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Assessing Transmission of Crop Diseases by Insect Vectors in a Landscape Context

JOURNAL OF ECONOMIC ENTOMOLOGY 2014年第1期107卷 1-10页

作者： Carriere, Y. Degain, B. Hartfield, K. A. Nolte, K. D. Marsh, S. E. Ellers-Kirk, C. Van Leeuwen, W. J. D. Liesner, L. Dutilleul, P. Palumbo, J. C. Univ Arizona Dept Entomol Tucson AZ 85721 USA Univ Arizona Sch Nat Resources & Environm Arizona Remote Sensing Ctr Tucson AZ 85721 USA Univ Arizona Yuma Co Cooperat Extens Yuma AZ 85364 USA Arizona Cotton Res & Protect Council Phoenix AZ 85040 USA McGill Univ Dept Plant Sci Quebec City PQ H9X 3V9 Canada

Theory indicates that landscape composition affects transmission of vector-borne crop diseases, but few empirical studies have investigated how landscape composition affects plant disease epidemiology. Since 2006, Bemisia tabaci (Gennadius) has vectored the cucurbit yellow stunting disorder virus (CYSDV) to cantaloupe and honeydew melons (Cucumis melo L.) in the southwestern United States and northern Mexico, causing significant reductions in yield of fall melons and increased use of insecticides. Here, we show that a landscape-based approach allowing simultaneous assessment of impacts of local (i.e., planting date) and regional (i.e., landscape composition) factors provides valuable insights on how to reduce crop disease risks. Specifically, we found that planting fall melon fields early in the growing season, eliminating plants germinating from seeds produced by spring melons after harvest, and planting fall melon fields away from cotton and spring melon fields may significantly reduce the incidence of CYSDV infection in fall melons. Because the largest scale of significance of the positive association between abundance of cotton and spring melon fields and CYSDV incidence was 1,750 and 3,000 m, respectively, reducing areas of cotton and spring melon fields within these distances from fall melon fields may decrease CYSDV incidence. Our results indicate that landscape-based studies will be fruitful to alleviate limitations imposed on crop production by vector-borne diseases.

关键词： Bemisia tabaci classification and regression tree cucurbit yellow stunting disorder virus integrated pest management landscape ecology

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Is it possible to identify cases of coronary artery bypass graft postoperative surgical site infection accurately from claims data?

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2014年第1期14卷 1-9页

作者： Yu, Tsung-Hsien Hou, Yu-Chang Lin, Kuan-Chia Chung, Kuo-Piao Natl Taiwan Univ Inst Healthcare Policy & Management Taipei 10764 Taiwan Natl Taiwan Univ Master Degree Program Publ Hlth Taipei 10764 Taiwan Minist Hlth & Welf Tao Yuan Gen Hosp Dept Chinese Med Taoyuan Taiwan Chuan Yuan Christian Univ Dept Biosci Technol Taoyuan Taiwan Natl Taipei Univ Nursing & Hlth Sci Dept Hlth Care & Management Taipei Taiwan

Background: Claims data has usually been used in recent studies to identify cases of healthcare-associated infection. However, several studies have indicated that the ICD-9-CM codes might be inappropriate for identifying such cases from claims data;therefore, several researchers developed alternative identification models to correctly identify more cases from claims data. The purpose of this study was to investigate three common approaches to develop alternative models for the identification of cases of coronary artery bypass graft (CABG) surgical site infection, and to compare the performance between these models and the ICD-9-CM model. Methods: The 2005-2008 National Health Insurance claims data and healthcare-associated infection surveillance data from two medical centers were used in this study for model development and model verification. In addition to the use of ICD-9-CM codes, this study also used classification algorithms, a multivariable regression model, and a decision tree model in the development of alternative identification models. In the classification algorithms, we defined three levels (strict, moderate, and loose) of the criteria in terms of their strictness. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were used to evaluate the performance of each model. Results: The ICD-9-CM-based model showed good specificity and negative predictive value, but sensitivity and positive predictive value were poor. Performances of the other models were varied, except for negative predictive value. Among the models, the performance of the decision tree model was excellent, especially in terms of positive predictive value. Conclusion: The accuracy of identification of cases of CABG surgical site infection is an important issue in claims data. Use of the decision tree model to identify such cases can improve the accuracy of patient-level outcome research. This model should be considered when performing future research u

