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Quantifying livestock vulnerability to snow disasters in the Tibetan Plateau: Comparing different modeling techniques for prediction

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2020年 48卷

作者： Ye, Tao Liu, Weihang Mu, Qingyang Zong, Shuo Li, Yijia Shi, Peijun Beijing Normal Univ Fac Geog Sci Key Lab Environm Change & Nat DisasterMinist Edu State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China Minist Emergency Management Acad Disaster Reduct & Emergency Management Beijing 100875 Peoples R China Minist Educ Beijing 100875 Peoples R China Peoples Govt Qinghai Prov Acad Plateau Sci & Sustainabil Xining 810016 Peoples R China Beijing Normal Univ Xining 810016 Peoples R China Boston Univ Frederick S Pardee Ctr Study Longer Range Future Boston MA 02215 USA Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China

Quantitative vulnerability relationships describing the susceptibility of socioeconomic losses in response to climate change are critical for natural disaster loss modeling and risk assessment. Modeling such vulnerability requires methods capable of handling complicated multi-factor, non-linear, and interactive relationships. Here, we compared the performance of generalized additive models (GAM) and random forest (RF) and boosted regression trees (BRT) in quantifying livestock vulnerability to snow disasters in the Tibetan Plateau for both explanatory and predictive purposes. Our results indicated promising explanatory power of these three modeling methods, with deviance-based R-2 up to 0.720. They consistently revealed geophysical and socioeconomic factors that contributed to higher mortality rates. Nevertheless, GAM model failed to identify the critical influence of snow depth, mainly due to its smoothing scheme when fitting models to data. They also differed in the selection of the most important socioeconomic variable to represent prevention capacity. From a predictive perspective, all three modeling methods also showed promising predictive power, yet RF had the smallest prediction error, with less number of predictors used. Therefore, the predictive version of RF may well be the best choice for use in future risk analyses, yet those of BRT and GAM can serve as an alternative if needed.

关键词： Livestock snow disaster Vulnerability generalized additive models Random forest Boosted regression trees

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The effect of bilingualism on brain development from early childhood to young adulthood

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BRAIN STRUCTURE & FUNCTION 2020年第7期225卷 2131-2152页

作者： Pliatsikas, Christos Meteyard, Lotte Verissimo, Joao DeLuca, Vincent Shattuck, Kyle Ullman, Michael T. Univ Reading Sch Psychol & Clin Language Sci Reading RG6 6AL Berks England Univ Nebrija Fac Lenguas & Educ Madrid Spain Univ Potsdam Potsdam Res Inst Multilingualism Potsdam Germany Univ Birmingham Sch Psychol Birmingham B15 2SA W Midlands England Georgetown Univ Dept Neurosci Washington DC 20057 USA Georgetown Univ Dept Neurol Washington DC USA

Bilingualism affects the structure of the brain in adults, as evidenced by experience-dependent grey and white matter changes in brain structures implicated in language learning, processing, and control. However, limited evidence exists on how bilingualism may influence brain development. We examined the developmental patterns of both grey and white matter structures in a cross-sectional study of a large sample (n = 711 for grey matter,n = 637 for white matter) of bilingual and monolingual participants, aged 3-21 years. Metrics of grey matter (thickness, volume, and surface area) and white matter (fractional anisotropy and mean diffusivity) were examined across 41 cortical and subcortical brain structures and 20 tracts, respectively. We used generalized additive modelling to analyze whether, how, and where the developmental trajectories of bilinguals and monolinguals might differ. Bilingual and monolingual participants manifested distinct developmental trajectories in both grey and white matter structures. As compared to monolinguals, bilinguals showed: (a) more grey matter (less developmental loss) starting during late childhood and adolescence, mainly in frontal and parietal regions (particularly in the inferior frontal gyrus pars opercularis, superior frontal cortex, inferior and superior parietal cortex, and precuneus);and (b) higher white matter integrity (greater developmental increase) starting during mid-late adolescence, specifically in striatal-inferior frontal fibers. The data suggest that there may be a developmental basis to the well-documented structural differences in the brain between bilingual and monolingual adults.

