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Ma H, Shi Z, Kim M, Liu B, Smith PJ, Liu Y, Wu G. Disentangling sex-dependent effects of APOE on diverse trajectories of cognitive decline in Alzheimer's disease. Neuroimage 2024; 292:120609. [PMID: 38614371 PMCID: PMC11069285 DOI: 10.1016/j.neuroimage.2024.120609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/15/2024] Open
Abstract
Current diagnostic systems for Alzheimer's disease (AD) rely upon clinical signs and symptoms, despite the fact that the multiplicity of clinical symptoms renders various neuropsychological assessments inadequate to reflect the underlying pathophysiological mechanisms. Since putative neuroimaging biomarkers play a crucial role in understanding the etiology of AD, we sought to stratify the diverse relationships between AD biomarkers and cognitive decline in the aging population and uncover risk factors contributing to the diversities in AD. To do so, we capitalized on a large amount of neuroimaging data from the ADNI study to examine the inflection points along the dynamic relationship between cognitive decline trajectories and whole-brain neuroimaging biomarkers, using a state-of-the-art statistical model of change point detection. Our findings indicated that the temporal relationship between AD biomarkers and cognitive decline may differ depending on the synergistic effect of genetic risk and biological sex. Specifically, tauopathy-PET biomarkers exhibit a more dynamic and age-dependent association with Mini-Mental State Examination scores (p<0.05), with inflection points at 72, 78, and 83 years old, compared with amyloid-PET and neurodegeneration (cortical thickness from MRI) biomarkers. In the landscape of health disparities in AD, our analysis indicated that biological sex moderates the rate of cognitive decline associated with APOE4 genotype. Meanwhile, we found that higher education levels may moderate the effect of APOE4, acting as a marker of cognitive reserve.
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Affiliation(s)
- Haixu Ma
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Zhuoyu Shi
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Minjeong Kim
- Department of Computer Science, University of North Carolina at Greensboro, NC 27412, USA
| | - Bin Liu
- Department of Statistics and Data Science, School of Management at Fudan University, Shanghai, 200433, PR China
| | - Patrick J Smith
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Genetics, Department of Biostatistics, University of North Carolina at Chapel Hill, NC 27599, USA.
| | - Guorong Wu
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, NC 27599, USA.
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Karami A, Niaki STA. An Online Support Vector Machine Algorithm for Dynamic Social Network Monitoring. Neural Netw 2024; 171:497-511. [PMID: 38159531 DOI: 10.1016/j.neunet.2023.12.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 11/20/2023] [Accepted: 12/13/2023] [Indexed: 01/03/2024]
Abstract
Online monitoring of social networks offers exciting features for platforms, enabling both technical and behavioral analysis. Numerous studies have explored the adaptation of traditional quality control methods for detecting change points within social networks. However, the current research studies face limitations such as an overreliance on case-based attributes, high computational costs, poor scalability with large networks, and low sensitivity in fast change point detection. This paper proposes a novel algorithm for social network monitoring using One-Class Support Vector Machines (OC-SVMs) to address these limitations. Additionally, using both nodal and network-level attributes makes it versatile for diverse social network applications and effectively detecting network disturbances. The algorithm utilizes a well-defined training data dictionary with an updating procedure for evolutionary networks, enhancing memory and time efficiency by reducing the processing of input data. Extensive numerical experiments are conducted using an EpiCNet model to simulate interactions in an online social network, covering six change scenarios to evaluate the proposed methodology. The results show lower Average Run Length (ARL) and Expected Delay Detection (EDD), demonstrating the superior accuracy and effectiveness of the OC-SVM algorithm compared to alternative methods. Applying OC-SVM to the Enron Email network indicates its capability to identify change points, reflecting the tumultuous timeline that led to Enron's downfall. This further validates the substantial advancement of OC-SVM in social network monitoring and opens doors to broader real-world applications.
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Affiliation(s)
- Arya Karami
- Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran; School of Mathematics and Statistics, University of New South Wales, Sydney, Australia
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3
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Sharma J, Singh O. Changes in agricultural land use and its consequences on crop productivity, diversity, and food availability in an agriculturally developed state of India. Environ Monit Assess 2023; 195:747. [PMID: 37243796 DOI: 10.1007/s10661-023-11222-9] [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: 02/22/2023] [Accepted: 04/04/2023] [Indexed: 05/29/2023]
Abstract
The present study, covering a period of 52 years (1966-2017), explores changes in agricultural land use and its consequences on crop productivity, diversity, and food availability in Haryana, an agriculturally developed state of India. The time series data on different parameters (area, production, yield, etc.) were collected from the secondary sources and analyzed with the help of compound annual growth rate, trend tests (simple linear regression and Mann-Kendall), and change point detection tests such as Pettitt, standard normal homogeneity, Buishand range, and Neumann ratio. Apart from above, the relative share of area and yield to total change in output was determined using decomposition analysis. The results revealed that agricultural land use became intensive and underwent significant alteration with multifold shifting in area from coarse cereals (maize, jowar, and bajra) to fine food grains (wheat and rice). The yield of all crops, especially wheat and rice witnessed a significant increase which subsequently led to an upsurge in their production. However, the production of maize, jowar, and pulses recorded negative growth despite of an increase in their yield. The results also revealed manifold increase in use of modern key inputs during the first two periods (1966-1985), but afterwards input use rate slowed down. Additionally, the decomposition analysis revealed that yield effect remained positive in changing the production of all crops, but area contributed positively only in wheat, rice, cotton, and oilseeds. The major findings of this study imply that the production of crops can be enhanced only through improvement in yield because there is no further scope left for horizontal expansion in cultivable area of the state.
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Affiliation(s)
- Jyoti Sharma
- Department of Geography, Kurukshetra University, Kurukshetra, 136119, Haryana, India
| | - Omvir Singh
- Department of Geography, Kurukshetra University, Kurukshetra, 136119, Haryana, India.
