(s)	Shiitake Mushrooms/chemistry ; Antioxidants ; Sodium Chloride, Dietary ; Sodium Chloride ; Flavonoids ; Water/chemistry ; Perception ; Amino Acids ; Nitrogen
Chemical Substances	Antioxidants ; Sodium Chloride, Dietary ; Sodium Chloride (451W47IQ8X) ; Flavonoids ; Water (059QF0KO0R) ; Amino Acids ; Nitrogen (N762921K75)
Language	English
Publishing date	2022-09-22
Publishing country	England
Document type	Journal Article
ZDB-ID	243123-3
ISSN	1873-7072 ; 0308-8146
ISSN (online)	1873-7072
ISSN	0308-8146
DOI	10.1016/j.foodchem.2022.134149
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Improved understanding on biochar effect in electron supplied anaerobic soil as evidenced by dechlorination and methanogenesis processes.

Zhu, Min / Zhang, Lujun / Xu, Jianming / He, Yan

The Science of the total environment

2022 Volume 857, Issue Pt 3, Page(s) 159346

Abstract: Research interest in biochar as an environmental remediation material has rapidly increased over the past few years. However, the effect of biochar on typical environmental processes in anaerobic soil environment has been insufficiently discussed. By ... ...

Abstract	Research interest in biochar as an environmental remediation material has rapidly increased over the past few years. However, the effect of biochar on typical environmental processes in anaerobic soil environment has been insufficiently discussed. By regulating the electron donors with sodium acetate or pyruvate, the effects and underpinning chemical-microbiological coupling mechanisms of biochar under anaerobic conditions were disclosed. Unlike the electron limited condition, the addition of electron donors alleviated the competition for electrons among various reduction processes in the soil. The effect of biochar in regulating the electron transfer processes was lessened. But more than doubled methane emissions were resulted by the exogenous substances, especially with the synergic effect of biochar. Biochar addition increased soil environmental heterogeneity. It might indirectly affect the reductive transformation of γ-HCH via increasing the bioavailability of pollutants through adsorption and promoting the metabolism of some rare microorganisms. Anaerolineaceae, Peptococcaceae and Methanosarcina had coherent phylogenetic patterns and were likely to be the enablers for the reductive dechlorination process in flooded soil. ENVIRONMENTAL IMPLICATION: Previous studies have widely reported the performance characteristics of biochar, but its effects under anaerobic environments are not systematically understood. By regulating the electron donors, the competition for electrons among various reduction processes in the soil might be alleviated, resulting in a lessened effect of biochar in regulating the electron transfer processes. The findings presented in this study highlight the role of biochar to the dynamic changes of reduction processes under anaerobic environments. The relevant soil conditions such as the electron donors and the functional microbial groups should be adequately considered for maximizing the all-around beneficial efficiency of biochar amendments.
MeSH term(s)	Soil ; Electrons ; Soil Pollutants/analysis ; Biodegradation, Environmental ; Anaerobiosis ; Phylogeny ; Charcoal
Chemical Substances	biochar ; Soil ; Soil Pollutants ; Charcoal (16291-96-6)
Language	English
Publishing date	2022-10-10
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2022.159346
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Article: Near real-time hurricane rainfall forecasting using convolutional neural network models with Integrated Multi-satellitE Retrievals for GPM (IMERG) product

Kim, Taereem / Yang, Tiantian / Zhang, Lujun / Hong, Yang

Atmospheric research. 2022 June 01, v. 270

2022

Abstract: Artificial Intelligence and Machine Learning (AI/ML) techniques are powerful tools, which can be applied to forecast spatial imagery in sequence. In this study, we applied a Convolutional Neural Network (CNN) model to predict the next few hours of ... ...

Abstract	Artificial Intelligence and Machine Learning (AI/ML) techniques are powerful tools, which can be applied to forecast spatial imagery in sequence. In this study, we applied a Convolutional Neural Network (CNN) model to predict the next few hours of rainfall observation imagery from NASA's Global Precipitation Measurement mission and its IMERG Early Run product in assist of hurricane predictions over the U.S.-Mexico Gulf coastal region. The IMERG Early Run is a satellite-based rainfall retrieval technique, providing rainfall measurement with a half-hourly temporal resolution, near-global scale coverage, and 4 h observation latency rainfall intensity imagery. The goal of this study is to extend the observation latency so that the IMERG Early Run product could potentially be used for real-time hurricane prediction and flash flood warning. In this study, we first build a sub-dataset from the IMERG Early Run product containing total 37 past hurricane events that hit the contiguous U.S. (CONUS) bordering the Gulf of Mexico from 2002 to 2019. Then, two CNN models with different model structures are built and tested to forecast the 37 hurricane events in a retrospective manner. Sensitivity experiments are conducted to select the optimal hyperparameters of the two CNN models, i.e., the number of convolution layers, filter size, minibatch size, number of filters, and pooling size. The prediction results show that the two employed CNN models generally provide satisfactory performance, showing averaged Accuracy in the categorical metrics is above 90% and averaged NSE in the continuous metrics is above 0.5. We found that the forecasting performance of the two CNN models is not significantly different; however, the CNN model without pooling layers always shows slightly better performance than the CNN model with pooling layers. We also found that both CNN models can predict the spatial range of hurricane rainfall well, but there are limitations to underestimate in larger rainfall rates. This study will serve as a novel investigation on how AI/ML models could help with real-time hurricane predictions, and provide a future reference for selecting the optimal CNN model parameters for processing spatial rainfall data.
Keywords	artificial intelligence ; coasts ; floods ; hurricanes ; meteorological data ; neural networks ; prediction ; rain ; rain intensity ; research ; satellites ; Gulf of Mexico
Language	English
Dates of publication	2022-0601
Publishing place	Elsevier B.V.
Document type	Article
ISSN	0169-8095
DOI	10.1016/j.atmosres.2022.106037
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