Predicting functional gene links from phylogenetic-statistical analyses of whole genomes

An important element of the developing field of proteomics is to understand protein-protein interactions and other functional links amongst genes. Across-species correlation methods for detecting functional links work on the premise that functionally linked proteins will tend to show a common pattern of presence and absence across a range of genomes. We describe a maximum likelihood statistical model for predicting functional gene linkages. The method detects independent instances of the correlated gain or loss of pairs of proteins on phylogenetic trees, reducing the high rates of false positives observed in conventional across-species methods that do not explicitly incorporate a phylogeny. We show, in a dataset of 10,551 protein pairs, that the phylogenetic method improves by up to 35% on across-species analyses at identifying known functionally linked proteins. The method shows that protein pairs with at least two to three correlated events of gain or loss are almost certainly functionally linked. Contingent evolution, in which one gene's presence or absence depends upon the presence of another, can also be detected phylogenetically, and may identify genes whose functional significance depends upon its interaction with other genes. Incorporating phylogenetic information improves the prediction of functional linkages. The improvement derives from having a lower rate of false positives and from detecting trends that across-species analyses miss. Phylogenetic methods can easily be incorporated into the screening of large-scale bioinformatics datasets to identify sets of protein links and to characterise gene networks.

Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity

Background: MHC Class I molecules present antigenic peptides to cytotoxic T cells, which forms an integral part of the adaptive immune response. Peptides are bound within a groove formed by the MHC heavy chain. Previous approaches to MHC Class I-peptide binding prediction have largely concentrated on the peptide anchor residues located at the P2 and C-terminus positions. Results: A large dataset comprising MHC-peptide structural complexes was created by remodelling pre-determined x-ray crystallographic structures. Static energetic analysis, following energy minimisation, was performed on the dataset in order to characterise interactions between bound peptides and the MHC Class I molecule, partitioning the interactions within the groove into van der Waals, electrostatic and total non-bonded energy contributions. Conclusion: The QSAR techniques of Genetic Function Approximation (GFA) and Genetic Partial Least Squares (G/PLS) algorithms were used to identify key interactions between the two molecules by comparing the calculated energy values with experimentally-determined BL50 data. Although the peptide termini binding interactions help ensure the stability of the MHC Class I-peptide complex, the central region of the peptide is also important in defining the specificity of the interaction. As thermodynamic studies indicate that peptide association and dissociation may be driven entropically, it may be necessary to incorporate entropic contributions into future calculations.

