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Motivation: Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles.
Results: A novel algorithm for the inference of biomolecular interaction networks from temporal expression data is presented. Linear time-varying models, which can represent a much wider class of time-series data than linear time-invariant models, are employed in the algorithm. From time-series expression profiles, the model parameters are identified by solving a non-linear optimization problem. In order to systematically reduce the set of possible solutions for the optimization problem, a filtering process is performed using a phase-portrait analysis with random numerical perturbations. The proposed approach has the advantages of not requiring the system to be in a stable steady state, of using time-series profiles which have been generated by a single experiment, and of allowing non-linear network interactions to be identified. The ability of the proposed algorithm to correctly infer network interactions is illustrated by its application to three examples: a non-linear model for cAMP oscillations in Dictyostelium discoideum, the cell-cycle data for Saccharomyces cerevisiae and a large-scale non-linear model of a group of synchronized Dictyostelium cells.
Availability: The software used in this article is available from http://sbie.kaist.ac.kr/software
Supplementary information: Supplementary data are available at Bioinformatics online.
Biomolecular regulatory networks can contain complicated non-linear interactions amongst many different molecules, where the result is not simply the sum of multiples of the inputs. Functional regulation in such systems involves multiple interactions between inputs and intermediates, forming a non-linear network. Depending on the number of different interactions involved, such networks can be modelled using stochastic equations, ordinary differential equations, or undirected/directed graphs with nodes and edges (Bornholdt, 2005). The problem of inferring biomolecular networks from time-series expression profiles so as to obtain a quantitative understanding of the underlying biomolecular mechanisms is a fundamental one for Systems Biology, and has been geared up by the ever increasing amount of high-throughput experimental data from real biomolecular networks (Cho et al., 2007).
Many different methods for inferring biomolecular networks have been proposed in the recent literature. They can roughly be divided into two main categories. The first uses perturbation experiments about a steady state to generate a Jacobian matrix which is then used to infer the network interactions (Bansal et al., 2006; Cho et al., 2005; Kholodenko et al., 2002; Schmidt et al., 2005; Sontag et al., 2004; Tegner et al., 2003). The perturbation-based approach requires several assumptions to be made about the system, such as the existence of a steady state and the possibility of conducting experiments which achieve just the right level of perturbation in the system. This means that such approaches are not suitable for application to biomolecular regulatory networks which do not have a stable steady state, such as those which display oscillatory dynamics, e.g. limit cycles. It is also well known that the size of the perturbation is critical for the success of these methods. If the perturbation is too large, the resulting kinetics are no longer near the original steady state and the inferred network may display completely different characteristics. On the other hand, if it is too small, then the resulting data may not contain sufficient information for the algorithm to infer the network interactions correctly. In addition, each additional perturbation experiment entails additional time and effort, and in some cases, multiple experiments may not even be possible.
The second category of inference methods employ probabilistic approaches, such as Bayesian methods, which uncover the network interactions using causal relations between the states. However, many such approaches can only infer network structures correctly when there are no feedback loops in the networks, i.e. the resulting networks are directed acyclic graphs (Chen et al., 2006; Friedman, 2004). Although dynamical Bayesian methods can overcome these problems, the resulting problem formulation needs to be solved using global optimization algorithms such as greedy search, simulated annealing and genetic algorithms (Yu et al., 2004), which are computationally expensive. This problem was partially addressed in Zhao et al. (2006) where dynamical Bayesian networks and probabilistic Boolean networks are combined to reduce the parametric search space for inferring the network. However, the amount of calculation required still quickly expands as the number of nodes increases, thus dividing large networks into several relatively small networks.
Since most biomolecular networks are inherently non-linear and the average interactions of individual molecules generally behave in a continuous fashion, it would seem appropriate to formulate such problems as a non-linear system identification problem using ordinary differential equations. However, unlike the identification problem for linear time-invariant systems, non-linear system identification problems are ill-posed in general, because not only the coefficients in the system but also the system structure must be estimated. System identification using linear time-invariant models, on the other hand, simply involves finding an optimal set of coefficients for a given model, since the model structure is fixed in advance. In the literature, there are many well-developed theoretical results for the linear system identification problem (Ljung, 1999). Any model of the system derived using purely linear approaches will, however, not be able to accurately represent the true non-linear behaviour of the real biomolecular network. For example, many biomolecular regulatory networks produce robust and stable limit cycle dynamics, circadian rhythms (Goldbeter, 1995), cAMP oscillation in Dictyostelium discoideum (Laub and Loomis, 1998), Ca2 + oscillations (Dellen et al., 2005). Such oscillatory dynamics cannot be produced by purely linear time-invariant systems, and therefore the underlying network can never be accurately identified using linear time-invariant models.
In previous work, Cho et al. (2006) proposed a simple but highly effective algorithm to infer biomolecular networks from just one set of time-series data using phase portrait analysis, where the phase portrait is a state trajectory on a plane spanned by the state values. Because of its simplicity it can be applied to very large-scale networks, but non-linear actions still cause difficulty for this approach as it is based purely on the use of linear models. In addition, any self interactions in the network cannot be identified using this approach. In order to address the above issues, in this article we adopt linear time-varying systems as our model for inferring biomolecular networks. Although linear time-varying systems are still in a linear form, they have a much richer range of dynamic responses than linear time-invariant systems. Hence, a wider range of time-series expression profiles, including oscillatory trajectories, can be approximated by such models.
Using linear time-varying models, the network inference problem is formulated as an optimization problem. The cost, which is the sum of the error between the measurement data and the states generated by the model, is minimized using an optimization algorithm and the optimal set of coefficients for the model is obtained. To the authors' knowledge, this is the first time that the use of linear time-varying systems for inferring biomolecular networks has been proposed. Moreover, to generate the cost function for the optimization algorithm, we use only time-series expression profiles that may be obtained by a single experiment. To reduce the possible number of solutions for the network, we propose an algorithm based on random perturbations, where the perturbations are numerical ones to the measurement data. Previous efforts to address this problem have attempted to exploit the sparsity property of biomolecular networks to reduce the number of alternative solutions. While many biomolecular networks including gene networks, protein–protein interactions, and metabolic networks are indeed sparsely connected (Gardner et al., 2005; Yeung et al., 2002), the levels of sparsity will vary from network to network, and moreover, measurement data from biological experiments will include noise whose statistical properties are not generally known. In addition, it is impossible to infer all interactions correctly using a single set of data. Therefore, robust inferring algorithms must give consistent answers for lightly perturbed measurements. In our approach, phase portraits are used to quantify the relative effect of each element in the network, and based on the results from several random perturbation the inconsistent network interactions are identified and declared as unidentifiable nodes. Inferring is then performed for the remaining consistent network connections.
