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Mastin N, Durell L, Brooks BW, Hering AS. Advancing statistical treatment of photolocomotor behavioral response study data. PLoS One 2024; 19:e0300636. [PMID: 38771799 PMCID: PMC11108188 DOI: 10.1371/journal.pone.0300636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 03/02/2024] [Indexed: 05/23/2024] Open
Abstract
Fish photolocomotor behavioral response (PBR) studies have become increasingly prevalent in pharmacological and toxicological research to assess the environmental impact of various chemicals. There is a need for a standard, reliable statistical method to analyze PBR data. The most common method currently used, univariate analysis of variance (ANOVA), does not account for temporal dependence in observations and leads to incomplete or unreliable conclusions. Repeated measures ANOVA, another commonly used method, has drawbacks in its interpretability for PBR study data. Because each observation is collected continuously over time, we instead consider each observation to be a function and apply functional ANOVA (FANOVA) to PBR data. Using the functional approach not only accounts for temporal dependency but also retains the full structure of the data and allows for straightforward interpretation in any subregion of the domain. Unlike the traditional univariate and repeated measures ANOVA, the FANOVA that we propose is nonparametric, requiring minimal assumptions. We demonstrate the disadvantages of univariate and repeated measures ANOVA using simulated data and show how they are overcome by applying FANOVA. We then apply one-way FANOVA to zebrafish data from a PBR study and discuss how those results can be reproduced for future PBR studies.
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Affiliation(s)
- Natalie Mastin
- Department of Statistical Science, Baylor University, Waco, TX, United States of America
| | - Luke Durell
- Department of Statistical Science, Baylor University, Waco, TX, United States of America
| | - Bryan W. Brooks
- Department of Environmental Science, Baylor University, Waco, TX, United States of America
- Institute of Biomedical Studies, Baylor University, Waco, TX, United States of America
| | - Amanda S. Hering
- Department of Statistical Science, Baylor University, Waco, TX, United States of America
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2
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Long AS, Reich BJ, Staicu AM, Meitzen J. A nonparametric test of group distributional differences for hierarchically clustered functional data. Biometrics 2023; 79:3778-3791. [PMID: 36805970 PMCID: PMC10695330 DOI: 10.1111/biom.13846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 02/03/2023] [Indexed: 02/22/2023]
Abstract
Biological sex and gender are critical variables in biomedical research, but are complicated by the presence of sex-specific natural hormone cycles, such as the estrous cycle in female rodents, typically divided into phases. A common feature of these cycles are fluctuating hormone levels that induce sex differences in many behaviors controlled by the electrophysiology of neurons, such as neuronal membrane potential in response to electrical stimulus, typically summarized using a priori defined metrics. In this paper, we propose a method to test for differences in the electrophysiological properties across estrous cycle phase without first defining a metric of interest. We do this by modeling membrane potential data in the frequency domain as realizations of a bivariate process, also depending on the electrical stimulus, by adopting existing methods for longitudinal functional data. We are then able to extract the main features of the bivariate signals through a set of basis function coefficients. We use these coefficients for testing, adapting methods for multivariate data to account for an induced hierarchical structure that is a product of the experimental design. We illustrate the performance of the proposed approach in simulations and then apply the method to experimental data.
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Affiliation(s)
- Alexander S Long
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, U.S.A
| | - Brian J Reich
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, U.S.A
| | - Ana-Maria Staicu
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, U.S.A
| | - John Meitzen
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, U.S.A
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3
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Zhu C, Wang JL. Testing homogeneity: the trouble with sparse functional data. J R Stat Soc Series B Stat Methodol 2023; 85:705-731. [PMID: 37521166 PMCID: PMC10376451 DOI: 10.1093/jrsssb/qkad021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 12/06/2022] [Accepted: 02/25/2023] [Indexed: 08/01/2023]
Abstract
Testing the homogeneity between two samples of functional data is an important task. While this is feasible for intensely measured functional data, we explain why it is challenging for sparsely measured functional data and show what can be done for such data. In particular, we show that testing the marginal homogeneity based on point-wise distributions is feasible under some mild constraints and propose a new two-sample statistic that works well with both intensively and sparsely measured functional data. The proposed test statistic is formulated upon energy distance, and the convergence rate of the test statistic to its population version is derived along with the consistency of the associated permutation test. The aptness of our method is demonstrated on both synthetic and real data sets.