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Cruz M, Ombao H, Gillen DL. A generalized interrupted time series model for assessing complex health care interventions. Stat Biosci 2022; 14:582-610. [PMID: 37234509 PMCID: PMC10208393 DOI: 10.1007/s12561-022-09346-6] [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: 10/02/2020] [Revised: 04/24/2022] [Accepted: 05/06/2022] [Indexed: 10/18/2022]
Abstract
Assessing the impact of complex interventions on measurable health outcomes is a growing concern in health care and health policy. Interrupted time series (ITS) designs borrow from traditional case-crossover designs and function as quasi-experimental methodology able to retrospectively analyze the impact of an intervention. Statistical models used to analyze ITS designs primarily focus on continuous-valued outcomes. We propose the "Generalized Robust ITS" (GRITS) model appropriate for outcomes whose underlying distribution belongs to the exponential family of distributions, thereby expanding the available methodology to adequately model binary and count responses. GRITS formally implements a test for the existence of a change point in discrete ITS. The methodology proposed is able to test for the existence of and estimate the change point, borrow information across units in multi-unit settings, and test for differences in the mean function and correlation pre- and post-intervention. The methodology is illustrated by analyzing patient falls from a hospital that implemented and evaluated a new care delivery model in multiple units.
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Affiliation(s)
- Maricela Cruz
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Hernando Ombao
- Biostatistics Group, King Abdullah University of Science and Technology Thuwal, Saudi Arabia
| | - Daniel L Gillen
- Department of Statistics, University of California Irvine, Irvine, CA, USA
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Talukdar S, Pal S, Naikoo MW, Parvez A, Rahman A. Trend analysis and forecasting of streamflow using random forest in the Punarbhaba River basin. Environ Monit Assess 2022; 195:153. [PMID: 36435930 DOI: 10.1007/s10661-022-10696-3] [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: 03/09/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
Streamflow rate changes due to damming are hydro-ecologically sensitive in present and future times. Very less studies have done an investigation of the damming effect on the streamflow along with future forecasting, which can be the solution for the existing problems. Therefore, this study aims to use the Pettitt test as well as standard normal homogeneity test (SNHT) to discover trends in streamflow with the future situation in the Punarbhaba River in Indo-Bangladesh from 1978 to 2017. Trend was spotted using Mann-Kendall test, Spearman's rank correlation approach, innovative trend analysis, and a linear regression model. The current work additionally uses advanced machine learning techniques like random forest (RF) to estimate flow regimes using historical time series data. 1992 appears to be a yard mark in this continuum of time series datasets, indicating a significant transformation in the streamflow regime. The MK test as well as Spearman's rho was used to find a significant negative trend for the average (-0.57), maximum (-0.62), and minimum (-0.48) flow regimes. The consistency of the flow regime has been losing consistency, and the variability of flow regime has increased from 2.1 to 6.7% of the average water level, 1.5 to 6.5% of the maximum streamflow, and 3.1 to 5.8% of the minimum streamflow in the post-change point phase. The forecast trend using random forest for streamflow up to 2030 are negative for all four seasons with a flow volume likely to be reduced by 0.67% to-5.23%. Annual and monthly streamflows revealed very negative tendencies, according to the conclusions of unique trend analysis. Flow declination of this magnitude impacts downstream habitat and environment. According to future estimates, the seasonal flow will decrease. Furthermore, the outcome of this research will give a wealth of data for river management and other places with comparable environment.
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Affiliation(s)
- Swapan Talukdar
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Swades Pal
- Department of Geography, University of Gour Banga, Mokdumpur, Malda, West Bengal, 732103, India
| | - Mohd Waseem Naikoo
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Ayesha Parvez
- School of Engineering, Engineering Service Rd, Henry Samueli, University of California, Irvine, CA, USA
| | - Atiqur Rahman
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India.
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Boubaker S, Liu Z, Zhang Y. Forecasting oil commodity spot price in a data-rich environment. Ann Oper Res 2022:1-18. [PMID: 36217322 PMCID: PMC9534472 DOI: 10.1007/s10479-022-05004-8] [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] [Figures] [Subscribe] [Scholar Register] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Statistical properties that vary with time represent a challenge for time series forecasting. This paper proposes a change point-adaptive-RNN (CP-ADARNN) framework to predict crude oil prices with high-dimensional monthly variables. We first detect the structural breaks in predictors using the change point technique, and subsequently train a prediction model based on ADARNN. Using 310 economic series as exogenous factors from 1993 to 2021 to predict the monthly return on the WTI crude oil real price, CP-ADARNN outperforms competing benchmarks by 12.5% in terms of the root mean square error and achieves a correlation of 0.706 between predicted and actual returns. Furthermore, the superiority of CP-ADARNN is robust for Brent oil price as well as during the COVID-19 pandemic. The findings of this paper provide new insights for investors and researchers in the oil market.
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Affiliation(s)
- Sabri Boubaker
- EM Normandie Business School, Métis Lab, Paris, France
- International School, Vietnam National University, Hanoi, Vietnam
- Swansea University, Swansea, United Kingdom
| | - Zhenya Liu
- School of Finance, Renmin University of China, Beijing, China
- China Financial Policy Research Center, Renmin University of China, Beijing, China
- CERGAM, Aix-Marseille University, Aix-en-Provence, France
| | - Yifan Zhang
- School of Finance, Renmin University of China, Beijing, China
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Novakovic A, Marshall AH. The CP-ABM approach for modelling COVID-19 infection dynamics and quantifying the effects of non-pharmaceutical interventions. Pattern Recognit 2022; 130:108790. [PMID: 35601479 PMCID: PMC9107333 DOI: 10.1016/j.patcog.2022.108790] [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] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/04/2022] [Accepted: 05/11/2022] [Indexed: 05/16/2023]
Abstract
The motivation for this research is to develop an approach that reliably captures the disease dynamics of COVID-19 for an entire population in order to identify the key events driving change in the epidemic through accurate estimation of daily COVID-19 cases. This has been achieved through the new CP-ABM approach which uniquely incorporates Change Point detection into an Agent Based Model taking advantage of genetic algorithms for calibration and an efficient infection centric procedure for computational efficiency. The CP-ABM is applied to the Northern Ireland population where it successfully captures patterns in COVID-19 infection dynamics over both waves of the pandemic and quantifies the significant effects of non-pharmaceutical interventions (NPI) on a national level for lockdowns and mask wearing. To our knowledge, there is no other approach to date that has captured NPI effectiveness and infection spreading dynamics for both waves of the COVID-19 pandemic for an entire country population.
