Suchergebnisse

AbstractThis paper examines the payments made to minority producers, focused on African American producers, from the COVID‐19 program, Coronavirus Food Assistance Program (CFAP), of the United States Department of Agriculture (USDA) and compares it with one of the other more recent ad hoc program payments, the Market Facilitation Program (MFP). There were two rounds of the CFAP, and combinedly (as of March 2022), the program made direct payments of $31.0 billion ($11.8 billion from CFAP 1 and $19.2 billion from CFAP 2) starting in 2020. The MFP made a total payment of $23.5 billion (in two rounds, MFP 2018 and MFP 2019) to producers affected by the retaliatory tariffs placed on US producers by trade partners across multiple years. CFAP made almost $600 million in direct payments to minority producers, including Black or African American producers. Black or African American only producers received more than $52 million in CFAP payments. CFAP payments were proportional to the value of agricultural commodity sold for most minority producers. The 2017 Census of Agriculture showed that the majority of minority producers, including African American producers but excluding Asian producers, raised livestock. CFAP made the highest payments to livestock minority producers. The CFAP payment distribution pattern shows that payments reached minority producers who often did not receive Government payments. CFAP made more payments and as a share of total program outlays to minority producers compared to MFP. However, for Black or African American only producers, even though the magnitude increased (because CFAP disbursed more funds compared to MFP), the share of payment received did not increase.

Funding Information: The authors thank Dr. Pirkko M Mattila, Dr. Jenni Lahtela and Bhiswa Ghimire for their valuable suggestions on how to improve the web-tool, Olle Hansson for the FIMM cluster server machine to host the web-tool and the database, and all the beta-testers for confirming the smooth operation and reproducibility of the analyses. This work was supported by the Academy of Finland (grants 295504, 310507, 326238, 340141 and 344698 to TA), European Union's Horizon 2020 Research and Innovation Programme (ERA PerMed JAKSTAT-TARGET), the Cancer Society of Finland (TA), the Sigrid Jusélius Foundation (TA), and the Norwegian Cancer Society (grant 216104 to TA). Publisher Copyright: © 2022, The Author(s). ; Identification of cell populations often relies on manual annotation of cell clusters using established marker genes. However, the selection of marker genes is a time-consuming process that may lead to sub-optimal annotations as the markers must be informative of both the individual cell clusters and various cell types present in the sample. Here, we developed a computational platform, ScType, which enables a fully-automated and ultra-fast cell-type identification based solely on a given scRNA-seq data, along with a comprehensive cell marker database as background information. Using six scRNA-seq datasets from various human and mouse tissues, we show how ScType provides unbiased and accurate cell type annotations by guaranteeing the specificity of positive and negative marker genes across cell clusters and cell types. We also demonstrate how ScType distinguishes between healthy and malignant cell populations, based on single-cell calling of single-nucleotide variants, making it a versatile tool for anticancerapplications. The widely applicable method is deployed both as an interactive web-tool (https://sctype.app), and as an open-source R-package. ; Peer reviewed

Funding Information: Academy of Finland [292611, 310507, 313267, 326238 to T.A.]; Cancer Society of Finland [to T.A.]; Sigrid Jus?lius Foundation [to TA]. Funding for open access charge: European Union's Horizon 2020 Research and Innovation Programme [ERA PerMed JAKSTAT-TARGET project]. Funding Information: Academy of Finland [292611, 310507, 313267, 326238 to T.A.]; Cancer Society of Finland [to T.A.]; Sigrid Jusélius Foundation [to TA]. Funding for open access charge: European Union's Horizon 2020 Research and Innovation Programme [ERA PerMed JAKSTAT-TARGET project]. Conflict of interest statement. None declared. Publisher Copyright: © The Author(s) 2020. ; SynergyFinder (https://synergyfinder.fimm.fi) is a stand-alone web-application for interactive analysis and visualization of drug combination screening data. Since its first release in 2017, SynergyFinder has become a widely used web-tool both for the discovery of novel synergistic drug combinations in pre-clinical model systems (e.g. cell lines or primary patient-derived cells), and for better understanding of mechanisms of combination treatment efficacy or resistance. Here, we describe the latest version of SynergyFinder (release 2.0), which has extensively been upgraded through the addition of novel features supporting especially higher-order combination data analytics and exploratory visualization of multi-drug synergy patterns, along with automated outlier detection procedure, extended curve-fitting functionality and statistical analysis of replicate measurements. A number of additional improvements were also implemented based on the user requests, including new visualization and export options, updated user interface, as well as enhanced stability and performance of the web-tool. With these improvements, SynergyFinder 2.0 is expected to greatly extend its potential applications in various areas of multi-drug combinatorial screening and precision medicine. ; Peer reviewed

AbstractUsing data from the 2009–2020 Agricultural Resource Management Surveys, we compare the financial position and performance of African American farms to that of other U.S. farms. The results suggest that, relative to others, the average African American farm has lower total value of production, net cash farm income, government payments, assets, and debts. We obtained mixed results regarding financial performance, with African American farms having lower profitability, liquidity, and efficiency than others but higher solvency. To our knowledge, this study is the first to provide a contemporary overview of the status of African American farms using detailed, farm‐level financial data.

AbstractThis paper evaluates producer risk management decisions accounting for government provided risk management programs. An analytical model is developed to investigate the effect of crop insurance and Farm Bill program choice on producer demand for hedging in the futures market. Simulation results show government programs has potential to alter the optimal hedging decisions of producers. Yield protection insurance is found to complement hedging in most locations, while revenue insurance is generally found to substitute for hedging. Farm Bill programs are found to have varying effects based on price level.

Mobility remains a vital part of the well-being of rural-living, older adults and transportation disadvantaged persons. This study seeks to identify research and policy needs related to rural transit for older people and the transportation disadvantaged. To obtain this goal, the multidisciplinary study team conducted two activities as part of a 2016 rural transportation conference: a survey of conference attendees and open discussion to elicit additional information. Results suggest the attendees felt the need for rural transit for older adults would continue to increase with public and private funding being critical issues. Respondents had similar opinions about challenges and opportunities across socioeconomic characteristics including age, gender, political leaning, rurality, and organizational function. This suggests an opportunity to mobilize support for public transportation.