Lack of association between central adiposity and lipaemia in UK Sikh men

OBJECTIVE: To determine whether the positive statistical associations between measures of total and regional adiposity and measures of glucose, insulin and triacylglycerol ( TAG) metabolism reported in Caucasian men, are also observed in UK Sikhs. DESIGN: A matched cross-sectional study in which each volunteer provided a blood sample after a 12-h overnight fast and had anthropometric measurements taken. SUBJECTS: A total of 55 healthy Caucasian and 55 healthy UK Sikh men were recruited. The Caucasian and Sikh men were matched for age ( 48.7 +/- 10.9 and 48.3 +/- 10.0 y, respectively) and body mass index (BMI) ( 26.1 +/- 2.8 and 26.3 +/- 3.2 kg/m(2), respectively). MEASUREMENTS: Anthropometric measurements were performed to assess total and regional fat depots. The concentrations of plasma total cholesterol, high-density cholesterol (HDL- C), low-density cholesterol (LDL-C) and small dense LDL (LDL3), TAG, glucose, fasting insulin (ins) and nonesterified fatty acids (NEFA) were analysed in fasted plasma. Surrogate measures of insulin resistance (HOMA-IR) and insulin sensitivity (RQUICKI) were calculated from insulin and glucose (HOMA-IR) and insulin, glucose and NEFA ( RQUICKI) measurements. RESULTS: The Sikh men had significantly higher body fat, with the sum of the four skinfold measurements (Ssk) ( P = 0.0001) and subscapular skinfold value (P = 0.009) higher compared with the Caucasian men. The Sikh volunteers also had characteristics of the metabolic syndrome: lower HDL-C (P = 0.07), higher TAG (P = 0.004), higher % LDL3 (P = 0.0001) and insulin resistance (P = 0.05). Both ethnic groups demonstrated positive correlations between insulin and waist circumference (Caucasian: r = 0.661, P = 0.0001; Sikh: r = 0.477, P = 0.0001). The Caucasian men also demonstrated significant positive correlations between central adiposity (r = 0.275, P = 0.04), other measures of adiposity (BMI and suprailiac skinfold) and plasma TAG, whereas the Sikh men showed no correlation for central adiposity (r = 0.019, ns) and TAG with a trend to a negative relationship between other measures ( Ssk and suprailiac) which reached near significance for subscapular skinfold and TAG (r = - 0.246, P = 0.007). The expected positive association between insulin and TAG was observed in the Caucasian men (r = 0.318, P = 0.04) but not in the Sikh men (r = 0.011, ns). CONCLUSIONS: In the Caucasian men, the expected positive association between plasma TAG and centralized body fat was observed. However, a lack of association between centralized, or any other measure of adiposity, and plasma TAG was observed in the matched Sikh men, although both ethnic groups showed the positive association between centralized body fat and insulin resistance, which was less strong for Sikhs. These findings in the Sikh men were not consistent with the hypothesis that there is a clear causal relationship between body fat and its distribution, insulin resistance, and lipid abnormalities associated with the metabolic syndrome, in this ethnic group.

A Packet/Frame sync detector based on statistical mode with application to wireless-USB

Wireless Personal Area Networks (WPANs) are offering high data rates suitable for interconnecting high bandwidth personal consumer devices (Wireless HD streaming, Wireless-USB and Bluetooth EDR). ECMA-368 is the Physical (PHY) and Media Access Control (MAC) backbone of many of these wireless devices. WPAN devices tend to operate in an ad-hoc based network and therefore it is important to successfully latch onto the network and become part of one of the available piconets. This paper presents a new algorithm for detecting the Packet/Fame Sync (PFS) signal in ECMA-368 to identify piconets and aid symbol timing. The algorithm is based on correlating the received PFS symbols with the expected locally stored symbols over the 24 or 12 PFS symbols, but selecting the likely TFC based on the highest statistical mode from the 24 or 12 best correlation results. The results are very favorable showing an improvement margin in the order of 11.5dB in reference sensitivity tests between the required performance using this algorithm and the performance of comparable systems.

Effects of beverage alcohol taxes and prices on drinking: a meta-analysis of 1003 estimates from 112 studies

Aims: We conducted a systematic review of studies examining relationships between measures of beverage alcohol tax or price levels and alcohol sales or self-reported drinking. A total of 112 studies of alcohol tax or price effects were found, containing 1003 estimates of the tax/price–consumption relationship. Design: Studies included analyses of alternative outcome measures, varying subgroups of the population, several statistical models, and using different units of analysis. Multiple estimates were coded from each study, along with numerous study characteristics. Using reported estimates, standard errors, t-ratios, sample sizes and other statistics, we calculated the partial correlation for the relationship between alcohol price or tax and sales or drinking measures for each major model or subgroup reported within each study. Random-effects models were used to combine studies for inverse variance weighted overall estimates of the magnitude and significance of the relationship between alcohol tax/price and drinking. Findings: Simple means of reported elasticities are -0.46 for beer, -0.69 for wine and -0.80 for spirits. Meta-analytical results document the highly significant relationships (P < 0.001) between alcohol tax or price measures and indices of sales or consumption of alcohol (aggregate-level r = -0.17 for beer, -0.30 for wine, -0.29 for spirits and -0.44 for total alcohol). Price/tax also affects heavy drinking significantly (mean reported elasticity = -0.28, individual-level r = -0.01, P < 0.01), but the magnitude of effect is smaller than effects on overall drinking. Conclusions: A large literature establishes that beverage alcohol prices and taxes are related inversely to drinking. Effects are large compared to other prevention policies and programs. Public policies that raise prices of alcohol are an effective means to reduce drinking.

