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Yan Q, Zhao Z, Liu D, Li J, Pan S, Duan J, Liu Z. Novel immune cross-talk between inflammatory bowel disease and IgA nephropathy. Ren Fail 2024; 46:2337288. [PMID: 38628140 PMCID: PMC11025414 DOI: 10.1080/0886022x.2024.2337288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 03/27/2024] [Indexed: 04/19/2024] Open
Abstract
The mechanisms underlying the complex correlation between immunoglobulin A nephropathy (IgAN) and inflammatory bowel disease (IBD) remain unclear. This study aimed to identify the optimal cross-talk genes, potential pathways, and mutual immune-infiltrating microenvironments between IBD and IgAN to elucidate the linkage between patients with IBD and IgAN. The IgAN and IBD datasets were obtained from the Gene Expression Omnibus (GEO). Three algorithms, CIBERSORTx, ssGSEA, and xCell, were used to evaluate the similarities in the infiltrating microenvironment between the two diseases. Weighted gene co-expression network analysis (WGCNA) was implemented in the IBD dataset to identify the major immune infiltration modules, and the Boruta algorithm, RFE algorithm, and LASSO regression were applied to filter the cross-talk genes. Next, multiple machine learning models were applied to confirm the optimal cross-talk genes. Finally, the relevant findings were validated using histology and immunohistochemistry analysis of IBD mice. Immune infiltration analysis showed no significant differences between IBD and IgAN samples in most immune cells. The three algorithms identified 10 diagnostic genes, MAPK3, NFKB1, FDX1, EPHX2, SYNPO, KDF1, METTL7A, RIDA, HSDL2, and RIPK2; FDX1 and NFKB1 were enhanced in the kidney of IBD mice. Kyoto Encyclopedia of Genes and Genomes analysis showed 15 mutual pathways between the two diseases, with lipid metabolism playing a vital role in the cross-talk. Our findings offer insights into the shared immune mechanisms of IgAN and IBD. These common pathways, diagnostic cross-talk genes, and cell-mediated abnormal immunity may inform further experimental studies.
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Affiliation(s)
- Qianqian Yan
- Department of Integrated Traditional and Western Nephrology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P. R. China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, P. R. China
| | - Zihao Zhao
- Department of Integrated Traditional and Western Nephrology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P. R. China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, P. R. China
| | - Dongwei Liu
- Department of Integrated Traditional and Western Nephrology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P. R. China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, P. R. China
- Henan Province Research Center for Kidney Disease, Zhengzhou, P. R. China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, P. R. China
| | - Jia Li
- Department of Integrated Traditional and Western Nephrology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P. R. China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, P. R. China
- Henan Province Research Center for Kidney Disease, Zhengzhou, P. R. China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, P. R. China
| | - Shaokang Pan
- Department of Integrated Traditional and Western Nephrology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P. R. China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, P. R. China
- Henan Province Research Center for Kidney Disease, Zhengzhou, P. R. China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, P. R. China
| | - Jiayu Duan
- Department of Integrated Traditional and Western Nephrology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P. R. China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, P. R. China
- Henan Province Research Center for Kidney Disease, Zhengzhou, P. R. China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, P. R. China
| | - Zhangsuo Liu
- Department of Integrated Traditional and Western Nephrology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P. R. China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, P. R. China
- Henan Province Research Center for Kidney Disease, Zhengzhou, P. R. China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, P. R. China
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Rezapour M, Yazdinejad M, Rajabi Kouchi F, Habibi Baghi M, Khorrami Z, Khavanin Zadeh M, Pourbaghi E, Rezapour H. Text mining of hypertension researches in the west Asia region: a 12-year trend analysis. Ren Fail 2024; 46:2337285. [PMID: 38616180 PMCID: PMC11018045 DOI: 10.1080/0886022x.2024.2337285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/27/2024] [Indexed: 04/16/2024] Open
Abstract
More than half of the world population lives in Asia and hypertension (HTN) is the most prevalent risk factor found in Asia. There are numerous articles published about HTN in Eastern Mediterranean Region (EMRO) and artificial intelligence (AI) methods can analyze articles and extract top trends in each country. Present analysis uses Latent Dirichlet allocation (LDA) as an algorithm of topic modeling (TM) in text mining, to obtain subjective topic-word distribution from the 2790 studies over the EMRO. The period of checked studied is last 12 years and results of LDA analyses show that HTN researches published in EMRO discuss on changes in BP and the factors affecting it. Among the countries in the region, most of these articles are related to I.R Iran and Egypt, which have an increasing trend from 2017 to 2018 and reached the highest level in 2021. Meanwhile, Iraq and Lebanon have been conducting research since 2010. The EMRO word cloud illustrates 'BMI', 'mortality', 'age', and 'meal', which represent important indicators, dangerous outcomes of high BP, and gender of HTN patients in EMRO, respectively.
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Affiliation(s)
- Mohammad Rezapour
- Faculty Member of the Iranian Ministry of Science, Research and Technology, Tehran, Iran
| | | | - Faezeh Rajabi Kouchi
- Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | | | - Zahra Khorrami
- Ophthalmic Epidemiology Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Morteza Khavanin Zadeh
- Hasheminejad Kidney Center, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Elmira Pourbaghi
- Faculty of Advanced Sciences and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Hassan Rezapour
- Department of Transportation and Urban Infrastructure Studies, Morgan State University, Baltimore, MD, USA
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Hohlmann B, Broessner P, Radermacher K. Ultrasound-based 3D bone modelling in computer assisted orthopedic surgery - a review and future challenges. Comput Assist Surg (Abingdon) 2024; 29:2276055. [PMID: 38261543 DOI: 10.1080/24699322.2023.2276055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2024] Open
Abstract
Computer-assisted orthopedic surgery requires precise representations of bone surfaces. To date, computed tomography constitutes the gold standard, but comes with a number of limitations, including costs, radiation and availability. Ultrasound has potential to become an alternative to computed tomography, yet suffers from low image quality and limited field-of-view. These shortcomings may be addressed by a fully automatic segmentation and model-based completion of 3D bone surfaces from ultrasound images. This survey summarizes the state-of-the-art in this field by introducing employed algorithms, and determining challenges and trends. For segmentation, a clear trend toward machine learning-based algorithms can be observed. For 3D bone model completion however, none of the published methods involve machine learning. Furthermore, data sets and metrics are identified as weak spots in current research, preventing development and evaluation of models that generalize well.
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Affiliation(s)
- Benjamin Hohlmann
- Chair of Medical Engineering, Rheinisch-Westfalische Technische Hochschule, Aachen, Germany
| | - Peter Broessner
- Chair of Medical Engineering, Rheinisch-Westfalische Technische Hochschule, Aachen, Germany
| | - Klaus Radermacher
- Chair of Medical Engineering, Rheinisch-Westfalische Technische Hochschule, Aachen, Germany
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Li X, Wang Z, Zhao W, Shi R, Zhu Y, Pan H, Wang D. Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease. Ren Fail 2024; 46:2315298. [PMID: 38357763 PMCID: PMC10877653 DOI: 10.1080/0886022x.2024.2315298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 02/01/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD). METHODS After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. RESULTS This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions. CONCLUSIONS In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.
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Affiliation(s)
- Xunliang Li
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhijuan Wang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wenman Zhao
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Rui Shi
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yuyu Zhu
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Haifeng Pan
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, China
| | - Deguang Wang
- Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Kidney Disease, Inflammation and Immunity Mediated Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
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Fang Z, Fu J, Chen X. A combined immune and exosome-related risk signature as prognostic biomakers in acute myeloid leukemia. Hematology 2024; 29:2300855. [PMID: 38186215 DOI: 10.1080/16078454.2023.2300855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 12/19/2023] [Indexed: 01/09/2024] Open
Abstract
OBJECTIVES Acute myeloid leukemia (AML) is one of the common hematological diseases with low survival rates. Studies have highlighted the dysregulated expression of immune-related and exosome-related genes (ERGs) in cancers. Nevertheless, it remains to be determined whether combining these genes have a prognostic significance in AML. METHODS Immune-ERG profiles for 151 AML patients from TCGA were analyzed. A risk model was constructed and optimized through the combination of univariate Cox regression and LASSO regression analysis. GEO datasets were utilized as the external validation for the robustness of the risk model. In addition, we performed KEGG and GO enrichment analyses to investigate the role played by these genes in AML. The variations in immune cell infiltrations among risk groups were assessed through four algorithms. Expression of hub gene in specific cell was analyzed by single-cell RNA seq. RESULTS A total of 85 immune-ERGs associated with prognosis were identified, enabling the construction of a risk model for AML. The risk model based on five immune-ERGs (CD37, NUCB2, LSP1, MGST1, and PLXNB1) demonstrated a correlation with the clinical outcomes. Additionally, age, FAB classification, cytogenetics risk, and risk score were identified as independent prognostic factors. The five immune-ERGs exhibited correlations with cytokine-cytokine receptor interaction, and antigen processing and presentation. Notably, the risk model demonstrated significant associations with immune responses and the expression of immune checkpoints. CONCLUSIONS An immune-ERG-based risk model was developed to effectively predict prognostic outcomes for AML patients. There is potential for immune therapy in AML targeting the five hub genes.
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Affiliation(s)
- Zenghui Fang
- Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, People's Republic of China
| | - Jiali Fu
- Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, People's Republic of China
| | - Xin Chen
- Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, People's Republic of China
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Shu S, Yang G, Han H, Zhan T, Dang H, Xu Y. Accurate Temperature Reconstruction in Radiofrequency Ablation for Atherosclerotic Plaques Based on Inverse Heat Transfer Analysis. J Biomech Eng 2024; 146:081010. [PMID: 38491980 DOI: 10.1115/1.4065111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 03/13/2024] [Indexed: 03/18/2024]
Abstract
Radio frequency ablation has emerged as a widely accepted treatment for atherosclerotic plaques. However, monitoring the temperature field distribution in the blood vessel wall during this procedure presents challenges. This limitation increases the risk of endothelial cell damage and inflammatory responses, potentially leading to lumen restenosis. The aim of this study is to accurately reconstruct the transient temperature distribution by solving a stochastic heat transfer model with uncertain parameters using an inverse heat transfer algorithm and temperature measurement data. The nonlinear least squares optimization method, Levenberg-Marquardt (LM), was employed to solve the inverse heat transfer problem for parameter estimation. Then, to improve the convergence of the algorithm and reduce the computational resources, a method of parameter sensitivity analysis was proposed to select parameters mainly affecting the temperature field. Furthermore, the robustness and accuracy of the algorithm were verified by introducing random noise to the temperature measurements. Despite the high level of temperature measurement noise (ξ = 5%) and larger initial guess deviation, the parameter estimation results remained closely aligned with the actual values, with an overall ERMS consistently below 0.05. The absolute errors between the reconstruction temperature at the measurement points TC1, TC2, and TC3, and the actual temperature, remained within 0.33 °C, 2.4 °C, and 1.17 °C, respectively. The Levenberg-Marquardt algorithm employed in this study proficiently tackled the ill-posed issue of inversion process and obtained a strong consistency between the reconstructed temperature the actual temperature.
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Affiliation(s)
- Shuang Shu
- Institute of Bio-thermal Science and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Guoliang Yang
- Institute of Bio-thermal Science and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Hengxin Han
- Institute of Bio-thermal Science and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Taijie Zhan
- Institute of Bio-thermal Science and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Hangyu Dang
- Institute of Bio-thermal Science and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yi Xu
- Institute of Bio-thermal Science and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China
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Zhang S, Duan X, Yan X, Yuan X, Zhang D, Liu Y, Wang Y, Shen S, Xuan S, Zhao J, Chen X, Luo S, Gu A. Multispectral detection of dietary fiber content in Chinese cabbage leaves across different growth periods. Food Chem 2024; 447:138895. [PMID: 38492298 DOI: 10.1016/j.foodchem.2024.138895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/13/2024] [Accepted: 02/26/2024] [Indexed: 03/18/2024]
Abstract
Multispectral imaging, combined with stoichiometric values, was used to construct a prediction model to measure changes in dietary fiber (DF) content in Chinese cabbage leaves across different growth periods. Based on all the spectral bands (365-970 nm) and characteristic spectral bands (430, 880, 590, 490, 690 nm), eight quantitative prediction models were established using four machine learning algorithms, namely random forest (RF), backpropagation neural network, radial basis function, and multiple linear regression. Finally, a quantitative prediction model of RF learning algorithm is constructed based on all spectral bands, which has good prediction accuracy and model robustness, prediction performance with R2 of 0.9023, root mean square error (RMSE) of 2.7182 g/100 g, residual predictive deviation (RPD) of 3.1220 > 3.0. In summary, this model efficiently detects changes in DF content across different growth periods of Chinese cabbage, which offers technical support for vegetable sorting and grading in the field.
