1
|
Xu Y, Tian C, Ma J, Li J, Zhang G. Grain transportation and consumption reshapes the α-HCH exposure picture of China. Sci Total Environ 2024; 927:172254. [PMID: 38583609 DOI: 10.1016/j.scitotenv.2024.172254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 04/09/2024]
Abstract
Socio-economic activities like food trade can increase the uncertainty of human risk of persistent organic pollutants (POPs). We compared the change in model predicted α-hexachlorocyclohexane (α-HCH) cancer risk (CR) with and without grain trade in mainland China. In scenario without grain logistics, α-HCH moved fast away from southern and southeastern China via northward atmospheric transport. However, the grain logistics from northeastern China delivers the α-HCH previously accumulated in northeastern sink back to densely populated areas in recent years, which enhance CR by >50 % in the southern seaboard of China. The northward movement of grain production center and recent grain deficiency in southern provinces induced by dietary pattern changes is identified as the major driving factors of the reversed transport of α-HCH. The finding highlights the potential of socio-economic activities that can otherwise offset the risk reduction effect of the geochemical cycle of POPs.
Collapse
Affiliation(s)
- Yue Xu
- State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China
| | - Chongguo Tian
- Key Laboratory of Coastal Zone Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
| | - Jianmin Ma
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Jun Li
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Gan Zhang
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China.
| |
Collapse
|
2
|
Zhou J, Zhang R, Guo J, Dai J, Zhang J, Zhang L, Miao Y. Estimation of aboveground biomass of senescence grassland in China's arid region using multi-source data. Sci Total Environ 2024; 918:170602. [PMID: 38325448 DOI: 10.1016/j.scitotenv.2024.170602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 12/15/2023] [Accepted: 01/29/2024] [Indexed: 02/09/2024]
Abstract
Aboveground Biomass (AGB) in the grassland senescence period is a key indicator for assessing grassland fire risk and autumnal pasture carrying capacity. Despite the advancement of remote sensing in rapid monitoring of AGB on a regional scale, accurately monitoring AGB during the senescence period in vast arid areas remains a major challenge. Using remote sensing, environmental data, and 356 samples of grassland senescence period AGB data, this study utilizes the Gram-Schmidt Pan Sharpening (GS) method, multivariate selection methods, and machine learning algorithms (RF, SVM, and BP_ANN) to construct a model for AGB during senescence grassland, and applies the optimal model to analyze spatio-temporal pattern changes in AGB from 2000 to 2021 in arid regions. The results indicate that the GS method effectively enhances the correlation between measured AGB and vegetation indices, reducing model error to some extent; The accuracy of grassland AGB inversion models based on a single vegetation index is low (0.03 ≤ |R| ≤ 0.63), while the RF model constructed with multiple variables selected by the Boruta algorithm is the optimal model for estimating AGB in arid regions during the senescence period (R2 = 0.71, RMSE = 519.74 kg/ha); In the span of 22 years, the annual average AGB in the senescence period of arid regions was 1413.85 kg/ha, with regions of higher AGB primarily located in the northeast and southwest of the study area. The area experiencing an increase in AGB during the senescence period (79.97 %) was significantly larger than that with decreased AGB (20.03 %).
Collapse
Affiliation(s)
- Jiahui Zhou
- College of Ecology and Environment, Xinjiang University, Urumqi 830046, China; Key Laboratory of Oasis Ecology of Education Ministry, Xinjiang University, Urumqi 830046, China.
| | - Renping Zhang
- College of Ecology and Environment, Xinjiang University, Urumqi 830046, China; Key Laboratory of Oasis Ecology of Education Ministry, Xinjiang University, Urumqi 830046, China.
| | - Jing Guo
- Xinjiang Academy Forestry, Urumqi 830000, China
| | - Junfeng Dai
- Xinjiang Uygur Autonomous Region Forestry and Grassland Bureau of Fire Prevention, Urumqi 830000, China
| | - Jianli Zhang
- Xinjiang Uygur Autonomous Region Grassland General Station, Urumqi 830000, China
| | - Liangliang Zhang
- College of Ecology and Environment, Xinjiang University, Urumqi 830046, China; Key Laboratory of Oasis Ecology of Education Ministry, Xinjiang University, Urumqi 830046, China.
