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Shortfin mako hot sets - Defining high bycatch conditions as a basis for bycatch mitigation

FISHERIES RESEARCH 2021年 244卷 106123-106123页

作者： O'Farrell, Halie B. Babcock, Elizabeth A. Univ Miami Rosenstiel Sch Marine & Atmospher Sci 4600 Rickenbacker Causeway Miami FL 33149 USA

Shortfin mako sharks, Isurus oxyrinchus, were listed under Appendix II of CITES in 2019 in part due to the results of the last stock assessment for the North Atlantic population, which determined the population is overfished and experiencing overfishing. With population numbers expected to continue to decline, the managing body, the International Commission for the Conservation of Atlantic Tunas (ICCAT), has called for efforts to reduce shortfin mako bycatch. We evaluate the potential for reducing mako bycatch by identifying mako hot sets, those with particularly high shortfin mako bycatch. Environmental conditions were evaluated for their influence on catch per unit effort (CPUE) of shortfin mako sharks. Standardized CPUEs were calculated from the US pelagic longline observer program (2004-2012) using a generalized additive model (GAM) with a delta-lognormal approach applied to the environmental variables sea surface height, sea surface temperature, bathymetry, and chlorophyll a concentration. Quantile regression (QR) was also performed to evaluate whether environmental variables can predict fishing conditions with high CPUE. The results of the GAM and QR methods were compared and assessed for their ability to predict and identify locations where shortfin mako CPUE is particularly high. The results suggest that using the binomial portion of the delta-lognormal model, the probability of positive bycatch, is the best basis to define an algorithm to avoid setting in conditions that might have high mako bycatch. Bycatch avoidance strategies built from probability of positive bycatch perform well enough at identifying hot sets to avoid half the shortfin mako bycatch with only a 20% reduction in effort.

关键词： Shortfin mako shark Pelagic longline fishery Bycatch mitigation Quantile regression generalized additive model

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Using a new framework of two-phase generalized additive models to incorporate prey abundance in spatial distribution models of juvenile slender lizardfish in Haizhou Bay, China

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MARINE BIOLOGY RESEARCH 2018年第5期14卷 508-523页

作者： Xue, Ying Tanaka, Kisei Yu, Huaming Chen, Yong Guan, Lisha Li, Zengguang Yu, Haiqing Xu, Binduo Ren, Yiping Wan, Rong Ocean Univ China Fisheries Coll Qingdao Peoples R China Univ Maine Sch Marine Sci Orono ME USA Ocean Univ China Coll Ocean & Atmospher Sci Qingdao Peoples R China Qingdao Natl Lab Marine Sci & Technol Lab Marine Fisheries Sci & Food Prod Proc Qingdao Peoples R China

The predictive skill of species distribution models depends on the quality and quantity of input information. In addition to the physical environmental variables, prey availability is also one of the main drivers regulating spatial distribution of marine species. However, prey distribution data have rarely been considered in habitat models due to the lack of information on non-commercial prey species. This may lead to an incomplete view of species distributions and biased model predictions. In this study, we developed a new framework of two-phase generalized additive models (GAMs) based on the Tweedie distribution to incorporate the predicted prey abundance as covariates in habitat models, and applied this framework to juvenile slender lizardfish Saurida elongata in Haizhou Bay, China. This study demonstrated that the predictive skill of habitat models could be greatly improved through incorporating prey abundance as explanatory variables. The importance of prey distribution data in the habitat model confirms the essentiality of including prey data while modelling species distribution. Spatial overlap and GAM analysis demonstrated that not all dominant prey can be selected as potential explanatory variables and only those prey species showing high spatiotemporal occurrences with predators should be incorporated. The framework derived in this study could be extended to other marine organisms to improve the predictive skill of habitat models and enhance our understanding of the ecological mechanisms underlying the distribution of marine species.

