KIT mutations and dose selection for imatinib in patients with advanced gastrointestinal stromal tumours.

A recent randomized EORTC phase III trial, comparing two doses of imatinib in patients with advanced gastrointestinal stromal tumours (GISTs), reported dose dependency for progression-free survival. The current analysis of that study aimed to assess if tumour mutational status correlates with clinical response to imatinib. Pre-treatment samples of GISTs from 377 patients enrolled in phase III study were analyzed for mutations of KIT or PDGFRA by combination of D-HPLC and direct sequencing of tumour genomic DNA. Mutation types were correlated with patients' survival data. The presence of exon 9-activating mutations in KIT was the strongest adverse prognostic factor for response to imatinib, increasing the relative risk of progression by 171% (P<0.0001) and the relative risk of death by 190% (P<0.0001) when compared with KIT exon 11 mutants. Similarly, the relative risk of progression was increased by 108% (P<0.0001) and the relative risk of death by 76% (P=0.028) in patients without detectable KIT or PDGFRA mutations. In patients whose tumours expressed an exon 9 KIT oncoprotein, treatment with the high-dose regimen resulted in a significantly superior progression-free survival (P=0.0013), with a reduction of the relative risk of 61%. We conclude that tumour genotype is of major prognostic significance for progression-free survival and overall survival in patients treated with imatinib for advanced GISTs. Our findings suggest the need for differential treatment of patients with GISTs, with KIT exon 9 mutant patients benefiting the most from the 800 mg daily dose of the drug.

Adaptive model selection using empirical complexities

Given $n$ independent replicates of a jointly distributed pair $(X,Y)\in {\cal R}^d \times {\cal R}$, we wish to select from a fixed sequence of model classes ${\cal F}_1, {\cal F}_2, \ldots$ a deterministic prediction rule $f: {\cal R}^d \to {\cal R}$ whose risk is small. We investigate the possibility of empirically assessingthe {\em complexity} of each model class, that is, the actual difficulty of the estimation problem within each class. The estimated complexities are in turn used to define an adaptive model selection procedure, which is based on complexity penalized empirical risk.The available data are divided into two parts. The first is used to form an empirical cover of each model class, and the second is used to select a candidate rule from each cover based on empirical risk. The covering radii are determined empirically to optimize a tight upper bound on the estimation error. An estimate is chosen from the list of candidates in order to minimize the sum of class complexity and empirical risk. A distinguishing feature of the approach is that the complexity of each model class is assessed empirically, based on the size of its empirical cover.Finite sample performance bounds are established for the estimates, and these bounds are applied to several non-parametric estimation problems. The estimates are shown to achieve a favorable tradeoff between approximation and estimation error, and to perform as well as if the distribution-dependent complexities of the model classes were known beforehand. In addition, it is shown that the estimate can be consistent,and even possess near optimal rates of convergence, when each model class has an infinite VC or pseudo dimension.For regression estimation with squared loss we modify our estimate to achieve a faster rate of convergence.