| | - Yuhao Miao
- College of Ecology and Environment, Xinjiang University, Urumqi 830046, China; Key Laboratory of Oasis Ecology of Education Ministry, Xinjiang University, Urumqi 830046, China.
| |
Collapse
|
3
|
Zhong X, Jacobsohn A, Dufour C, Schwartz C, Sterckeman T. Evaluating a mass balance model for soil trace metals using the historical data from the King's Kitchen Garden (Versailles, France). J Hazard Mater 2024; 465:133259. [PMID: 38118194 DOI: 10.1016/j.jhazmat.2023.133259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/29/2023] [Accepted: 12/12/2023] [Indexed: 12/22/2023]
Abstract
The mass balance of reconstituted Cd, Cu, Pb and Zn fluxes from 1683 to 2021 was compared to the current levels of the soil used only for vegetable production in the King's Kitchen Garden in Versailles (France). This comparison was made on the basis of 4 scenarios of organic matter application in the 18th and 19th centuries and by an uncertainty analysis over the entire period. The topsoil contamination falls within that of French kitchen gardens. Modelling of past fluxes predicted the correct trend (an increase) and order of magnitude of the soil metal contents. It produced a relatively accurate evaluation of the Cu and Zn contents. The model underestimated the Pb contents by about 80%, revealing a large and unknown source of soil contamination by this metal. The calculation overestimated the current Cd levels by about 100%, probably due to various biases, for example on atmospheric fallout or the composition of organic amendments. This assessment shows that modelling the mass balance of trace metal fluxes can be used to predict the long-term trend in the levels of these elements in cultivated soils, providing the input data are chosen according to realistic scenarios.
Collapse
Affiliation(s)
- Xueqian Zhong
- Université de Lorraine, INRAE, Laboratoire Sols et Environnement, F-54000 Nancy, France
| | - Antoine Jacobsohn
- École nationale supérieure de paysage, Potager du Roi, 78000 Versailles, France
| | - Christine Dufour
- École nationale supérieure de paysage, Potager du Roi, 78000 Versailles, France
| | - Christophe Schwartz
- Université de Lorraine, INRAE, Laboratoire Sols et Environnement, F-54000 Nancy, France
| | - Thibault Sterckeman
- Université de Lorraine, INRAE, Laboratoire Sols et Environnement, F-54000 Nancy, France.
| |
Collapse
|
4
|
Singh FA, Afzal N, Smithline SJ, Thalhauser CJ. Assessing the performance of QSP models: biology as the driver for validation. J Pharmacokinet Pharmacodyn 2023:10.1007/s10928-023-09871-x. [PMID: 37386340 DOI: 10.1007/s10928-023-09871-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/15/2023] [Indexed: 07/01/2023]
Abstract
Validation of a quantitative model is a critical step in establishing confidence in the model's suitability for whatever analysis it was designed. While processes for validation are well-established in the statistical sciences, the field of quantitative systems pharmacology (QSP) has taken a more piecemeal approach to defining and demonstrating validation. Although classical statistical methods can be used in a QSP context, proper validation of a mechanistic systems model requires a more nuanced approach to what precisely is being validated, and what role said validation plays in the larger context of the analysis. In this review, we summarize current thoughts of QSP validation in the scientific community, contrast the aims of statistical validation from several contexts (including inference, pharmacometrics analysis, and machine learning) with the challenges faced in QSP analysis, and use examples from published QSP models to define different stages or levels of validation, any of which may be sufficient depending on the context at hand.
Collapse
Affiliation(s)
- Fulya Akpinar Singh
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA
| | - Nasrin Afzal
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA
| | - Shepard J Smithline
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA
| | - Craig J Thalhauser
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA.
| |
Collapse
|
5
|
Grassini L, Magrini A, Conti E. Formative-reflective scheme for the assessment of tourism destination competitiveness: an analysis of Italian municipalities. Qual Quant 2022; 57:1-26. [PMID: 36097442 PMCID: PMC9453737 DOI: 10.1007/s11135-022-01519-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/20/2022] [Indexed: 11/25/2022]
Abstract
In this article, we propose a formative-reflective scheme for the assessment of Tourism Destination Competitiveness (TDC) based on a combined use of Partial Least Squares-Path Modelling (PLS-PM) and the method recently proposed by Fattore, Pelagatti, and Vittadini (FPV). TDC is conceived as a construct reflecting the tourism performance of a destination, and several determinants are considered, including endowed resources, created resources, and supporting factors. The proposed scheme is applied to a case study on 1575 Italian municipalities for which the Italian National Institute of Statistics released data on tourist flows. Our contribution is innovative for three aspects: (i) the consistency of the formative-reflective scheme for TDC assessment is discussed on a theoretical basis; (ii) an empirical comparison between PLS-PM and the FPV method is performed; (iii) data with higher granularity than most studies on TDC assessment are employed. Our findings highlight that endowed resources are the primary driver of TDC, followed by created resources and supporting factors, and emphasize that the best ranked destinations are big cities with a multifaceted tourism alongside sea and mountain destinations with cultural attractions.
