Molecular systematics of the Cactaceae

Bayesian, maximum-likelihood, and maximum-parsimony phylogenies, constructed using nucleotide sequences from the plastid gene region trnK-matK, are employed to investigate relationships within the Cactaceae. These phylogenies sample 666 plants representing 532 of the 1438 species recognized in the family. All four subfamilies, all nine tribes, and 69% of currently recognized genera of Cactaceae are sampled. We found strong support for three of the four currently recognized subfamilies, although relationships between subfamilies were not well defined. Major clades recovered within the largest subfamilies, Opuntioideae and Cactoideae, are reviewed; only three of the nine currently accepted tribes delimited within these subfamilies, the Cacteae, Rhipsalideae, and Opuntieae, are monophyletic, although the Opuntieae were recovered in only the Bayesian and maximum-likelihood analyses, not in the maximum-parsimony analysis, and more data are needed to reveal the status of the Cylindropuntieae, which may yet be monophyletic. Of the 42 genera with more than one exemplar in our study, only 17 were monophyletic; 14 of these genera were from subfamily Cactoideae and three from subfamily Opuntioideae. We present a synopsis of the status of the currently recognized genera

Generalized beta generated distributions

This article introduces generalized beta-generated (GBG) distributions. Sub-models include all classical beta-generated, Kumaraswamy-generated and exponentiated distributions. They are maximum entropy distributions under three intuitive conditions, which show that the classical beta generator skewness parameters only control tail entropy and an additional shape parameter is needed to add entropy to the centre of the parent distribution. This parameter controls skewness without necessarily differentiating tail weights. The GBG class also has tractable properties: we present various expansions for moments, generating function and quantiles. The model parameters are estimated by maximum likelihood and the usefulness of the new class is illustrated by means of some real data sets.

A neurofuzzy classifier for two class problems

A neurofuzzy classifier identification algorithm is introduced for two class problems. The initial fuzzy base construction is based on fuzzy clustering utilizing a Gaussian mixture model (GMM) and the analysis of covariance (ANOVA) decomposition. The expectation maximization (EM) algorithm is applied to determine the parameters of the fuzzy membership functions. Then neurofuzzy model is identified via the supervised subspace orthogonal least square (OLS) algorithm. Finally a logistic regression model is applied to produce the class probability. The effectiveness of the proposed neurofuzzy classifier has been demonstrated using a real data set.

Who adopts Green Revolution (GR) technology in Ghana?

Sub-Saharan Africa in general and Ghana in particular, missed out on the Green revolution. Efforts are being made to re-introduce the revolution, and this calls for more socio-economic research into the factors influencing the adoption of new technologies, hence, this study. The study sought to find out how socio-economic factors contribute to adoption of Green revolution technology in Ghana. The method of analysis involved a maximum likelihood estimation of a probit model. The proportion of Green revolution inputs was found to be greater for the following: households whose heads had formal education, households with higher levels of non-farm income, credit and labor supply as well as those living in urban centers. It is recommended that levels of complementary inputs such as credit, extension services and infrastructure are increased. Also, households must be encouraged to form farmer-groups as an important source of farm labor. Furthermore, the fundamental problems of illiteracy must be addressed through increasing the levels of formal and non-formal education; and the gap between the rural and urban centers must be bridged through infrastructural and rural development. However, care must be taken to ensure that small-scale farmers are not marginalized, in terms of access to these complementary inputs that go with effective adoption of new technology. With these policies well implemented, Ghana can catch up with her Asian counterparts in this re-introduction of the revolution.

Adoption of green revolution services and poverty reduction in Ghana

In Sub-Saharan Africa (SSA) the technological advances of the Green Revolution (GR) have not been very successful. However, the efforts being made to re-introduce the revolution call for more socio-economic research into the adoption and the effects of the new technologies. The paper discusses an investigation on the effects of GR technology adoption on poverty among households in Ghana. Maximum likelihood estimation of a poverty model within the framework of Heckman's two stage method of correcting for sample selection was employed. Technology adoption was found to have positive effects in reducing poverty. Other factors that reduce poverty include education, credit, durable assets, living in the forest belt and in the south of the country. Technology adoption itself was also facilitated by education, credit, non-farm income and household labour supply as well as living in urban centres. Inarguably, technology adoption can be taken seriously by increasing the levels of complementary inputs such as credit, extension services and infrastructure. Above all, the fundamental problems of illiteracy, inequality and lack of effective markets must be addressed through increasing the levels of formal and non-formal education, equitable distribution of the 'national cake' and a more pragmatic management of the ongoing Structural Adjustment Programme.