关键词： Administrative data Identification model CABG Surgical site infection Decision tree classification and regression tree

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Predicting Hepatic Steatosis in a Racially and Ethnically Diverse Cohort of Adolescent Girls

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JOURNAL OF PEDIATRICS 2014年第2期165卷 319-U153页

作者： Rehm, Jennifer L. Connor, Ellen L. Wolfgram, Peter M. Eickhoff, Jens C. Reeder, Scott B. Allen, David B. Univ Wisconsin Sch Med & Publ Hlth Dept Pediat Madison WI 53792 USA Med Coll Wisconsin Dept Pediat Milwaukee WI 53226 USA Univ Wisconsin Sch Med & Publ Hlth Dept Biostat & Med Informat Madison WI 53792 USA Univ Wisconsin Sch Med & Publ Hlth Dept Radiol Madison WI 53792 USA Univ Wisconsin Sch Med & Publ Hlth Dept Med Phys Madison WI 53792 USA

Objective To develop a risk assessment model for early detection of hepatic steatosis using common anthropometric and metabolic markers. Study design This was a cross-sectional study of 134 adolescent and young adult females, age 11-22 years (mean 13.3 +/- 2 years) from a middle school and clinics in Madison, Wisconsin. The ethnic distribution was 27% Hispanic and 73% non-Hispanic;the racial distribution was 64% Caucasian, 31% African-American, and 5% Asian, Fasting glucose, fasting insulin, alanine aminotransferase (ALT), body mass index (BMI), waist circumference (WC), and other metabolic markers were assessed. Hepatic fat was quantified using magnetic resonance imaging proton density fat fraction (MR-PDFF). Hepatic steatosis was defined as MR-PDFF >5.5%. Outcome measures were sensitivity, specificity, and positive predictive value (PPV) of BMI, WC, ALT, fasting insulin, and ethnicity as predictors of hepatic steatosis, individually and combined, in a risk assessment model. classification and regression tree methodology was used to construct a decision tree for predicting hepatic steatosis. Results MR-PDFF revealed hepatic steatosis in 16% of subjects (27% overweight, 3% nonoverweight). Hispanic ethnicity conferred an OR of 4.26 (95% CI, 1.65-11.04;P = .003) for hepatic steatosis. BMI and ALT did not independently predict hepatic steatosis. A BMI >85% combined with ALT >65 U/L had 9% sensitivity, 100% specificity, and 100% PPV. Lowering the ALT value to 24 U/L increased the sensitivity to 68%, but reduced the PPV to 47%. A risk assessment model incorporating fasting insulin, total cholesterol, WC, and ethnicity increased sensitivity to 64%, specificity to 99% and PPV to 93%. Conclusion A risk assessment model can increase specificity, sensitivity, and PPV for identifying the risk of hepatic steatosis and guide the efficient use of biopsy or imaging for early detection and intervention.

关键词： ALT Alanine aminotransferase AST Aspartate aminotransferase BMI Body mass index CART classification and regression tree HDL High-density lipoprotein HgbA1c Hemoglobin A1c HOMA-IR Homeostatic model assessment of insulin resistance MRI Magnetic resonance imaging MR-PDFF Magnetic resonance imaging proton density fat fraction NAFLD Nonalcoholic fatty liver disease NASH Nonalcoholic steatohepatitis NPV Negative predictive value PPV Positive predictive value ROC Receiver operating characteristic WC Waist circumference

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Prosopis: a global assessment of the biogeography, benefits, impacts and management of one of the world's worst woody invasive plant taxa

