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Ran W, Chen J, Zhao Y, Zhang N, Luo G, Zhao Z, Song Y. Global climate change-driven impacts on the Asian distribution of Limassolla leafhoppers, with implications for biological and environmental conservation. Ecol Evol 2024; 14:e70003. [PMID: 39026963 PMCID: PMC11257772 DOI: 10.1002/ece3.70003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 06/04/2024] [Accepted: 06/28/2024] [Indexed: 07/20/2024] Open
Abstract
Knowing the impacts of global climate change on the habitat suitability distribution of Limassolla leafhoppers contributes to understanding the feedback of organisms on climate change from a macroecological perspective, and provides important scientific basis for protecting the ecological environment and biodiversity. However, there is limited knowledge on this aspect. Thus, our study aimed to address this gap by analyzing Asian habitat suitability and centroid shifts of Limassolla based on 19 bioclimatic variables and occurrence records. Selecting five ecological niche models with the outstanding predictive performance (Maxlike, generalized linear model, generalized additive model, random forest, and maximum entropy) along with their ensemble model from 12 models, the current habitat suitability of Limassolla and its future habitat suitability under two Shared Socio-economic Pathways (SSP1-2.6 and SSP5-8.5) in the 2050s and 2090s were predicted. The results showed that the prediction results of the five models are generally consistent. Based on ensemble model, 11 potential biodiversity hotspots with high suitability were identified. With climate change, the suitable range of Limassolla will experience both expansion and contraction. In SSP5-8.52050s, the expansion area is 118.56 × 104 km2, while the contraction area is 25.40 × 104 km2; in SSP1-2.62090s, the expansion area is 91.71 × 104 km2, and the contraction area is 26.54 × 104 km2. Furthermore, the distribution core of Limassolla will shift toward higher latitudes in the northeast direction, and the precipitation of warmest quarter was found to have the greatest impact on the distribution of Limassolla. Our research results supported our four hypotheses. Finally, this research suggests establishing ecological reserves in identified contraction to prevent habitat loss, enhancing the protection of biodiversity hotspots, and pursuing a sustainable development path with reduced emissions.
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Affiliation(s)
- Weiwei Ran
- School of Karst ScienceGuizhou Normal UniversityGuiyangChina
- State Engineering Technology Institute for Karst Desertification ControlGuiyangChina
| | - Jiajia Chen
- School of Karst ScienceGuizhou Normal UniversityGuiyangChina
- State Engineering Technology Institute for Karst Desertification ControlGuiyangChina
| | - Yuanqi Zhao
- School of Karst ScienceGuizhou Normal UniversityGuiyangChina
- State Engineering Technology Institute for Karst Desertification ControlGuiyangChina
| | - Ni Zhang
- School of Karst ScienceGuizhou Normal UniversityGuiyangChina
- State Engineering Technology Institute for Karst Desertification ControlGuiyangChina
| | - Guimei Luo
- School of Karst ScienceGuizhou Normal UniversityGuiyangChina
- State Engineering Technology Institute for Karst Desertification ControlGuiyangChina
| | - Zhibing Zhao
- School of Karst ScienceGuizhou Normal UniversityGuiyangChina
- State Engineering Technology Institute for Karst Desertification ControlGuiyangChina
- School of Food Science and EngineeringGuiyang UniversityGuiyangChina
| | - Yuehua Song
- School of Karst ScienceGuizhou Normal UniversityGuiyangChina
- State Engineering Technology Institute for Karst Desertification ControlGuiyangChina
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Jahangiri M, Rahim F, Saki N, Saki Malehi A. Application of Bayesian Decision Tree in Hematology Research: Differential Diagnosis of β-Thalassemia Trait from Iron Deficiency Anemia. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6401105. [PMID: 34795791 PMCID: PMC8594992 DOI: 10.1155/2021/6401105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 09/21/2021] [Accepted: 10/11/2021] [Indexed: 11/17/2022]
Abstract