Collapse
Affiliation(s)
- Laura Grassini
- Department of Statistics, Computer Science, Applications, University of Florence, Florence, Italy
| | - Alessandro Magrini
- Department of Statistics, Computer Science, Applications, University of Florence, Florence, Italy
| | - Enrico Conti
- Regional Institute for Economic Planning of Tuscany (IRPET), Florence, Italy
| |
Collapse
|
6
|
Androulakis IP. Towards a comprehensive assessment of QSP models: what would it take? J Pharmacokinet Pharmacodyn 2022:10.1007/s10928-022-09820-0. [PMID: 35962928 PMCID: PMC9922790 DOI: 10.1007/s10928-022-09820-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 07/15/2022] [Indexed: 10/15/2022]
Abstract
Quantitative Systems Pharmacology (QSP) has emerged as a powerful ensemble of approaches aiming at developing integrated mathematical and computational models elucidating the complex interactions between pharmacology, physiology, and disease. As the field grows and matures its applications expand beyond the boundaries of research and development and slowly enter the decision making and regulatory arenas. However, widespread acceptance and eventual adoption of a new modeling approach requires assessment criteria and quantifiable metrics that establish credibility and increase confidence in model predictions. QSP aims to provide an integrated understanding of pathology in the context of therapeutic interventions. Because of its ambitious nature and the fact that QSP emerged in an uncoordinated manner as a result of activities distributed across organizations and academic institutions, high entropy characterizes the tools, methods, and computational methodologies and approaches used. The eventual acceptance of QSP model predictions as supporting material for an application to a regulatory agency will require that two key aspects are considered: (1) increase confidence in the QSP framework, which drives standardization and assessment; and (2) careful articulation of the expectations. Both rely heavily on our ability to rigorously and consistently assess QSP models. In this manuscript, we wish to discuss the meaning and purpose of such an assessment in the context of QSP model development and elaborate on the differentiating features of QSP that render such an endeavor challenging. We argue that QSP establishes a conceptual, integrative framework rather than a specific and well-defined computational methodology. QSP elicits the use of a wide variety of modeling and computational methodologies optimized with respect to specific applications and available data modalities, which exceed the data structures employed by chemometrics and PK/PD models. While the range of options fosters creativity and promises to substantially advance our ability to design pharmaceutical interventions rationally and optimally, our expectations of QSP models need to be clearly articulated and agreed on, with assessment emphasizing the scope of QSP studies rather than the methods used. Nevertheless, QSP should not be considered an independent approach, rather one of many in the broader continuum of computational models.
Collapse
Affiliation(s)
- Ioannis P Androulakis
- Biomedical Engineering Department and Chemical & Biochemical Engineering Department, Rutgers, The State University of New Jersey, New Brunswick, USA.