National Adaptation Programme of Action on Climate Change - 2008 - 2012

The archipelago of Cape Verde is made up of ten islands and nine islets and is located between latitudes 14º 28' N and 17º 12' N and longitudes 22º 40' W and 25º 22' W. It is located approximately 500 km from the Senegal coast in West Africa (Figure 1). The islands are divided into two groups: Windward and Leeward. The Windward group is composed of the islands of Santo Antão, São Vicente, Santa Luzia, São Nicolau, Sal and Boavista; and the Leeward group is composed of the islands Maio, Santiago, Fogo and Brava. The archipelago has a total land surface of 4,033 km2 and an Economic Exclusive Zone (ZEE) that extends for approximately 734,000 km2. In general, the relief is very steep, culminating with high elevations (e.g. 2,829 m on Fogo and 1,979 m on Santo Antão). The surface area, geophysical configuration and geology vary greatly from one island to the next. Cape Verde, due to its geomorphology, has a dense and complex hydrographical network. However, there are no permanent water courses and temporary water courses run only during the rainy season. These temporary water courses drain quickly towards the main watersheds, where, unless captured by artificial means, continue rapidly to lower areas and to the sea. This applies equally to the flatter islands. The largest watershed is Rabil with an area of 199.2 km2. The watershed areas on other islands extend over less than 70 km2. Cape Verde is both a least developed country (LDC) and a small island development state (SIDS). In 2002, the population of Cape Verde was estimated at approximately 451,000, of whom 52% were women and 48% men. The population was growing at an average 2.4% per year, and the urban population was estimated at 53.7 %. Over the past 15 years, the Government has implemented a successful development strategy, leading to a sustained economic growth anchored on development of the private sector and the integration of Cape Verde into the world economy. During this period, the tertiary sector has become increasingly important, with strong growth in the tourism, transport, banking and trade sectors. Overall, the quality of life indicators show substantial improvements in almost all areas: housing conditions, access to drinking water and sanitation, use of modern energy in both lighting and cooking, access to health services and education. Despite these overall socio-economic successes, the primary sector has witnessed limited progress. Weak performance in the primary sector has had a severe negative impact on the incomes and poverty risks faced by rural workers1. Moreover, relative poverty has increased significantly during the past decade. The poverty profile shows that: (i) extreme poverty is mostly found in rural areas, although it has also increased in urban areas; (ii) poverty is more likely to occur when the head of the household is a woman; (iii) poverty increases with family size; (iv) education significantly affects poverty; (v) the predominantly agricultural islands of Santo Antão and Fogo have the highest poverty rates; (vi) unemployment affects the poor more than the nonpoor; (vii) agriculture and fisheries workers are more likely to be poor than those in other sectors. Therefore, the fight against poverty and income inequalities remains one of the greatest challenges for Cape Verde authorities. The various governments of Cape Verde over the last decade have demonstrated a commitment to improving governance, notably by encouraging a democratic culture that guarantees stability and democratic changes without conflicts. This democratic governance offers a space for a wider participation of citizens in public management and consolidates social cohesion. However, there are some remaining challenges related to democratic governance and the gains must be systematically monitored. Finally, it is worth emphasizing that the country’s insularity has stimulated a movement to decentralized governance, although social inequalities and contrasts from one island to the next constitute, at the same time, challenges and opportunities.

Iowa Civil Rights Commission, Agency Performance Plan and Action Plan, 2006

Annual Report, Agency Performance plan

Iowa Civil Rights Commission, Agency Performance Plan and Action Plan, 2007

Annual Report, Agency Performance plan

Department of Human Rights, Division of Community Action Agencies Performance Plan, FY 2009

Agency Performance Plan, Department of Human Rights, Division of Community Action Agencies

Lottery Action, Vol. 15, no. 1, January 7-27, 2008

Iowa Lottery Authority Newsletter for Lottery Retailers

Lottery Action, Vol. 15, no. 5, March 10-23, 2008

Iowa Lottery Authority Newsletter for Lottery Retailers

Lottery Action, Vol. 15, no. 6, March 21-April 8, 2008

Iowa Lottery Authority Newsletter For Lottery Retailers

Lottery Action, Vol. 15, no. 7, April 7, 2008

Iowa Lottery Authority Newsletter For Lottery Retailers

Lottery Action, Vol. 15, no. 11, June 2-15, 2008

Iowa Lottery Authority Newsletter For Lottery Retailers

Lottery Action, Vol. 15, no. 14 July 12-August 3, 2008

Iowa Lottery Authority Newsletter For Lottery Retailers

Lottery Action, Vol. 15, no. 15, August 4, 17, 2008

Iowa Lottery Authority Newsletter For Lottery Retailers

Lottery Action, Vol. 15, no. 18, August 18 - September 7, 2008

Iowa Lottery Authority Newsletter For Lottery Retailers

Lottery Action, Vol.15, no. 17, October 8-21, 2008

Iowa Lottery Authority Newsletter For Lottery Retailers