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Affiliation(s)
- Shaoliang Zhang
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Xin Duan
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Xinglong Yan
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Xiaoxue Yuan
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Dongfang Zhang
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Yuanming Liu
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Yanhua Wang
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Shuxing Shen
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Shuxin Xuan
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Jianjun Zhao
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Xueping Chen
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Shuangxia Luo
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Aixia Gu
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, 071000 Baoding, China.
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Bernardini LG, Rosinger C, Bodner G, Keiblinger KM, Izquierdo-Verdiguier E, Spiegel H, Retzlaff CO, Holzinger A. Learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction? N Biotechnol 2024; 81:20-31. [PMID: 38462171 DOI: 10.1016/j.nbt.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/24/2024] [Accepted: 03/06/2024] [Indexed: 03/12/2024]
Abstract
In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while process-based models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils. Therefore, we recommend applying ensembles of ML algorithms with process-based models to combine advantages inherent to both approaches.
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Affiliation(s)
| | - Christoph Rosinger
- Institute of Agronomy, University of Natural Resources and Life Sciences (BOKU) Vienna, Konrad Lorenz-Straße 24, 3430 Tulln an der Donau, Austria; Institute of Soil Research, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria.
| | - Gernot Bodner
- Institute of Agronomy, University of Natural Resources and Life Sciences (BOKU) Vienna, Konrad Lorenz-Straße 24, 3430 Tulln an der Donau, Austria
| | - Katharina M Keiblinger
- Institute of Soil Research, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria
| | - Emma Izquierdo-Verdiguier
- Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria
| | - Heide Spiegel
- Austrian Agency for Health and Food Safety (AGES), Institute for Soil Health and Plant Nutrition, Spargelfeldstraße 191, 1226 Vienna, Austria
| | - Carl O Retzlaff
- Human-Centered AI Lab, Institute of Forest Engineering, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria
| | - Andreas Holzinger
- Human-Centered AI Lab, Institute of Forest Engineering, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter Jordan-Straße 82, 1190 Vienna, Austria
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Yang H, Ai J, Zhu Y, Shi Q, Yu Q. Rapid classification of coffee origin by combining mass spectrometry analysis of coffee aroma with deep learning. Food Chem 2024; 446:138811. [PMID: 38412809 DOI: 10.1016/j.foodchem.2024.138811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/11/2024] [Accepted: 02/19/2024] [Indexed: 02/29/2024]
Abstract
Mislabeling the geographical origin of coffee is a prevalent form of fraud. In this study, a rapid, nondestructive, and high-throughput method combining mass spectrometry (MS) analysis and intelligence algorithms to classify coffee origin was developed. Specifically, volatile compounds in coffee aroma were detected using self-aspiration corona discharge ionization mass spectrometry (SACDI-MS), and the acquired MS data were processed using a customized deep learning algorithm to perform origin authentication automatically. To facilitate high-throughput analysis, an air curtain sampling device was designed and coupled with SACDI-MS to prevent volatile mixing and signal overlap. An accuracy of 99.78% was achieved in the classification of coffee samples from six origins at a throughput of 1 s per sample. The proposed approach may be effective in preventing coffee fraud owing to its straightforward operation, rapidity, and high accuracy and thus benefit consumers.
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Affiliation(s)
- Huang Yang
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Jiawen Ai
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Yanping Zhu
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Qinhao Shi
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Quan Yu
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
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10
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Chen H, Ren L, Yang Y, Long W, Lan W, Yang J, Fu H. Three-dimensional fluorescence combined with alternating trilinear decomposition and random forest algorithm for the rapid prediction of species, geographical origin and main components of Glycyrrhizae Radix et Rhizoma (Gancao). Food Chem 2024; 444:138603. [PMID: 38330604 DOI: 10.1016/j.foodchem.2024.138603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/07/2024] [Accepted: 01/25/2024] [Indexed: 02/10/2024]
Abstract
Glycyrrhizae Radix et Rhizoma (Gancao) is a functional food whose quality varies significantly between distinct geographical sources owing to the influence of genetics and the geographical environment. This study employed three-dimensional fluorescence coupled with alternating trilinear decomposition (ATLD) and random forest (RF) algorithms to rapidly predict Gancao species, geographical origins, and primary constituents. Seven fluorescent components were resolved from the three-dimensional fluorescence of the ATLD for subsequent analysis. Results indicated that the RF model distinguished Gancao from various species and origins better than other algorithms, achieving an accuracy of 94.4 % and 88.9 %, respectively. Furthermore, the RF regressor algorithm was used to predict the concentrations of liquiritin and glycyrrhizic acid in Gancao, with 96.4 % and 95.6 % prediction accuracies compared to HPLC, respectively. This approach offers a novel means of objectively evaluating the origin of food and holds substantial promise for food quality assessment.
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Affiliation(s)
- Hengye Chen
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Lixue Ren
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Yinan Yang
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Wanjun Long
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Wei Lan
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China
| | - Jian Yang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijng 100700, PR China
| | - Haiyan Fu
- The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China.
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11
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Goncu B. Identification of suitable reference genes for RT-qPCR studies in human parathyroid tissue glandular cells. Gene 2024; 912:148380. [PMID: 38490511 DOI: 10.1016/j.gene.2024.148380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/06/2024] [Accepted: 03/13/2024] [Indexed: 03/17/2024]
Abstract
Identifying a proper reference gene allows us to understand fundamental changes in many biological processes. Normalization during gene expression analyses is essential for every tissue/cell type, including parathyroid tissue glandular cells. Quantitative method of gene expression analyses via qRT-PCR method provides the accurate examination of every target gene. There are limited reports to present commonly used reference genes in human parathyroid tissues rather than for glandular cell types. This study aims to determine and compare the most stable to least stable genes for parathyroid tissue cells. 43 human parathyroid tissue obtained from primary and secondary hyperparathyroidism patients and glandular cells isolated enzymatically by the removal of extracellular matrix components. After extraction of the total RNA, cDNA synthesis was performed, then qRT-PCR evaluated 14 candidate reference genes. Stability was determined by RefFinder software (Delta ct, BestKeeper, Genorm, and NormFinder algorithms), and the outcome was evaluated for five groups. Even if assessed with different groups, the most stable genes were RPLP0 and GAPDH, while the CLTC and RNA 18S were the least stable. We have confirmed the comprehensive ranking of the most stable three genes alone with the NormFinder algorithm to understand intergroup variation and found out that RPLP0>GAPDH>PGK1. Lastly, comparisons of relative target gene (GCM2) expression revealed similar expression patterns for the most stable reference genes. The most stable reference gene is recommended for the stages where stability is evaluated using the results of four different approaches using RefFinder. We aspire for this study to assist future research to conduct thorough assessments of appropriate reference genes before engaging in gene expression analyses for parathyroid tissue.
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Affiliation(s)
- Beyza Goncu
- Bezmialem Vakif University, Vocational School of Health Services, Department of Medical Services and Techniques, Istanbul, Turkiye; Bezmialem Vakif University Hospital, Organ Transplantation Center, Parathyroid Transplantation Unit, Istanbul, Turkiye.
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12
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Zhang T, Wang Y, Sun J, Liang J, Wang B, Xu X, Xu J, Liu L. Precision in wheat flour classification: Harnessing the power of deep learning and two-dimensional correlation spectrum (2DCOS). Spectrochim Acta A Mol Biomol Spectrosc 2024; 314:124112. [PMID: 38518439 DOI: 10.1016/j.saa.2024.124112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/28/2024] [Accepted: 03/02/2024] [Indexed: 03/24/2024]
Abstract
Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduces an innovative method for wheat flour recognition, combining deep learning (DL) with Two-dimensional correlation spectrum (2DCOS). In this investigation, 316 samples from four distinct types of wheat flour were collected using a near-infrared (NIR) spectrometer, and the raw spectra of each sample underwent preprocessing employing diverse methods. The discrete generalized 2DCOS algorithm was applied to generate 3792 2DCOS images from the preprocessed spectral data. We trained a deep learning model tailored for flour 2DCOS images - EfficientNet. Ultimately, this DL model achieved 100% accuracy in identifying wheat flour within the test set. The findings demonstrate the viability of directly transforming spectra into two-dimensional images for species recognition using 2DCOS and DL. Compared to the traditional stoichiometric method Partial Least Squares Discriminant Analysis (PLS_DA), machine learning methods Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and deep learning methods one-dimensional convolutional neural network (1DCNN) and residual neural network (ResNet), the model proposed in this paper is better suited for wheat flour identification, boasting the highest accuracy. This study offers a fresh perspective on wheat flour type identification and successfully integrates the latest advancements in deep learning with 2DCOS for spectral type identification. Furthermore, this approach can be extended to the spectral identification of other products, presenting a novel avenue for future research in the field.
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Affiliation(s)
- Tianrui Zhang
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Yifan Wang
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Jiansong Sun
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Jing Liang
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Bin Wang
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
| | - Xiaoxuan Xu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Yunnan Research Institute, Nankai University, Kunming 650091, China
| | - Jing Xu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
| | - Lei Liu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China
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13
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Carvalho Macruz FBD, Dias ALMP, Andrade CS, Nucci MP, Rimkus CDM, Lucato LT, Rocha AJD, Kitamura FC. The new era of artificial intelligence in neuroradiology: current research and promising tools. Arq Neuropsiquiatr 2024; 82:1-12. [PMID: 38565188 PMCID: PMC10987255 DOI: 10.1055/s-0044-1779486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/13/2023] [Indexed: 04/04/2024]
Abstract
Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.
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Affiliation(s)
- Fabíola Bezerra de Carvalho Macruz
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Rede D'Or São Luiz, Departamento de Radiologia e Diagnóstico por Imagem, São Paulo SP, Brazil.
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
- Academia Nacional de Medicina, Rio de Janeiro RJ, Brazil.
| | | | | | - Mariana Penteado Nucci
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
| | - Carolina de Medeiros Rimkus
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Rede D'Or São Luiz, Departamento de Radiologia e Diagnóstico por Imagem, São Paulo SP, Brazil.
- Universidade de São Paulo, Laboratório de Investigação Médica em Ressonância Magnética (LIM 44), São Paulo SP, Brazil.
| | - Leandro Tavares Lucato
- Universidade de São Paulo, Hospital das Clínicas, Departamento de Radiologia e Oncologia, Seção de Neurorradiologia, Faculdade de Medicina, São Paulo SP, Brazil.
- Diagnósticos da América SA, São Paulo SP, Brazil.
| | | | - Felipe Campos Kitamura
- Diagnósticos da América SA, São Paulo SP, Brazil.
- Universidade Federal de São Paulo, São Paulo SP, Brazil.
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Kratochvíla J, Jiřík R, Bartoš M, Standara M, Starčuk Z, Taxt T. Blind deconvolution decreases requirements on temporal resolution of DCE-MRI: Application to 2nd generation pharmacokinetic modeling. Magn Reson Imaging 2024; 109:238-248. [PMID: 38508292 DOI: 10.1016/j.mri.2024.03.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 03/08/2024] [Accepted: 03/16/2024] [Indexed: 03/22/2024]
Abstract
PURPOSE Dynamic Contrast-Enhanced (DCE) MRI with 2nd generation pharmacokinetic models provides estimates of plasma flow and permeability surface-area product in contrast to the broadly used 1st generation models (e.g. the Tofts models). However, the use of 2nd generation models requires higher frequency with which the dynamic images are acquired (around 1.5 s per image). Blind deconvolution can decrease the demands on temporal resolution as shown previously for one of the 1st generation models. Here, the temporal-resolution requirements achievable for blind deconvolution with a 2nd generation model are studied. METHODS The 2nd generation model is formulated as the distributed-capillary adiabatic-tissue-homogeneity (DCATH) model. Blind deconvolution is based on Parker's model of the arterial input function. The accuracy and precision of the estimated arterial input functions and the perfusion parameters is evaluated on synthetic and real clinical datasets with different levels of the temporal resolution. RESULTS The estimated arterial input functions remained unchanged from their reference high-temporal-resolution estimates (obtained with the sampling interval around 1 s) when increasing the sampling interval up to about 5 s for synthetic data and up to 3.6-4.8 s for real data. Further increasing of the sampling intervals led to systematic distortions, such as lowering and broadening of the 1st pass peak. The resulting perfusion-parameter estimation error was below 10% for the sampling intervals up to 3 s (synthetic data), in line with the real data perfusion-parameter boxplots which remained unchanged up to the sampling interval 3.6 s. CONCLUSION We show that use of blind deconvolution decreases the demands on temporal resolution in DCE-MRI from about 1.5 s (in case of measured arterial input functions) to 3-4 s. This can be exploited in increased spatial resolution or larger organ coverage.
