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He N, Kou C. Predicting verbal and performance intelligence quotients from multimodal data in individuals with attention deficit/hyperactivity disorder. Int J Dev Neurosci 2024; 84:217-226. [PMID: 38387863 DOI: 10.1002/jdn.10320] [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: 10/27/2023] [Revised: 01/10/2024] [Accepted: 02/05/2024] [Indexed: 02/24/2024] Open
Abstract
Despite the importance of understanding how intelligence is ingrained in the function and structure of the brain in some neurological disorders, the alterations of intelligence-associated neurological factors in atypical neurodevelopmental disorders, such as attention deficit/hyperactivity disorder (ADHD), are limited. Therefore, we aimed to explore the relationship between the brain functional and morphological characteristics and the intellectual performance of 139 patients with ADHD. Resting-state functional and T1-weighted structural magnetic resonance imaging (MRI) data and intellectual-performance data of the patients were collected. The MRI data were preprocessed to extract four indicators characterizing the participants' brain features: fractional amplitude of low-frequency fluctuation, regional homogeneity, and gray and white matter volumes. Then, we used a two-layer feature-selection method with support vector regression models based on three kernel functions to predict the verbal and performance intelligent quotients of the patients, along with ten fold cross-validation to evaluate the models' predictive performance. All models showed good performance; the correlation coefficients between the predicted and observed values for each predictive phenotypic variable were >0.41, with statistical significance. The brain features that could best predict the intellectual performance of the patients were concentrated in the superior and inferior frontal gyrus of the prefrontal areas, the angular gyrus and precuneus of the parietal lobe, the inferior and middle temporal gyrus of the temporal lobe, and part of the cerebellar regions. Thus, the voxel-based brain-feature indicators could adequately predict the intellectual performance of patients with ADHD, providing a foundation for future neuroimaging studies of this disorder.
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Affiliation(s)
- Ningning He
- School of Mathematics and Statistics, Zhoukou Normal University, Zhoukou, People's Republic of China
| | - Chao Kou
- School of Foreign Languages, Zhoukou Normal University, Zhoukou, People's Republic of China
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Chan MK, Tan SJ, Yeow ATH, Ng SC, Lau WJ. Zeolite-Based Poly(vinylidene fluoride) Ultrafiltration Membrane: Characterization and Molecular Weight Cut-Off Estimation with Support Vector Regression Modelling. Membranes (Basel) 2024; 14:91. [PMID: 38668119 PMCID: PMC11052103 DOI: 10.3390/membranes14040091] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 03/29/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024]
Abstract
Zeolite serves as a promising additive for enhancing the hydrophilicity of polymeric membranes, yet its utilization for bolstering the mechanical strength of the membrane remains limited. In this study, poly(vinylidene fluoride) (PVDF) membranes were modified by incorporating various concentrations of zeolite (0.5-2 wt%) to improve not only their mechanical properties, but also other features for water filtration. Membranes with and without zeolite incorporation were fabricated via a dry-wet phase inversion technique, followed by the application of a series of characterization techniques in order to study their morphological structure, mechanical strength, and hydrophilicity. The membrane filtration performance for each membrane was evaluated based on pure water flux and Bovine Serum Albumin (BSA) rejection. Field-Emission Scanning Electron Microscopy (FESEM) images revealed a dense, microvoid-free structure across all of the PVDF membranes, contributing to a high pristine PVDF membrane tensile strength of 14 MPa. The addition of 0.5 wt% zeolite significantly improved the tensile strength up to 19.4 MPa. Additionally, the incorporation of 1 wt% zeolite into PVDF membrane yielded improvements in membrane hydrophilicity (contact angle of 67.84°), pure water flux (63.49% increase), and high BSA rejection (95.76%) compared to pristine PVDF membranes. To further improve the characterization of the zeolite-modified PVDF membranes, the Support Vector Regression (SVR) model was adopted to estimate the molecular weight cut off (MWCO) of the membranes. A coefficient of determination (R2) value of 0.855 was obtained, suggesting that the SVR model predicted the MWCO accurately. The findings of this study showed that the utilization of zeolite is promising in enhancing both the mechanical properties and separation performance of PVDF membranes for application in ultrafiltration processes.
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Affiliation(s)
- Mieow Kee Chan
- Centre for Water Research, Faculty of Engineering and the Built Environment, SEGi University, Petaling Jaya 47810, Malaysia;
| | - Syee Jia Tan
- Centre for Water Research, Faculty of Engineering and the Built Environment, SEGi University, Petaling Jaya 47810, Malaysia;
| | - Andrew T. H. Yeow
- Department of Chemical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia;
| | - Sok Choo Ng
- Faculty of Arts and Science, International University of Malaya-Wales, Kuala Lumpur 50480, Malaysia;
| | - Woei Jye Lau
- Advanced Membrane Technology Research Centre, Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia;
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Shao W, Lin X, Huang Y, Qu L, Weihai Z, Liu H. Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm. J Xray Sci Technol 2024:XST240015. [PMID: 38607729 DOI: 10.3233/xst-240015] [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] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/14/2024]
Abstract
PURPOSE This study aims to propose and develop a fast, accurate, and robust prediction method of patient-specific organ doses from CT examinations using minimized computational resources. MATERIALS AND METHODS We randomly selected the image data of 723 patients who underwent thoracic CT examinations. We performed auto-segmentation based on the selected data to generate the regions of interest (ROIs) of thoracic organs using the DeepViewer software. For each patient, radiomics features of the thoracic ROIs were extracted via the Pyradiomics package. The support vector regression (SVR) model was trained based on the radiomics features and reference organ dose obtained by Monte Carlo (MC) simulation. The root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-squared) were evaluated. The robustness was verified by randomly assigning patients to the train and test sets of data and comparing regression metrics of different patient assignments. RESULTS For the right lung, left lung, lungs, esophagus, heart, and trachea, results showed that the trained SVR model achieved the RMSEs of 2 mGy to 2.8 mGy on the test sets, 1.5 mGy to 2.5 mGy on the train sets. The calculated MAPE ranged from 0.1 to 0.18 on the test sets, and 0.08 to 0.15 on the train sets. The calculated R-squared was 0.75 to 0.89 on test sets. CONCLUSIONS By combined utilization of the SVR algorithm and thoracic radiomics features, patient-specific thoracic organ doses could be predicted accurately, fast, and robustly in one second even using one single CPU core.
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Affiliation(s)
- Wencheng Shao
- Institute of Radiation Medicine, Fudan University, Shanghai, China
| | - Xin Lin
- Institute of Radiation Medicine, Fudan University, Shanghai, China
| | - Ying Huang
- Department of Nuclear Science and Technology, Institute of Modern Physics, Fudan University, Shanghai, China
- Key Lab of Nuclear Physics & Ion-Beam Appl. (MOE), Fudan University, Shanghai, China
- Department of Radiation Oncology, Shanghai Jiao Tong University Chest Hospital Shanghai, China
| | - Liangyong Qu
- Department of Radiology, Shanghai Zhongye Hospital, Shanghai, China
| | - Zhuo Weihai
- Institute of Radiation Medicine, Fudan University, Shanghai, China
| | - Haikuan Liu
- Institute of Radiation Medicine, Fudan University, Shanghai, China
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Behnamfar M, Stevenson A, Tariq M, Sarwat A. Vehicle Position Detection Based on Machine Learning Algorithms in Dynamic Wireless Charging. Sensors (Basel) 2024; 24:2346. [PMID: 38610560 PMCID: PMC11013965 DOI: 10.3390/s24072346] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/19/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024]
Abstract
Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle's position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position.
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Affiliation(s)
| | | | | | - Arif Sarwat
- Electrical and Computer Engineering Department, Florida International University, Miami, FL 33174, USA; (M.B.); (A.S.); (M.T.)
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Khalilian M, Roussel M, Godefroy O, Aarabi A. Predicting functional impairments with lesion-derived disconnectome mapping: Validation in stroke patients with motor deficits. Eur J Neurosci 2024. [PMID: 38578844 DOI: 10.1111/ejn.16334] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 02/24/2024] [Accepted: 03/07/2024] [Indexed: 04/07/2024]
Abstract
Focal structural damage to white matter tracts can result in functional deficits in stroke patients. Traditional voxel-based lesion-symptom mapping is commonly used to localize brain structures linked to neurological deficits. Emerging evidence suggests that the impact of structural focal damage may extend beyond immediate lesion sites. In this study, we present a disconnectome mapping approach based on support vector regression (SVR) to identify brain structures and white matter pathways associated with functional deficits in stroke patients. For clinical validation, we utilized imaging data from 340 stroke patients exhibiting motor deficits. A disconnectome map was initially derived from lesions for each patient. Bootstrap sampling was then employed to balance the sample size between a minority group of patients exhibiting right or left motor deficits and those without deficits. Subsequently, SVR analysis was used to identify voxels associated with motor deficits (p < .005). Our disconnectome-based analysis significantly outperformed alternative lesion-symptom approaches in identifying major white matter pathways within the corticospinal tracts associated with upper-lower limb motor deficits. Bootstrapping significantly increased the sensitivity (80%-87%) for identifying patients with motor deficits, with a minimum lesion size of 32 and 235 mm3 for the right and left motor deficit, respectively. Overall, the lesion-based methods achieved lower sensitivities compared with those based on disconnection maps. The primary contribution of our approach lies in introducing a bootstrapped disconnectome-based mapping approach to identify lesion-derived white matter disconnections associated with functional deficits, particularly efficient in handling imbalanced data.
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Affiliation(s)
- Maedeh Khalilian
- Laboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, France
| | - Martine Roussel
- Laboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, France
| | - Olivier Godefroy
- Laboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, France
- Faculty of Medicine, University of Picardy Jules Verne, Amiens, France
- Neurology Department, Amiens University Hospital, Amiens, France
| | - Ardalan Aarabi
- Laboratory of Functional Neuroscience and Pathologies (UR UPJV 4559), University Research Center (CURS), University of Picardy Jules Verne, Amiens, France
- Faculty of Medicine, University of Picardy Jules Verne, Amiens, France
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Shan X, Yan H, Li H, Liu F, Li P, Zhao J, Guo W. Abnormal regional activity in the prefrontal-limbic circuit at rest: Potential imaging markers and treatment predictors in drug-naive anxiety disorders. CNS Neurosci Ther 2024; 30:e14523. [PMID: 37990350 PMCID: PMC11017453 DOI: 10.1111/cns.14523] [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: 06/06/2023] [Revised: 10/07/2023] [Accepted: 10/29/2023] [Indexed: 11/23/2023] Open
Abstract
BACKGROUND Previous research has identified functional impairments within the prefrontal-limbic circuit in individuals with anxiety disorders. However, the link between these deficiencies, clinical symptoms, and responses to antipsychotic treatment is still not fully understood. This study aimed to investigate abnormal regional activity within the prefrontal-limbic circuit among drug-naive individuals diagnosed with generalized anxiety disorder (GAD) and panic disorder (PD) and to analyze changes following treatment. METHODS Resting-state magnetic resonance imaging was performed on a cohort of 118 anxiety disorder patients (64 GAD, 54 PD) and 61 healthy controls (HCs) at baseline. Among them, 52 patients with GAD and 44 patients with PD underwent a 4-week treatment regimen of paroxetine. Fractional amplitude of low-frequency fluctuation (fALFF) measurements and pattern classification techniques were employed to analyze the data in accordance with the human Brainnetome atlas. RESULTS Both patients with GAD and PD demonstrated decreased fALFF in the right cHipp subregion of the hippocampus and increased fALFF in specified subregions of the cingulate and orbitofrontal lobe. Notably, patients with PD exhibited significantly higher fALFF in the left A24cd subregion compared to patients with GAD, while other ROI subregions showed no significant variations between the two patient groups. Whole-brain analysis revealed abnormal fALFF in both patient groups, primarily in specific areas of the cingulate and parasingulate gyrus, as well as the inferior and medial orbitofrontal gyrus (OFG). Following a 4-week treatment period, specific subregions in the GAD and PD groups showed a significant decrease in fALFF. Further analysis using support vector regression indicated that fALFF measurements in the right A13 and right A24cd subregions may be predictive of treatment response among anxiety disorder patients. CONCLUSIONS Aberrant functional activity in certain subregions of the prefrontal-limbic circuit appears to be linked to the manifestation of anxiety disorders. These findings suggest potential imaging indicators for individual responses to antipsychotic treatment.