Adiposity, insulin and lipid metabolism in post-menopausal women

OBJECTIVE: To investigate relationships between body fat and its distribution and carbohydrate and lipid tolerance using statistical comparisons in post-menopausal women. DESIGN: Sequential meal, postprandial study (600 min) which included a mixed standard breakfast (30 g fat) and lunch (44 g fat) given at 0 and 270 min, respectively, after an overnight fast. SUBJECTS: Twenty-eight post-menopausal women with a diverse range of body weight (body mass index (BMI), mean 27.2, range 20.5-38.8 kg/m2) and abdominal fat deposition (waist, mean 86.4, range 63.5-124.0 cm). Women with BMI <18 or >37 kg/m2, age>80 y and taking hormone replacement therapy (HRT) were excluded. MEASUREMENTS: Anthropometric measurements were performed to assess total and regional fat deposits. The concentrations of plasma total cholesterol, high density lipoprotein (HDL) cholesterol, triacylglycerol (TAG), glucose, insulin (ins), non-esterified fatty acids (NEFA) and apolipoprotein (apo) B-48 were analysed in plasma collected at baseline (fasted state) and at 13 postprandial time points for a 600 min period. RESULTS: Insulin concentrations in the fasted and fed state were significantly correlated with all measures of adiposity (BMI, waist, waist-hip ratio (W/H), waist-height ratio (W/Ht) and sum of skinfold thickness (SSk)). After controlling for BMI, waist remained significantly and positively associated with fasted insulin (r=0.559) with waist contributing 53% to the variability after multiple regression analysis. After controlling for waist, BMI remained significantly correlated with postprandial (IAUC) insulin (r=0.535) contributing 66% of the variability of this measurement. No association was found between any measures of adiposity and glucose concentrations, although insulin concentration in relation to glucose concentration (glucose-insulin ratio) was significantly negatively correlated with all measures of adiposity. A significant positive correlation was found between fasted TAG and BMI (r=0.416), waist (r=0.393) and Ssk (r=0.457) and postprandial (AUC) TAG with BMI (r=0.385) and Ssk (r=0.406). A significantly higher postprandial apolipoprotein (apo) B-48 response was observed in those women with high BMI (>27 kg/m2). Fasting levels of NEFA were significantly and positively correlated with all measures of adiposity (except W/H). No association was found between cholesterol containing particles and any measure of adiposity. CONCLUSION: Hyperinsulinaemia associated with increasing body fat and central fat distribution is associated with normal glucose but not TAG or NEFA concentrations in postmenopausal women.

Model inter-comparison between statistical and dynamic model assessments of the long-term stability of blanket peat in Great Britain (1940-2099)

We compared output from 3 dynamic process-based models (DMs: ECOSSE, MILLENNIA and the Durham Carbon Model) and 9 bioclimatic envelope models (BCEMs; including BBOG ensemble and PEATSTASH) ranging from simple threshold to semi-process-based models. Model simulations were run at 4 British peatland sites using historical climate data and climate projections under a medium (A1B) emissions scenario from the 11-RCM (regional climate model) ensemble underpinning UKCP09. The models showed that blanket peatlands are vulnerable to projected climate change; however, predictions varied between models as well as between sites. All BCEMs predicted a shift from presence to absence of a climate associated with blanket peat, where the sites with the lowest total annual precipitation were closest to the presence/absence threshold. DMs showed a more variable response. ECOSSE predicted a decline in net C sink and shift to net C source by the end of this century. The Durham Carbon Model predicted a smaller decline in the net C sink strength, but no shift to net C source. MILLENNIA predicted a slight overall increase in the net C sink. In contrast to the BCEM projections, the DMs predicted that the sites with coolest temperatures and greatest total annual precipitation showed the largest change in carbon sinks. In this model inter-comparison, the greatest variation in model output in response to climate change projections was not between the BCEMs and DMs but between the DMs themselves, because of different approaches to modelling soil organic matter pools and decomposition amongst other processes. The difference in the sign of the response has major implications for future climate feedbacks, climate policy and peatland management. Enhanced data collection, in particular monitoring peatland response to current change, would significantly improve model development and projections of future change.

