A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

The variability of tropical ice cloud properties as a function of the large-scale context from ground-based radar-lidar observations over Darwin, Australia

The high complexity of cloud parameterizations now held in models puts more pressure on observational studies to provide useful means to evaluate them. One approach to the problem put forth in the modelling community is to evaluate under what atmospheric conditions the parameterizations fail to simulate the cloud properties and under what conditions they do a good job. It is the ambition of this paper to characterize the variability of the statistical properties of tropical ice clouds in different tropical "regimes" recently identified in the literature to aid the development of better process-oriented parameterizations in models. For this purpose, the statistical properties of non-precipitating tropical ice clouds over Darwin, Australia are characterized using ground-based radar-lidar observations from the Atmospheric Radiation Measurement (ARM) Program. The ice cloud properties analysed are the frequency of ice cloud occurrence, the morphological properties (cloud top height and thickness), and the microphysical and radiative properties (ice water content, visible extinction, effective radius, and total concentration). The variability of these tropical ice cloud properties is then studied as a function of the large-scale cloud regimes derived from the International Satellite Cloud Climatology Project (ISCCP), the amplitude and phase of the Madden-Julian Oscillation (MJO), and the large-scale atmospheric regime as derived from a long-term record of radiosonde observations over Darwin. The vertical variability of ice cloud occurrence and microphysical properties is largest in all regimes (1.5 order of magnitude for ice water content and extinction, a factor 3 in effective radius, and three orders of magnitude in concentration, typically). 98 % of ice clouds in our dataset are characterized by either a small cloud fraction (smaller than 0.3) or a very large cloud fraction (larger than 0.9). In the ice part of the troposphere three distinct layers characterized by different statistically-dominant microphysical processes are identified. The variability of the ice cloud properties as a function of the large-scale atmospheric regime, cloud regime, and MJO phase is large, producing mean differences of up to a factor 8 in the frequency of ice cloud occurrence between large-scale atmospheric regimes and mean differences of a factor 2 typically in all microphysical properties. Finally, the diurnal cycle of the frequency of occurrence of ice clouds is also very different between regimes and MJO phases, with diurnal amplitudes of the vertically-integrated frequency of ice cloud occurrence ranging from as low as 0.2 (weak diurnal amplitude) to values in excess of 2.0 (very large diurnal amplitude). Modellers should now use these results to check if their model cloud parameterizations are capable of translating a given atmospheric forcing into the correct statistical ice cloud properties.

Impacts of plant-based foods in ancestral hominin diets on the metabolism and function of gut microbiota in vitro

Ancestral human populations had diets containing more indigestible plant material than present-day diets in industrialized countries. One hypothesis for the rise in prevalence of obesity is that physiological mechanisms for controlling appetite evolved to match a diet with plant fiber content higher than that of present-day diets. We investigated how diet affects gut microbiota and colon cells by comparing human microbial communities with those from a primate that has an extreme plant-based diet, namely, the gelada baboon, which is a grazer. The effects of potato (high starch) versus grass (high lignin and cellulose) diets on human-derived versus gelada-derived fecal communities were compared in vitro. We especially focused on the production of short-chain fatty acids, which are hypothesized to be key metabolites influencing appetite regulation pathways. The results confirmed that diet has a major effect on bacterial numbers, short-chain fatty acid production, and the release of hormones involved in appetite suppression. The potato diet yielded greater production of short-chain fatty acids and hormone release than the grass diet, even in the gelada cultures, which we had expected should be better adapted to the grass diet. The strong effects of diet on hormone release could not be explained, however, solely by short-chain fatty acid concentrations. Nuclear magnetic resonance spectroscopy found changes in additional metabolites, including betaine and isoleucine, that might play key roles in inhibiting and stimulating appetite suppression pathways. Our study results indicate that a broader array of metabolites might be involved in triggering gut hormone release in humans than previously thought. IMPORTANCE: One theory for rising levels of obesity in western populations is that the body's mechanisms for controlling appetite evolved to match ancestral diets with more low-energy plant foods. We investigated this idea by comparing the effects of diet on appetite suppression pathways via the use of gut bacterial communities from humans and gelada baboons, which are modern-day primates with an extreme diet of low-energy plant food, namely, grass. We found that diet does play a major role in affecting gut bacteria and the production of a hormone that suppresses appetite but not in the direction predicted by the ancestral diet hypothesis. Also, bacterial products were correlated with hormone release that were different from those normally thought to play this role. By comparing microbiota and diets outside the natural range for modern humans, we found a relationship between diet and appetite pathways that was more complex than previously hypothesized on the basis of more-controlled studies of the effects of single compounds.