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Affiliation(s)
- Xiaoxiao Shan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
- Department of Psychiatry, Shandong Mental Health CenterShandong UniversityJinanShandongChina
| | - Haohao Yan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Huabing Li
- Department of RadiologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Feng Liu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Ping Li
- Department of PsychiatryQiqihar Medical UniversityQiqiharHeilongjiangChina
| | - Jingping Zhao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
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Chen J, Zhu L, Wang J. Quantitative structure-property relationship modelling on autoignition temperature: evaluation and comparative analysis. SAR QSAR Environ Res 2024; 35:199-218. [PMID: 38372083 DOI: 10.1080/1062936x.2024.2312527] [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: 11/06/2023] [Accepted: 01/25/2024] [Indexed: 02/20/2024]
Abstract
The autoignition temperature (AIT) serves as a crucial indicator for assessing the potential hazards associated with a chemical substance. In order to gain deeper insights into model performance and facilitate the establishment of effective methodological practices for AIT predictions, this study conducts a benchmark investigation on Quantitative Structure-Property Relationship (QSPR) modelling for AIT. As novelties of this work, three significant advancements are implemented in the AIT modelling process, including explicit consideration of data quality, utilization of state-of-the-art feature engineering workflows, and the innovative application of graph-based deep learning techniques, which are employed for the first time in AIT prediction. Specifically, three traditional QSPR models (multi-linear regression, support vector regression, and artificial neural networks) are evaluated, alongside the assessment of a deep-learning model employing message passing neural network architecture supplemented by graph-data augmentation techniques.
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Affiliation(s)
- J Chen
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - L Zhu
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - J Wang
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
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Sperber C, Wiesen D, Karnath H, de Haan B. The neuroanatomy of visual extinction following right hemisphere brain damage: Insights from multivariate and Bayesian lesion analyses in acute stroke. Hum Brain Mapp 2024; 45:e26639. [PMID: 38433712 PMCID: PMC10910281 DOI: 10.1002/hbm.26639] [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: 07/14/2023] [Revised: 01/08/2024] [Accepted: 02/06/2024] [Indexed: 03/05/2024] Open
Abstract
Multi-target attention, that is, the ability to attend and respond to multiple visual targets presented simultaneously on the horizontal meridian across both visual fields, is essential for everyday real-world behaviour. Given the close link between the neuropsychological deficit of extinction and attentional limits in healthy subjects, investigating the anatomy that underlies extinction is uniquely capable of providing important insights concerning the anatomy critical for normal multi-target attention. Previous studies into the brain areas critical for multi-target attention and its failure in extinction patients have, however, produced heterogeneous results. In the current study, we used multivariate and Bayesian lesion analysis approaches to investigate the anatomical substrate of visual extinction in a large sample of 108 acute right hemisphere stroke patients. The use of acute stroke patient data and multivariate/Bayesian lesion analysis approaches allowed us to address limitations associated with previous studies and so obtain a more complete picture of the functional network associated with visual extinction. Our results demonstrate that the right temporo-parietal junction (TPJ) is critically associated with visual extinction. The Bayesian lesion analysis additionally implicated the right intraparietal sulcus (IPS), in line with the results of studies in neurologically healthy participants that highlighted the IPS as the area critical for multi-target attention. Our findings resolve the seemingly conflicting previous findings, and emphasise the urgent need for further research to clarify the precise cognitive role of the right TPJ in multi-target attention and its failure in extinction patients.
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Affiliation(s)
- Christoph Sperber
- Center of Neurology, Division of NeuropsychologyHertie‐Institute for Clinical Brain Research, University of TübingenTübingenGermany
- Department of NeurologyInselspital, University Hospital BernBernSwitzerland
| | - Daniel Wiesen
- Center of Neurology, Division of NeuropsychologyHertie‐Institute for Clinical Brain Research, University of TübingenTübingenGermany
| | - Hans‐Otto Karnath
- Center of Neurology, Division of NeuropsychologyHertie‐Institute for Clinical Brain Research, University of TübingenTübingenGermany
- Department of PsychologyUniversity of South CarolinaColumbiaSouth CarolinaUSA
| | - Bianca de Haan
- Centre for Cognitive Neuroscience, College of Health and Life Sciences, Brunel University LondonUxbridgeUK
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Gao J, Liu Y. Prediction and the influencing factor study of colorectal cancer hospitalization costs in China based on machine learning-random forest and support vector regression: a retrospective study. Front Public Health 2024; 12:1211220. [PMID: 38389946 PMCID: PMC10881792 DOI: 10.3389/fpubh.2024.1211220] [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: 04/24/2023] [Accepted: 01/23/2024] [Indexed: 02/24/2024] Open
Abstract
Aims As people's standard of living improves, the incidence of colorectal cancer is increasing, and colorectal cancer hospitalization costs are relatively high. Therefore, predicting the cost of hospitalization for colorectal cancer patients can provide guidance for controlling healthcare costs and for the development of related policies. Methods This study used the first page of medical record data on colorectal cancer inpatient cases of a tertiary first-class hospital in Shenzhen from 2018 to 2022. The impacting factors of hospitalization costs for colorectal cancer were analyzed. Random forest and support vector regression models were used to establish predictive models of the cost of hospitalization for colorectal cancer patients and to compare and evaluate. Results In colorectal cancer inpatients, major procedures, length of stay, level of procedure, Charlson comorbidity index, age, and medical payment method were the important influencing factors. In terms of the test set, the R2 of the Random forest model was 0.833, the R2 of the Support vector regression model was 0.824; the root mean square error (RMSE) of the Random forest model was 0.029, and the RMSE of the Support vector regression model was 0.032. In the Random Forest model, the weight of the major procedure was the highest (0.286). Conclusion Major procedures and length of stay have the greatest impacts on hospital costs for colorectal cancer patients. The random forest model is a better method to predict the hospitalization costs for colorectal cancer patients than the support vector regression.
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Affiliation(s)
- Jun Gao
- Department of Medical Record Statistics, Peking University Shenzhen Hospital, Shenzhen, China
- School of Public Healthy, Guilin Medical University, Guilin, China
| | - Yan Liu
- Department of Medical Record Statistics, Peking University Shenzhen Hospital, Shenzhen, China
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Shao W, Lin X, Yi Y, Huang Y, Qu L, Zhuo W, Liu H. Fast prediction of patient-specific organ doses in brain CT scans using support vector regression algorithm. Phys Med Biol 2024; 69:025010. [PMID: 38086079 DOI: 10.1088/1361-6560/ad14c7] [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: 08/31/2023] [Accepted: 12/12/2023] [Indexed: 01/11/2024]
Abstract
Objectives. This study aims to develop a method for predicting patient-specific head organ doses by training a support vector regression (SVR) model based on radiomics features and graphics processing unit (GPU)-calculated reference doses.Methods. In this study, 237 patients who underwent brain CT scans were selected, and their CT data were transferred to an autosegmentation software to segment head regions of interest (ROIs). Subsequently, radiomics features were extracted from the CT data and ROIs, and the benchmark organ doses were computed using fast GPU-accelerated Monte Carlo (MC) simulations. The SVR organ dose prediction model was then trained using the radiomics features and benchmark doses. For the predicted organ doses, the relative root mean squared error (RRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were evaluated. The robustness of organ dose prediction was verified by changing the patient samples on the training and test sets randomly.Results. For all head organs, the maximal difference between the reference and predicted dose was less than 1 mGy. For the brain, the organ dose was predicted with an absolute error of 1.3%, and theR2reached up to 0.88. For the eyes and lens, the organ doses predicted by SVR achieved an RRMSE of less than 13%, the MAPE ranged from 4.5% to 5.5%, and theR2values were more than 0.7.Conclusions. Patient-specific head organ doses from CT examinations can be predicted within one second with high accuracy, speed, and robustness by training an SVR using radiomics features.
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Affiliation(s)
- Wencheng Shao
- Institute of Radiation Medicine, Fudan University, Shanghai, People's Republic of China
| | - Xin Lin
- Institute of Radiation Medicine, Fudan University, Shanghai, People's Republic of China
| | - Yanling Yi
- Institute of Radiation Medicine, Fudan University, Shanghai, People's Republic of China
| | - Ying Huang
- Department of Nuclear Science and Technology, Institute of Modern Physics, Fudan University, Shanghai, People's Republic of China
- Key Lab of Nucl. Phys. & Ion-Beam Appl. (MOE), Fudan University, Shanghai, People's Republic of China
- Department of Radiation Oncology, Shanghai Jiao Tong University Chest Hospital Shanghai, People's Republic of China
| | - Liangyong Qu
- Department of Radiology, Shanghai Zhongye Hospital, Shanghai, People's Republic of China
| | - Weihai Zhuo
- Institute of Radiation Medicine, Fudan University, Shanghai, People's Republic of China
| | - Haikuan Liu
- Institute of Radiation Medicine, Fudan University, Shanghai, People's Republic of China
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Babatunde HA, Collins J, Lukman R, Saxton R, Andersen T, McDougal OM. SVR Chemometrics to Quantify β-Lactoglobulin and α-Lactalbumin in Milk Using MIR. Foods 2024; 13:166. [PMID: 38201194 PMCID: PMC10778881 DOI: 10.3390/foods13010166] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/26/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Protein content variation in milk can impact the quality and consistency of dairy products, necessitating access to in-line real time monitoring. Here, we present a chemometric approach for the qualitative and quantitative monitoring of β-lactoglobulin and α-lactalbumin, using mid-infrared spectroscopy (MIR). In this study, we employed Hotelling T2 and Q-residual for outlier detection, automated preprocessing using nippy, conducted wavenumber selection with genetic algorithms, and evaluated four chemometric models, including partial least squares, support vector regression (SVR), ridge, and logistic regression to accurately predict the concentrations of β-lactoglobulin and α-lactalbumin in milk. For the quantitative analysis of these two whey proteins, SVR performed the best to interpret protein concentration from 197 MIR spectra originating from 42 Cornell University samples of preserved pasteurized modified milk. The R2 values obtained for β-lactoglobulin and α-lactalbumin using leave one out cross-validation (LOOCV) are 92.8% and 92.7%, respectively, which is the highest correlation reported to date. Our approach introduced a combination of preprocessing automation, genetic algorithm-based wavenumber selection, and used Optuna to optimize the framework for tuning hyperparameters of the chemometric models, resulting in the best chemometric analysis of MIR data to quantitate β-lactoglobulin and α-lactalbumin to date.
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Affiliation(s)
| | - Joseph Collins
- Biomolecular Sciences Graduate Program, Boise State University, Boise, ID 83725, USA;
| | - Rianat Lukman
- Department of Chemistry and Biochemistry, Boise State University, Boise, ID 83725, USA; (R.L.); (R.S.)
| | - Rose Saxton
- Department of Chemistry and Biochemistry, Boise State University, Boise, ID 83725, USA; (R.L.); (R.S.)
| | | | - Owen M. McDougal
- Department of Chemistry and Biochemistry, Boise State University, Boise, ID 83725, USA; (R.L.); (R.S.)
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Yao B, Xu Y, Jing J, Zhang W, Guo Y, Zhang Z, Zhang S, Liu J, Xue C. Comparison and Verification of Three Algorithms for Accuracy Improvement of Quartz Resonant Pressure Sensors. Micromachines (Basel) 2023; 15:23. [PMID: 38258142 PMCID: PMC10819135 DOI: 10.3390/mi15010023] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 12/16/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024]
Abstract
Pressure measurement is of great importance due to its wide range of applications in many fields. AT-cut quartz, with its exceptional precision and durability, stands out as an excellent pressure transducer due to its superior accuracy and stable performance over time. However, its intrinsic temperature dependence significantly hinders its potential application in varying temperature environments. Herein, three different learning algorithms (i.e., multivariate polynomial regression, multilayer perceptron networks, and support vector regression) are elaborated in detail and applied to establish the prediction models for compensating the temperature effect of the resonant pressure sensor, respectively. The AC-cut quartz, which is sensitive to temperature variations, is paired with the AT-cut quartz, providing the essential temperature information. The output frequencies derived from the AT-cut and AC-cut quartzes are selected as input data for these learning algorithms. Through experimental validation, all three methods are effective, and a remarkable improvement in accuracy can be achieved. Among the three methods, the MPR model has exceptionally high accuracy in predicting pressure. The calculated residual error over the temperature range of -10-40 °C is less than 0.008% of 40 MPa full scale (FS). An intelligent automatic compensation and real-time processing system for the resonant pressure sensor is developed as well, which may contribute to improving the efficiency in online calibration and large-scale industrialization. This paper paves a promising way for the temperature compensation of resonant pressure sensors.