| |
Collapse
|
7
|
Ge J, Hou M, Liang T, Feng Q, Meng X, Liu J, Bao X, Gao H. Spatiotemporal dynamics of grassland aboveground biomass and its driving factors in North China over the past 20 years. Sci Total Environ 2022; 826:154226. [PMID: 35240176 DOI: 10.1016/j.scitotenv.2022.154226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 02/23/2022] [Accepted: 02/25/2022] [Indexed: 06/14/2023]
Abstract
Although remote sensing has enabled rapid monitoring of grassland aboveground biomass (AGB) at a regional scale, it is still a difficult challenge to construct an accurate estimation model of grassland AGB in a vast region to support the AGB dynamics analysis over a long time series. In this study, extensive grassland AGB measurements (collected in North China during the grassland growing season of 2000-2019), MODIS data, and environmental factors (climate, topography and soil) were employed to construct the grassland AGB models using four machine learning algorithms (random forest, support vector machine, artificial neural network and extreme learning machine) combined with four variable selections. The spatial distributions of annual grassland AGB from 2000 to 2019 were simulated based on the optimal AGB model. The temporal change and future trend of AGB series from 2000 to 2019 were comprehensively analyzed by the slope model and Hurst exponent. The influences of natural and anthropogenic factors on grassland AGB dynamics were explored quantitatively using the Geodetector model. The results showed that (1) the random forest model constructed from the variables selected by the successive projections algorithm is the optimal grassland AGB model. (2) The 20-year average grassland AGB in North China showed an overall spatial distribution of being low in the central and western parts and high in the southeastern part. (3) The annual maximum grassland AGB in most regions (82.71%) showed an increasing trend during 2000-2019; and most of the grasslands with a decreasing trend of AGB were located in regions with low AGB values and arid climates. (4) The future trend of grassland AGB after the study period may be optimistic, as reflected by more grassland AGB was predicted to increase rather than decrease (70.38% vs. 29.62%). (5) The main driving factors of spatiotemporal dynamics of grassland AGB were precipitation, soil type, and livestock density; the interactive influence of two drivers on AGB showed mutual enhancement.
Collapse
Affiliation(s)
- Jing Ge
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, PR China
| | - Mengjing Hou
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, PR China
| | - Tiangang Liang
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, PR China.
| | - Qisheng Feng
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, PR China
| | - Xinyue Meng
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, PR China
| | - Jie Liu
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, PR China
| | - Xuying Bao
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, PR China
| | - Hongyuan Gao
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, PR China
| |
Collapse
|
8
|
Russo M, Scarpa B. Learning in Medicine: The Importance of Statistical Thinking. Methods Mol Biol 2022; 2486:215-232. [PMID: 35437725 DOI: 10.1007/978-1-0716-2265-0_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In many fields, including medicine and biology, there has been in the last years an increasing diffusion and availability of complex data from different sources. Examples include biological experiments or data from health care providers. These data encompass information that can potentially enhance therapeutic advancement and constitute the core of modern system medicine. When analyzing these complex data, it is important to appropriately quantify uncertainty, avoiding using only algorithmic and automated approaches, which are not always appropriate. Improper application of algorithmic approaches, which ignore domain knowledge, could result in filling the literature with imprecise and/or misleading conclusions. In this chapter, we highlight the importance of statistical thinking when leveraging complex data and models to enhance science progress. In particular, we discuss the reproducibility and replicability issues, the importance of uncertainty quantification, and some common pitfalls in the analysis of big data.
Collapse
Affiliation(s)
- Massimiliano Russo
- Harvard-MIT Center for Regulatory Science, Harvard Medical School & Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Bruno Scarpa
- Department of Statistical Sciences, University of Padova, Padova, Italy
| |
Collapse
|
9
|
Ding Z, Wang G, Yang H, Zhang P, Fu D, Yang Z, Wang X, Wang X, Xia Z, Zhang C, Cai W, Yuan B, Jia D, Chen B, Huang C, Zhang J, Li Y, Yang S, He R. A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram. Med Biol Eng Comput 2021; 60:33-45. [PMID: 34677739 PMCID: PMC8724189 DOI: 10.1007/s11517-021-02420-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 07/26/2021] [Indexed: 11/28/2022]
Abstract
Computerized interpretation of electrocardiogram plays an important role in daily cardiovascular healthcare. However, inaccurate interpretations lead to misdiagnoses and delay proper treatments. In this work, we built a high-quality Chinese 12-lead resting electrocardiogram dataset with 15,357 records, and called for a community effort to improve the performances of CIE through the China ECG AI Contest 2019. This dataset covers most types of ECG interpretations, including the normal type, 8 common abnormal types, and the other type which includes both uncommon abnormal and noise signals. Based on the Contest, we systematically assessed and analyzed a set of top-performing methods, most of which are deep neural networks, with both their commonalities and characteristics. This study establishes the benchmarks for computerized interpretation of 12-lead resting electrocardiogram and provides insights for the development of new methods. A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram ![]()
Collapse
Affiliation(s)
- Zijian Ding
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Guijin Wang
- Department of Electronic Engineering, Tsinghua University, Beijing, China.