Interactions between uncoupling protein 2 gene polymorphisms, obesity and alcohol intake on liver function: a large meta-analysed population-based study

BACKGROUND AND OBJECTIVE: Given the role of uncoupling protein 2 (UCP2) in the accumulation of fat in the hepatocytes and in the enhancement of protective mechanisms in acute ethanol intake, we hypothesised that UCP2 polymorphisms are likely to cause liver disease through their interactions with obesity and alcohol intake. To test this hypothesis, we investigated the interaction between tagging polymorphisms in the UCP2 gene (rs2306819, rs599277 and rs659366), alcohol intake and obesity traits such as BMI and waist circumference (WC) on alanine aminotransferase (ALT) and gamma glutamyl transferase (GGT) in a large meta-analysis of data sets from three populations (n=20 242). DESIGN AND METHODS: The study populations included the Northern Finland Birth Cohort 1966 (n=4996), Netherlands Study of Depression and Anxiety (n=1883) and LifeLines Cohort Study (n=13 363). Interactions between the polymorphisms and obesity and alcohol intake on dichotomised ALT and GGT levels were assessed using logistic regression and the likelihood ratio test. RESULTS: In the meta-analysis of the three cohorts, none of the three UCP2 polymorphisms were associated with GGT or ALT levels. There was no evidence for interaction between the polymorphisms and alcohol intake on GGT and ALT levels. In contrast, the association of WC and BMI with GGT levels varied by rs659366 genotype (Pinteraction=0.03 and 0.007, respectively; adjusted for age, gender, high alcohol intake, diabetes, hypertension and serum lipid concentrations). CONCLUSION: In conclusion, our findings in 20 242 individuals suggest that UCP2 gene polymorphisms may cause liver dysfunction through the interaction with body fat rather than alcohol intake.

A stochastic parameterization for deep convection based on equilibrium statistics

A stochastic parameterization scheme for deep convection is described, suitable for use in both climate and NWP models. Theoretical arguments and the results of cloud-resolving models, are discussed in order to motivate the form of the scheme. In the deterministic limit, it tends to a spectrum of entraining/detraining plumes and is similar to other current parameterizations. The stochastic variability describes the local fluctuations about a large-scale equilibrium state. Plumes are drawn at random from a probability distribution function (pdf) that defines the chance of finding a plume of given cloud-base mass flux within each model grid box. The normalization of the pdf is given by the ensemble-mean mass flux, and this is computed with a CAPE closure method. The characteristics of each plume produced are determined using an adaptation of the plume model from the Kain-Fritsch parameterization. Initial tests in the single column version of the Unified Model verify that the scheme is effective in producing the desired distributions of convective variability without adversely affecting the mean state.

Prediction leaching of potassium using the convective-dispersive and the convective log-normal transfer function models

There is increasing concern about soil enrichment with K+ and subsequent potential losses following long-term application of poor quality water to agricultural land. Different models are increasingly being used for predicting or analyzing water flow and chemical transport in soils and groundwater. The convective-dispersive equation (CDE) and the convective log-normal transfer function (CLT) models were fitted to the potassium (K+) leaching data. The CDE and CLT models produced equivalent goodness of fit. Simulated breakthrough curves for a range of CaCl2 concentration based on parameters of 15 mmol l(-1) CaCl2 were characterised by an early peak position associated with higher K+ concentration as the CaCl2 concentration used in leaching experiments decreased. In another method, the parameters estimated from 15 mmol l(-1) CaCl2 solution were used for all other CaCl2 concentrations, and the best value of retardation factor (R) was optimised for each data set. A better prediction was found. With decreasing CaCl2 concentration the value of R is required to be more than that measured (except for 10 mmol l(-1) CaCl2), if the estimated parameters of 15 mmol l(-1) CaCl2 are used. The two models suffer from the fact that they need to be calibrated against a data set, and some of their parameters are not measurable and cannot be determined independently.