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Affiliation(s)
- Changbo Zhu
- Address for correspondence: Changbo Zhu, Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Jane-Ling Wang
- Department of Statistics, University of California, Davis, Davis, United States
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4
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Kheiri SK, Vahedi Z, Sun H, Megahed FM, Cavuoto LA. Functional ANOVA for Upper Extremity Fatigue Analysis during Dynamic Order Picking. IISE Trans Occup Ergon Hum Factors 2023; 11:123-135. [PMID: 38536045 DOI: 10.1080/24725838.2024.2331182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 03/12/2024] [Indexed: 04/20/2024]
Abstract
OCCUPATIONAL APPLICATIONSMusculoskeletal disorders are prevalent among warehouse workers who engage in repetitive and dynamic tasks. To prevent such injuries, it is vital to identify the factors that influence fatigue in the upper extremities during these repetitive activities. Our study reveals that task factors, namely the bottle mass and picking rate, significantly influence upper extremity fatigue. In most cases, the fatigue indicator is a functional variable, meaning that the fatigue score or measurement is a curve captured over time, which could be modeled as a function. In this study, we demonstrate that functional data analysis tools, such as functional analysis of variance (FANOVA), prove more effective than traditional methods in specifying how task factors contribute to the development of fatigue in the upper extremities. Furthermore, since there are inherent differences among workers that could affect their fatigue development process, the data heterogeneity could be tackled by employing clustering methods.
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Affiliation(s)
| | - Zahra Vahedi
- Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, USA
| | - Hongyue Sun
- Mechanical Engineering, University of Georgia, Athens, GA, USA
| | - Fadel M Megahed
- Information Systems & Analytics, Miami University, Oxford, OH, USA
| | - Lora A Cavuoto
- Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, USA
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5
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SHIEH DENISE, OGDEN RTODD. Permutation-Based Inference for Function-on-Scalar Regression With an Application in PET Brain Imaging. J Nonparametr Stat 2023; 35:820-838. [PMID: 38046382 PMCID: PMC10688779 DOI: 10.1080/10485252.2023.2206926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 04/19/2023] [Indexed: 12/05/2023]
Abstract
The density of various proteins throughout the human brain can be studied through the use of positron emission tomography (PET) imaging. We report here on data from a study of serotonin transporter (5-HTT) binding. While PET imaging data analysis is most commonly performed on data that are aggregated into several discrete a priori regions of interest, in this study, primary interest is on measures of 5-HTT binding potential that are made at many locations along a continuous anatomically defined tract, one that was chosen to follow serotonergic axons. Our goal is to characterize the binding patterns along this tract and also to determine how such patterns differ between control subjects and depressed patients. Due to the nature of our data, we utilize function-on-scalar regression modeling to make optimal use of our data. Inference on both main effects (position along the tract; diagnostic group) and their interactions is made using permutation testing strategies that do not require distributional assumptions. Also, to investigate the question of homogeneity we implement a permutation testing strategy, which adapts a "block bootstrapping" approach from time series analysis to the functional data setting.