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Affiliation(s)
- Aleksandar Novakovic
- School of Mathematics and Physics, Queen's University Belfast, University Road, Belfast, BT7 1NN, Northern Ireland, United Kingdom
- Joint Research Centre in AI for Health and Wellness, Faculty of Business and IT, Ontario Tech University, 2000 Simcoe Street North, Oshawa, Ontario L1G 0C5, Canada
| | - Adele H Marshall
- School of Mathematics and Physics, Queen's University Belfast, University Road, Belfast, BT7 1NN, Northern Ireland, United Kingdom
- Joint Research Centre in AI for Health and Wellness, Faculty of Business and IT, Ontario Tech University, 2000 Simcoe Street North, Oshawa, Ontario L1G 0C5, Canada
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He Y, Burghardt KA, Lerman K. Leveraging change point detection to discover natural experiments in data. EPJ Data Sci 2022; 11:49. [PMID: 36090462 PMCID: PMC9440658 DOI: 10.1140/epjds/s13688-022-00361-7] [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] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 08/09/2022] [Indexed: 06/15/2023]
Abstract
Change point detection has many practical applications, from anomaly detection in data to scene changes in robotics; however, finding changes in high dimensional data is an ongoing challenge. We describe a self-training model-agnostic framework to detect changes in arbitrarily complex data. The method consists of two steps. First, it labels data as before or after a candidate change point and trains a classifier to predict these labels. The accuracy of this classifier varies for different candidate change points. By modeling the accuracy change we can infer the true change point and fraction of data affected by the change (a proxy for detection confidence). We demonstrate how our framework can achieve low bias over a wide range of conditions and detect changes in high dimensional, noisy data more accurately than alternative methods. We use the framework to identify changes in real-world data and measure their effects using regression discontinuity designs, thereby uncovering potential natural experiments, such as the effect of pandemic lockdowns on air pollution and the effect of policy changes on performance and persistence in a learning platform. Our method opens new avenues for data-driven discovery due to its flexibility, accuracy and robustness in identifying changes in data.
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Affiliation(s)
- Yuzi He
- Information Sciences Institute, University of Southern California, Marina del Rey, CA USA
- Department of Physics and Astronomy, University of Southern California, Los Angeles, CA USA
| | - Keith A. Burghardt
- Information Sciences Institute, University of Southern California, Marina del Rey, CA USA
| | - Kristina Lerman
- Information Sciences Institute, University of Southern California, Marina del Rey, CA USA
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Chen X, Wang H, Lyu W, Xu R. The Mann-Kendall-Sneyers test to identify the change points of COVID-19 time series in the United States. BMC Med Res Methodol 2022; 22:233. [PMID: 36042407 PMCID: PMC9424808 DOI: 10.1186/s12874-022-01714-6] [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: 09/04/2021] [Accepted: 08/19/2022] [Indexed: 11/22/2022] Open
Abstract
Background One critical variable in the time series analysis is the change point, which is the point where an abrupt change occurs in chronologically ordered observations. Existing parametric models for change point detection, such as the linear regression model and the Bayesian model, require that observations are normally distributed and that the trend line cannot have extreme variability. To overcome the limitations of the parametric model, we apply a nonparametric method, the Mann-Kendall-Sneyers (MKS) test, to change point detection for the state-level COVID-19 case time series data of the United States in the early outbreak of the pandemic. Methods The MKS test is implemented for change point detection. The forward sequence and the backward sequence are calculated based on the new weekly cases between March 22, 2020 and January 31, 2021 for each of the 50 states. Points of intersection between the two sequences falling within the 95% confidence intervals are identified as the change points. The results are compared with two other change point detection methods, the pruned exact linear time (PELT) method and the regression-based method. Also, an open-access tool by Microsoft Excel is developed to facilitate the model implementation. Results By applying the MKS test to COVID-19 cases in the United States, we have identified that 30 states (60.0%) have at least one change point within the 95% confidence intervals. Of these states, 26 states have one change point, 4 states (i.e., LA, OH, VA, and WA) have two change points, and one state (GA) has three change points. Additionally, most downward changes appear in the Northeastern states (e.g., CT, MA, NJ, NY) at the first development stage (March 23 through May 31, 2020); most upward changes appear in the Western states (e.g., AZ, CA, CO, NM, WA, WY) and the Midwestern states (e.g., IL, IN, MI, MN, OH, WI) at the third development stage (November 19, 2020 through January 31, 2021). Conclusions This study is among the first to explore the potential of the MKS test applied for change point detection of COVID-19 cases. The MKS test is characterized by several advantages, including high computational efficiency, easy implementation, the ability to identify the change of direction, and no assumption for data distribution. However, due to its conservative nature in change point detection and moderate agreement with other methods, we recommend using the MKS test primarily for initial pattern identification and data pruning, especially in large data. With modification, the method can be further applied to other health data, such as injuries, disabilities, and mortalities.