Uncoupling protein 2 and 3 gene polymorphisms and their association with type 2 diabetes in asian indians

BACKGROUND: this study examined the association of -866G/A, Ala55Val, 45bpI/D, and -55C/T polymorphisms at the uncoupling protein (UCP) 3-2 loci with type 2 diabetes in Asian Indians. METHODS: a case-control study was performed among 1,406 unrelated subjects (487 with type 2 diabetes and 919 normal glucose-tolerant [NGT]), chosen from the Chennai Urban Rural Epidemiology Study, an ongoing population-based study in Southern India. The polymorphisms were genotyped using polymerase chain reaction-restriction fragment length polymorphism and direct sequencing. Haplotype frequencies were estimated using an expectation-maximization algorithm. Linkage disequilibrium was estimated from the estimates of haplotypic frequencies. RESULTS: the genotype (P = 0.00006) and the allele (P = 0.00007) frequencies of Ala55Val of the UCP2 gene showed a significant protective effect against the development of type 2 diabetes. The odds ratios (adjusted for age, sex, and body mass index) for diabetes for individuals carrying Ala/Val was 0.72, and that for individuals carrying Val/Val was 0.37. Homeostasis insulin resistance model assessment and 2-h plasma glucose were significantly lower among Val-allele carriers compared to the Ala/Ala genotype within the NGT group. The genotype (P = 0.02) and the allele (P = 0.002) frequencies of -55C/T of the UCP3 gene showed a significant protective effect against the development of diabetes. The odds ratio for diabetes for individuals carrying CT was 0.79, and that for individuals carrying TT was 0.61. The haplotype analyses further confirmed the association of Ala55Val with diabetes, where the haplotypes carrying the Ala allele were significantly higher in the cases compared to controls. CONCLUSIONS: Ala55Val and -55C/T polymorphisms at the UCP3-2 loci are associated with a significantly reduced risk of developing type 2 diabetes in Asian Indians.

Effect of polymorphisms in the PPARGC1A gene on body fat in Asian Indians

OBJECTIVE: To evaluate whether polymorphisms in the peroxisome proliferator-activated receptor-gamma coactivator-1 alpha (PPARGC1A) gene were related to body fat in Asian Indians. METHODS: Three polymorphisms of PPARGC1A gene, the Thr394Thr, Gly482Ser and +A2962G, were genotyped on 82 type 2 diabetic and 82 normal glucose tolerant (NGT) subjects randomly chosen from the Chennai Urban Rural Epidemiology Study using PCR-RFLP, and the nature of the variants were confirmed using direct sequencing. Linkage disequilibrium (LD) was estimated from the estimates of haplotypic frequencies using an expectation-maximization algorithm. Visceral, subcutaneous and total abdominal fat were measured using computed tomography, whereas dual X-ray absorptiometry was used to measure central abdominal and total body fat. RESULTS: None of the three polymorphisms studied were in LD. The genotype (0.59 vs 0.32, P=0.001) and allele (0.30 vs 0.17, P=0.007) frequencies of Thr394Thr polymorphism were significantly higher in type 2 diabetic subjects compared to those in NGT subjects. The odds ratio for diabetes (adjusted for age, sex and body mass index) for the susceptible genotype, XA (GA+AA) of Thr394Thr polymorphism, was 2.53 (95% confidence intervals: 1.30-5.04, P=0.009). Visceral and subcutaneous fat were significantly higher in NGT subjects with XA genotype of the Thr394Thr polymorphism compared to those with GG genotype (visceral fat: XA 148.2+/-46.9 vs GG 106.5+/-51.9 cm(2), P=0.001; subcutaneous fat: XA 271.8+/-167.1 vs GG 181.5+/-78.5 cm(2), P=0.001). Abdominal (XA 4521.9+/-1749.6 vs GG 3445.2+/-1443.4 g, P=0.004), central abdominal (XA 1689.0+/-524.0 vs GG 1228.5+/-438.7 g, P<0.0001) and non-abdominal fat (XA 18763.8+/-8789.4 vs GG 13160.4+/-4255.3 g, P<0.0001) were also significantly higher in the NGT subjects with XA genotype compared to those with GG genotype. The Gly482Ser and +A2962G polymorphisms were not associated with any of the body fat measures. CONCLUSION: Among Asian Indians, the Thr394Thr (G --> A) polymorphism is associated with increased total, visceral and subcutaneous body fat.

