Molecular characterization of Citrus tristeza virus isolates from Cyprus on the basis of the coat protein gene

Within the context of a program in Cyprus for the control of Citrus tristeza virus (CTV), the coat protein (CP) genes of 12 local isolates of the virus that induced different symptoms on host trees, were compared to those of known isolates. The CP genes were reverse-transcribed (RT) and amplified by polymerase chain reaction (PCR) and the resulting amplicons were cloned and sequenced. Nucleotide sequence analysis revealed no signs of geographic speciation. All the sequences obtained clustered close to those of previously known isolates of worldwide origin that are in five distinct groups. The nucleotide diversity was high compared to that found using a worldwide database of CP gene sequences. These data support the existence of different CTV introductions into Cyprus or an introduction from a location in which CTV is relatively diverse. Some of the isolates induced stem pitting on branches of grapefruit and sweet orange. Such isolates have not been noted often in the Mediterranean basin. They were close in CP sequence to isolate B249 from Venezuela, which induces stem pitting, and are of particular concern for the whole region.

Temperature modelling of an homogeneous medium using genetically selected RBF (LIC)

Temperature modelling of human tissue subjected to ultrasound for therapeutic use is essencial for an accurate instrumental assessment and calibration. In this paper punctual temperature modeling of a homogeneous medium, radiated by therapeutic ultrasound, is presented.

Estimation of unemployment rates in small areas of Portugal: a best linear unbiased prediction approach versus a hierarchical Bayes approach

The high level of unemployment is one of the major problems in most European countries nowadays. Hence, the demand for small area labor market statistics has rapidly increased over the past few years. The Labour Force Survey (LFS) conducted by the Portuguese Statistical Office is the main source of official statistics on the labour market at the macro level (e.g. NUTS2 and national level). However, the LFS was not designed to produce reliable statistics at the micro level (e.g. NUTS3, municipalities or further disaggregate level) due to small sample sizes. Consequently, traditional design-based estimators are not appropriate. A solution to this problem is to consider model-based estimators that "borrow information" from related areas or past samples by using auxiliary information. This paper reviews, under the model-based approach, Best Linear Unbiased Predictors and an estimator based on the posterior predictive distribution of a Hierarchical Bayesian model. The goal of this paper is to analyze the possibility to produce accurate unemployment rate statistics at micro level from the Portuguese LFS using these kinds of stimators. This paper discusses the advantages of using each approach and the viability of its implementation.