Three biomolecular network systems are used as examples in the article for testing the proposed inferring algorithm. The first is an in silico example, the Laub–Loomis model for D.discoideum cAMP oscillation (Laub and Loomis, 1998). This model captures many of the most important features of several classes of biomolecular networks that oscillate with constant frequencies. The second is an in vivo example, the cell cycle of the budding yeast cell, Saccharomyces cerevisiae, for which we use the actual experimental data for inferring the cell-cycle network (Spellman et al., 1998). The third is a large size network example, a group of D.discoideum cells. Note that the biomolecular network underlying cAMP oscillations in D.discoideum cannot be inferred from any of the current perturbation-based approaches found in the literature, since, during this particular phase of its development it does not have steady states but displays limit cycle oscillations. Hence, the Jacobian, which gives the local network interactions, is not constant but periodically time varying (Kim et al., 2006; Ma and Igiesias, 2002), and there is currently no method that can identify a time-varying Jacobian from time-series data. For the second example, the cell-cycle data is generated from time-series expression profiles of gene levels for which there are no experimental perturbation data provided. Hence, perturbation-based approaches are also not applicable for this system.
2.1 Approximating non-linear interactions using linear time-varying models
The dynamics of most biomolecular regulatory networks arise from complex biomolecular interactions which are non-linear, and can be written as follows:is corrupted by some measurements noise vi(tk) and tk is the sampling time. Typically, in experiments the sampling interval tk + 1 − tk is not necessarily the same for all k and the statistical properties of the noise are also generally unknown.
The estimation of fi(·), which involves fitting the time profile of the states and finding the structure of the function, is an ill-posed problem. On the other hand, if the model is assumed to be linear time invariant as follows:2006):
2.2 Optimization problem and reducing the problem dimension and measurement noise effects
By using the linear time-varying model, the following optimization problem can be formulated:4), where is the numerically perturbed measurement, which will be defined later, and xi(t) is the solution of (4). For a fixed i, the number of parameters to be estimated is 3n + 2 including the initial condition of (4), xi(t1). The only computational limitation on the size of problems which may be solved by our algorithm is that arising from the optimization algorithm chosen to solve (6). In this article, we show that a standard simplex search method implemented in MATLAB (Mathworks, 2007) is able to cope with even relatively large-scale problems. However, if the number of nodes is greater than several hundred, optimization algorithms which have been specifically developed for problems involving large numbers of decision variables would be more appropriate, e.g. the simultaneous perturbation algorithm proposed in Spall (2003).
Note that the cost function is normalized by the maximum value of the measurements of each state. The optimization problem is formulated separately for each dxi(t)/dt in order to reduce the rate of increase in the number of parameters to be estimated as the dimension of xi(t) increases. If the problem is formulated for all xi(t), the number of parameters increases according to 3n² + n + 1. The price to be paid for reducing the number of parameters to be computed in this way is the increased effect of noise, since in order to solve (4) all the states except xi(t) have to be interpolated from the measurements and this will necessarily introduce the direct effect of noise on the estimate. To reduce this effect some elements of the noise could be filtered out before the data are used in the interpolation, e.g. by using Principal Component Analysis (Sanguinetti, et al., 2005), or the noisy measurements could be replaced by the solution of the differential equation, (4). After the optimal solution is obtained for (6), if the optimal cost is smaller than a certain bound, for example 10% of the maximum of the measurements, the measurements are replaced by the solution of the differential equations, under the assumption that the model gives less noisy data without deteriorating the original measurements significantly. As the optimization problem is solved from x1 to xn, more measurements may be replaced. At the final stage, all measurements except the measurements for xn could be replaced by the filtered states. To remove the unbalanced noise effect, the same procedure is repeated in the opposite direction, i.e. starting from xn − 1 to x1 since the earlier states may be affected more by the noise. The cost bound should be a bigger number when the measurement noise is expected to be large. However, if the measurement is too noisy, then the inferring results cannot be good. On the other hand, if the measurement noise is mild, the high frequency noise can be removed by replacing the measurement by the model output. The 10% bound is the reasonable level for removing the mild noise effect and it is fixed to 0.1.
Note that (6) is a very flexible expression. It can cope with cases where the sampling time is not evenly distributed, and weighting can also be used in the cost when the error bar at each sampling time is given. The optimization problem is, however, non-linear, and hence it may have many local solutions. If biologically plausible ranges for the parameters are known, these can be used in choosing a better initial guess for the optimization, in order to improve the chances of finding the globally optimal solution. Initial values for the parameters may also be chosen by inspecting the finite difference magnitude of , since, although the data are corrupted by noise, the rate of change of xi(t) should still not be very different from the magnitude of the finite difference. The initial guess for ω can be obtained by calculating the dominant frequency of the measurement data using Fourier transforms (Proakis and Manolakis, 2006).
2.3 Measuring the strength of interactions using phase portraits
The phase portrait of a system is a trajectory of states drawn on a plane. Phase portraits have long been used as a useful graphical tool in dynamical systems theory. Usually, a 2D phase portrait is drawn from two states and several dynamic properties such as the location and the stability of equilibriums, the region of attraction of equilibrium points, etc, are inspected (Khalil, 2002). The idea of inferring biomolecular interactions using phase portraits was first presented in Cho et al. (2006). In the article, the slope index of two states, xi(t) and xj(t), was defined as the average slope of the curve drawn by xi(t) and xj(t) on the phase portrait. By inspecting the sign of the slope for two given time points, i.e. [xj(tk + 1) − xj(tk)] / [xi(tk + 1) − xi(tk)], it can be decided whether the slope is tilted to the right or left. The average value is then calculated from multiple points on the curve. Depending on the sign of the slope index, it can hence be deduced whether xi(t) activates or inhibits xj(t).
