1
|
Wen X, Wang Y, Shao Z. The spatiotemporal trend of human brucellosis in China and driving factors using interpretability analysis. Sci Rep 2024; 14:4880. [PMID: 38418566 PMCID: PMC10901783 DOI: 10.1038/s41598-024-55034-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/19/2024] [Indexed: 03/01/2024] Open
Abstract
Human brucellosis has reemerged in China, with a distinct change in its geographical distribution. The incidence of human brucellosis has significantly risen in inland regions of China. To gain insights into epidemic characteristics and identify factors influencing the geographic spread of human brucellosis, our study utilized the Extreme Gradient Boosting (XGBoost) algorithm and interpretable machine learning techniques. The results showed a consistent upward trend in the incidence of human brucellosis, with a significant increase of 8.20% from 2004 to 2021 (95% CI: 1.70, 15.10). The northern region continued to face a serious human situation, with a gradual upward trend. Meanwhile, the western and southern regions have experienced a gradual spread of human brucellosis, encompassing all regions of China over the past decade. Further analysis using Shapley Additive Explanations (SHAP) demonstrated that higher Gross Domestic Product (GDP) per capita and increased funding for education have the potential to reduce the spread. Conversely, the expansion of human brucellosis showed a positive correlation with bed availability per 1000 individuals, humidity, railway mileage, and GDP. These findings strongly suggest that socioeconomic factors play a more significant role in the spread of human brucellosis than other factors.
Collapse
Affiliation(s)
- Xiaohui Wen
- Department of Epidemiology, Air Force Medical University, Xi'an, 710000, China
| | - Yun Wang
- Central Sterile Services Department, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710000, China
| | - Zhongjun Shao
- Department of Epidemiology, Air Force Medical University, Xi'an, 710000, China.
| |
Collapse
|
2
|
Zhang X, Mei LC, Gao YY, Hao GF, Song BA. Web tools support predicting protein-nucleic acid complexes stability with affinity changes. WILEY INTERDISCIPLINARY REVIEWS. RNA 2023; 14:e1781. [PMID: 36693636 DOI: 10.1002/wrna.1781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/10/2022] [Accepted: 11/28/2022] [Indexed: 01/26/2023]
Abstract
Numerous biological processes, such as transcription, replication, and translation, rely on protein-nucleic acid interactions (PNIs). Demonstrating the binding stability of protein-nucleic acid complexes is vital to deciphering the code for PNIs. Numerous web-based tools have been developed to attach importance to protein-nucleic acid stability, facilitating the prediction of PNIs characteristics rapidly. However, the data and tools are dispersed and lack comprehensive integration to understand the stability of PNIs better. In this review, we first summarize existing databases for evaluating the stability of protein-nucleic acid binding. Then, we compare and evaluate the pros and cons of web tools for forecasting the interaction energies of protein-nucleic acid complexes. Finally, we discuss the application of combining models and capabilities of PNIs. We may hope these web-based tools will facilitate the discovery of recognition mechanisms for protein-nucleic acid binding stability. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.
Collapse
Affiliation(s)
- Xiao Zhang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
| | - Long-Can Mei
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Yang-Yang Gao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Bao-An Song
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
| |
Collapse
|
3
|
Dai L, Zhang L, Chen Z, Ding W. Collaborative granular sieving: A deterministic multievolutionary algorithm for multimodal optimization problems. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
4
|
Tao H, Salih S, Oudah AY, Abba SI, Ameen AMS, Awadh SM, Alawi OA, Mostafa RR, Surendran UP, Yaseen ZM. Development of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams, United States. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:35841-35861. [PMID: 35061183 DOI: 10.1007/s11356-022-18554-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2023]
Abstract
Natural streams longitudinal dispersion coefficient (Kx) is an essential indicator for pollutants transport and its determination is very important. Kx is influenced by several parameters, including river hydraulic geometry, sediment properties, and other morphological characteristics, and thus its calculation is a highly complex engineering problem. In this research, three relatively explored machine learning (ML) models, including Random Forest (RF), Gradient Boosting Decision Tree (GTB), and XGboost-Grid, were proposed for the Kx determination. The modeling scheme on building the prediction matrix was adopted from the well-established literature. Several input combinations were tested for better predictability performance for the Kx. The modeling performance was tested based on the data division for the training and testing (70-30% and 80-20%). Based on the attained modeling results, XGboost-Grid reported the best prediction results over the training and testing phase compared to RF and GTB models. The development of the newly established machine learning model revealed an excellent computed-aided technology for the Kx simulation.