Peroxisome proliferator-activated receptor-gamma co-activator-1alpha (PGC-1alpha) gene polymorphisms and their relationship to Type 2 diabetes in Asian Indians

AIMS: The objective of the present investigation was to examine the relationship of three polymorphisms, Thr394Thr, Gly482Ser and +A2962G, of the peroxisome proliferator activated receptor-gamma co-activator-1 alpha (PGC-1alpha) gene with Type 2 diabetes in Asian Indians. METHODS: The study group comprised 515 Type 2 diabetic and 882 normal glucose tolerant subjects chosen from the Chennai Urban Rural Epidemiology Study, an ongoing population-based study in southern India. The three polymorphisms were genotyped using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Haplotype frequencies were estimated using an expectation-maximization (EM) algorithm. Linkage disequilibrium was estimated from the estimates of haplotypic frequencies. RESULTS: The three polymorphisms studied were not in linkage disequilibrium. With respect to the Thr394Thr polymorphism, 20% of the Type 2 diabetic patients (103/515) had the GA genotype compared with 12% of the normal glucose tolerance (NGT) subjects (108/882) (P = 0.0004). The frequency of the A allele was also higher in Type 2 diabetic subjects (0.11) compared with NGT subjects (0.07) (P = 0.002). Regression analysis revealed the odds ratio for Type 2 diabetes for the susceptible genotype (XA) to be 1.683 (95% confidence intervals: 1.264-2.241, P = 0.0004). Age adjusted glycated haemoglobin (P = 0.003), serum cholesterol (P = 0.001) and low-density lipoprotein (LDL) cholesterol (P = 0.001) levels and systolic blood pressure (P = 0.001) were higher in the NGT subjects with the XA genotype compared with GG genotype. There were no differences in genotype or allelic distribution between the Type 2 diabetic and NGT subjects with respect to the Gly482Ser and +A2962G polymorphisms. CONCLUSIONS: The A allele of Thr394Thr (G --> A) polymorphism of the PGC-1 gene is associated with Type 2 diabetes in Asian Indian subjects and the XA genotype confers 1.6 times higher risk for Type 2 diabetes compared with the GG genotype in this population.

Robust evaluation of fixed-event forecast rationality

In this paper we introduce a new testing procedure for evaluating the rationality of fixed-event forecasts based on a pseudo-maximum likelihood estimator. The procedure is designed to be robust to departures in the normality assumption. A model is introduced to show that such departures are likely when forecasters experience a credibility loss when they make large changes to their forecasts. The test is illustrated using monthly fixed-event forecasts produced by four UK institutions. Use of the robust test leads to the conclusion that certain forecasts are rational while use of the Gaussian-based test implies that certain forecasts are irrational. The difference in the results is due to the nature of the underlying data. Copyright © 2001 John Wiley & Sons, Ltd.

Extending the Lincoln–Petersen estimator for multiple identifications in one source

The Lincoln–Petersen estimator is one of the most popular estimators used in capture–recapture studies. It was developed for a sampling situation in which two sources independently identify members of a target population. For each of the two sources, it is determined if a unit of the target population is identified or not. This leads to a 2 × 2 table with frequencies f11, f10, f01, f00 indicating the number of units identified by both sources, by the first but not the second source, by the second but not the first source and not identified by any of the two sources, respectively. However, f00 is unobserved so that the 2 × 2 table is incomplete and the Lincoln–Petersen estimator provides an estimate for f00. In this paper, we consider a generalization of this situation for which one source provides not only a binary identification outcome but also a count outcome of how many times a unit has been identified. Using a truncated Poisson count model, truncating multiple identifications larger than two, we propose a maximum likelihood estimator of the Poisson parameter and, ultimately, of the population size. This estimator shows benefits, in comparison with Lincoln–Petersen’s, in terms of bias and efficiency. It is possible to test the homogeneity assumption that is not testable in the Lincoln–Petersen framework. The approach is applied to surveillance data on syphilis from Izmir, Turkey.

Interaction among non-toxic phytoplankton, toxic phytoplankton and zooplankton: inferences from field observations

We explore the mutual dependencies and interactions among different groups of species of the plankton population, based on an analysis of the long-term field observations carried out by our group in the North–West coast of the Bay of Bengal. The plankton community is structured into three groups of species, namely, non-toxic phytoplankton (NTP), toxic phytoplankton (TPP) and zooplankton. To find the pair-wise dependencies among the three groups of plankton, Pearson and partial correlation coefficients are calculated. To explore the simultaneous interaction among all the three groups, a time series analysis is performed. Following an Expectation Maximization (E-M) algorithm, those data points which are missing due to irregularities in sampling are estimated, and with the completed data set a Vector Auto-Regressive (VAR) model is analyzed. The overall analysis demonstrates that toxin-producing phytoplankton play two distinct roles: the inhibition on consumption of toxic substances reduces the abundance of zooplankton, and the toxic materials released by TPP significantly compensate for the competitive disadvantages among phytoplankton species. Our study suggests that the presence of TPP might be a possible cause for the generation of a complex interaction among the large number of phytoplankton and zooplankton species that might be responsible for the prolonged coexistence of the plankton species in a fluctuating biomass.