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Affiliation(s)
- Bin Yao
- Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China; (B.Y.); (Y.G.); (S.Z.); (J.L.)
| | - Yanbo Xu
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China; (Y.X.); (J.J.); (W.Z.)
| | - Junming Jing
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China; (Y.X.); (J.J.); (W.Z.)
| | - Wenjun Zhang
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China; (Y.X.); (J.J.); (W.Z.)
| | - Yuzhen Guo
- Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China; (B.Y.); (Y.G.); (S.Z.); (J.L.)
| | - Zengxing Zhang
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China; (Y.X.); (J.J.); (W.Z.)
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, China
| | - Shiqiang Zhang
- Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China; (B.Y.); (Y.G.); (S.Z.); (J.L.)
| | - Jianwei Liu
- Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China; (B.Y.); (Y.G.); (S.Z.); (J.L.)
| | - Chenyang Xue
- Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China; (B.Y.); (Y.G.); (S.Z.); (J.L.)
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, China
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13
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Jornkokgoud K, Baggio T, Faysal M, Bakiaj R, Wongupparaj P, Job R, Grecucci A. Predicting narcissistic personality traits from brain and psychological features: A supervised machine learning approach. Soc Neurosci 2023; 18:257-270. [PMID: 37497589 DOI: 10.1080/17470919.2023.2242094] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 06/28/2023] [Accepted: 07/22/2023] [Indexed: 07/28/2023]
Abstract
Narcissism is a multifaceted construct often linked to pathological conditions whose neural correlates are still poorly understood. Previous studies have reported inconsistent findings related to the neural underpinnings of narcissism, probably due to methodological limitations such as the low number of participants or the use of mass univariate methods. The present study aimed to overcome the previous methodological limitations and to build a predictive model of narcissistic traits based on neural and psychological features. In this respect, two machine learning-based methods (Kernel Ridge Regression and Support Vector Regression) were used to predict narcissistic traits from brain structural organization and from other relevant normal and abnormal personality features. Results showed that a circuit including the lateral and middle frontal gyri, the angular gyrus, Rolandic operculum, and Heschl's gyrus successfully predicted narcissistic personality traits (p < 0.003). Moreover, narcissistic traits were predicted by normal (openness, agreeableness, conscientiousness) and abnormal (borderline, antisocial, insecure, addicted, negativistic, machiavellianism) personality traits. This study is the first to predict narcissistic personality traits via a supervised machine learning approach. As such, these results may expand the possibility of deriving personality traits from neural and psychological features.
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Affiliation(s)
- Khanitin Jornkokgoud
- Cognitive Science and Innovation Research Unit (CSIRU), College of Research Methodology and Cognitive Science (RMCS), Burapha University, Chonburi, Thailand
| | - Teresa Baggio
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
| | - Md Faysal
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
| | - Richard Bakiaj
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
| | - Peera Wongupparaj
- Cognitive Science and Innovation Research Unit (CSIRU), College of Research Methodology and Cognitive Science (RMCS), Burapha University, Chonburi, Thailand
| | - Remo Job
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
- Centre for Medical Sciences (CISMed), University of Trento, Trento, Italy
| | - Alessandro Grecucci
- Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Rovereto, Italy
- Centre for Medical Sciences (CISMed), University of Trento, Trento, Italy
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14
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Tuerxun N, Zheng J, Wang R, Wang L, Liu L. Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms. Front Plant Sci 2023; 14:1260772. [PMID: 38034562 PMCID: PMC10682207 DOI: 10.3389/fpls.2023.1260772] [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] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/27/2023] [Indexed: 12/02/2023]
Abstract
The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2 = 0.99, RMSE=0.61) and measured dataset (R2 = 0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data.
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Affiliation(s)
- Nigela Tuerxun
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
| | - Jianghua Zheng
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
| | - Renjun Wang
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
| | - Lei Wang
- Institute of Modern Forestry, Xinjiang Academy of Forestry Sciences, Urumqi, China
| | - Liang Liu
- College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi, China
- Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi, China
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15
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He Q, Zhang H, Li T, Zhang X, Li X, Dong C. NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States. Sensors (Basel) 2023; 23:9107. [PMID: 38005495 PMCID: PMC10675699 DOI: 10.3390/s23229107] [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: 09/06/2023] [Revised: 11/05/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023]
Abstract
Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the development of precision agriculture in tea plantations. In recent years, spectral data have become an important tool for the non-destructive testing of soil physicochemical properties. In this study, a support vector regression (SVR) model was constructed to model the hydrolyzed nitrogen, available potassium, and effective phosphorus in tea plantation soils of different grain sizes. Then, the successful projections algorithm (SPA) and least-angle regression (LAR) and bootstrapping soft shrinkage (BOSS) variable importance screening methods were used to optimize the variables in the soil physicochemical properties. The findings demonstrated that soil particle sizes of 0.25-0.5 mm produced the best predictions for all three physicochemical properties. After further using the dimensionality reduction approach, the LAR algorithm (R2C = 0.979, R2P = 0.976, RPD = 6.613) performed optimally in the prediction model for hydrolytic nitrogen at a soil particle size of 0.25~0.5. The models using data dimensionality reduction and those that used the BOSS method to estimate available potassium (R2C = 0.977, R2P = 0.981, RPD = 7.222) and effective phosphorus (R2C = 0.969, R2P = 0.964, RPD = 5.163) had the best accuracy. In order to offer a reference for the accurate detection of soil physicochemical properties in tea plantations, this study investigated the modeling effect of each physicochemical property under various soil particle sizes and integrated the regression model with various downscaling strategies.
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Affiliation(s)
- Qinghai He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310008, China; (Q.H.); (X.L.)
- Shandong Academy of Agricultural Machinery Science, Jinan 250100, China;
| | - Haowen Zhang
- Shandong Academy of Agricultural Machinery Science, Jinan 250100, China;
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China;
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271000, China;
| | - Tianhua Li
- College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271000, China;
| | - Xiaojia Zhang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China;
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310008, China; (Q.H.); (X.L.)
| | - Chunwang Dong
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China;
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16
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Xie S, Yao R, Yan Y, Lin H, Zhang P, Chen Y. Hybrid Machine-Learning-Based Prediction Model for the Peak Dilation Angle of Rock Discontinuities. Materials (Basel) 2023; 16:6387. [PMID: 37834523 PMCID: PMC10573530 DOI: 10.3390/ma16196387] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/21/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023]
Abstract
The peak dilation angle is an important mechanical feature of rock discontinuities, which is significant in assessing the mechanical behaviour of rock masses. Previous studies have shown that the efficiency and accuracy of traditional experimental methods and analytical models in determining the shear dilation angle are not completely satisfactory. Machine learning methods are popular due to their efficient prediction of outcomes for multiple influencing factors. In this paper, a novel hybrid machine learning model is proposed for predicting the peak dilation angle. The model incorporates support vector regression (SVR) techniques as the primary prediction tools, augmented with the grid search optimization algorithm to enhance prediction performance and optimize hyperparameters. The proposed model was employed on eighty-nine datasets with six input variables encompassing morphology and mechanical property parameters. Comparative analysis is conducted between the proposed model, the original SVR model, and existing analytical models. The results show that the proposed model surpasses both the original SVR model and analytical models, with a coefficient of determination (R2) of 0.917 and a mean absolute percentage error (MAPE) of 4.5%. Additionally, the study also reveals that normal stress is the most influential mechanical property parameter affecting the peak dilation angle. Consequently, the proposed model was shown to be effective in predicting the peak dilation angle of rock discontinuities.
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Affiliation(s)
- Shijie Xie
- School of Civil Engineering, Southeast University, Nanjing 210096, China
| | - Rubing Yao
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China (Y.C.)
- School of Qilu Transportation, Shandong University, Jinan 250061, China
| | - Yatao Yan
- School of Civil Engineering, Southeast University, Nanjing 210096, China
| | - Hang Lin
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China (Y.C.)
| | - Peilei Zhang
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China (Y.C.)
| | - Yifan Chen
- School of Resources and Safety Engineering, Central South University, Changsha 410083, China (Y.C.)
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17
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Liu S, Lei T, Li G, Liu S, Chu X, Hao D, Xiao G, Khan AA, Haq TU, Sameeh MY, Aziz T, Tashkandi M, He G. Rapid detection of micronutrient components in infant formula milk powder using near-infrared spectroscopy. Front Nutr 2023; 10:1273374. [PMID: 37810922 PMCID: PMC10556746 DOI: 10.3389/fnut.2023.1273374] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 08/17/2023] [Indexed: 10/10/2023] Open
Abstract
In order to achieve rapid detection of galactooligosaccharides (GOS), fructooligosaccharides (FOS), calcium (Ca), and vitamin C (Vc), four micronutrient components in infant formula milk powder, this study employed four methods, namely Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Normalization (Nor), and Savitzky-Golay Smoothing (SG), to preprocess the acquired original spectra of the milk powder. Then, the Competitive Adaptive Reweighted Sampling (CARS) algorithm and Random Frog (RF) algorithm were used to extract representative characteristic wavelengths. Furthermore, Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) models were established to predict the contents of GOS, FOS, Ca, and Vc in infant formula milk powder. The results indicated that after SNV preprocessing, the original spectra of GOS and FOS could effectively extract feature wavelengths using the CARS algorithm, leading to favorable predictive results through the CARS-SVR model. Similarly, after MSC preprocessing, the original spectra of Ca and Vc could efficiently extract feature wavelengths using the CARS algorithm, resulting in optimal predictive outcomes via the CARS-SVR model. This study provides insights for the realization of online nutritional component detection and optimization control in the production process of infant formula.
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Affiliation(s)
- Shaoli Liu
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China
| | - Ting Lei
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China
| | - Guipu Li
- Beingmate (Hangzhou) Food Research Institute Co., Ltd., Hangzhou, Zhejiang, China
| | - Shuming Liu
- Beingmate Dairy Co., Ltd., Anda, Heilongjiang, China
| | - Xiaojun Chu
- Beingmate (Hangzhou) Food Research Institute Co., Ltd., Hangzhou, Zhejiang, China
| | - Donghai Hao
- Beingmate Dairy Co., Ltd., Anda, Heilongjiang, China
| | - Gongnian Xiao
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China
| | - Ayaz Ali Khan
- Department of Biotechnology, University of Malakand, Chakdara, Pakistan
| | - Taqweem Ul Haq
- Department of Biotechnology, University of Malakand, Chakdara, Pakistan
| | - Manal Y. Sameeh
- Chemistry Department, Al-Leith University College, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Tariq Aziz
- Department of Agriculture, University of Ioannina, Ioannina, Greece
| | - Manal Tashkandi
- College of Science, Department of Biochemistry, University of Jeddah, Jeddah, Saudi Arabia
| | - Guanghua He
- School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China
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18
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Jing H, Zhang C, Yan H, Li X, Liang J, Liang W, Ou Y, Wu W, Guo H, Deng W, Xie G, Guo W. Deviant spontaneous neural activity as a potential early-response predictor for therapeutic interventions in patients with schizophrenia. Front Neurosci 2023; 17:1243168. [PMID: 37727324 PMCID: PMC10505796 DOI: 10.3389/fnins.2023.1243168] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 08/18/2023] [Indexed: 09/21/2023] Open
Abstract
Objective Previous studies have established significant differences in the neuroimaging characteristics between healthy controls (HCs) and patients with schizophrenia (SCZ). However, the relationship between homotopic connectivity and clinical features in patients with SCZ is not yet fully understood. Furthermore, there are currently no established neuroimaging biomarkers available for the diagnosis of SCZ or for predicting early treatment response. The aim of this study is to investigate the association between regional homogeneity and specific clinical features in SCZ patients. Methods We conducted a longitudinal investigation involving 56 patients with SCZ and 51 HCs. The SCZ patients underwent a 3-month antipsychotic treatment. Resting-state functional magnetic resonance imaging (fMRI), regional homogeneity (ReHo), support vector machine (SVM), and support vector regression (SVR) were used for data acquisition and analysis. Results In comparison to HCs, individuals with SCZ demonstrated reduced ReHo values in the right postcentral/precentral gyrus, left postcentral/inferior parietal gyrus, left middle/inferior occipital gyrus, and right middle temporal/inferior occipital gyrus, and increased ReHo values in the right putamen. It is noteworthy that there was decreased ReHo values in the right inferior parietal gyrus after treatment compared to baseline data. Conclusion The observed decrease in ReHo values in the sensorimotor network and increase in ReHo values in the right putamen may represent distinctive neurobiological characteristics of patients with SCZ, as well as a potential neuroimaging biomarker for distinguishing between patients with SCZ and HCs. Furthermore, ReHo values in the sensorimotor network and right putamen may serve as predictive indicators for early treatment response in patients with SCZ.
