Increasing anthocyanin content in Queen Garnet plum and correlations with in-field measures

While plums are traditionally bred for fresh fruit traits such as size, sweetness, yield and disease resistance the Queensland Government breeding program for Japanese plum ( Prunus salicina Lindl.) also selected for anthocyanin content to develop a new plum selection named 'Queen Garnet'. When ripe or overripe, it has a near black skin and deep red flesh colour, which when combined, result in exceptionally high anthocyanin content, reaching up to 277 mg/100 g fruit. The skin fraction contributes 36-66% of the total anthocyanin content of fruit. The plum is now being commercially grown to be processed into a range of functional products from food colourants to premium health products. These are sold on the basis of anthocyanin and antioxidant content. Protocols for increasing anthocyanin content have therefore been researched to maximise the total anthocyanin yield rather than fresh fruit weight and taste. The principal approach is through selective harvest of overripe plums high in colour, although post-harvest storage at 21°C results in further anthocyanin synthesis. Modified processing is also required to ensure recovery of anthocyanins from the skin fraction. The plum products have entered testing for assessing health properties beginning with an initial proof of in vivo bioavailability of the anthocyanins.

Genetic correlations of young bull reproductive traits and heifer puberty traits with female reproductive performance in two tropical beef genotypes in northern Australia

Genetic correlations of young bull and heifer puberty traits with measures of early and lifetime female reproductive performance were estimated in two tropical beef cattle genotypes. Heifer age at puberty was highly (r(g) = -0.71 +/- 0.11) and moderately (r(g) = -0.40 +/- 0.20) genetically correlated with pregnancy rate at first annual mating (mating 1) and lifetime annual calving rate, respectively in Brahman (BRAH). In Tropical Composite (TCOMP), heifer age at puberty was highly correlated with reproductive outcomes from the first re-breed (mating 2), mainly due to its association with lactation anoestrous interval (r(g) = 0.72 +/- 0.17). Scrotal circumference were correlated with heifer age at puberty (r(g) = -0.41 +/- 0.11 at 12 months in BRAH; -0.30 +/- 0.13 at 6 months in TCOMP) but correlations were lower with later female reproduction traits. Bull insulin-like growth factor-I was correlated with heifer age at puberty (r(g) = -0.56 +/- 0.11 in BRAH; -0.43 +/- 0.11 in TCOMP) and blood luteinising hormone concentration was moderately correlated with lactation anoestrous interval (r(g) = 0.59 +/- 0.23) in TCOMP. Semen quality traits, including mass activity, motility and percent normal sperm were genetically correlated with lactation anoestrus and female lifetime female reproductive traits in both genotypes, but the magnitudes of the relationships differed with bull age at measurement. Preputial eversion and sheath scores were genetically associated with lifetime calving and weaning rates in both genotypes. Several of the early-in-life male and female measures examined were moderately to highly genetically correlated with early and lifetime female reproduction traits and may be useful as indirect selection criteria for improving female reproduction in tropical breeds in northern Australia.

Dark-energy constraints and correlations with systematics from CFHTLS weak lensing, SNLS supernovae Ia and WMAP5

Aims We combine measurements of weak gravitational lensing from the CFHTLS-Wide survey, supernovae Ia from CFHT SNLS and CMB anisotropies from WMAP5 to obtain joint constraints on cosmological parameters, in particular, the dark-energy equation-of-state parameter w. We assess the influence of systematics in the data on the results and look for possible correlations with cosmological parameters. Methods We implemented an MCMC algorithm to sample the parameter space of a flat CDM model with a dark-energy component of constant w. Systematics in the data are parametrised and included in the analysis. We determine the influence of photometric calibration of SNIa data on cosmological results by calculating the response of the distance modulus to photometric zero-point variations. The weak lensing data set is tested for anomalous field-to-field variations and a systematic shape measurement bias for high-redshift galaxies. Results Ignoring photometric uncertainties for SNLS biases cosmological parameters by at most 20% of the statistical errors, using supernovae alone; the parameter uncertainties are underestimated by 10%. The weak-lensing field-to-field variance between 1 deg2-MegaCam pointings is 5-15% higher than predicted from N-body simulations. We find no bias in the lensing signal at high redshift, within the framework of a simple model, and marginalising over cosmological parameters. Assuming a systematic underestimation of the lensing signal, the normalisation increases by up to 8%. Combining all three probes we obtain -0.10 < 1 + w < 0.06 at 68% confidence ( -0.18 < 1 + w < 0.12 at 95%), including systematic errors. Our results are therefore consistent with the cosmological constant . Systematics in the data increase the error bars by up to 35%; the best-fit values change by less than 0.15.

