Validation of three new measure-correlate-predict models for the long-term prospection of the wind resource

The estimation of the long-term wind resource at a prospective site based on a relatively short on-site measurement campaign is an indispensable task in the development of a commercial wind farm. The typical industry approach is based on the measure-correlate-predict �MCP� method where a relational model between the site wind velocity data and the data obtained from a suitable reference site is built from concurrent records. In a subsequent step, a long-term prediction for the prospective site is obtained from a combination of the relational model and the historic reference data. In the present paper, a systematic study is presented where three new MCP models, together with two published reference models �a simple linear regression and the variance ratio method�, have been evaluated based on concurrent synthetic wind speed time series for two sites, simulating the prospective and the reference site. The synthetic method has the advantage of generating time series with the desired statistical properties, including Weibull scale and shape factors, required to evaluate the five methods under all plausible conditions. In this work, first a systematic discussion of the statistical fundamentals behind MCP methods is provided and three new models, one based on a nonlinear regression and two �termed kernel methods� derived from the use of conditional probability density functions, are proposed. All models are evaluated by using five metrics under a wide range of values of the correlation coefficient, the Weibull scale, and the Weibull shape factor. Only one of all models, a kernel method based on bivariate Weibull probability functions, is capable of accurately predicting all performance metrics studied.

Anti-proliferative effects of physiological concentrations of enterolactone in models of prostate tumourigenesis

SCOPE: There is evidence that a mammalian lignan, enterolactone (ENL), decreases the proliferation rate of prostate cancer cells, although previous studies have used concentrations difficult to achieve through dietary modification. We have therefore investigated the anti-proliferative effects of ENL in an in vitro model of prostate tumourigenesis at concentrations reported to occur in a range of male populations. METHODS AND RESULTS: The effects of 0.1 and 1 μM ENL on three markers of viability and proliferation (metabolic activity, growth kinetics, and cell cycle progression) were assessed in the RWPE-1, WPE1-NA22, WPE1-NB14, WPE1-NB11, WPE1-NB26, LNCaP, and PC-3 cell lines over 72 h. Based on these data, we quantified the expression levels of 12 genes involved in the control of DNA replication initiation using TaqMan real-time PCR in the WPE1-NA22, WPE1-NB14, WPE1-NB11, and WPE1-NB26 cell lines. ENL significantly inhibited the abnormal proliferation of the WPE1-NB14 and WPE1-NB11 cell lines and appears to be a consequence of decreased expression of abnormal chromatin licensing and DNA replication factor 1. CONCLUSION: In contrast to previous studies, concentrations of ENL that are reported after dietary intervention restrict the proliferation of early-stage tumourigenic prostate cell lines by inhibiting the abnormal formation of complexes that initiate DNA replication.

Midlatitude Forcing Mechanisms for Glacier Mass Balance Investigated Using General Circulation Models

A process-oriented modeling approach is applied in order to simulate glacier mass balance for individual glaciers using statistically downscaled general circulation models (GCMs). Glacier-specific seasonal sensitivity characteristics based on a mass balance model of intermediate complexity are used to simulate mass balances of Nigardsbreen (Norway) and Rhonegletscher (Switzerland). Simulations using reanalyses (ECMWF) for the period 1979–93 are in good agreement with in situ mass balance measurements for Nigardsbreen. The method is applied to multicentury integrations of coupled (ECHAM4/OPYC) and mixed-layer (ECHAM4/MLO) GCMs excluding external forcing. A high correlation between decadal variations in the North Atlantic oscillation (NAO) and mass balance of the glaciers is found. The dominant factor for this relationship is the strong impact of winter precipitation associated with the NAO. A high NAO phase means enhanced (reduced) winter precipitation for Nigardsbreen (Rhonegletscher), typically leading to a higher (lower) than normal annual mass balance. This mechanism, entirely due to internal variations in the climate system, can explain observed strong positive mass balances for Nigardsbreen and other maritime Norwegian glaciers within the period 1980–95. It can also partly be responsible for recent strong negative mass balances of Alpine glaciers.

The ModFOLD4 server for the quality assessment of 3D protein models

Once you have generated a 3D model of a protein, how do you know whether it bears any resemblance to the actual structure? To determine the usefulness of 3D models of proteins, they must be assessed in terms of their quality by methods that predict their similarity to the native structure. The ModFOLD4 server is the latest version of our leading independent server for the estimation of both the global and local (per-residue) quality of 3D protein models. The server produces both machine readable and graphical output, providing users with intuitive visual reports on the quality of predicted protein tertiary structures. The ModFOLD4 server is freely available to all at: http://www.reading.ac.uk/bioinf/ModFOLD/.