关键词： Bilingualism Brain development Grey matter White matter generalized additive models

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Multidimensional penalized splines for incidence and mortality-trend analyses and validation of national cancer-incidence estimates

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INTERNATIONAL JOURNAL OF EPIDEMIOLOGY 2020年第4期49卷 1294-1306页

作者： Uhry, Zoe Chatignoux, Edouard Dantony, Emmanuelle Colonna, Marc Roche, Laurent Fauvernier, Mathieu Defossez, Gautier Leguyader-Peyrou, Sandra Monnereau, Alain Grosclaude, Pascale Bossard, Nadine Remontet, Laurent Sante Publ France Direct Malad Non Transmissibles & Traumatismes St Maurice France Hosp Civils Lyon Serv Biostat Bioinformat Pole Sante Publ Lyon France Univ Lyon 1 Lab Biometrie & Biol Evolut CNRS UMR 5558 Villeurbanne France Registre Canc Isere Grenoble France Registre Canc Poitou Charentes Poitiers France Inst Bergonie Registre Hemopathies Malignes Gironde Bordeaux France Inst Univ Canc Toulouse Oncopole IUCT O Inst Claudius Regaud Registre Canc Tarn Canc Toulouse France Univ Toulouse III Lab Epidemiol & Anal Sante Publ LEASP INSERM UMR 1027 Toulouse France

Background: Cancer-incidence and mortality-trend analyses require appropriate statistical modelling. In countries without a nationwide cancer registry, an additional issue is estimating national incidence from local-registry data. The objectives of this study were to (i) promote the use of multidimensional penalized splines (MPS) for trend analyses;(ii) estimate the national cancer-incidence trends, using MPS, from only local-registry data;and (iii) propose a validation process of these estimates. Methods: We used an MPS model of age and year for trend analyses in France over 1990-2015 with a projection up to 2018. Validation was performed for 22 cancer sites and relied essentially on comparison with reference estimates that used the incidence/healthcare ratio over the period 2011-2015. Alternative estimates that used the incidence/mortality ratio were also used to validate the trends. Results: In the validation assessment, the relative differences of the incidence estimates (2011-2015) with the reference estimates were <5% except for testis cancer in men and < 7% except for larynx cancer in women. Trends could be correctly derived since 1990 despite incomplete histories in some registries. The proposed method was applied to estimate the incidence and mortality trends of female lung cancer and prostate cancer in France. Conclusions: The validation process confirmed the validity of the national French estimates;it may be applied in other countries to help in choosing the most appropriate national estimation method according to country-specific contexts. MPS form a powerful statistical tool for trend analyses;they allow trends to vary smoothly with age and are suitable for modelling simple as well as complex trends thanks to penalization. Detailed trend analyses of lung and prostate cancers illustrated the suitability of MPS and the epidemiological interest of such analyses.

关键词： incidence mortality penalized splines generalized additive models trend analyses cancer cancer registry

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Suitable habitats of fish species in the Barents Sea

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FISHERIES OCEANOGRAPHY 2020年第6期29卷 526-540页

作者： Husson, Berengere Certain, Gregoire Filin, Anatoly Planque, Benjamin Havforskningsinst Avdeling Tromso Tromso Norway Univ Montpellier IFREMER CNRS MARBECIRD Sete France Fed State Budget Sci Inst Polar Branch Russian Fed Res Inst Fisheries & Oceanog PINRO Murmansk Murmansk Oblast Russia

Many marine species exhibit poleward migrations following climate change. The Barents Sea, a doorstep to the fast-warming Arctic, is experiencing large scale changes in its environment and its communities. Tracking and anticipating changes for management and conservation purposes at the scale of the ecosystem necessitate quantitative knowledge on individual species distribution drivers. This paper aims at identifying the factors controlling demersal habitats in the Barents Sea, investigating for which species we can predict current and future habitats and inferring those most likely to respond to climate change. We used non-linear quantile regressions (QGAM) to model the upper quantile of the biomass response of 33 fish species to 10 environmental gradients and revealed three environmental niche typologies. Four main predictors seem to be limiting species habitat: bottom and surface temperature, salinity, and depth. We highlighted three cases of present and future habitat predictability: (a) Habitats of widespread species are not likely to be limited by the existing conditions within the Barents Sea. (b) Habitats limited by a single factor are predictable and could shift if impacted by climate change. If the factor is depth, the habitat may stagnate or shrink if the environment becomes unsuitable. (c) Habitats limited by several factors are also predictable but need to be predicted from QGAM applied on projected environmental maps. These modeled suitable habitats can serve as input to species distribution forecasts and end-to-end models, and inform fisheries and conservation management.