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Affiliation(s)
- Jiří Kratochvíla
- Czech Academy of Sciences, Institute of Scientific Instruments, Královopolská 147, 612 64 Brno, Czech Republic.
| | - Radovan Jiřík
- Czech Academy of Sciences, Institute of Scientific Instruments, Královopolská 147, 612 64 Brno, Czech Republic
| | - Michal Bartoš
- Czech Academy of Sciences, Institute of Information Technology and Automation, Pod Vodárenskou věží 4, 182 08 Praha 8, Czech Republic
| | - Michal Standara
- Department of Radiology, Masaryk Memorial Cancer Institute, Žlutý kopec 7, 656 53 Brno, Czech Republic
| | - Zenon Starčuk
- Czech Academy of Sciences, Institute of Scientific Instruments, Královopolská 147, 612 64 Brno, Czech Republic
| | - Torfinn Taxt
- Department of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen, Norway
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15
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Raj P, Paidi SK, Conway L, Chatterjee A, Barman I. CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging. J Biomed Opt 2024; 29:S22706. [PMID: 38638450 PMCID: PMC11025678 DOI: 10.1117/1.jbo.29.s2.s22706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/20/2024]
Abstract
Significance Three-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. It has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw three-dimensional (3D) tomograms is not well-developed. We focus on a critical, yet often underappreciated, step of the analysis pipeline that of 3D cell segmentation from the acquired tomograms. Aim We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the 3D segmentation of QPI images. Approach The cell segmentation algorithm mimics the gemstone extraction process, initiating with a coarse 3D extrusion from a two-dimensional (2D) segmented mask to outline the cell structure. A 2D image is generated, and a segmentation algorithm identifies the boundary in the x - y plane. Leveraging cell continuity in consecutive z -stacks, a refined 3D segmentation, akin to fine chiseling in gemstone carving, completes the process. Results The CellSNAP algorithm outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 s per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused AI-based segmentation tools. Conclusion Our proposed method is less memory intensive and significantly faster than existing methods. The method can be easily implemented on a student laptop. Since the approach is rule-based, there is no need to collect a lot of imaging data and manually annotate them to perform machine learning based training of the model. We envision our work will lead to broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.
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Affiliation(s)
- Piyush Raj
- Johns Hopkins University, Department of Mechanical Engineering, Baltimore, Maryland, United States
| | - Santosh Kumar Paidi
- Johns Hopkins University, Department of Mechanical Engineering, Baltimore, Maryland, United States
| | - Lauren Conway
- Johns Hopkins University, Department of Chemical and Biomolecular Engineering, Baltimore, Maryland, United States
| | - Arnab Chatterjee
- Johns Hopkins University, Department of Mechanical Engineering, Baltimore, Maryland, United States
| | - Ishan Barman
- Johns Hopkins University, Department of Mechanical Engineering, Baltimore, Maryland, United States
- The Johns Hopkins University, School of Medicine, The Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, Maryland, United States
- Johns Hopkins University, Department of Oncology, Baltimore, Maryland, United States
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16
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Chen X, Wu W, Chiew M. Motion compensated structured low-rank reconstruction for 3D multi-shot EPI. Magn Reson Med 2024; 91:2443-2458. [PMID: 38361309 DOI: 10.1002/mrm.30019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/08/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024]
Abstract
PURPOSE The 3D multi-shot EPI imaging offers several benefits including higher SNR and high isotropic resolution compared to 2D single shot EPI. However, it suffers from shot-to-shot inconsistencies arising from physiologically induced phase variations and bulk motion. This work proposed a motion compensated structured low-rank (mcSLR) reconstruction method to address both issues for 3D multi-shot EPI. METHODS Structured low-rank reconstruction has been successfully used in previous work to deal with inter-shot phase variations for 3D multi-shot EPI imaging. It circumvents the estimation of phase variations by reconstructing an individual image for each phase state which are then sum-of-squares combined, exploiting their linear interdependency encoded in structured low-rank constraints. However, structured low-rank constraints become less effective in the presence of inter-shot motion, which corrupts image magnitude consistency and invalidates the linear relationship between shots. Thus, this work jointly models inter-shot phase variations and motion corruptions by incorporating rigid motion compensation for structured low-rank reconstruction, where motion estimates are obtained in a fully data-driven way without relying on external hardware or imaging navigators. RESULTS Simulation and in vivo experiments at 7T have demonstrated that the mcSLR method can effectively reduce image artifacts and improve the robustness of 3D multi-shot EPI, outperforming existing methods which only address inter-shot phase variations or motion, but not both. CONCLUSION The proposed mcSLR reconstruction compensates for rigid motion, and thus improves the validity of structured low-rank constraints, resulting in improved robustness of 3D multi-shot EPI to both inter-shot motion and phase variations.
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Affiliation(s)
- Xi Chen
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Mark Chiew
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
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17
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Abbasi-Rad S, Cloos MA, Jin J, O'Brien K, Barth M. B 1 + inhomogeneity mitigation for diffusion weighted MRI at 7T using TR-FOCI pulses. Magn Reson Med 2024; 91:2508-2518. [PMID: 38321602 DOI: 10.1002/mrm.30024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 12/14/2023] [Accepted: 01/07/2024] [Indexed: 02/08/2024]
Abstract
PURPOSE The purpose of this study is to improve the image quality of diffusion-weighted images obtained with a single RF transmit channel 7 T MRI setup using time-resampled frequency-offset corrected inversion (TR-FOCI) pulses to refocus the spins in a twice-refocused spin-echo readout scheme. METHODS We replaced the conventional Shinnar-Le Roux-pulses in the twice refocused diffusion sequence with TR-FOCI pulses. The slice profiles were evaluated in simulation and experimentally in phantoms. The image quality was evaluated in vivo comparing the Shinnar-Le Roux and TR-FOCI implementation using a b value of 0 and of 1000 s/mm2. RESULTS The b0 and diffusion-weighted images acquired using the modified sequence improved the image quality across the whole brain. A region of interest-based analysis showed an SNR increase of 113% and 66% for the nondiffusion-weighted (b0) and the diffusion-weighted (b = 1000 s/mm2) images in the temporal lobes, respectively. Investigation of all slices showed that the adiabatic pulses mitigatedB 1 + $$ {B}_1^{+} $$ inhomogeneity globally using a conventional single-channel transmission setup. CONCLUSION The TR-FOCI pulse can be used in a twice-refocused spin-echo diffusion pulse sequence to mitigate the impact ofB 1 + $$ {B}_1^{+} $$ inhomogeneity on the signal intensity across the brain at 7 T. However, further work is needed to address SAR limitations.
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Affiliation(s)
- Shahrokh Abbasi-Rad
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland, Australia
| | - Martijn A Cloos
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, St Lucia, Queensland, Australia
| | - Jin Jin
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, St Lucia, Queensland, Australia
- Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Kieran O'Brien
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, St Lucia, Queensland, Australia
- Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Markus Barth
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, St Lucia, Queensland, Australia
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, Queensland, Australia
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18
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Wang D, Han C, Zhang Z, Zhai T, Lin H, Yang B, Cui Y, Lin Y, Zhao Z, Zhao L, Liang C, Zeng A, Pan D, Chen X, Shi Z, Liu Z. FedDUS: Lung tumor segmentation on CT images through federated semi-supervised with dynamic update strategy. Comput Methods Programs Biomed 2024; 249:108141. [PMID: 38574423 DOI: 10.1016/j.cmpb.2024.108141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 02/27/2024] [Accepted: 03/19/2024] [Indexed: 04/06/2024]
Abstract
BACKGROUND AND OBJECTIVE Lung tumor annotation is a key upstream task for further diagnosis and prognosis. Although deep learning techniques have promoted automation of lung tumor segmentation, there remain challenges impeding its application in clinical practice, such as a lack of prior annotation for model training and data-sharing among centers. METHODS In this paper, we use data from six centers to design a novel federated semi-supervised learning (FSSL) framework with dynamic model aggregation and improve segmentation performance for lung tumors. To be specific, we propose a dynamically updated algorithm to deal with model parameter aggregation in FSSL, which takes advantage of both the quality and quantity of client data. Moreover, to increase the accessibility of data in the federated learning (FL) network, we explore the FAIR data principle while the previous federated methods never involve. RESULT The experimental results show that the segmentation performance of our model in six centers is 0.9348, 0.8436, 0.8328, 0.7776, 0.8870 and 0.8460 respectively, which is superior to traditional deep learning methods and recent federated semi-supervised learning methods. CONCLUSION The experimental results demonstrate that our method is superior to the existing FSSL methods. In addition, our proposed dynamic update strategy effectively utilizes the quality and quantity information of client data and shows efficiency in lung tumor segmentation. The source code is released on (https://github.com/GDPHMediaLab/FedDUS).
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Affiliation(s)
- Dan Wang
- School of Computers, Guangdong University of Technology, Guangzhou 510006, China
| | - Chu Han
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Zhen Zhang
- Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Tiantian Zhai
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Huan Lin
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Baoyao Yang
- School of Computers, Guangdong University of Technology, Guangzhou 510006, China
| | - Yanfen Cui
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, China
| | - Yinbing Lin
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Zhihe Zhao
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Lujun Zhao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - An Zeng
- School of Computers, Guangdong University of Technology, Guangzhou 510006, China.
| | - Dan Pan
- School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.
| | - Zhenwei Shi
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
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Wong SM, Akbulatov A, Macsemchuk CA, Headrick A, Luo P, Drake JM, Waspe AC. An augmented hybrid multibaseline and referenceless MR thermometry motion compensation algorithm for MRgHIFU hyperthermia. Magn Reson Med 2024; 91:2266-2277. [PMID: 38181187 DOI: 10.1002/mrm.29988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 12/04/2023] [Accepted: 12/08/2023] [Indexed: 01/07/2024]
Abstract
PURPOSE A hybrid principal component analysis and projection onto dipole fields (PCA-PDF) MR thermometry motion compensation algorithm was optimized with atlas image augmentation and validated. METHODS Experiments were conducted on a 3T Philips MRI and Profound V1 Sonalleve high intensity focused ultrasound (high intensity focused ultrasound system. An MR-compatible robot was configured to induce motion on custom gelatin phantoms. Trials with periodic and sporadic motion were introduced on phantoms while hyperthermia was administered. The PCA-PDF algorithm was augmented with a predictive atlas to better compensate for larger sporadic motion. RESULTS During periodic motion, the temperature SD in the thermometry was improved from1 . 1 ± 0 . 1 $$ 1.1\pm 0.1 $$ to0 . 5 ± 0 . 1 ∘ $$ 0.5\pm 0.{1}^{\circ } $$ C with both the original and augmented PCA-PDF application. For large sporadic motion, the augmented atlas improved the motion compensation from the original PCA-PDF correction from8 . 8 ± 0 . 5 $$ 8.8\pm 0.5 $$ to0 . 7 ± 0 . 1 ∘ $$ 0.7\pm 0.{1}^{\circ } $$ C. CONCLUSION The PCA-PDF algorithm improved temperature accuracy to <1°C during periodic motion, but was not able to adequately address sporadic motion. By augmenting the PCA-PDF algorithm, temperature SD during large sporadic motion was also reduced to <1°C, greatly improving the original PCA-PDF algorithm.