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Affiliation(s)
- Xiang Chen
- Department of Geography, University of Connecticut, Storrs, CT, 06269, USA. .,Institute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut, Storrs, CT, 06269, USA.
| | - Hui Wang
- Department of Geosciences, Mississippi State University, Mississippi State, MS, 39762, USA
| | - Weixuan Lyu
- Department of Geography, University of Connecticut, Storrs, CT, 06269, USA
| | - Ran Xu
- Institute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut, Storrs, CT, 06269, USA.,Department of Allied Health Sciences, University of Connecticut, Storrs, CT, 06269, USA
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Sieg M, Sciesielski LK, Kirschner KM, Kruppa J. Change point detection for clustered expression data. BMC Genomics 2022; 23:491. [PMID: 35794534 PMCID: PMC9261071 DOI: 10.1186/s12864-022-08680-9] [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: 02/16/2022] [Accepted: 06/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To detect changes in biological processes, samples are often studied at several time points. We examined expression data measured at different developmental stages, or more broadly, historical data. Hence, the main assumption of our proposed methodology was the independence between the examined samples over time. In addition, however, the examinations were clustered at each time point by measuring littermates from relatively few mother mice at each developmental stage. As each examination was lethal, we had an independent data structure over the entire history, but a dependent data structure at a particular time point. Over the course of these historical data, we wanted to identify abrupt changes in the parameter of interest - change points. RESULTS In this study, we demonstrated the application of generalized hypothesis testing using a linear mixed effects model as a possible method to detect change points. The coefficients from the linear mixed model were used in multiple contrast tests and the effect estimates were visualized with their respective simultaneous confidence intervals. The latter were used to determine the change point(s). In small simulation studies, we modelled different courses with abrupt changes and compared the influence of different contrast matrices. We found two contrasts, both capable of answering different research questions in change point detection: The Sequen contrast to detect individual change points and the McDermott contrast to find change points due to overall progression. We provide the R code for direct use with provided examples. The applicability of those tests for real experimental data was shown with in-vivo data from a preclinical study. CONCLUSION Simultaneous confidence intervals estimated by multiple contrast tests using the model fit from a linear mixed model were capable to determine change points in clustered expression data. The confidence intervals directly delivered interpretable effect estimates representing the strength of the potential change point. Hence, scientists can define biologically relevant threshold of effect strength depending on their research question. We found two rarely used contrasts best fitted for detection of a possible change point: the Sequen and McDermott contrasts.
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Affiliation(s)
- Miriam Sieg
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Biometry and Clinical Epidemiology, Charitéplatz 1, Berlin, 10117, Germany.
| | - Lina Katrin Sciesielski
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Neonatology, Charitéplatz 1, Berlin, 10117, Germany
| | - Karin Michaela Kirschner
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Translational Physiology, Charitéplatz 1, Berlin, 10117, Germany
| | - Jochen Kruppa
- Hochschule Osnabrück - University of Applied Sciences, Albrechtstr. 30, Osnabrück, 49076, Germany
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Lamooki SR, Hajifar S, Kang J, Sun H, Megahed FM, Cavuoto LA. A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor. Appl Ergon 2022; 102:103732. [PMID: 35287084 DOI: 10.1016/j.apergo.2022.103732] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 02/18/2022] [Accepted: 02/24/2022] [Indexed: 06/14/2023]
Abstract
Existing ergonomic risk assessment tools require monitoring of multiple risk factors. To eliminate the direct observation, we investigated the effectiveness of an end-to-end framework that works with the data from a single wearable sensor. The framework is used to identify the performed task as the major contextual risk factor, and then estimate the task duration and number of repetitions as two main indicators of task intensity. For evaluation of the framework, we recruited 37 participants to complete 10 simulated work tasks in a laboratory setting. In testing, we achieved an average accuracy of 92% for task identification, 7.3% error in estimation of task duration, and 7.1% error for counting the number of task repetitions. Moreover, we showed the utility of the framework outputs in two ergonomic tools to estimate the risk of injury. Overall, we indicated the feasibility of using data from wearable sensors to automate the ergonomic risk assessment in workplaces.
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Affiliation(s)
- Saeb Ragani Lamooki
- Department of Mechanical Engineering, University at Buffalo, Buffalo, NY, 14260, USA.
| | - Sahand Hajifar
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, 14260, USA.
| | - Jiyeon Kang
- Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, 14260, USA.
| | - Hongyue Sun
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, 14260, USA.
| | - Fadel M Megahed
- Farmer School of Business, Miami University, Oxford, OH, 45056, USA.
| | - Lora A Cavuoto
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, 14260, USA.
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Jiang F, Jin H, Gao Y, Xie X, Cummings J, Raj A, Nagarajan S. Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging. Neuroimage 2022; 254:119131. [PMID: 35337963 PMCID: PMC9942947 DOI: 10.1016/j.neuroimage.2022.119131] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [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: 10/13/2021] [Revised: 02/04/2022] [Accepted: 03/21/2022] [Indexed: 01/26/2023] Open
Abstract
Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods that are inherently non-adaptive, have various limitations such as high-dimensionality, an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multiply functional imaging modalities. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between a low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. TVDN is applicable to multiple modalities of functional neuroimaging such as fMRI and MEG/EEG. The estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.
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Affiliation(s)
- Fei Jiang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA.
| | - Huaqing Jin
- Department of Statistics and Actuarial Science, the University of Hong Kong, CN, Hong Kong
| | - Yijing Gao
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA
| | - Xihe Xie
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA
| | - Jennifer Cummings
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
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Yokoyama H, Kitajo K. Detecting changes in dynamical structures in synchronous neural oscillations using probabilistic inference. Neuroimage 2022; 252:119052. [PMID: 35247547 DOI: 10.1016/j.neuroimage.2022.119052] [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] [Received: 07/28/2021] [Revised: 12/06/2021] [Accepted: 03/01/2022] [Indexed: 11/28/2022] Open
Abstract
Recent neuroscience studies have suggested that cognitive functions and learning capacity are reflected in the time-evolving dynamics of brain networks. However, an efficient method to detect changes in dynamical brain structures using neural data has yet to be established. To address this issue, we developed a new model-based approach to detect change points in dynamical network structures by combining the model-based network estimation with a phase-coupled oscillator model and sequential Bayesian inference. By giving the model parameter as the prior distribution, applying Bayesian inference allows the extent of temporal changes in dynamic brain networks to be quantified by comparing the prior distribution with the posterior distribution using information theoretical criteria. For this, we used the Kullback-Leibler divergence as an index of such changes. To validate our method, we applied it to numerical data and electroencephalography data. As a result, we confirmed that the Kullback-Leibler divergence only increased when changes in dynamical network structures occurred. Our proposed method successfully estimated both directed network couplings and change points of dynamical structures in the numerical and electroencephalography data. These results suggest that our proposed method can reveal the neural basis of dynamic brain networks.