The Universal Common Good: Faith-Based Partnerships and Sustainable Development

Partnership is increasingly espoused as the best relationship between members of the sustainable development aid chain, and implies a respect for the position of all and a desire to avoid a situation where one group dominates another. It also implies a form of relationship that is not just 'better' for the sake of it but that is more able to help achieve sustainable development. However, given the inevitable inequalities in power between donors that have the resources and field partners that do not it can be hard to put this ideal into practice. This paper explores the function of partnership within a group of closely related institutions that comprise the Catholic Church development chain. The research focussed on three Catholic Church based donors (one from the USA and two from Europe) and their partners in Abuja Ecclesiastical Province, Nigeria. Relationships between and within various strata of the Church in Nigeria were also examined. Relationships were 'patchy' at all levels. One of the donors had a significant operational presence in Nigeria and this was regarded by some respondents as a parallel structure that seriously undermined local bodies. However, while problems existed, there was a sense of inter-dependence arising from a shared sense of values and Catholic Social Teaching, which allowed partners to work through their stresses and conflicts. It is the innate sustainability of the aid chain itself founded upon a set of shared values that provided the space and time for problems to be addressed. Copyright (C) 2008 John Wiley & Sons, Ltd and ERP Environment.

Projected gradient approach to the numerical solution of the SCoTLASS

The SCoTLASS problem-principal component analysis modified so that the components satisfy the Least Absolute Shrinkage and Selection Operator (LASSO) constraint-is reformulated as a dynamical system on the unit sphere. The LASSO inequality constraint is tackled by exterior penalty function. A globally convergent algorithm is developed based on the projected gradient approach. The algorithm is illustrated numerically and discussed on a well-known data set. (c) 2004 Elsevier B.V. All rights reserved.

Phylogeny, function, and evolution of the cupins, a structurally conserved, functionally diverse superfamily of proteins

The cupin superfamily is a group of functionally diverse proteins that are found in all three kingdoms of life, Archaea, Eubacteria, and Eukaryota. These proteins have a characteristic signature domain comprising two histidine- containing motifs separated by an intermotif region of variable length. This domain consists of six beta strands within a conserved beta barrel structure. Most cupins, such as microbial phosphomannose isomerases (PMIs), AraC- type transcriptional regulators, and cereal oxalate oxidases (OXOs), contain only a single domain, whereas others, such as seed storage proteins and oxalate decarboxylases (OXDCs), are bi-cupins with two pairs of motifs. Although some cupins have known functions and have been characterized at the biochemical level, the majority are known only from gene cloning or sequencing projects. In this study, phylogenetic analyses were conducted on the conserved domain to investigate the evolution and structure/function relationships of cupins, with an emphasis on single- domain plant germin-like proteins (GLPs). An unrooted phylogeny of cupins from a wide spectrum of evolutionary lineages identified three main clusters, microbial PMIs, OXDCs, and plant GLPs. The sister group to the plant GLPs in the global analysis was then used to root a phylogeny of all available plant GLPs. The resulting phylogeny contained three main clades, classifying the GLPs into distinct subfamilies. It is suggested that these subfamilies correlate with functional categories, one of which contains the bifunctional barley germin that has both OXO and superoxide dismutase (SOD) activity. It is proposed that GLPs function primarily as SODs, enzymes that protect plants from the effects of oxidative stress. Closer inspection of the DNA sequence encoding the intermotif region in plant GLPs showed global conservation of thymine in the second codon position, a character associated with hydrophobic residues. Since many of these proteins are multimeric and enzymatically inactive in their monomeric state, this conservation of hydrophobicity is thought to be associated with the need to maintain the various monomer- monomer interactions. The type of structure-based predictive analysis presented in this paper is an important approach for understanding gene function and evolution in an era when genomes from a wide range of organisms are being sequenced at a rapid rate.

