Association between ovarian follicle development and pregnancy rates in dairy cows undergoing spontaneous oestrous cycles

Ovarian follicle development continues in a wave-like manner during the bovine oestrous cycle giving rise to variation in the duration of ovulatory follicle development. The objectives of the present study were to determine whether a relationship exists between the duration of ovulatory follicle development and pregnancy rates following artificial insemination (AI) in dairy cows undergoing spontaneous oestrous cycles, and to identify factors influencing follicle turnover and pregnancy rate and the relationship between these two variables. Follicle development was monitored by daily transrectal ultrasonography from 10 days after oestrus until the subsequent oestrus in 158 lactating dairy cows. The cows were artificially inseminated following the second observed oestrus and pregnancy was diagnosed 35 days later. The predominant pattern of follicle development was two follicle waves (74.7%) with three follicle waves in 22.1% of oestrous cycles and four or more follicle waves in 3.2% of oestrous cycles. The interval from ovulatory follicle emergence to oestrus (EOI) was 3 days longer (P < 0.0001) in cows with two follicle waves than in those with three waves. Ovulatory follicles from two-wave oestrous cycles grew more slowly but were approximately 2 mm larger (P < 0.0001) on the day of oestrus. Twin ovulations were observed in 14.2% of oestrous cycles and occurred more frequently (P < 0.001) in three-wave oestrous cycles; consequently EOI was shorter in cows with twin ovulations. Overall, 57.0% of the cows were diagnosed pregnant 35 days after AI. Linear logistic regression analysis revealed an inverse relationship between EOI and the proportion of cows diagnosed pregnant, among all cows (n = 158; P < 0.01) and amongst those with single ovulations (n = 145; P < 0.05). Mean EOI was approximately I day shorter (P < 0.01) in cows that became pregnant than in non-pregnant cows; however, pregnancy rates did not differ significantly among cows with different patterns of follicle development. These findings confirm and extend previous observations in pharmacologically manipulated cattle and show, for the first time, that in dairy cows undergoing spontaneous oestrous cycles, natural variation in the duration of post-emergence ovulatory follicle development has a significant effect on pregnancy rate, presumably reflecting variation in oocyte developmental competence.

Influence-matrix diagnostic of a data assimilation system

The influence matrix is used in ordinary least-squares applications for monitoring statistical multiple-regression analyses. Concepts related to the influence matrix provide diagnostics on the influence of individual data on the analysis - the analysis change that would occur by leaving one observation out, and the effective information content (degrees of freedom for signal) in any sub-set of the analysed data. In this paper, the corresponding concepts have been derived in the context of linear statistical data assimilation in numerical weather prediction. An approximate method to compute the diagonal elements of the influence matrix (the self-sensitivities) has been developed for a large-dimension variational data assimilation system (the four-dimensional variational system of the European Centre for Medium-Range Weather Forecasts). Results show that, in the boreal spring 2003 operational system, 15% of the global influence is due to the assimilated observations in any one analysis, and the complementary 85% is the influence of the prior (background) information, a short-range forecast containing information from earlier assimilated observations. About 25% of the observational information is currently provided by surface-based observing systems, and 75% by satellite systems. Low-influence data points usually occur in data-rich areas, while high-influence data points are in data-sparse areas or in dynamically active regions. Background-error correlations also play an important role: high correlation diminishes the observation influence and amplifies the importance of the surrounding real and pseudo observations (prior information in observation space). Incorrect specifications of background and observation-error covariance matrices can be identified, interpreted and better understood by the use of influence-matrix diagnostics for the variety of observation types and observed variables used in the data assimilation system. Copyright © 2004 Royal Meteorological Society

Association between ovarian follicle development and pregnancy rates in dairy cows undergoing spontaneous oestrous cycles