Assessing the vulnerability of blanket peat to climate change using an ensemble of statistical bioclimatic envelope models

We assessed the vulnerability of blanket peat to climate change in Great Britain using an ensemble of 8 bioclimatic envelope models. We used 4 published models that ranged from simple threshold models, based on total annual precipitation, to Generalised Linear Models (GLMs, based on mean annual temperature). In addition, 4 new models were developed which included measures of water deficit as threshold, classification tree, GLM and generalised additive models (GAM). Models that included measures of both hydrological conditions and maximum temperature provided a better fit to the mapped peat area than models based on hydrological variables alone. Under UKCIP02 projections for high (A1F1) and low (B1) greenhouse gas emission scenarios, 7 out of the 8 models showed a decline in the bioclimatic space associated with blanket peat. Eastern regions (Northumbria, North York Moors, Orkney) were shown to be more vulnerable than higher-altitude, western areas (Highlands, Western Isles and Argyle, Bute and The Trossachs). These results suggest a long-term decline in the distribution of actively growing blanket peat, especially under the high emissions scenario, although it is emphasised that existing peatlands may well persist for decades under a changing climate. Observational data from long-term monitoring and manipulation experiments in combination with process-based models are required to explore the nature and magnitude of climate change impacts on these vulnerable areas more fully.

A study of the ethene-ozone reaction with photoelectron spectroscopy: measurement of product branching ratios and atmospheric implications

The ozone-ethene reaction has been investigated at low pressure in a flow-tube interfaced to a u.v. photoelectron spectrometer. Photoelectron spectra recorded as a function of reaction time have been used to estimate partial pressures of the reagents and products, using photoionization cross-sections for selected photoelectron bands of the reagents and products, which have been measured separately. Product yields compare favourably with results of other studies, and the production of oxygen and acetaldehyde have been measured as a function of time for the first time. A reaction scheme developed for the ozone-ethene reaction has been used to simulate the reagents and products as a function of time. The results obtained are in good agreement with the experimental measurements. For each of the observed products, the simulations allow the main reaction (or reactions) for production of that product to be established. The product yields have been used in a global model to estimate their global annual emissions in the atmosphere. Of particular interest are the calculated global annual emissions of formaldehyde (0.96 ± 0.10 Tg) and formic acid, (0.05 ± 0.01 Tg) which are estimated as 0.04% and 0.7% of the total annual emission respectively.

Prediction of processed cheese instrumental texture and meltability by mid-infrared spectroscopy coupled with chemometric tools

The objective of this study was to determine the potential of mid-infrared spectroscopy coupled with multidimensional statistical analysis for the prediction of processed cheese instrumental texture and meltability attributes. Processed cheeses (n = 32) of varying composition were manufactured in a pilot plant. Following two and four weeks storage at 4 degrees C samples were analysed using texture profile analysis, two meltability tests (computer vision, Olson and Price) and mid-infrared spectroscopy (4000-640 cm(-1)). Partial least squares regression was used to develop predictive models for all measured attributes. Five attributes were successfully modelled with varying degrees of accuracy. The computer vision meltability model allowed for discrimination between high and low melt values (R-2 = 0.64). The hardness and springiness models gave approximate quantitative results (R-2 = 0.77) and the cohesiveness (R-2 = 0.81) and Olson and Price meltability (R-2 = 0.88) models gave good prediction results. (c) 2006 Elsevier Ltd. All rights reserved..