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Affiliation(s)
- Suzanne M Wong
- The Wilfred and Joyce Posluns Centre for Image-Guided Innovation and Theraputic Intervention, The Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Arthur Akbulatov
- The Wilfred and Joyce Posluns Centre for Image-Guided Innovation and Theraputic Intervention, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Craig A Macsemchuk
- The Wilfred and Joyce Posluns Centre for Image-Guided Innovation and Theraputic Intervention, The Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Andrew Headrick
- The Wilfred and Joyce Posluns Centre for Image-Guided Innovation and Theraputic Intervention, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Phoebe Luo
- The Wilfred and Joyce Posluns Centre for Image-Guided Innovation and Theraputic Intervention, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - James M Drake
- The Wilfred and Joyce Posluns Centre for Image-Guided Innovation and Theraputic Intervention, The Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Adam C Waspe
- The Wilfred and Joyce Posluns Centre for Image-Guided Innovation and Theraputic Intervention, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
- Department of Material Science and Engineering, University of Toronto, Toronto, Ontario, Canada
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20
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Murazaki H, Wada T, Togao O, Obara M, Helle M, Kobayashi K, Ishigami K, Kato T. Improved temporal resolution and acceleration on 4D-MR angiography based on superselective pseudo-continuous arterial spin labeling combined with CENTRA-keyhole and view-sharing (4D-S-PACK) using an interpolation algorithm on the temporal axis and compressed sensing-sensitivity encoding (CS-SENSE). Magn Reson Imaging 2024; 109:1-9. [PMID: 38417470 DOI: 10.1016/j.mri.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 03/01/2024]
Abstract
PURPOSE Two major drawbacks of 4D-MR angiography based on superselective pseudo-continuous arterial spin labeling combined with CENTRA-keyhole and view-sharing (4D-S-PACK) are the low temporal resolution and long scanning time. We investigated the feasibility of increasing the temporal resolution and accelerating the scanning time on 4D-S-PACK by using CS-SENSE and PhyZiodynamics, a novel image-processing program that interpolates images between phases to generate new phases and reduces image noise. METHODS Seven healthy volunteers were scanned with a 3.0 T MR scanner to visualize the internal carotid artery (ICA) system. PhyZiodynamics is a novel image-processing that interpolates images between phases to generate new phases and reduces image noise, and by increasing temporal resolution using PhyZiodynamics, inflow dynamic data (reference) were acquired by changing the labeling durations (100-2000 msec, 31 phases) in 4D-S-PACK. From this set of data, we selected seven time intervals to calculate interpolated time points with up to 61 intervals using ×10 for the generation of interpolated phases with PhyZiodynamics. In the denoising process of PhyZiodynamics, we processed the none, low, medium, high noise reduction dataset images. The time intensity curve (TIC), the contrast-to-noise ratio (CNR) were evaluated. In accelerating with CS-SENSE for 4D-S-PACK, 4D-S-PACK were scanned different SENSE or CS-SENSE acceleration factors: SENSE3, CS3-6. Signal intensity (SI), CNR, were evaluated for accelerating the 4D-S-PACK. With regard to arterial vascular visualization, we evaluated the middle cerebral artery (MCA: M1-4 segments). RESULTS In increasing temporal resolution, the TIC showed a similar trend between the reference dataset and the interpolated dataset. As the noise reduction weight increased, the CNR of the interpolated dataset were increased compared to that of the reference dataset. In accelerating 4D-S-PACK, the SI values of the SENSE3 dataset and CS dataset with CS3-6 were no significant differences. The image noise increased with the increase of acceleration factor, and the CNR decreased with the increase of acceleration factor. Significant differences in CNR were observed between acceleration factor of SENSE3 and CS6 for the M1-4 (P < 0.05). Visualization of small arteries (M4) became less reliable in CS5 or CS6 images. Significant differences were found for the scores of M2, M3 and M4 segments between SENSE3 and CS6. CONCLUSION With PhyZiodynamics and CS-SENSE in 4D-S-PACK, we were able to shorten the scan time while improving the temporal resolution.
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Affiliation(s)
- Hiroo Murazaki
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan.
| | - Tatsuhiro Wada
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan
| | - Osamu Togao
- Department of Molecular Imaging & Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | | | | | - Kouji Kobayashi
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Toyoyuki Kato
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan
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21
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Zheng J, Ju X, Zhang N, Xu D. A novel predefined-time neurodynamic approach for mixed variational inequality problems and applications. Neural Netw 2024; 174:106247. [PMID: 38518707 DOI: 10.1016/j.neunet.2024.106247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/20/2024] [Accepted: 03/15/2024] [Indexed: 03/24/2024]
Abstract
In this paper, we propose a novel neurodynamic approach with predefined-time stability that offers a solution to address mixed variational inequality problems. Our approach introduces an adjustable time parameter, thereby enhancing flexibility and applicability compared to conventional fixed-time stability methods. By satisfying certain conditions, the proposed approach is capable of converging to a unique solution within a predefined-time, which sets it apart from fixed-time stability and finite-time stability approaches. Furthermore, our approach can be extended to address a wide range of mathematical optimization problems, including variational inequalities, nonlinear complementarity problems, sparse signal recovery problems, and nash equilibria seeking problems in noncooperative games. We provide numerical simulations to validate the theoretical derivation and showcase the effectiveness and feasibility of our proposed method.
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Affiliation(s)
- Jinlan Zheng
- Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China
| | - Xingxing Ju
- College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China; Shaanxi Key Laboratory of Information Communication Network and Security, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China
| | - Naimin Zhang
- College of Mathematics and Physics, Wenzhou University, Wenzhou 325035, China
| | - Dongpo Xu
- Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China.
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22
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Boukelkal N, Rahal S, Rebhi R, Hamadache M. QSPR for the prediction of critical micelle concentration of different classes of surfactants using machine learning algorithms. J Mol Graph Model 2024; 129:108757. [PMID: 38503002 DOI: 10.1016/j.jmgm.2024.108757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 03/07/2024] [Accepted: 03/10/2024] [Indexed: 03/21/2024]
Abstract
The determination of the critical micelle concentration (CMC) is a crucial factor when evaluating surfactants, making it an essential tool in studying the properties of surfactants in various industrial fields. In this present research, we assembled a comprehensive set of 593 different classes of surfactants including, anionic, cationic, nonionic, zwitterionic, and Gemini surfactants to establish a link between their molecular structure and the negative logarithmic value of critical micelle concentration (pCMC) utilizing quantitative structure-property relationship (QSPR) methodologies. Statistical analysis revealed that a set of 14 significant Mordred descriptors (SlogP, GATS6d, nAcid, GATS8dv, GATS4dv, PEOE_VSA11, GATS8d, ATS0p, GATS1d, MATS5p, GATS3d, NdssC, GATS6dv and EState_VSA4), along with temperature, served as appropriate inputs. Different machine learning methods, such as multiple linear regression (MLR), random forest regression (RFR), artificial neural network (ANN), and support vector regression (SVM), were employed in this study to build QSPR models. According to the statistical coefficients of QSPR models, SVR with Dragonfly hyperparameter optimization (SVR-DA) was the most accurate in predicting pCMC values, achieving (R2 = 0.9740, Q2 = 0.9739, r‾m2 = 0.9627, and Δrm2 = 0.0244) for the entire dataset.
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Affiliation(s)
- Nada Boukelkal
- Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Yahia Fares, Faculty of Technology, Department of Process Engineering and Environment, Medea, 26000, Algeria.
| | - Soufiane Rahal
- Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Yahia Fares, Faculty of Technology, Department of Process Engineering and Environment, Medea, 26000, Algeria
| | - Redha Rebhi
- Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Yahia Fares, Faculty of Technology, Department of Process Engineering and Environment, Medea, 26000, Algeria
| | - Mabrouk Hamadache
- Biomaterials and Transport Phenomena Laboratory (LBMPT), University of Yahia Fares, Faculty of Technology, Department of Process Engineering and Environment, Medea, 26000, Algeria
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23
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Qin X, Quan Y, Ji H. Enhanced deep unrolling networks for snapshot compressive hyperspectral imaging. Neural Netw 2024; 174:106250. [PMID: 38531122 DOI: 10.1016/j.neunet.2024.106250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 02/01/2024] [Accepted: 03/18/2024] [Indexed: 03/28/2024]
Abstract
Snapshot compressive hyperspectral imaging necessitates the reconstruction of a complete hyperspectral image from its compressive snapshot measurement, presenting a challenging inverse problem. This paper proposes an enhanced deep unrolling neural network, called EDUNet, to tackle this problem. The EDUNet is constructed via the deep unrolling of a proximal gradient descent algorithm and introduces two innovative modules for gradient-driven update and proximal mapping reflectivity. The gradient-driven update module leverages a memory-assistant descent approach inspired by momentum-based acceleration techniques, for enhancing the unrolled reconstruction process and improving convergence. The proximal mapping is modeled by a sub-network with a cross-stage spectral self-attention, which effectively exploits the inherent self-similarities present in hyperspectral images along the spectral axis. It also enhances feature flow throughout the network, contributing to reconstruction performance gain. Furthermore, we introduce a spectral geometry consistency loss, encouraging EDUNet to prioritize the geometric layouts of spectral curves, leading to a more precise capture of spectral information in hyperspectral images. Experiments are conducted using three benchmark datasets including KAIST, ICVL, and Harvard, along with some real data, comprising a total of 73 samples. The experimental results demonstrate that EDUNet outperforms 15 competing models across four metrics including PSNR, SSIM, SAM, and ERGAS.
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Affiliation(s)
- Xinran Qin
- School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.
| | - Yuhui Quan
- School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China; Pazhou Lab, Guangzhou 510335, China.
| | - Hui Ji
- Department of Mathematics, National University of Singapore, 119076, Singapore.
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24
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Chen P, Fu R, Shi Y, Liu C, Yang C, Su Y, Lu T, Zhou P, He W, Guo Q, Fei C. Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion. Food Chem 2024; 442:138408. [PMID: 38241985 DOI: 10.1016/j.foodchem.2024.138408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/21/2024]
Abstract
This study utilized computer vision to extract color and texture features of Pericarpium Citri Reticulatae (PCR). The ultra-fast gas-phase electronic nose (UF-GC-E-nose) technique successfully identified 98 volatile components, including olefins, alcohols, and esters, which significantly contribute to the flavor profile of PCR. Multivariate statistical Analysis was applied to the appearance traits of PCR, identifying 57 potential marker-trait factors (VIP > 1 and P < 0.05) from the 118 trait factors that can distinguish PCR from different origins. These factors include color, texture, and odor traits. By integrating multivariate statistical Analysis with the BP neural network algorithm, a novel artificial intelligence algorithm was developed and optimized for traceability of PCR origin. This algorithm achieved a 100% discrimination rate in differentiating PCR samples from various origins. This study offers a valuable reference and data support for developing intelligent algorithms that utilize data fusion from multiple intelligent sensory technologies to achieve rapid traceability of food origins.
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Affiliation(s)
- Peng Chen
- Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China
| | - Rao Fu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yabo Shi
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Chang Liu
- Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China
| | - Chenlu Yang
- Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China
| | - Yong Su
- Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China
| | - Tulin Lu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Peina Zhou
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Weitong He
- Jiangsu Wigroup Technologies Co., Ltd., Nanjing 210000, China
| | - Qiaosheng Guo
- Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China.
| | - Chenghao Fei
- Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210095, China.
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25
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Xu M, Ma Q, Zhang H, Kong D, Zeng T. MEF-UNet: An end-to-end ultrasound image segmentation algorithm based on multi-scale feature extraction and fusion. Comput Med Imaging Graph 2024; 114:102370. [PMID: 38513396 DOI: 10.1016/j.compmedimag.2024.102370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 03/10/2024] [Accepted: 03/13/2024] [Indexed: 03/23/2024]
Abstract
Ultrasound image segmentation is a challenging task due to the complexity of lesion types, fuzzy boundaries, and low-contrast images along with the presence of noises and artifacts. To address these issues, we propose an end-to-end multi-scale feature extraction and fusion network (MEF-UNet) for the automatic segmentation of ultrasound images. Specifically, we first design a selective feature extraction encoder, including detail extraction stage and structure extraction stage, to precisely capture the edge details and overall shape features of the lesions. In order to enhance the representation capacity of contextual information, we develop a context information storage module in the skip-connection section, responsible for integrating information from adjacent two-layer feature maps. In addition, we design a multi-scale feature fusion module in the decoder section to merge feature maps with different scales. Experimental results indicate that our MEF-UNet can significantly improve the segmentation results in both quantitative analysis and visual effects.
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Affiliation(s)
- Mengqi Xu
- School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, 210044, China
| | - Qianting Ma
- School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, 210044, China.
| | - Huajie Zhang
- School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, 210044, China
| | - Dexing Kong
- School of Mathematical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310027, China
| | - Tieyong Zeng
- Department of Mathematics, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China
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26
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Lin S, Yang M, Liu C, Wang Z, Long X. A pretrain-finetune approach for improving model generalizability in outcome prediction of acute respiratory distress syndrome patients. Int J Med Inform 2024; 186:105397. [PMID: 38507979 DOI: 10.1016/j.ijmedinf.2024.105397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/20/2023] [Accepted: 02/25/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND Early prediction of acute respiratory distress syndrome (ARDS) of critically ill patients in intensive care units (ICUs) has been intensively studied in the past years. Yet a prediction model trained on data from one hospital might not be well generalized to other hospitals. It is therefore essential to develop an accurate and generalizable ARDS prediction model adaptive to different hospital or medical centers. METHODS We analyzed electronic medical records of 200,859 and 50,920 hospitalized patients within 24 h after being diagnosed with ARDS from the Philips eICU Institute (eICU-CRD) and the Medical Information Mart for Intensive Care (MIMIC-IV) dataset, respectively. Patients were sorted into three groups, including rapid death, long stay, and recovery, based on their condition or outcome between 24 and 72 h after ARDS diagnosis. To improve prediction performance and generalizability, a "pretrain-finetune" approach was applied, where we pretrained models on the eICU-CRD dataset and performed model finetuning using only a part (35%) of the MIMIC-IV dataset, and then tested the finetuned models on the remaining data from the MIMIC-IV dataset. Well-known machine-learning algorithms, including logistic regression, random forest, extreme gradient boosting, and multilayer perceptron neural networks, were employed to predict ARDS outcomes. Prediction performance was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS Results show that, in general, multilayer perceptron neural networks outperformed the other models. The use of pretrain-finetune yielded improved performance in predicting ARDS outcomes achieving a micro-AUC of 0.870 for the MIMIC-IV dataset, an improvement of 0.046 over the pretrain model. CONCLUSIONS The proposed pretrain-finetune approach can effectively improve model generalizability from one to another dataset in ARDS prediction.