| | - Huazhong Yang
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Ping Zhang
- Department of Cardiology, Beijing Tsinghua Changgung Hospital, Beijing, China.,School of clinical Medicine, Tsinghua University, Beijing, China
| | - Dapeng Fu
- Chinese Academy of Sciences Zhong Guan Cun Hospital, Beijing, China
| | - Zhen Yang
- ECG Center, Tianjin Wuqing District People's Hospital, Tianjin, China
| | - Xinkang Wang
- ECG Diagnosis Department, Fujian Provincial Hospital, Fuzhou, China
| | - Xia Wang
- Beijing Tsingdata Technology Development Co., LTD., Beijing, China
| | - Zhourui Xia
- Tsinghua-Berkerley Shenzhen Institute, Shenzhen, China
| | - Chiming Zhang
- Southwest University of Science and Technology, Mianyang, China
| | - Wenjie Cai
- University of Shanghai for Science and Technology, Shanghai, China
| | | | - Dongya Jia
- Guangzhou Shiyuan Electronic Technology Company LTD, Guangzhou, China
| | - Bo Chen
- 1st Military Delegate Room of Dalian Regional, Dalian, China
| | | | - Jing Zhang
- University of Science and Technology of China, Hefei, China
| | - Yi Li
- China Wuhan Zoncare, LTD., Wuhan, China
| | - Shan Yang
- Chengdu Spaceon Electronics CO., LTD., Chengdu, China
| | - Runnan He
- Harbin Institute of Technology, Harbin, China
| |
Collapse
|
10
|
Houska T, Kraft P, Jehn FU, Bestian K, Kraus D, Breuer L. Detection of hidden model errors by combining single and multi-criteria calibration. Sci Total Environ 2021; 777:146218. [PMID: 33689893 DOI: 10.1016/j.scitotenv.2021.146218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 02/19/2021] [Accepted: 02/25/2021] [Indexed: 06/12/2023]
Abstract
Environmental models aim to reproduce landscape processes with mathematical equations. Observations are used for validation. The performance and uncertainties are quantified either by single or multi-criteria model assessment. In a case-study, we combine both approaches. We use a coupled hydro-biogeochemistry landscape-scale model to simulate 14 target values on discharge, stream nitrate as well as soil moisture, soil temperature and trace gas emissions (N2O, CO2) from different land uses. We reveal typical mistakes that happen during both, single and multi-criteria model assessment. Such as overestimated uncertainty in multi-criteria and ignored wrong model processes in single-criterion calibration. These mistakes can mislead the development of water quality and in general all environmental models. Only the combination of both approaches reveals the five types of posterior probability distributions for model parameters. Each type allocates a specific type of error. We identify and locate mismatched parameter values, obsolete parameters, flawed model structures and wrong process representations. The presented method can guide model users and developers to the so far hidden errors in their models. We emphasize to include observations from physical, chemical, biological and ecological processes in the model assessment, rather than the typical discipline specific assessments.
Collapse
Affiliation(s)
- T Houska
- Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, 35392 Giessen, Germany.
| | - P Kraft
- Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, 35392 Giessen, Germany
| | - F U Jehn
- Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, 35392 Giessen, Germany
| | - K Bestian
- Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, 35392 Giessen, Germany
| | - D Kraus
- Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), 82467 Garmisch-Partenkirchen, Germany
| | - L Breuer
- Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, 35392 Giessen, Germany; Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, 35392 Giessen, Germany
| |
Collapse
|
11
|
Abstract
Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized at atomic resolution using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. In the following chapter, we present an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of a similar protocol has resulted in models of useful accuracy for domains in more than half of all known protein sequences.
