Factors affecting innovation and imitation of ICT in the agrifood sector

Diffusion of innovations has gained a lot of attention and concerns different scientific fields. Many studies, which examine the determining factors of technological innovations in the agricultural and agrifood sector, have been conducted using the widely used Technology Accepted Model, for a random sample of farmers or firms engaged in agricultural sector. In the present study, a holistic examination of the determining factors that affect the propensity of firms to innovate or imitate, is conducted. The diffusion of ICT tools of firms which are engaged in the NACE 02/03 as well as in the NACE 10/11 classifications for 49 heterogeneous national markets is examined, using the Bass model. The innovation parameter is positively associated with rural income, female employment, export activity and education of farmers, while the imitation parameter is increased in countries whose societies are characterized by uncertainty avoidance

Selecting Forecasting Methods

I examined six ways of selecting forecasting methods: Convenience, “what’s easy,” is inexpensive, but risky. Market popularity, “what others do,” sounds appealing but is unlikely to be of value because popularity and success may not be related and because it overlooks some methods. Structured judgment, “what experts advise,” which is to rate methods against prespecified criteria, is promising. Statistical criteria, “what should work,” are widely used and valuable, but risky if applied narrowly. Relative track records, “what has worked in this situation,” are expensive because they depend on conducting evaluation studies. Guidelines from prior research, “what works in this type of situation,” relies on published research and offers a low-cost, effective approach to selection. Using a systematic review of prior research, I developed a flow chart to guide forecasters in selecting among ten forecasting methods. Some key findings: Given enough data, quantitative methods are more accurate than judgmental methods. When large changes are expected, causal methods are more accurate than naive methods. Simple methods are preferable to complex methods; they are easier to understand, less expensive, and seldom less accurate. To select a judgmental method, determine whether there are large changes, frequent forecasts, conflicts among decision makers, and policy considerations. To select a quantitative method, consider the level of knowledge about relationships, the amount of change involved, the type of data, the need for policy analysis, and the extent of domain knowledge. When selection is difficult, combine forecasts from different methods

Genome-Wide Association Study of Lp-PLA2 Activity and Mass in the Framingham Heart Study

Lipoprotein-associated phospholipase A2 (Lp-PLA2) is an emerging risk factor and therapeutic target for cardiovascular disease. The activity and mass of this enzyme are heritable traits, but major genetic determinants have not been explored in a systematic, genome-wide fashion. We carried out a genome-wide association study of Lp-PLA2 activity and mass in 6,668 Caucasian subjects from the population-based Framingham Heart Study. Clinical data and genotypes from the Affymetrix 550K SNP array were obtained from the open-access Framingham SHARe project. Each polymorphism that passed quality control was tested for associations with Lp-PLA2 activity and mass using linear mixed models implemented in the R statistical package, accounting for familial correlations, and controlling for age, sex, smoking, lipid-lowering-medication use, and cohort. For Lp-PLA2 activity, polymorphisms at four independent loci reached genome-wide significance, including the APOE/APOC1 region on chromosome 19 (p = 6×10−24); CELSR2/PSRC1 on chromosome 1 (p = 3×10−15); SCARB1 on chromosome 12 (p = 1×10−8) and ZNF259/BUD13 in the APOA5/APOA1 gene region on chromosome 11 (p = 4×10−8). All of these remained significant after accounting for associations with LDL cholesterol, HDL cholesterol, or triglycerides. For Lp-PLA2 mass, 12 SNPs achieved genome-wide significance, all clustering in a region on chromosome 6p12.3 near the PLA2G7 gene. Our analyses demonstrate that genetic polymorphisms may contribute to inter-individual variation in Lp-PLA2 activity and mass

A Review of Time Courses and Predictors of Lipid Changes with Fenofibric Acid-Statin Combination

Fibrates activate peroxisome proliferator activated receptor α and exert beneficial effects on triglycerides, high-density lipoprotein cholesterol, and low density lipoprotein subspecies. Fenofibric acid (FA) has been studied in a large number of patients with mixed dyslipidemia, combined with a low- or moderate-dose statin. The combination of FA with simvastatin, atorvastatin and rosuvastatin resulted in greater improvement of the overall lipid profile compared with the corresponding statin dose. The long-term efficacy of FA combined with low- or moderate- dose statin has been demonstrated in a wide range of patients, including patients with type 2 diabetes mellitus, metabolic syndrome, or elderly subjects. The FA and statin combination seems to be a reasonable option to further reduce cardiovascular risk in high-risk populations, although trials examining cardiovascular disease events are missing