PPARGC1A sequence variation and cardiovascular risk-factor levels: a study of the main genetic effects and gene x environment interactions in children from the European Youth Heart Study.

AIMS/HYPOTHESIS: The PPARGC1A gene coactivates multiple nuclear transcription factors involved in cellular energy metabolism and vascular stasis. In the present study, we genotyped 35 tagging polymorphisms to capture all common PPARGC1A nucleotide sequence variations and tested for association with metabolic and cardiovascular traits in 2,101 Danish and Estonian boys and girls from the European Youth Heart Study, a multicentre school-based cross-sectional cohort study. METHODS: Fasting plasma glucose concentrations, anthropometric variables and blood pressure were measured. Habitual physical activity and aerobic fitness were objectively assessed using uniaxial accelerometry and a maximal aerobic exercise stress test on a bicycle ergometer, respectively. RESULTS: In adjusted models, nominally significant associations were observed for BMI (rs10018239, p = 0.039), waist circumference (rs7656250, p = 0.012; rs8192678 [Gly482Ser], p = 0.015; rs3755863, p = 0.02; rs10018239, beta = -0.01 cm per minor allele copy, p = 0.043), systolic blood pressure (rs2970869, p = 0.018) and fasting glucose concentrations (rs11724368, p = 0.045). Stronger associations were observed for aerobic fitness (rs7656250, p = 0.005; rs13117172, p = 0.008) and fasting glucose concentrations (rs7657071, p = 0.002). None remained significant after correcting for the number of statistical comparisons. We proceeded by testing for gene x physical activity interactions for the polymorphisms that showed nominal evidence of association in the main effect models. None of these tests was statistically significant. CONCLUSIONS/INTERPRETATION: Variants at PPARGC1A may influence several metabolic traits in this European paediatric cohort. However, variation at PPARGC1A is unlikely to have a major impact on cardiovascular or metabolic health in these children.

Transcriptional dysregulation of coding and non-coding genes in cellular models of Huntington's disease

HD (Huntington's disease) is a late onset heritable neurodegenerative disorder that is characterized by neuronal dysfunction and death, particularly in the cerebral cortex and medium spiny neurons of the striatum. This is followed by progressive chorea, dementia and emotional dysfunction, eventually resulting in death. HD is caused by an expanded CAG repeat in the first exon of the HD gene that results in an abnormally elongated polyQ (polyglutamine) tract in its protein product, Htt (Huntingtin). Wild-type Htt is largely cytoplasmic; however, in HD, proteolytic N-terminal fragments of Htt form insoluble deposits in both the cytoplasm and nucleus, provoking the idea that mutHtt (mutant Htt) causes transcriptional dysfunction. While a number of specific transcription factors and co-factors have been proposed as mediators of mutHtt toxicity, the causal relationship between these Htt/transcription factor interactions and HD pathology remains unknown. Previous work has highlighted REST [RE1 (repressor element 1)-silencing transcription factor] as one such transcription factor. REST is a master regulator of neuronal genes, repressing their expression. Many of its direct target genes are known or suspected to have a role in HD pathogenesis, including BDNF (brain-derived neurotrophic factor). Recent evidence has also shown that REST regulates transcription of regulatory miRNAs (microRNAs), many of which are known to regulate neuronal gene expression and are dysregulated in HD. Thus repression of miRNAs constitutes a second, indirect mechanism by which REST can alter the neuronal transcriptome in HD. We will describe the evidence that disruption to the REST regulon brought about by a loss of interaction between REST and mutHtt may be a key contributory factor in the widespread dysregulation of gene expression in HD.

Conditional mean functions of non-linear models of US output

We compare a number of models of post War US output growth in terms of the degree and pattern of non-linearity they impart to the conditional mean, where we condition on either the previous period's growth rate, or the previous two periods' growth rates. The conditional means are estimated non-parametrically using a nearest-neighbour technique on data simulated from the models. In this way, we condense the complex, dynamic, responses that may be present in to graphical displays of the implied conditional mean.

Modeling plant transpiration under limited soil water: comparison of different plant and soil hydraulic parameterizations and preliminary implications for their use in land surface models