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AOB PLANTS 2014年第6期6卷 plu027页

作者： Shackleton, Ross T. Le Maitre, David C. Pasiecznik, Nick M. Richardson, David M. Univ Stellenbosch Ctr Invas Biol Dept Bot & Zool ZA-7602 Matieland South Africa CSIR Nat Resources & Environm ZA-7599 Stellenbosch South Africa Agroforestry Enterprises F-71550 Villebeuf Cussy En Morvan France

Invasive species cause ecological, economic and social impacts and are key drivers of global change. This is the case for the genus Prosopis (mesquite;Fabaceae) where several taxa are among the world's most damaging invasive species. Many contentious issues ('conflicts of interest') surround these taxa, and management interventions have not yet sustainably reduced the negative impacts. There is an urgent need to better understand the factors that drive invasions and shape management actions, and to compare the effectiveness of different management approaches. This paper presents a global review of Prosopis, focusing on its distribution, impacts, benefits and approaches to management. Prosopis was found to occur in a 129 countries globally and many more countries are climatically suitable. All areas with naturalized or invasive Prosopis species at present are suitable for more taxa and many Asian and Mediterranean countries with no records of Prosopis are bioclimatically suitable. Several Prosopis species have substantial impacts on biodiversity, ecosystem services, and local and regional economies in their native and even more so in their invasive ranges;others provide multiple benefits to local communities. Management efforts are underway in only a small part of the invaded range. Countries where more research has been done are more likely to implement formal management than those where little published research is available. Management strategies differ among countries;developed nations use mainly mechanical and chemical control whereas developing nations tend to apply control through utilization approaches. A range of countries are also using biological control. Key gaps in knowledge and promising options for management are highlighted.

关键词： classification and regression tree distribution global review impacts logistic regression management mesquite tree invasions

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Multilocus analysis of hormonal, neurotransmitter, inflammatory pathways and genome-wide associated variants in migraine susceptibility

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EUROPEAN JOURNAL OF NEUROLOGY 2014年第7期21卷 1011-1020页

作者： Ghosh, J. Pradhan, S. Mittal, B. Sanjay Gandhi Postgrad Inst Med Sci SGPGIMS Dept Genet Lucknow Uttar Pradesh India Sanjay Gandhi Postgrad Inst Med Sci SGPGIMS Dept Neurol Lucknow Uttar Pradesh India

Background and purpose Migraine pathophysiology involves a complex interplay of processes wherein the hormonal, neurotransmitter and inflammatory pathways interact to influence the migraine phenotype. However, all studies pertaining to the role of genetic variants in migraine have been restricted to a specific pathway and none of the studies has looked into inter-pathway genetic analysis. Our aim was to combine all the genetic variants from our previously reported studies to conduct higher order gene-gene interaction analysis using different multi-analytical approaches. Methods The study group included 324 migraine patients and 134 healthy controls. The study included 20 polymorphisms from hormonal, neurotransmitter, inflammatory and genome-wide associated variants from our published reports. Univariate and multivariate analyses were carried out by logistic regression. classification and regression tree (CART) analysis was performed to build a decision tree via recursive partitioning. The high order genetic interactions associated with migraine risk were analyzed using multifactor dimensionality reduction (MDR). Results Univariate analysis revealed significant associations of polymorphisms in CYP19A1, ESR1, TNFA and PRDM16 genes with migraine susceptibility. Multiple regression analysis found significant results for four markers in CYP19A1, TNFA, ESR1 and LRP1 genes. In CART, the most prominent splitting variable was CYP19A1 polymorphism followed by TNFA, ESR1 and PRDM16 markers. The MDR analysis identified markers of CYP19A1, CYP19A1- TNFA, CYP19A1- ESR1- TNFA and CYP19A1- ESR1- TRPM8- PRDM16 as best models for one, two, three and four factors, respectively. Conclusions The present study suggests interactions amongst hormonal, inflammatory and genome-wide associated variants but not with neurotransmitter pathway variants in migraine susceptibility.