In this article, we adapt this approach to the case of time-varying models. The slope index is now defined using xj(t) and aij(t)xj(t), since we are considering the direct effect of xj(t) on the time derivative of xi(t). The slope index, SI(·, ·), is therefore defined as follows:Figure 1, the slope index can be seen as the slope of the dashed line passing through the phase portrait. A positive SI means that when xj(t) increases or decreases, the time derivative of xi(t) increases or decreases in a synchronous fashion, hence, we can say xj(t) activates xi(t). Correspondingly, a negative SI means that xj(t) inhibits xi(t). While the slope index indicates the type of reaction, i.e. activation or inhibition, the following indicates the magnitude of the reaction, i.e. how much xj(t) affects xi(t): Figure 1, for example, since ρ12 is significantly smaller than ρ11, the effect of x2(t) on dx1(t)/dt could be negligible compared to the effect of x1(t) on dx1(t)/dt.
SI and ρij are multiplied by each other and the result is defined as the interaction strength (IS) matrix, whose ith row and jth column element is given by
2.4 Inferring the biomolecular interactions using random perturbations
The network interactions could be inferred by inspecting the sign of the elements of the IS matrix. However, there is a fundamental difficulty in inferring biomolecular interaction networks. The chances of finding the correct combinations of aij(t) values to represent the network are very low because of the presence of multiple solutions. Even if an algorithm shows a high success ratio for a specific example, this may simply have happened by chance and the same algorithm may show bad performance in a different example. In addition, it is clear that the set of multiple solutions cannot be reduced to one unique solution when just using one set of time-series profiles, except for some special cases. Hence, an inferring algorithm, when used with one set of data, must decide whether a specific connection can be identifiable. If it is not identifiable, it is declared as an unknown. If it is identifiable, then it is inferred further whether it is activation, inhibition or no connection. To do this, artificial noise is added into the measurements as follows:6), is solved. The larger σw, the larger the terms in the IS matrix will fluctuate. The amount of the fluctuation shows the level of robustness of the interaction to be inferred. To obtain the mean and the SD of each term in the IS matrix with artificial perturbations in the measurements, the optimization problem is solved N times with different random artificial noises.
To remove the interactions that are not easy to identify, four steps of filtering are performed to construct the interaction matrix (IM) based on the mean and the SD of the IS matrices obtained from the N optimizations with different sets of random perturbation of the measurements. That is, for each optimization process, an IS matrix is obtained. Since the measurements are perturbed randomly for each optimization process, the N IS matrices may be slightly or significantly different from each other. The higher the variation is, the less the confidence is. This filtering procedure allows us to find the connections that cannot be decided consistently for a given measurement dataset, or which are too weak for it to be said that any directional connection exists. Firstly, all connections having large SDs are declared as being unidentifiable.is the mean value and γm is a threshold in [0,1]. As γm approaches 1, more nodes are declared as being disconnected. Thirdly, Supplementary Material.
if IM(i , j) is empty, it is declared as unidentifiable,
if sign[IM(i,j)] > 0, xj(t) activates xi(t),
if sign[IM(i,j)] < 0, xj(t) inhibits xi(t) and
if IM(i, j) is zero, there is no direct interaction, from xi(t) to xj(t).
The algorithm is tested using two small size examples: a D.discoideum cAMP oscillations model and the S.cerevisiae cell-cycle data, and one large size example: 15 synchronized Dictyostelium cells. Dictyostelium and Saccharomyces were also used to test an inferring algorithm based on the use of epistasis analysis in Van Driessche et al. (2005) and St Onge et al. (2007). Further details about the method of epistasis analysis can be found in Huges (2005). In the following results, the optimization problem, (6), was solved using a simplex search method in the MATLAB (Mathworks, 2007).
3.1 In silico example: Dictyostelium discoideum cAMP oscillations
Dictyostelium discoideum cells signal each other by emitting stable oscillations of cAMP at the beginning of the aggregation phase of their development. The oscillations continue during chemotaxis towards higher gradients of cAMP concentration as D.discoideum aggregate to survive. A model for the biomolecular network underlying the stable oscillations in cAMP was postulated by Laub and Loomis (1998). The corresponding non-linear differential equations are given in the Supplementary Material.
The Laub–Loomis model exhibits a stable limit cycle, i.e. whenever the states are perturbed, they immediately converge back into the limit cycle. This type of trajectory cannot be modelled using a linear time-invariant system. In fact, the Jacobian along the limit cycle is periodically time varying, and hence the estimation of the Jacobian is also a very difficult problem. Here, without any perturbation experiment, a total of 16 measurements for each state are generated with a sampling time interval of 2 min, where the measurement noise vi(tk) is the zero-mean Gaussian distribution with the variance equal to the 5% of the mean value of {xi(t1),xi(t2),…,xi(tp)}. Note that the statistical properties of vi(tk) are unknown in general. Some measurement examples are shown in Figure S1 (Supplementary Material). Using this set of measurements, the non-linear system is assumed to be in the linear time-varying form given by (4) with the coefficients (5) and the optimization problem, (6), is then solved with the numerically perturbed measurement given by (10). The artificial perturbation wi(tk) is added, which is the zero-mean Gaussian and the variance equal to the 5% of the mean value of . After the optimization problem is solved, the amount of effect from each xj(t) to xi(t), i.e. ρij, and the slope index is identified. This procedure is repeated 10 times (N = 10) and the inferring procedure is performed with 10-IS matrices.
The variation in the success ratios with respect to the thresholds are shown in Figure 2 after performing 100 Monte Carlo simulations with a different set of measurements for each simulation. The first column shows the success ratio between the number of nodes that are inferred as activation and the nodes that are indeed activation. The second column shows the success ratio for the inhibition and the third column shows the success ratio for no connection. Each row gives the mean, the worst and the best results for the 100 Monte Carlo simulations. The worst is the 3σ bound, which is three times the SD of the success ratios for the Monte Carlo simulations, and the corresponding threshold values guarantee that the success ratio is higher than the worst bound with the probability 99.73%. The true positive and negative success ratios from the dynamic Bayesian approaches are 22% and 76%, where activation and inhibition are not distinguished but only the existence of connections are (Cho et al., 2006). These boundaries are indicated by the solid lines in the first row. For a wide range of the threshold values, the success ratios could be higher than those generated by the Bayesian approaches.