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Affiliation(s)
- Huan Jing
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Chunguo Zhang
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Haohao Yan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaoling Li
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Jiaquan Liang
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Wenting Liang
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Yangpan Ou
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Weibin Wu
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Huagui Guo
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Wen Deng
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Guojun Xie
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong, China
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
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Karim AA, Pardede E, Mann S. A Model Selection Approach for Time Series Forecasting: Incorporating Google Trends Data in Australian Macro Indicators. Entropy (Basel) 2023; 25:1144. [PMID: 37628174 PMCID: PMC10453655 DOI: 10.3390/e25081144] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/12/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023]
Abstract
This study examined whether the behaviour of Internet search users obtained from Google Trends contributes to the forecasting of two Australian macroeconomic indicators: monthly unemployment rate and monthly number of short-term visitors. We assessed the performance of traditional time series linear regression (SARIMA) against a widely used machine learning technique (support vector regression) and a deep learning technique (convolutional neural network) in forecasting both indicators across different data settings. Our study focused on the out-of-sample forecasting performance of the SARIMA, SVR, and CNN models and forecasting the two Australian indicators. We adopted a multi-step approach to compare the performance of the models built over different forecasting horizons and assessed the impact of incorporating Google Trends data in the modelling process. Our approach supports a data-driven framework, which reduces the number of features prior to selecting the best-performing model. The experiments showed that incorporating Internet search data in the forecasting models improved the forecasting accuracy and that the results were dependent on the forecasting horizon, as well as the technique. To the best of our knowledge, this study is the first to assess the usefulness of Google search data in the context of these two economic variables. An extensive comparison of the performance of traditional and machine learning techniques on different data settings was conducted to enable the selection of an efficient model, including the forecasting technique, horizon, and modelling features.
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Affiliation(s)
| | - Eric Pardede
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia
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Yoosefzadeh-Najafabadi M, Torabi S, Tulpan D, Rajcan I, Eskandari M. Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits. Plants (Basel) 2023; 12:2659. [PMID: 37514272 PMCID: PMC10383196 DOI: 10.3390/plants12142659] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/12/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023]
Abstract
Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.
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Affiliation(s)
| | - Sepideh Torabi
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
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Yang X, Qiu H, Zhang Y, Zhang P. Quantitative structure-activity relationship study of amide derivatives as xanthine oxidase inhibitors using machine learning. Front Pharmacol 2023; 14:1227536. [PMID: 37456753 PMCID: PMC10339742 DOI: 10.3389/fphar.2023.1227536] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 06/16/2023] [Indexed: 07/18/2023] Open
Abstract
The target of the study is to predict the inhibitory effect of amide derivatives on xanthine oxidase (XO) by building several models, which are based on the theory of the quantitative structure-activity relationship (QSAR). The heuristic method (HM) was used to linearly select descriptors and build a linear model. XGBoost was used to non-linearly select descriptors, and radial basis kernel function support vector regression (RBF SVR), polynomial kernel function SVR (poly SVR), linear kernel function SVR (linear SVR), mix-kernel function SVR (MIX SVR), and random forest (RF) were adopted to establish non-linear models, in which the MIX-SVR method gives the best result. The kernel function of MIX SVR has strong abilities of learning and generalization of established models simultaneously, which is because it is a combination of the linear kernel function, the radial basis kernel function, and the polynomial kernel function. In order to test the robustness of the models, leave-one-out cross validation (LOOCV) was adopted. In a training set, R2 = 0.97 and RMSE = 0.01; in a test set, R2 = 0.95, RMSE = 0.01, and Rcv2 = 0.96. This result is in line with the experimental expectations, which indicate that the MIX-SVR modeling approach has good applications in the study of amide derivatives.
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Zafar S, Nizami IF, Rehman MU, Majid M, Ryu J. NISQE: Non-Intrusive Speech Quality Evaluator Based on Natural Statistics of Mean Subtracted Contrast Normalized Coefficients of Spectrogram. Sensors (Basel) 2023; 23:5652. [PMID: 37420818 DOI: 10.3390/s23125652] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/01/2023] [Accepted: 06/14/2023] [Indexed: 07/09/2023]
Abstract
With the evolution in technology, communication based on the voice has gained importance in applications such as online conferencing, online meetings, voice-over internet protocol (VoIP), etc. Limiting factors such as environmental noise, encoding and decoding of the speech signal, and limitations of technology may degrade the quality of the speech signal. Therefore, there is a requirement for continuous quality assessment of the speech signal. Speech quality assessment (SQA) enables the system to automatically tune network parameters to improve speech quality. Furthermore, there are many speech transmitters and receivers that are used for voice processing including mobile devices and high-performance computers that can benefit from SQA. SQA plays a significant role in the evaluation of speech-processing systems. Non-intrusive speech quality assessment (NI-SQA) is a challenging task due to the unavailability of pristine speech signals in real-world scenarios. The success of NI-SQA techniques highly relies on the features used to assess speech quality. Various NI-SQA methods are available that extract features from speech signals in different domains, but they do not take into account the natural structure of the speech signals for assessment of speech quality. This work proposes a method for NI-SQA based on the natural structure of the speech signals that are approximated using the natural spectrogram statistical (NSS) properties derived from the speech signal spectrogram. The pristine version of the speech signal follows a structured natural pattern that is disrupted when distortion is introduced in the speech signal. The deviation of NSS properties between the pristine and distorted speech signals is utilized to predict speech quality. The proposed methodology shows better performance in comparison to state-of-the-art NI-SQA methods on the Centre for Speech Technology Voice Cloning Toolkit corpus (VCTK-Corpus) with a Spearman's rank-ordered correlation constant (SRC) of 0.902, Pearson correlation constant (PCC) of 0.960, and root mean squared error (RMSE) of 0.206. Conversely, on the NOIZEUS-960 database, the proposed methodology shows an SRC of 0.958, PCC of 0.960, and RMSE of 0.114.
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Affiliation(s)
- Shakeel Zafar
- Department of Computer Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
| | - Imran Fareed Nizami
- Department of Electrical Engineering, Bahria University, Islamabad 44000, Pakistan
| | - Mobeen Ur Rehman
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Muhammad Majid
- Department of Computer Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
| | - Jihyoung Ryu
- Electronics and Telecommunications Research Institute (ETRI), Gwangju 61012, Republic of Korea
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23
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Prasetya V, Vito V, Tanawi IN, Aldila D, Hertono GF. Predicting potential areas at risk of the Dengue Hemorrhagic Fever in Jakarta, Indonesia-analyzing the accuracy of predictive hot spot analysis in the absence of small geographical area data. Infect Ecol Epidemiol 2023; 13:2218207. [PMID: 37325468 PMCID: PMC10262815 DOI: 10.1080/20008686.2023.2218207] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 05/22/2023] [Indexed: 06/17/2023] Open
Abstract
Dengue Hemorrhagic Fever (DHF), a more severe form of dengue, is one of the most rapidly spreading mosquito-borne diseases in the world. This study is motivated by the rising DHF incidence in Jakarta, the capital city of Indonesia. We mainly utilized hot spot analysis, which employs spatial statistics to find at-risk areas for DHF outbreaks in Jakarta's five municipalities. However, producing informative results from hot spot analysis requires a complete set of data on each of Jakarta's 42 districts, which is not available. We thus propose the idea of using small area estimation (SAE) and machine learning to make up for the lack of data. To evaluate whether this proposed method is effective, we compare the hot spot results from the estimation with the actual data of each district. The results show that the estimated hot spot map is similar to the hot spot map from the actual data. This implies that it is possible to find potential at-risk areas of dengue fever without a complete dataset in every small geographic area. We expect that this research can increase the performance of DHF control measures at the district level, even in the absence of small area data.
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Affiliation(s)
- Valentino Prasetya
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia
| | - Valentino Vito
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia
| | - Ivan N. Tanawi
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia
| | - Dipo Aldila
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia
| | - Gatot F. Hertono
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia
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24
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Giamarelos N, Papadimitrakis M, Stogiannos M, Zois EN, Livanos NAI, Alexandridis A. A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons. Sensors (Basel) 2023; 23:5436. [PMID: 37420606 DOI: 10.3390/s23125436] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/26/2023] [Accepted: 06/02/2023] [Indexed: 07/09/2023]
Abstract
The increasing penetration of renewable energy sources tends to redirect the power systems community's interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons constitutes an essential electric utility task in network planning, operation, and management. This paper presents a novel mixed power-load forecasting scheme for multiple prediction horizons ranging from 15 min to 24 h ahead. The proposed approach makes use of a pool of models trained by several machine-learning methods with different characteristics, namely neural networks, linear regression, support vector regression, random forests, and sparse regression. The final prediction values are calculated using an online decision mechanism based on weighting the individual models according to their past performance. The proposed scheme is evaluated on real electrical load data sensed from a high voltage/medium voltage substation and is shown to be highly effective, as it results in R2 coefficient values ranging from 0.99 to 0.79 for prediction horizons ranging from 15 min to 24 h ahead, respectively. The method is compared to several state-of-the-art machine-learning approaches, as well as a different ensemble method, producing highly competitive results in terms of prediction accuracy.
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Affiliation(s)
- Nikolaos Giamarelos
- Department of Electrical and Electronic Engineering, University of West Attica, Thivon 250, 122 41 Aigaleo, Greece
| | - Myron Papadimitrakis
- Department of Electrical and Electronic Engineering, University of West Attica, Thivon 250, 122 41 Aigaleo, Greece
| | - Marios Stogiannos
- Department of Electrical and Electronic Engineering, University of West Attica, Thivon 250, 122 41 Aigaleo, Greece
| | - Elias N Zois
- Department of Electrical and Electronic Engineering, University of West Attica, Thivon 250, 122 41 Aigaleo, Greece
| | - Nikolaos-Antonios I Livanos
- Department of Electrical and Electronic Engineering, University of West Attica, Thivon 250, 122 41 Aigaleo, Greece
- EMTECH SPACE P.C., Korinthou 32 & S. Davaki, Metamorfosi, 144 51 Athens, Greece
| | - Alex Alexandridis
- Department of Electrical and Electronic Engineering, University of West Attica, Thivon 250, 122 41 Aigaleo, Greece
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25
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Lauro PM, Lee S, Amaya DE, Liu DD, Akbar U, Asaad WF. Concurrent decoding of distinct neurophysiological fingerprints of tremor and bradykinesia in Parkinson's disease. eLife 2023; 12:e84135. [PMID: 37249217 PMCID: PMC10264071 DOI: 10.7554/elife.84135] [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: 10/12/2022] [Accepted: 05/26/2023] [Indexed: 05/31/2023] Open
Abstract
Parkinson's disease (PD) is characterized by distinct motor phenomena that are expressed asynchronously. Understanding the neurophysiological correlates of these motor states could facilitate monitoring of disease progression and allow improved assessments of therapeutic efficacy, as well as enable optimal closed-loop neuromodulation. We examined neural activity in the basal ganglia and cortex of 31 subjects with PD during a quantitative motor task to decode tremor and bradykinesia - two cardinal motor signs of PD - and relatively asymptomatic periods of behavior. Support vector regression analysis of microelectrode and electrocorticography recordings revealed that tremor and bradykinesia had nearly opposite neural signatures, while effective motor control displayed unique, differentiating features. The neurophysiological signatures of these motor states depended on the signal type and location. Cortical decoding generally outperformed subcortical decoding. Within the subthalamic nucleus (STN), tremor and bradykinesia were better decoded from distinct subregions. These results demonstrate how to leverage neurophysiology to more precisely treat PD.