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Affiliation(s)
- Hiroshi Yokoyama
- Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Aichi, 444-8585, Japan; Department of Physiological Sciences, School of Life Science, Graduate University for Advanced Studies (SOKENDAI), Okazaki, Aichi, 444-8585, Japan.
| | - Keiichi Kitajo
- Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki, Aichi, 444-8585, Japan; Department of Physiological Sciences, School of Life Science, Graduate University for Advanced Studies (SOKENDAI), Okazaki, Aichi, 444-8585, Japan.
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14
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Abstract
Change point analysis aims to detect structural changes in a data sequence. It has always been an active research area since it was introduced in the 1950s. In modern statistical applications, however, high-throughput data with increasing dimensions are ubiquitous in fields ranging from economics, finance to genetics and engineering. For those problems, the earlier works are typically no longer applicable. As a result, the problem of testing a change point for high dimensional data sequences has been an important yet challenging task. In this paper, we first focus on models for at most one change point, and review recent state-of-art techniques for change point testing of high dimensional mean vectors and compare their theoretical properties. Based on that, we provide a survey of some extensions to general high dimensional parameters beyond mean vectors as well as strategies for testing multiple change points in high dimensions. Finally, we discuss some open problems for possible future research directions.
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Affiliation(s)
- Bin Liu
- School of Management, Fudan University, Shanghai, 200433, China
| | - Xinsheng Zhang
- School of Management, Fudan University, Shanghai, 200433, China
| | - Yufeng Liu
- Department of Statistics and Operations Research, Department of Genetics, and Department of Biostatistics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, U.S.A.,Corresponding author. . (Yufeng Liu)
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15
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Bian Z, Zuo F, Gao J, Chen Y, Pavuluri Venkata SSC, Duran Bernardes S, Ozbay K, Ban XJ, Wang J. Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle. Transp Res Part A Policy Pract 2021; 145:269-283. [PMID: 36569966 PMCID: PMC9759401 DOI: 10.1016/j.tra.2021.01.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 12/12/2020] [Accepted: 01/24/2021] [Indexed: 05/04/2023]
Abstract
The unprecedented challenges caused by the COVID-19 pandemic demand timely action. However, due to the complex nature of policy making, a lag may exist between the time a problem is recognized and the time a policy has its impact on a system. To understand this lag and to expedite decision making, this study proposes a change point detection framework using likelihood ratio, regression structure and a Bayesian change point detection method. The objective is to quantify the time lag effect reflected in transportation systems when authorities take action in response to the COVID-19 pandemic. Using travel patterns as an indicator of policy effectiveness, the length of policy lag and magnitude of policy impacts on the road system, mass transit, and micromobility are investigated through the case studies of New York City (NYC), and Seattle-two U.S. cities significantly affected by COVID-19. The quantitative findings show that the National declaration of emergency had no policy lag while stay-at-home and reopening policies had a lead effect on mobility. The magnitude of impact largely depended on the land use and sociodemographic characteristics of the area, as well as the type of transportation system.
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Affiliation(s)
- Zilin Bian
- Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, 6 MetroTech Center, 4th Floor, Brooklyn, NY 11201, USA
| | - Fan Zuo
- Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, 6 MetroTech Center, 4th Floor, Brooklyn, NY 11201, USA
| | - Jingqin Gao
- Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, 6 MetroTech Center, 4th Floor, Brooklyn, NY 11201, USA
| | - Yanyan Chen
- Department of Civil and Environmental Engineering, University of Washington, 121G More Hall, Seattle, WA 98195, USA
| | | | - Suzana Duran Bernardes
- Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, 6 MetroTech Center, 4th Floor, Brooklyn, NY 11201, USA
| | - Kaan Ozbay
- Department of Civil and Urban Engineering & Center for Urban Science and Progress (CUSP), Tandon School of Engineering, New York University, 6 MetroTech Center, 4th Floor, Brooklyn, NY 11201, USA
| | - Xuegang Jeff Ban
- Department of Civil and Environmental Engineering, University of Washington, 121G More Hall, Seattle, WA 98195, USA
| | - Jingxing Wang
- Department of Civil and Environmental Engineering, University of Washington, 121G More Hall, Seattle, WA 98195, USA
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16
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Ahmadi SH, Javanbakht Z. Assessing the physical and empirical reference evapotranspiration (ETo) models and time series analyses of the influencing weather variables on ETo in a semi-arid area. J Environ Manage 2020; 276:111278. [PMID: 32906072 DOI: 10.1016/j.jenvman.2020.111278] [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: 03/21/2020] [Revised: 06/10/2020] [Accepted: 08/19/2020] [Indexed: 06/11/2023]
Abstract
Accurate estimation of irrigation requirement is necessary for conserving the quantity and quality of water resources. Generally, irrigation requirement is estimated by calculating reference evapotranspiration (ETo). In this study, radiation-based, temperature-based, and combination-based ETo models were assessed based on the monthly averaged weather data between 1987 and 2017. The combination-based Standardized ASCE Penman-Monteith (ASCE PM Std.) was selected as the benchmark model due to its global acceptance and accuracy. Results showed that the combination-based Penman models were ranked as the top models among the other ETo models. However, if some weather variables are missing, the Priestly-Taylor model followed by the Makkink and Turc models (all as radiation-based models) were the next recommended ETo models.The performance of the temperature-based models and some other radiation-based models (FAO24 Radiation and Jensen-Haise) were not satisfactory. Trend and change point detection analyses on air temperature, relative humidity, and wind speed showed that the study area is getting warmer and drier, which indicate that ETo would increase in the study area. Therefore, it is recommended to use the ETo models that consider the majority of the weather variables that influence ETo. The results of this study could serve as a reliable guide for selection of appropriate ETo models to protect water resources in arid and semi-arid areas. .
