1
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Lokre O, Perk TG, Weisman AJ, Govindan RM, Chen S, Chen M, Eickhoff J, Liu G, Jeraj R. Quantitative evaluation of lesion response heterogeneity for superior prognostication of clinical outcome. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06764-0. [PMID: 38819668 DOI: 10.1007/s00259-024-06764-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 05/12/2024] [Indexed: 06/01/2024]
Abstract
PURPOSE Standardized reporting of treatment response in oncology patients has traditionally relied on methods like RECIST, PERCIST and Deauville score. These endpoints assess only a few lesions, potentially overlooking the response heterogeneity of all disease. This study hypothesizes that comprehensive spatial-temporal evaluation of all individual lesions is necessary for superior prognostication of clinical outcome. METHODS [18F]FDG PET/CT scans from 241 patients (127 diffuse large B-cell lymphoma (DLBCL) and 114 non-small cell lung cancer (NSCLC)) were retrospectively obtained at baseline and either during chemotherapy or post-chemoradiotherapy. An automated TRAQinform IQ software (AIQ Solutions) analyzed the images, performing quantification of change in regions of interest suspicious of cancer (lesion-ROI). Multivariable Cox proportional hazards (CoxPH) models were trained to predict overall survival (OS) with varied sets of quantitative features and lesion-ROI, compared by bootstrapping with C-index and t-tests. The best-fit model was compared to automated versions of previously established methods like RECIST, PERCIST and Deauville score. RESULTS Multivariable CoxPH models demonstrated superior prognostic power when trained with features quantifying response heterogeneity in all individual lesion-ROI in DLBCL (C-index = 0.84, p < 0.001) and NSCLC (C-index = 0.71, p < 0.001). Prognostic power significantly deteriorated (p < 0.001) when using subsets of lesion-ROI (C-index = 0.78 and 0.67 for DLBCL and NSCLC, respectively) or excluding response heterogeneity (C-index = 0.67 and 0.70). RECIST, PERCIST, and Deauville score could not significantly associate with OS (C-index < 0.65 and p > 0.1), performing significantly worse than the multivariable models (p < 0.001). CONCLUSIONS Quantitative evaluation of response heterogeneity of all individual lesions is necessary for the superior prognostication of clinical outcome.
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Affiliation(s)
- Ojaswita Lokre
- AIQ Solutions, 8000 Excelsior Dr Suite 400, Madison, WI, 53717, United States of America.
| | - Timothy G Perk
- AIQ Solutions, 8000 Excelsior Dr Suite 400, Madison, WI, 53717, United States of America
| | - Amy J Weisman
- AIQ Solutions, 8000 Excelsior Dr Suite 400, Madison, WI, 53717, United States of America
| | | | - Song Chen
- Department of Nuclear Medicine, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Meijie Chen
- Department of Nuclear Medicine, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jens Eickhoff
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States of America
| | - Glenn Liu
- AIQ Solutions, 8000 Excelsior Dr Suite 400, Madison, WI, 53717, United States of America
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States of America
| | - Robert Jeraj
- AIQ Solutions, 8000 Excelsior Dr Suite 400, Madison, WI, 53717, United States of America
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States of America
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2
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Burkhart MC, Lee LY, Vaghari D, Toh AQ, Chong E, Chen C, Tiňo P, Kourtzi Z. Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction. Sci Rep 2024; 14:10755. [PMID: 38729989 PMCID: PMC11087538 DOI: 10.1038/s41598-024-60914-w] [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: 12/13/2023] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
Predicting the course of neurodegenerative disorders early has potential to greatly improve clinical management and patient outcomes. A key challenge for early prediction in real-world clinical settings is the lack of labeled data (i.e., clinical diagnosis). In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i.e., trajectories) and stratifies individuals without using clinical diagnosis for model training. MTM learns the relationship between states comprising expensive, invasive biomarkers (β-amyloid, grey matter density) and readily obtainable cognitive observations. MTM training on trajectories stratifies individuals into clinically meaningful clusters more reliably than MTM training on baseline data alone and is robust to missing data (i.e., cognitive data alone or single assessments). Extracting an individualized cognitive health index (i.e., MTM-derived cluster membership index) allows us to predict progression to AD more precisely than standard clinical assessments (i.e., cognitive tests or MRI scans alone). Importantly, MTM generalizes successfully from research cohort to real-world clinical data from memory clinic patients with missing data, enhancing the clinical utility of our approach. Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled data (i.e., clinical diagnosis) with strong potential for translation to clinical practice.
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Affiliation(s)
- Michael C Burkhart
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Liz Y Lee
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Delshad Vaghari
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
| | - An Qi Toh
- Department of Pharmacology, Memory, Aging, and Cognition Center, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Eddie Chong
- Department of Pharmacology, Memory, Aging, and Cognition Center, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Christopher Chen
- Department of Pharmacology, Memory, Aging, and Cognition Center, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Peter Tiňo
- School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK
| | - Zoe Kourtzi
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK.
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3
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Zheng N, Kheirollahi A, Yilmaz Y. Addressing age measurement errors in fish growth estimation from length-stratified samples. Biometrics 2024; 80:ujae029. [PMID: 38647000 DOI: 10.1093/biomtc/ujae029] [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: 07/14/2023] [Revised: 03/25/2024] [Accepted: 04/01/2024] [Indexed: 04/25/2024]
Abstract
Fish growth models are crucial for fisheries stock assessments and are commonly estimated using fish length-at-age data. This data is widely collected using length-stratified age sampling (LSAS), a cost-effective two-phase response-selective sampling method. The data may contain age measurement errors (MEs). We propose a methodology that accounts for both LSAS and age MEs to accurately estimate fish growth. The proposed methods use empirical proportion likelihood methodology for LSAS and the structural errors in variables methodology for age MEs. We provide a measure of uncertainty for parameter estimates and standardized residuals for model validation. To model the age distribution, we employ a continuation ratio-logit model that is consistent with the random nature of the true age distribution. We also apply a discretization approach for age and length distributions, which significantly improves computational efficiency and is consistent with the discrete age and length data typically encountered in practice. Our simulation study shows that neglecting age MEs can lead to significant bias in growth estimation, even with small but non-negligible age MEs. However, our new approach performs well regardless of the magnitude of age MEs and accurately estimates SEs of parameter estimators. Real data analysis demonstrates the effectiveness of the proposed model validation device. Computer codes to implement the methodology are provided.
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Affiliation(s)
- Nan Zheng
- Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, NL A1C 5S7 Canada
| | - Atefeh Kheirollahi
- Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, NL A1C 5S7 Canada
| | - Yildiz Yilmaz
- Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, NL A1C 5S7 Canada
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4
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Zhu W, Li J, Wu L, Du F, Zhou Y, Diao K, Zeng H. A latent profile analysis of doctors' joy in work at public hospitals. Front Psychol 2024; 15:1330078. [PMID: 38577117 PMCID: PMC10991811 DOI: 10.3389/fpsyg.2024.1330078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 03/04/2024] [Indexed: 04/06/2024] Open
Abstract
Introduction When doctors' work stress increases, their joy in work decreases, severely affecting the quality of care and threatening patient safety. Analysis of the latent categories of joy in work of doctors in public hospitals and the differences in the characteristics of each category can help uncover hidden messages that enhance doctors' joy in work. Methods Questionnaires were administered to 426 doctors working in public hospitals using the general information questionnaire and the public hospital doctor's joy in work evaluation scale. Upon identifying their potential categories using latent profile analysis, chi-square test, and multinomial logistic regression were performed to analyze the differences in the characteristics of each category. Results The 426 public hospital doctors could be divided into three potential categories: "low joy in work" (11.27%), "medium joy in work" (59.86%), and "high joy in work" (28.87%). Most of the doctors did not have much joy in work, with 71.13% of them having "low to medium joy in work." Doctors who work in secondary or tertiary hospitals, have a personnel agency or contract, and are older than 45 years are more likely to belong to the "low joy in work" category. Some of the protective factors are having an average monthly income (RMB) of 10,001-15,000 yuan and having a fair or good self-rated health status. Conclusion There are obvious classification characteristics of doctors' level of joy in work. Hospital managers can take commensurate actions to improve their joy in work, thereby improving patient safety and the quality of medical services.
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Affiliation(s)
- Weilin Zhu
- Shenzhen Health Development Research and Data Management Center, Shenzhen, China
| | - Jiayi Li
- Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Liqun Wu
- Shenzhen Health Development Research and Data Management Center, Shenzhen, China
| | - Fang Du
- Shenzhen Health Development Research and Data Management Center, Shenzhen, China
| | - Yi Zhou
- Shenzhen Health Development Research and Data Management Center, Shenzhen, China
| | - Kaichuan Diao
- Shenzhen Health Development Research and Data Management Center, Shenzhen, China
| | - Huatang Zeng
- Shenzhen Health Development Research and Data Management Center, Shenzhen, China
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Wang SH, Siebenhühner F, Arnulfo G, Myrov V, Nobili L, Breakspear M, Palva S, Palva JM. Critical-like Brain Dynamics in a Continuum from Second- to First-Order Phase Transition. J Neurosci 2023; 43:7642-7656. [PMID: 37816599 PMCID: PMC10634584 DOI: 10.1523/jneurosci.1889-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 06/07/2023] [Accepted: 09/25/2023] [Indexed: 10/12/2023] Open
Abstract
The classic brain criticality hypothesis postulates that the brain benefits from operating near a continuous second-order phase transition. Slow feedback regulation of neuronal activity could, however, lead to a discontinuous first-order transition and thereby bistable activity. Observations of bistability in awake brain activity have nonetheless remained scarce and its functional significance unclear. Moreover, there is no empirical evidence to support the hypothesis that the human brain could flexibly operate near either a first- or second-order phase transition despite such a continuum being common in models. Here, using computational modeling, we found bistable synchronization dynamics to emerge through elevated positive feedback and occur exclusively in a regimen of critical-like dynamics. We then assessed bistability in vivo with resting-state MEG in healthy adults (7 females, 11 males) and stereo-electroencephalography in epilepsy patients (28 females, 36 males). This analysis revealed that a large fraction of the neocortices exhibited varying degrees of bistability in neuronal oscillations from 3 to 200 Hz. In line with our modeling results, the neuronal bistability was positively correlated with classic assessment of brain criticality across narrow-band frequencies. Excessive bistability was predictive of epileptic pathophysiology in the patients, whereas moderate bistability was positively correlated with task performance in the healthy subjects. These empirical findings thus reveal the human brain as a one-of-a-kind complex system that exhibits critical-like dynamics in a continuum between continuous and discontinuous phase transitions.SIGNIFICANCE STATEMENT In the model, while synchrony per se was controlled by connectivity, increasing positive local feedback led to gradually emerging bistable synchrony with scale-free dynamics, suggesting a continuum between second- and first-order phase transitions in synchrony dynamics inside a critical-like regimen. In resting-state MEG and SEEG, bistability of ongoing neuronal oscillations was pervasive across brain areas and frequency bands and was observed only with concurring critical-like dynamics as the modeling predicted. As evidence for functional relevance, moderate bistability was positively correlated with executive functioning in the healthy subjects, and excessive bistability was associated with epileptic pathophysiology. These findings show that critical-like neuronal dynamics in vivo involves both continuous and discontinuous phase transitions in a frequency-, neuroanatomy-, and state-dependent manner.