关键词： Haizhou Bay Saurida elongate generalized additive model prey biotic variables habitat

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The Relationship between Mobility and COVID-19 in Germany: modeling Case Occurrence using Apple's Mobility Trends Data

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Methods of Information in Medicine 2021年第6期59卷 179-182页

作者： Mark David Walker Mihály Sulyok Department of the Natural and Built Environment Sheffield Hallam University Sheffield United Kingdom Department of Pathology Institute of Pathology and Neuropathology Eberhard Karls University Tübingen Germany Institute of Tropical Medicine Eberhard Karls University Tübingen Germany

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Fluctuations in distribution and relative abundance of Japanese Spanish mackerel, Scomberomorus niphonius, in the Yellow Sea, East China Sea and Sea of Japan

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REGIONAL STUDIES IN MARINE SCIENCE 2021年 48卷

作者： Ohshimo, Seiji Muko, Soyoka Yoda, Mari Kurota, Hiroyuki Fisheries Resources Inst Nagasaki Stn 1551-8 Taira Machi Nagasaki 8512213 Japan

To evaluate the habitat and abundance of Japanese Spanish mackerel, we analyzed catch per unit effort (CPUE) of large and mid-sized Japanese purse-seine vessels in the Yellow Sea, East China Sea and Sea of Japan using a generalized additive model and generalized linear model. An area of high CPUE was observed in the Yellow and East China seas from 1994 to 1997, but disappeared after 1998. The high-CPUE area firstly developed in the Yellow Sea from October to December, and moved to the southern Yellow Sea, East China Sea and coastal Japan from January to March. Based on the generalized additive model, Japanese Spanish mackerel appeared to prefer regions of low temperature at 10 m depth. The lowest CPUE was observed in 1996, and increased by 2000. Annual CPUE values declined from 2000 to 2003, increased subsequently (with some fluctuations) and remained largely stable after 2010. Our results comprise the first stock assessment report of Japanese Spanish mackerel based on the Japanese large and mid-sized purse seine fishery in the Yellow Sea, East China Sea and Sea of Japan. Further cooperative assessment with China and Korea is needed for accurate stock assessment and management of this species. (C) 2021 Elsevier B.V. All rights reserved.

关键词： CPUE generalized additive model generalized linear model

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Projections for COVID-19 pandemic in India and effect of temperature and humidity

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DIABETES & METABOLIC SYNDROME-CLINICAL RESEARCH & REVIEWS 2020年第5期14卷 801-805页

作者： Goswami, Kuldeep Bharali, Sulaxana Hazarika, Jiten Dibrugarh Univ Dept Stat Dibrugarh 4 Assam India

Background and aims: As, the COVID-19 has been deemed a pandemic by World Health Organization (WHO), and since it spreads everywhere throughout the world, investigation in relation to this disease is very much essential. Investigation of pattern in the occurrence of COVID-19, to check the influence of different meteorological factors on the incidence of COVID-19 and prediction of incidence of COVID-19 are the objectives of this paper. Methods: For trend analysis, Sen's Slope and Man-Kendall test have been used, generalized additive model (GAM) of regression has been used to check the influence of different meteorological factors on the incidence and to predict the frequency of COVID-19, and Verhulst (Logistic) Population model has been used. Results: Statistically significant linear trend found for the daily-confirmed cases of COVID-19. The regression analysis indicates that there is some influence of the interaction of average temperature (AT) and average relative humidity (ARH) on the incidence of COVID-19. However, this result is not consistent throughout the study area. The projections have been made up to 21st May, 2020. Conclusions: Trend and regression analysis give an idea of the incidence of COVID-19 in India while projection made by Verhulst (Logistic) Population model for the confirmed cases of the study area are encouraging as the sample prediction is as same as the actual number of confirmed COVID-19 cases. (c) 2020 Diabetes India. Published by Elsevier Ltd. All rights reserved.

关键词： COVID-19 generalized additive model Logistic population model Pandemic Trend

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WaveICA 2.0: a novel batch effect removal method for untargeted metabolomics data without using batch information

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METABOLOMICS 2021年第10期17卷 87-87页

作者： Deng, Kui Zhao, Falin Rong, Zhiwei Cao, Lei Zhang, Liuchao Li, Kang Hou, Yan Zhu, Zheng-Jiang Chinese Acad Sci Interdisciplinary Res Ctr Biol & Chem Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Organ Chem Shanghai Peoples R China Harbin Med Univ Sch Publ Hlth Dept Epidemiol & Biostat Harbin Peoples R China Westlake Univ Sch Life Sci Key Lab Growth Regulat & Translat Res Zhejiang Pr Hangzhou Peoples R China Inst Basic Med Sci Westlake Inst Adv Study Hangzhou Peoples R China Peking Univ Sch Publ Hlth Dept Biostat Beijing Peoples R China Hangzhou Normal Univ Sch Med Dept Hlth Management Hangzhou Peoples R China