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Affiliation(s)
- Seyed Hamid Ahmadi
- Water Engineering Department, School of Agriculture, Shiraz University, Shiraz, Iran; Drought Research Center, Shiraz University, Shiraz, Iran.
| | - Zahra Javanbakht
- Water Engineering Department, School of Agriculture, Shiraz University, Shiraz, Iran
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17
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Sakizadeh M, Chua LHC. Environmental impact of Karkheh Dam in the southern part of Iran on groundwater quality by intervention and trend analysis. Environ Monit Assess 2020; 192:683. [PMID: 33026556 DOI: 10.1007/s10661-020-08629-z] [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: 03/29/2019] [Accepted: 09/24/2020] [Indexed: 06/11/2023]
Abstract
The main objective of this research was to investigate the impact of the construction of Karkheh Dam in 2001 (referred to as the intervention time), on groundwater quality. The time series of total dissolved solids (TDS) and other water quality data including potassium (K+), sodium (Na+), magnesium (Mg2+), calcium (Ca2+), bicarbonate (HCO3-), sulfate (SO42-), and chloride (Cl-) for the period between 1996 and 2012 were analyzed. The magnitude of the trend by Sen's slope estimator for HCO3-, SO42-, and TDS was 0.005, - 0.02 and - 3.04, where a decline expected for SO42- and TDS, whereas for HCO3-, an increase was expected. According to the Pettitt's test, the mean of TDS decreased from 2306.9 mg/l between 1996 and 2002 to 797.2 mg/l between 2002 and 2012. During this time, the standard deviation of TDS declined from 2187.1 to 132.0 mg/l. The results of change point detection by the Pruned Exact Linear Time (PELT) algorithm were consistent with that of Pettitt's test providing confirmation that a change point in Ca2+, Mg2+, SO42-, and TDS time series data occurred in 2002.The findings from intervention analysis using the Bayesian structural time series (BSTS) technique showed that TDS concentration during the post-intervention period had an average value of 1127 mg/l compared with 1972 mg/l, before the dam construction. The time series of TDS demonstrated a decrease of about 43% following the intervention time.
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Affiliation(s)
- Mohammad Sakizadeh
- Environmental Engineering and Management Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
- Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
| | - Lloyd H C Chua
- School of Engineering, Faculty of Science Engineering & Built Environment, Deakin University, 75 Pigdons Road, Waurn Ponds, VIC, 3220, Australia
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18
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Mancho-Fora N, Montalà-Flaquer M, Farràs-Permanyer L, Zarabozo-Hurtado D, Gallardo-Moreno GB, Gudayol-Farré E, Peró-Cebollero M, Guàrdia-Olmos J. Network change point detection in resting-state functional connectivity dynamics of mild cognitive impairment patients. Int J Clin Health Psychol 2020; 20:200-212. [PMID: 32994793 PMCID: PMC7501449 DOI: 10.1016/j.ijchp.2020.07.005] [Citation(s) in RCA: 2] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 07/16/2020] [Indexed: 10/27/2022] Open
Abstract
Background/Objective: This study aims to characterize the differences on the short-term temporal network dynamics of the undirected and weighted whole-brain functional connectivity between healthy aging individuals and people with mild cognitive impairment (MCI). The Network Change Point Detection algorithm was applied to identify the significant change points in the resting-state fMRI register, and we analyzed the fluctuations in the topological properties of the sub-networks between significant change points. Method: Ten MCI patients matched by gender and age in 1:1 ratio to healthy controls screened during patient recruitment. A neuropsychological evaluation was done to both groups as well as functional magnetic images were obtained with a Philips 3.0T. All the images were preprocessed and statistically analyzed through dynamic point estimation tools. Results: No statistically significant differences were found between groups in the number of significant change points in the functional connectivity networks. However, an interaction effect of age and state was detected on the intra-participant variability of the network strength. Conclusions: The progression of states was associated to higher variability in the patient's group. Additionally, higher performance in the prospective and retrospective memory scale was associated with higher median network strength.
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Affiliation(s)
| | - Marc Montalà-Flaquer
- Facultat de Psicologia, Universitat de Barcelona, Spain.,UB Institute of Complex Systems, Universitat de Barcelona, Spain
| | | | | | | | - Esteban Gudayol-Farré
- Facultad de Psicología, Universidad Miochoacana San Nicolás de Hidalgo, Morelia, Mexico
| | - Maribel Peró-Cebollero
- Facultat de Psicologia, Universitat de Barcelona, Spain.,UB Institute of Complex Systems, Universitat de Barcelona, Spain.,Institute of Neuroscience, Universitat de Barcelona, Spain
| | - Joan Guàrdia-Olmos
- Facultat de Psicologia, Universitat de Barcelona, Spain.,UB Institute of Complex Systems, Universitat de Barcelona, Spain.,Institute of Neuroscience, Universitat de Barcelona, Spain
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19
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Masnadi-Shirazi M, Maurya MR, Pao G, Ke E, Verma IM, Subramaniam S. Time varying causal network reconstruction of a mouse cell cycle. BMC Bioinformatics 2019; 20:294. [PMID: 31142274 PMCID: PMC6542064 DOI: 10.1186/s12859-019-2895-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [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: 05/01/2019] [Accepted: 05/13/2019] [Indexed: 12/21/2022] Open
Abstract
Background Biochemical networks are often described through static or time-averaged measurements of the component macromolecules. Temporal variation in these components plays an important role in both describing the dynamical nature of the network as well as providing insights into causal mechanisms. Few methods exist, specifically for systems with many variables, for analyzing time series data to identify distinct temporal regimes and the corresponding time-varying causal networks and mechanisms. Results In this study, we use well-constructed temporal transcriptional measurements in a mammalian cell during a cell cycle, to identify dynamical networks and mechanisms describing the cell cycle. The methods we have used and developed in part deal with Granger causality, Vector Autoregression, Estimation Stability with Cross Validation and a nonparametric change point detection algorithm that enable estimating temporally evolving directed networks that provide a comprehensive picture of the crosstalk among different molecular components. We applied our approach to RNA-seq time-course data spanning nearly two cell cycles from Mouse Embryonic Fibroblast (MEF) primary cells. The change-point detection algorithm is able to extract precise information on the duration and timing of cell cycle phases. Using Least Absolute Shrinkage and Selection Operator (LASSO) and Estimation Stability with Cross Validation (ES-CV), we were able to, without any prior biological knowledge, extract information on the phase-specific causal interaction of cell cycle genes, as well as temporal interdependencies of biological mechanisms through a complete cell cycle. Conclusions The temporal dependence of cellular components we provide in our model goes beyond what is known in the literature. Furthermore, our inference of dynamic interplay of multiple intracellular mechanisms and their temporal dependence on one another can be used to predict time-varying cellular responses, and provide insight on the design of precise experiments for modulating the regulation of the cell cycle. Electronic supplementary material The online version of this article (10.1186/s12859-019-2895-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Maryam Masnadi-Shirazi