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Affiliation(s)
- Songlu Lin
- Instrument Science and Electrical Engineering, Jilin University, Changchun, China; Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands
| | - Meicheng Yang
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands; State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Chengyu Liu
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Zhihong Wang
- Instrument Science and Electrical Engineering, Jilin University, Changchun, China
| | - Xi Long
- Biomedical Diagnostics Lab, Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands
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Schulz F, Nachtkamp K, Oster HS, Mittelman M, Gattermann N, Schweier S, Barthuber C, Germing U. Validation of a novel algorithm with a high specificity in ruling out MDS. Int J Lab Hematol 2024; 46:510-514. [PMID: 38284270 DOI: 10.1111/ijlh.14234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/10/2024] [Indexed: 01/30/2024]
Abstract
INTRODUCTION A previously published web-based App using Gradient-boosted models (GBMs) of eight laboratory parameters was established by Oster et al. to facilitate diagnosis or exclusion of myelodysplastic syndromes (MDS) in patients. METHODS To validate their algorithm, we compared 175 anemic patients with MDS diagnosis from our German MDS Registry with 1378 non-MDS anemic patients who consulted various specialties in the Düsseldorf university hospital. RESULTS Based on hemoglobin level, leukocyte and platelet count, mean corpuscular volume, absolute neutrophil count, absolute monocyte count, glucose and creatinine, plus the patients' gender and age, we could not reproduce a high negative predictive value (NPV), but confirmed a useful specificity of 90.9% and a positive predictive value (PPV) of 77.1%. 1192 of 1378 controls were correctly categorized as "probably not MDS (pnMDS)" patients. A total of 65 patients were wrongly classified as "probable MDS (pMDS)," of whom 48 had alternative explanations for their altered laboratory results. In a second analysis, we included 29 patients with chronic myelomonocytic leukemia (CMML) resulting in only one label as possible MDS, suggesting that highly proliferative bone marrow disorders are correctly excluded. CONCLUSION The possibility of reliably excluding MDS from differential diagnosis based on peripheral blood lab work appears to be attractive for patients and physicians alike while the confirmation of MDS diagnosis still requires a bone marrow biopsy.
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Affiliation(s)
- Felicitas Schulz
- Department of Hematology, Oncology and Clinical Immunology, University Hospital of Düsseldorf, Düsseldorf, Germany
| | - Kathrin Nachtkamp
- Department of Hematology, Oncology and Clinical Immunology, University Hospital of Düsseldorf, Düsseldorf, Germany
| | - Howard S Oster
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Internal Medicine A, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Moshe Mittelman
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Hematology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Norbert Gattermann
- Department of Hematology, Oncology and Clinical Immunology, University Hospital of Düsseldorf, Düsseldorf, Germany
| | - Sarah Schweier
- Department of Laboratory Medicine, Universitätsklinik Düsseldorf, Düsseldorf, Germany
| | - Carmen Barthuber
- Department of Laboratory Medicine, Universitätsklinik Düsseldorf, Düsseldorf, Germany
| | - Ulrich Germing
- Department of Hematology, Oncology and Clinical Immunology, University Hospital of Düsseldorf, Düsseldorf, Germany
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Gu S, Zhu F. BAGAIL: Multi-modal imitation learning from imbalanced demonstrations. Neural Netw 2024; 174:106251. [PMID: 38552352 DOI: 10.1016/j.neunet.2024.106251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 01/19/2024] [Accepted: 03/18/2024] [Indexed: 04/14/2024]
Abstract
Expert demonstrations in imitation learning often contain different behavioral modes, e.g., driving modes such as driving on the left, keeping the lane, and driving on the right in the driving tasks. Although most existing multi-modal imitation learning methods allow learning from demonstrations of multiple modes, they have strict constraints on the data of each mode, generally requiring a near data ratio of all modes. Otherwise, it tends to fall into a mode collapse or only learn the data distribution of the mode that has the largest data volume. To address the problem, an algorithm that balances real-fake loss and classification loss by modifying the output of the discriminator, referred to as BAlanced Generative Adversarial Imitation Learning (BAGAIL), is proposed. With this modification, the generator is only rewarded for generating real trajectories with correct modes. BAGAIL is therefore able to deal with imbalanced expert demonstrations and carry out efficient learning for each mode. The learning process of BAGAIL is divided into a pre-training stage and an imitation learning stage. During the pre-training stage, BAGAIL initializes the generator parameters by means of conditional Behavioral Cloning, laying the foundation for the direction of parameter optimization. During the imitation learning stage, BAGAIL optimizes the parameters by using the adversary between the generator and the modified discriminator so that the finally obtained policy can successfully learn the distribution of imbalanced expert data. The experiments showed that BAGAIL accurately distinguished different behavioral modes with imbalanced demonstrations. What is more, the learning result of each mode is close to the expert standard and more stable than other multi-modal imitation learning methods.
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Affiliation(s)
- Sijia Gu
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, 215006, China.
| | - Fei Zhu
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, 215006, China.
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29
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Liu J, Wang L, Yerudkar A, Liu Y. Set stabilization of logical control networks: A minimum node control approach. Neural Netw 2024; 174:106266. [PMID: 38552353 DOI: 10.1016/j.neunet.2024.106266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 02/26/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024]
Abstract
In network systems, control using minimum nodes or pinning control can be effectively used for stabilization problems to cut down the cost of control. In this paper, we investigate the set stabilization problem of logical control networks. In particular, we study the set stabilization problem of probabilistic Boolean networks (PBNs) and probabilistic Boolean control networks (PBCNs) via controlling minimal nodes. Firstly, an algorithm is given to search for the minimum index set of pinning nodes. Then, based on the analysis of its high computational complexity, we present optimized algorithms with lower computational complexity to ascertain the network control using minimum node sets. Moreover, some sufficient and necessary conditions are proposed to ensure the feasibility and effectiveness of the proposed algorithms. Furthermore, a theorem is presented for PBCNs to devise all state-feedback controllers corresponding to the set of pinning nodes. Finally, two models of gene regulatory networks are considered to show the efficacy of obtained results.
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Affiliation(s)
- Jiayang Liu
- School of International Business, Jinhua Open University, Jinhua, 321022, PR China.
| | - Lina Wang
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, PR China.
| | - Amol Yerudkar
- School of Mathematical Sciences, Zhejiang Normal University, Jinhua, 321004, PR China.
| | - Yang Liu
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Jinhua, 321004, PR China; School of Mathematical Sciences, Zhejiang Normal University, Jinhua, 321004, PR China; School of Automation and Electrical Engineering, Linyi University, Linyi, 276000, PR China.
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Gunaratne C, Ison R, Price CC, Modave F, Tighe P. Development of a Probabilistic Boolean network (PBN) to model intraoperative blood pressure management. Comput Methods Programs Biomed 2024; 249:108143. [PMID: 38552333 DOI: 10.1016/j.cmpb.2024.108143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 03/15/2024] [Accepted: 03/21/2024] [Indexed: 04/21/2024]
Abstract
BACKGROUND Blood pressure is a vital sign for organ perfusion that anesthesiologists measure and modulate during surgery. However, current decision-making processes rely heavily on clinicians' experience, which can lead to variability in treatment across surgeries. With the advent of machine learning, we can now create models to predict the outcomes of interventions and guide perioperative decision-making. The first step in this process involves translating the clinical decision-making process into a framework understood by an algorithm. Probabilistic Boolean networks (PBNs) provide an information-rich approach to this problem. A PBN trends toward a steady state, and its decisions are easily understood via its Boolean predictor functions. We hypothesize that a PBN can be developed that corrects hemodynamic instability in patients by selecting clinical interventions to maintain blood pressure within a given range. METHODS Data on patients over the age of 65 undergoing surgery with general anesthesia from 2018 to 2020 were drawn from the UF Health PRECEDE data set with IRB approval (IRB201700747). Parameters examined included heart rate, blood pressure, and frequency of medications given 15 min after anesthetic induction and 15 min before awakening. The medication frequency data were truncated into a 66/33 split for the training and validation set used in the PBN. The model was coded using Python 3 and evaluated by comparing the frequency of medications chosen by the program to the values in the testing set via linear regression analysis. RESULTS The network developed successfully models a hemodynamically unstable patient and corrects the imbalance by administering medications. This is evidenced by the model achieving a stable, steady state matrix in all iterations. However, the model's ability to emulate clinical drug selection was variable. It was successful with its use of vasodilator selection but struggled with the appropriate selection of vasopressors. CONCLUSIONS The PBN has demonstrated the ability to choose appropriate interventions based on a patient's current vitals. Additional work must be done to have the network emulate the frequency at which drugs are selected from in clinical practice. In its current state, the model provides an understanding of how a PBN behaves in the context of correcting hemodynamic instability and can aid in developing more robust models in the future.
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Affiliation(s)
- Chamara Gunaratne
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA.
| | - Ron Ison
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Catherine C Price
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA; Department of Clinical and Health Psychology, University of Florida College of Medicine, 1600 SW Archer Road, Gainesville, FL 32610, USA
| | - Francois Modave
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Patrick Tighe
- Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA
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Chen Y, Guo M, Chen K, Jiang X, Ding Z, Zhang H, Lu M, Qi D, Dong C. Predictive models for sensory score and physicochemical composition of Yuezhou Longjing tea using near-infrared spectroscopy and data fusion. Talanta 2024; 273:125892. [PMID: 38493609 DOI: 10.1016/j.talanta.2024.125892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/16/2024] [Accepted: 03/07/2024] [Indexed: 03/19/2024]
Abstract
In this study, NIR quantitative prediction model was established for sensory score and physicochemical components of different varieties and quality grades of Yuezhou Longjing tea. Firstly, L, a, b color factors and diffuse reflection spectral data are collected for each sample. Subsequently, the original spectrum is preprocessed. Three techniques for selecting variables, CARS, BOSS, and SPA, were utilized to extract optimal feature bands. Finally, the spectral data extracted from feature bands were fused with L, a and b color factors to build SVR and PLSR prediction models. enabling the rapid non-destructive discrimination of different varieties and grades of Yuezhou Longjing tea. The outcomes demonstrated that BOSS was the best variable selection technique for sensory score and the distinctive caffeine wavelengths, CARS, however, was the best variable selection technique for catechins distinctive wavelengths. Additionally, the middle-level data fusion-based non-linear prediction models greatly outperformed the linear prediction models. For the prediction models of sensory score, catechins, and caffeine, the relative percent deviation (RPD) values were 2.8, 1.6, and 2.6, respectively, suggesting the good predictive ability of the models. In conclusion, evaluating the quality of the five Yuezhou Longjing tea varieties using near-infrared spectroscopy and data fusion have proved as feasible.
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Affiliation(s)
- Yong Chen
- College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China
| | - Mengqi Guo
- College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China; Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Kai Chen
- Shangrao Normal University, The Innovation Institute of Agricultural Technology, College of Life Science, Shangrao, 334001, China
| | - Xinfeng Jiang
- Jiangxi Institute of Economic Crops, Nanchang, 330046, China
| | - Zezhong Ding
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Haowen Zhang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Min Lu
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Dandan Qi
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China.
| | - Chunwang Dong
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China.
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He Z, Lefebvre PM, Soullié P, Doguet M, Ambarki K, Chen B, Odille F. Phantom evaluation of electrical conductivity mapping by MRI: Comparison to vector network analyzer measurements and spatial resolution assessment. Magn Reson Med 2024; 91:2374-2390. [PMID: 38225861 DOI: 10.1002/mrm.30009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/17/2024]
Abstract
PURPOSE To evaluate the performance of various MR electrical properties tomography (MR-EPT) methods at 3 T in terms of absolute quantification and spatial resolution limit for electrical conductivity. METHODS Absolute quantification as well as spatial resolution performance were evaluated on homogeneous phantoms and a phantom with holes of different sizes, respectively. Ground-truth conductivities were measured with an open-ended coaxial probe connected to a vector network analyzer (VNA). Four widely used MR-EPT reconstruction methods were investigated: phase-based Helmholtz (PB), phase-based convection-reaction (PB-cr), image-based (IB), and generalized-image-based (GIB). These methods were compared using the same complex images from a 1 mm-isotropic UTE sequence. Alternative transceive phase acquisition sequences were also compared in PB and PB-cr. RESULTS In large homogeneous phantoms, all methods showed a strong correlation with ground truth conductivities (r > 0.99); however, GIB was the best in terms of accuracy, spatial uniformity, and robustness to boundary artifacts. In the resolution phantom, the normalized root-mean-squared error of all methods grew rapidly (>0.40) when the hole size was below 10 mm, with simplified methods (PB and IB), or below 5 mm, with generalized methods (PB-cr and GIB). CONCLUSION VNA measurements are essential to assess the accuracy of MR-EPT. In this study, all tested MR-EPT methods correlated strongly with the VNA measurements. The UTE sequence is recommended for MR-EPT, with the GIB method providing good accuracy for structures down to 5 mm. Structures below 5 mm may still be detected in the conductivity maps, but with significantly lower accuracy.