3.2 In vivo example: Saccharomyces cerevisiae cell cycle
The cell cycle, in which a cell duplicates its contents and divides into two, is an essential activity of all life and involves some highly elaborate regulatory mechanisms. The cell cycle is divided into four phases, which are genome duplication (S-phase), nuclear division (mitosis or M-phase) and two gap phases (G1- and G2-phase) (Bähler, 2005). In this example, S.cerevisiae cell-cycle microarray data is used to infer the underlying biomolecular network interactions (Spellman, 1998). The measurements include a total of 17 gene-expression levels with 16 samples each, and the complete network interactions are shown in Figure S2 (Supplementary Material). This network is chosen because their interactions are well studied (Bähler, 2005). The samples are from α Factor-based synchronization experiment. As noted by Friedman (2004), this is a very difficult problem, due to the fact that we are attempting to infer the interaction networks solely from the gene-expression levels, without any information on protein activity levels. This is a problem because many activations and inhibitions are performed through post-transcriptional protein modifications. Such information is, however, usually unavailable from current measurement technology, as in the case of the cell-cycle data used here. This represents a fundamental limitation when biomolecular networks are to be inferred from gene-expression levels only (Friedman, 2004).
The inferring is performed for the given experimental data, where N = 10 and wi(tk) is the zero-mean Gaussian with the variance equal to 5% of the mean of . The inferring result with γs = 0.6 and γm = 0.18 is shown in Figure S2 (Supplementary Material) and a part of the inferring matrix is shown in Table 1. The true activation/inhibition and the true negative ratios for different threshold values are shown in Figure S3 (Supplementary Material) The figure is generated with 50 Monte Carlo simulations of the algorithm for the same measurement set. On average, the true activation ratio is above a 20% success ratio for a wide region of the threshold values. The true inhibition ratio has a smaller region where the ratio is above 20%. On the other hand, because of the sparse property of the network, the true negative ratio remains high for most of the threshold values.
The incorrect inferring results are given in grey and the correct ones are given in bold. The matrix is also used for designing some perturbation experiments. For example, since the first and the fifth columns have the most number of inconclusive elements, we may want to perturb either Ace2 or Cln1 to maximize the possible information to be obtained.
3.3 In silico large size example: multiple Dictyostelium discoideum cells cAMP oscillations
To test the algorithm for large size problems, the extended model for Dictyostelium cAMP oscillations is used (Kim et al., 2007). The model includes the effect of interactions between multiple cells (See Supplementary Material). For 15 cells, the number of states is 105 (15 × 7) and the number of edges to be inferred is 11 025 (105 × 105). The algorithm calculation time for N = 10 is about 64 hours in Windows XP, Intel Core2 Quad CPU, 2.66 GHz and 2.75 GB of RAM and the simplex method is used for solving the optimization problem. The calculation time will be reduced significantly by using some parallel computing technique and/or randomization approach for the optimization. The inferring results is shown in Figure S4 (Supplementary Material). The true activation and the true inhibition rate are lower compared to the previous examples as we expected since the number of possible network connections are increased. However, the true negative ratio stays high since the network satisfies the sparse property, which can be applied for many biomolecular networks.
We presented a novel algorithm for inferring biomolecular networks, which uses linear time-varying models. The algorithm uses phase portraits to construct a systematic filtering method to reject the inconsistent elements in the network, based on numerical perturbations. The algorithm was tested on three examples: the Laub–Loomis model for D.discoideum cAMP oscillation, the cell cycle of budding yeast cell, S.cerevisiae and the group of Dictyostelium cells. The success ratios are sensitive to two threshold values in the algorithm, which are for finding nodes that are not connected or inconsistent with the perturbation. If the first threshold is too small, most of interactions will be considered to be connected. On the other hand, if it is too large, most of them will be considered to be disconnected. Hence, ideally, the threshold for finding no connection has to be between 0.1 and 0.5 given that in general biological networks are not fully connected. The value of the threshold for rejection depends on the richness of the given measurement data and the number of types of genes. It should be inversely proportional to the richness of the data and proportional to the number of types of genes. Since there is no clear way to quantify the richness of signals, the threshold can in practice be decided purely based on the number of types of genes, since the greater the number of types of genes, the higher is the number of possible network connections. Hence, the threshold must be larger as the number of types of genes increases. This can be clearly seen in three examples considered in this article. Dictyostelium has seven different type of molecules while Saccharomyces has 17 and the group of Dictyostelium has 105. As shown in the figures, the highest success ratios for Saccharomyces and the group of Dictyostelium are achieved with a higher value of the rejection bound than was seen for Dictyostelium.
The proposed algorithm has many practical advantages and potential extensions. First, the error bars or noise in the measurement can be weighted appropriately when the cost function is formulated. Second, the measurements do not need to be evenly spaced in time. Third, the algorithm can infer not only feedback paths but also self activations or inhibitions in networks. Fourth, the number of unknowns to be estimated by the optimization algorithm does not increase exponentially with the number of nodes in the network. Fifth, only time-series expression profiles are used and no perturbation experiments are required. Sixth, inferring results can be used to design some optimal perturbation experiments, Finally, the linear time-varying models employed by the algorithm can effectively capture many non-linear interactions, which cannot be approximated by linear time-invariant models, including limit cycle oscillations.
This study was initiated during a visit of the the first author to the K.-H.Cho's; Systems Biology Laboratory in Korea. The first author appreciates the hospitality and many helpful and interesting discussions with the lab members.
Funding: This work was carried out under UK EPSRC Research Grant GR/S61874/01 and BBSRC Research Grant BB/D015340/1. It was supported by a Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MOST) (M10503010001-07N030100112) and also supported by the Korea Ministry of Science and Technology through a Nuclear Research Grant (M20708000001-07B0800-00110) and the 21C Frontier Microbial Genomics and Application Center Program.
Conflict of Interest: none declared.


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Aerial Cinematography
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What should one make of the indicators coming out of Beijing that have regularly fallen short of market expectations; the most recent being an August PMI – further revised downwards – showing manufacturing intentions hitting a nine-month low?
No need for Chinese contortions
As China attempts to rebalance its economy, data indicators continue to fall short of expectations. Beijing needs to tread a fine line, without major adjustments, to achieve the desired structural transition.
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Young girls who were diagnosed ADHD have a higher risk of adult obesity, a new study said.
When surgical trainees choose to work longer hours, the quality of patient care does not decline, a new study reported.
A startling report by the Nordic Cochrane Center and University College London (UCL) revealed a startling conclusion linking anti-depressants with doubled suicide risks in children and teens.
A promising new treatment known as proton treatment for children with brain tumors offer the same level of efficacy as the conventional photon radiotherapy but with significantly lower IQ loss and fewer side effects.