- Department of Electrical and Computer Engineering and Bioengineering, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Mano R Maurya
- Department of Bioengineering and San Diego Supercomputer center, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Gerald Pao
- Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Eugene Ke
- Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Inder M Verma
- Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Shankar Subramaniam
- Department of Bioengineering, Departments of Computer Science and Engineering, Cellular and Molecular Medicine, and the Graduate Program in Bioinformatics, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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20
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Xiao Z, Hu S, Zhang Q, Tian X, Chen Y, Wang J, Chen Z. Ensembles of change-point detectors: implications for real-time BMI applications. J Comput Neurosci 2018; 46:107-124. [PMID: 30206733 DOI: 10.1007/s10827-018-0694-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [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: 11/26/2017] [Revised: 08/22/2018] [Accepted: 08/30/2018] [Indexed: 12/29/2022]
Abstract
Brain-machine interfaces (BMIs) have been widely used to study basic and translational neuroscience questions. In real-time closed-loop neuroscience experiments, many practical issues arise, such as trial-by-trial variability, and spike sorting noise or multi-unit activity. In this paper, we propose a new framework for change-point detection based on ensembles of independent detectors in the context of BMI application for detecting acute pain signals. Motivated from ensemble learning, our proposed "ensembles of change-point detectors" (ECPDs) integrate multiple decisions from independent detectors, which may be derived based on data recorded from different trials, data recorded from different brain regions, data of different modalities, or models derived from different learning methods. By integrating multiple sources of information, the ECPDs aim to improve detection accuracy (in terms of true positive and true negative rates) and achieve an optimal trade-off of sensitivity and specificity. We validate our method using computer simulations and experimental recordings from freely behaving rats. Our results have shown superior and robust performance of ECPDS in detecting the onset of acute pain signals based on neuronal population spike activity (or combined with local field potentials) recorded from single or multiple brain regions.
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Affiliation(s)
- Zhengdong Xiao
- Department of Instrument Science and Technology, Zhejiang University, Hangzhou, Zhejiang, 310027, China.,Department of Psychiatry, New York University School of Medicine, New York, NY, 10016, USA
| | - Sile Hu
- Department of Instrument Science and Technology, Zhejiang University, Hangzhou, Zhejiang, 310027, China.,Department of Psychiatry, New York University School of Medicine, New York, NY, 10016, USA
| | - Qiaosheng Zhang
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University School of Medicine, New York, NY, 10016, USA
| | - Xiang Tian
- Department of Instrument Science and Technology, Zhejiang University, Hangzhou, Zhejiang, 310027, China.,Zhejiang Provincial Key Laboratory for Network Multimedia Technologies, Key Laboratory for Biomedical Engineering of Ministry of Education of China, Zhejiang University, Hangzhou, Zhejiang, 310027, China
| | - Yaowu Chen
- Department of Instrument Science and Technology, Zhejiang University, Hangzhou, Zhejiang, 310027, China.,Zhejiang Provincial Key Laboratory for Network Multimedia Technologies, Key Laboratory for Biomedical Engineering of Ministry of Education of China, Zhejiang University, Hangzhou, Zhejiang, 310027, China
| | - Jing Wang
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University School of Medicine, New York, NY, 10016, USA.,Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, 10016, USA
| | - Zhe Chen
- Department of Psychiatry, New York University School of Medicine, New York, NY, 10016, USA. .,Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, 10016, USA.
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21
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Li H, Fan Y. Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI. Med Image Comput Comput Assist Interv 2018; 11072:232-239. [PMID: 30320310 DOI: 10.1007/978-3-030-00931-1_27] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Dynamic functional connectivity analysis provides valuable information for understanding brain functional activity underlying different cognitive processes. Besides sliding window based approaches, a variety of methods have been developed to automatically split the entire functional MRI scan into segments by detecting change points of functional signals to facilitate better characterization of temporally dynamic functional connectivity patterns. However, these methods are based on certain assumptions for the functional signals, such as Gaussian distribution, which are not necessarily suitable for the fMRI data. In this study, we develop a deep learning based framework for adaptively detecting temporally dynamic functional state transitions in a data-driven way without any explicit modeling assumptions, by leveraging recent advances in recurrent neural networks (RNNs) for sequence modeling. Particularly, we solve this problem in an anomaly detection framework with an assumption that the functional profile of one single time point could be reliably predicted based on its preceding profiles within a stable functional state, while large prediction errors would occur around change points of functional states. We evaluate the proposed method using both task and resting-state fMRI data obtained from the human connectome project and experimental results have demonstrated that the proposed change point detection method could effectively identify change points between different task events and split the resting-state fMRI into segments with distinct functional connectivity patterns.
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Affiliation(s)
- Hongming Li
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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22
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Cabrieto J, Tuerlinckx F, Kuppens P, Grassmann M, Ceulemans E. Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods. Behav Res Methods 2017; 49:988-1005. [PMID: 27383753 DOI: 10.3758/s13428-016-0754-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP. Since these methods use different statistical approaches, two issues need to be tackled. First, applied researchers may find it hard to appraise the differences between the methods. Second, a direct comparison of the relative performance of all these methods for capturing change points signaling correlation changes is still lacking. Therefore, we present the basic principles behind DeCon, E-divisive, Multirank, and KCP and the corresponding algorithms, to make them more accessible to readers. We further compared their performance through extensive simulations using the settings of Bulteel et al. (Biological Psychology, 98 (1), 29-42, 2014) implying changes in mean and in correlation structure and those of Matteson and James (Journal of the American Statistical Association, 109 (505), 334-345, 2014) implying different numbers of (noise) variables. KCP emerged as the best method in almost all settings. However, in case of more than two noise variables, only DeCon performed adequately in detecting correlation changes.
