Applying linear time-varying constraints to econometric models: With an application to demand systems

When linear equality constraints are invariant through time they can be incorporated into estimation by restricted least squares. If, however, the constraints are time-varying, this standard methodology cannot be applied. In this paper we show how to incorporate linear time-varying constraints into the estimation of econometric models. The method involves the augmentation of the observation equation of a state-space model prior to estimation by the Kalman filter. Numerical optimisation routines are used for the estimation. A simple example drawn from demand analysis is used to illustrate the method and its application.

Application of inter simple sequence repeat (ISSR) markers to plant genetics

Microsatellites or simple sequence repeats (SSRs) are ubiquitous in eukaryotic genomes. Single-locus SSR markers have been developed for a number of species, although there is a major bottleneck in developing SSR markers whereby flanking sequences must be known to design 5'-anchors for polymerase chain reaction (PCR) primers. Inter SSR (ISSR) fingerprinting was developed such that no sequence knowledge was required. Primers based on a repeat sequence, such as (CA)(n), can be made with a degenerate 3'-anchor, such as (CA)(8)RG or (AGC)(6)TY. The resultant PCR reaction amplifies the sequence between two SSRs, yielding a multilocus marker system useful for fingerprinting, diversity analysis and genome mapping. PCR products are radiolabelled with P-32 or P-33 via end-labelling or PCR incorporation, and separated on a polyacrylamide sequencing gel prior to autoradiographic visualisation. A typical reaction yields 20-100 bands per lane depending on the species and primer. We have used ISSR fingerprinting in a number of plant species, and report here some results on two important tropical species, sorghum and banana. Previous investigators have demonstrated that ISSR analysis usually detects a higher level of polymorphism than that detected with restriction fragment length polymorphism (RFLP) or random amplified polymorphic DNA (RAPD) analyses. Our data indicate that this is not a result of greater polymorphism genetically, but rather technical reasons related to the detection methodology used for ISSR analysis.

Relative dispersion of intravascular transit times during isolated human limb perfusions for recurrent melanoma

Aims We have characterized the relative dispersion of vascular and extravascular markers in the limbs of three patients undergoing isolated limb perfusions with the cytotoxic melphalan for recurrent malignant melanoma both before and after melphalan dosing. Methods A bolus of injectate containing [Cr-51] labelled red blood cells, [C-14]-sucrose and [H-3]-water was injected into an iliac or femoral artery and outflow samples collected at 1 s intervals by a fraction collector. The radioactivity due to each isotype was analysed by either gamma [Cr-51] or beta [C-14 and H-3] counting. The moments of the outflow fraction-time profiles were estimated by a nonparametric (numerical integration) method and a parametric model (sum of two inverse Gaussian functions). Results The availability, mean transit time and normalised variance (CV2) obtained for labelled red blood cells, sucrose and water were similar before and after melphalan dosing and with the two methods of calculation but varied between the patients. Conclusions The vascular space is not well-stirred but characterized by a CV2 similar that reported previously for in situ rat hind limb and rat liver perfusions. A flow-limited blood-tissue exchange was observed for the permeating indicators. Administration of melphalan did not influence the distribution characteristics of the indicators.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.