OBJECTIVE Several discriminating techniques have been proposed to discriminate between β-thalassemia trait (βTT) and iron deficiency anemia (IDA). These discrimination techniques are essential clinically, but they are challenging and typically difficult. This study is the first application of the Bayesian tree-based method for differential diagnosis of βTT from IDA. METHOD This cross-sectional study included 907 patients with ages over 18 years old and a mean (±SD) age of 25 ± 16.1 with either βTT or IDA. Hematological parameters were measured using a Sysmex KX-21 automated hematology analyzer. Bayesian Logit Treed (BLTREED) and Classification and Regression Trees (CART) were implemented to discriminate βTT from IDA based on the hematological parameters. RESULTS This study proposes an automatic detection model of beta-thalassemia carriers based on a Bayesian tree-based method. The BLTREED model and CART showed that mean corpuscular volume (MCV) was the main predictor in diagnostic discrimination. According to the test dataset, CART indicated higher sensitivity and negative predictive value than BLTREED for differential diagnosis of βTT from IDA. However, the CART algorithm had a high false-positive rate. Overall, the BLTREED model showed better performance concerning the area under the curve (AUC). CONCLUSIONS The BLTREED model showed excellent diagnostic accuracy for differentiating βTT from IDA. In addition, understanding tree-based methods are easy and do not need statistical experience. Thus, it can help physicians in making the right clinical decision. So, the proposed model could support medical decisions in the differential diagnosis of βTT from IDA to avoid much more expensive, time-consuming laboratory tests, especially in countries with limited recourses or poor health services.
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Affiliation(s)
- Mina Jahangiri
- Ph.D. Student, Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Fakher Rahim
- Thalassemia & Hemoglobinopathy Research Center, Research Institute of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Najmaldin Saki
- Thalassemia & Hemoglobinopathy Research Center, Research Institute of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Amal Saki Malehi
- Thalassemia & Hemoglobinopathy Research Center, Research Institute of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
- Department of Biostatistics and Epidemiology, Faculty of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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Wang N, Mengersen K, Tong S, Kimlin M, Zhou M, Wang L, Yin P, Xu Z, Cheng J, Zhang Y, Hu W. Short-term association between ambient air pollution and lung cancer mortality. ENVIRONMENTAL RESEARCH 2019; 179:108748. [PMID: 31561053 DOI: 10.1016/j.envres.2019.108748] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 09/08/2019] [Accepted: 09/16/2019] [Indexed: 05/20/2023]
Abstract
RATIONALE Long-term exposure to air pollution has been associated with increased lung cancer incidence and mortality. However, the short-term association between air pollution and lung cancer mortality (LCM) remains largely unknown. METHODS We collected daily data on particulate matter with diameter <2.5 μm (PM2.5), particulate matter with diameter < 10 μm (PM10), sulfur dioxide (SO2), and ozone (O3), and LCM in three of the biggest cities in China, i.e. Beijing, Chongqing, and Guangzhou, from 2013 to 2015. We first estimated city-specific relationships between air pollutants and LCM using time-series generalized linear models, adjusting for potential confounders. A classification and regression tree (CART) model was used to stratify LCM risk based on combinations of air pollutants and meteorological factors in each city. Then we pooled the city-specific associations using random-effects meta-analysis. Meta regression was used to explore if city-specific characteristics modified the air pollution-LCM association. Finally, we stratified the analyses by season, age, and sex. RESULTS Over the entire period, the current-day concentrations of PM2.5 and PM10 in Chongqing and PM2.5, PM10, and SO2 in Guangzhou were positively associated with LCM (Excess risk ranged from 0.72% (95% CI 0.27%-1.17%) to 6.06% (95% CI 0.76%-11.64%) with each 10 μg/m3 increment in different pollutants), but the association between current-day air pollution and LCM in Beijing was not significant (P > 0.05). When considering the environmental and weather factors simultaneously, current-day PM2.5, relative humidity, and PM10 were the most important factors associated with LCM in Beijing, Chongqing, and Guangzhou, respectively. LCM risk related with daily PM2.5, PM10, and SO2 significantly increased with the increasing annual mean temperature and humidity of the city, while LCM risk related with daily O3 significantly increased with the increases of latitude, annual mean O3 concentration, and socioeconomic level. After stratification, the current-day PM2.5, PM10, and O3 during the warm season in Beijing and PM2.5, PM10, and SO2 during the cool season in Chongqing and Guangzhou were positively associated with LCM (Excess risk ranged from 0.93% (95% CI 0.42%-1.45%) to 7.16% (95% CI 0.64%-14.09%) with each 10 μg/m3 increment in different pollutants). Male and the elderly lung cancer patients were more sensitive to the short-term effect of air pollution. CONCLUSIONS Lung cancer patients should enhance protection measures against air pollution. More attentions should be paid for the high PM2.5, PM10, and O3 during the warm season in Beijing, and high PM2.5, PM10, and SO2 during the cool season in Chongqing and Guangzhou.