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Affiliation(s)
- Zhongzheng He
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
| | | | - Paul Soullié
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
| | - Martin Doguet
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
- BioSerenity, Paris, France
| | | | - Bailiang Chen
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
- CIC-IT 1433, INSERM, Université de Lorraine and CHRU Nancy, Nancy, France
| | - Freddy Odille
- IADI U1254, INSERM and Université de Lorraine, Nancy, France
- CIC-IT 1433, INSERM, Université de Lorraine and CHRU Nancy, Nancy, France
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Yang GJ, Kim JH, Lee SW. Geometry-driven self-supervision for 3D human pose estimation. Neural Netw 2024; 174:106237. [PMID: 38513508 DOI: 10.1016/j.neunet.2024.106237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 02/23/2024] [Accepted: 03/11/2024] [Indexed: 03/23/2024]
Abstract
Although 3D human pose estimation has recently made strides, it is still difficult to precisely recreate a 3D human posture from a single image without the aid of 3D annotation for the following reasons. Firstly, the process of reconstruction inherently suffers from ambiguity, as multiple 3D poses can be projected onto the same 2D pose. Secondly, accurately measuring camera rotation without laborious camera calibration is a difficult task. While some approaches attempt to address these issues using traditional computer vision algorithms, they are not differentiable and cannot be optimized through training. This paper introduces two modules that explicitly leverage geometry to overcome these challenges, without requiring any 3D ground-truth or camera parameters. The first module, known as the relative depth estimation module, effectively mitigates depth ambiguity by narrowing down the possible depths for each joint to only two candidates. The second module, referred to as the differentiable pose alignment module, calculates camera rotation by aligning poses from different views. The use of these geometrically interpretable modules reduces the complexity of training and yields superior performance. By adopting our proposed method, we achieve state-of-the-art results on standard benchmark datasets, surpassing other self-supervised methods and even outperforming several fully-supervised approaches that heavily rely on 3D annotations.
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Affiliation(s)
- Geon-Jun Yang
- Department of Artificial Intelligence, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, Republic of Korea
| | - Jun-Hee Kim
- Department of Artificial Intelligence, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, Republic of Korea
| | - Seong-Whan Lee
- Department of Artificial Intelligence, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, Republic of Korea.
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Zhang Y, Fang Z, Fan J. Generalization analysis of deep CNNs under maximum correntropy criterion. Neural Netw 2024; 174:106226. [PMID: 38490117 DOI: 10.1016/j.neunet.2024.106226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 02/01/2024] [Accepted: 03/04/2024] [Indexed: 03/17/2024]
Abstract
Convolutional neural networks (CNNs) have gained immense popularity in recent years, finding their utility in diverse fields such as image recognition, natural language processing, and bio-informatics. Despite the remarkable progress made in deep learning theory, most studies on CNNs, especially in regression tasks, tend to heavily rely on the least squares loss function. However, there are situations where such learning algorithms may not suffice, particularly in the presence of heavy-tailed noises or outliers. This predicament emphasizes the necessity of exploring alternative loss functions that can handle such scenarios more effectively, thereby unleashing the true potential of CNNs. In this paper, we investigate the generalization error of deep CNNs with the rectified linear unit (ReLU) activation function for robust regression problems within an information-theoretic learning framework. Our study demonstrates that when the regression function exhibits an additive ridge structure and the noise possesses a finite pth moment, the empirical risk minimization scheme, generated by the maximum correntropy criterion and deep CNNs, achieves fast convergence rates. Notably, these rates align with the mini-max optimal convergence rates attained by fully connected neural network model with the Huber loss function up to a logarithmic factor. Additionally, we further establish the convergence rates of deep CNNs under the maximum correntropy criterion when the regression function resides in a Sobolev space on the sphere.
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Affiliation(s)
- Yingqiao Zhang
- Department of Mathematics, Hong Kong Baptist University, Kowloon, Hong Kong, China.
| | - Zhiying Fang
- Institute of Applied Mathematics, Shenzhen Polytechnic University, Shahexi Road 4089, Shenzhen, 518000, Guangdong, China.
| | - Jun Fan
- Department of Mathematics, Hong Kong Baptist University, Kowloon, Hong Kong, China.
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Yuan T, Li Z, Liu B, Tang Y, Liu Y. ARPruning: An automatic channel pruning based on attention map ranking. Neural Netw 2024; 174:106220. [PMID: 38447427 DOI: 10.1016/j.neunet.2024.106220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 01/22/2024] [Accepted: 02/27/2024] [Indexed: 03/08/2024]
Abstract
Structured pruning is a representative model compression technology for convolutional neural networks (CNNs), aiming to prune some less important filters or channels of CNNs. Most recent structured pruning methods have established some criteria to measure the importance of filters, which are mainly based on the magnitude of weights or other parameters in CNNs. However, these judgment criteria lack explainability, and it is insufficient to simply rely on the numerical values of the network parameters to assess the relationship between the channel and the model performance. Moreover, directly utilizing these pruning criteria for global pruning may lead to suboptimal solutions, therefore, it is necessary to complement search algorithms to determine the pruning ratio for each layer. To address these issues, we propose ARPruning (Attention-map-based Ranking Pruning), which reconstructs a new pruning criterion as the importance of the intra-layer channels and further develops a new local neighborhood search algorithm for determining the optimal inter-layer pruning ratio. To measure the relationship between the channel to be pruned and the model performance, we construct an intra-layer channel importance criterion by considering the attention map for each layer. Then, we propose an automatic pruning strategy searching method that can search for the optimal solution effectively and efficiently. By integrating the well-designed pruning criteria and search strategy, our ARPruning can not only maintain a high compression rate but also achieve outstanding accuracy. In our work, it is also experimentally concluded that compared with state-of-the-art pruning methods, our ARPruning method is capable of achieving better compression results. The code can be obtained at https://github.com/dozingLee/ARPruning.
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Affiliation(s)
| | - Zulin Li
- Beijing University of Technology, China
| | - Bo Liu
- Beijing University of Technology, China.
| | - Yinan Tang
- Inspur Electronic Information Industry Co., Ltd, China
| | - Yujia Liu
- Beijing University of Technology, China
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36
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Yang M, He L, Liu W, Zhang Y, Huang H. Performance improvement of atherosclerosis risk assessment based on feature interaction. Comput Methods Programs Biomed 2024; 249:108139. [PMID: 38554640 DOI: 10.1016/j.cmpb.2024.108139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 03/06/2024] [Accepted: 03/18/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND AND OBJECTIVE Cardiovascular disease is a leading cause of mortality and premature death. Early intervention in asymptomatic individuals through risk assessment can reduce the incidence of disease. Atherosclerosis is a major cause of cardiovascular disease and early detection can effectively prevent and treat it. In this study, we used real patient data to evaluate the risk of atherosclerosis, assisting doctors in diagnosis and reducing the incidence of cardiovascular disease. METHODS We proposed a multi-stage atherosclerosis risk assessment model that includes three main stages: (i) SMOTE and decorrelation weighting algorithm technology were added to the causal stability middle layer to address class imbalance in the dataset and reduce the impact of feature-induced dataset distribution shifts on model differences. (ii) The feature interaction layer considered possible feature interactions and classified features by different categories. By adding more effective feature information, the accuracy and generalizability of the model were improved. (iii) In the integrated model layer, we chose LightGBM as the decision tree integration model for risk assessment because it has higher accuracy and robustness compared to other machine learning algorithms. RESULTS The final model used a dataset containing 21 original features and 17 interaction features, achieving excellent performance under a 10-fold cross-validation strategy. The macro accuracy reached 93.86%, macro precision was 94.82%, macro recall was 93.52%, and macro F1 score was as high as 93.37%. These indicators demonstrate the accuracy and robustness of the model in atherosclerosis risk assessment. CONCLUSION The model provides strong support for the prevention and diagnosis of cardiovascular disease. Through atherosclerosis risk assessment, the model can help doctors develop personalized prevention and treatment plans, which is of great significance for the prevention and treatment of cardiovascular disease.
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Affiliation(s)
- Mengdie Yang
- School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Lidan He
- School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Wenjun Liu
- School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Yudong Zhang
- School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Hui Huang
- Department of Ultrasound, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China
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Liu H, Zhu L, Ji Z, Zhang M, Yang X. Porphyrin fluorescence imaging for real-time monitoring and visualization of the freshness of beef stored at different temperatures. Food Chem 2024; 442:138420. [PMID: 38237294 DOI: 10.1016/j.foodchem.2024.138420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 02/15/2024]
Abstract
This study presents a novel fluorescence imaging method for the real-time monitoring of beef quality deterioration and freshness. The fluorescence property of porphyrin in the form of heme can be used to characterize quality changes in beef during storage. Therefore, a fluorescence imaging system with an excitation light source of 440 nm and a CCD camera with a specific wavelength filter of 595 nm was constructed, and the porphyrin fluorescence images of beef samples stored at different temperatures were then collected. The quantitative model for predicting the microbial freshness indicator (TVC) of beef was built with the support vector machine regression (SVR) algorithm and produced satisfactory results with Rc2 and Rp2 of 0.858 and 0.812, respectively. The classification model based on the support vector machine (SVM) algorithm classified beef freshness into "fresh" and "spoiled", with calibration and prediction accuracy of 100 % and 90.9 %, respectively.
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Affiliation(s)
- Huan Liu
- Research Center of Information Technology, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100097, China; Key Laboratory of Cold Chain Logistics Technology for Agro-product, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
| | - Lei Zhu
- Research Center of Information Technology, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Zengtao Ji
- Research Center of Information Technology, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100097, China; Key Laboratory of Cold Chain Logistics Technology for Agro-product, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
| | - Min Zhang
- Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing 100125, China.
| | - Xinting Yang
- Research Center of Information Technology, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100097, China; Key Laboratory of Cold Chain Logistics Technology for Agro-product, Ministry of Agriculture and Rural Affairs, Beijing 100097, China.
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Abdulsalam AJ, Kara M, Özçakar L. Sarcopenia is not a Sonographic/Morphological diagnosis only: ISarcoPRM algorithm revisited. J Clin Anesth 2024; 94:111420. [PMID: 38394923 DOI: 10.1016/j.jclinane.2024.111420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024]
Affiliation(s)
- Ahmad J Abdulsalam
- Department of Physical and Rehabilitation Medicine, Hacettepe University Medical School, Ankara, Turkey; Department of Physical Medicine and Rehabilitation, Mubarak Alkabeer Hospital, Jabriya, Kuwait.
| | - Murat Kara
- Department of Physical and Rehabilitation Medicine, Hacettepe University Medical School, Ankara, Turkey
| | - Levent Özçakar
- Department of Physical and Rehabilitation Medicine, Hacettepe University Medical School, Ankara, Turkey
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Xie GB, Yu Y, Lin ZY, Chen RB, Xie JH, Liu ZG. 4 mC site recognition algorithm based on pruned pre-trained DNABert-Pruning model and fused artificial feature encoding. Anal Biochem 2024; 689:115492. [PMID: 38458307 DOI: 10.1016/j.ab.2024.115492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/21/2024] [Indexed: 03/10/2024]
Abstract
DNA 4 mC plays a crucial role in the genetic expression process of organisms. However, existing deep learning algorithms have shortcomings in the ability to represent DNA sequence features. In this paper, we propose a 4 mC site identification algorithm, DNABert-4mC, based on a fusion of the pruned pre-training DNABert-Pruning model and artificial feature encoding to identify 4 mC sites. The algorithm prunes and compresses the DNABert model, resulting in the pruned pre-training model DNABert-Pruning. This model reduces the number of parameters and removes redundancy from output features, yielding more precise feature representations while upholding accuracy.Simultaneously, the algorithm constructs an artificial feature encoding module to assist the DNABert-Pruning model in feature representation, effectively supplementing the information that is missing from the pre-trained features. The algorithm also introduces the AFF-4mC fusion strategy, which combines artificial feature encoding with the DNABert-Pruning model, to improve the feature representation capability of DNA sequences in multi-semantic spaces and better extract 4 mC sites and the distribution of nucleotide importance within the sequence. In experiments on six independent test sets, the DNABert-4mC algorithm achieved an average AUC value of 93.81%, outperforming seven other advanced algorithms with improvements of 2.05%, 5.02%, 11.32%, 5.90%, 12.02%, 2.42% and 2.34%, respectively.