Ovarian follicle development continues in a wave-like manner during the bovine oestrous cycle giving rise to variation in the duration of ovulatory follicle development. The objectives of the present study were to determine whether a relationship exists between the duration of ovulatory follicle development and pregnancy rates following artificial insemination (AI) in dairy cows undergoing spontaneous oestrous cycles, and to identify factors influencing follicle turnover and pregnancy rate and the relationship between these two variables. Follicle development was monitored by daily transrectal ultrasonography from 10 days after oestrus until the subsequent oestrus in 158 lactating dairy cows. The cows were artificially inseminated following the second observed oestrus and pregnancy was diagnosed 35 days later. The predominant pattern of follicle development was two follicle waves (74.7%) with three follicle waves in 22.1% of oestrous cycles and four or more follicle waves in 3.2% of oestrous cycles. The interval from ovulatory follicle emergence to oestrus (EOI) was 3 days longer (P < 0.0001) in cows with two follicle waves than in those with three waves. Ovulatory follicles from two-wave oestrous cycles grew more slowly but were approximately 2 mm larger (P < 0.0001) on the day of oestrus. Twin ovulations were observed in 14.2% of oestrous cycles and occurred more frequently (P < 0.001) in three-wave oestrous cycles; consequently EOI was shorter in cows with twin ovulations. Overall, 57.0% of the cows were diagnosed pregnant 35 days after AI. Linear logistic regression analysis revealed an inverse relationship between EOI and the proportion of cows diagnosed pregnant, among all cows (n = 158; P < 0.01) and amongst those with single ovulations (n = 145; P < 0.05). Mean EOI was approximately I day shorter (P < 0.01) in cows that became pregnant than in non-pregnant cows; however, pregnancy rates did not differ significantly among cows with different patterns of follicle development. These findings confirm and extend previous observations in pharmacologically manipulated cattle and show, for the first time, that in dairy cows undergoing spontaneous oestrous cycles, natural variation in the duration of post-emergence ovulatory follicle development has a significant effect on pregnancy rate, presumably reflecting variation in oocyte developmental competence.

Associations between apolipoprotein e genotype and circulating F-2-isoprostane levels in humans

Apolipoprotein E (apoE), an important determinant of plasma lipoprotein metabolism, has three common alleles (ε 2, ε 3, and ε 4). Population studies have shown that the risk of diseases characterized by oxidative damage, such as coronary heart disease and Alzheimer's disease, is significantly higher in ε 4 carriers. We evaluated the association between apoE genotypes and plasma F-2-isoprostane levels, an index of lipid peroxidation, in humans. Two hundred seventy-four healthy subjects (104 males, 170 females; 46.9 &PLUSMN; 13.0 yr; 200 whites, 74 blacks; 81 nonsmokers, 64 passive smokers, and 129 active smokers) recruited for a randomized clinical antioxidant intervention trial were included in this analysis. ApoE genotype was determined by PCR and restriction enzyme digestion. Free plasma F2-isoprostane was measured by GC-MS. Genotype groups were compared using multiple regression analysis with adjustment for sex, age, race, smoking status, body mass index, plasma ascorbic acid, and β-carotene. Subjects with ε 3/ε 4 and ε 4/ε 4 genotype (ε 4-carriers) and with ε 2/ε 3 and ε 3/ε 3 (non-ε 4-carriers) were pooled for analysis. In subjects with high cholesterol levels (total cholesterol above 200 mg/dl), plasma F-2-isoprostane levels were 29% higher in ε 4 carriers than in non-ε 4-carriers (P= 0.0056). High-cholesterol subjects that are ε 4 carriers have significantly higher levels of lipid peroxidation as assessed by circulating F-2-isoprostane levels.

State-space model identification of a wireless THz network

We model the large scale fading of wireless THz communications links deployed in a metropolitan area taking into account reception through direct line of sight, ground or wall reflection and diffraction. The movement of the receiver in the three dimensions is modelled by an autonomous dynamic linear system in state-space whereas the geometric relations involved in the attenuation and multi-path propagation of the electric field are described by a static non-linear mapping. A subspace algorithm in conjunction with polynomial regression is used to identify a Wiener model from time-domain measurements of the field intensity.

Nonlinear system identification using particle swarm optimisation tuned radial basis function models

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.

MIMO Wiener model identification for large scale fading of wireless mobile communications links

The large scale fading of wireless mobile communications links is modelled assuming the mobile receiver motion is described by a dynamic linear system in state-space. The geometric relations involved in the attenuation and multi-path propagation of the electric field are described by a static non-linear mapping. A Wiener system subspace identification algorithm in conjunction with polynomial regression is used to identify a model from time-domain estimates of the field intensity assuming a multitude of emitters and an antenna array at the receiver end.