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Affiliation(s)
- Ning Wang
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kerrie Mengersen
- School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Shilu Tong
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia; Shanghai Children's Medical Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China; School of Public Health, Institute of Environment and Population Health, Anhui Medical University, Hefei, China
| | - Michael Kimlin
- Health Research Institute, University of the Sunshine Coast, Sippy Downs, Queensland, Australia
| | - Maigeng Zhou
- National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Lijun Wang
- National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Peng Yin
- National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhiwei Xu
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Jian Cheng
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Yuzhou Zhang
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Wenbiao Hu
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
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Guan B, Zhao Y, Sun W. Ant colony optimization with an automatic adjustment mechanism for detecting epistatic interactions. Comput Biol Chem 2018; 77:354-362. [DOI: 10.1016/j.compbiolchem.2018.11.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 10/01/2018] [Accepted: 11/05/2018] [Indexed: 12/13/2022]
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Zhu H, Han Q, Zhang D, Wang Y, Gao J, Geng W, Yang X, Chen X. A diagnostic model for minimal change disease based on biological parameters. PeerJ 2018; 6:e4237. [PMID: 29340242 PMCID: PMC5768169 DOI: 10.7717/peerj.4237] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 12/15/2017] [Indexed: 01/10/2023] Open
Abstract
Background Minimal change disease (MCD) is a kind of nephrotic syndrome (NS). In this study, we aimed to establish a mathematical diagnostic model based on biological parameters to classify MCD. Methods A total of 798 NS patients were divided into MCD group and control group. The comparison of biological indicators between two groups were performed with t-tests. Logistic regression was used to establish the diagnostic model, and the diagnostic value of the model was estimated using receiver operating characteristic (ROC) analysis. Results Thirteen indicators including Anti-phospholipase A2 receptor (anti-PLA2R) (P = 0.000), Total protein (TP) (P = 0.000), Albumin (ALB) (P = 0.000), Direct bilirubin (DB) (P = 0.002), Creatinine (Cr) (P = 0.000), Total cholesterol (CH) (P = 0.000), Lactate dehydrogenase (LDH) (P = 0.007), High density lipoprotein cholesterol (HDL) (P = 0.000), Low density lipoprotein cholesterol (LDL) (P = 0.000), Thrombin time (TT) (P = 0.000), Plasma fibrinogen (FIB) (P = 0.000), Immunoglobulin A (IgA) (P = 0.008) and Complement 3 (C3) (P = 0.019) were significantly correlated with MCD. Furthermore, the area under ROC curves of CH, HDL, LDL, TT and FIB were more than 0.70. Logistic analysis demonstrated that CH and TT were risk factors for MCD. According to the ROC of “CH+TT”, the AUC was 0.827, with the sensitivity of 83.0% and the specificity of 69.8% (P = 0.000). Conclusion The established diagnostic model with CH and TT could be used for classified diagnosis of MCD.