A breakthrough study has revealed how some genes can affect the development of Schizophrenia, a complicated psychiatric condition that the scientists have spent years in understanding
A landmark schizophrenia research published in the scientific journal Nature offers promising results that would finally de-mystify the psychiatric disorder and its causes.
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A new study has made researchers create monkeys with a rare human gene related to autism.
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A new research shows that improperly managed deliveries may not be the main reason for brain-damaged babies.
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If you are a kind and considerate driver, chances are you will live longer, says the new study conducted by Queensland researchers. They are the first study to show how aggressive driving can have a poor long term impact
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Welcome to NCAA After the Game Career Center
Development Coordinator - New Opportunity for 2017
American Academy of Child & Adolescent Psychiatry
The primary responsibility is to provide administrative and fundraising support to the Development Department. This includes coordinating, executing, and maintaining the Departments fundraising initiatives and activities that support and advance the vision, mission, and impact of AACAP.
Coordinates and Implements Donor Acknowledgement, Stewardship Activities, and Fundraising Metrics:
Supports Development Programs and Initiatives
Industry and Corporate Sponsorship Liaison
Provides Support to the Deputy Director of Development
Additional Salary Information: This is an entry level professional position that requires some work/volunteer experience. Compensation will be competitive with other similar entry level positions in not-for-profit association space. Cover letter must include starting wage expectations for immediate consideration.
Internal Number: AEZ_01062017
About American Academy of Child & Adolescent Psychiatry
The Mission of the American Academy of Child and Adolescent Psychiatry is to promote the healthy development of children, adolescents, and families through advocacy, education, and research, and to meet the professional needs of child and adolescent psychiatrists throughout their careers. The Academy is a 501(c)(3) non-profit membership based organization, composed of over 8,000 child and adol...escent psychiatrists and other interested physicians. Its members actively research, evaluate, diagnose, and treat psychiatric disorders and pride themselves on giving direction to and responding quickly to new developments in addressing the health care needs of children and their families.
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6)Investigations/Info Analysis 7)Business Continuity Management 8)Nodal &Regulatory- Telecom
6)Investigations/Info Analysis 7)Business Continuity Management 8)Nodal &Regulatory- Telecom
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Polymer MIS - Market Intelligence Source - Mumbai
405, DURGA, A-8 Veera Indl. Estate, Off Veera Desai Road, Behind Balaji Telefilms, Andheri (W),. Mumbai. Mumbai. Show phone number
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Brass Copper & Alloy India Limited - Mumbai
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1992 self-starter, dedicated engineers ventured to seed the Asia's largest industry of Cable Accessories and Connectors. The values set further then, has been...
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Kbm Extrusion Machines Pvt. Ltd. - Mumbai
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B/3, Nand Jyot Industrial Estate, Safed Pool, Sakinaka, Andheri (east). Mumbai. Mumbai. Show phone number
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Poli-film India Pvt. Ltd. - Mumbai
No. 8, Hill Crest, N.s. Road No. 10, Juhu Scheme. Mumbai. Mumbai. Show phone number
Ultimate surface protection solutions provider, poli-film india pvt. Commenced its business operations in 2008. Since then, we have been serving customers as...
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Multi Plast Industries - Mumbai
10, Gobind Udyog Bhavan, Opp Model Town, B.r Road, Mulund(w), Mulund West. Mumbai. Mumbai. Show phone number
Using high quality raw materials like pet resin, pet preforms and plastic granules, our entire range of is widely appreciated for its light. multi plast industries...
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Prima Plastics Ltd. - Mumbai
41, National House, Saki-vihar Road, Powai, Andheri (east). Mumbai. Mumbai. Show phone number
Through our high quality , careful product development and planned marketin. Our product array includes chairs, baby and the likes, tables & stools, trolleys &...
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Star Enterprises - Mumbai
166, Khan Manzil, Modi Street, 1st Floor, Fort. Mumbai. Mumbai. Show phone number
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K. G. Industries - Mumbai
116, Damji Shamji Industrial Complex, Off Mahakali Caves Road, Near Sanghi Oxygen, Andheri (e), Midc Chakala. Mumbai. Mumbai. Show phone number
We, an overview owing to attractive range of and prompt services, k. The are made by using premium quality raw material that are procured from reputed vendors of...
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208, Sati Industrial Estate, I.b. Patel Road, Goregaon East, Goregaon East. Mumbai. Mumbai. Show phone number
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Nipa Industry - Mumbai
No. 255, Shanti Industrial Estate, 2nd Floor, S. N. Road, Near Tambe Nagar, Mulund (w), Mulund West. Mumbai. Mumbai. Show phone number
Plastic pharmaceutical tablet bottle, side handle bottle, blow molded plastic containers, we are providing packaging plastic bottles, plastic shampoo bottle,...
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YP - The Real Yellow PagesSM - helps you find the right local businesses to meet your specific needs. Search results are sorted by a combination of factors to give you a set of choices in response to your search criteria. These factors are similar to those you might use to determine which business to select from a local Yellow Pages directory, including proximity to where you are searching, expertise in the specific services or products you need, and comprehensive business information to help evaluate a business's suitability for you. "Preferred" listings, or those with featured website buttons, indicate YP advertisers who directly provide information about their businesses to help consumers make more informed buying decisions. YP advertisers receive higher placement in the default ordering of search results and may appear in sponsored listings on the top, side, or bottom of the search results page.
1. Michelle's Cajun Shack
100 Highway 332 WLake Jackson, TX 77566
2. Red Lobster
104 Highway 332 WLake Jackson, TX 77566
From Business: Fresh is in our food: Each day, you'll find an updated selection of fresh fish on our Today's Fresh Fish menu, not to mention live Maine lobster, freshly baked Ched…
3. Tokyo Japanese Seafood & Steakhouse
Excellent Chicken dinner, the rice is FABULOUS!! Love the Salad dressing.. We had take-out and the host was very nice, quick service. Great Exp…
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If you want to eat great seafood at a great price in a relaxed, come-as-you-are atmosphere, Lake Jackson Seafood is the place for you. Even the hear…
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6. Off The Hook Seafood Market
Great seafood & outstanding service. Mary, the owner, is a pleasure to do business with. Call ahead & she'll have the shrimp peeled for you!