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Affiliation(s)
- Sheng H Wang
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Doctoral Programme Brain & Mind, University of Helsinki, 00014 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, 00290 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Felix Siebenhühner
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, 00290 Helsinki, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, 16136 Genoa, Italy
| | - Vladislav Myrov
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Lino Nobili
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Children's Sciences, University of Genoa, 16136 Genoa, Italy
- Child Neuropsychiatry Unit, Istituto Di Ricovero e Cura a Carattere Scientifico Istituto Giannina Gaslini, 16147 Genoa, Italy
- Centre of Epilepsy Surgery "C. Munari," Department of Neuroscience, Niguarda Hospital, 20162 Milan, Italy
| | - Michael Breakspear
- College of Engineering, Science and Environment, College of Health and Medicine, University of Newcastle, Callaghan, 2308 Australia
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom
| | - J Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom
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Peng Z, Zhao T, Shi J, Kerr YH, Rodríguez-Fernández NJ, Yao P, Che T. An RFI-suppressed SMOS L-band multi-angular brightness temperature dataset spanning over a decade (since 2010). Sci Data 2023; 10:599. [PMID: 37684228 PMCID: PMC10491810 DOI: 10.1038/s41597-023-02499-z] [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: 05/24/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
The Soil Moisture Ocean Salinity (SMOS) was the first mission providing L-band multi-angular brightness temperature (TB) at the global scale. However, radio frequency interferences (RFI) and aliasing effects degrade, when present SMOS TBs, and thus affect the retrieval of land parameters. To alleviate this, a refined SMOS multi-angular TB dataset was generated based on a two-step regression approach. This approach smooths the TBs and reconstructs data at the incidence angle with large TB uncertainties. Compared with Centre Aval de Traitement des Données SMOS (CATDS) TB product, this dataset shows a better relationship with the Soil Moisture Active Passive (SMAP) TB and enhanced correlation with in-situ measured soil moisture. This RFI-suppressed SMOS TB dataset, spanning more than a decade (since 2010), is expected to provide opportunities for better retrieval of land parameters and scientific applications.
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Affiliation(s)
- Zhiqing Peng
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Tianjie Zhao
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jiancheng Shi
- National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Yann H Kerr
- Centre d'Etudes Spatiales de la Biosphère (CESBIO), Université de Toulouse, Centre National d'Etudes Spatiales (CNES), Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Dévelopement (IRD), Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAé), Université Paul Sabatier, 18 av. Edouard Belin, bpi 2801, 31401, Toulouse, France
| | - Nemesio J Rodríguez-Fernández
- Centre d'Etudes Spatiales de la Biosphère (CESBIO), Université de Toulouse, Centre National d'Etudes Spatiales (CNES), Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Dévelopement (IRD), Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAé), Université Paul Sabatier, 18 av. Edouard Belin, bpi 2801, 31401, Toulouse, France
| | - Panpan Yao
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
| | - Tao Che
- Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
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Sciannameo V, Azzolina D, Lanera C, Acar AŞ, Corciulo MA, Comoretto RI, Berchialla P, Gregori D. Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models. Healthcare (Basel) 2023; 11:2363. [PMID: 37628560 PMCID: PMC10454512 DOI: 10.3390/healthcare11162363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/06/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023] Open
Abstract
The COVID-19 outbreak involved a spread of prediction efforts, especially in the early pandemic phase. A better understanding of the epidemiological implications of the different models seems crucial for tailoring prevention policies. This study aims to explore the concordance and discrepancies in outbreak prediction produced by models implemented and used in the first wave of the epidemic. To evaluate the performance of the model, an analysis was carried out on Italian pandemic data from February 24, 2020. The epidemic models were fitted to data collected at 20, 30, 40, 50, 60, 70, 80, 90, and 98 days (the entire time series). At each time step, we made predictions until May 31, 2020. The Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) were calculated. The GAM model is the most suitable parameterization for predicting the number of new cases; exponential or Poisson models help predict the cumulative number of cases. When the goal is to predict the epidemic peak, GAM, ARIMA, or Bayesian models are preferable. However, the prediction of the pandemic peak could be made carefully during the early stages of the epidemic because the forecast is affected by high uncertainty and may very likely produce the wrong results.
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Affiliation(s)
- Veronica Sciannameo
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
- Center of Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torino, 10124 Turin, Italy;
| | - Danila Azzolina
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
- Department of Environmental and Preventive Sciences, University of Ferrara, 44121 Ferrara, Italy
| | - Corrado Lanera
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
| | | | - Maria Assunta Corciulo
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
| | - Rosanna Irene Comoretto
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
- Department of Public Health and Pediatrics, University of Torino, 10124 Turin, Italy
| | - Paola Berchialla
- Center of Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torino, 10124 Turin, Italy;
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy; (V.S.); (D.A.); (C.L.); (M.A.C.); (R.I.C.)
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8
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Bayesian inference for survival prediction of childhood Leukemia. Comput Biol Med 2023; 156:106713. [PMID: 36863191 DOI: 10.1016/j.compbiomed.2023.106713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/09/2023] [Accepted: 02/26/2023] [Indexed: 03/03/2023]
Abstract
BACKGROUND Childhood Leukemia is the most common type of cancer among children. Nearly 39% of cancer-induced childhood deaths are attributable to Leukemia. Nevertheless, early intervention has long been underdeveloped. Moreover, there are still a group of children succumbing to their cancer due to the cancer care resource disparity. Therefore, it calls for an accurate predictive approach to improve childhood Leukemia survival and mitigate these disparities. Existing survival predictions rely on a single best model, which fails to consider model uncertainties in predictions. Prediction from a single model is brittle, with model uncertainty neglected, and inaccurate prediction could lead to serious ethical and economic consequences. METHODS To address these challenges, we develop a Bayesian survival model to predict patient-specific survivals by taking model uncertainty into account. Specifically, we first develop a survival model predict time-varying survival probabilities. Second, we place different prior distributions over various model parameters and estimate their posterior distribution with full Bayesian inference. Third, we predict the patient-specific survival probabilities changing with respect to time by considering model uncertainty induced by posterior distribution. RESULTS Concordance index of the proposed model is 0.93. Moreover, the standardized survival probability of the censored group is higher than that of the deceased group. CONCLUSIONS Experimental results indicate that the proposed model is robust and accurate in predicting patient-specific survivals. It can also help clinicians track the contribution of multiple clinical attributes, thereby enabling well-informed intervention and timely medical care for childhood Leukemia.
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9
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Li K, Figarella K, Su X, Kovalchuk Y, Gorzolka J, Neher JJ, Mojtahedi N, Casadei N, Hedrich UBS, Garaschuk O. Endogenous but not sensory-driven activity controls migration, morphogenesis and survival of adult-born juxtaglomerular neurons in the mouse olfactory bulb. Cell Mol Life Sci 2023; 80:98. [PMID: 36932186 PMCID: PMC10023654 DOI: 10.1007/s00018-023-04753-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/06/2023] [Accepted: 03/07/2023] [Indexed: 03/19/2023]
Abstract
The development and survival of adult-born neurons are believed to be driven by sensory signaling. Here, in vivo analyses of motility, morphology and Ca2+ signaling, as well as transcriptome analyses of adult-born juxtaglomerular cells with reduced endogenous excitability (via cell-specific overexpression of either Kv1.2 or Kir2.1 K+ channels), revealed a pronounced impairment of migration, morphogenesis, survival, and functional integration of these cells into the mouse olfactory bulb, accompanied by a reduction in cytosolic Ca2+ fluctuations, phosphorylation of CREB and pCREB-mediated gene expression. Moreover, K+ channel overexpression strongly downregulated genes involved in neuronal migration, differentiation, and morphogenesis and upregulated apoptosis-related genes, thus locking adult-born cells in an immature and vulnerable state. Surprisingly, cells deprived of sensory-driven activity developed normally. Together, the data reveal signaling pathways connecting the endogenous intermittent neuronal activity/Ca2+ fluctuations as well as enhanced Kv1.2/Kir2.1 K+ channel function to migration, maturation, and survival of adult-born neurons.