Optimal piecewise locally linear modeling

Associative memory networks such as Radial Basis Functions, Neurofuzzy and Fuzzy Logic used for modelling nonlinear processes suffer from the curse of dimensionality (COD), in that as the input dimension increases the parameterization, computation cost, training data requirements, etc. increase exponentially. Here a new algorithm is introduced for the construction of a Delaunay input space partitioned optimal piecewise locally linear models to overcome the COD as well as generate locally linear models directly amenable to linear control and estimation algorithms. The training of the model is configured as a new mixture of experts network with a new fast decision rule derived using convex set theory. A very fast simulated reannealing (VFSR) algorithm is utilized to search a global optimal solution of the Delaunay input space partition. A benchmark non-linear time series is used to demonstrate the new approach.

Data based constructive identification - overcoming the curse of dimensionality

The modelling of a nonlinear stochastic dynamical processes from data involves solving the problems of data gathering, preprocessing, model architecture selection, learning or adaptation, parametric evaluation and model validation. For a given model architecture such as associative memory networks, a common problem in non-linear modelling is the problem of "the curse of dimensionality". A series of complementary data based constructive identification schemes, mainly based on but not limited to an operating point dependent fuzzy models, are introduced in this paper with the aim to overcome the curse of dimensionality. These include (i) a mixture of experts algorithm based on a forward constrained regression algorithm; (ii) an inherent parsimonious delaunay input space partition based piecewise local lineal modelling concept; (iii) a neurofuzzy model constructive approach based on forward orthogonal least squares and optimal experimental design and finally (iv) the neurofuzzy model construction algorithm based on basis functions that are Bézier Bernstein polynomial functions and the additive decomposition. Illustrative examples demonstrate their applicability, showing that the final major hurdle in data based modelling has almost been removed.

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.

Uncertainty in the relationship between climate forcing and hydrological response in UK catchments

This paper assesses the relationship between amount of climate forcing – as indexed by global mean temperature change – and hydrological response in a sample of UK catchments. It constructs climate scenarios representing different changes in global mean temperature from an ensemble of 21 climate models assessed in the IPCC AR4. The results show a considerable range in impact between the 21 climate models, with – for example - change in summer runoff at a 2oC increase in global mean temperature varying between -40% and +20%. There is evidence of clustering in the results, particularly in projected changes in summer runoff and indicators of low flows, implying that the ensemble mean is not an appropriate generalised indicator of impact, and that the standard deviation of responses does not adequately characterise uncertainty. The uncertainty in hydrological impact is therefore best characterised by considering the shape of the distribution of responses across multiple climate scenarios. For some climate model patterns, and some catchments, there is also evidence that linear climate change forcings produce non-linear hydrological impacts. For most variables and catchments, the effects of climate change are apparent above the effects of natural multi-decadal variability with an increase in global mean temperature above 1oC, but there are differences between catchments. Based on the scenarios represented in the ensemble, the effect of climate change in northern upland catchments will be seen soonest in indicators of high flows, but in southern catchments effects will be apparent soonest in measures of summer and low flows. The uncertainty in response between different climate model patterns is considerably greater than the range due to uncertainty in hydrological model parameterisation.

Measures of observation impact in non-Gaussian data assimilation

ABSTRACT Non-Gaussian/non-linear data assimilation is becoming an increasingly important area of research in the Geosciences as the resolution and non-linearity of models are increased and more and more non-linear observation operators are being used. In this study, we look at the effect of relaxing the assumption of a Gaussian prior on the impact of observations within the data assimilation system. Three different measures of observation impact are studied: the sensitivity of the posterior mean to the observations, mutual information and relative entropy. The sensitivity of the posterior mean is derived analytically when the prior is modelled by a simplified Gaussian mixture and the observation errors are Gaussian. It is found that the sensitivity is a strong function of the value of the observation and proportional to the posterior variance. Similarly, relative entropy is found to be a strong function of the value of the observation. However, the errors in estimating these two measures using a Gaussian approximation to the prior can differ significantly. This hampers conclusions about the effect of the non-Gaussian prior on observation impact. Mutual information does not depend on the value of the observation and is seen to be close to its Gaussian approximation. These findings are illustrated with the particle filter applied to the Lorenz ’63 system. This article is concluded with a discussion of the appropriateness of these measures of observation impact for different situations.