7. China Town Restaurant
Average Cantonese buffet, nothing special. Best to go either right when lunch or dinner is open, as they tend to keep the same food out until compl…
8. Backman's Seafood & Takeout
222 Acacia StLake Jackson, TX 77566
9. Michelle's Cajun Shack
321 N Gulf BlvdFreeport, TX 77541
10. Talk About Good
2105 N Brazosport BlvdFreeport, TX 77541
11. On The River Restaurant
they were the nicest people and it wasn't high priced there were about 20 of us
12. Pier 30 Bar and Grill
1601 E Highway 332Freeport, TX 77541
From Business: Pier 30 Bar and Grill is a full bar and restaurant. Our kitchen is open daily from 11 am to 10 pm, and the bar is open daily 11 am to 2 am. We have both indoor and …
13. Mediterranean Gardens Cafe
1130 Highway 332Clute, TX 77531
14. Red Lobster
Pretty nice place. Not bad at all. Staff were nice and very helpful when it came to changing my food. Food was ok too.
15. Capt Mark's Seafood Market
603 W 2nd StFreeport, TX 77541
16. Giovanni's Italian Kitchen
Awesome food. Went with wife for anniversary. She liked it so much that she took her parents the next day and her girlfriends the day after that.
17. Dido's
Great food, great service and a beautiful view of the San Bernard River! The huge outdoor, screened-in patio was perfect for watching the sunset ov…
18. Baytown Seafood Freeport
Nice service and simply delicious food. i love coming to eat here. i actually wish i was there right now.
19. China Buffet
Best place and great service
20. 2js Restaurant
21. Edgewater Grill
I am handicapped. No parking spaces labeled such, or the ramp for same. The mgr. Was polite. Not from here. Zokay. Met a nice couple from Argentina …
22. Smithhart's Texas Grill
I eat the Club Salad all the time and it is just wonderful. You can expect all of the food to be fresh, no matter when you come eat. The staff…
23. Blue Ocean Seafood
we went here for lunch , myself my wife and my son . We were served food that tasted spoiled ! Even though we sent the food back , three fully un…
24. Waterfront Restaurant
190 CR 255Matagorda, TX 77457
25. Rudy's Roo's Seafood
1515 N Brazosport BlvdFreeport, TX 77541
26. Sokle
1202 N Gulf BlvdFreeport, TX 77541
27. Creek Side Bar
2614 Miller RdFreeport, TX 77541
28. La Sirenita Seafood
4014 E Highway 332.Freeport, TX 77541
29. Western Seafood Company
301 W Brazos StFreeport, TX 77541
30. Baron's Seafood
1160 E Brazos StFreeport, TX 77541


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Subcategory: Combines
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Tractors, Caterpillar (4292) in London, ON
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Grain Handling, Grain Dryers (4124) in Kindersley, SK
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Tractors, Various (4319) in edmonton, AB
97 HP, 525 hrs., with LL 7101 FEL, 40 gear shuttle shift trans, PTO, 3 PTH, 78" QA bucket, like new condition, $54,000 OBO.


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Narcotic Drugs & Psychotropic Substances
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Update: Westboro Protesters Vastly Outnumbered by Counter-Protesting Brooklynites
Protest greeted by crowds of ill-wishers.By Lindsay Robertson
Protest greeted by crowds of ill-wishers.By Lindsay Robertson
The crazies are descending on Brooklyn Technical High School this afternoon.By Jessica Pressler
The editor of the scrappy borough journal is happy about this. And so are we.By Chris Rovzar
Students said he had 'child-bearing hips.'By Tim Murphy
Gersh Kuntzman, did you make up these quotes?By Tim Murphy
How can someone make you feel sorry for a city bureaucrat?
What would a victory at Nathan's be if it wasn't over Kobayashi?
G-train riders protesting in Fort Greene! Eighth-graders protesting in the South Bronx! Party poopers protesting on Park Avenue! What will you choose to protest? Read our daily boroughs report and decide.
That Awkward Clip of Donald and Melania at the Inauguration Is Definitely Real
Trump's Latest Executive Order Will Affect Women's Health Worldwide
Becoming an American in the Age of Trump
FEC Document Reveals Trump Hired Actors to Attend Campaign Launch Event
Josh Kushner, Ivanka Trump's Brother-in-Law, Was Reportedly Spotted at the Women's March
SNL Writer Katie Rich Suspended for Barron Trump Tweet
Donald Trump's Presidency Is the Twilight Zone Episode About a Terrifying 6-Year-Old
Steve Buscemi Approves of Man's 'Shut the Fuck Up, Donny' Women's March Sign
What You Need to Know About the 89800 'Count Me' Text
A Scene from the D.C. Women's March


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text: 15 Jan 2017 The world's 20 cheapest cities Previous slide Next slide 9 of 20 View All Skip Ad 11= Pretoria Another South African city snatches joint 11th place. "South Africa's capital was founded by Voortrekkers and retains a rather stern, bureaucratic atmosphere, albeit softened by a large student population," says Francisca Kellett. "Although relatively safe to stroll around, there isn't much to keep you in the city for long – it's best to combine Pretoria with Johannesburg." Back to image


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"Remmie" is a gorgeous 5 year old registered gelding. He is 15.1hh and put together real nice. He has been shown in 4H and was the 2014 overall Grand Champion…$3,600
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Bus from Kassel to Oberhausen and return
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Bus Oberhausen - Kassel 183 km
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1 Distributed Nuclear Norm Minimization for Matrix Completion Morteza Mardani, Gonzalo Mateos and Georgios Giannakis ECE Department, University of Minnesota Acknowledgments: MURI (AFOSR FA ) grant Cesme, Turkey June 19, 2012
2 Learning from "Big Data" ` Data are widely available, what is scarce is the ability to extract wisdom from them' Hal Varian, Google's chief economist BIG Fast Productive Revealing Ubiquitous Smart K. Cukier, ``Harnessing the data deluge,'' Nov Messy
33 Context Imputation of network data Goal: Given few incomplete rows per agent, impute missing entries in a distributed fashion by leveraging low-rank of the data matrix. Preference modeling Network cartography Smart metering
(as) has low rank Goal: denoise observed entries, impute missing ones 44 Low-rank matrix completion Consider matrix, set Given incomplete (noisy) data Nuclear-norm minimization [Fazel'02],[Candes-Recht'09] Sampling operator Noisy Noise-free s.t. ? ? ? ? ? ? ? ? ? ? ? ?
55 Problem statement Goal: Given per node and single-hop exchanges, find n Network: undirected, connected graph (P1) ? ? ? ? ? ? ? ? ? ? Challenges Nuclear norm is not separable Global optimization variable
66 Separable regularization Key result [Recht et al'11] New formulation equivalent to (P1) (P2) Proposition 1. If stationary pt. of (P2) and, then is a global optimum of (P1). Nonconvex; reduces complexity: LxρLxρ ≥rank[X]
77 Distributed estimator Network connectivity (P2) (P3) (P3) Consensus with neighboring nodes Alternating-directions method of multipliers (ADMM) solver Method [Glowinski-Marrocco'75], [Gabay-Mercier'76] Learning over networks [Schizas et al'07] Primal variables per agent : Message passing: n
8 8 Distributed iterations
99 Highly parallelizable with simple recursions Unconstrained QPs per agent No SVD per iteration Low overhead for message exchanges is and is small Comm. cost independent of network size Recap: (P1) (P2) (P3) Centralized Convex Sep. regul. Nonconvex Consensus Nonconvex Stationary (P3) Stationary (P2) Global (P1) Attractive features
10 Optimality Proposition 2. If converges to and, then: i) ii) is the global optimum of (P1). ADMM can converge even for non-convex problems [Boyd et al'11] Simple distributed algorithm for optimal matrix imputation Centralized performance guarantees e.g., [Candes-Recht'09] carry over
11 Synthetic data Random network topology N=20, L=66, T=66 Data ,
12 Real data Abilene network data (Aug 18-22,2011) End-to-end latency matrix N=9, L=T=N 80% missing data Network distance prediction [Liau et al'12] Data: Relative error: 10%
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Preventing Holiday Weekend Cargo Theft31 August 2016
Cargo theft is more likely to occur over long holiday weekends than at any other time of the year. ARC put together best practices to prevent holiday weekend cargo thefts.
Emerging Risks Initiative - Risk radar update 201616 January 2017
The Chief Risk Officer Forum (CRO) has launched an updated version of the Emerging Risk Radar.
Allianz Risk Barometer 201711 January 2017
The sixth annual Allianz Risk Barometer identifies the top risks facing businesses in 2017, based on the responses of risk experts around the world.
Infrared Thermography Risk Bulletin30 November 2016
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Snow and Ice Removal Safeguards30 November 2016
Snow and ice removal may seem like a mundane task, especially knowing that it will eventually melt. This bulletin provides tips and reminders for evaluating and managing the exposures created by harsh winter weather.
Water Risks - Emerging Risk Initiative29 November 2016
The Chief Risk Officer Forum (CRO), a group of professional risk managers from the insurance industry including AGCS experts, has launched a new report on water risks.


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Bengaluru Tops Most Dynamic Cities List, Silicon Valley Takes ...In a proud new development, Bengaluru has been crowned the most dynamic city in the world. At the World Economic Forum Annual Meeting being held in Davos, Jones Lang LaSalle (JLL) has published its list of world's most dynamic cities - the main ... NDTV, 2 hours ago
Bengaluru pips Silicon Valley to become world's most dynamic city - ZeeNews.com, 3 hours ago
Bengaluru becomes most dynamic city in the world, Silicon Valley of US at third spot - India TV, 5 hours ago
18,451 images for valley
Fresh WD Approaching, Valley To Receive Heavy Snowfall
Srinagar: A fresh Western Disturbance (WD) will likely hit the state on January 22, resulting in heavy snowfall, which would once again affect air and surface connectivity from the Valley. Meanwhile, authorities Wednesday issued a fresh avalanche ...Kashmir Observer, 8 minutes ago Fresh snowfall in Kashmir Valley Day After India, 10 hours ago Fresh snowfall disrupts life in Kashmir Valley Daijiworldcom, 10 hours ago Fresh snowfall hits daily life in Kashmir Valley New Delhi Pioneer, 18 hours ago
More from: Zee News, Zee News...and 21 other sources
Ponzi scam arrests not linked to TMC's anti-note ban stand:CBI
CBI today said the arrest of two TMC leaders in connection with the Rose Valley ponzi scam was not related to the party's vehement opposition to demonetisation and was merely "co-incidental". "The probe into the Rose Valley scam had started in 2014.Business Standard, 5 hours ago Is Mamata worried over CBI's next move in Rose Valley scam? Rediff.com, 7 hours ago Rose Valley chit fund scam: Prime accused Gautam Kundu hospitalised, misses Court hearing Financial Express, 1 day ago Rose Valley scam: Prime Minister's Office denies giving instructions to CBI India.com, 1 day ago
185 Illegal Structures Built in 3 Valley Resorts
Jammu: As many as 185 illegal constructions were detected in highly eco-sensitive and barred areas of three world famous tourist resorts of Kashmir Valley during the last three years, the state government said today. During last three years, 185 ...Kashmir Observer, 8 minutes ago 185 illegal structures came up in Kashmir's 3 tourist resorts Business Standard, 2 hours ago
Silicon Valley IPO contender DocuSign hires CEO after long search
By Heather Somerville SAN FRANCISCO, Jan 18 (Reuters) - After a marathon search lasting more than a year, Silicon Valley company DocuSign announced on Wednesday it hired a new chief executive. DocuSign, a $3 billion company that makes software to ...Sharenet, 1 hour ago
CBI plans to move Rose Valley and Saradha chiefs from jail to the grilling table...
CBI has drawn up plans to bring Rose Valley chairman Gautam Kundu and Saradha boss Sudipto Sen from Kolkata jails to the interrogation table in Bhabaneswar where TMC MPs Tapas Paul and Sudip Bandyopadhyay are held for questioning, giving the ...Hindustan Times, 4 hours ago Rose Valley and Saradha cases: Ex-TMC MP Kunal Ghosh questioned by CBI Economic Times, 2 days ago Saradha scam: TMC MP Kunal Ghosh interrogated by CBI Business Standard, 1 week ago
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Srinagar : With the decrease of the unrest in the Kashmir valley, the Central Reserve Police Force has decided to remove 102 companies currently active in the Valley. According to the sources, 45 companies of the force already have been withdrawn ...Kashmir Observer, 4 hours ago
World urged to stop Indian atrocities in held valley
SADIQABAD-International community must take notice of Indian atrocities being committed against innocent Kashmiris in the held Kashmir. RY Khan Chamber of Commerce and Industry member Qamar Sultan Zia stated while talking to media here the other ...The Nation Pakistan, 14 hours ago Separatists lash out at CM over AFSPA Chandigarh Tribune, 19 hours ago Mehbooba slams those opposing return of Kashmiri Pandits Deccan Herald, 1 day ago Kashmir unrest: Mehbooba Mufti assures suitable compensation for rehabilitation of victims Sify, 2 days ago
Snowfall in Srinagar affects life across Valley; Srinagar-Jammu highway remain closed for third day
The latest spell of snowfall in Srinagar and other areas has affected the normal activities across the Kashmir Valley. The Srinagar-Jammu National Highway shall remain closed for the third consecutive day on 18 January. The trucks and passenger vehicles ...catchnews.com, 11 hours ago Snowfall in Srinagar affects normal activity across valley Ani News, 11 hours ago Valley back to normal after two-day shutdown Chandigarh Tribune, 2 days ago
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Chhota Bhangal under 2-ft snow
Thirty villages of the Chhota Bhangal valley remained cut off with the rest of the state for the second day today following heavy snow between Jhatingri and Kothi Kohar. All roads leading to the valley are under two to three feet of snow. Most of ...Chandigarh Tribune, 20 hours ago Kashmir valley remains cut off as snowfall continues Press Trust of India, 1 day ago Valley, Doda region remain cut off on 2nd day Kashmir Times, 21 hours ago Kashmir Valley remain cut off due to snowfall Press Trust of India, 1 hour ago
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Dyeing proteins to peer deeper inside living cells
Super-resolution microscopy that allows researchers to see inside nerve cells, track disease-causing proteins and watch cell division in living organisms earned three scientists the Nobel Prize for chemistry in 2014. Building on that breakthrough, a Marie Curie post-doctoral fellow has developed a pioneering technique to peer even more closely inside living cells at the nano-scale, using fluorescent dyes to label the tiny amino acids that are the building blocks of all proteins.
Published: 20 January 2017
Understanding cancer progression to improve treatment
As Europe's population ages, cancer has become the leading cause of death. Therapeutic targeting - changing the activity of a protein or nucleic acid with a stimulus - is of limited use because of the complexity of cancer: changes take place within the tumour cell itself, but also in the cell's microenvironment. An EU-funded project will increase understanding of cancer progression and provide the basis for new, targeted approaches.
Published: 19 January 2017
Training the antiviral drug developers of the future
Viral infections are a major course of disease worldwide. Licensed antiviral drugs are currently only available for a few viruses, such as HIV, influenza and hepatitis C. Through a multi-disciplinary, inter-sectoral training programme, the ANTIVIRALS project is training a new generation of experts to develop novel antiviral drugs
Published: 10 January 2017
Targeted treatments for advanced-stage bowel cancer
The EU-funded MoTriColor project is seeking to develop novel therapies for advanced-stage bowel cancer patients by targeting treatments according to each patient's disease subtype.
Published: 6 January 2017
A targeted approach to treating osteoarthritis
Nanocarriers are tiny substances that can be used to transport other materials, such as drugs, to specific areas of the body. The EU-funded TARGETCARE project plans to use them to treat joint and intervertebral disc diseases - improving patients' mobility and quality of life.
Published: 19 December 2016
New production processes promise to lower cost of biopharmaceuticals
Drugs derived from biological sources play a major role in modern healthcare, but producing them cost-effectively to keep up with rising demand is a major challenge for the pharmaceutical industry. EU-funded researchers are developing more efficient biopharmaceutical manufacturing processes that should expand patients' access to these highly effective therapies.
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Understanding the health benefits of fermented foods
EU-funded researchers have developed an affordable data analysis toolbox for finding and interpreting the genomic sequence of fermented food - the key to better understanding their potential benefits and risks.
Published: 9 December 2016
Achieving sharper optical tools with plasma and metamaterials
An EU-funded project has given researchers a clearer insight into how the next generation of optical tools - including medical diagnostic devices and security detectors - could be achieved.
Published: 1 December 2016
Fixing the faults in immune systems
For sufferers of primary immune deficiencies, every infection contracted could lead to a serious or debilitating illness. EU scientists have their sights set on a cure for the most severe forms of the disease's 200 variations, and have already had very positive results in clinical trials. The findings could also help treat other conditions.
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One small step for a protein...
EU-funded researchers have built a new microscope that can track single molecules, and have used it to clarify how materials are transported inside living cells. It could have wide application in science, medicine and industry.
Published: 24 November 2016


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1840 $10 AU55 NGC....Click the image to load the highest resolution version.
1840 Liberty Eagle, AU55
1840 $10 AU55 NGC. A Choice AU example of this underrated
date, areas of luster remain on this specimen displaying yellow and
green-gold color. Scattered abrasions are present, with a couple of
them being slightly larger, but posing little distraction, if any.
The 1840 mintage consisted of 47,338 coins. Census: 27 in 55, 23
finer (12/13).
Underrated Issue in High Grade
From the collection of Donald E. Bently, sold for the benefit of the Bently Foundation.(Registry values: N1793) (NGC ID# 262G, PCGS# 8581)
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Nord Anglia Education, the UK's leading private education company, invited offers for its university, accountancy and publishing operations yesterday as it narrows its focus to running nursery, primary and secondary schools.
Analysts said the businesses up for sale could attract offers of more than £20m, compared with the £18m Nord Anglia paid for them three years ago.
Kevin McNeany, the founder of Nord Anglia, revealed the plans along with the £5.8m purchase of Bright Horizons, an operator of 10 day-care nurseries in the South and West of England. The acquisition will double the size of the Nord Anglia's own pre-school operations, which are currently focused on the North-west, Midlands and Yorkshire under the Princess Christian name. It also runs a further 12 nurseries attached to private schools that it owns.
Demand for nursery care has increased rapidly in recent years as more mothers decide to return to work after having children. Provision of services has lagged that demand for some time. Nord Anglia said that most of Bright Horizons' nurseries make a profit but the business as a whole fell into the red last year as it funded the opening of two new nurseries. Pre-tax losses totalled £290,000 in the year to May, compared with a profit of £70,000 in the previous year, on a 37 per cent rise in turnover to £3.39m.
Analysts believe Nord Anglia is well placed to cash in on the Government's plans to allow more private companies to move into the state sector. It will become the first profit-making company to manage a state school after winning a ground-breaking contract to run Abbeylands comprehensive, in Surrey, for seven years.
Shares in Nord Anglia, which also runs key education services for several London councils including Hackney, Haringey and Westminster, rose 3.5p to 265p. The company currently makes £1.5m profit a year from publicly funded schools.Reuse content


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