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Affiliation(s)
- Kaizhen Li
- Department of Neurophysiology, Institute of Physiology, University of Tübingen, Tübingen, Germany
- Department of Physiology, University of Bern, Bern, Switzerland
| | - Katherine Figarella
- Department of Neurophysiology, Institute of Physiology, University of Tübingen, Tübingen, Germany
| | - Xin Su
- Department of Neurophysiology, Institute of Physiology, University of Tübingen, Tübingen, Germany
| | - Yury Kovalchuk
- Department of Neurophysiology, Institute of Physiology, University of Tübingen, Tübingen, Germany
| | - Jessika Gorzolka
- Department of Neurophysiology, Institute of Physiology, University of Tübingen, Tübingen, Germany
| | - Jonas J Neher
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Nima Mojtahedi
- Department of Neurophysiology, Institute of Physiology, University of Tübingen, Tübingen, Germany
| | - Nicolas Casadei
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- NGS Competence Center Tübingen, Tübingen, Germany
| | - Ulrike B S Hedrich
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Olga Garaschuk
- Department of Neurophysiology, Institute of Physiology, University of Tübingen, Tübingen, Germany.
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10
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Marcisz A, Polanska J. Can T1-Weighted Magnetic Resonance Imaging Significantly Improve Mini-Mental State Examination-Based Distinguishing Between Mild Cognitive Impairment and Early-Stage Alzheimer's Disease? J Alzheimers Dis 2023; 92:941-957. [PMID: 36806505 PMCID: PMC10116132 DOI: 10.3233/jad-220806] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/23/2023] [Indexed: 02/19/2023]
Abstract
BACKGROUND Detecting early-stage Alzheimer's disease (AD) is still problematic in clinical practice. This work aimed to find T1-weighted MRI-based markers for AD and mild cognitive impairment (MCI) to improve the screening process. OBJECTIVE Our assumption was to build a screening model that would be accessible and easy to use for physicians in their daily clinical routine. METHODS The multinomial logistic regression was used to detect status: AD, MCI, and normal control (NC) combined with the Bayesian information criterion for model selection. Several T1-weighted MRI-based radiomic features were considered explanatory variables in the prediction model. RESULTS The best radiomic predictor was the relative brain volume. The proposed method confirmed its quality by achieving a balanced accuracy of 95.18%, AUC of 93.25%, NPV of 97.93%, and PPV of 90.48% for classifying AD versus NC for the European DTI Study on Dementia (EDSD). The comparison of the two models: with the MMSE score only as an independent variable and corrected for the relative brain value and age, shows that the addition of the T1-weighted MRI-based biomarker improves the quality of MCI detection (AUC: 67.04% versus 71.08%) while maintaining quality for AD (AUC: 93.35% versus 93.25%). Additionally, among MCI patients predicted as AD inconsistently with the original diagnosis, 60% from ADNI and 76.47% from EDSD were re-diagnosed as AD within a 48-month follow-up. It shows that our model can detect AD patients a few years earlier than a standard medical diagnosis. CONCLUSION The created method is non-invasive, inexpensive, clinically accessible, and efficiently supports AD/MCI screening.
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Affiliation(s)
- Anna Marcisz
- Department of Data Science and Engineering, The Silesian University of Technology, Gliwice, Poland
| | | | - Joanna Polanska
- Department of Data Science and Engineering, The Silesian University of Technology, Gliwice, Poland
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11
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Shibak A, Maghsoudi A, Rokouei M, Farhangfar H, Faraji-Arough H. Investigation of egg production curve in ostrich using nonlinear functions. Poult Sci 2022; 102:102333. [PMID: 36463766 PMCID: PMC9719868 DOI: 10.1016/j.psj.2022.102333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 11/21/2022] Open
Abstract
In most countries, ostrich farming is considered a developing branch of the efficient poultry industry. The profitability of ostrich farm requires specific consideration of productions features such as the female fertility, egg production, hatchability, and growth performance. Hence, this study aimed to fit nonlinear functions to describe the ostrich egg production pattern to achieve the most appropriate and recommendable mathematical function for future studies. For this purpose, 14,507 daily records of 184 female ostriches in 5 production seasons (periods) during 2016 to 2021 were used. Five nonlinear functions including Incomplete gamma (Wood function), Corrected gamma (McNally), nonlinear Logistic (Yang), Logistic (Nelder), and Lokhorst were fitted for modeling the egg production curve in ostrich. The goodness of fit criteria's including Mean Square Error (MSE), Likelihood Ratio Test (LRT), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to evaluate and selection of the best function. The results indicated that the Wood and the McNally functions with a slight difference in all fitting criteria were the best-fitted functions and the Yang function with the highest values of MSE, LRT, AIC, BIC, were the most inappropriate function to describe the ostrich egg production curve. The McNally and the Wood can be recommended as appropriate functions to describe egg production during 5 production seasons in the studied ostrich flock.
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Affiliation(s)
- Abbas Shibak
- Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran
| | - Ali Maghsoudi
- Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran,Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran,Corresponding author:
| | - Mohammad Rokouei
- Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran
| | - Homayoun Farhangfar
- Department of Animal Science, Faculty of Agriculture, University of Birjand, Birjand, Iran
| | - Hadi Faraji-Arough
- Department of Ostrich, Special Domestic Animals Institute, Research Institute of Zabol, Zabol, Iran
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12
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Zhao G, Feng Q, Chen C, Zhou Z, Yu Y. Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:7400-7416. [PMID: 34822325 DOI: 10.1109/tpami.2021.3130759] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
During clinical practice, radiologists often use attributes, e.g., morphological and appearance characteristics of a lesion, to aid disease diagnosis. Effectively modeling attributes as well as all relationships involving attributes could boost the generalization ability and verifiability of medical image diagnosis algorithms. In this paper, we introduce a hybrid neuro-probabilistic reasoning algorithm for verifiable attribute-based medical image diagnosis. There are two parallel branches in our hybrid algorithm, a Bayesian network branch performing probabilistic causal relationship reasoning and a graph convolutional network branch performing more generic relational modeling and reasoning using a feature representation. Tight coupling between these two branches is achieved via a cross-network attention mechanism and the fusion of their classification results. We have successfully applied our hybrid reasoning algorithm to two challenging medical image diagnosis tasks. On the LIDC-IDRI benchmark dataset for benign-malignant classification of pulmonary nodules in CT images, our method achieves a new state-of-the-art accuracy of 95.36% and an AUC of 96.54%. Our method also achieves a 3.24% accuracy improvement on an in-house chest X-ray image dataset for tuberculosis diagnosis. Our ablation study indicates that our hybrid algorithm achieves a much better generalization performance than a pure neural network architecture under very limited training data.
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13
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Zhilova M. New Edgeworth-type expansions with finite sample guarantees. Ann Stat 2022. [DOI: 10.1214/22-aos2192] [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)
- Mayya Zhilova
- School of Mathematics, Georgia Institute of Technology
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14
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Tajuddin RRM, Ismail N, Ibrahim K. Several two-component mixture distributions for count data. COMMUN STAT-SIMUL C 2022. [DOI: 10.1080/03610918.2020.1722834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
| | - Noriszura Ismail
- Department of Mathematical Sciences, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | - Kamarulzaman Ibrahim
- Department of Mathematical Sciences, Universiti Kebangsaan Malaysia, Bangi, Malaysia
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15
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Anele CR, Goldani MZ, Schüler-Faccini L, da Silva CH. Prevalence of Congenital Anomaly and Its Relationship with Maternal Education and Age According to Local Development in the Extreme South of Brazil. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19138079. [PMID: 35805738 PMCID: PMC9265685 DOI: 10.3390/ijerph19138079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/24/2022] [Accepted: 06/28/2022] [Indexed: 12/04/2022]
Abstract
Congenital anomalies (CA) contribute to disabilities and health conditions throughout life. Furthermore, they can cause emotional distress to the mothers and children, who may also experience limitations in individual and social development. This study investigated the prevalence of CA and the relationship with maternal education and age according to local development in the extreme south of Brazil. This is a retrospective observational study with birth data from the Live Birth Information System from 2000 to 2017. The association between age and maternal education with the presence of CA was verified using multiple Poisson regression for robust variances in models adjusted for those variables with a preliminary significant association. A total of 5131 (1.5%) had some CA identified at birth between 2000 and 2017. Only advanced age (≥36 years) was associated with CA regardless of macro-region development (p ≤ 0.001). The highest risk was observed in regions with medium development (RR = 1.60; 95% CI 1.30−1.97). Maternal education (<8 years of study) was associated with CA only in mothers from macro-regions with very high development (RR = 1.27; 95% CI 1.03−1.54). These analyses confirmed that women of advanced age are at greater risk of having children with a CA regardless of maternal education and local development, but social characteristics can also have an influence, as regions with higher development had lower prevalence of CA.
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Affiliation(s)
- Carolina Ribeiro Anele
- Postgraduate Program in Child and Adolescent Health, Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Rua Ramiro Barcelos, 2400, Porto Alegre 90035-003, RS, Brazil; (C.R.A.); (M.Z.G.); (L.S.-F.)
| | - Marcelo Zubaran Goldani
- Postgraduate Program in Child and Adolescent Health, Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Rua Ramiro Barcelos, 2400, Porto Alegre 90035-003, RS, Brazil; (C.R.A.); (M.Z.G.); (L.S.-F.)
- Pediatrics and Primary Health Care Service, Hospital de Clínicas de Porto Alegre (HCPA), Rua Ramiro Barcelos, 2350, Porto Alegre 90620-110, RS, Brazil
- Department of Pediatrics, Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Rua Ramiro Barcelos, 2400, Porto Alegre 90035-003, RS, Brazil
| | - Lavínia Schüler-Faccini
- Postgraduate Program in Child and Adolescent Health, Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Rua Ramiro Barcelos, 2400, Porto Alegre 90035-003, RS, Brazil; (C.R.A.); (M.Z.G.); (L.S.-F.)
- Department of Genetics, Universidade Federal do Rio Grande do Sul (UFRGS), Avenida Bento Gonçalves, 9500, Porto Alegre 91501-970, RS, Brazil
- Instituto Nacional de Genética Médica Populacional (INAGEMP), Medical Genetics Service, Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos, 2350, Porto Alegre 90035-003, RS, Brazil
| | - Clécio Homrich da Silva
- Postgraduate Program in Child and Adolescent Health, Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Rua Ramiro Barcelos, 2400, Porto Alegre 90035-003, RS, Brazil; (C.R.A.); (M.Z.G.); (L.S.-F.)