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23
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Huang H, Thompson W, Paulus MP. Computational Dysfunctions in Anxiety: Failure to Differentiate Signal From Noise. Biol Psychiatry 2017; 82:440-446. [PMID: 28838468 PMCID: PMC5576575 DOI: 10.1016/j.biopsych.2017.07.007] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 07/13/2017] [Accepted: 07/13/2017] [Indexed: 01/04/2023]
Abstract
BACKGROUND Differentiating whether an action leads to an outcome by chance or by an underlying statistical regularity that signals environmental change profoundly affects adaptive behavior. Previous studies have shown that anxious individuals may not appropriately differentiate between these situations. This investigation aims to precisely quantify the process deficit in anxious individuals and determine the degree to which these process dysfunctions are specific to anxiety. METHODS One hundred twenty-two subjects recruited as part of an ongoing large clinical population study completed a change point detection task. Reinforcement learning models were used to explicate observed behavioral differences in low anxiety (Overall Anxiety Severity and Impairment Scale score ≤ 8) and high anxiety (Overall Anxiety Severity and Impairment Scale score ≥ 9) groups. RESULTS High anxiety individuals used a suboptimal decision strategy characterized by a higher lose-shift rate. Computational models and simulations revealed that this difference was related to a higher base learning rate. These findings are better explained in a context-dependent reinforcement learning model. CONCLUSIONS Anxious subjects' exaggerated response to uncertainty leads to a suboptimal decision strategy that makes it difficult for these individuals to determine whether an action is associated with an outcome by chance or by some statistical regularity. These findings have important implications for developing new behavioral intervention strategies using learning models.
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Affiliation(s)
- He Huang
- Laureate Institute for Brain Research, Tulsa, OK
| | - Wesley Thompson
- Laureate Institute for Brain Research, Tulsa, OK,Psychiatry, University of California San Diego, La Jolla, CA
| | - Martin P. Paulus
- Laureate Institute for Brain Research, Tulsa, OK,Psychiatry, University of California San Diego, La Jolla, CA
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24
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Messer M, Costa KM, Roeper J, Schneider G. Multi-scale detection of rate changes in spike trains with weak dependencies. J Comput Neurosci 2016; 42:187-201. [PMID: 28025784 DOI: 10.1007/s10827-016-0635-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [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: 07/17/2016] [Revised: 11/23/2016] [Accepted: 12/07/2016] [Indexed: 11/28/2022]
Abstract
The statistical analysis of neuronal spike trains by models of point processes often relies on the assumption of constant process parameters. However, it is a well-known problem that the parameters of empirical spike trains can be highly variable, such as for example the firing rate. In order to test the null hypothesis of a constant rate and to estimate the change points, a Multiple Filter Test (MFT) and a corresponding algorithm (MFA) have been proposed that can be applied under the assumption of independent inter spike intervals (ISIs). As empirical spike trains often show weak dependencies in the correlation structure of ISIs, we extend the MFT here to point processes associated with short range dependencies. By specifically estimating serial dependencies in the test statistic, we show that the new MFT can be applied to a variety of empirical firing patterns, including positive and negative serial correlations as well as tonic and bursty firing. The new MFT is applied to a data set of empirical spike trains with serial correlations, and simulations show improved performance against methods that assume independence. In case of positive correlations, our new MFT is necessary to reduce the number of false positives, which can be highly enhanced when falsely assuming independence. For the frequent case of negative correlations, the new MFT shows an improved detection probability of change points and thus, also a higher potential of signal extraction from noisy spike trains.
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Affiliation(s)
- Michael Messer
- Institute of Mathematics, Johann Wolfgang Goethe University Frankfurt, Frankfurt, Germany
| | - Kauê M Costa
- Institute of Neurophysiology, Johann Wolfgang Goethe University Frankfurt, Frankfurt, Germany
| | - Jochen Roeper
- Institute of Neurophysiology, Johann Wolfgang Goethe University Frankfurt, Frankfurt, Germany
| | - Gaby Schneider
- Institute of Mathematics, Johann Wolfgang Goethe University Frankfurt, Frankfurt, Germany.
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25
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Abstract
Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.
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Affiliation(s)
- Samaneh Aminikhanghahi
- School of Electrical Engineering and Computer Science Washington State University, Pullman, WA
| | - Diane J Cook
- School of Electrical Engineering and Computer Science Washington State University, Pullman, WA
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26
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Sprint G, Cook DJ, Schmitter-Edgecombe M. Unsupervised detection and analysis of changes in everyday physical activity data. J Biomed Inform 2016; 63:54-65. [PMID: 27471222 DOI: 10.1016/j.jbi.2016.07.020] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [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: 02/23/2016] [Revised: 06/08/2016] [Accepted: 07/22/2016] [Indexed: 11/27/2022]
Abstract
Sensor-based time series data can be utilized to monitor changes in human behavior as a person makes a significant lifestyle change, such as progress toward a fitness goal. Recently, wearable sensors have increased in popularity as people aspire to be more conscientious of their physical health. Automatically detecting and tracking behavior changes from wearable sensor-collected physical activity data can provide a valuable monitoring and motivating tool. In this paper, we formalize the problem of unsupervised physical activity change detection and address the problem with our Physical Activity Change Detection (PACD) approach. PACD is a framework that detects changes between time periods, determines significance of the detected changes, and analyzes the nature of the changes. We compare the abilities of three change detection algorithms from the literature and one proposed algorithm to capture different types of changes as part of PACD. We illustrate and evaluate PACD on synthetic data and using Fitbit data collected from older adults who participated in a health intervention study. Results indicate PACD detects several changes in both datasets. The proposed change algorithms and analysis methods are useful data mining techniques for unsupervised, window-based change detection with potential to track users' physical activity and motivate progress toward their health goals.
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Affiliation(s)
- Gina Sprint
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States.
| | - Diane J Cook
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States.
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