Collapse
|
12
|
Oetzmann von Sochaczewski C, Deigendesch N, Lindner A, Baumgart J, Schröder A, Heimann A, Muensterer OJ. Comparing Aachen Minipigs and Pietrain Piglets as Models of Experimental Pediatric Urology to Human Reference Data. Eur Surg Res 2020; 61:95-100. [PMID: 33161395 DOI: 10.1159/000511399] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 09/02/2020] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Swine had special roles in the development of minimally invasive procedures to treat vesicoureteral reflux, and minipigs have been gaining ground in recent years in experimental pediatric urology as they combine small size with less vulnerable adult physiology, but their suitability as a model has never been assessed. We therefore compared a landrace piglet with a juvenile minipig to elucidate comparability. METHODS We evaluated five 3-week old Pietrain piglets and five 3-month old Aachen Minipigs as representatives of landrace and minipig models based on their expected bodyweight being similar to a newborn human. We compared renal weight, volume - via the ellipsoid formula - and ureteral length. In addition, we calculated porcine renal function via Gasthuys' formula. In order to compare the groups with previously published values for infants, we used resampling techniques to allow comparison to humans. RESULTS Renal weight was higher in humans than in Pietrain piglets (ΔL = 7.6 g; ΔR = 5.4 g) and Aachen Minipigs (ΔL = 11 g; ΔR = 9.4 g). Renal volumes in humans were higher than in both Pietrain piglets (ΔL = 5.6 mL, p < 0.001; ΔR = 3.7 mL, p = 0.004) and Aachen Minipigs (ΔL = 8.1 mL; ΔR = 6.6 mL; both p < 0.001). Ureteral lengths in humans and both pig breeds were comparable as were estimated renal functions between both pig breeds. DISCUSSION AND CONCLUSION Both landrace piglets and juvenile minipigs are suitable models for experimental pediatric urology as parameters did not differ between them. In addition, the anatomic parameters are comparable or smaller than in infants. This might facilitate translational research as technical failure is less likely in larger organs. Additional research is necessary to cover higher age ranges than those included in the present pilot study.
Collapse
Affiliation(s)
| | - Nikolaus Deigendesch
- Institut für medizinische Genetik und Pathologie, Universitätsspital Basel, Basel, Switzerland
| | - Andreas Lindner
- Klinik und Poliklinik für Kinderchirurgie, Universitätsmedizin der Johannes-Gutenberg-Universität Mainz, Mainz, Germany
| | - Jan Baumgart
- Translational Animal Research Center, Johannes-Gutenberg-Universität Mainz, Mainz, Germany
| | - Arne Schröder
- Klinik für Kinder- und Jugendmedizin, Elisabeth-Krankenhaus Essen, Essen, Germany
| | - Axel Heimann
- Institut für neurochirurgische Pathophysiologie, Universitätsmedizin der Johannes-Gutenberg-Universität Mainz, Mainz, Germany
| | - Oliver J Muensterer
- Klinik und Poliklinik für Kinderchirurgie, Universitätsmedizin der Johannes-Gutenberg-Universität Mainz, Mainz, Germany
| |
Collapse
|
13
|
Lu C, Yang Y. On assessing binary regression models based on ungrouped data. Biometrics 2018; 75:5-12. [PMID: 30229867 DOI: 10.1111/biom.12969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 07/01/2018] [Accepted: 08/01/2018] [Indexed: 11/26/2022]
Abstract
Assessing a binary regression model based on ungrouped data is a commonly encountered but very challenging problem. Although tests, such as Hosmer-Lemeshow test and le Cessie-van Houwelingen test, have been devised and widely used in applications, they often have low power in detecting lack of fit and not much theoretical justification has been made on when they can work well. In this article, we propose a new approach based on a cross-validation voting system to address the problem. In addition to a theoretical guarantee that the probabilities of type I and II errors both converge to zero as the sample size increases for the new method under proper conditions, our simulation results demonstrate that it performs very well.
Collapse
Affiliation(s)
- Chunling Lu
- Division of Global Health, Brigham and Women's Hospital & Department of Global Health and Social Medicine Harvard University, Boston, U.S.A
| | - Yuhong Yang
- School of Statistics, University of Minnesota, Minnesota, U.S.A
| |
Collapse
|
14
|
Nowakowska M. Selected aspects of prior and likelihood information for a Bayesian classifier in a road safety analysis. Accid Anal Prev 2017; 101:97-106. [PMID: 28213206 DOI: 10.1016/j.aap.2017.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Revised: 10/21/2016] [Accepted: 01/16/2017] [Indexed: 06/06/2023]
Abstract
The development of the Bayesian logistic regression model classifying the road accident severity is discussed. The already exploited informative priors (method of moments, maximum likelihood estimation, and two-stage Bayesian updating), along with the original idea of a Boot prior proposal, are investigated when no expert opinion has been available. In addition, two possible approaches to updating the priors, in the form of unbalanced and balanced training data sets, are presented. The obtained logistic Bayesian models are assessed on the basis of a deviance information criterion (DIC), highest probability density (HPD) intervals, and coefficients of variation estimated for the model parameters. The verification of the model accuracy has been based on sensitivity, specificity and the harmonic mean of sensitivity and specificity, all calculated from a test data set. The models obtained from the balanced training data set have a better classification quality than the ones obtained from the unbalanced training data set. The two-stage Bayesian updating prior model and the Boot prior model, both identified with the use of the balanced training data set, outperform the non-informative, method of moments, and maximum likelihood estimation prior models. It is important to note that one should be careful when interpreting the parameters since different priors can lead to different models.