- Pediatrics and Primary Health Care Service, Hospital de Clínicas de Porto Alegre (HCPA), Rua Ramiro Barcelos, 2350, Porto Alegre 90620-110, RS, Brazil
- Department of Pediatrics, Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Rua Ramiro Barcelos, 2400, Porto Alegre 90035-003, RS, Brazil
- Correspondence: ; Tel.: +55-51-33085601
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16
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nTreeClus: A tree-based sequence encoder for clustering categorical series. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.076] [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]
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17
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Ferritin Trajectories over Repeated Whole Blood Donations: Results from the FIND+ Study. J Clin Med 2022; 11:jcm11133581. [PMID: 35806867 PMCID: PMC9267857 DOI: 10.3390/jcm11133581] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 06/14/2022] [Accepted: 06/17/2022] [Indexed: 12/26/2022] Open
Abstract
Background: Depending on post-donation erythropoiesis, available iron stores, and iron absorption rates, optimal donation intervals may differ between donors. This project aims to define subpopulations of donors with different ferritin trajectories over repeated donations. Methods: Ferritin levels of 300 new whole blood donors were measured from stored (lookback) samples from each donation over two years in an observational cohort study. Latent classes of ferritin level trajectories were investigated separately using growth mixture models for male and female donors. General linear mixed models assessed associations of ferritin levels with subsequent iron deficiency and/or low hemoglobin. Results: Two groups of donors were identified using group-based trajectory modeling in both genders. Ferritin levels showed rather linear reductions among 42.9% of male donors and 87.7% of female donors. For the remaining groups of donors, steeper declines in ferritin levels were observed. Ferritin levels at baseline and the end of follow-up varied greatly between groups. Conclusions: Repeated ferritin measurements show depleting iron stores in all-new whole blood donors, the level at which mainly depends on baseline ferritin levels. Tailored, less intensive donation strategies might help to prevent low iron in donors, and could be supported with ferritin monitoring and/or iron supplementation.
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18
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Amato MT, Giménez D. Quantifying root turnover in grasslands from biomass dynamics: Application of the growth-maintenance respiration paradigm and re-analysis of historical data. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2022.109940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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19
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Zhao YJ, Ma T, Zhang L, Ran X, Zhang RY, Ku Y. Atypically larger variability of resource allocation accounts for visual working memory deficits in schizophrenia. PLoS Comput Biol 2021; 17:e1009544. [PMID: 34748538 PMCID: PMC8601612 DOI: 10.1371/journal.pcbi.1009544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 11/18/2021] [Accepted: 10/07/2021] [Indexed: 11/18/2022] Open
Abstract
Working memory (WM) deficits have been widely documented in schizophrenia (SZ), and almost all existing studies attributed the deficits to decreased capacity as compared to healthy control (HC) subjects. Recent developments in WM research suggest that other components, such as precision, also mediate behavioral performance. It remains unclear how different WM components jointly contribute to deficits in schizophrenia. We measured the performance of 60 SZ (31 females) and 61 HC (29 females) in a classical delay-estimation visual working memory (VWM) task and evaluated several influential computational models proposed in basic science of VWM to disentangle the effect of various memory components. We show that the model assuming variable precision (VP) across items and trials is the best model to explain the performance of both groups. According to the VP model, SZ exhibited abnormally larger variability of allocating memory resources rather than resources or capacity per se. Finally, individual differences in the resource allocation variability predicted variation of symptom severity in SZ, highlighting its functional relevance to schizophrenic pathology. This finding was further verified using distinct visual features and subject cohorts. These results provide an alternative view instead of the widely accepted decreased-capacity theory and highlight the key role of elevated resource allocation variability in generating atypical VWM behavior in schizophrenia. Our findings also shed new light on the utility of Bayesian observer models to characterize mechanisms of mental deficits in clinical neuroscience. Working memory is a core cognitive function related to a broad range of cognitive domains such as problem-solving, attention, executive control, and IQ. Although working memory deficits have been well-documented in schizophrenia, the underlying mechanisms remain unclear. Conventional working memory theories attribute working memory deficits in schizophrenia to their reduced memory capacity, overlooking the potential roles of other memory components, such as precision. In this study, we take the approach of computational psychiatry and use computational modeling to uncover the major determinants of working memory deficits. We assess working memory performance of a large cohort of participants (60 schizophrenia patients and 61 demographic matched healthy controls) and evaluate multiple mainstream computational models of visual working memory. The variable precision model turns out to be the best model for both groups. We further find that the poorer performance of schizophrenia patients arises from heterogeneous distribution of memory resources when encoding items in memory. This resource allocation variability can also predict symptom severity in schizophrenia. Our study highlights the use of computational models in psychiatric researches.
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Affiliation(s)
- Yi-Jie Zhao
- Center for Brain and Mental Well-being, Department of Psychology, Sun Yat-sen University, Guangzhou, China
- Peng Cheng Laboratory, Shenzhen, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Tianye Ma
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Li Zhang
- Shanghai Changning Mental Health Center, Shanghai, China
| | - Xuemei Ran
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Ru-Yuan Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Institute of Psychology and Behavioral Science, Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China
- * E-mail: (R-YZ); (YK)
| | - Yixuan Ku
- Center for Brain and Mental Well-being, Department of Psychology, Sun Yat-sen University, Guangzhou, China
- Peng Cheng Laboratory, Shenzhen, China
- * E-mail: (R-YZ); (YK)
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20
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Fish J, DeWitt A, AlMomani AAR, Laurienti PJ, Bollt E. Entropic regression with neurologically motivated applications. CHAOS (WOODBURY, N.Y.) 2021; 31:113105. [PMID: 34881577 DOI: 10.1063/5.0039333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 10/14/2021] [Indexed: 06/13/2023]
Abstract
The ultimate goal of cognitive neuroscience is to understand the mechanistic neural processes underlying the functional organization of the brain. The key to this study is understanding the structure of both the structural and functional connectivity between anatomical regions. In this paper, we use an information theoretic approach, which defines direct information flow in terms of causation entropy, to improve upon the accuracy of the recovery of the true network structure over popularly used methods for this task such as correlation and least absolute shrinkage and selection operator regression. The method outlined above is tested on synthetic data, which is produced by following previous work in which a simple dynamical model of the brain is used, simulated on top of a real network of anatomical brain regions reconstructed from diffusion tensor imaging. We demonstrate the effectiveness of the method of AlMomani et al. [Chaos 30, 013107 (2020)] when applied to data simulated on the realistic diffusion tensor imaging network, as well as on randomly generated small-world and Erdös-Rényi networks.
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Affiliation(s)
- Jeremie Fish
- Department of Electrical and Computer Engineering, Clarkson University, Potsdam, New York 13699, USA
| | - Alexander DeWitt
- Department of Electrical and Computer Engineering, Clarkson University, Potsdam, New York 13699, USA
| | - Abd AlRahman R AlMomani
- Department of Electrical and Computer Engineering, Clarkson University, Potsdam, New York 13699, USA
| | - Paul J Laurienti
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina 27101, USA
| | - Erik Bollt
- Department of Electrical and Computer Engineering, Clarkson University, Potsdam, New York 13699, USA
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21
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Skinner SP, Follmer AH, Ubbink M, Poulos TL, Houwing-Duistermaat JJ, Paci E. Partial Opening of Cytochrome P450cam (CYP101A1) Is Driven by Allostery and Putidaredoxin Binding. Biochemistry 2021; 60:2932-2942. [PMID: 34519197 DOI: 10.1021/acs.biochem.1c00406] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Cytochrome P450cam (CYP101A1) catalyzes the regio- and stereo-specific 5-exo-hydroxylation of camphor via a multistep catalytic cycle that involves two-electron transfer steps, with an absolute requirement that the second electron be donated by the ferrodoxin, putidaredoxin (Pdx). Whether P450cam, once camphor has bound to the active site and the substrate entry channel has closed, opens up upon Pdx binding, during the second electron transfer step, or it remains closed is still a matter of debate. A potential allosteric site for camphor binding has been identified and postulated to play a role in the binding of Pdx. Here, we have revisited paramagnetic NMR spectroscopy data and determined a heterogeneous ensemble of structures that explains the data, provides a complete representation of the P450cam/Pdx complex in solution, and reconciles alternative hypotheses. The allosteric camphor binding site is always present, and the conformational changes induced by camphor binding to this site facilitates Pdx binding. We also determined that the state to which Pdx binds comprises an ensemble of structures that have features of both the open and closed state. These results demonstrate that there is a finely balanced interaction between allosteric camphor binding and the binding of Pdx at high camphor concentrations.
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Affiliation(s)
- Simon P Skinner
- School of Molecular and Cell Biology and Astbury Centre, University of Leeds, Leeds LS2 9JT, U.K
| | - Alec H Follmer
- Department of Chemistry, University of California, Irvine, California 92697-3900, United States
| | - Marcellus Ubbink
- Leiden University, Institute of Chemistry, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Thomas L Poulos
- Department of Chemistry, University of California, Irvine, California 92697-3900, United States.,Department of Molecular Biology and Biochemistry, University of California, Irvine, California 92697-3900, United States.,Department of Pharmaceutical Sciences, University of California, Irvine, California 92697-3900, United States
| | | | - Emanuele Paci
- School of Molecular and Cell Biology and Astbury Centre, University of Leeds, Leeds LS2 9JT, U.K
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22
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Chang HY, Richards RA, Chen Y. Effects of environmental factors on reproductive potential of the Gulf of Maine northern shrimp (Pandalus borealis). Glob Ecol Conserv 2021. [DOI: 10.1016/j.gecco.2021.e01774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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23
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Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach. SUSTAINABILITY 2021. [DOI: 10.3390/su131910720] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The water level in a river defines the nature of flow and is fundamental to flood analysis. Extreme fluctuation in water levels in rivers, such as floods and droughts, are catastrophic in every manner; therefore, forecasting at an early stage would prevent possible disasters and relief efforts could be set up on time. This study aims to digitally model the water level in the Kabul River to prevent and alleviate the effects of any change in water level in this river downstream. This study used a machine learning tool known as the automatic autoregressive integrated moving average for statistical methodological analysis for forecasting the river flow. Based on the hydrological data collected from the water level of Kabul River in Swat, the water levels from 2011–2030 were forecasted, which were based on the lowest value of Akaike Information Criterion as 9.216. It was concluded that the water flow started to increase from the year 2011 till it reached its peak value in the year 2019–2020, and then the water level will maintain its maximum level to 250 cumecs and minimum level to 10 cumecs till 2030. The need for this research is justified as it could prove helpful in establishing guidelines for hydrological designers, the planning and management of water, hydropower engineering projects, as an indicator for weather prediction, and for the people who are greatly dependent on the Kabul River for their survival.
