The Design of Multiyear Crop Insurance Contracts

Crop Production/Industries, Risk and Uncertainty,

AGROBEST: an efficient Agrobacterium-mediated transient expression method for versatile gene function analyses in Arabidopsis seedlings

Background: Transient gene expression via Agrobacterium-mediated DNA transfer offers a simple and fast method to analyze transgene functions. Although Arabidopsis is the most-studied model plant with powerful genetic and genomic resources, achieving highly efficient and consistent transient expression for gene function analysis in Arabidopsis remains challenging. Results: We developed a highly efficient and robust Agrobacterium-mediated transient expression system, named AGROBEST (Agrobacterium-mediated enhanced seedling transformation), which achieves versatile analysis of diverse gene functions in intact Arabidopsis seedlings. Using β-glucuronidase (GUS) as a reporter for Agrobacterium-mediated transformation assay, we show that the use of a specific disarmed Agrobacterium strain with vir gene pre-induction resulted in homogenous GUS staining in cotyledons of young Arabidopsis seedlings. Optimization with AB salts in plant culture medium buffered with acidic pH 5.5 during Agrobacterium infection greatly enhanced the transient expression levels, which were significantly higher than with two existing methods. Importantly, the optimized method conferred 100% infected seedlings with highly increased transient expression in shoots and also transformation events in roots of ~70% infected seedlings in both the immune receptor mutant efr-1 and wild-type Col-0 seedlings. Finally, we demonstrated the versatile applicability of the method for examining transcription factor action and circadian reporter-gene regulation as well as protein subcellular localization and protein–protein interactions in physiological contexts. Conclusions: AGROBEST is a simple, fast, reliable, and robust transient expression system enabling high transient expression and transformation efficiency in Arabidopsis seedlings. Demonstration of the proof-of-concept experiments elevates the transient expression technology to the level of functional studies in Arabidopsis seedlings in addition to previous applications in fluorescent protein localization and protein–protein interaction studies. In addition, AGROBEST offers a new way to dissect the molecular mechanisms involved in Agrobacterium-mediated DNA transfer

The iNOS/Src/FAK axis is critical in Toll-like receptor-mediated cell motility in macrophages

AbstractThe Toll-like receptors (TLRs) play a pivotal role in innate immunity for the detection of highly conserved, pathogen-expressed molecules. Previously, we demonstrated that lipopolysaccharide (LPS, TLR4 ligand)-increased macrophage motility required the participation of Src and FAK, which was inducible nitric oxide synthase (iNOS)-dependent. To investigate whether this iNOS/Src/FAK pathway is a general mechanism for macrophages to mobilize in response to engagement of TLRs other than TLR4, peptidoglycan (PGN, TLR2 ligand), polyinosinic–polycytidylic acid (polyI:C, TLR3 ligand) and CpG-oligodeoxynucleotides (CpG, TLR9 ligand) were used to treat macrophages in this study. Like LPS stimulation, simultaneous increase of cell motility and Src (but not Fgr, Hck, and Lyn) was detected in RAW264.7, peritoneal macrophages, and bone marrow-derived macrophages exposed to PGN, polyI:C and CpG. Attenuation of Src suppressed PGN-, polyI:C-, and CpG-elicited movement and the level of FAK Pi-Tyr861, which could be reversed by the reintroduction of siRNA-resistant Src. Besides, knockdown of FAK reduced the mobility of macrophages stimulated with anyone of these TLR ligands. Remarkably, PGN-, polyI:C-, and CpG-induced Src expression, FAK Pi-Tyr861, and cell mobility were inhibited in macrophages devoid of iNOS, indicating the importance of iNOS. These findings corroborate that iNOS/Src/FAK axis occupies a central role in macrophage locomotion in response to engagement of TLRs

Identification of a New Peptide for Fibrosarcoma Tumor Targeting and Imaging In Vivo

A 12-mer amino acid peptide SATTHYRLQAAN, denominated TK4, was isolated from a phage-display library with fibrosarcoma tumor-binding activity. In vivo biodistribution analysis of TK4-displaying phage showed a significant increased phage titer in implanted tumor up to 10-fold in comparison with normal tissues after systemic administration in mouse. Competition assay confirmed that the binding of TK4-phage to tumor cells depends on the TK4 peptide. Intravenous injection of 131I-labeled synthetic TK4 peptide in mice showed a tumor retention of 3.3% and 2.7% ID/g at 1- and 4-hour postinjection, respectively. Tumor-to-muscle ratio was 1.1, 5.7, and 3.2 at 1-, 4-, and 24-hour, respectively, and tumors were imaged on a digital γ-camera at 4-hour postinjection. The present data suggest that TK4 holds promise as a lead structure for tumor targeting, and it could be further applied in the development of diagnostic or therapeutic agent