关键词： classification and regression tree gene-gene interaction migraine multifactor dimensionality reduction multilocus multivariate polymorphisms susceptibility

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Understanding biophysical and socio-economic determinants of maize (Zea mays L.) yield variability in eastern India

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NJAS-WAGENINGEN JOURNAL OF LIFE SCIENCES 2014年第1期70-71卷 79-93页

作者： Banerjee, Hirak Goswami, Rupak Chakraborty, Somsubhra Dutta, Sudarshan Majumdar, Kaushik Satyanarayana, T. Jat, M. L. Zingore, Shamie Bidhan Chandra Krishi Viswavidyalaya Nadia 741252 W Bengal India Ramakrishna Mission Vivekananda Univ RKMVU IRDM Fac Ctr Kolkata 700103 India Int Plant Nutr Inst HUDA South Asia Program Gurgaon 122016 Haryana India Int Maize & Wheat Improvement Ctr CIMMYT New Delhi 110012 India Int Plant Nutr Inst Sub Saharan Africa Program IFDC East & Southern Africa Div ICIPE Compound Nairobi Kenya

The aim of this paper was to investigate the key factors limiting maize (Zea mays L.) productivity in eastern India to develop effective crop and nutrient management strategies to reduce yield gap. A series of farm surveys was conducted in two distinct agro-ecological zones of eastern India to evaluate the importance of crop management and structural constraints for maize productivity in a range of socio-economic settings prevalent in smallholder farms. Surveys revealed yield gap and yield variations among farms across growing seasons. Lower yields of farmers were mainly associated with farmer's ethnic origin, availability of family labor, land ownership, legumes in cropping sequence, irrigation constraints, seed type, optimal plant population, labor and capital investment, and use of organic manure. These constraints varied strongly between sites as well as growing seasons. Stochastic Frontier Analysis suggested intensification of farm input use and removal of socio-economic and structural constraints for increasing efficiency in maize production. The use of multivariate classification and regression tree analysis revealed that maize yield was affected by multiple and interacting production constraints, differentiating the surveyed farms in six distinct resource groups. These farm types lend scope for introducing typology-specific crop management practices through appropriate participatory on-farm evaluation/trials. Summarily, this research indicated that interacting production constraints should be addressed simultaneously, considering the need of different farm types, if significant productivity improvements are to be achieved. This will be, however, more challenging for less endowed farms due to lack of social and financial capital to improve management intensity. A typology-specific farm support strategy may be formulated to offset this lack of entitlement among resource-poor farmers. (C) 2014 Royal Netherlands Society for Agricultural Sciences. Published by El

关键词： classification and regression tree Eastern India Farm typology Production constraints Smallholder farms Stochastic Frontier Analysis Yield gap

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Assessment of fracture risk by the FRAX algorithm in men and women with and without type 2 diabetes mellitus: a cross- sectional study

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DIABETES-METABOLISM RESEARCH AND REVIEWS 2014年第4期30卷 313-322页

作者： Carnevale, Vincenzo Morano, Susanna Fontana, Andrea Annese, Maria Antonietta Fallarino, Mara Filardi, Tiziana Copetti, Massimiliano Pellegrini, Fabio Romagnoli, Elisabetta Eller-Vainicher, Cristina Zhukouskaya, Volha V. Chiodini, Iacopo D'Amico, Graziella IRCCS Unit Internal Med Casa Sollievo Sofferenza Hosp I-71013 San Giovanni Rotondo FG Italy Univ Roma La Sapienza Dept Internal Med & Med Specialties Rome Italy IRCCS Unit Biostat Casa Sollievo Sofferenza Hosp I-71013 San Giovanni Rotondo FG Italy Ist Ric Farmacol Mario Negri Consorzio Mario Negri Sud Unit Biostat DCPE I-66030 Santa Maria Imbaro Italy Univ Milan Unit Endocrinol & Diabetol Dept Clin Sci & Community Hlth Fdn IRCCS Ca GranadaOsped Maggiore Policlin Milan Italy