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Affiliation(s)
- Peter M Lauro
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
- The Warren Alpert Medical School, Brown UniversityProvidenceUnited States
| | - Shane Lee
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
- Norman Prince Neurosciences Institute, Rhode Island HospitalProvidenceUnited States
- Department of Neurosurgery, Rhode Island HospitalProvidenceUnited States
| | - Daniel E Amaya
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - David D Liu
- Department of Neurosurgery, Brigham and Women’s HospitalBostonUnited States
| | - Umer Akbar
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
- The Warren Alpert Medical School, Brown UniversityProvidenceUnited States
- Norman Prince Neurosciences Institute, Rhode Island HospitalProvidenceUnited States
- Department of Neurology, Rhode Island HospitalProvidenceUnited States
| | - Wael F Asaad
- Department of Neuroscience, Brown UniversityProvidenceUnited States
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
- The Warren Alpert Medical School, Brown UniversityProvidenceUnited States
- Norman Prince Neurosciences Institute, Rhode Island HospitalProvidenceUnited States
- Department of Neurosurgery, Rhode Island HospitalProvidenceUnited States
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26
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Weaver NA, Mamdani MH, Lim JS, Biesbroek JM, Biessels GJ, Huenges Wajer IMC, Kang Y, Kim BJ, Lee BC, Lee KJ, Yu KH, Bae HJ, Bzdok D, Kuijf HJ. Disentangling poststroke cognitive deficits and their neuroanatomical correlates through combined multivariable and multioutcome lesion-symptom mapping. Hum Brain Mapp 2023; 44:2266-2278. [PMID: 36661231 PMCID: PMC10028652 DOI: 10.1002/hbm.26208] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 12/21/2022] [Accepted: 01/04/2023] [Indexed: 01/21/2023] Open
Abstract
Studies in patients with brain lesions play a fundamental role in unraveling the brain's functional anatomy. Lesion-symptom mapping (LSM) techniques can relate lesion location to cognitive performance. However, a limitation of current LSM approaches is that they can only evaluate one cognitive outcome at a time, without considering interdependencies between different cognitive tests. To overcome this challenge, we implemented canonical correlation analysis (CCA) as combined multivariable and multioutcome LSM approach. We performed a proof-of-concept study on 1075 patients with acute ischemic stroke to explore whether addition of CCA to a multivariable single-outcome LSM approach (support vector regression) could identify infarct locations associated with deficits in three well-defined verbal memory functions (encoding, consolidation, retrieval) based on four verbal memory subscores derived from the Seoul Verbal Learning Test (immediate recall, delayed recall, recognition, learning ability). We evaluated whether CCA could extract cognitive score patterns that matched prior knowledge of these verbal memory functions, and if these patterns could be linked to more specific infarct locations than through single-outcome LSM alone. Two of the canonical modes identified with CCA showed distinct cognitive patterns that matched prior knowledge on encoding and consolidation. In addition, CCA revealed that each canonical mode was linked to a distinct infarct pattern, while with multivariable single-outcome LSM individual verbal memory subscores were associated with largely overlapping patterns. In conclusion, our findings demonstrate that CCA can complement single-outcome LSM techniques to help disentangle cognitive functions and their neuroanatomical correlates.
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Affiliation(s)
- Nick A Weaver
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, The Netherlands
| | - Muhammad Hasnain Mamdani
- Department of Biomedical Engineering, Faculty of Medicine, McConnell Brain Imaging Centre, School of Computer Science, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | | | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, The Netherlands
| | - Irene M C Huenges Wajer
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, The Netherlands
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - Yeonwook Kang
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, Republic of Korea
- Department of Psychology, Hallym University, Chuncheon, Republic of Korea
| | - Beom Joon Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Byung-Chul Lee
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Keon-Joo Lee
- Department of Neurology, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Kyung-Ho Yu
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym Neurological Institute, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Hee-Joon Bae
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Danilo Bzdok
- Department of Biomedical Engineering, Faculty of Medicine, McConnell Brain Imaging Centre, School of Computer Science, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
- Mila-Quebec Artificial Intelligence Institute, Montreal, Canada
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
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27
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Zhang M, Kong P, Xia A, Tuo W, Lv Y, Wang S. Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability Identification Strategy of Wavelet Singular Spectrum. Biomimetics (Basel) 2023; 8:biomimetics8020132. [PMID: 37092384 PMCID: PMC10123602 DOI: 10.3390/biomimetics8020132] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/15/2023] [Accepted: 03/18/2023] [Indexed: 04/25/2023] Open
Abstract
The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and active control of the aeroengine. In this paper, an aerodynamic system modeling method combination with the wavelet transform and gray wolf algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion technology based on the feature correlation of chaotic data. Because of the chaotic characteristic represented by the sequence, the correlation-correlation (C-C) algorithm is adopted to reconstruct the phase space of the spatial modal. On the premise of finding out the local law of the dynamic system variety, the machine learning method is applied to model the reconstructed low-frequency components and high-frequency components, respectively. As the key part, the parameters of the SVR model are optimized by the gray wolf optimization algorithm (GWO) from the biological view inspired by the predatory behavior of gray wolves. In the definition of the hunting behaviors of gray wolves by mathematical equations, it is superior to algorithms such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy of the model, the multi-resolution and equivalent frequency distribution of the wavelet transform (WT) are used to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better prediction accuracy and reliability with the wavelet reconstruction coefficients as the inputs. In order to effectively identify the sign of the instability in the modeling system, a wavelet singular information entropy algorithm is proposed to detect the stall inception. By using the three sigma criteria as the identification strategy, the instability early warning can be given about 102r in advance, which is helpful for the active control.
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Affiliation(s)
- Mingming Zhang
- Faculty of Science, Beijing University of Technology, Beijing 100124, China
- Key Laboratory of Modern Measurement and Control Technology, Beijing Information Science and Technology University, Beijing 100192, China
- Zhengzhou Aerotropolis Institute of Artificial Intelligence, Zhengzhou 451162, China
| | - Pan Kong
- Faculty of Science, Beijing University of Technology, Beijing 100124, China
| | - Aiguo Xia
- Beijing Aeronautical Technology Research Center, Beijing 100076, China
| | - Wei Tuo
- Beijing Aeronautical Technology Research Center, Beijing 100076, China
| | - Yongzhao Lv
- Beijing Aeronautical Technology Research Center, Beijing 100076, China
| | - Shaohong Wang
- Key Laboratory of Modern Measurement and Control Technology, Beijing Information Science and Technology University, Beijing 100192, China
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28
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Kim WS, Heo DW, Shen J, Tsogt U, Odkhuu S, Kim SW, Suk HI, Ham BJ, Rami FZ, Kang CY, Sui J, Chung YC. Stage-Specific Brain Aging in First-Episode Schizophrenia and Treatment-Resistant Schizophrenia. Int J Neuropsychopharmacol 2023; 26:207-216. [PMID: 36545813 PMCID: PMC10032294 DOI: 10.1093/ijnp/pyac080] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 11/18/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Brain age is a popular brain-based biomarker that offers a powerful strategy for using neuroscience in clinical practice. We investigated the brain-predicted age difference (PAD) in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging data. The association between brain-PAD and clinical parameters was also assessed. METHODS We developed brain age prediction models for the association between 77 average structural brain measures and age in a training sample of controls (HCs) using ridge regression, support vector regression, and relevance vector regression. The trained models in the controls were applied to the test samples of the controls and 3 patient groups to obtain brain-based age estimates. The correlations were tested between the brain PAD and clinical measures in the patient groups. RESULTS Model performance indicated that, regardless of the type of regression metric, the best model was support vector regression and the worst model was relevance vector regression for the training HCs. Accelerated brain aging was identified in patients with SCZ, FE-SSDs, and TRS compared with the HCs. A significant difference in brain PAD was observed between FE-SSDs and TRS using the ridge regression algorithm. Symptom severity, the Social and Occupational Functioning Assessment Scale, chlorpromazine equivalents, and cognitive function were correlated with the brain PAD in the patient groups. CONCLUSIONS These findings suggest additional progressive neuronal changes in the brain after SCZ onset. Therefore, pharmacological or psychosocial interventions targeting brain health should be developed and provided during the early course of SCZ.
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Affiliation(s)
- Woo-Sung Kim
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
| | - Da-Woon Heo
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
- Department of Artificial Intelligence, Korea University, Seoul, Korea
| | - Jie Shen
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Uyanga Tsogt
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Soyolsaikhan Odkhuu
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Sung-Wan Kim
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Heung-Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
- Department of Artificial Intelligence, Korea University, Seoul, Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Fatima Zahra Rami
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Chae Yeong Kang
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Young-Chul Chung
- Department of Psychiatry, Jeonbuk National University, Medical School, Jeonju, Korea
- Department of Psychiatry, Jeonbuk National University Hospital, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
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Tang N, Yuan M, Chen Z, Ma J, Sun R, Yang Y, He Q, Guo X, Hu S, Zhou J. Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants. Int J Environ Res Public Health 2023; 20:3910. [PMID: 36900920 PMCID: PMC10002212 DOI: 10.3390/ijerph20053910] [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] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/15/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Tuberculosis (TB) is a public health problem worldwide, and the influence of meteorological and air pollutants on the incidence of tuberculosis have been attracting interest from researchers. It is of great importance to use machine learning to build a prediction model of tuberculosis incidence influenced by meteorological and air pollutants for timely and applicable measures of both prevention and control. METHODS The data of daily TB notifications, meteorological factors and air pollutants in Changde City, Hunan Province ranging from 2010 to 2021 were collected. Spearman rank correlation analysis was conducted to analyze the correlation between the daily TB notifications and the meteorological factors or air pollutants. Based on the correlation analysis results, machine learning methods, including support vector regression, random forest regression and a BP neural network model, were utilized to construct the incidence prediction model of tuberculosis. RMSE, MAE and MAPE were performed to evaluate the constructed model for selecting the best prediction model. RESULTS (1) From the year 2010 to 2021, the overall incidence of tuberculosis in Changde City showed a downward trend. (2) The daily TB notifications was positively correlated with average temperature (r = 0.231), maximum temperature (r = 0.194), minimum temperature (r = 0.165), sunshine duration (r = 0.329), PM2.5 (r = 0.097), PM10 (r = 0.215) and O3 (r = 0.084) (p < 0.05). However, there was a significant negative correlation between the daily TB notifications and mean air pressure (r = -0.119), precipitation (r = -0.063), relative humidity (r = -0.084), CO (r = -0.038) and SO2 (r = -0.034) (p < 0.05). (3) The random forest regression model had the best fitting effect, while the BP neural network model exhibited the best prediction. (4) The validation set of the BP neural network model, including average daily temperature, sunshine hours and PM10, showed the lowest root mean square error, mean absolute error and mean absolute percentage error, followed by support vector regression. CONCLUSIONS The prediction trend of the BP neural network model, including average daily temperature, sunshine hours and PM10, successfully mimics the actual incidence, and the peak incidence highly coincides with the actual aggregation time, with a high accuracy and a minimum error. Taken together, these data suggest that the BP neural network model can predict the incidence trend of tuberculosis in Changde City.
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Affiliation(s)
- Na Tang
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Maoxiang Yuan
- Changde Center for Disease Control and Prevention, Changde 415000, China
| | - Zhijun Chen
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Jian Ma
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Rui Sun
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Yide Yang
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Quanyuan He
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Xiaowei Guo
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Shixiong Hu
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China
| | - Junhua Zhou
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
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30
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Pushpa B, Baskaran B, Vivekanandan S, Gokul P. Liver fat analysis using optimized support vector machine with support vector regression. Technol Health Care 2022; 31:867-886. [PMID: 36617796 DOI: 10.3233/thc-220254] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
BACKGROUND Fatty liver disease is a common condition caused by excess fat in the liver. It consists of two types: Alcoholic Fatty Liver Disease, also called alcoholic steatohepatitis, and Non-Alcoholic Fatty Liver Disease (NAFLD). As per epidemiological studies, fatty liver encompasses 9% to 32% of the general population in India and affects overweight people. OBJECTIVE An Optimized Support Vector Machine with Support Vector Regression model is proposed to evaluate the volume of liver fat by image analysis (LFA-OSVM-SVR). METHOD The input computed tomography (CT) liver images are collected from the Chennai liver foundation and Liver Segmentation (LiTS) datasets. Here, input datasets are pre-processed using Gaussian smoothing filter and bypass filter to reduce noise and improve image intensity. The proposed U-Net method is used to perform the liver segmentation. The Optimized Support Vector Machine is used to classify the liver images as fatty liver image and normal images. The support vector regression (SVR) is utilized for analyzing the fat in percentage. RESULTS The LFA-OSVM-SVR model effectively analyzed the liver fat from CT scan images. The proposed approach is activated in python and its efficiency is analyzed under certain performance metrics. CONCLUSION The proposed LFA-OSVM-SVR method attains 33.4%, 28.3%, 25.7% improved accuracy with 55%, 47.7%, 32.6% lower error rate for fatty image classification and 30%, 21%, 19.5% improved accuracy with 57.9%, 46.5%, 31.76% lower error rate for normal image classificationthan compared to existing methods such as Convolutional Neural Network (CNN) with Fractional Differential Enhancement (FDE) (CNN-FDE), Fully Convolutional Networks (FCN) and Non-negative Matrix Factorization (NMF) (FCN-NMF), and Deep Learning with Fully Convolutional Networks (FCN) (DL-FCN).