Introduction Untargeted metabolomics based on liquid chromatography-mass spectrometry is inevitably affected by batch effects that are caused by non-biological systematic bias. Previously, we developed a novel method called WaveICA to remove batch effects for untargeted metabolomics data. To detect batch effect information, the method relies on a batch label. However, it cannot be used in the scenario in which there is only one batch of data or the batch label is unknown. Objectives We aim to improve the WaveICA method to remove batch effects for untargeted metabolomics data without using batch information. Methods We improved the WaveICA method by developing WaveICA 2.0 to remove batch effects for metabolomics data, and provided an R package WaveICA_2.0 to implement this method. Results The performance of the WaveICA 2.0 method was evaluated on real metabolomics data. For metabolomics data with three batches, the performance of the WaveICA 2.0 method was similar to that of the WaveICA method in terms of gathering quality control samples (QCSs) and subject samples together in principle component analysis score plots, increasing the similarity of QCSs, increasing differential peaks, and improving classification accuracy. For metabolomics data with only one batch, the WaveICA 2.0 method had a strong ability to remove intensity drift and reveal more biological information and outperformed the QC-RLSC and QC-SVRC methods in our study using our metabolomics data. Conclusion Our results demonstrated that the WaveICA 2.0 method can be used in practice to remove batch effects for untargeted metabolomics data without batch information.

关键词： Untargeted metabolomics Wavelet transform Batch effects Intensity drift removal generalized additive model

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Application of nonlinear land use regression models for ambient air pollutants and air quality index

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ATMOSPHERIC POLLUTION RESEARCH 2021年第10期12卷

作者： Zhang, Licheng Tian, Xue Zhao, Yuhan Liu, Lulu Li, Zhiwei Tao, Lixin Wang, Xiaonan Guo, Xiuhua Luo, Yanxia Capital Med Univ Sch Publ Hlth 10 XitoutiaoYouanmen Wai St Beijing 100069 Peoples R China Beijing Municipal Key Lab Clin Epidemiol 10 XitoutiaoYouanmen Wai St Beijing 100069 Peoples R China Beijing Canc Hosp Beijing Peoples R China

Air pollution is a major global environmental problem that affects health. In view of this, it is important to improve the prediction method of air pollutant concentrations to obtain accurate exposure estimates. Currently, land use regression (LUR) models are widely used to predict the fine-scale spatial variation of air pollutants. However, most of previous studies used linear regression methods such as generalized linear models (GLM) with less applicability to fit LUR models. Considering the potential nonlinear relationship between predictor variables, this study adopted generalized additive models (GAM) to derive LUR models of air pollutants (including PM2.5, PM10, CO, NO2, SO2, and O-3) and air quality index (AQI) in Beijing with annual resolution. These models were based on routine monitoring data from 35 national regulatory monitoring sites and combined with a set of predictor variables such as land-use type, traffic and industrial emissions, population density, and meteorological factors. Results indicated that compared with traditional methods, the GAM approach significantly improved the performance of LUR models with explanatory power adjusted R-2 levels ranging from 70 to 90%, and the cross-validation analysis also showed high prediction accuracy of the GAM approach. Besides, this approach emphasized the importance of meteorology in predicting air pollutant concentrations and AQI values. Generally, this study provides a feasible way to determine exposure assessment in heavily polluted cities and future support for long-term environmental epidemiological studies.

关键词： Air pollutant Air quality index Land use regression model generalized additive model generalized linear model Meteorology

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Predicting COVID-19 mortality risk in Toronto, Canada: a comparison of tree-based and regression-based machine learning methods

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BMC MEDICAL RESEARCH METHODOLOGY 2021年第1期21卷 267-267页

作者： Feng, Cindy Kephart, George Juarez-Colunga, Elizabeth Dalhousie Univ Dept Community Hlth & Epidemiol Fac Med 5790 Univ Ave Halifax NS B3H 1V7 Canada Univ Colorado Dept Biostat & Informat Anschutz Med Campus Aurora CO 80045 USA

Background Coronavirus disease (COVID-19) presents an unprecedented threat to global health worldwide. Accurately predicting the mortality risk among the infected individuals is crucial for prioritizing medical care and mitigating the healthcare system's burden. The present study aimed to assess the predictive accuracy of machine learning methods to predict the COVID-19 mortality risk. Methods We compared the performance of classification tree, random forest (RF), extreme gradient boosting (XGBoost), logistic regression, generalized additive model (GAM) and linear discriminant analysis (LDA) to predict the mortality risk among 49,216 COVID-19 positive cases in Toronto, Canada, reported from March 1 to December 10, 2020. We used repeated split-sample validation and k-steps-ahead forecasting validation. Predictive models were estimated using training samples, and predictive accuracy of the methods for the testing samples was assessed using the area under the receiver operating characteristic curve, Brier's score, calibration intercept and calibration slope. Results We found XGBoost is highly discriminative, with an AUC of 0.9669 and has superior performance over conventional tree-based methods, i.e., classification tree or RF methods for predicting COVID-19 mortality risk. Regression-based methods (logistic, GAM and LASSO) had comparable performance to the XGBoost with slightly lower AUCs and higher Brier's scores. Conclusions XGBoost offers superior performance over conventional tree-based methods and minor improvement over regression-based methods for predicting COVID-19 mortality risk in the study population.