Crude oil and agricultural futures: An analysis of correlation dynamics

Correlations between oil and agricultural commodities have varied over previous decades, impacted by renewable fuels policy and turbulent economic conditions. We estimate smooth transition conditional correlation models for 12 agricultural commodities and WTI crude oil. While a structural change in correlations occurred concurrently with the introduction of biofuel policy, oil and food price levels are also key influences. High correlation between biofuel feedstocks and oil is more likely to occur when food and oil price levels are high. Correlation with oil returns is strong for biofuel feedstocks, unlike with other agricultural futures, suggesting limited contagion from energy to food markets.

Statistical significance of sequential firing patterns in multi-neuronal spike trains

Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

A conditional random field-based downscaling method for assessment of climate change impact on multisite daily precipitation in the Mahanadi basin

Downscaling to station-scale hydrologic variables from large-scale atmospheric variables simulated by general circulation models (GCMs) is usually necessary to assess the hydrologic impact of climate change. This work presents CRF-downscaling, a new probabilistic downscaling method that represents the daily precipitation sequence as a conditional random field (CRF). The conditional distribution of the precipitation sequence at a site, given the daily atmospheric (large-scale) variable sequence, is modeled as a linear chain CRF. CRFs do not make assumptions on independence of observations, which gives them flexibility in using high-dimensional feature vectors. Maximum likelihood parameter estimation for the model is performed using limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization. Maximum a posteriori estimation is used to determine the most likely precipitation sequence for a given set of atmospheric input variables using the Viterbi algorithm. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework. The model is used to project the future cumulative distribution function of precipitation. Uncertainty in precipitation prediction is addressed through a modified Viterbi algorithm that predicts the n most likely sequences. The model is applied for downscaling monsoon (June-September) daily precipitation at eight sites in the Mahanadi basin in Orissa, India, using the MIROC3.2 medium-resolution GCM. The predicted distributions at all sites show an increase in the number of wet days, and also an increase in wet day precipitation amounts. A comparison of current and future predicted probability density functions for daily precipitation shows a change in shape of the density function with decreasing probability of lower precipitation and increasing probability of higher precipitation.

Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains

We consider the problem of detecting statistically significant sequential patterns in multineuronal spike trains. These patterns are characterized by ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data-mining scheme to efficiently discover such patterns, which occur often enough in the data. Here we propose a method to determine the statistical significance of such repeating patterns. The novelty of our approach is that we use a compound null hypothesis that not only includes models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a probabilistic model that captures the counting process and use this to derive a test of significance for rejecting such a compound null hypothesis. The structure of our null hypothesis also allows us to rank-order different significant patterns. We illustrate the effectiveness of our approach using spike trains generated with a simulator.

Vibrational phase relaxation of O–H stretch in bulk water: Role of large amplitude angular jumps and negative cross-correlations among the forces on the O–H bond

The ultrafast vibrational phase relaxation of O–H stretch in bulk water is investigated in molecular dynamics simulations. The dephasing time (T2) of the O–H stretch in bulk water calculated from the frequency fluctuation time correlation function (Cω(t)) is in the range of 70–80 femtosecond (fs), which is comparable to the characteristic timescale obtained from the vibrational echo peak shift measurements using infrared photon echo [W.P. de Boeij, M.S. Pshenichnikov, D.A. Wiersma, Ann. Rev. Phys. Chem. 49 (1998) 99]. The ultrafast decay of Cω(t) is found to be responsible for the ultrashort T2 in bulk water. Careful analysis reveals the following two interesting reasons for the ultrafast decay of Cω(t). (A) The large amplitude angular jumps of water molecules (within 30–40 fs time duration) provide a large scale contribution to the mean square vibrational frequency fluctuation and gives rise to the rapid spectral diffusion on 100 fs time scale. (B) The projected force, due to all the atoms of the solvent molecules on the oxygen (FO(t)) and hydrogen (FH(t)) atom of the O–H bond exhibit a large negative cross-correlation (NCC). We further find that this NCC is partly responsible for a weak, non-Arrhenius temperature dependence of the dephasing rate.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.