关键词： climate change environmental niche generalized additive models habitat suitability models limiting factors quantile regression species distribution

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Age-specific shifts in Greenland halibut (Reinhardtius hippoglossoides) distribution in response to changing ocean climate

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ICES JOURNAL OF MARINE SCIENCE 2020年第1期77卷 230-240页

作者： Wheeland, Laura J. Morgan, M. Joanne Fisheries & Oceans Canada Northwest Atlantic Fisheries Ctr POB 5667 St John NF A1C 5X1 Canada

Fish distribution is strongly influenced by temperature, with populations predicted to respond to ongoing changes in ocean climate by adapting distribution to maintain preferred thermal habitats. Over the last several decades, the Northwest Atlantic off Newfoundland, Canada has experienced wide variation in temperature and significant changes in the structure of the marine community. A period of particularly cold ocean conditions from the mid-1980s to mid-1990s saw Greenland halibut occupying deeper waters, and moving farther offshore and southward. Distributional shifts across periods of varying thermal conditions were most pronounced in the youngest ages. While no specific nursery areas were evident, these young fish typically occurred in shallower waters where temperatures were more variable, moving deeper with age. Sensitivity to changing ocean climate may vary with age. This adaptation to shifting temperatures suggests that this species is likely to alter its distribution in the face of continued changes in ocean climate. Age-specific differences in response to changing temperature have potential implications for ecosystem interactions and fisheries management.

关键词： climate depth distribution generalized additive models Reinhardtius hippoglossoides temperature

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Airborne particulate matter, population mobility and COVID-19: a multi-city study in China

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BMC PUBLIC HEALTH 2020年第1期20卷 1-10页

作者： Wang, Bo Liu, Jiangtao Li, Yanlin Fu, Shihua Xu, Xiaocheng Li, Lanyu Zhou, Ji Liu, Xingrong He, Xiaotao Yan, Jun Shi, Yanjun Niu, Jingping Yang, Yong Li, Yiyao Luo, Bin Zhang, Kai Lanzhou Univ Sch Publ Hlth Inst Occupat Hlth & Environm Hlth Lanzhou 730000 Gansu Peoples R China Shanghai Meteorol Bur Shanghai Key Lab Meteorol & Hlth Shanghai 200030 Peoples R China Lanzhou Univ Dept Gen Surg Hosp 1 Lanzhou 730000 Gansu Peoples R China Lanzhou Univ Sch Publ Hlth Inst Epidemiol & Stat Lanzhou 730000 Gansu Peoples R China Univ Memphis Sch Publ Hlth Div Social & Behav Sci Memphis TN 38152 USA Univ Texas Hlth Sci Ctr Houston Sch Publ Hlth Dept Biostat & Data Sci Houston TX 77030 USA China Meteorol Adm Shanghai Typhoon Inst Shanghai 200030 Peoples R China Univ Texas Hlth Sci Ctr Houston Sch Publ Hlth Dept Epidemiol Human Genet & Environm Sci Houston TX 77030 USA Univ Texas Hlth Sci Ctr Houston Southwest Ctr Occupat & Environm Hlth Sch Publ Hlth Houston TX 77030 USA