Collapse
Affiliation(s)
- Marzena Nowakowska
- Faculty of Management and Computer Modelling, Kielce University of Technology, Al. Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, Poland.
| |
Collapse
|
15
|
Lottaz C, Gronwald W, Spang R, Engelmann JC. High-Dimensional Profiling for Computational Diagnosis. Methods Mol Biol 2017; 1526:205-229. [PMID: 27896744 DOI: 10.1007/978-1-4939-6613-4_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
New technologies allow for high-dimensional profiling of patients. For instance, genome-wide gene expression analysis in tumors or in blood is feasible with microarrays, if all transcripts are known, or even without this restriction using high-throughput RNA sequencing. Other technologies like NMR finger printing allow for high-dimensional profiling of metabolites in blood or urine. Such technologies for high-dimensional patient profiling represent novel possibilities for molecular diagnostics. In clinical profiling studies, researchers aim to predict disease type, survival, or treatment response for new patients using high-dimensional profiles. In this process, they encounter a series of obstacles and pitfalls. We review fundamental issues from machine learning and recommend a procedure for the computational aspects of a clinical profiling study.
Collapse
Affiliation(s)
- Claudio Lottaz
- Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
| | - Wolfram Gronwald
- Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Rainer Spang
- Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Julia C Engelmann
- Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| |
Collapse
|
16
|
Abstract
Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized at atomic resolution using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. In the following chapter, we present an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of a similar protocol has resulted in models of useful accuracy for domains in more than half of all known protein sequences.
Collapse
Affiliation(s)
- Benjamin Webb
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, CA, 94143, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, CA, 94143, USA.
| |
Collapse
|
17
|
Ohsawa M, Ogasawara K, Okayama A. Tightrope walking: achieving the best possible balance between better model fit and accurate prediction: response to letter (IJC-D-14-00417). Int J Cardiol 2014; 174:791-4. [PMID: 24794062 DOI: 10.1016/j.ijcard.2014.04.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2014] [Accepted: 04/09/2014] [Indexed: 11/30/2022]
Affiliation(s)
- Masaki Ohsawa
- Department of Hygiene and Preventive Medicine, Iwate Medical University, Japan.
| | | | - Akira Okayama
- The First Institute of Health Service, Japan Anti-Tuberculosis Association, Japan
| |
Collapse
|
18
|
Pandurangan AP, Shakeel S, Butcher SJ, Topf M. Combined approaches to flexible fitting and assessment in virus capsids undergoing conformational change. J Struct Biol 2014; 185:427-39. [PMID: 24333899 DOI: 10.1016/j.jsb.2013.12.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2013] [Revised: 11/28/2013] [Accepted: 12/06/2013] [Indexed: 01/25/2023]
Abstract
Fitting of atomic components into electron cryo-microscopy (cryoEM) density maps is routinely used to understand the structure and function of macromolecular machines. Many fitting methods have been developed, but a standard protocol for successful fitting and assessment of fitted models has yet to be agreed upon among the experts in the field. Here, we created and tested a protocol that highlights important issues related to homology modelling, density map segmentation, rigid and flexible fitting, as well as the assessment of fits. As part of it, we use two different flexible fitting methods (Flex-EM and iMODfit) and demonstrate how combining the analysis of multiple fits and model assessment could result in an improved model. The protocol is applied to the case of the mature and empty capsids of Coxsackievirus A7 (CAV7) by flexibly fitting homology models into the corresponding cryoEM density maps at 8.2 and 6.1 Å resolution. As a result, and due to the improved homology models (derived from recently solved crystal structures of a close homolog – EV71 capsid – in mature and empty forms), the final models present an improvement over previously published models. In close agreement with the capsid expansion observed in the EV71 structures, the new CAV7 models reveal that the expansion is accompanied by ∼5° counterclockwise rotation of the asymmetric unit, predominantly contributed by the capsid protein VP1. The protocol could be applied not only to viral capsids but also to many other complexes characterised by a combination of atomic structure modelling and cryoEM density fitting.
Collapse
|