Sequential split vector quantization of LSF parameters using conditional PDF

A better performing product code vector quantization (VQ) method is proposed for coding the line spectrum frequency (LSF) parameters; the method is referred to as sequential split vector quantization (SeSVQ). The split sub-vectors of the full LSF vector are quantized in sequence and thus uses conditional distribution derived from the previous quantized sub-vectors. Unlike the traditional split vector quantization (SVQ) method, SeSVQ exploits the inter sub-vector correlation and thus provides improved rate-distortion performance, but at the expense of higher memory. We investigate the quantization performance of SeSVQ over traditional SVQ and transform domain split VQ (TrSVQ) methods. Compared to SVQ, SeSVQ saves 1 bit and nearly 3 bits, for telephone-band and wide-band speech coding applications respectively.

Conditional Moments in Asset Pricing

A better understanding of stock price changes is important in guiding many economic activities. Since prices often do not change without good reasons, searching for related explanatory variables has involved many enthusiasts. This book seeks answers from prices per se by relating price changes to their conditional moments. This is based on the belief that prices are the products of a complex psychological and economic process and their conditional moments derive ultimately from these psychological and economic shocks. Utilizing information about conditional moments hence makes it an attractive alternative to using other selective financial variables in explaining price changes. The first paper examines the relation between the conditional mean and the conditional variance using information about moments in three types of conditional distributions; it finds that the significance of the estimated mean and variance ratio can be affected by the assumed distributions and the time variations in skewness. The second paper decomposes the conditional industry volatility into a concurrent market component and an industry specific component; it finds that market volatility is on average responsible for a rather small share of total industry volatility — 6 to 9 percent in UK and 2 to 3 percent in Germany. The third paper looks at the heteroskedasticity in stock returns through an ARCH process supplemented with a set of conditioning information variables; it finds that the heteroskedasticity in stock returns allows for several forms of heteroskedasticity that include deterministic changes in variances due to seasonal factors, random adjustments in variances due to market and macro factors, and ARCH processes with past information. The fourth paper examines the role of higher moments — especially skewness and kurtosis — in determining the expected returns; it finds that total skewness and total kurtosis are more relevant non-beta risk measures and that they are costly to be diversified due either to the possible eliminations of their desirable parts or to the unsustainability of diversification strategies based on them.

Analysis of Dynamic Heterogeneity in a Glass Former from the Spatial Correlations of Mobility

The growth of characteristic length scales associated with dynamic heterogeneity in glass-forming liquids is investigated in an extensive computational study of a four-point, time-dependent structure factor defined from spatial correlations of mobility, for a model liquid for system sizes extending up to 351 232 particles, in constant-energy and constant-temperature ensembles. Our estimates for dynamic correlation lengths and susceptibilities are consistent with previous results from finite size scaling. We find scaling exponents that are inconsistent with predictions from inhomogeneous mode coupling theory and a recent simulation confirmation of these predictions.

The Influence of World Factors on Finnish Stock Market Volatility

This paper investigates to what extent the volatility of Finnish stock portfolios is transmitted through the "world volatility". We operationalize the volatility processes of Finnish leverage, industry, and size portfolio returns by asymmetric GARCH specifications according to Glosten et al. (1993). We use daily return data for January, 2, 1987 to December 30, 1998. We find that the world shock significantly enters the domestic models, and that the impact has increased over time. This applies also for the variance ratios, and the correlations to the world. The larger the firm, the larger is the world impact. The conditional variance is higher during recessions. The asymmetry parameter is surprisingly non-significant, and the leverage hypothesis cannot be verified. The return generating process of the domestic portfolio returns does usually not include the world information set, thus indicating that the returns are generated by a segmented conditional asset pricing model.

Effects of electron correlations in conjugated organic molecules and solids

The discrepancies between the non-interacting models and experimental results for conjugated systems is highlighted in this brief review. The interacting model hamiltonians correctly give the forbidden singlet state below the optical gap in polyenes and also explain both the nonvanishing optical gap in polyacetylenes and the vanishing optical gap in symmetric cyanine dyes. The negative spin densities in polyene radicals is also understood in terms of a correlated picture. The role of electron-electron interactions in other strongly correlated systems, such as polydiacetylene and mixed and segregated stack charge transfer solids, are also briefly discussed.