Nonlinear set-membership estimation: a support vector machine approach

In this paper a support vector machine (SVM) approach for characterizing the feasible parameter set (FPS) in non-linear set-membership estimation problems is presented. It iteratively solves a regression problem from which an approximation of the boundary of the FPS can be determined. To guarantee convergence to the boundary the procedure includes a no-derivative line search and for an appropriate coverage of points on the FPS boundary it is suggested to start with a sequential box pavement procedure. The SVM approach is illustrated on a simple sine and exponential model with two parameters and an agro-forestry simulation model.

An Earth Observation Land Data Assimilation System (EO-LDAS)

Current methods for estimating vegetation parameters are generally sub-optimal in the way they exploit information and do not generally consider uncertainties. We look forward to a future where operational dataassimilation schemes improve estimates by tracking land surface processes and exploiting multiple types of observations. Dataassimilation schemes seek to combine observations and models in a statistically optimal way taking into account uncertainty in both, but have not yet been much exploited in this area. The EO-LDAS scheme and prototype, developed under ESA funding, is designed to exploit the anticipated wealth of data that will be available under GMES missions, such as the Sentinel family of satellites, to provide improved mapping of land surface biophysical parameters. This paper describes the EO-LDAS implementation, and explores some of its core functionality. EO-LDAS is a weak constraint variational dataassimilationsystem. The prototype provides a mechanism for constraint based on a prior estimate of the state vector, a linear dynamic model, and EarthObservationdata (top-of-canopy reflectance here). The observation operator is a non-linear optical radiative transfer model for a vegetation canopy with a soil lower boundary, operating over the range 400 to 2500 nm. Adjoint codes for all model and operator components are provided in the prototype by automatic differentiation of the computer codes. In this paper, EO-LDAS is applied to the problem of daily estimation of six of the parameters controlling the radiative transfer operator over the course of a year (> 2000 state vector elements). Zero and first order process model constraints are implemented and explored as the dynamic model. The assimilation estimates all state vector elements simultaneously. This is performed in the context of a typical Sentinel-2 MSI operating scenario, using synthetic MSI observations simulated with the observation operator, with uncertainties typical of those achieved by optical sensors supposed for the data. The experiments consider a baseline state vector estimation case where dynamic constraints are applied, and assess the impact of dynamic constraints on the a posteriori uncertainties. The results demonstrate that reductions in uncertainty by a factor of up to two might be obtained by applying the sorts of dynamic constraints used here. The hyperparameter (dynamic model uncertainty) required to control the assimilation are estimated by a cross-validation exercise. The result of the assimilation is seen to be robust to missing observations with quite large data gaps.

Scale sensitivity of drivers of environmental change across Europe

The development of effective environmental management plans and policies requires a sound understanding of the driving forces involved in shaping and altering the structure and function of ecosystems. However, driving forces, especially anthropogenic ones, are defined and operate at multiple administrative levels, which do not always match ecological scales. This paper presents an innovative methodology of analysing drivers of change by developing a typology of scale sensitivity of drivers that classifies and describes the way they operate across multiple administrative levels. Scale sensitivity varies considerably among drivers, which can be classified into five broad categories depending on the response of ‘evenness’ and ‘intensity change’ when moving across administrative levels. Indirect drivers tend to show low scale sensitivity, whereas direct drivers show high scale sensitivity, as they operate in a non-linear way across the administrative scale. Thus policies addressing direct drivers of change, in particular, need to take scale into consideration during their formulation. Moreover, such policies must have a strong spatial focus, which can be achieved either by encouraging local–regional policy making or by introducing high flexibility in (inter)national policies to accommodate increased differentiation at lower administrative levels. High quality data is available for several drivers, however, the availability of consistent data at all levels for non-anthropogenic drivers is a major constraint to mapping and assessing their scale sensitivity. This lack of data may hinder effective policy making for environmental management, since it restricts the ability to fully account for scale sensitivity of natural drivers in policy design.