Marketing Reform Interventions in the Performance of World Bank Financed Agricultural Programmes in Trans-Nzoia County, Kenya

The main objective of this study was examine contribution of marketing reform interventions on the performance of agricultural programmes funded by the World Bank in Trans-Nzoia County, Kenya. The study arose out of the need to quantify the worth of reform packages currently implemented in the agriculture sector thorough innovative interventions. The sample size of this study was 268 respondents determined using the simplified Yamane formula of proportions. Pragmatism school of thought was the best suited philosophy to guide this study as it complemented the epistemological, methodological and axiological underpinnings desired for mixed-mode research. Results obtained showed β weight of 0.181 (F- value (0.029); ρ-value= 0.05) implying that marketing reforms contributed positively to the performance of agricultural programmes. Further analysis generated R=0.125, R2= 0.016 and adjusted R2 =0.012 indicating a better fit for the model and that marketing reform contributed to the performance of agricultural programmes by 1.6%. The analysis also generated F- value (0.029); (p<0.05) and the F-calculated (4.796) being significantly larger than the critical value (F=2.454) suggesting up to 95% chance the model’s strength in explaining it is statistically significant. These results support outcomes theory by providing documented analysis and empirical evidence to support the formulation of research-based policies and regulations. Findings from the study will therefore contribute immensely to the growth of project management discipline and agricultural marketing practices in Kenya and globally.

A Robust Approach for Identification of Cancer Biomarkers and Candidate Drugs

Background: Identification of cancer biomarkers that are differentially expressed (DE) under two biological conditions is an important task in many microarray studies. There exist several methods in the literature in this regards and most of these methods designed especially for unpaired samples, which does not satisfy the requirements of paired samples where the gene expressions are taken from the same patients before and after treatment. Furthermore, the traditional biomarker identification methods based on either p-values or fold change (FC) values.  However, sometimes, p-value based results do not comply with FC based results due to the smaller variance of gene expressions. There are some methods that combine both p-values and FC values to solve this problem. But, these methods also show weak performance for small-sample case in presence of outlying expressions. To overcome this problem, in this paper an attempt is made to develop a hybrid robust SAM-FC approach by combining rank of FC values and rank of p-values based on SAM statistic using minimum β-divergence method, which is designed for paired samples. This method introduces a weight function known as β-weight function. This weight function produces larger weights corresponding to usual/normal expressions and smaller weights for unusual/outlying expressions. The β-weight function plays the significant role on the performance of the proposed method. Results: The proposed method uses β-weight function as a measure of outlier detection by setting β=0.2. We unify both classical and robust estimates using β-weight function such that maximum likelihood estimators (MLEs) are used in absence of outliers and minimum β-divergence estimators are used in presence of outliers to obtain reasonable p-values and FC values in the proposed method. We examined the performance of proposed method in a comparison of some popular methods (t-test, SAM, LIMMA, Wilcoxon, WAD, RP and FCROS) using both simulated and real gene expression profiles for both small-and large-sample cases. From the simulation and a real spike in data analysis results we observed that the proposed method outperforms other methods for small-sample case in presence of outliers and it keeps almost equal performance with other robust methods (Wilcoxon, RP and FCROS) otherwise. From a head-and-neck cancer (HNC) dataset the proposed method identified 2 genes (CYP3A4, NOVA1) that are significantly enriched in linoleic acid metabolism, drug metabolism, steroid hormone biosynthesis and metabolic pathways. The survival analysis through Kaplan-Meier curve revealed that combined effect of these 2 genes has prognostic capability and they might be promising biomarker of HNC. Moreover, we retrieved the 12 candidate drugs based on gene interaction from glad4u and drug bank databases. Conclusion The identified drugs showed statistical significance and critical role of the proteins indicate that these proteins might be therapeutic target in cancer. Thus, elucidating the associations between the drugs identified in the present study require further investigations.

Gritty, Hardy, and Resilient: Predictors of Sport Engagement and Life Satisfaction in Wheelchair Basketball Players

The purpose of the current study was to predict both general and sport-specific quality of life using measures of grit, hardiness, and resilience. Seventy-five adults (74 men, 1 woman) who are wheelchair basketball athletes participated in the current study. Twenty-six percent of the variance in life satisfaction was accounted for. Both hardiness and resilience accounted for meaningful variance, as indicated by their significant beta weights. Twenty-two percent of the variance in sport engagement was predicted; resilience and grit accounted for meaningful variance, as indicated by their significant beta weight. The regression results indicate that athletes reporting the highest levels of grit and resilience tended to also be the most engaged in their sport, and athletes with high levels of hardiness and resilience reported the highest quality of life. The descriptive results support an affirmation model of disability for the current sample of wheelchair athletes in that they reported moderate to strong levels of resiliency, grit, hardiness, sport engagement, and a high quality of life.

Conditional Probabilities, Potential Surprise, and the Conjunction Fallacy

Tversky and Kahneman (1983) found that a relationship of positive conditional dependence between the components of a conjunction of two events increases the prevalence of the conjunction fallacy. Consistent with this finding, the results of two experiments reveal that dependence leads to higher estimates for the conjunctive probability and a higher incidence of the fallacy. However, contrary to the theoretical account proposed by Tversky and Kahneman, the actual magnitude of the conditional relationship does not directly affect the extent of the fallacy; all that is necessary is for a positive conditional relationship to exist. The pattern of results obtained can be accounted for in terms of Shackle's (1969) “potential surprise” theory of subjective probability. Surprise theory predicts that the impact of the conditional event will be at its maximum where the relationship is a negative one. Tversky and Kahneman's model, on the other hand, predicts the maximum effect when the relationship is positive. In all 12 scenarios tested, multiple regression analysis revealed that the standardized beta weight associated with the conditional event was greater when the relationship was a negative one. Thus the outcome was supportive of the surprise model rather than Tversky and Kahneman's account.