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Affiliation(s)
- B Pushpa
- Department of Electronics and Communication Engineering, Kings Engineering College, Chennai, Tamil Nadu, India
| | - B Baskaran
- Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, Annamalai University, Chidambaram, Tamil Nadu, India
| | - S Vivekanandan
- Managing Director and Liver Transplant Surgeon, Department of HPB and Liver Transplantation, RPS Hospitals, Chennai, Tamil Nadu, India
| | - P Gokul
- Department of Biotechnology, Saveetha school of engineering, Chennai, Tamil Nadu, India
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Jin S, Cao M, Qian Q, Zhang G, Wang Y. Study on an Assembly Prediction Method of RV Reducer Based on IGWO Algorithm and SVR Model. Sensors (Basel) 2022; 23:366. [PMID: 36616963 PMCID: PMC9824458 DOI: 10.3390/s23010366] [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] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/15/2022] [Accepted: 12/26/2022] [Indexed: 06/17/2023]
Abstract
This paper proposes a new method for predicting rotation error based on improved grey wolf-optimized support vector regression (IGWO-SVR), because the existing rotation error research methods cannot meet the production beat and product quality requirements of enterprises, because of the disadvantages of its being time-consuming and having poor calculation accuracy. First, the grey wolf algorithm is improved based on the optimal Latin hypercube sampling initialization, nonlinear convergence factor, and dynamic weights to improve its accuracy in optimizing the parameters of the support vector regression (SVR) model. Then, the IGWO-SVR prediction model between the manufacturing error of critical parts and the rotation error is established with the RV-40E reducer as a case. The results show that the improved grey wolf algorithm shows better parameter optimization performance, and the IGWO-SVR method shows better prediction performance than the existing back propagation (BP) neural network and BP neural network optimized by the sparrow search algorithm rotation error prediction methods, as well as the SVR models optimized by particle swarm algorithm and grey wolf algorithm. The mean squared error of IGWO-SVR model is 0.026, the running time is 7.843 s, and the maximum relative error is 13.5%, which can meet the requirements of production beat and product quality. Therefore, the IGWO-SVR method can be well applied to the rotate vector (RV) reducer parts-matching model to improve product quality and reduce rework rate and cost.
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Balakrishnan N, Baskar G, Balaji S, Kullappan M, Krishna Mohan S. Machine learning modeling to identify affinity improved biobetter anticancer drug trastuzumab and the insight of molecular recognition of trastuzumab towards its antigen HER2. J Biomol Struct Dyn 2022; 40:11638-11652. [PMID: 34392800 DOI: 10.1080/07391102.2021.1961866] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In the present study, a machine learning (ML) model was developed to predict the epistatic phenomena of combination mutants to improve the anticancer antibody-drug trastuzumab's binding affinity towards its antigen human epidermal growth factor receptor 2 (HER2). An ML algorithm, Support Vector Regression (SVR) was used to develop ML models with a data set consists of 193 affinity values of single mutants of trastuzumab and its associated various amino acid sequence derived descriptors. The subset selection of descriptors and SVR hyperparameters were done using the Genetic Algorithm (GA) within the SVR and the wrapper approach called GA-SVR. A 100 evolutionary cycles of GA produced the best 100 probable GA-SVR models based on their fitness score (Q2) estimated using a stratified 5 fold cross-validation procedure. The final ML model found to be highly predictive of test data set of six combination mutants and one single mutant with Rpre2 = 0.71. The analysis of descriptors in the ML model highlighted the importance of mutant induced secondary structural variation causes the binding affinity variation of the trastuzumab. The same was verified using a short 20 ns and a long 100 ns in duplicate molecular dynamics simulation of a wild and mutant variant of trastuzumab. The secondary structure induced affinity change due to mutations in the CDR-H3 is a novel insight that came out of this study. That should help rational mutant selection to develop a biobetter trastuzumab with a multifold improved binding affinity into the market quickly.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | - Gurunathan Baskar
- Department of Biotechnology, St. Joseph's College of Engineering, Chennai, India
| | - Sathyanarayan Balaji
- Department of Biotechnology, Bannari Amman Institute of Technology, Erode, India
| | - Malathi Kullappan
- Department of Research, Panimalar Medical College Hospital & Research Institute, Chennai, India
| | - Surapaneni Krishna Mohan
- Department of Biochemistry, Panimalar Medical College Hospital & Research Institute, Chennai, India.,Department of Molecular Virology, Panimalar Medical College Hospital & Research Institute, Chennai, India.,Department of Clinical Skills & Simulation, Panimalar Medical College Hospital & Research Institute, Chennai, India
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Eysenbach G, Pan Y, Zhao L, Niu Z, Guo Q, Zhao B. A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study. J Med Internet Res 2022; 24:e41819. [PMID: 36485032 PMCID: PMC9789495 DOI: 10.2196/41819] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/04/2022] [Accepted: 11/15/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND The treatment and care of adults and children with traumatic brain injury (TBI) constitute an intractable global health problem. Predicting the prognosis and length of hospital stay of patients with TBI may improve therapeutic effects and significantly reduce societal health care burden. Applying novel machine learning methods to the field of TBI may be valuable for determining the prognosis and cost-effectiveness of clinical treatment. OBJECTIVE We aimed to combine multiple machine learning approaches to build hybrid models for predicting the prognosis and length of hospital stay for adults and children with TBI. METHODS We collected relevant clinical information from patients treated at the Neurosurgery Center of the Second Affiliated Hospital of Anhui Medical University between May 2017 and May 2022, of which 80% was used for training the model and 20% for testing via screening and data splitting. We trained and tested the machine learning models using 5 cross-validations to avoid overfitting. In the machine learning models, 11 types of independent variables were used as input variables and Glasgow Outcome Scale score, used to evaluate patients' prognosis, and patient length of stay were used as output variables. Once the models were trained, we obtained and compared the errors of each machine learning model from 5 rounds of cross-validation to select the best predictive model. The model was then externally tested using clinical data of patients treated at the First Affiliated Hospital of Anhui Medical University from June 2021 to February 2022. RESULTS The final convolutional neural network-support vector machine (CNN-SVM) model predicted Glasgow Outcome Scale score with an accuracy of 93% and 93.69% in the test and external validation sets, respectively, and an area under the curve of 94.68% and 94.32% in the test and external validation sets, respectively. The mean absolute percentage error of the final built convolutional neural network-support vector regression (CNN-SVR) model predicting inpatient time in the test set and external validation set was 10.72% and 10.44%, respectively. The coefficient of determination (R2) was 0.93 and 0.92 in the test set and external validation set, respectively. Compared with back-propagation neural network, CNN, and SVM models built separately, our hybrid model was identified to be optimal and had high confidence. CONCLUSIONS This study demonstrates the clinical utility of 2 hybrid models built by combining multiple machine learning approaches to accurately predict the prognosis and length of stay in hospital for adults and children with TBI. Application of these models may reduce the burden on physicians when assessing TBI and assist clinicians in the medical decision-making process.
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Affiliation(s)
| | - Yifeng Pan
- The School of Big Data and Artificial Intelligence, Anhui Xinhua University, Hefei, China
| | - Luotong Zhao
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Zhaoyi Niu
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Qingguo Guo
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Bing Zhao
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
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Wang XH, Zhao B, Li L. Mapping white matter structural covariance connectivity for single subject using wavelet transform with T1-weighted anatomical brain MRI. Front Neurosci 2022; 16:1038514. [PMID: 36507319 PMCID: PMC9727234 DOI: 10.3389/fnins.2022.1038514] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/08/2022] [Indexed: 11/24/2022] Open
Abstract
Introduction Current studies of structural covariance networks were focused on the gray matter in the human brain. The structural covariance connectivity in the white matter remains largely unexplored. This paper aimed to build novel metrics that can infer white matter structural covariance connectivity, and to explore the predictive power of the proposed features. Methods To this end, a cohort of 315 adult subjects with the anatomical brain MRI datasets were obtained from the publicly available Dallas Lifespan Brain Study (DLBS) project. The 3D wavelet transform was applied on the individual voxel-based morphology (VBM) volume to obtain the white matter structural covariance connectivity. The predictive models for cognitive functions were built using support vector regression (SVR). Results The predictive models exhibited comparable performance with previous studies. The novel features successfully predicted the individual ability of digit comparison (DC) (r = 0.41 ± 0.01, p < 0.01) and digit symbol (DSYM) (r = 0.5 ± 0.01, p < 0.01). The sensorimotor-related white matter system exhibited as the most predictive network node. Furthermore, the node strengths of sensorimotor mode were significantly correlated to cognitive scores. Discussion The results suggested that the white matter structural covariance connectivity was informative and had potential for predictive tasks of brain-behavior research.
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Bode S, Schubert E, Hogendoorn H, Feuerriegel D. Decoding continuous variables from event-related potential (ERP) data with linear support vector regression using the Decision Decoding Toolbox (DDTBOX). Front Neurosci 2022; 16:989589. [PMID: 36408410 PMCID: PMC9669708 DOI: 10.3389/fnins.2022.989589] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 10/14/2022] [Indexed: 11/04/2023] Open
Abstract
Multivariate classification analysis for event-related potential (ERP) data is a powerful tool for predicting cognitive variables. However, classification is often restricted to categorical variables and under-utilises continuous data, such as response times, response force, or subjective ratings. An alternative approach is support vector regression (SVR), which uses single-trial data to predict continuous variables of interest. In this tutorial-style paper, we demonstrate how SVR is implemented in the Decision Decoding Toolbox (DDTBOX). To illustrate in more detail how results depend on specific toolbox settings and data features, we report results from two simulation studies resembling real EEG data, and one real ERP-data set, in which we predicted continuous variables across a range of analysis parameters. Across all studies, we demonstrate that SVR is effective for analysis windows ranging from 2 to 100 ms, and relatively unaffected by temporal averaging. Prediction is still successful when only a small number of channels encode true information, and the analysis is robust to temporal jittering of the relevant information in the signal. Our results show that SVR as implemented in DDTBOX can reliably predict continuous, more nuanced variables, which may not be well-captured by classification analysis. In sum, we demonstrate that linear SVR is a powerful tool for the investigation of single-trial EEG data in relation to continuous variables, and we provide practical guidance for users.
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Affiliation(s)
- Stefan Bode
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, VIC, Australia
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36
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Siti Nurhidayah Sharin, Mohamad Khairil Radzali, Muhamad Shirwan Abdullah Sani. A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia. Healthcare Analytics 2022; 2. [PMID: 37520622 PMCID: PMC9293790 DOI: 10.1016/j.health.2022.100080] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/13/2022] [Accepted: 07/14/2022] [Indexed: 05/27/2023]
Abstract
This study aims to (1) correlate and visualise the Coronavirus disease 19 (COVID-19) pandemic spread via Spearman rank coefficients of network analysis (NA) and (2) predict the cumulative number of COVID-19 confirmed and death cases via support vector regression (SVR) based on COVID-19 dataset in Malaysia between July 2020 to June 2021. The NA indicated increasing connectivity between different states throughout the time frame, revealing the most complex network of COVID-19 transmission in the second quarter of 2021. The SVR model predicted future COVID-19 cases and deaths in Malaysia in the second half of 2021. The study demonstrated that the NA and SVR could provide relatively simple yet valuable artificial intelligence techniques for visualising the degree of connectivity and predicting pandemic risk based on confirmed COVID-19 cases and deaths. The Malaysian health authorities used the NA and SVR model results for preventive measures in highly populated states.
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Chen JT, He PG, Jiang JS, Yang YF, Wang SY, Pan CH, Zeng L, He YF, Chen ZH, Lin HJ, Pan JM. In vivo prediction of abdominal fat and breast muscle in broiler chicken using live body measurements based on machine learning. Poult Sci 2022; 102:102239. [PMID: 36335741 PMCID: PMC9646972 DOI: 10.1016/j.psj.2022.102239] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/01/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022] Open
Abstract
The purpose of this study was to predict the carcass characteristics of broilers using support vector regression (SVR) and artificial neural network (ANN) model methods. Data were obtained from 176 yellow feather broilers aged 100-day-old (90 males and 86 females). The input variables were live body measurements, including external measurements and B-ultrasound measurements. The predictors of the model were the weight of abdominal fat and breast muscle in male and female broilers, respectively. After descriptive statistics and correlation analysis, the datasets were randomly divided into train set and test set according to the ratio of 7:3 to establish the model. The results of this study demonstrated that it is feasible to use machine learning methods to predict carcass characteristics of broilers based on live body measurements. Compared with the ANN method, the SVR method achieved better prediction results, for predicting breast muscle (male: R2 = 0.950; female: R2 = 0.955) and abdominal fat (male: R2 = 0.802; female: R2 = 0.944) in the test set. Consequently, the SVR method can be considered to predict breast muscle and abdominal fat of broiler chickens, except for abdominal fat in male broilers. However, further revaluation of the SVR method is suggested.