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Affiliation(s)
- DENISE SHIEH
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
| | - R TODD OGDEN
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
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6
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Chang H, McKeague IW. Empirical likelihood‐based inference for functional means with application to wearable device data. J R Stat Soc Series B Stat Methodol 2022. [DOI: 10.1111/rssb.12543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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7
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Kim M. Application of functional ANOVA and functional MANOVA. KOREAN JOURNAL OF APPLIED STATISTICS 2022. [DOI: 10.5351/kjas.2022.35.5.579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Mijeong Kim
- Department of Statistics, Ewha Womans University
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8
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Hlávka Z, Hlubinka D, Koňasová K. Functional ANOVA based on empirical characteristic functionals. J MULTIVARIATE ANAL 2022. [DOI: 10.1016/j.jmva.2021.104878] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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9
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Chowdhury J, Chaudhuri P. Multi-sample comparison using spatial signs for infinite dimensional data. Electron J Stat 2022. [DOI: 10.1214/22-ejs2054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Joydeep Chowdhury
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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Affiliation(s)
- Łukasz Smaga
- Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poznań, Poland
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11
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Huang WH, Huang LS, Yang CT. Invariant tests for functional data with application to an earthquake impact study. J MULTIVARIATE ANAL 2021. [DOI: 10.1016/j.jmva.2021.104894] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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12
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13
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Acal C, Aguilera AM, Sarra A, Evangelista A, Di Battista T, Palermi S. Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2021; 36:1083-1101. [PMID: 34456623 PMCID: PMC8383262 DOI: 10.1007/s00477-021-02071-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/24/2021] [Indexed: 06/13/2023]
Abstract
Faced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of NO 2 , PM 10 , PM 2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the PM 10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.
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Affiliation(s)
- Christian Acal
- Department of Statistics and O.R. and IMAG, University of Granada, Granada, Spain
| | - Ana M. Aguilera
- Department of Statistics and O.R. and IMAG, University of Granada, Granada, Spain
| | - Annalina Sarra
- Department of Philosophical, Pedagogical and Economic-Quantitative Sciences, University G. d’Annunzio, V.le Pindaro, 42, 65127 Pescara, Italy
| | - Adelia Evangelista
- Department of Philosophical, Pedagogical and Economic-Quantitative Sciences, University G. d’Annunzio, V.le Pindaro, 42, 65127 Pescara, Italy
| | - Tonio Di Battista
- Department of Philosophical, Pedagogical and Economic-Quantitative Sciences, University G. d’Annunzio, V.le Pindaro, 42, 65127 Pescara, Italy
| | - Sergio Palermi
- Agency of Environmental Protection of Abruzzo (ARTA), V.le G. Marconi, 51, 65127 Pescara, Italy
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Lin Z, Lopes ME, Müller HG. High-Dimensional MANOVA Via Bootstrapping and Its Application to Functional and Sparse Count Data. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1920959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Zhenhua Lin
- Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Miles E. Lopes
- Department of Statistics, University of California, Davis, CA
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15
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Qiu Z, Chen J, Zhang JT. Two-sample tests for multivariate functional data with applications. Comput Stat Data Anal 2021. [DOI: 10.1016/j.csda.2020.107160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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16
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Asymptotic normality of a generalized maximum mean discrepancy estimator. Stat Probab Lett 2021. [DOI: 10.1016/j.spl.2020.108961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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17
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Chakraborty S, Dey T, Mukherjee A, Alberts JL, Linder SM. Functional modeling of pedaling kinematics for the Stroke patients. J Biopharm Stat 2020; 30:674-688. [PMID: 32129143 DOI: 10.1080/10543406.2020.1730872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Understanding deficits in motor control through the analysis of pedaling biomechanics plays a key role in the treatment of stroke patients. A thorough study of the impact of different exercise patterns and workloads on the change between pre- and post-treatment movement patterns in the patients is therefore of utmost importance to the clinicians. The objective of this study was to analyze the difference between pre- and post-treatment pedaling torques when the patients are subject to different exercise groups with varying workloads. The effects of affected vs unaffected side along with the covariates age and BMI have also been accounted for in this work. Two different three-way ANOVA-based approaches have been implemented here. In the first approach, a random projection-based ANOVA technique has been performed treating the pedaling torques as functional response, whereas the second approach utilizes distance measures to summarize the difference between pre- and post-treatment torques and perform nonparametric tests on it. Bayesian bootstrap has been used here to perform tests on the median distance. A group of stroke patients have been studied in the Cleveland Clinic categorizing them into different exercise groups and workload patterns. The data obtained have been analyzed with the aforementioned techniques, and the results have been reported here. These techniques turn out to be promising and will help clinicians recommend personalized treatment to stroke patients for optimal results.