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Affiliation(s)
- Guo-Bo Xie
- Guangdong University of Technology, Guangzhou, 510000, China
| | - Yi Yu
- Guangdong University of Technology, Guangzhou, 510000, China
| | - Zhi-Yi Lin
- Guangdong University of Technology, Guangzhou, 510000, China.
| | - Rui-Bin Chen
- Guangdong University of Technology, Guangzhou, 510000, China
| | - Jian-Hui Xie
- Guangdong University of Technology, Guangzhou, 510000, China
| | - Zhen-Guo Liu
- Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan 2nd Road, Guangzhou, 510080, China.
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Meyer NK, In MH, Black DF, Campeau NG, Welker KM, Huston J, Halverson MA, Bernstein MA, Trzasko JD. Model-based iterative reconstruction for direct imaging with point spread function encoded echo planar MRI. Magn Reson Imaging 2024; 109:189-202. [PMID: 38490504 DOI: 10.1016/j.mri.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/08/2024] [Accepted: 03/08/2024] [Indexed: 03/17/2024]
Abstract
BACKGROUND Echo planar imaging (EPI) is a fast measurement technique commonly used in magnetic resonance imaging (MRI), but is highly sensitive to measurement non-idealities in reconstruction. Point spread function (PSF)-encoded EPI is a multi-shot strategy which alleviates distortion, but acquisition of encodings suitable for direct distortion-free imaging prolongs scan time. In this work, a model-based iterative reconstruction (MBIR) framework is introduced for direct imaging with PSF-EPI to improve image quality and acceleration potential. METHODS An MBIR platform was developed for accelerated PSF-EPI. The reconstruction utilizes a subspace representation, is regularized to promote local low-rankedness (LLR), and uses variable splitting for efficient iteration. Comparisons were made against standard reconstructions from prospectively accelerated PSF-EPI data and with retrospective subsampling. Exploring aggressive partial Fourier acceleration of the PSF-encoding dimension, additional comparisons were made against an extension of Homodyne to direct PSF-EPI in numerical experiments. A neuroradiologists' assessment was completed comparing images reconstructed with MBIR from retrospectively truncated data directly against images obtained with standard reconstructions from non-truncated datasets. RESULTS Image quality results were consistently superior for MBIR relative to standard and Homodyne reconstructions. As the MBIR signal model and reconstruction allow for arbitrary sampling of the PSF space, random sampling of the PSF-encoding dimension was also demonstrated, with quantitative assessments indicating best performance achieved through nonuniform PSF sampling combined with partial Fourier. With retrospective subsampling, MBIR reconstructs high-quality images from sub-minute scan datasets. MBIR was shown to be superior in a neuroradiologists' assessment with respect to three of five performance criteria, with equivalence for the remaining two. CONCLUSIONS A novel image reconstruction framework is introduced for direct imaging with PSF-EPI, enabling arbitrary PSF space sampling and reconstruction of diagnostic-quality images from highly accelerated PSF-encoded EPI data.
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Affiliation(s)
- Nolan K Meyer
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Myung-Ho In
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - David F Black
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Norbert G Campeau
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Kirk M Welker
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - John Huston
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Maria A Halverson
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Matt A Bernstein
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Joshua D Trzasko
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
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Li P, Huang J, Wu H, Zhang Z, Qi C. SecureNet: Proactive intellectual property protection and model security defense for DNNs based on backdoor learning. Neural Netw 2024; 174:106199. [PMID: 38452664 DOI: 10.1016/j.neunet.2024.106199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 01/26/2024] [Accepted: 02/20/2024] [Indexed: 03/09/2024]
Abstract
With the widespread application of deep neural networks (DNNs), the risk of privacy breaches against DNN models is constantly on the rise, resulting in an increasing need for intellectual property (IP) protection for such models. Although neural network watermarking techniques are widely used to safeguard the IP of DNNs, they can only achieve passive protection and cannot actively prevent unauthorized users from illicit use or embezzlement of the trained DNN models. Therefore, the development of proactive protection techniques to prevent IP infringement is imperative. To this end, we propose SecureNet, a key-based access license framework for DNN models. The proposed approach involves injecting license keys into the model through backdoor learning, enabling correct model functionality only when the appropriate license key is included in the input. To ensure the reusability of DNN models, we also propose a license key replacement algorithm. In addition, based on SecureNet, we designed defense mechanisms against adversarial attacks and backdoor attacks, respectively. Furthermore, we introduce a fine-grained authorization method that enables flexible granting of model permissions to different users. We have designed four license-key schemes with different privileges, tailored to various scenarios. We evaluated SecureNet on five benchmark datasets including MNIST, Cifar10, Cifar100, FaceScrub, and CelebA, and assessed its performance on six classic DNN models: LeNet-5, VGG16, ResNet18, ResNet101, NFNet-F5, and MobileNetV3. The results demonstrate that our approach outperforms the state-of-the-art model parameter encryption methods by at least 95% in terms of computational efficiency. Additionally, it provides effective defense against adversarial attacks and backdoor attacks without compromising the model's overall performance.
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Affiliation(s)
- Peihao Li
- Southeast University, Nanjing, 211189, Jiangsu, China.
| | - Jie Huang
- Southeast University, Nanjing, 211189, Jiangsu, China; Purple Mountain Laboratories, Nanjing, 210096, Jiangsu, China.
| | - Huaqing Wu
- University of Calgary, Calgary, T2N 1N4, Alberta, Canada.
| | - Zeping Zhang
- Southeast University, Nanjing, 211189, Jiangsu, China.
| | - Chunyang Qi
- Southeast University, Nanjing, 211189, Jiangsu, China.
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42
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Zhang Z, Che K, Yang S, Xu W. Communication-efficient distributed cubic Newton with compressed lazy Hessian. Neural Netw 2024; 174:106212. [PMID: 38479185 DOI: 10.1016/j.neunet.2024.106212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 01/28/2024] [Accepted: 02/23/2024] [Indexed: 04/14/2024]
Abstract
Recently, second-order distributed optimization algorithms have been becoming a research hot in distributed learning, due to their faster convergence rate than the first-order algorithms. However, second-order algorithms always suffer from serious communication bottleneck. To conquer such challenge, we propose communication-efficient second-order distributed optimization algorithms in the parameter-server framework, by incorporating cubic Newton methods with compressed lazy Hessian. Specifically, our algorithms require each worker communicate compressed Hessians with the server only at some particular iterations, which can save both communication bits and communication rounds. For non-convex problems, we theoretically prove that our algorithms can reduce the communication cost comparing to the state-of-the-art second-order algorithms, while maintaining the same iteration complexity order O(ϵ-3/2) as the centralized cubic Newton methods. By further using gradient regularization technique, our algorithms can achieve global convergence for convex problems. Moreover, for strongly convex problems, our algorithms can achieve local superlinear convergence rate without any requirement on initial conditions. Finally, numerical experiments are conducted to show the high efficiency of the proposed algorithms.
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Affiliation(s)
- Zhen Zhang
- School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, PR China.
| | - Keqin Che
- School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, PR China.
| | - Shaofu Yang
- School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, PR China.
| | - Wenying Xu
- School of Mathematics, Southeast University, Nanjing, Jiangsu, PR China.
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Zhang Q, Li J, Ye Q, Lin Y, Chen X, Fu YG. DWSSA: Alleviating over-smoothness for deep Graph Neural Networks. Neural Netw 2024; 174:106228. [PMID: 38461705 DOI: 10.1016/j.neunet.2024.106228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 01/15/2024] [Accepted: 03/05/2024] [Indexed: 03/12/2024]
Abstract
Graph Neural Networks (GNNs) have demonstrated great potential in achieving outstanding performance in various graph-related tasks, e.g., graph classification and link prediction. However, most of them suffer from the following issue: shallow networks capture very limited knowledge. Prior works design deep GNNs with more layers to solve the issue, which however introduces a new challenge, i.e., the infamous over-smoothness. Graph representation over emphasizes node features but only considers the static graph structure with a uniform weight are the key reasons for the over-smoothness issue. To alleviate the issue, this paper proposes a Dynamic Weighting Strategy (DWS) for addressing over-smoothness. We first employ Fuzzy C-Means (FCM) to cluster all nodes into several groups and get each node's fuzzy assignment, based on which a novel metric function is devised for dynamically adjusting the aggregation weights. This dynamic weighting strategy not only enables the intra-cluster interactions, but also inter-cluster aggregations, which well addresses undifferentiated aggregation caused by uniform weights. Based on DWS, we further design a Structure Augmentation (SA) step for addressing the issue of underutilizing the graph structure, where some potentially meaningful connections (i.e., edges) are added to the original graph structure via a parallelable KNN algorithm. In general, the optimized Dynamic Weighting Strategy with Structure Augmentation (DWSSA) alleviates over-smoothness by reducing noisy aggregations and utilizing topological knowledge. Extensive experiments on eleven homophilous or heterophilous graph benchmarks demonstrate the effectiveness of our proposed method DWSSA in alleviating over-smoothness and enhancing deep GNNs performance.
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Affiliation(s)
- Qirong Zhang
- College of Computer and Data Science, Fuzhou University, Fuzhou 350116, PR China
| | - Jin Li
- College of Computer and Data Science, Fuzhou University, Fuzhou 350116, PR China
| | - Qingqing Ye
- Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region of China
| | - Yuxi Lin
- College of Computer and Data Science, Fuzhou University, Fuzhou 350116, PR China
| | - Xinlong Chen
- College of Computer and Data Science, Fuzhou University, Fuzhou 350116, PR China
| | - Yang-Geng Fu
- College of Computer and Data Science, Fuzhou University, Fuzhou 350116, PR China.
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Huang Y, Zhang X, Hu Y, Johnston AR, Jones CK, Zbijewski WB, Siewerdsen JH, Helm PA, Witham TF, Uneri A. Deformable registration of preoperative MR and intraoperative long-length tomosynthesis images for guidance of spine surgery via image synthesis. Comput Med Imaging Graph 2024; 114:102365. [PMID: 38471330 DOI: 10.1016/j.compmedimag.2024.102365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 01/31/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024]
Abstract
PURPOSE Improved integration and use of preoperative imaging during surgery hold significant potential for enhancing treatment planning and instrument guidance through surgical navigation. Despite its prevalent use in diagnostic settings, MR imaging is rarely used for navigation in spine surgery. This study aims to leverage MR imaging for intraoperative visualization of spine anatomy, particularly in cases where CT imaging is unavailable or when minimizing radiation exposure is essential, such as in pediatric surgery. METHODS This work presents a method for deformable 3D-2D registration of preoperative MR images with a novel intraoperative long-length tomosynthesis imaging modality (viz., Long-Film [LF]). A conditional generative adversarial network is used to translate MR images to an intermediate bone image suitable for registration, followed by a model-based 3D-2D registration algorithm to deformably map the synthesized images to LF images. The algorithm's performance was evaluated on cadaveric specimens with implanted markers and controlled deformation, and in clinical images of patients undergoing spine surgery as part of a large-scale clinical study on LF imaging. RESULTS The proposed method yielded a median 2D projection distance error of 2.0 mm (interquartile range [IQR]: 1.1-3.3 mm) and a 3D target registration error of 1.5 mm (IQR: 0.8-2.1 mm) in cadaver studies. Notably, the multi-scale approach exhibited significantly higher accuracy compared to rigid solutions and effectively managed the challenges posed by piecewise rigid spine deformation. The robustness and consistency of the method were evaluated on clinical images, yielding no outliers on vertebrae without surgical instrumentation and 3% outliers on vertebrae with instrumentation. CONCLUSIONS This work constitutes the first reported approach for deformable MR to LF registration based on deep image synthesis. The proposed framework provides access to the preoperative annotations and planning information during surgery and enables surgical navigation within the context of MR images and/or dual-plane LF images.