关键词： COVID-19 mortality Predictive model generalized additive model Classification trees Extreme gradient boosting

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The association between extreme temperature and pulmonary tuberculosis in Shandong Province, China, 2005-2016: a mixed method evaluation

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BMC INFECTIOUS DISEASES 2021年第1期21卷 402-402页

作者： Chen, Dongzhen Lu, Hua Zhang, Shengyang Yin, Jia Liu, Xuena Zhang, Yixin Dai, Bingqin Li, Xiaomei Ding, Guoyong Shandong First Med Univ & Shandong Acad Med Sci Sch Publ Hlth Dept Epidemiol 619 Changcheng Rd Tai An 271016 Shandong Peoples R China Taian Ctr Dis Prevent Control Tai An 271000 Shandong Peoples R China Shandong Ctr Dis Control & Prevent Jinan 250014 Shandong Peoples R China

Background The effects of extreme temperature on infectious diseases are complex and far-reaching. There are few studies to access the relationship of pulmonary tuberculosis (PTB) with extreme temperature. The study aimed to identify whether there was association between extreme temperature and the reported morbidity of PTB in Shandong Province, China, from 2005 to 2016. Methods A generalized additive model (GAM) was firstly conducted to evaluate the relationship between daily reported incidence rate of PTB and extreme temperature events in the prefecture-level cities. Then, the effect estimates were pooled using meta-analysis at the provincial level. The fixed-effect model or random-effect model was selected based on the result of heterogeneity test. Results Among the 446,016 PTB reported cases, the majority of reported cases occurred in spring. The higher reported incidence rate areas were located in Liaocheng, Taian, Linyi and Heze. Extreme low temperature had an impact on the reported incidence of PTB in only one prefecture-level city, i.e., Binzhou (RR = 0.903, 95% CI: 0.817-0.999). While, extreme high temperature was found to have a positive effect on reported morbidity of PTB in Binzhou (RR = 0.924, 95% CI: 0.856-0.997) and Weihai (RR = 0.910, 95% CI: 0.843-0.982). Meta-analysis showed that extreme high temperature was associated with a decreased risk of PTB (RR = 0.982, 95% CI: 0.966-0.998). However, extreme low temperature was no relationship with the reported incidence of PTB. Conclusion Our findings are suggested that extreme high temperature has significantly decreased the risk of PTB at the provincial levels. The findings have implications for developing strategies to response to climate change.

关键词： Pulmonary tuberculosis Extreme temperature generalized additive model Meta-analysis

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Analysis of debt-paying ability of real estate enterprises based on fuzzy mathematics and K-means algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第5期37卷 6403-6414页

作者： Qiang, Qunli Anhui Jianzhu Univ Sch Econ & Management Hefei Anhui Peoples R China

Currently, there is a certain fluctuation in the real estate industry, so it is particularly important to analyze the solvency of real estate enterprises. In order to find a reliable model suitable for studying the difference in house prices, this study collects the research data through data collection, and uses the K-means clustering method to construct the corresponding model as a basic research in combination with the machine learning research method. At the same time, this paper compares the analysis effects of several common machine learning models and finds the advantages and disadvantages of these methods through mathematical statistics. In addition, combined with practice, this paper constructs a nonlinear generalized additive model, and based on machine learning technology, validates the validity of the model based on data analysis, the collected predictors. In view of the improvement of the solvency of real estate enterprises, diversified operation of real estate enterprises can maintain reasonable cash flow and make up for the defect of poor liquidity of real estate. Furthermore, this paper uses the stability method to find the optimal model. In addition, the generalized additive model effectively reveals the complex nonlinear relationship between continuous predictors and house prices. Through research, it can be seen that the nonlinear generalized additive model based on machine learning can play an important role in real estate industry forecasting and has certain theoretical reference significance for subsequent related research.

关键词： Real estate generalized additive model machine learning K-means Algorithm

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