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Affiliation(s)
- Sounak Chakraborty
- Department of Statistics, University of Missouri - Columbia , Columbia, Missouri, USA
| | - Tanujit Dey
- Center for Surgery and Public Health, Brigham and Women's Hospital , Boston, Massachusetts, USA.,Department of Quantitative Health Sciences, Cleveland Clinic , Cleveland, Ohio, USA
| | - Anish Mukherjee
- Department of Quantitative Health Sciences, Cleveland Clinic , Cleveland, Ohio, USA.,Department of Population and Quantitative Health Sciences, Case Western Reserve University , Cleveland, Ohio, USA
| | - Jay L Alberts
- Department of Biomedical Engineering, Cleveland Clinic , Cleveland, Ohio, USA
| | - Susan M Linder
- Department of Biomedical Engineering, Cleveland Clinic , Cleveland, Ohio, USA
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18
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Lopes ME, Lin Z, Müller HG. Bootstrapping max statistics in high dimensions: Near-parametric rates under weak variance decay and application to functional and multinomial data. Ann Stat 2020. [DOI: 10.1214/19-aos1844] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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19
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Smaga Ł, Zhang J. Linear hypothesis testing for weighted functional data with applications. Scand Stat Theory Appl 2019. [DOI: 10.1111/sjos.12414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Łukasz Smaga
- Faculty of Mathematics and Computer Science Adam Mickiewicz University
| | - Jin‐Ting Zhang
- Department of Statistics and Applied Probability National University of Singapore
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20
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The left cerebral hemisphere may be dominant for the control of bimanual symmetric reach-to-grasp movements. Exp Brain Res 2019; 237:3297-3311. [PMID: 31664489 DOI: 10.1007/s00221-019-05672-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Accepted: 10/19/2019] [Indexed: 12/20/2022]
Abstract
Previous research has established that the left cerebral hemisphere is dominant for the control of continuous bimanual movements. The lateralisation of motor control for discrete bimanual movements, in contrast, is underexplored. The purpose of the current study was to investigate which (if either) hemisphere is dominant for discrete bimanual movements. Twenty-one participants made bimanual reach-to-grasp movements towards pieces of candy. Participants grasped the candy to either place it in their mouths (grasp-to-eat) or in a receptacle near their mouths (grasp-to-place). Research has shown smaller maximum grip apertures (MGAs) for unimanual grasp-to-eat movements than unimanual grasp-to-place movements when controlled by the left hemisphere. In Experiment 1, participants made bimanual symmetric movements where both hands made grasp-to-eat or grasp-to-place movements. We hypothesised that a left hemisphere dominance for bimanual movements would cause smaller MGAs in both hands during bimanual grasp-to-eat movements compared to those in bimanual grasp-to-place movements. The results revealed that MGAs were indeed smaller for bimanual grasp-to-eat movements than grasp-to-place movements. This supports that the left hemisphere may be dominant for the control of bimanual symmetric movements, which agrees with studies on continuous bimanual movements. In Experiment 2, participants made bimanual asymmetric movements where one hand made a grasp-to-eat movement while the other hand made a grasp-to-place movement. The results failed to support the potential predictions of left hemisphere dominance, right hemisphere dominance, or contralateral control.
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21
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Ibarrondo O, Lopez-Oceja A, Baeta M, M de Pancorbo M. A Statistical Method to Enhance the Analysis of the Differences Among High-Resolution Melting (HRM) Curves of PCR-Amplified DNA Fragments. J Food Sci 2019; 84:2719-2728. [PMID: 31578715 DOI: 10.1111/1750-3841.14814] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 08/20/2019] [Accepted: 08/22/2019] [Indexed: 11/30/2022]
Abstract
Consistent differences among melting curves of PCR-amplified DNA fragments are treated by normalizing the relative fluorescence units (RFU) and performing a clustering analysis, but statistically significant differences among curves are not usually determined. In the present study, an analysis based on functional data analysis (FDA) was implemented to evaluate the existence of statistically significant differences between normalized RFU curves obtained from PCR-HRM (high-resolution melting) analysis by using ANOVA for functional data. The effectiveness of the FDA method was analyzed with data from a set of samples of eight animal species of interest in food analysis, as well as mixtures of DNA from these species, analyzed by PCR-HRM to differentiate them. The statistical method described in this study has been demonstrated to be a robust and precise tool to discriminate among melting curves derived from HRM analysis. This method has advantages over the current comparison methods. PRACTICAL APPLICATION: As long as food fraud and mislabeling exist, new techniques for species identification are needed. High-resolution melting (HRM) has been shown to be a rapid, reliable and inexpensive species identification method. In the present study, functional data analysis (FDA) was applied to HRM curves of DNA from eight animal species used for food, as well as to mixtures of these species in different proportions. FDA has advantages over the usual methods, providing a deeper statistical analysis and facilitating the data interpretation as shown by the HRM analysis for a clearer comparison among individual species and mixtures of species.