The pgip family in soybean and three other legume species: evidence for a birth-and-death model of evolution

Background Polygalacturonase-inhibiting proteins (PGIPs) are leucine-rich repeat (LRR) plant cell wall glycoproteins involved in plant immunity. They are typically encoded by gene families with a small number of gene copies whose evolutionary origin has been poorly investigated. Here we report the complete characterization of the full complement of the pgip family in soybean (Glycine max [L.] Merr.) and the characterization of the genomic region surrounding the pgip family in four legume species. Results BAC clone and genome sequence analyses showed that the soybean genome contains two pgip loci. Each locus is composed of three clustered genes that are induced following infection with the fungal pathogen Sclerotinia sclerotiorum (Lib.) de Bary, and remnant sequences of pgip genes. The analyzed homeologous soybean genomic regions (about 126 Kb) that include the pgip loci are strongly conserved and this conservation extends also to the genomes of the legume species Phaseolus vulgaris L., Medicago truncatula Gaertn. and Cicer arietinum L., each containing a single pgip locus. Maximum likelihood-based gene trees suggest that the genes within the pgip clusters have independently undergone tandem duplication in each species. Conclusions The paleopolyploid soybean genome contains two pgip loci comprised in large and highly conserved duplicated regions, which are also conserved in bean, M. truncatula and C. arietinum. The genomic features of these legume pgip families suggest that the forces driving the evolution of pgip genes follow the birth-and-death model, similar to that proposed for the evolution of resistance (R) genes of NBS-LRR-type.

Designing a sampling scheme to reveal correlations between weeds and soil properties at multiple spatial scales

Weeds tend to aggregate in patches within fields and there is evidence that this is partly owing to variation in soil properties. Because the processes driving soil heterogeneity operate at different scales, the strength of the relationships between soil properties and weed density would also be expected to be scale-dependent. Quantifying these effects of scale on weed patch dynamics is essential to guide the design of discrete sampling protocols for mapping weed distribution. We have developed a general method that uses novel within-field nested sampling and residual maximum likelihood (REML) estimation to explore scale-dependent relationships between weeds and soil properties. We have validated the method using a case study of Alopecurus myosuroides in winter wheat. Using REML, we partitioned the variance and covariance into scale-specific components and estimated the correlations between the weed counts and soil properties at each scale. We used variograms to quantify the spatial structure in the data and to map variables by kriging. Our methodology successfully captured the effect of scale on a number of edaphic drivers of weed patchiness. The overall Pearson correlations between A. myosuroides and soil organic matter and clay content were weak and masked the stronger correlations at >50 m. Knowing how the variance was partitioned across the spatial scales we optimized the sampling design to focus sampling effort at those scales that contributed most to the total variance. The methods have the potential to guide patch spraying of weeds by identifying areas of the field that are vulnerable to weed establishment.

Data Assimilation and Inverse Methods in Terms of a Probabilistic Formulation

The weak-constraint inverse for nonlinear dynamical models is discussed and derived in terms of a probabilistic formulation. The well-known result that for Gaussian error statistics the minimum of the weak-constraint inverse is equal to the maximum-likelihood estimate is rederived. Then several methods based on ensemble statistics that can be used to find the smoother (as opposed to the filter) solution are introduced and compared to traditional methods. A strong point of the new methods is that they avoid the integration of adjoint equations, which is a complex task for real oceanographic or atmospheric applications. they also avoid iterative searches in a Hilbert space, and error estimates can be obtained without much additional computational effort. the feasibility of the new methods is illustrated in a two-layer quasigeostrophic model.

Sparse density estimation on multinomial manifold combining local component analysis

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion combining local component analysis for the finite mixture model. We start with a Parzen window estimator which has the Gaussian kernels with a common covariance matrix, the local component analysis is initially applied to find the covariance matrix using expectation maximization algorithm. Since the constraint on the mixing coefficients of a finite mixture model is on the multinomial manifold, we then use the well-known Riemannian trust-region algorithm to find the set of sparse mixing coefficients. The first and second order Riemannian geometry of the multinomial manifold are utilized in the Riemannian trust-region algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators.