Towards a nutrient export risk matrix approach to managing agricultural pollution at source

A generic Nutrient Export Risk Matrix (NERM) approach is presented. This provides advice to farmers and policy makers on good practice for reducing nutrient loss and is intended to persuade them to implement such measures. Combined with a range of nutrient transport modelling tools and field experiments, NERMs can play an important role in reducing nutrient export from agricultural land. The Phosphorus Export Risk Matrix (PERM) is presented as an example NERM. The PERM integrates hydrological understanding of runoff with a number of agronomic and policy factors into a clear problem-solving framework. This allows farmers and policy makers to visualise strategies for reducing phosphorus loss through proactive land management. The risk Of Pollution is assessed by a series of informed questions relating to farming intensity and practice. This information is combined with the concept of runoff management to point towards simple, practical remedial strategies which do not compromise farmers' ability to obtain sound economic returns from their crop and livestock.

Very large inverse problems in atmosphere and ocean modelling

For the very large nonlinear dynamical systems that arise in a wide range of physical, biological and environmental problems, the data needed to initialize a numerical forecasting model are seldom available. To generate accurate estimates of the expected states of the system, both current and future, the technique of ‘data assimilation’ is used to combine the numerical model predictions with observations of the system measured over time. Assimilation of data is an inverse problem that for very large-scale systems is generally ill-posed. In four-dimensional variational assimilation schemes, the dynamical model equations provide constraints that act to spread information into data sparse regions, enabling the state of the system to be reconstructed accurately. The mechanism for this is not well understood. Singular value decomposition techniques are applied here to the observability matrix of the system in order to analyse the critical features in this process. Simplified models are used to demonstrate how information is propagated from observed regions into unobserved areas. The impact of the size of the observational noise and the temporal position of the observations is examined. The best signal-to-noise ratio needed to extract the most information from the observations is estimated using Tikhonov regularization theory. Copyright © 2005 John Wiley & Sons, Ltd.

Can 4D-Var use dynamical information from targeted observations of a baroclinic structure?

Targeted observations are generally taken in regions of high baroclinicity, but often show little impact. One plausible explanation is that important dynamical information, such as upshear tilt, is not extracted from the targeted observations by the data assimilation scheme and used to correct initial condition error. This is investigated by generating pseudo targeted observations which contain a singular vector (SV) structure that is not present in the background field or routine observations, i.e. assuming that the background has an initial condition error with tilted growing structure. Experiments were performed for a single case-study with varying numbers of pseudo targeted observations. These were assimilated by the Met Office four-dimensional variational (4D-Var) data assimilation scheme, which uses a 6 h window for observations and background-error covariances calculated using the National Meteorological Centre (NMC) method. The forecasts were run using the operational Met Office Unified Model on a 24 km grid. The results presented clearly demonstrate that a 6 h window 4D-Var system is capable of extracting baroclinic information from a limited set of observations and using it to correct initial condition error. To capture the SV structure well (projection of 0.72 in total energy), 50 sondes over an area of 1×106 km2 were required. When the SV was represented by only eight sondes along an example targeting flight track covering a smaller area, the projection onto the SV structure was lower; the resulting forecast perturbations showed an SV structure with increased tilt and reduced initial energy. The total energy contained in the perturbations decreased as the SV structure was less well described by the set of observations (i.e. as fewer pseudo observations were assimilated). The assimilated perturbation had lower energy than the SV unless the pseudo observations were assimilated with the dropsonde observation errors halved from operational values. Copyright © 2010 Royal Meteorological Society

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

The absorption of vitamin E is influenced by the amount of fat in a meal and the food matrix

Vitamin E absorption requires the presence of fat; however, limited information exists on the influence of fat quantity on optimal absorption. In the present study we compared the absorption of stable-isotope-labelled vitamin E following meals of varying fat content and source. In a randomised four-way cross-over study, eight healthy individuals consumed a capsule containing 150 mg H-2-labelled RRR-alpha-tocopheryl acetate with a test meal of toast with butter (17.5 g fat), cereal with full-fat milk (17.5 g fat), cereal with semi-skimmed milk (2.7 g fat) and water (0g fat). Blood was taken at 0, 0.5, 1, 1.5, 2, 3, 6 and 9 h following ingestion, chylomicrons were isolated, and H-2-labelled alpha-tocopherol was analysed in the chylomicron and plasma samples. There was a significant time (P<0.001) and treatment effect (P<0.001) in H-2-labelled alpha-tocopherol concentration in both chylomicrons and plasma between the test meals. H-2-labelled alpha-tocopherol concentration was significantly greater with the higher-fat toast and butter meal compared with the low-fat cereal meal or water (P< 0.001), and a trend towards greater concentration compared with the high-fat cereal meal (P= 0.065). There was significantly greater H-2-labelled α-tocopherol concentration with the high-fat cereal meal compared with the low-fat cereal meal (P< 0.05). The H-2-labelled alpha-tocopherol concentration following either the low-fat cereal meal or water was low. These results demonstrate that both the amount of fat and the food matrix influence vitamin E absorption. These factors should be considered by consumers and for future vitamin E intervention studies.

Absorption of isoflavones in humans: effects of food matrix and processing

If soy isoflavones are to be effective in preventing or treating a range of diseases, they must be bioavailable, and thus understanding factors which may alter their bioavailability needs to be elucidated. However, to date there is little information on whether the pharmacokinetic profile following ingestion of a defined dose is influenced by the food matrix in which the isoflavone is given or by the processing method used. Three different foods (cookies, chocolate bars and juice) were prepared, and their isoflavone contents were determined. We compared the urinary and serum concentrations of daidzein, genistein and equol following the consumption of three different foods, each of which contained 50 mg of isoflavones. After the technological processing of the different test foods, differences in aglycone levels were observed. The plasma levels of the isoflavone precursor daidzein were not altered by food matrix. Urinary daidzein recovery was similar for all three foods ingested with total urinary output of 33-34% of ingested dose. Peak genistein concentrations were attained in serum earlier following consumption of a liquid matrix rather than a solid matrix, although there was a lower total urinary recovery of genistein following ingestion of juice than that of the two other foods. (c) 2006 Elsevier Inc. All rights reserved.

Semantic-driven context-aware visual information indexing and retrieval: applied in the film post-production domain

A large volume of visual content is inaccessible until effective and efficient indexing and retrieval of such data is achieved. In this paper, we introduce the DREAM system, which is a knowledge-assisted semantic-driven context-aware visual information retrieval system applied in the film post production domain. We mainly focus on the automatic labelling and topic map related aspects of the framework. The use of the context- related collateral knowledge, represented by a novel probabilistic based visual keyword co-occurrence matrix, had been proven effective via the experiments conducted during system evaluation. The automatically generated semantic labels were fed into the Topic Map Engine which can automatically construct ontological networks using Topic Maps technology, which dramatically enhances the indexing and retrieval performance of the system towards an even higher semantic level.

Parallel Hybrid Monte Carlo Algorithms for Matrix Computations

In this paper we consider hybrid (fast stochastic approximation and deterministic refinement) algorithms for Matrix Inversion (MI) and Solving Systems of Linear Equations (SLAE). Monte Carlo methods are used for the stochastic approximation, since it is known that they are very efficient in finding a quick rough approximation of the element or a row of the inverse matrix or finding a component of the solution vector. We show how the stochastic approximation of the MI can be combined with a deterministic refinement procedure to obtain MI with the required precision and further solve the SLAE using MI. We employ a splitting A = D – C of a given non-singular matrix A, where D is a diagonal dominant matrix and matrix C is a diagonal matrix. In our algorithm for solving SLAE and MI different choices of D can be considered in order to control the norm of matrix T = D –1C, of the resulting SLAE and to minimize the number of the Markov Chains required to reach given precision. Further we run the algorithms on a mini-Grid and investigate their efficiency depending on the granularity. Corresponding experimental results are presented.

A sparse parallel hybrid Monte Carlo algorithm for matrix computations

In this paper we introduce a new algorithm, based on the successful work of Fathi and Alexandrov, on hybrid Monte Carlo algorithms for matrix inversion and solving systems of linear algebraic equations. This algorithm consists of two parts, approximate inversion by Monte Carlo and iterative refinement using a deterministic method. Here we present a parallel hybrid Monte Carlo algorithm, which uses Monte Carlo to generate an approximate inverse and that improves the accuracy of the inverse with an iterative refinement. The new algorithm is applied efficiently to sparse non-singular matrices. When we are solving a system of linear algebraic equations, Bx = b, the inverse matrix is used to compute the solution vector x = B(-1)b. We present results that show the efficiency of the parallel hybrid Monte Carlo algorithm in the case of sparse matrices.

Highly accurate detection of ovarian cancer using CA125 but limited improvement with serum matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling

Objectives: Our objective was to test the performance of CA125 in classifying serum samples from a cohort of malignant and benign ovarian cancers and age-matched healthy controls and to assess whether combining information from matrix-assisted laser desorption/ionization (MALDI) time-of-flight profiling could improve diagnostic performance. Materials and Methods: Serum samples from women with ovarian neoplasms and healthy volunteers were subjected to CA125 assay and MALDI time-of-flight mass spectrometry (MS) profiling. Models were built from training data sets using discriminatory MALDI MS peaks in combination with CA125 values and tested their ability to classify blinded test samples. These were compared with models using CA125 threshold levels from 193 patients with ovarian cancer, 290 with benign neoplasm, and 2236 postmenopausal healthy controls. Results: Using a CA125 cutoff of 30 U/mL, an overall sensitivity of 94.8% (96.6% specificity) was obtained when comparing malignancies versus healthy postmenopausal controls, whereas a cutoff of 65 U/mL provided a sensitivity of 83.9% (99.6% specificity). High classification accuracies were obtained for early-stage cancers (93.5% sensitivity). Reasons for high accuracies include recruitment bias, restriction to postmenopausal women, and inclusion of only primary invasive epithelial ovarian cancer cases. The combination of MS profiling information with CA125 did not significantly improve the specificity/accuracy compared with classifications on the basis of CA125 alone. Conclusions: We report unexpectedly good performance of serum CA125 using threshold classification in discriminating healthy controls and women with benign masses from those with invasive ovarian cancer. This highlights the dependence of diagnostic tests on the characteristics of the study population and the crucial need for authors to provide sufficient relevant details to allow comparison. Our study also shows that MS profiling information adds little to diagnostic accuracy. This finding is in contrast with other reports and shows the limitations of serum MS profiling for biomarker discovery and as a diagnostic tool

Extreme value distribution for singular measures

In this paper we perform an analytical and numerical study of Extreme Value distributions in discrete dynamical systems that have a singular measure. Using the block maxima approach described in Faranda et al. [2011] we show that, numerically, the Extreme Value distribution for these maps can be associated to the Generalised Extreme Value family where the parameters scale with the information dimension. The numerical analysis are performed on a few low dimensional maps. For the middle third Cantor set and the Sierpinskij triangle obtained using Iterated Function Systems, experimental parameters show a very good agreement with the theoretical values. For strange attractors like Lozi and H\`enon maps a slower convergence to the Generalised Extreme Value distribution is observed. Even in presence of large statistics the observed convergence is slower if compared with the maps which have an absolute continuous invariant measure. Nevertheless and within the uncertainty computed range, the results are in good agreement with the theoretical estimates.

High Matrix Metalloproteinase Activity Is a Hallmark of Periapical Granulomas

Introduction: The inability to distinguish periapical cysts from granulomas before performing root canal treatment leads to uncertainty in treatment outcomes because cysts have lower healing rates. Searching for differential expression of molecules within cysts or granulomas could provide information with regard to the identity of the lesion or suggest mechanistic differences that may form the basis for future therapeutic intervention. Thus, we investigated whether granulomas and cysts exhibit differential expression of extracellular matrix (ECM) molecules. Methods: Human periapical granulomas, periapical cysts, and healthy periodontal ligament tissues were used to investigate the differential expression of ECM molecules by microarray analysis. Because matrix metalloproteinases (MMP) showed the highest differential expression in the microarray analysis, MMPs were further examined by in situ zymography and immunohistochemistry. Data were analyzed by using one-way analysis of variance followed by the Tu-key test. Results: We observed that cysts and granulomas differentially expressed several ECM molecules, especially those from the MMP family. Compared with cysts, granulomas exhibited higher MMP enzymatic activity in areas stained for MMP-9. These areas were composed of polymorphonuclear cells (PMNs) in contrast to cysts. Similarly, MMP-13 was expressed by a greater number of cells in granulomas compared with cysts. Conclusion: Our findings indicate that high enzymatic MIMP activity in PMNs together with MMP-9 and MMP-13 stained cells could be a molecular signature of granulomas unlike periapical cysts. (J Endod 2009;35:1234-1242)

Importance of estimating matrix quality for modeling species distribution in complex tropical landscapes: a test with Atlantic forest small mammals

Information to guide decision making is especially urgent in human dominated landscapes in the tropics, where urban and agricultural frontiers are still expanding in an unplanned manner. Nevertheless, most studies that have investigated the influence of landscape structure on species distribution have not considered the heterogeneity of altered habitats of the matrix, which is usually high in human dominated landscapes. Using the distribution of small mammals in forest remnants and in the four main altered habitats in an Atlantic forest landscape, we investigated 1) how explanatory power of models describing species distribution in forest remnants varies between landscape structure variables that do or do not incorporate matrix quality and 2) the importance of spatial scale for analyzing the influence of landscape structure. We used standardized sampling in remnants and altered habitats to generate two indices of habitat quality, corresponding to the abundance and to the occurrence of small mammals. For each remnant, we calculated habitat quantity and connectivity in different spatial scales, considering or not the quality of surrounding habitats. The incorporation of matrix quality increased model explanatory power across all spatial scales for half the species that occurred in the matrix, but only when taking into account the distance between habitat patches (connectivity). These connectivity models were also less affected by spatial scale than habitat quantity models. The few consistent responses to the variation in spatial scales indicate that despite their small size, small mammals perceive landscape features at large spatial scales. Matrix quality index corresponding to species occurrence presented a better or similar performance compared to that of species abundance. Results indicate the importance of the matrix for the dynamics of fragmented landscapes and suggest that relatively simple indices can improve our understanding of species distribution, and could be applied in modeling, monitoring and managing complex tropical landscapes.

A Time Efficient Optical Model for GATE Simulation of a LYSO Scintillation Matrix Used in PET Applications

A time efficient optical model is proposed for GATE simulation of a LYSO scintillation matrix coupled to a photomultiplier. The purpose is to avoid the excessively long computation time when activating the optical processes in GATE. The usefulness of the model is demonstrated by comparing the simulated and experimental energy spectra obtained with the dual planar head equipment for dosimetry with a positron emission tomograph ( DoPET). The procedure to apply the model is divided in two steps. Firstly, a simplified simulation of a single crystal element of DoPET is used to fit an analytic function that models the optical attenuation inside the crystal. In a second step, the model is employed to calculate the influence of this attenuation in the energy registered by the tomograph. The use of the proposed optical model is around three orders of magnitude faster than a GATE simulation with optical processes enabled. A good agreement was found between the experimental and simulated data using the optical model. The results indicate that optical interactions inside the crystal elements play an important role on the energy resolution and induce a considerable degradation of the spectra information acquired by DoPET. Finally, the same approach employed by the proposed optical model could be useful to simulate a scintillation matrix coupled to a photomultiplier using single or dual readout scheme.

Normalization procedure for relaxation studies in NMR quantum information processing

NMR quantum information processing studies rely on the reconstruction of the density matrix representing the so-called pseudo-pure states (PPS). An initially pure part of a PPS state undergoes unitary and non-unitary (relaxation) transformations during a computation process, causing a ""loss of purity"" until the equilibrium is reached. Besides, upon relaxation, the nuclear polarization varies in time, a fact which must be taken into account when comparing density matrices at different instants. Attempting to use time-fixed normalization procedures when relaxation is present, leads to various anomalies on matrices populations. On this paper we propose a method which takes into account the time-dependence of the normalization factor. From a generic form for the deviation density matrix an expression for the relaxing initial pure state is deduced. The method is exemplified with an experiment of relaxation of the concurrence of a pseudo-entangled state, which exhibits the phenomenon of sudden death, and the relaxation of the Wigner function of a pseudo-cat state.