Germin and germin-like proteins: evolution, structure, and function

Germin and germin-like proteins (GLPs) are encoded by a family of genes found in all plants. They are part of the cupin superfamily of biochemically diverse proteins, a superfamily that has a conserved tertiary structure, though with limited similarity in primary sequence. The subgroups of GLPs have different enzyme functions that include the two hydrogen peroxide-generating enzymes, oxalate oxidase (OxO) and superoxide dismutase. This review summarizes the sequence and structural details of GLPs and also discusses their evolutionary progression, particularly their amplification in gene number during the evolution of the land plants. In terms of function, the GLPs are known to be differentially expressed during specific periods of plant growth and development, a pattern of evolutionary subfunctionalization. They are also implicated in the response of plants to biotic (viruses, bacteria, mycorrhizae, fungi, insects, nematodes, and parasitic plants) and abiotic (salt, heat/cold, drought, nutrient, and metal) stress. Most detailed data come from studies of fungal pathogenesis in cereals. This involvement with the protection of plants from environmental stress of various types has led to numerous plant breeding studies that have found links between GLPs and QTLs for disease and stress resistance. In addition the OxO enzyme has considerable commercial significance, based principally on its use in the medical diagnosis of oxalate concentration in plasma and urine. Finally, this review provides information on the nutritional importance of these proteins in the human diet, as several members are known to be allergenic, a feature related to their thermal stability and evolutionary connection to the seed storage proteins, also members of the cupin superfamily.

Determinants of fruit and vegetable intake in England: a re-examination based on quantile regression

Objective To examine die sociodemographic determinants of fruit and vegetable (F&V) consumption in England and determine the differential effects of socioeconomic variables at various parts of the intake distribution, with a special focus on severely inadequate intakes Design Quantile regression, expressing F&V intake as a function of sociodemographic variables, is employed. Here, quantile regression flexibly allows variables such as ethnicity to exert effects on F&V intake that. vary depending oil existing levels of intake. Setting The 2003 Health survey of England. Subjects Data were from 11044 adult individuals. Results The influence of particular sociodemographic variables is found to vary significantly across the intake distribution We conclude that women consume more F&V than men, Asians and Hacks mole dian Whites, co-habiting individuals more than single-living ones Increased incomes and education also boost intake However, the key general finding of the present study is that the influence of most variables is relatively weak in the area of greatest concern, i e among those with the most inadequate intakes in any reference group. Conclusions. Our findings emphasise the importance of allowing the effects of socio-economic drivers to vary across the intake distribution The main finding, that variables which exert significant influence on F&V Intake at other parts Of the conditional distribution have a relatively weak influence at the lower tail, is cause for concern. It implies that in any defined group, those consuming the lease F&V are hard to influence using compaigns or policy levers.

A role for the thiol isomerase. protein ERP5 in platelet function

Formation and rearrangement of disulfide bonds during the correct folding of nascent proteins is modulated by a family of enzymes known as thiol isomerases, which include protein disulfide isomerase (PDI), endoplasmic reticulum protein 5 (ERP5), and ERP57. Recent evidence supports an alternative role for this family of proteins on the surface of cells, where they are involved in receptor 'remodeling and recognition. In platelets, blocking PDI with inhibitory antibodies inhibits a number of platelet activation pathways, including aggregation, secretion, and fibrinogen binding. Analysis of human platelet membrane fractions identified the presence of the thiol isomerase protein ERP5. Further study showed that ERP5 is resident mainly on platelet intracellular membranes, although it is rapidly recruited to the cell, surface in response to a range of platelet agonists. Blocking cell-surface ERP5 using inhibitory antibodies leads to a decrease in platelet aggregation in response to agonists, and a decrease in fibrinogen binding and P-selectin exposure. It is Possible that this is based on the disruption of integrin function, as we observed that ERP5 becomes physically associated with the integrin beta(3) subunit during platelet stimulation. These results provide new insights into the involvement of thiol isomerases and regulation of platelet activation. (C) 2005 by The American Society of Hematology.