BackgroundCoronavirus disease 2019 (COVID-19) is an emerging infectious disease, which has caused numerous deaths and health problems worldwide. This study aims to examine the effects of airborne particulate matter (PM) pollution and population mobility on COVID-19 across *** obtained daily confirmed cases of COVID-19, air particulate matter (PM2.5, PM10), weather parameters such as ambient temperature (AT) and absolute humidity (AH), and population mobility scale index (MSI) in 63 cities of China on a daily basis (excluding Wuhan) from January 01 to March 02, 2020. Then, the generalized additive models (GAM) with a quasi-Poisson distribution were fitted to estimate the effects of PM10, PM2.5 and MSI on daily confirmed COVID-19 *** found each 1 unit increase in daily MSI was significantly positively associated with daily confirmed cases of COVID-19 in all lag days and the strongest estimated RR (1.21, 95% CIs:1.14 similar to 1.28) was observed at lag 014. In PM analysis, we found each 10 mu g/m(3) increase in the concentration of PM10 and PM2.5 was positively associated with the confirmed cases of COVID-19, and the estimated strongest RRs (both at lag 7) were 1.05 (95% CIs: 1.04, 1.07) and 1.06 (95% CIs: 1.04, 1.07), respectively. A similar trend was also found in all cumulative lag periods (from lag 01 to lag 014). The strongest effects for both PM10 and PM2.5 were at lag 014, and the RRs of each 10 mu g/m(3) increase were 1.18 (95% CIs:1.14, 1.22) and 1.23 (95% CIs:1.18, 1.29), *** mobility and airborne particulate matter may be associated with an increased risk of COVID-19 transmission.

关键词： COVID-19 Particulate matter Population mobility generalized additive models

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Association of Environmental Factors in the Taiwan Strait with Distributions and Habitat Characteristics of Three Swimming Crabs

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REMOTE SENSING 2020年第14期12卷

作者： Naimullah, Muhamad Lan, Kuo-Wei Liao, Cheng-Hsin Hsiao, Po-Yuan Liang, Yen-Rong Chiu, Ting-Chen Natl Taiwan Ocean Univ Dept Environm Biol Fisheries Sci 2 Pei Ning Rd Keelung 20224 Taiwan Natl Taiwan Ocean Univ Ctr Excellence Oceans 2 Pei Ning Rd Keelung 20224 Taiwan

Information regarding the oceanic environment is crucial for determining species distributions and their habitat preferences. However, in studies on crustaceans, especially swimming crabs, such information remains poorly utilized, and its effects on crab communities in the Taiwan Strait (TS) has not been well documented. The purpose of this study was to understand the relationship between the catch rates of three swimming crab species and environmental factors in the TS. We fitted generalized additive models (GAMs) to logbooks and voyage data recorder data from Taiwanese crab vessels (2011-2015), developed a species distribution model, and predicted catch rates for these three swimming crab species based on the GAM output. The chlorophyll-a(Chl-a) concentration was related to the high catch rates ofChrybdis feriatusandPortunus sanguinolentus, whereas bottom temperature (BT) was related to high catch rates ofPortunus pelagicus. The variance percentages for each crab species indicated that high catch rates ofC. feriatusandP. sanguinolentusoccurred in a Chl-aconcentration > 0.5 mg/m(3), whereasP. pelagicuscatch rates exhibited negative correlations with BTs > 25 degrees C. The model predicted high catch rates ofC. feriatusin the north of the TS during autumn and winter, whereasP. pelagicuswas observed to the south during summer and autumn.P. sanguinolentuswas predicted to be widely distributed around the TS and distributed further to the northern area during autumn and winter. These findings revealed that each species responds to spatiotemporal environmental variations. Understanding the distributions and habitats of these three crabs is vital in fisheries resource management and conservation planning.

关键词： crustacean Portunidae commercial crab generalized additive models habitat preference

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The cut-off value of impaired fasting glucose should be lower: Based on the associations of fasting blood glucose with blood lipids

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PRIMARY CARE DIABETES 2020年第2期14卷 147-153页

作者： Sun, Mengzi Wang, Min Sun, Chong Zheng, Pingping Lv, Yaogai Pu, Liyuan Li, Shuo Yao, Yan Jin, Lina Jilin Univ Sch Publ Hlth Epidemiol & Biostat 1163 Xinmin St Changchun 130021 Jilin Peoples R China