A Two-Stage Random Forest-Based Pathway Analysis Method

Pathway analysis provides a powerful approach for identifying the joint effect of genes grouped into biologically-based pathways on disease. Pathway analysis is also an attractive approach for a secondary analysis of genome-wide association study (GWAS) data that may still yield new results from these valuable datasets. Most of the current pathway analysis methods focused on testing the cumulative main effects of genes in a pathway. However, for complex diseases, gene-gene interactions are expected to play a critical role in disease etiology. We extended a random forest-based method for pathway analysis by incorporating a two-stage design. We used simulations to verify that the proposed method has the correct type I error rates. We also used simulations to show that the method is more powerful than the original random forest-based pathway approach and the set-based test implemented in PLINK in the presence of gene-gene interactions. Finally, we applied the method to a breast cancer GWAS dataset and a lung cancer GWAS dataset and interesting pathways were identified that have implications for breast and lung cancers

Developing, Pricing and Evaluating Efficiency of Weather-Based Index Insurance Contracts in Agriculture in Taiwan

[[conferencetype]]國際[[conferencedate]]20140627~20140701[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Denver, US

Three Essays on A Multiyear Crop Insurance Contract

[[abstract]]This study focused on how to redesign MPCI and GRP so that they are more attractive to farmers. Here we propose multiyear MPCI and GRP that insurance terms are extended to more than a year. Our simulation results showed that the actuarially-fair rate for a multiyear insurance program was lower than the actuarially-fair rate for a single year insurance program when the correlation of yield distribution among years decreased. Our real data results also showed that the correlations of yields among years are not strong. Therefore, the proposed multiyear insurance program can be practical and will provide more interests for farmers to participate in the MPCI and GRP. We also discussed farmers’ welfare under different crop insurance plans. We are concerned about producers’ participation and producers’ behaviors if multiyear insurance plans become available. As optimal coverage levels can reflect both the decision to participate and producers’ behavior when buying insurance, we used empirical models to simulate a producer’s decisions given price and yield risks and with various degrees of risk aversion. We focused on three scenarios of risk aversion that represent the risk preferences commonly reported in the empirical literature, high risk aversion, and risk neutrality

The Design of Multiyear Crop Insurance Contracts

[[abstract]]Agriculture production suffers potential risks because of the yield and price instabilities which result from unpredictable factors. These factors can be caused by natural disasters such as fire, drought, flooding and pest damage. Yield volatility causes price movements and income instability for farmers. In order to help protect farmers from production, price and income risks, the Federal Crop Insurance Program provides various types of insurance. Some insurance is based on the farm level, such as the farm-level yield insurance (Multiple Peril Crop Insurance or MPCI), farm-level revenue index insurance and farm-level revenue insurance with harvest price feature. Other types of insurance are based on area level, such as the area-level yield insurance (Group Risk Plan or GRP), area-level revenue index insurance and area-level revenue index insurance with harvest price feature. Current multiple peril (MPCI) and group risk (GRP) crop insurance plans are designed to mitigate monetary fluctuations resulting from yield losses for a single year. However, yield realizations (or yield realization tendency) can vary from year to year and may depend on the correlation of yield realizations among years. Indemnities and actuarially-fair rates, which are given by the expected loss divided by liability, for MPCI and GRP are related to yield realizations. If poor yield realizations can be offset by another year’s better yield realizations, the actuarially-fair rate is expected to decrease when current MPCI and GRP are extended to multiple periods. Therefore, in this proposed multiyear MPCI and GRP, insurance terms are extended to more than a year. The premium, liability and indemnity are also determined by a multiyear term. That is, they are calculated based on the aggregated yields for two or three years. To implement the multiyear MPCI and GRP contracts, we need to model the multivariate multiyear yield distribution and understand the correlation of yields among years. Further, the probability of loss, expected loss, liability, actuarially-fair rate, the optimum premium, the optimum time to pay premium during multiple periods will be investigated. The objectives of this study are to 1) understand the relationship between correlation of yields among years and actuarially-fair rates for multiyear MPCI and GRP crop insurance policies. 2) investigate how to model multiyear corn yield distributions for farm (MPCI) and county level data (GRP) and to estimate correlation of yields across years; 3) investigate how to design an efficient multiyear MPCI and GRP. We have used simulations to demonstrate that actuarially-fair rates can be reduced if a multiyear insurance plan is considered and the Pearson correlation coefficient of yields between two consecutive years is less than 1. The simulation results showed that actuarially-fair rate for a multiyear insurance plan decreases when the correlation of yields between two consecutive years also decreases. This implies that in practice, if the yields of two consecutive years are not completely correlated, our proposed multiyear insurance program can perform better than current single year insurance program. Therefore, it is important to estimate the correlation of yields using real data to demonstrate the feasibility of our method. Farm data for ten years in Iowa, Illinois, Ohio and Indiana were obtained from ftp://ftp.rma.usda.gov/pub/Miscellaneous_Files/yield98/. County data from 1928 to 2007 in Iowa, Illinois, Ohio and Indiana were obtained from www.nass.usda.gov. The top ten productive counties in these 4 states were used in the empirical analysis. We calculated Pearson correlation coefficients for farm and county data for 1) each farm in Iowa, Illinois, and Ohio States and; 2) each county in the four States after farm level data are aggregated to county level data; 3) the top ten productive counties in Iowa, Illinois, Ohio and Indiana States. We found that values of Pearson correlation coefficients vary considerably for farm data. However, most of them are not significantly correlated (i.e. p values > 0.05). It is also notable that there is no significant correlation at the significance levels 0.01 and 0.025 in the top ten productive counties in the four States. The empirical results demonstrate that the proposed multiyear insurance plan can be practical. Other than calculating sample correlation coefficients based on the Pearson correlation coefficient, we also estimated the correlation of yields among years by modeling the joint distribution of yields. There are two methods to modeling yield distributions- parametric methods (such as Weibull, the log-normal, the gamma, the logistic distributions and mixtures of parametric distributions) and nonparametric kernel methods. Since the dependence structure needs to be considered when a multiyear yield distribution is estimated but current parametric methods usually do not have closed forms for multivariate distributions, parametric copula methods (Frank’s Copula and Farlie-Gumbel-Morgenstern copula in the Archimedean Copula Family, Gaussian Copula and t-Copula in the Normal Copula Family) were used to approximate the multiyear yield distribution. The results also agreed with the Pearson correlation coefficients that the correlations of yields between two consecutive years are not strong. This study focused on how to redesign MPCI and GRP so that they are more attractive to farmers. Here we propose multiyear MPCI and GRP that insurance terms are extended to more than a year. Our simulation results showed that the actuarially-fair rate for a multiyear insurance program was lower than the actuarially-fair rate for a single year insurance program when the correlation of yield distribution among years decreased. Our real data results also showed that the correlations of yields among years are not strong. Therefore, the proposed multiyear insurance program can be practical and will provide more interests for farmers to participate in the MPCI and GRP.[[sponsorship]]American Agricultural Economics Association[[conferencetype]]國際[[conferencedate]]20100725~20100727[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Denver, US

Predicting Long-Term Care Service Demands for Cancer Patients: A Machine Learning Approach

Background: Long-term care (LTC) service demands among cancer patients are significantly understudied, leading to gaps in healthcare resource allocation and policymaking. Objective: This study aimed to predict LTC service demands for cancer patients and identify the crucial factors. Methods: 3333 cases of cancers were included. We further developed two specialized prediction models: a Unified Prediction Model (UPM) and a Category-Specific Prediction Model (CSPM). The UPM offered generalized forecasts by treating all services as identical, while the CSPM built individual predictive models for each specific service type. Sensitivity analysis was also conducted to find optimal usage cutoff points for determining the usage and non-usage cases. Results: Service usage differences in lung, liver, brain, and pancreatic cancers were significant. For the UPM, the top 20 performance model cutoff points were adopted, such as through Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), and XGBoost (XGB), achieving an AUROC range of 0.707 to 0.728. The CSPM demonstrated performance with an AUROC ranging from 0.777 to 0.837 for the top five most frequently used services. The most critical predictive factors were the types of cancer, patients’ age and female caregivers, and specific health needs. Conclusion: The results of our study provide valuable information for healthcare decisions, resource allocation optimization, and personalized long-term care usage for cancer patients.補正完畢US

Prevalence and predictive modeling of undiagnosed diabetes and impaired fasting glucose in Taiwan: a Taiwan Biobank study

Introduction: We investigated the prevalence of undiagnosed diabetes and impaired fasting glucose (IFG) in individuals without known diabetes in Taiwan and developed a risk prediction model for identifying undiagnosed diabetes and IFG. Research design and methods: Using data from a large population-based Taiwan Biobank study linked with the National Health Insurance Research Database, we estimated the standardized prevalence of undiagnosed diabetes and IFG between 2012 and 2020. We used the forward continuation ratio model with the Lasso penalty, modeling undiagnosed diabetes, IFG, and healthy reference group (individuals without diabetes or IFG) as three ordinal outcomes, to identify the risk factors and construct the prediction model. Two models were created: Model 1 predicts undiagnosed diabetes, IFG_110 (ie, fasting glucose between 110 mg/dL and 125 mg/dL), and the healthy reference group, while Model 2 predicts undiagnosed diabetes, IFG_100 (ie, fasting glucose between 100 mg/dL and 125 mg/dL), and the healthy reference group. Results: The standardized prevalence of undiagnosed diabetes for 2012-2014, 2015-2016, 2017-2018, and 2019-2020 was 1.11%, 0.99%, 1.16%, and 0.99%, respectively. For these periods, the standardized prevalence of IFG_110 and IFG_100 was 4.49%, 3.73%, 4.30%, and 4.66% and 21.0%, 18.26%, 20.16%, and 21.08%, respectively. Significant risk prediction factors were age, body mass index, waist to hip ratio, education level, personal monthly income, betel nut chewing, self-reported hypertension, and family history of diabetes. The area under the curve (AUC) for predicting undiagnosed diabetes in Models 1 and 2 was 80.39% and 77.87%, respectively. The AUC for predicting undiagnosed diabetes or IFG in Models 1 and 2 was 78.25% and 74.39%, respectively. Conclusions: Our results showed the changes in the prevalence of undiagnosed diabetes and IFG. The identified risk factors and the prediction models could be helpful in identifying individuals with undiagnosed diabetes or individuals with a high risk of developing diabetes in Taiwan.補正完畢TW