Collapse
Affiliation(s)
- Hai Tao
- School of Electronics and Information Engineering, Ankang University, Ankang, China
- School of Computer Sciences, Baoji University of Arts and Sciences, Shaanxi, China
- Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
| | - Sinan Salih
- Computer Science Department, Dijlah University College, Al-Dora, Baghdad, Iraq
- Artificial Intelligence Research Unit (AIRU), Dijlah University College, Al-Dora, Baghdad, Iraq
| | - Atheer Y Oudah
- Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Thi-Qar, Iraq
- Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq
| | - S I Abba
- Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia
- Faculty of Engineering, Department of Civil Engineering, Baze University, Abuja, Nigeria
| | | | | | - Omer A Alawi
- Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor Bahru, Malaysia
| | - Reham R Mostafa
- Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, 35516, Egypt
| | - Udayar Pillai Surendran
- Land and Water Management Research Group, Centre for Water Resources Development and Management (CWRDM), Kozhikode, Kerala, India
| | - Zaher Mundher Yaseen
- Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080, Chelyabinsk, Russia.
- New era and development in civil engineering research group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq.
- College of Creative Design, Asia University, Taichung City, Taiwan.
| |
Collapse
|
5
|
Prediction of Conformance Control Performance for Cyclic-Steam-Stimulated Horizontal Well Using the XGBoost: A Case Study in the Chunfeng Heavy Oil Reservoir. ENERGIES 2021. [DOI: 10.3390/en14238161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Conformance control is an effective method to enhance heavy oil recovery for cyclic-steam-stimulated horizontal wells. The numerical simulation technique is frequently used prior to field applications to evaluate the incremental oil production with conformance control in order to ensure cost-efficiency. However, conventional numerical simulations require the use of specific thermal numerical simulators that are usually expensive and computationally inefficient. This paper proposed the use of the extreme gradient boosting (XGBoost) trees to estimate the incremental oil production of conformance control with N2-foam and gel for cyclic-steam-stimulated horizontal wells. A database consisting of 1000 data points was constructed using numerical simulations based on the geological and fluid properties of the heavy oil reservoir in the Chunfeng Oilfield, which was then used for training and validating the XGBoost model. Results show that the XGBoost model is capable of estimating the incremental oil production with relatively high accuracy. The mean absolute errors (MAEs), mean relative errors (MRE) and correlation coefficients are 12.37/80.89 t, 0.09%/0.059% and 0.99/0.98 for the training/validation sets, respectively. The validity of the prediction model was further confirmed by comparison with numerical simulations for six real production wells in the Chunfeng Oilfield. The permutation indices (PI) based on the XGBoost model indicate that net to gross ratio (NTG) and the cumulative injection of the plugging agent exerts the most significant effects on the enhanced oil production. The proposed method can be easily transferred to other heavy oil reservoirs, provided efficient training data are available.
Collapse
|
6
|
Ghimire S, Yaseen ZM, Farooque AA, Deo RC, Zhang J, Tao X. Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks. Sci Rep 2021; 11:17497. [PMID: 34471166 PMCID: PMC8410863 DOI: 10.1038/s41598-021-96751-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 08/13/2021] [Indexed: 11/09/2022] Open
Abstract
Streamflow (Qflow) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Qflow (short-term) at Brisbane River and Teewah Creek, Australia. The CNN layers were used to extract the features of Qflow time-series, while the LSTM networks use these features from CNN for Qflow time series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models. Qflow prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-Weeks, and 9-Months, respectively. With the help of different performance metrics and graphical analysis visualization, the experimental results reveal that with small residual error between the actual and predicted Qflow, the CNN-LSTM model outperforms all the benchmarked conventional AI models as well as ensemble models for all the time intervals. With 84% of Qflow prediction error below the range of 0.05 m3 s-1, CNN-LSTM demonstrates a better performance compared to 80% and 66% for LSTM and DNN, respectively. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. Thus, CNN-LSTM has significant practical value in Qflow prediction.
Collapse
Affiliation(s)
- Sujan Ghimire
- School of Sciences, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Zaher Mundher Yaseen
- New era and development in civil engineering research group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq.
- College of Creative Design, Asia University, Taichung City, Taiwan.
| | - Aitazaz A Farooque
- Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE, C1A4P3, Canada
| | - Ravinesh C Deo
- School of Sciences, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Ji Zhang
- School of Sciences, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Xiaohui Tao
- School of Sciences, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| |
Collapse
|