Objective: The previous study served impaired fasting glucose (IFG), a pre-diabetic state which was much greater than that of diabetic patients, have reached no consensus on its cut-off value. In this study, we aimed to explore the cut-off value of IFG via the associations of blood lipids with fasting plasma glucose (FPG). Design: A cross-sectional study in Jilin province, China, 2012. Sample: 13,093 participants aged 18-79 years in Jilin province were involved in the study. The Lamba-Mu-Sigma (LMS) method and generalized additive model (GAM) were used to explore the associations of different levels of FPG and blood lipids. Results: The distributions of FPG, triglyceride (TG) and high-density lipoprotein cholesterol (HDL-c) were significantly different by gender (p <0.01). In general, both LMS and GAM results showed that the TG and HDL-c curves had a steeper tendency when the FPG was greater than 6.0 mmol/L. Conclusion: FPG was positively associated with TG while negatively associated with HDL-c, and the associations were stronger in females. The cut-off value of IFG was suggested to be 6.0 mmol/L. (C) 2019 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

关键词： Blood lipids Cut-off value Impaired fasting glucose Lamba-Mu-Sigma method generalized additive models

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ACTUARIAL APPLICATIONS OF WORD EMBEDDING models

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ASTIN BULLETIN 2020年第1期50卷 1-24页

作者： Lee, Gee Y. Manski, Scott Maiti, Tapabrata Michigan State Univ Dept Stat & Probabil Dept Math C337 Wells Hall619 Red Cedar Rd E Lansing MI 48824 USA Michigan State Univ Dept Stat & Probabil C511 Wells Hall619 Red Cedar Rd E Lansing MI 48824 USA Michigan State Univ Dept Stat & Probabil C424 Wells Hall619 Red Cedar Rd E Lansing MI 48824 USA

In insurance analytics, textual descriptions of claims are often discarded, because traditional empirical analyses require numeric descriptor variables. This paper demonstrates how textual data can be easily used in insurance analytics. Using the concept of word similarities, we illustrate how to extract variables from text and incorporate them into claims analyses using standard generalized linear model or generalized additive regression model. This procedure is applied to the Wisconsin Local Government Property Insurance Fund (LGPIF) data, in order to demonstrate how insurance claims management and risk mitigation procedures can be improved. We illustrate two applications. First, we show how the claims classification problem can be solved using textual information. Second, we analyze the relationship between risk metrics and the probability of large losses. We obtain good results for both applications, where short textual descriptions of insurance claims are used for the extraction of features.

关键词： Risk mitigation insurance claims adjustment word embedding text mining word2vec GloVe word similarity generalized additive models actuarial modeling classification logistic regression loss modeling

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Comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India

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ECOLOGICAL INDICATORS 2021年 129卷

作者： Mondal, Rubina Bhat, Anuradha Indian Inst Sci Educ & Res Kolkata Dept Biol Sci Mohanpur 741246 W Bengal India

Over the past several decades, ecologists have been striving to develop models that accurately describe specieshabitat relationships across ecological communities. Statistical models that explain ecological dynamics need to consider the nuances of the complex interactions between communities and ecological factors. Here, we used multiple linear mixed models (LMM), generalized additive models (GAM), multivariate adaptive regression splines (MARS), and artificial neural networks (ANN) to model species richness and diversity of freshwater fishes in eastern and central India. The models were based on fish abundance and associated ecological data over three years across the study regions. We developed global models using all predictors after removing highly correlated variables (Pearson's r > 0.7). Results revealed conductivity, water temperature, and water velocity as the most important predictive factors of both species richness and diversity. We, then, built two subsets of selected factors to build predictive models for diversity and richness- one variable set containing common significant factors as revealed from the four different modeling methods used and the second, using an automatic feature selection technique. Amongst the modeling methods used in our study, ANN was found to create the best fit models for explaining nonlinearities between response variables and predictors. The importance of variable selection is highlighted, given that subset 1 (common consensual factors) creates more homogeneity in predictions compared to using subset 2 (automated feature selection). Contrary to similar studies in recent years, which show machine learning (ML) methods to typically outperform conventional methods, our results revealed that ANN performed at par with other methods in terms of predictive power. Our findings underline the need for a judicious choice of modeling techniques based on the availability of the data and the ecological communities being studied.

关键词： Freshwater fish Artificial neural network Linear mixed models Multivariate adaptive regression splines generalized additive models

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