BackgroundThe FRAX algorithm is a diffuse tool to assess fracture risk, but it has not been clinically applied in European patients with diabetes. We investigated FRAX-estimated fracture risk in patients with type 2 diabetes mellitus (DM), compared with concomitantly enrolled control subjects. MethodsIn our multicentric cross-sectional study, we assessed the FRAX scores of 974 DM and 777 control subjects from three Italian diabetes outpatient clinics, and in DM. We tested the association between parameters and complications of the disease and FRAX scores. ResultsDM had significantly lower FRAX-estimated probability of both major osteoporotic fracture (MOF) and hip fracture (HF) than control subjects (6.355.07% versus 7.756.93%, p<0.001, and 2.17 +/- 3.07% versus 2.91 +/- 4.56%, p=0.023, respectively). When grouping by gender, such differences were found only in men. In DM, the frequency of previous fracture was higher than in control subjects (29.88% versus 20.46%, p<0.001). In diabetic patients, age, sex, body mass index, HbA(1c) and hypoglycaemia are significantly associated with FRAX scores;gender-specific regression models differed. Among DM, the tree-based regression (classification and regression tree (CART)) analysis identified groups of patients with different mean FRAX scores. In female DM aged >65years with or without obesity, MOF>20% was found in 5.66% and 13.53% and HF>3% in 40.57% and 63.91% of patients, respectively. ConclusionsPatients with DM had mean FRAX scores lower than control subjects, despite the higher number of previous fractures. Some features and complications of DM did associate with FRAX scores. Among DM patients, the CART analysis identified subgroups with higher FRAX scores. However, despite its potential utility, concerns still remain for using FRAX in DM patients. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： type 2 diabetes fractures FRAX classification and regression tree

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Diagnostic and Predictive Capability of Routine Laboratory Tests for the Diagnosis and Staging of Equine Inflammatory Disease

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JOURNAL OF VETERINARY INTERNAL MEDICINE 2014年第5期28卷 1587-1593页

作者： Hooijberg, E. H. van den Hoven, R. Tichy, A. Schwendenwein, I. Univ Vet Med A-1210 Vienna Austria Univ Vet Med Dept Small Anim & Horses Equine Hosp A-1210 Vienna Austria

Background: A wide spectrum of laboratory tests is available to aid diagnosis and classification of equine inflammatory disease. Objectives: To compare diagnostic efficacy and combined predictive capability of the myeloperoxidase index (MPXI), and plasma fibrinogen, iron and serum amyloid A (SAA) concentrations for the diagnosis of inflammation. Animals: Twenty-six hospitalized horses with systemic inflammation (SI), 114 with local inflammation (LI) and 61 healthy horses or those with noninflammatory disease (NI) were included. Methods: A retrospective study was performed;clinicopathologic data from horses were compared between groups. Receiver-operator characteristic (ROC) curves were used to evaluate diagnostic efficacy;classification and regression tree analysis (CART) and logistic regression analysis were used to generate diagnostic algorithms. Results: Horses with SI had significantly higher SAA than horses with LI (P = .007) and NI (P < .001) and lower iron concentrations than horses with LI (P < .001) and NI (P < .001). Fibrinogen concentration was higher in horses with inflammation than in those without inflammation (P = .002). There was no difference between the SI and LI groups. White blood cell count, neutrophil count and MPXI were similar between groups. SAA had the highest accuracy for diagnosing inflammation (area under ROC curve [AUC], 0.83 +/- 0.06) and iron and SAA concentration had the highest accuracy for differentiating SI from LI (AUC, 0.80 +/- 0.09 and 0.73 +/- 0.10 respectively). Predictive modeling failed to generate useful algorithms and classification of cases was moderate. Conclusions and Clinical Importance: Very high SAA and low iron concentrations may reflect SI, but diagnostic guidelines based on quantitative results of inflammatory markers could not be formulated.