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Affiliation(s)
- Jin-Tian Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China,Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Peng-Guang He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China,Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Jin-Song Jiang
- Hangzhou LightTalk Biotechnology Co., Ltd., Hangzhou 310020, China
| | - Ye-Feng Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China,Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Shou-Yi Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China,Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Cheng-Hao Pan
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China,Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Li Zeng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China,Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Ye-Fan He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China,Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Zhong-Hao Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China,Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Hong-Jian Lin
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China,Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Jin-Ming Pan
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China,Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China,Corresponding author:
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Miley K, Michalowski M, Yu F, Leng E, McMorris BJ, Vinogradov S. Predictive models for social functioning in healthy young adults: A machine learning study integrating neuroanatomical, cognitive, and behavioral data. Soc Neurosci 2022; 17:414-427. [PMID: 36196662 PMCID: PMC9707316 DOI: 10.1080/17470919.2022.2132285] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 09/14/2022] [Indexed: 10/10/2022]
Abstract
Poor social functioning is an emerging public health problem associated with physical and mental health consequences. Developing prognostic tools is critical to identify individuals at risk for poor social functioning and guide interventions. We aimed to inform prediction models of social functioning by evaluating models relying on bio-behavioral data using machine learning. With data from the Human Connectome Project Healthy Young Adult sample (age 22-35, N = 1,101), we built Support Vector Regression models to estimate social functioning from variable sets of brain morphology to behavior with increasing complexity: 1) brain-only model, 2) brain-cognition model, 3) cognition-behavioral model, and 4) combined brain-cognition-behavioral model. Predictive accuracy of each model was assessed and the importance of individual variables for model performance was determined. The combined and cognition-behavioral models significantly predicted social functioning, whereas the brain-only and brain-cognition models did not. Negative affect, psychological wellbeing, extraversion, withdrawal, and cortical thickness of the rostral middle-frontal and superior-temporal regions were the most important predictors in the combined model. Results demonstrate that social functioning can be accurately predicted using machine learning methods. Behavioral markers may be more significant predictors of social functioning than brain measures for healthy young adults and may represent important leverage points for preventative intervention.
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Affiliation(s)
- Kathleen Miley
- School of Nursing, University of Minnesota, Minneapolis MN, United States
| | - Martin Michalowski
- School of Nursing, University of Minnesota, Minneapolis MN, United States
| | - Fang Yu
- Edson College of Nursing and Health Innovation, Arizona State University, Phoenix, AZ, United States
| | - Ethan Leng
- Department of Biomedical Engineering, University of Minnesota, Minneapolis MN, United States
| | | | - Sophia Vinogradov
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis MN, United States
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Kim M, Okuyucu O, Ordu E, Ordu S, Arslan Ö, Ko J. Prediction of Undrained Shear Strength by the GMDH-Type Neural Network Using SPT-Value and Soil Physical Properties. Materials (Basel) 2022; 15:6385. [PMID: 36143696 PMCID: PMC9502201 DOI: 10.3390/ma15186385] [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] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 08/31/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
This study presents a novel method for predicting the undrained shear strength (cu) using artificial intelligence technology. The cu value is critical in geotechnical applications and difficult to directly determine without laboratory tests. The group method of data handling (GMDH)-type neural network (NN) was utilized for the prediction of cu. The GMDH-type NN models were designed with various combinations of input parameters. In the prediction, the effective stress (σv'), standard penetration test result (NSPT), liquid limit (LL), plastic limit (PL), and plasticity index (PI) were used as input parameters in the design of the prediction models. In addition, the GMDH-type NN models were compared with the most commonly used method (i.e., linear regression) and other regression models such as random forest (RF) and support vector regression (SVR) models as comparative methods. In order to evaluate each model, the correlation coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE) were calculated for different input parameter combinations. The most effective model, the GMDH-type NN with input parameters (e.g., σv', NSPT, LL, PL, PI), had a higher correlation coefficient (R2 = 0.83) and lower error rates (MAE = 14.64 and RMSE = 22.74) than other methods used in the prediction of cu value. Furthermore, the impact of input variables on the model output was investigated using the SHAP (SHApley Additive ExPlanations) technique based on the extreme gradient boosting (XGBoost) ensemble learning algorithm. The results demonstrated that using the GMDH-type NN is an efficient method in obtaining a new empirical mathematical model to provide a reliable prediction of the undrained shear strength of soils.
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Affiliation(s)
- Mintae Kim
- School of Civil, Environmental, and Architectural Engineering, Korea University, Seoul 02841, Korea
| | - Osman Okuyucu
- Department of Civil Engineering, Tekirdağ Namık Kemal University, Tekirdağ 59860, Turkey
| | - Ertuğrul Ordu
- Department of Civil Engineering, Tekirdağ Namık Kemal University, Tekirdağ 59860, Turkey
| | - Seyma Ordu
- Department of Environmental Engineering, Tekirdağ Namık Kemal University, Tekirdağ 59860, Turkey
| | - Özkan Arslan
- Department of Electronics and Communication Engineering, Tekirdağ Namık Kemal University, Tekirdağ 59860, Turkey
| | - Junyoung Ko
- Department of Civil Engineering, Chungnam National University, Daejeon 34134, Korea
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Lee SH, Lee SH, Kim SW, Park HY, Lim K, Jung H. Estimation of Functional Fitness of Korean Older Adults Using Machine Learning Techniques: The National Fitness Award 2015-2019. Int J Environ Res Public Health 2022; 19:9754. [PMID: 35955109 PMCID: PMC9367869 DOI: 10.3390/ijerph19159754] [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] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/03/2022] [Accepted: 08/05/2022] [Indexed: 06/15/2023]
Abstract
Measuring functional fitness (FF) to track the decline in physical abilities is important in order to maintain a healthy life in old age. This paper aims to develop an estimation model of FF variables, which represents strength, flexibility, and aerobic endurance, using easy-to-measure physical parameters for Korean older adults aged over 65 years old. The estimation models were developed using various machine learning techniques and were trained with the National Fitness Award datasets from 2015 to 2019 compiled by the Korea Sports Promotion Foundation. The machine-learning-based nonlinear regression models were employed to improve the performance of the previous linear regression models. To derive the optimal estimation model that showed the best estimation accuracy, we developed five different machine-learning-based estimation models and compares the estimation accuracy not only among the machine learning models, but also with the previous linear regression model. The coefficient of determination of the FF variables was used to compare the performance of each model; the mean absolute percentage error (MAPE) and standard error of estimation (SEE) were used to evaluate the model performance. The deep neural network (DNN) model presented the best performance among the regression models for the estimation of all of the FF variables. The coefficient of determination in the HGS test was 0.784, while those of the others were less than 0.5 meaning that the HGS of older adults can be reliably estimated using easy-to-measure independent variables.
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Affiliation(s)
- Sang-Hun Lee
- Division of Mechanical and Aerospace Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
| | - Seung-Hun Lee
- Division of Mechanical and Aerospace Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
| | - Sung-Woo Kim
- Physical Activity and Performance Institute, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
| | - Hun-Young Park
- Physical Activity and Performance Institute, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
- Department of Sports Medicine and Science, Graduate School, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
| | - Kiwon Lim
- Physical Activity and Performance Institute, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
- Department of Sports Medicine and Science, Graduate School, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
- Department of Physical Education, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
| | - Hoeryong Jung
- Division of Mechanical and Aerospace Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
- Department of Sports Medicine and Science, Graduate School, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea
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Chen P, Wang H, Cao S, Lv X. Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation. Materials (Basel) 2022; 15:4971. [PMID: 35888438 DOI: 10.3390/ma15144971] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/08/2022] [Accepted: 07/11/2022] [Indexed: 02/01/2023]
Abstract
The prediction and control of the mechanical behaviours of fibre-reinforced polymer (FRP)-confined circular concrete columns subjected to axial loading are directly related to the safety of the structures. One challenge in building a mechanical model is understanding the complex relationship between the main parameters affecting the phenomenon. Artificial intelligence (AI) algorithms can overcome this challenge. In this study, 298 test data points were considered for FRP-confined circular concrete columns. Six parameters, such as the diameter-to-fibre thickness ratio (D/t) and the tensile strength of the FRP (ffrp) were set as the input sets. The existing models were compared with the test data. In addition, artificial neural networks (ANNs) and support vector regression (SVR) were used to predict the mechanical behaviour of FRP-confined circular concrete columns. The study showed that the predictive accuracy of the compressive strength in the existing models was higher than the peak compressive strain for the high dispersion of material deformation. The predictive accuracy of the ANN and SVR was higher than that of the existing models. The ANN and SVR can predict the compressive strength and peak compressive strain of FRP-confined circular concrete columns and can be used to predict the mechanical behaviour of FRP-confined circular concrete columns.
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Yang XY, Liu R, Luo J, Huang FF, Wang PC, Yang XJ, Wu H, Zhou Y, Li ZJ. Comprehensive Cortical Structural Features Predict the Efficacy of Cognitive Behavioral Therapy in Obsessive-Compulsive Disorder. Brain Sci 2022; 12. [PMID: 35884728 DOI: 10.3390/brainsci12070921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/30/2022] [Accepted: 07/09/2022] [Indexed: 12/10/2022] Open
Abstract
Although cognitive behavioral therapy (CBT) is effective for patients with obsessive-compulsive disorder (OCD), 40% of OCD patients show a poor response to CBT. This study aimed to identify the cortical structural factors that predict CBT outcomes in OCD patients. A total of 56 patients with OCD received baseline structural MRI (sMRI) scanning and 14 individual CBT sessions. The linear support vector regression (SVR) models were used to identify the predictive performance of sMRI indices, including gray matter volume, cortical thickness, sulcal depth, and gyrification value. The patients’ OC symptoms decreased significantly after CBT intervention (p < 0.001). We found the model with the comprehensive variables exhibited better performance than the models with single structural indices (MAE = 0.14, MSE = 0.03, R2 = 0.36), showing a significant correlation between the true value and the predicted value (r = 0.63, p < 0.001). The results indicated that a model integrating four cortical structural features can accurately predict the effectiveness of CBT for OCD. Future models incorporating other brain indicators, including brain functional indicators, EEG indicators, neurotransmitters, etc., which might be more accurate for predicting the effectiveness of CBT for OCD, are needed.
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Li H, Huang X, Zhou D, Cao A, Su M, Wang Y, Guo L. Forecasting Carbon Price in China: A Multimodel Comparison. Int J Environ Res Public Health 2022; 19:6217. [PMID: 35627753 DOI: 10.3390/ijerph19106217] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 11/29/2022]
Abstract
With the global concern for carbon dioxide, the carbon emission trading market is becoming more and more important. An accurate forecast of carbon price plays a significant role in understanding the dynamics of the carbon trading market and achieving national emission reduction targets. Carbon prices are influenced by many factors, which makes carbon price forecasting a complicated problem. In recent years, deep learning models are widely used in price forecasting, because they have high forecasting accuracy when dealing with nonlinear time series data. In this paper, Multivariate Long Short-Term Memory (LSTM) in deep learning is used to forecast carbon prices in China, which takes into account the factors affecting the carbon price. The historical time series data of carbon prices in Hubei (HBEA) and Guangdong (GDEA) and three traditional energy prices affecting carbon prices from 5 May 2014 to 22 July 2021 are collected to form two data sets. To prove the forecast effect of our model, this paper not only uses Multivariate LSTM, Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Recurrent Neural Network (RNN) to forecast the same data, but also compares the forecast results of Multivariate LSTM with the existing research on HBEA and GDEA forecast based on deep learning recently. The results show that the MAE, MSE, and RMSE obtained by the Multivariate LSTM are all smaller than other prediction models, which proves that the model is more suitable for carbon price forecast and offers a new approach to carbon prices forecast. This research conclusion also provides some policy implications.
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Zhu J, Li A, Qin F, Chang L. A New Robust Adaptive Filter Aided by Machine Learning Method for SINS/DVL Integrated Navigation System. Sensors (Basel) 2022; 22:s22103792. [PMID: 35632201 PMCID: PMC9144316 DOI: 10.3390/s22103792] [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] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 02/05/2023]
Abstract
As an important means of underwater navigation and positioning, the accuracy of SINS/DVL integrated navigation system greatly affects the efficiency of underwater work. Considering the complexity and change of the underwater environment, it is necessary to enhance the robustness and adaptability of the SINS/DVL integrated navigation system. Therefore, this paper proposes a new adaptive filter based on support vector regression. The method abandons the elimination of outliers generated by Doppler Velocity Logger (DVL) in the measurement process from the inside of the filter in the form of probability density function modeling. Instead, outliers are eliminated from the perspective of external sensors, which effectively improves the robustness of the filter. At the same time, a new Variational Bayesian (VB) strategy is adopted to reduce the influence of inaccurate process noise and measurement noise, and improve the adaptiveness of the filter. Their advantages complement each other, effectively improve the stability of filter. Simulation and ship-borne tests are carried out. The test results show that the method proposed in this paper has higher navigation accuracy.