Accurate estimates of how soil water stress affects plant transpiration are crucial for reliable land surface model (LSM) predictions. Current LSMs generally use a water stress factor, β, dependent on soil moisture content, θ, that ranges linearly between β = 1 for unstressed vegetation and β = 0 when wilting point is reached. This paper explores the feasibility of replacing the current approach with equations that use soil water potential as their independent variable, or with a set of equations that involve hydraulic and chemical signaling, thereby ensuring feedbacks between the entire soil–root–xylem–leaf system. A comparison with the original linear θ-based water stress parameterization, and with its improved curvi-linear version, was conducted. Assessment of model suitability was focused on their ability to simulate the correct (as derived from experimental data) curve shape of relative transpiration versus fraction of transpirable soil water. We used model sensitivity analyses under progressive soil drying conditions, employing two commonly used approaches to calculate water retention and hydraulic conductivity curves. Furthermore, for each of these hydraulic parameterizations we used two different parameter sets, for 3 soil texture types; a total of 12 soil hydraulic permutations. Results showed that the resulting transpiration reduction functions (TRFs) varied considerably among the models. The fact that soil hydraulic conductivity played a major role in the model that involved hydraulic and chemical signaling led to unrealistic values of β, and hence TRF, for many soil hydraulic parameter sets. However, this model is much better equipped to simulate the behavior of different plant species. Based on these findings, we only recommend implementation of this approach into LSMs if great care with choice of soil hydraulic parameters is taken

Nuclear Factor-kappaB controls the reaggregation of 3D neurosphere cultures in vitro

The approach of reaggregation involves the regeneration and self-renewal of histotypical 3D spheres from isolated tissue kept in suspension culture. Reaggregated spheres can be used as tumour, genetic, biohybrid and neurosphere models. In addition the functional superiority of 3D aggregates over conventional 2D cultures developed the use of neurospheres for brain engineering of CNS diseases. Thus 3D aggregate cultures created enormous interest in mechanisms that regulate the formation of multicellular aggregates in vitro. Here we analyzed mechanisms guiding the development of 3D neurosphere cultures. Adult neural stem cells can be cultured as self-adherent clusters, called neurospheres. Neurospheres are characterised as heterogeneous clusters containing unequal stem cell sub-types. Tumour necrosis factor-alpha (TNF-alpha is one of the crucial inflammatory cytokines with multiple actions on several cell types. TNF-alpha strongly activates the canonical Nuclear Factor Kappa-B (NF- kappaB) pathway. In order to investigate further functions of TNF in neural stem cells (NSCs) we tested the hypothesis that TNF is able to modulate the motility and/or migratory behaviour of SVZ derived adult neural stem cells. We observed a significantly faster sphere formation in TNF treated cultures than in untreated controls. The very fast aggregation of isolated NSCs (<2h) is a commonly observed phenomenon, though the mechanisms of 3D neurosphere formation remain largely unclear. Here we demonstrate for the first time, increased aggregation and enhanced motility of isolated NSCs in response to the TNF-stimulus. Moreover, this phenomenon is largely dependent on activated transcription factor NF-kappaB. Both, the pharmacological blockade of NF-kappaB pathway by pyrrolidine dithiocarbamate (PDTC) or Bay11-7082 and genetic blockade by expression of a transdominant-negative super-repressor IkappaB-AA1 led to decreased aggregation.

ENSO representation in climate models: from CMIP3 to CMIP5

We analyse the ability of CMIP3 and CMIP5 coupled ocean–atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Niño-Southern Oscillation (ENSO). The CMIP5 multi-model ensemble displays an encouraging 30 % reduction of the pervasive cold bias in the western Pacific, but no quantum leap in ENSO performance compared to CMIP3. CMIP3 and CMIP5 can thus be considered as one large ensemble (CMIP3 + CMIP5) for multi-model ENSO analysis. The too large diversity in CMIP3 ENSO amplitude is however reduced by a factor of two in CMIP5 and the ENSO life cycle (location of surface temperature anomalies, seasonal phase locking) is modestly improved. Other fundamental ENSO characteristics such as central Pacific precipitation anomalies however remain poorly represented. The sea surface temperature (SST)-latent heat flux feedback is slightly improved in the CMIP5 ensemble but the wind-SST feedback is still underestimated by 20–50 % and the shortwave-SST feedbacks remain underestimated by a factor of two. The improvement in ENSO amplitudes might therefore result from error compensations. The ability of CMIP models to simulate the SST-shortwave feedback, a major source of erroneous ENSO in CGCMs, is further detailed. In observations, this feedback is strongly nonlinear because the real atmosphere switches from subsident (positive feedback) to convective (negative feedback) regimes under the effect of seasonal and interannual variations. Only one-third of CMIP3 + CMIP5 models reproduce this regime shift, with the other models remaining locked in one of the two regimes. The modelled shortwave feedback nonlinearity increases with ENSO amplitude and the amplitude of this feedback in the spring strongly relates with the models ability to simulate ENSO phase locking. In a final stage, a subset of metrics is proposed in order to synthesize the ability of each CMIP3 and CMIP5 models to simulate ENSO main characteristics and key atmospheric feedbacks.

A constrained recursive least squares algorithm for adaptive combination of multiple models

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l1-norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l2-norm is applied as an approximation to the l1-norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.