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Affiliation(s)
- Yixuan Huang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Xiaoxuan Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Yicheng Hu
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States
| | - Ashley R Johnston
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Craig K Jones
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States
| | - Wojciech B Zbijewski
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Jeffrey H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | | | - Timothy F Witham
- Department of Neurosurgery, Johns Hopkins Medicine, Baltimore, MD, United States
| | - Ali Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
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Huo Z, Wen K, Luo Y, Neji R, Kunze KP, Ferreira PF, Pennell DJ, Scott AD, Nielles-Vallespin S. Referenceless Nyquist ghost correction outperforms standard navigator-based method and improves efficiency of in vivo diffusion tensor cardiovascular magnetic resonance. Magn Reson Med 2024; 91:2403-2416. [PMID: 38263908 DOI: 10.1002/mrm.30012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 11/20/2023] [Accepted: 12/28/2023] [Indexed: 01/25/2024]
Abstract
PURPOSE The study aims to assess the potential of referenceless methods of EPI ghost correction to accelerate the acquisition of in vivo diffusion tensor cardiovascular magnetic resonance (DT-CMR) data using both computational simulations and data from in vivo experiments. METHODS Three referenceless EPI ghost correction methods were evaluated on mid-ventricular short axis DT-CMR spin echo and STEAM datasets from 20 healthy subjects at 3T. The reduced field of view excitation technique was used to automatically quantify the Nyquist ghosts, and DT-CMR images were fit to a linear ghost model for correction. RESULTS Numerical simulation showed the singular value decomposition (SVD) method is the least sensitive to noise, followed by Ghost/Object method and entropy-based method. In vivo experiments showed significant ghost reduction for all correction methods, with referenceless methods outperforming navigator methods for both spin echo and STEAM sequences at b = 32, 150, 450, and 600 smm - 2 $$ {\mathrm{smm}}^{-2} $$ . It is worth noting that as the strength of the diffusion encoding increases, the performance gap between the referenceless method and the navigator-based method diminishes. CONCLUSION Referenceless ghost correction effectively reduces Nyquist ghost in DT-CMR data, showing promise for enhancing the accuracy and efficiency of measurements in clinical practice without the need for any additional reference scans.
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Affiliation(s)
- Zimu Huo
- CMR Unit, Royal Brompton Hosptial, Guy's and St Thomas' NHS Foundation Trust, London, UK
- Department of Bioengineering, Imperial College London, London, UK
| | - Ke Wen
- CMR Unit, Royal Brompton Hosptial, Guy's and St Thomas' NHS Foundation Trust, London, UK
- NHLI, Imperial College London, London, UK
| | - Yaqing Luo
- CMR Unit, Royal Brompton Hosptial, Guy's and St Thomas' NHS Foundation Trust, London, UK
- NHLI, Imperial College London, London, UK
| | - Radhouene Neji
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | - Karl P Kunze
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | - Pedro F Ferreira
- CMR Unit, Royal Brompton Hosptial, Guy's and St Thomas' NHS Foundation Trust, London, UK
- NHLI, Imperial College London, London, UK
| | - Dudley J Pennell
- CMR Unit, Royal Brompton Hosptial, Guy's and St Thomas' NHS Foundation Trust, London, UK
- NHLI, Imperial College London, London, UK
| | - Andrew D Scott
- CMR Unit, Royal Brompton Hosptial, Guy's and St Thomas' NHS Foundation Trust, London, UK
- NHLI, Imperial College London, London, UK
| | - Sonia Nielles-Vallespin
- CMR Unit, Royal Brompton Hosptial, Guy's and St Thomas' NHS Foundation Trust, London, UK
- NHLI, Imperial College London, London, UK
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46
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Wang J, Qi Y. Multi-level feature fusion and joint refinement for simultaneous object pose estimation and camera localization. Neural Netw 2024; 174:106238. [PMID: 38508048 DOI: 10.1016/j.neunet.2024.106238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 01/22/2024] [Accepted: 03/13/2024] [Indexed: 03/22/2024]
Abstract
Object pose estimation and camera localization are critical in various applications. However, achieving algorithm universality, which refers to category-level pose estimation and scene-independent camera localization, presents challenges for both techniques. Although the two tasks keep close relationships due to spatial geometry constraints, different tasks require distinct feature extractions. This paper pays attention to a unified RGB-D based framework that simultaneously performs category-level object pose estimation and scene-independent camera localization. The framework consists of a pose estimation branch called SLO-ObjNet, a localization branch called SLO-LocNet, a pose confidence calculation process and object-level optimization. At the start, we obtain the initial camera and object results from SLO-LocNet and SLO-ObjNet. In these two networks, we design there-level feature fusion modules as well as the loss function to achieve feature sharing between two tasks. Then the proposed approach involves a confidence calculation process to determine the accuracy of object poses obtained. Additionally, an object-level Bundle Adjustment (BA) optimization algorithm is further used to improve the precision of these techniques. The BA algorithm establishes relationships among feature points, objects, and cameras with the usage of camera-point, camera-object, and object-point metrics. To evaluate the performance of this approach, experiments are conducted on localization and pose estimation datasets including REAL275, CAMERA25, LineMOD, YCB-Video, 7 Scenes, ScanNet and TUM RGB-D. The results show that this approach outperforms existing methods in terms of both estimation and localization accuracy. Additionally, SLO-LocNet and SLO-ObjNet are trained on ScanNet data and tested on 7 Scenes and TUM RGB-D datasets to demonstrate its universality performance. Finally, we also highlight the positive effects of fusion modules, loss function, confidence process and BA for improving overall performance.
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Affiliation(s)
- Junyi Wang
- School of Computer Science and Technology, Shandong University, Qingdao, China; Qingdao Research Institute of Beihang University, Qingdao, China.
| | - Yue Qi
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China; Qingdao Research Institute of Beihang University, Qingdao, China.
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47
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Liu G, Tian L, Wen Y, Yu W, Zhou W. Cosine convolutional neural network and its application for seizure detection. Neural Netw 2024; 174:106267. [PMID: 38555723 DOI: 10.1016/j.neunet.2024.106267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 02/23/2024] [Accepted: 03/22/2024] [Indexed: 04/02/2024]
Abstract
Traditional convolutional neural networks (CNNs) often suffer from high memory consumption and redundancy in their kernel representations, leading to overfitting problems and limiting their application in real-time, low-power scenarios such as seizure detection systems. In this work, a novel cosine convolutional neural network (CosCNN), which replaces traditional kernels with the robust cosine kernel modulated by only two learnable factors, is presented, and its effectiveness is validated on the tasks of seizure detection. Meanwhile, based on the cosine lookup table and KL-divergence, an effective post-training quantization algorithm is proposed for CosCNN hardware implementation. With quantization, CosCNN can achieve a nearly 75% reduction in the memory cost with almost no accuracy loss. Moreover, we design a configurable cosine convolution accelerator on Field Programmable Gate Array (FPGA) and deploy the quantized CosCNN on Zedboard, proving the proposed seizure detection system can operate in real-time and low-power scenarios. Extensive experiments and comparisons were conducted using two publicly available epileptic EEG databases, the Bonn database and the CHB-MIT database. The results highlight the performance superiority of the CosCNN over traditional CNNs as well as other seizure detection methods.
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Affiliation(s)
- Guoyang Liu
- School of Integrated Circuits, Shandong University, Jinan 250100, China
| | - Lan Tian
- School of Integrated Circuits, Shandong University, Jinan 250100, China
| | - Yiming Wen
- School of Integrated Circuits, Shandong University, Jinan 250100, China
| | - Weize Yu
- School of Integrated Circuits, Shandong University, Jinan 250100, China
| | - Weidong Zhou
- School of Integrated Circuits, Shandong University, Jinan 250100, China.
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48
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Zeng Y, Liu X, Wang Z, Gao W, Zhang S, Wang Y, Liu Y, Yu H. Multi-scale characterization and analysis of cellular viscoelastic mechanical phenotypes by atomic force microscopy. Microsc Res Tech 2024; 87:1157-1167. [PMID: 38284615 DOI: 10.1002/jemt.24505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/14/2024] [Accepted: 01/16/2024] [Indexed: 01/30/2024]
Abstract
The viscoelasticity of cells serves as a biomarker that reveals changes induced by malignant transformation, which aids the cytological examinations. However, differences in the measurement methods and parameters have prevented the consistent and effective characterization of the viscoelastic phenotype of cells. To address this issue, nanomechanical indentation experiments were conducted using an atomic force microscope (AFM). Multiple indentation methods were applied, and the indentation parameters were gradually varied to measure the viscoelasticity of normal liver cells and cancerous liver cells to create a database. This database was employed to train machine-learning algorithms in order to analyze the differences in the viscoelasticity of different types of cells and as well as to identify the optimal measurement methods and parameters. These findings indicated that the measurement speed significantly influenced viscoelasticity and that the classification difference between the two cell types was most evident at 5 μm/s. In addition, the precision and the area under the receiver operating characteristic curve were comparatively analyzed for various widely employed machine-learning algorithms. Unlike previous studies, this research validated the effectiveness of measurement parameters and methods with the assistance of machine-learning algorithms. Furthermore, the results confirmed that the viscoelasticity obtained from the multiparameter indentation measurement could be effectively used for cell classification. RESEARCH HIGHLIGHTS: This study aimed to analyze the viscoelasticity of liver cancer cells and liver cells. Different nano-indentation methods and parameters were used to measure the viscoelasticity of the two kinds of cells. The neural network algorithm was used to reverse analyze the dataset, and the methods and parameters for accurate classification and identification of cells are successfully found.
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Affiliation(s)
- Yi Zeng
- International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun, China
- Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun, China
| | - Xianping Liu
- School of Engineering, University of Warwick, Coventry, UK
| | - Zuobin Wang
- International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun, China
- Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun, China
- JR3CN & IRAC, University of Bedfordshire, Luton, UK
| | - Wei Gao
- School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, China
- School of Electronic Information Engineering, Changchun University, Changchun, China
| | - Shengli Zhang
- International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun, China
- Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun, China
| | - Ying Wang
- International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun, China
- Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun, China
| | - Yunqing Liu
- School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, China
| | - Haiyue Yu
- International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun, China
- Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun, China
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Stephany R, Earls C. PDE-LEARN: Using deep learning to discover partial differential equations from noisy, limited data. Neural Netw 2024; 174:106242. [PMID: 38521016 DOI: 10.1016/j.neunet.2024.106242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 02/24/2024] [Accepted: 03/13/2024] [Indexed: 03/25/2024]
Abstract
In this paper, we introduce PDE-LEARN, a novel deep learning algorithm that can identify governing partial differential equations (PDEs) directly from noisy, limited measurements of a physical system of interest. PDE-LEARN uses a Rational Neural Network, U, to approximate the system response function and a sparse, trainable vector, ξ, to characterize the hidden PDE that the system response function satisfies. Our approach couples the training of U and ξ using a loss function that (1) makes U approximate the system response function, (2) encapsulates the fact that U satisfies a hidden PDE that ξ characterizes, and (3) promotes sparsity in ξ using ideas from iteratively reweighted least-squares. Further, PDE-LEARN can simultaneously learn from several data sets, allowing it to incorporate results from multiple experiments. This approach yields a robust algorithm to discover PDEs directly from realistic scientific data. We demonstrate the efficacy of PDE-LEARN by identifying several PDEs from noisy and limited measurements.
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Affiliation(s)
- Robert Stephany
- Center for Applied Mathematics, Cornell University, Ithaca, NY 14850, United States.
| | - Christopher Earls
- Center for Applied Mathematics, Cornell University, Ithaca, NY 14850, United States; School of Civil & Environmental Engineering, Cornell University, Ithaca, NY 14850, United States
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50
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Ma J, Yu G. Distribution-free Bayesian regularized learning framework for semi-supervised learning. Neural Netw 2024; 174:106262. [PMID: 38547803 DOI: 10.1016/j.neunet.2024.106262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 02/09/2024] [Accepted: 03/19/2024] [Indexed: 04/14/2024]
Abstract
In machine learning it is often necessary to assume or know the distribution of the data, however it is difficult to do so in practical applications. Aiming to this problem, this work, we propose a novel distribution-free Bayesian regularized learning framework for semi-supervised learning, which is called Hessian regularized twin minimax probability extreme learning machine (HRTMPELM). In this framework, we attempt to construct two non-parallel hyperplanes by introducing the high separation probability assumption, such that each hyperplane separates samples from one class with maximum probability while moving away from samples from the other class. Subsidiently, the framework can be utilized to construct reasonable semi-supervised classifiers by using the information of the inherent geometric distribution of the samples through the Hessian regularization term. Additionally, the proposed framework controls the misclassification error of samples by minimizing the upper limit of the worst-case misclassification probability, and improves the generalization performance of the model by introducing the idea of regularization to avoid the occurrence of ill-posedness and overfitting problems. More importantly, the framework has no hyperparameters, making the learning process very simplified and efficient. Finally, a simple and reliable algorithm with globally optimal solutions via multivariate Chebyshev inequalities is designed for solving the proposed learning framework. Experiments on multiple datasets demonstrate the reliability and effectiveness of the proposed learning framework compared to other methods. Especially, we applied the framework to Ningxia wolfberry quality detection, which greatly enriches and facilitates the application of machine learning algorithms in the agricultural field.
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Affiliation(s)
- Jun Ma
- School of Mathematics and Information Sciences, North Minzu University, Yinchuan Ningxia 750021, PR China.
| | - Guolin Yu
- School of Mathematics and Information Sciences, North Minzu University, Yinchuan Ningxia 750021, PR China.
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