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Affiliation(s)
- Oliver Ibarrondo
- BIOMICS Research Group, Univ. of the Basque Country, UPV/EHU, Vitoria-Gasteiz, 01006, Spain
| | - Andrés Lopez-Oceja
- BIOMICS Research Group, Univ. of the Basque Country, UPV/EHU, Vitoria-Gasteiz, 01006, Spain
| | - Miriam Baeta
- BIOMICS Research Group, Univ. of the Basque Country, UPV/EHU, Vitoria-Gasteiz, 01006, Spain
| | - Marian M de Pancorbo
- BIOMICS Research Group, Univ. of the Basque Country, UPV/EHU, Vitoria-Gasteiz, 01006, Spain
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22
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Causeur D, Sheu CF, Perthame E, Rufini F. A functional generalized F-test for signal detection with applications to event-related potentials significance analysis. Biometrics 2019; 76:246-256. [PMID: 31301147 DOI: 10.1111/biom.13118] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 07/02/2019] [Indexed: 11/28/2022]
Abstract
Motivated by the analysis of complex dependent functional data such as event-related brain potentials (ERP), this paper considers a time-varying coefficient multivariate regression model with fixed-time covariates for testing global hypotheses about population mean curves. Based on a reduced-rank modeling of the time correlation of the stochastic process of pointwise test statistics, a functional generalized F-test is proposed and its asymptotic null distribution is derived. Our analytical results show that the proposed test is more powerful than functional analysis of variance testing methods and competing signal detection procedures for dependent data. Simulation studies confirm such power gain for data with patterns of dependence similar to those observed in ERPs. The new testing procedure is illustrated with an analysis of the ERP data from a study of neural correlates of impulse control.
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Affiliation(s)
- David Causeur
- IRMAR UMR CNRS 6625, Agrocampus Ouest, Rennes Cedex, France
| | - Ching-Fan Sheu
- Institute of Education, National Cheng Kung University, Tainan, Taiwan
| | - Emeline Perthame
- Bioinformatique et Biostatistique, Bioinformatics and Biostatistics Hub C3BI, USR 3756 IP CNRS, Institut Pasteur, Paris, France
| | - Flavia Rufini
- Department of Statistics and Computer Science, Agrocampus Ouest, Rennes Cedex, France
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23
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Special Issue on Biostatistics. Comput Stat Data Anal 2019. [DOI: 10.1016/j.csda.2019.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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24
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A note on repeated measures analysis for functional data. ASTA ADVANCES IN STATISTICAL ANALYSIS 2019. [DOI: 10.1007/s10182-018-00348-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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25
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fdANOVA: an R software package for analysis of variance for univariate and multivariate functional data. Comput Stat 2018. [DOI: 10.1007/s00180-018-0842-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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26
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Guo J, Zhou B, Zhang JT. New Tests for Equality of Several Covariance Functions for Functional Data. J Am Stat Assoc 2018. [DOI: 10.1080/01621459.2018.1483827] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Jia Guo
- College of Economics and Management, Zhejiang University of Technology, Hangzhou, China
- ACEMS and School of Mathematics and Statistics, University of Melbourne, Melbourne, Parkville VIC, Australia
| | - Bu Zhou
- School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China
| | - Jin-Ting Zhang
- Department of Statistics and Applied Probability, National University of Singapore, Singapore
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