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Cheng KS, Su YL, Kuo LC, Yang TH, Lee CL, Chen W, Liu SH. Muscle Mass Measurement Using Machine Learning Algorithms with Electrical Impedance Myography. Sensors (Basel) 2022; 22:s22083087. [PMID: 35459072 PMCID: PMC9031580 DOI: 10.3390/s22083087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/15/2022] [Accepted: 04/16/2022] [Indexed: 02/04/2023]
Abstract
Sarcopenia is a wild chronic disease among elderly people. Although it does not entail a life-threatening risk, it will increase the adverse risk due to the associated unsteady gait, fall, fractures, and functional disability. The import factors in diagnosing sarcopenia are muscle mass and strength. The examination of muscle mass must be carried in the clinic. However, the loss of muscle mass can be improved by rehabilitation that can be performed in non-medical environments. Electronic impedance myography (EIM) can measure some parameters of muscles that have the correlations with muscle mass and strength. The goal of this study is to use machine learning algorithms to estimate the total mass of thigh muscles (MoTM) with the parameters of EIM and body information. We explored the seven major muscles of lower limbs. The feature selection methods, including recursive feature elimination (RFE) and feature combination, were used to select the optimal features based on the ridge regression (RR) and support vector regression (SVR) models. The optimal features were the resistance of rectus femoris normalized by the thigh circumference, phase of tibialis anterior combined with the gender, and body information, height, and weight. There were 96 subjects involved in this study. The performances of estimating the MoTM used the regression coefficient (r2) and root-mean-square error (RMSE), which were 0.800 and 0.929, and 1.432 kg and 0.980 kg for RR and SVR models, respectively. Thus, the proposed method could have the potential to support people examining their muscle mass in non-medical environments.
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Affiliation(s)
- Kuo-Sheng Cheng
- Department of Biomedical Engineering, National Cheng Kung University, Tainai 701, Taiwan; (K.-S.C.); (Y.-L.S.); (T.-H.Y.)
| | - Ya-Ling Su
- Department of Biomedical Engineering, National Cheng Kung University, Tainai 701, Taiwan; (K.-S.C.); (Y.-L.S.); (T.-H.Y.)
| | - Li-Chieh Kuo
- Department of Occupational Therapy, National Cheng Kung University, Tainan 701, Taiwan;
| | - Tai-Hua Yang
- Department of Biomedical Engineering, National Cheng Kung University, Tainai 701, Taiwan; (K.-S.C.); (Y.-L.S.); (T.-H.Y.)
| | - Chia-Lin Lee
- Department of Physical Education, National Kaohsiung Normal University, Kaohsiung City 80201, Taiwan;
| | - Wenxi Chen
- Biomedical Information Engineering Laboratory, The University of Aizu, Aizu-Wakamatsu City, Fukushima 965-8580, Japan;
| | - Shing-Hong Liu
- Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
- Correspondence: ; Tel.: +886-4-233230000-7811
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Mulrennan K, Munir N, Creedon L, Donovan J, Lyons JG, McAfee M. NIR-Based Intelligent Sensing of Product Yield Stress for High-Value Bioresorbable Polymer Processing. Sensors (Basel) 2022; 22:2835. [PMID: 35458820 PMCID: PMC9028237 DOI: 10.3390/s22082835] [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] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/03/2022] [Accepted: 04/04/2022] [Indexed: 06/14/2023]
Abstract
PLA (polylactide) is a bioresorbable polymer used in implantable medical and drug delivery devices. Like other bioresorbable polymers, PLA needs to be processed carefully to avoid degradation. In this work we combine in-process temperature, pressure, and NIR spectroscopy measurements with multivariate regression methods for prediction of the mechanical strength of an extruded PLA product. The potential to use such a method as an intelligent sensor for real-time quality analysis is evaluated based on regulatory guidelines for the medical device industry. It is shown that for the predictions to be robust to processing at different times and to slight changes in the processing conditions, the fusion of both NIR and conventional process sensor data is required. Partial least squares (PLS), which is the established 'soft sensing' method in the industry, performs the best of the linear methods but demonstrates poor reliability over the full range of processing conditions. Conversely, both random forest (RF) and support vector regression (SVR) show excellent performance for all criteria when used with a prior principal component (PC) dimension reduction step. While linear methods currently dominate for soft sensing of mixture concentrations in highly conservative, regulated industries such as the medical device industry, this work indicates that nonlinear methods may outperform them in the prediction of mechanical properties from complex physicochemical sensor data. The nonlinear methods show the potential to meet industrial standards for robustness, despite the relatively small amount of training data typically available in high-value material processing.
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Affiliation(s)
- Konrad Mulrennan
- Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland; (K.M.); (N.M.); (L.C.); (J.D.)
- Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
| | - Nimra Munir
- Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland; (K.M.); (N.M.); (L.C.); (J.D.)
- Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
| | - Leo Creedon
- Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland; (K.M.); (N.M.); (L.C.); (J.D.)
- Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
| | - John Donovan
- Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland; (K.M.); (N.M.); (L.C.); (J.D.)
- Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
| | - John G. Lyons
- Faculty of Engineering and Informatics, Technological University of the Shannon, Dublin Road, N37 HD68 Athlone, Ireland;
| | - Marion McAfee
- Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland; (K.M.); (N.M.); (L.C.); (J.D.)
- Centre for Precision Engineering, Materials and Manufacturing (PEM Centre), Atlantic Technological University, ATU Sligo, Ash Lane, F91 YW50 Sligo, Ireland
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Brier LM, Zhang X, Bice AR, Gaines SH, Landsness EC, Lee JM, Anastasio MA, Culver JP. A Multivariate Functional Connectivity Approach to Mapping Brain Networks and Imputing Neural Activity in Mice. Cereb Cortex 2022; 32:1593-1607. [PMID: 34541601 PMCID: PMC9016290 DOI: 10.1093/cercor/bhab282] [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: 03/12/2021] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 11/13/2022] Open
Abstract
Temporal correlation analysis of spontaneous brain activity (e.g., Pearson "functional connectivity," FC) has provided insights into the functional organization of the human brain. However, bivariate analysis techniques such as this are often susceptible to confounding physiological processes (e.g., sleep, Mayer-waves, breathing, motion), which makes it difficult to accurately map connectivity in health and disease as these physiological processes affect FC. In contrast, a multivariate approach to imputing individual neural networks from spontaneous neuroimaging data could be influential to our conceptual understanding of FC and provide performance advantages. Therefore, we analyzed neural calcium imaging data from Thy1-GCaMP6f mice while either awake, asleep, anesthetized, during low and high bouts of motion, or before and after photothrombotic stroke. A linear support vector regression approach was used to determine the optimal weights for integrating the signals from the remaining pixels to accurately predict neural activity in a region of interest (ROI). The resultant weight maps for each ROI were interpreted as multivariate functional connectivity (MFC), resembled anatomical connectivity, and demonstrated a sparser set of strong focused positive connections than traditional FC. While global variations in data have large effects on standard correlation FC analysis, the MFC mapping methods were mostly impervious. Lastly, MFC analysis provided a more powerful connectivity deficit detection following stroke compared to traditional FC.
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Affiliation(s)
- Lindsey M Brier
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Xiaohui Zhang
- Department of Bioengineering, University of Illinois, Urbana-Champaign, IL 61801, USA
| | - Annie R Bice
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Seana H Gaines
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Eric C Landsness
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Jin-Moo Lee
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Mark A Anastasio
- Department of Bioengineering, University of Illinois, Urbana-Champaign, IL 61801, USA
| | - Joseph P Culver
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University School of Engineering, St. Louis, MO 63105, USA
- Department of Electrical and Systems Engineering, Washington University School of Engineering, St. Louis, MO 63112, USA
- Department of Physics, Washington University School of Arts and Science, St. Louis, MO 63130, USA
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Piltan F, Toma RN, Shon D, Im K, Choi HK, Yoo DS, Kim JM. Strict-Feedback Backstepping Digital Twin and Machine Learning Solution in AE Signals for Bearing Crack Identification. Sensors (Basel) 2022; 22:539. [PMID: 35062499 DOI: 10.3390/s22020539] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 01/07/2022] [Accepted: 01/08/2022] [Indexed: 11/30/2022]
Abstract
Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and motor speeds. The proposed method has three main steps. In the first step, the strict-feedback backstepping digital twin is designed for acoustic emission signal modeling and estimation. After that, the acoustic emission residual signal is generated. Finally, a support vector machine is recommended for crack type/size classification. The proposed digital twin is presented in two steps, (a) AE signal modeling and (b) AE signal estimation. The AE signal in normal conditions is modeled using an autoregressive technique, the Laguerre algorithm, a support vector regression technique and a Gaussian process regression procedure. To design the proposed digital twin, a strict-feedback backstepping observer, an integral term, a support vector regression and a fuzzy logic algorithm are suggested for AE signal estimation. The Ulsan Industrial Artificial Intelligence (UIAI) Lab’s bearing dataset was used to test the efficiency of the combined strict-feedback backstepping digital twin and machine learning technique for bearing crack type/size diagnosis. The average accuracies of the crack type diagnosis and crack size diagnosis of acoustic emission signals for the bearings used in the proposed algorithm were 97.13% and 96.9%, respectively.
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Almohammed F, Sihag P, Sammen SS, Ostrowski KA, Singh K, Prasad CVSR, Zajdel P. Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete. Materials (Basel) 2022; 15:489. [PMID: 35057207 DOI: 10.3390/ma15020489] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 12/30/2021] [Accepted: 01/04/2022] [Indexed: 11/17/2022]
Abstract
In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set.
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Beauchamp JA, Khurram OU, Dewald J, Heckman CJ, Pearcey G. A computational approach for generating continuous estimates of motor unit discharge rates and visualizing population discharge characteristics. J Neural Eng 2021; 19. [PMID: 34937005 DOI: 10.1088/1741-2552/ac4594] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 12/22/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Successive improvements in high density surface electromyography and decomposition techniques have facilitated an increasing yield in decomposed motor unit (MU) spike times. Though these advancements enhance the generalizability of findings and promote the application of MU discharge characteristics to inform the neural control of motor output, limitations remain. Specifically, 1) common approaches for generating smooth estimates of MU discharge rates introduce artifacts in quantification, which may bias findings, and 2) discharge characteristics of large MU populations are often difficult to visualize. APPROACH In the present study, we propose support vector regression (SVR) as an improved approach for generating smooth continuous estimates of discharge rate and compare the fit characteristics of SVR to traditionally used methods, including Hanning window filtering and polynomial regression. Furthermore, we introduce ensembles as a method to visualize the discharge characteristics of large MU populations. We define ensembles as the average discharge profile of a subpopulation of MUs, composed of a time normalized ensemble average of all units within this subpopulation. Analysis was conducted with MUs decomposed from the tibialis anterior (N = 2128), medial gastrocnemius (N = 2673), and soleus (N = 1190) during isometric plantarflexion and dorsiflexion contractions. MAIN RESULT Compared to traditional approaches, we found SVR to alleviate commonly observed inaccuracies and produce significantly less absolute fit error in the initial phase of MU discharge and throughout the entire duration of discharge. Additionally, we found the visualization of MU populations as ensembles to intuitively represent population discharge characteristics with appropriate accuracy for visualization. SIGNIFICANCE The results and methods outlined here provide an improved method for generating estimates of MU discharge rate with SVR and present a unique approach to visualizing MU populations with ensembles. In combination, the use of SVR and generation of ensembles represent an efficient method for rendering population discharge characteristics.
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Affiliation(s)
- James A Beauchamp
- Northwestern University Feinberg School of Medicine, 645 N Michigan Ave., Chicago, Illinois, 60611-3008, UNITED STATES
| | - Obaid U Khurram
- Northwestern University Feinberg School of Medicine, 645 N Michigan Ave., Chicago, Illinois, 60611-3008, UNITED STATES
| | - Julius Dewald
- Physical Therapy and Human Movement Sciences, Northwestern University Feinberg School of Medicine, 645 N Michigan Ave., Chicago, Illinois, 60611-3008, UNITED STATES
| | - C J Heckman
- Northwestern University Feinberg School of Medicine, 311 E Superior, Chicago, Illinois, 60611-3008, UNITED STATES
| | - Gregory Pearcey
- Northwestern University Feinberg School of Medicine, 645 N Michigan Ave., Chicago, Illinois, 60611-3008, UNITED STATES
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