80 research outputs found

    AN ANALYSIS OF FACTORS THAT AFFECT THE QUALITY OF FEDERAL LAND BANK LOANS

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    Financial conditions existing in agriculture are placing severe pressure on lenders as well as borrowers. Data from both good and foreclosed Federal Land Bank loans were analyzed to determine the most important characteristics leading to the failure of loans. The analysis was completed by comparing means through t-tests and the development of a discriminant model. The ratio of total debt service to total income, the debt to asset ratio, the ratio of total loan amount to appraised value, and the ratio of acres in security to acres owned were determined to be the most important discriminating variables.Agricultural Finance,

    OPTIMAL IRRIGATION PIVOT LOCATION ON IRREGULARLY SHAPED FIELDS

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    Although annual rainfall in the Southeast is adequate, its distribution is a potential constraint to agricultural production. Farmers require production information concerning efficient use of irrigation technology adapted to regional growing conditions. Selection of optimal position, size, and number of pivots in center pivot irrigation systems poses special problems on small, irregularly shaped fields. In the southeastern United States, field size and shape are often varied and irregular. A mixed integer programming model was constructed to assist in irrigation investment decisions. The model is illustrated using irrigated peanut production in southeast Alabama. Results indicate the importance of economic engineering considerations.Farm Management,

    Efficacy of aerobic exercise and a prudent diet for improving selected lipids and lipoproteins in adults: a meta-analysis of randomized controlled trials

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    Background Studies addressing the effects of aerobic exercise and a prudent diet on lipid and lipoprotein concentrations in adults have reached conflicting conclusions. The purpose of this study was to determine the effects of aerobic exercise combined with a prudent diet on lipid and lipoprotein concentrations in adults. Methods Studies were located by searching nine electronic databases, cross-referencing, and expert review. Two independent reviewers selected studies that met the following criteria: (1) randomized controlled trials, (2) aerobic exercise combined with diet recommendations (saturated/trans fat intake less than 10% of total calories and cholesterol less than 300 mg/day and/or fiber intake ≥25 g/day in women and ≥35 grams per day in men), (3) intervention ≥4 weeks, (4) humans ≥18 years of age, (5) published studies, including dissertations and Master\u27s theses, (6) studies published in any language, (7) studies published between January 1, 1955 and May 1, 2009, (8) assessment of one or more of the following lipid and lipoprotein concentrations: total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), ratio of TC to HDL-C, non-HDL-C, low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG). Two reviewers independently extracted all data. Random-effects models that account for heterogeneity and 95% confidence intervals were used to pool findings. Results Of the 1,401 citations reviewed, six studies representing 16 groups (8 intervention, 8 control) and up to 559 men and women (282 intervention, 277 control) met the criteria for analysis. Statistically significant intervention minus control reductions were found for TC (-15.5 mg/dl, 95% CI, -20.3 to -10.7), TC:HDL-C (-0.4 mg/dl, 95% CI, -0.7 to -0.2), LDL-C (-9.2 mg/dl, 95% CI, -12.7 to -5.8) and TG (-10.6 mg/dl, 95% CI, -17.2 to -4.0) but not HDL-C (-0.5 mg/dl, 95% CI, -4.0 to 3.1). Changes were equivalent to reductions of 7.5%, 6.6%, 7.2% and 18.2% respectively, for TC, TC:HDL-C, LDL-C and TG. Because of missing variance statistics, non-HDL-C was excluded. Conclusions Aerobic exercise combined with a prudent diet is highly efficacious for improving TC, TC:HDL-C, LDL-C and TG, but not HDL-C concentrations, in adults. However, additional studies are needed, including effectiveness studies using intention-to-treat analysis

    Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery

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    Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer
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