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24
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Tomlinson MF, Greenwood D, Mucha-Kruczyński M. Asymmetric excitation of left- and right-tail extreme events probed using a Hawkes model: Application to financial returns. Phys Rev E 2021; 104:024112. [PMID: 34525535 DOI: 10.1103/physreve.104.024112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 07/13/2021] [Indexed: 11/07/2022]
Abstract
We construct a two-tailed peaks-over-threshold Hawkes model that captures asymmetric self- and cross-excitation in and between left- and right-tail extreme values within a time series. We demonstrate its applicability by investigating extreme gains and losses within the daily log-returns of the S&P 500 equity index. We find that the arrivals of extreme losses and gains are described by a common conditional intensity to which losses contribute twice as much as gains. However, the contribution of the former decays almost five times more quickly than that of the latter. We attribute these asymmetries to the different reactions of market traders to extreme upward and downward movements of asset prices: an example of negativity bias, wherein trauma is more salient than euphoria.
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Affiliation(s)
- Matthew F Tomlinson
- Department of Physics, University of Bath, Bath BA2 7AY, United Kingdom.,Centre for Networks and Collective Behaviour, University of Bath, Bath BA2 7AY, United Kingdom
| | - David Greenwood
- CheckRisk LLP, 4 Miles's Buildings, George Street, Bath BA1 2QS, United Kingdom
| | - Marcin Mucha-Kruczyński
- Department of Physics, University of Bath, Bath BA2 7AY, United Kingdom.,Centre for Nanoscience and Nanotechnology, University of Bath, Bath BA2 7AY, United Kingdom
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25
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Crapse J, Pappireddi N, Gupta M, Shvartsman SY, Wieschaus E, Wühr M. Evaluating the Arrhenius equation for developmental processes. Mol Syst Biol 2021; 17:e9895. [PMID: 34414660 PMCID: PMC8377445 DOI: 10.15252/msb.20209895] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 07/20/2021] [Accepted: 07/21/2021] [Indexed: 11/15/2022] Open
Abstract
The famous Arrhenius equation is well suited to describing the temperature dependence of chemical reactions but has also been used for complicated biological processes. Here, we evaluate how well the simple Arrhenius equation predicts complex multi-step biological processes, using frog and fruit fly embryogenesis as two canonical models. We find that the Arrhenius equation provides a good approximation for the temperature dependence of embryogenesis, even though individual developmental intervals scale differently with temperature. At low and high temperatures, however, we observed significant departures from idealized Arrhenius Law behavior. When we model multi-step reactions of idealized chemical networks, we are unable to generate comparable deviations from linearity. In contrast, we find the two enzymes GAPDH and β-galactosidase show non-linearity in the Arrhenius plot similar to our observations of embryonic development. Thus, we find that complex embryonic development can be well approximated by the simple Arrhenius equation regardless of non-uniform developmental scaling and propose that the observed departure from this law likely results more from non-idealized individual steps rather than from the complexity of the system.
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Affiliation(s)
- Joseph Crapse
- Undergraduate Integrated Science CurriculumPrinceton UniversityPrincetonNJUSA
- Department of Molecular BiologyPrinceton UniversityPrincetonNJUSA
- Lewis‐Sigler Institute for Integrative GenomicsPrinceton UniversityPrincetonNJUSA
| | - Nishant Pappireddi
- Department of Molecular BiologyPrinceton UniversityPrincetonNJUSA
- Lewis‐Sigler Institute for Integrative GenomicsPrinceton UniversityPrincetonNJUSA
| | - Meera Gupta
- Department of Molecular BiologyPrinceton UniversityPrincetonNJUSA
- Lewis‐Sigler Institute for Integrative GenomicsPrinceton UniversityPrincetonNJUSA
- Department of Chemical and Biological EngineeringPrinceton UniversityPrincetonNJUSA
| | - Stanislav Y Shvartsman
- Undergraduate Integrated Science CurriculumPrinceton UniversityPrincetonNJUSA
- Department of Molecular BiologyPrinceton UniversityPrincetonNJUSA
- Lewis‐Sigler Institute for Integrative GenomicsPrinceton UniversityPrincetonNJUSA
- Center for Computational BiologyFlatiron InstituteSimons FoundationNew YorkNYUSA
| | - Eric Wieschaus
- Undergraduate Integrated Science CurriculumPrinceton UniversityPrincetonNJUSA
- Department of Molecular BiologyPrinceton UniversityPrincetonNJUSA
- Lewis‐Sigler Institute for Integrative GenomicsPrinceton UniversityPrincetonNJUSA
| | - Martin Wühr
- Undergraduate Integrated Science CurriculumPrinceton UniversityPrincetonNJUSA
- Department of Molecular BiologyPrinceton UniversityPrincetonNJUSA
- Lewis‐Sigler Institute for Integrative GenomicsPrinceton UniversityPrincetonNJUSA
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26
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Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks. SENSORS 2021; 21:s21144733. [PMID: 34300473 PMCID: PMC8309591 DOI: 10.3390/s21144733] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 07/02/2021] [Accepted: 07/06/2021] [Indexed: 11/17/2022]
Abstract
We exploit the use of a controller area network (CAN-bus) to monitor sensors on the buses of local public transportation in a big European city. The aim is to advise fleet managers and policymakers on how to reduce fuel consumption so that air pollution is controlled and public services are improved. We deploy heuristic algorithms and exhaustive ones to generate Bayesian networks among the monitored variables. The aim is to describe the relevant relationships between the variables, to discover and confirm the possible cause–effect relationships, to predict the fuel consumption dependent on the contextual conditions of traffic, and to enable an intervention analysis to be conducted on the variables so that our goals are achieved. We propose a validation technique using Bayesian networks based on Granger causality: it relies upon observations of the time series formed by successive values of the variables in time. We use the same method based on Granger causality to rank the Bayesian networks obtained as well. A comparison of the Bayesian networks discovered against the ground truth is proposed in a synthetic data set, specifically generated for this study: the results confirm the validity of the Bayesian networks that agree on most of the existing relationships.
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27
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Sun T, Qiang S, Lu C, Xu F. Composition-dependent energetic contribution of complex salt bridges to collagen stability. Biophys J 2021; 120:3429-3436. [PMID: 34181903 DOI: 10.1016/j.bpj.2021.05.028] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 04/14/2021] [Accepted: 05/26/2021] [Indexed: 11/16/2022] Open
Abstract
Complex salt bridges, on which three or more charged residues interplay simultaneously, cannot be considered as addition of individual salt bridges. This is still an intriguing problem in protein folding and stability. Here, we used an obligated ABC-type collagen heterotrimer as a platform to study the relationship between energetic contributions and conformational details of three-body complex salt bridges anchored by positively charged residues, K and R. Eight complex salt bridges were constructed by engineering point mutations in the heterotrimer. The circular dichroism measurements showed that the K-anchored complex salt bridges were stronger than the R-anchored ones. The molecular dynamics simulation revealed that both types of salt bridges had distinct dynamic features. The energetic contribution of K-anchored salt bridges was mainly determined by strong single bridges. In the R-anchored complex salt bridges, both side-chain electrostatic interactions and side-chain-backbone hydrogen bonding were involved. An empirical equation was proposed to predict the energetic contributions with high accuracy (R2 = 0.93). This work could help us take insights into the mechanisms of composition-dependent behaviors of the complex salt bridges on protein surface.
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Affiliation(s)
- Tiantian Sun
- Ministry of Education Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Jiangnan University, Wuxi, China
| | - Shumin Qiang
- Ministry of Education Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Jiangnan University, Wuxi, China
| | - Cheng Lu
- Ministry of Education Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Jiangnan University, Wuxi, China.
| | - Fei Xu
- Ministry of Education Key Laboratory of Carbohydrate Chemistry and Biotechnology, School of Biotechnology, Jiangnan University, Wuxi, China.
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28
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Cavanna F, Alvarado J. Quantification of the mesh structure of bundled actin filaments. SOFT MATTER 2021; 17:5034-5043. [PMID: 33912871 DOI: 10.1039/d1sm00428j] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Biopolymer networks are essential for a wide variety of cellular functions. The biopolymer actin is known to self-assemble into a variety of spatial structures in response to physiological and physical mechanisms. So far, the mechanics of networks of single actin filaments and bundles has previously been described. However, the spatial structure of actin bundles remains poorly understood. Here, we investigate this question by bundling actin filaments with systematically varied concentrations of known physical bundling agents (MgCl2 and PEG) and physiological bundling agents (α-actinin and fascin). We image bundled actin networks with confocal microscopy and perform analysis to describe their mesh size and the nearest-distance distribution, which we call "mesh structure". We find that the mesh size ξ scales universally with actin concentration as ξ ∼ [actin]-1/2. However, the dependence of ξ on the concentration of the bundling agent depends on the agent used. Finally, we find that nearest-distance distributions are best fit by Weibull and Gamma distributions. A complete understanding of the mesh structure of biopolymer networks leads to a more mechanistic understanding of the structure of the cytoskeleton, and can be exploited to design filters with variable porosity for microfluidic devices.