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Affiliation(s)
- Hanyu Zhu
- Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center of Kidney Diseases, Beijing Key Laboratory of Kidney Disease, Beijing, China
| | - Qiuxia Han
- Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dong Zhang
- Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center of Kidney Diseases, Beijing Key Laboratory of Kidney Disease, Beijing, China
| | - Yong Wang
- Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center of Kidney Diseases, Beijing Key Laboratory of Kidney Disease, Beijing, China
| | - Jing Gao
- Department of Clinical Biochemistry, Chinese PLA General Hospital, Beijing, China
| | - Wenjia Geng
- Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Xiaoli Yang
- Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center of Kidney Diseases, Beijing Key Laboratory of Kidney Disease, Beijing, China
| | - Xiangmei Chen
- Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center of Kidney Diseases, Beijing Key Laboratory of Kidney Disease, Beijing, China
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Risk assessment of dengue fever in Zhongshan, China: a time-series regression tree analysis. Epidemiol Infect 2016; 145:451-461. [DOI: 10.1017/s095026881600265x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
SUMMARYDengue fever (DF) is the most prevalent and rapidly spreading mosquito-borne disease globally. Control of DF is limited by barriers to vector control and integrated management approaches. This study aimed to explore the potential risk factors for autochthonous DF transmission and to estimate the threshold effects of high-order interactions among risk factors. A time-series regression tree model was applied to estimate the hierarchical relationship between reported autochthonous DF cases and the potential risk factors including the timeliness of DF surveillance systems (median time interval between symptom onset date and diagnosis date, MTIOD), mosquito density, imported cases and meteorological factors in Zhongshan, China from 2001 to 2013. We found that MTIOD was the most influential factor in autochthonous DF transmission. Monthly autochthonous DF incidence rate increased by 36·02-fold [relative risk (RR) 36·02, 95% confidence interval (CI) 25·26–46·78, compared to the average DF incidence rate during the study period] when the 2-month lagged moving average of MTIOD was >4·15 days and the 3-month lagged moving average of the mean Breteau Index (BI) was ⩾16·57. If the 2-month lagged moving average MTIOD was between 1·11 and 4·15 days and the monthly maximum diurnal temperature range at a lag of 1 month was <9·6 °C, the monthly mean autochthonous DF incidence rate increased by 14·67-fold (RR 14·67, 95% CI 8·84–20·51, compared to the average DF incidence rate during the study period). This study demonstrates that the timeliness of DF surveillance systems, mosquito density and diurnal temperature range play critical roles in the autochthonous DF transmission in Zhongshan. Better assessment and prediction of the risk of DF transmission is beneficial for establishing scientific strategies for DF early warning surveillance and control.
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Gao J, Wang Y, Dong Z, Yan Z, Jia X, Tian Y. A novel differential diagnostic model based on multiple biological parameters for immunoglobulin A nephropathy. BMC Med Inform Decis Mak 2012; 12:58. [PMID: 22738421 PMCID: PMC3488968 DOI: 10.1186/1472-6947-12-58] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2011] [Accepted: 06/27/2012] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Immunoglobulin A nephropathy (IgAN) is the most common form of glomerulonephritis in China. An accurate diagnosis of IgAN is dependent on renal biopsies, and there is lack of non-invasive and practical classification methods for discriminating IgAN from other primary kidney diseases. The objective of this study was to develop a classification model for the auxiliary diagnosis of IgAN using multiparameter analysis with various biological parameters. METHODS To establish an optimal classification model, 121 cases (58 IgAN vs. 63 non-IgAN) were recruited and statistically analyzed. The model was then validated in another 180 cases. RESULTS Of the 57 biological parameters, there were 16 parameters that were significantly different (P < 0.05) between IgAN and non-IgAN. The combination of fibrinogen, serum immunoglobulin A level, and manifestation was found to be significant in predicting IgAN. The validation accuracies of the logistic regression and discriminant analysis models were 77.5 and 77.0%, respectively at a predictive probability cut-off of 0.5, and 81.1 and 79.9%, respectively, at a predictive probability cut-off of 0.40. When the predicted probability of the equation containing the combination of fibrinogen, serum IgA level, and manifestation was more than 0.59, a patient had at least an 85.0% probability of having IgAN. When the predicted probability was lower than 0.26, a patient had at least an 88.5% probability of having non-IgAN. The results of the net reclassification improvement certificated serum Immunoglobulin A and fibrinogen had classification power for discriminating IgAN from non-IgAN. CONCLUSIONS These models possess potential clinical applications in distinguishing IgAN from other primary kidney diseases.
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Affiliation(s)
- Jing Gao
- Department of Clinical Biochemistry, State Key Laboratory of Kidney Disease, Chinese PLA General Hospital, Beijing 100853, China
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