关键词： classification and regression tree Iron Myeloperoxidase index Serum amyloid A

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Correlations between weather conditions and common vole (Microtus arvalis) densities identified by regression tree analysis

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BASIC AND APPLIED ECOLOGY 2014年第1期15卷 75-84页

作者： Esther, Alexandra Imholt, Christian Perner, Joerg Schumacher, Jens Jacob, Jens Julius Kuehn Inst Fed Res Ctr Cultivated Plants Inst Plant Protect Hort & Forests D-48161 Munster Germany UAS Umwelt & Agrarstudien GmbH D-07743 Jena Germany Univ Jena Inst Stochast D-07743 Jena Germany

Population dynamics of fluctuating and cyclic rodent populations can be impacted by particular weather parameters. In temperate areas there are interrelations between different weather parameters, which make identification difficult. However, this is necessary because small rodents are relevant for both the food web and crop damage especially in the face of climate change. We used both, boosted regression tree and classification and regression tree methods to identify weather conditions correlating with the active burrow index (ABI) of common voles (Microtus arvalis) from 1974 to 1998 in the high outbreak risk area of Central Germany. Highest ABI occurred in perennial crops in fall with a maximum of more than 2000 active burrows per 1000 m(2). Boosted regression tree analysis showed that between 12 and 20 weather parameters could have a relative influence on vole ABIs ranging from 2% to 19%. classification and regression tree analysis highlighted that the number of days with snow cover in December and March, rainfall amount in spring and maximum temperature in October seem to be key indicators for ABIs in the following year in spring. Monthly maximum temperatures of February to June and the amount of precipitation in April and July were correlated to ABIs in fall. Quantitative validation showed an agreement of ABI distribution on regional scale >85%. This represents the first study to identify complex weather conditions including single parameter thresholds correlated with common vole abundance in a temperate area. The results have the potential to aid the development of predictive models for small rodent dynamics and they inspire further detailed search for regulative mechanisms of small mammal dynamics.

关键词： classification and regression tree Boosted regression trees Functional crop types Population dynamics Small mammal Rodent Temperate area

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A comparative study of variable selection methods in the context of developing psychiatric screening instruments

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STATISTICS IN MEDICINE 2014年第3期33卷 401-421页

作者： Lu, Feihan Petkova, Eva Columbia Univ Dept Stat New York NY 10027 USA NYU Dept Child & Adolescent Psychiat New York NY 10016 USA Nathan S Kline Inst Psychiat Res Orangeburg NY 10962 USA

The development of screening instruments for psychiatric disorders involves item selection from a pool of items in existing questionnaires assessing clinical and behavioral phenotypes. A screening instrument should consist of only a few items and have good accuracy in classifying cases and non-cases. Variable/item selection methods such as Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, classification and regression tree, Random Forest, and the two-sample t-test can be used in such context. Unlike situations where variable selection methods are most commonly applied (e.g., ultra high-dimensional genetic or imaging data), psychiatric data usually have lower dimensions and are characterized by the following factors: correlations and possible interactions among predictors, unobservability of important variables (i.e., true variables not measured by available questionnaires), amount and pattern of missing values in the predictors, and prevalence of cases in the training data. We investigate how these factors affect the performance of several variable selection methods and compare them with respect to selection performance and prediction error rate via simulations. Our results demonstrated that: (1) for complete data, LASSO and Elastic Net outperformed other methods with respect to variable selection and future data prediction, and (2) for certain types of incomplete data, Random Forest induced bias in imputation, leading to incorrect ranking of variable importance. We propose the Imputed-LASSO combining Random Forest imputation and LASSO;this approach offsets the bias in Random Forest and offers a simple yet efficient item selection approach for missing data. As an illustration, we apply the methods to items from the standard Autism Diagnostic Interview-Revised version. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： least absolute shrinkage and selection operator elastic net classification and regression tree random forest two-sample t-test missing data imputation

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