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Affiliation(s)
- Francis Cavanna
- UT Austin Department of Physics, 2515 Speedway, Austin, Texas, USA.
| | - José Alvarado
- UT Austin Department of Physics, 2515 Speedway, Austin, Texas, USA.
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29
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Randall EB, Randolph NZ, Alexanderian A, Olufsen MS. Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model. J Theor Biol 2021; 526:110759. [PMID: 33984355 DOI: 10.1016/j.jtbi.2021.110759] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 05/03/2021] [Accepted: 05/04/2021] [Indexed: 12/30/2022]
Abstract
In this study, we develop a methodology for model reduction and selection informed by global sensitivity analysis (GSA) methods. We apply these techniques to a control model that takes systolic blood pressure and thoracic tissue pressure data as inputs and predicts heart rate in response to the Valsalva maneuver (VM). The study compares four GSA methods based on Sobol' indices (SIs) quantifying the parameter influence on the difference between the model output and the heart rate data. The GSA methods include standard scalar SIs determining the average parameter influence over the time interval studied and three time-varying methods analyzing how parameter influence changes over time. The time-varying methods include a new technique, termed limited-memory SIs, predicting parameter influence using a moving window approach. Using the limited-memory SIs, we perform model reduction and selection to analyze the necessity of modeling both the aortic and carotid baroreceptor regions in response to the VM. We compare the original model to systematically reduced models including (i) the aortic and carotid regions, (ii) the aortic region only, and (iii) the carotid region only. Model selection is done quantitatively using the Akaike and Bayesian Information Criteria and qualitatively by comparing the neurological predictions. Results show that it is necessary to incorporate both the aortic and carotid regions to model the VM.
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Affiliation(s)
- E Benjamin Randall
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, United States; Department of Mathematics, North Carolina State University, Raleigh, NC, United States.
| | - Nicholas Z Randolph
- Department of Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Department of Mathematics, North Carolina State University, Raleigh, NC, United States.
| | - Alen Alexanderian
- Department of Mathematics, North Carolina State University, Raleigh, NC, United States.
| | - Mette S Olufsen
- Department of Mathematics, North Carolina State University, Raleigh, NC, United States.
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30
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Setzu M, Guidotti R, Monreale A, Turini F, Pedreschi D, Giannotti F. GLocalX - From Local to Global Explanations of Black Box AI Models. ARTIF INTELL 2021. [DOI: 10.1016/j.artint.2021.103457] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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31
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Schumacher FL, Lachos VH, Matos LA. Scale mixture of skew-normal linear mixed models with within-subject serial dependence. Stat Med 2021; 40:1790-1810. [PMID: 33438305 DOI: 10.1002/sim.8870] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 12/09/2020] [Accepted: 12/16/2020] [Indexed: 11/09/2022]
Abstract
In longitudinal studies, repeated measures are collected over time and hence they tend to be serially correlated. These studies are commonly analyzed using linear mixed models (LMMs), and in this article we consider an extension of the skew-normal/independent LMM, where the error term has a dependence structure, such as damped exponential correlation or autoregressive correlation of order p. The proposed model provides flexibility in capturing the effects of skewness and heavy tails simultaneously when continuous repeated measures are serially correlated. For this robust model, we present an efficient EM-type algorithm for parameters estimation via maximum likelihood and the observed information matrix is derived analytically to account for standard errors. The methodology is illustrated through an application to schizophrenia data and some simulation studies. The proposed algorithm and methods are implemented in the new R package skewlmm.
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Affiliation(s)
| | - Victor H Lachos
- Department of Statistics, University of Connecticut, Storrs, Connecticut, USA
| | - Larissa A Matos
- Department of Statistics, Universidade Estadual de Campinas, São Paulo, Brazil
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32
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Zheng Q, Delingette H, Fung K, Petersen SE, Ayache N. Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs. Front Cardiovasc Med 2020; 7:539788. [PMID: 33313075 PMCID: PMC7701336 DOI: 10.3389/fcvm.2020.539788] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 08/12/2020] [Indexed: 12/12/2022] Open
Abstract
We perform unsupervised analysis of image-derived shape and motion features extracted from 3,822 cardiac Magnetic resonance imaging (MRIs) of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion. Second, a feature selection is performed to remove highly correlated feature pairs. Third, clustering is carried out using a Gaussian mixture model on the selected features. After analysis, we identify 2 small clusters that probably correspond to 2 pathological categories. Further confirmation using a trained classification model and dimensionality reduction tools is carried out to support this finding. Moreover, we examine the differences between the other large clusters and compare our measures with the ground truth.
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Affiliation(s)
- Qiao Zheng
- Université Côte d'Azur, Inria, Sophia Antipolis, Valbonne, France
| | - Hervé Delingette
- Université Côte d'Azur, Inria, Sophia Antipolis, Valbonne, France
| | - Kenneth Fung
- National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health National Health Service Trust, London, United Kingdom
| | - Steffen E. Petersen
- National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health National Health Service Trust, London, United Kingdom
| | - Nicholas Ayache
- Université Côte d'Azur, Inria, Sophia Antipolis, Valbonne, France
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33
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Bhaduri M. On modifications to the Poisson-triggered hidden Markov paradigm through partitioned empirical recurrence rates ratios and its applications to natural hazards monitoring. Sci Rep 2020; 10:15889. [PMID: 32985544 PMCID: PMC7523003 DOI: 10.1038/s41598-020-72803-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 09/07/2020] [Indexed: 12/03/2022] Open
Abstract
Hidden Markov models (HMMs), especially those with a Poisson density governing the latent state-dependent emission probabilities, have enjoyed substantial and undeniable success in modeling natural hazards. Classifications among these hazards, induced through quantifiable properties such as varying intensities or geographic proximities, often exist, enabling the creation of an empirical recurrence rates ratio (ERRR), a smoothing statistic that is gradually gaining currency in modeling literature due to its demonstrated ability in unearthing interactions. Embracing these tools, this study puts forth a refreshing monitoring alternative where the unobserved state transition probability matrix in the likelihood of the Poisson based HMM is replaced by the observed transition probabilities of a discretized ERRR. Analyzing examples from Hawaiian volcanic and West Atlantic hurricane interactions, this work illustrates how the discretized ERRR may be interpreted as an observed version of the unobserved hidden Markov chain that generates one of the two interacting processes. Surveying different facets of traditional inference such as global state decoding, hidden state predictions, one-out conditional distributions, and implementing related computational algorithms, we find that the latest proposal estimates the chances of observing a high-risk period, one threatening several hazards, more accurately than its established counterpart. Strongly intuitive and devoid of forbidding technicalities, the new prescription launches a vision of surer forecasts and stands versatile enough to be applicable to other types of hazard monitoring (such as landslides, earthquakes, floods), especially those with meager occurrence probabilities.
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Affiliation(s)
- Moinak Bhaduri
- Department of Mathematical Sciences, Bentley University, Waltham, MA, 02452, United States.
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34
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Di Serio C, Scala S, Vicard P. Bayesian networks for cell differentiation process assessment. Stat (Int Stat Inst) 2020. [DOI: 10.1002/sta4.287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Clelia Di Serio
- University Centre for Statistics in the Biomedical Sciences Vita‐Salute San Raffaele University Milan 20132 Italy
| | - Serena Scala
- San Raffaele Telethon Institute for Gene Therapy (TIGET) Milan 20132 Italy
| | - Paola Vicard
- Department of Economics University Roma Tre Rome 00154 Italy
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35
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The impact of learning on perceptual decisions and its implication for speed-accuracy tradeoffs. Nat Commun 2020; 11:2757. [PMID: 32488065 PMCID: PMC7265464 DOI: 10.1038/s41467-020-16196-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Accepted: 04/01/2020] [Indexed: 11/16/2022] Open
Abstract
In standard models of perceptual decision-making, noisy sensory evidence is considered to be the primary source of choice errors and the accumulation of evidence needed to overcome this noise gives rise to speed-accuracy tradeoffs. Here, we investigated how the history of recent choices and their outcomes interact with these processes using a combination of theory and experiment. We found that the speed and accuracy of performance of rats on olfactory decision tasks could be best explained by a Bayesian model that combines reinforcement-based learning with accumulation of uncertain sensory evidence. This model predicted the specific pattern of trial history effects that were found in the data. The results suggest that learning is a critical factor contributing to speed-accuracy tradeoffs in decision-making, and that task history effects are not simply biases but rather the signatures of an optimal learning strategy. Here, the authors show that rats’ performance on olfactory decision tasks is best explained by a Bayesian model that combines reinforcement-based learning with accumulation of uncertain sensory evidence. The results suggest that learning is a critical factor contributing to speed-accuracy tradeoffs.
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36
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Imig D, Pollak N, Allgöwer F, Rehm M. Sample-based modeling reveals bidirectional interplay between cell cycle progression and extrinsic apoptosis. PLoS Comput Biol 2020; 16:e1007812. [PMID: 32497127 PMCID: PMC7271993 DOI: 10.1371/journal.pcbi.1007812] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 03/23/2020] [Indexed: 11/22/2022] Open
Abstract
Apoptotic cell death can be initiated through the extrinsic and intrinsic signaling pathways. While cell cycle progression promotes the responsiveness to intrinsic apoptosis induced by genotoxic stress or spindle poisons, this has not yet been studied conclusively for extrinsic apoptosis. Here, we combined fluorescence-based time-lapse monitoring of cell cycle progression and cell death execution by long-term time-lapse microscopy with sampling-based mathematical modeling to study cell cycle dependency of TRAIL-induced extrinsic apoptosis in NCI-H460/geminin cells. In particular, we investigated the interaction of cell death timing and progression of cell cycle states. We not only found that TRAIL prolongs cycle progression, but in reverse also that cell cycle progression affects the kinetics of TRAIL-induced apoptosis: Cells exposed to TRAIL in G1 died significantly faster than cells stimulated in S/G2/M. The connection between cell cycle state and apoptosis progression was captured by developing a mathematical model, for which parameter estimation revealed that apoptosis progression decelerates in the second half of the cell cycle. Similar results were also obtained when studying HCT-116 cells. Our results therefore reject the null hypothesis of independence between cell cycle progression and extrinsic apoptosis and, supported by simulations and experiments of synchronized cell populations, suggest that unwanted escape from TRAIL-induced apoptosis can be reduced by enriching the fraction of cells in G1 phase. Besides novel insight into the interrelation of cell cycle progression and extrinsic apoptosis signaling kinetics, our findings are therefore also relevant for optimizing future TRAIL-based treatment strategies.