Modeling data with structural and temporal correlation using lower level and higher level multilevel models

Novel imaging techniques are playing an increasingly important role in drug development, providing insight into the mechanism of action of new chemical entities. The data sets obtained by these methods can be large with complex inter-relationships, but the most appropriate statistical analysis for handling this data is often uncertain - precisely because of the exploratory nature of the way the data are collected. We present an example from a clinical trial using magnetic resonance imaging to assess changes in atherosclerotic plaques following treatment with a tool compound with established clinical benefit. We compared two specific approaches to handle the correlations due to physical location and repeated measurements: two-level and four-level multilevel models. The two methods identified similar structural variables, but higher level multilevel models had the advantage of explaining a greater proportion of variation, and the modeling assumptions appeared to be better satisfied.

Quantitative analysis of mannitol polymorphs: FT-Raman spectroscopy

Mannitol is a polymorphic excipient which is usually used in pharmaceutical products as the beta form, although other polymorphs (alpha and delta) are common contaminants. Binary mixtures containing beta and delta mannitol were prepared to quantify the concentration of the beta form using FT-Raman spectroscopy. Spectral regions characteristic of each form were selected and peak intensity ratios of beta peaks to delta peaks were calculated. Using these ratios, a correlation curve was established which was then validated by analysing further samples of known composition. The results indicate that levels down to 2% beta could be quantified using this novel, non-destructive approach. Potential errors associated with quantitative studies using FT-Raman spectroscopy were also researched. The principal source of variability arose from inhomogeneities on mixing of the samples; a significant reduction of these errors was observed by reducing and controlling the particle size range. The results show that FT-Raman spectroscopy can be used to rapidly and accurately quantitate polymorphic mixtures.

Statistical evaluation of the early-stage development of Jatropha energy plantations in Northeastern Mexico

A systematic evaluation of agricultural factors affecting the adaptation of the tropical oil plant Jatropha curcas L. to the semi-arid subtropical climate in Northeastern Mexico has been conducted. The factors studied include plant density and topology, as well as fungi and virus abundances. A multiple regression analysis shows that total fruit production can be well predicted by the area per plant and the total presence of fungi. Four common herbicides and a mechanical weed control measure were established at a dedicated test array and their impact on plant productivity was assessed.

Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions

Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO2 concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2 to 5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6 to 9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2 to 5 years and 6 to 9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6 to 9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions.

Decline and recovery of total column ozone using a multimodel time series analysis

Simulations of 15 coupled chemistry climate models, for the period 1960–2100, are presented. The models include a detailed stratosphere, as well as including a realistic representation of the tropospheric climate. The simulations assume a consistent set of changing greenhouse gas concentrations, as well as temporally varying chlorofluorocarbon concentrations in accordance with observations for the past and expectations for the future. The ozone results are analyzed using a nonparametric additive statistical model. Comparisons are made with observations for the recent past, and the recovery of ozone, indicated by a return to 1960 and 1980 values, is investigated as a function of latitude. Although chlorine amounts are simulated to return to 1980 values by about 2050, with only weak latitudinal variations, column ozone amounts recover at different rates due to the influence of greenhouse gas changes. In the tropics, simulated peak ozone amounts occur by about 2050 and thereafter total ozone column declines. Consequently, simulated ozone does not recover to values which existed prior to the early 1980s. The results also show a distinct hemispheric asymmetry, with recovery to 1980 values in the Northern Hemisphere extratropics ahead of the chlorine return by about 20 years. In the Southern Hemisphere midlatitudes, ozone is simulated to return to 1980 levels only 10 years ahead of chlorine. In the Antarctic, annually averaged ozone recovers at about the same rate as chlorine in high latitudes and hence does not return to 1960s values until the last decade of the simulations.