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Affiliation(s)
- Dirke Imig
- University of Stuttgart, Institute for Systems Theory and Automatic Control, Pfaffenwaldring 9, Stuttgart, Germany
| | - Nadine Pollak
- University of Stuttgart, Institute of Cell Biology and Immunology, Allmandring 31, Stuttgart, Germany
- University of Stuttgart, Stuttgart Research Center Systems Biology, Nobelstr. 15, Stuttgart, Germany
| | - Frank Allgöwer
- University of Stuttgart, Institute for Systems Theory and Automatic Control, Pfaffenwaldring 9, Stuttgart, Germany
- University of Stuttgart, Stuttgart Research Center Systems Biology, Nobelstr. 15, Stuttgart, Germany
| | - Markus Rehm
- University of Stuttgart, Institute of Cell Biology and Immunology, Allmandring 31, Stuttgart, Germany
- University of Stuttgart, Stuttgart Research Center Systems Biology, Nobelstr. 15, Stuttgart, Germany
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37
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Zhang Y, Hamada M. MoAIMS: efficient software for detection of enriched regions of MeRIP-Seq. BMC Bioinformatics 2020; 21:103. [PMID: 32171255 PMCID: PMC7071693 DOI: 10.1186/s12859-020-3430-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 02/24/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) is a popular sequencing method for studying RNA modifications and, in particular, for N6-methyladenosine (m6A), the most abundant RNA methylation modification found in various species. The detection of enriched regions is a main challenge of MeRIP-Seq analysis, however current tools either require a long time or do not fully utilize features of RNA sequencing such as strand information which could cause ambiguous calling. On the other hand, with more attention on the treatment experiments of MeRIP-Seq, biologists need intuitive evaluation on the treatment effect from comparison. Therefore, efficient and user-friendly software that can solve these tasks must be developed. RESULTS We developed a software named "model-based analysis and inference of MeRIP-Seq (MoAIMS)" to detect enriched regions of MeRIP-Seq and infer signal proportion based on a mixture negative-binomial model. MoAIMS is designed for transcriptome immunoprecipitation sequencing experiments; therefore, it is compatible with different RNA sequencing protocols. MoAIMS offers excellent processing speed and competitive performance when compared with other tools. When MoAIMS is applied to studies of m6A, the detected enriched regions contain known biological features of m6A. Furthermore, signal proportion inferred from MoAIMS for m6A treatment datasets (perturbation of m6A methyltransferases) showed a decreasing trend that is consistent with experimental observations, suggesting that the signal proportion can be used as an intuitive indicator of treatment effect. CONCLUSIONS MoAIMS is efficient and easy-to-use software implemented in R. MoAIMS can not only detect enriched regions of MeRIP-Seq efficiently but also provide intuitive evaluation on treatment effect for MeRIP-Seq treatment datasets.
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Affiliation(s)
- Yiqian Zhang
- Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1 Okubo Shinjuku-ku, Tokyo, 169-8555 Japan
- AIST-Waseda University Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), 3-4-1, Okubo Shinjuku-ku, Tokyo, 169-8555 Japan
| | - Michiaki Hamada
- Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1 Okubo Shinjuku-ku, Tokyo, 169-8555 Japan
- AIST-Waseda University Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), 3-4-1, Okubo Shinjuku-ku, Tokyo, 169-8555 Japan
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-41-6 Aomi, Koto-ku, Tokyo, 135-0064 Japan
- Institute for Medical-oriented Structural Biology, Waseda University, 2-2, Wakamatsu-cho Shinjuku-ku, Tokyo, 162-8480 Japan
- Graduate School of Medicine, Nippon Medical School, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8602 Japan
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38
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Richter F, Haegeman B, Etienne RS, Wit EC. Introducing a general class of species diversification models for phylogenetic trees. STAT NEERL 2020. [DOI: 10.1111/stan.12205] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Francisco Richter
- Bernoulli Institute for Mathematics, Computer Science and Artificial IntelligenceUniversity of Groningen Groningen The Netherlands
- Groningen Institute for Evolutionary Life SciencesUniversity of Groningen Groningen The Netherlands
| | - Bart Haegeman
- Theoretical and Experimental Ecology StationCNRS and Paul Sabatier University Toulouse France
| | - Rampal S. Etienne
- Groningen Institute for Evolutionary Life SciencesUniversity of Groningen Groningen The Netherlands
| | - Ernst C. Wit
- Bernoulli Institute for Mathematics, Computer Science and Artificial IntelligenceUniversity of Groningen Groningen The Netherlands
- Institute of Computational ScienceUniversità della Svizzera italiana (USI) Lugano Switzerland
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39
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Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 17:ijerph17010096. [PMID: 31877756 PMCID: PMC6981826 DOI: 10.3390/ijerph17010096] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/13/2019] [Accepted: 12/18/2019] [Indexed: 11/23/2022]
Abstract
This study analyses factors associated with cyclist injury severity, focusing on vehicle type, route environment, and interactions between them. Data analysed was collected by Spanish police during 2016 and includes records relating to 12,318 drivers and cyclist involving in collisions with at least one injured cyclist, of whom 7230 were injured cyclists. Bayesian methods were used to model relationships between cyclist injury severity and circumstances related to the crash, with the outcome variable being whether a cyclist was killed or seriously injured (KSI) rather than slightly injured. Factors in the model included those relating to the injured cyclist, the route environment, and involved motorists. Injury severity among cyclists was likely to be higher where an Heavy Goods Vehicle (HGV) was involved, and certain route conditions (bicycle infrastructure, 30 kph zones, and urban zones) were associated with lower injury severity. Interactions exist between the two: collisions involving large vehicles in lower-risk environments are less likely to lead to KSIs than collisions involving large vehicles in higher-risk environments. Finally, motorists involved in a collision were more likely than the injured cyclists to have committed an error or infraction. The study supports the creation of infrastructure that separates cyclists from motor traffic. Also, action needs to be taken to address motorist behaviour, given the imbalance between responsibility and risk.
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40
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Abdelmoez MN, Oguchi Y, Ozaki Y, Yokokawa R, Kotera H, Shintaku H. Distinct Kinetics in Electrophoretic Extraction of Cytoplasmic RNA from Single Cells. Anal Chem 2019; 92:1485-1492. [PMID: 31805233 DOI: 10.1021/acs.analchem.9b04739] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The physical fractionation of cytoplasmic versus nuclear components of cells is a key step for studying the subcellular localization of molecules. The application of an electric field is an emerging method for subcellular fractionation of proteins and nucleic acids from single cells. However, the multibiophysical process that involves electrical lysis of cytoplasmic membranes, electrophoresis, and diffusion of charged molecules remains unclear. Here we study RNA dynamics in single cells during the electrophoretic extraction via a microfluidic system that enables stringent fractionation of the subcellular components leveraging a focused electric field. We identified two distinct kinetics in the extraction of RNA molecules, which were respectively associated with soluble RNA and mitochondrial RNA. We show that the extraction kinetics of soluble RNA is dominated by electrophoresis over diffusion and has a time constant of 0.15 s. Interestingly, the extraction of mitochondrial RNA showed unexpected heterogeneity in the extraction with slower kinetics (3.8 s), while reproducibly resulting in the extraction of 98.9% ± 2% after 40 s. Together, we uncover that the microfluidic system uniquely offers length bias-free fractionation of RNA molecules for quantitative analysis of correlations among subcellular compartments by exploiting the homogeneous electrophoretic properties of RNA.
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Affiliation(s)
- Mahmoud N Abdelmoez
- RIKEN Cluster for Pioneering Research , Wako , Saitama , 351-0198 Japan.,Department of Micro Engineering, Graduate School of Engineering , Kyoto University , Kyoto , 606-8501 Japan
| | - Yusuke Oguchi
- RIKEN Cluster for Pioneering Research , Wako , Saitama , 351-0198 Japan
| | - Yuka Ozaki
- RIKEN Cluster for Pioneering Research , Wako , Saitama , 351-0198 Japan
| | - Ryuji Yokokawa
- Department of Micro Engineering, Graduate School of Engineering , Kyoto University , Kyoto , 606-8501 Japan
| | - Hidetoshi Kotera
- Department of Micro Engineering, Graduate School of Engineering , Kyoto University , Kyoto , 606-8501 Japan
| | - Hirofumi Shintaku
- RIKEN Cluster for Pioneering Research , Wako , Saitama , 351-0198 Japan
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41
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Kirn SL, Hinders MK. Dynamic wavelet fingerprint for differentiation of tweet storm types. SOCIAL NETWORK ANALYSIS AND MINING 2019. [DOI: 10.1007/s13278-019-0617-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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42
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Ludwig J, Zu Siederdissen CH, Liu Z, Stadler PF, Müller S. flowEMMi: an automated model-based clustering tool for microbial cytometric data. BMC Bioinformatics 2019; 20:643. [PMID: 31815609 PMCID: PMC6902487 DOI: 10.1186/s12859-019-3152-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 10/10/2019] [Indexed: 12/17/2022] Open
Abstract
Background Flow cytometry (FCM) is a powerful single-cell based measurement method to ascertain multidimensional optical properties of millions of cells. FCM is widely used in medical diagnostics and health research. There is also a broad range of applications in the analysis of complex microbial communities. The main concern in microbial community analyses is to track the dynamics of microbial subcommunities. So far, this can be achieved with the help of time-consuming manual clustering procedures that require extensive user-dependent input. In addition, several tools have recently been developed by using different approaches which, however, focus mainly on the clustering of medical FCM data or of microbial samples with a well-known background, while much less work has been done on high-throughput, online algorithms for two-channel FCM. Results We bridge this gap with flowEMMi, a model-based clustering tool based on multivariate Gaussian mixture models with subsampling and foreground/background separation. These extensions provide a fast and accurate identification of cell clusters in FCM data, in particular for microbial community FCM data that are often affected by irrelevant information like technical noise, beads or cell debris. flowEMMi outperforms other available tools with regard to running time and information content of the clustering results and provides near-online results and optional heuristics to reduce the running-time further. Conclusions flowEMMi is a useful tool for the automated cluster analysis of microbial FCM data. It overcomes the user-dependent and time-consuming manual clustering procedure and provides consistent results with ancillary information and statistical proof.
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Affiliation(s)
- Joachim Ludwig
- Department of Environmental Microbiology, Research Group Flow Cytometry, Helmholtz Centre for Environmental Research, Permoserstraße 15, Leipzig, 04318, Germany
| | | | - Zishu Liu
- Department of Environmental Microbiology, Research Group Flow Cytometry, Helmholtz Centre for Environmental Research, Permoserstraße 15, Leipzig, 04318, Germany
| | - Peter F Stadler
- Department of Computer Science, University Leipzig, Härtelstr. 16-18, Leipzig, 04107, Germany
| | - Susann Müller
- Department of Environmental Microbiology, Research Group Flow Cytometry, Helmholtz Centre for Environmental Research, Permoserstraße 15, Leipzig, 04318, Germany
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Jin H, Moseley HNB. Moiety modeling framework for deriving moiety abundances from mass spectrometry measured isotopologues. BMC Bioinformatics 2019; 20:524. [PMID: 31660850 PMCID: PMC6816163 DOI: 10.1186/s12859-019-3096-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 09/10/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Stable isotope tracing can follow individual atoms through metabolic transformations through the detection of the incorporation of stable isotope within metabolites. This resulting data can be interpreted in terms related to metabolic flux. However, detection of a stable isotope in metabolites by mass spectrometry produces a profile of isotopologue peaks that requires deconvolution to ascertain the localization of isotope incorporation. RESULTS To aid the interpretation of the mass spectroscopy isotopologue profile, we have developed a moiety modeling framework for deconvoluting metabolite isotopologue profiles involving single and multiple isotope tracers. This moiety modeling framework provides facilities for moiety model representation, moiety model optimization, and moiety model selection. The moiety_modeling package was developed from the idea of metabolite decomposition into moiety units based on metabolic transformations, i.e. a moiety model. The SAGA-optimize package, solving a boundary-value inverse problem through a combined simulated annealing and genetic algorithm, was developed for model optimization. Additional optimization methods from the Python scipy library are utilized as well. Several forms of the Akaike information criterion and Bayesian information criterion are provided for selecting between moiety models. Moiety models and associated isotopologue data are defined in a JSONized format. By testing the moiety modeling framework on the timecourses of 13C isotopologue data for uridine diphosphate N-acetyl-D-glucosamine (UDP-GlcNAc) in human prostate cancer LnCaP-LN3 cells, we were able to confirm its robust performance in isotopologue deconvolution and moiety model selection. CONCLUSIONS SAGA-optimize is a useful Python package for solving boundary-value inverse problems, and the moiety_modeling package is an easy-to-use tool for mass spectroscopy isotopologue profile deconvolution involving single and multiple isotope tracers. Both packages are freely available on GitHub and via the Python Package Index.
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Affiliation(s)
- Huan Jin
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY, USA
| | - Hunter N B Moseley
- Department of Molecular & Cellular Biochemistry, University of Kentucky, Lexington, KY, USA. .,Markey Cancer Center, University of Kentucky, Lexington, KY, USA. .,Resource Center for Stable Isotope Resolved Metabolomics, University of Kentucky, Lexington, KY, USA. .,Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA.
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Modeling determinants of time-to-circumcision of girls: a comparison of various parametric shared frailty models. HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY 2019. [DOI: 10.1007/s10742-019-00199-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Kossakowski JJ, Gordijn MCM, Riese H, Waldorp LJ. Applying a Dynamical Systems Model and Network Theory to Major Depressive Disorder. Front Psychol 2019; 10:1762. [PMID: 31447730 PMCID: PMC6692450 DOI: 10.3389/fpsyg.2019.01762] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 07/15/2019] [Indexed: 11/23/2022] Open
Abstract
Mental disorders like major depressive disorder can be modeled as complex dynamical systems. In this study we investigate the dynamic behavior of individuals to see whether or not we can expect a transition to another mood state. We introduce a mean field model to a binomial process, where we reduce a dynamic multidimensional system (stochastic cellular automaton) to a one-dimensional system to analyse the dynamics. Using maximum likelihood estimation, we can estimate the parameter of interest which, in combination with a bifurcation diagram, reflects the expectancy that someone has to transition to another mood state. After numerically illustrating the proposed method with simulated data, we apply this method to two empirical examples, where we show its use in a clinical sample consisting of patients diagnosed with major depressive disorder, and a general population sample. Results showed that the majority of the clinical sample was categorized as having an expectancy for a transition, while the majority of the general population sample did not have this expectancy. We conclude that the mean field model has great potential in assessing the expectancy for a transition between mood states. With some extensions it could, in the future, aid clinical therapists in the treatment of depressed patients.
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Affiliation(s)
| | - Marijke C. M. Gordijn
- Department of Chronobiology, GeLifes, University of Groningen, Groningen, Netherlands
| | - Harriëtte Riese
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Lourens J. Waldorp
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
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Filce C, Hyett J, Sahota D, Wilson K, McLennan A. Developing a quality assurance program for transvaginal cervical length measurement at 18-21 weeks' gestation. Aust N Z J Obstet Gynaecol 2019; 60:55-62. [PMID: 31141167 DOI: 10.1111/ajo.12984] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Accepted: 03/21/2019] [Indexed: 01/12/2023]
Abstract
BACKGROUND Preterm birth is the leading cause of death in children under the age of five years. Transvaginal cervical length (TVCL) assessment can be used to predict preterm delivery risk at the mid-trimester scan. To optimise the screening tool, developing and maintaining quality standards is important. AIMS To develop an Australian reference range for TVCL at 18.0-21.0 weeks' gestation, quality standards for measurement and audit mechanisms for ultrasound operators. MATERIALS AND METHODS A retrospective audit was performed of consecutive patients scanned at 18.0-21.0 weeks' gestation. Each TVCL measurement ultrasound image was reviewed, and exclusions were made based on a defined set of quality criteria. Fractional polynomial Bayesian methodology was used to establish a reference range. Central tendency, dispersion plots and cumulative sum charts for operators in the original reference range cohort were created. These plots were then applied to a second validation cohort of operators to establish the efficacy of this quality assurance audit tool. RESULTS Median TVCL from 1031 participants was 36.0 mm (interquartile range 32.7-40.0 mm), which was independent of gestational age. The quality audit tool was applied to 15 operators from the reference cohort with a mean cervix length multiple of the median of 1.01 and a mean SD log10 cervix length multiple of the median of 0.06. Of the 22 operators in the validation cohort, 20 (90.9%) demonstrated ideal or acceptable central tendency results, and 19 (86.4%) remained in the appropriate cumulative sum zone. CONCLUSION An Australian cervix length measurement reference range at 18.0-21.0 weeks' gestation has been developed along with a validated quality assurance audit tool for ultrasound operators.
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Affiliation(s)
- Casey Filce
- Discipline of Obstetrics, Gynaecology and Neonatology, Faculty of Medicine, University of Sydney, Sydney, New South Wales, Australia
| | - Jon Hyett
- Discipline of Obstetrics, Gynaecology and Neonatology, Faculty of Medicine, University of Sydney, Sydney, New South Wales, Australia
| | - Daljit Sahota
- Department of Obstetrics and Gynaecology, Faculty of Medicine, Chinese University of Hong Kong, New Territories, Hong Kong
| | - Kate Wilson
- Discipline of Obstetrics, Gynaecology and Neonatology, Faculty of Medicine, University of Sydney, Sydney, New South Wales, Australia
| | - Andrew McLennan
- Discipline of Obstetrics, Gynaecology and Neonatology, Faculty of Medicine, University of Sydney, Sydney, New South Wales, Australia
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A method of rapid testing of radioactivity of different materials. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2019. [DOI: 10.1016/j.jrras.2016.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Moser U, Schramm D. Multivariate dynamic time warping in automotive applications: A review. INTELL DATA ANAL 2019. [DOI: 10.3233/ida-184130] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Uwe Moser
- Department of Autonomous Driving and Driver Assistance, BMW Group, 80788 Munich, Germany
| | - Dieter Schramm
- Chair of Mechatronics, Faculty of Engineering Sciences, University of Duisburg-Essen, 47057 Duisburg, Germany
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Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2019. [DOI: 10.3390/make1010032] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
When performing a regression or classification analysis, one needs to specify a statistical model. This model should avoid the overfitting and underfitting of data, and achieve a low generalization error that characterizes its prediction performance. In order to identify such a model, one needs to decide which model to select from candidate model families based on performance evaluations. In this paper, we review the theoretical framework of model selection and model assessment, including error-complexity curves, the bias-variance tradeoff, and learning curves for evaluating statistical models. We discuss criterion-based, step-wise selection procedures and resampling methods for model selection, whereas cross-validation provides the most simple and generic means for computationally estimating all required entities. To make the theoretical concepts transparent, we present worked examples for linear regression models. However, our conceptual presentation is extensible to more general models, as well as classification problems.
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