text:
Don't let the enablers of the credit bubble off the hook, the bond manager urges.
Bill Gross, the manager of the world's biggest bond fund and co-chief investment officer at Pimco, gives the credit rating agencies Moody's and Standard & Poor's another good hiding in his monthly commentary out Wednesday.
He says investors should ignore their ratings, because the agencies have shown little intelligence or common sense in recent years.
Seattle News tv Seattle Kiro Videos News The comments come as the agencies are coming under fire for their role in the subprime crisis as well as a bubbling crisis in Europe, where downgrades of several financially stressed Southern European nations have sent markets tumbling.
Gross writes that the rating agencies have managed to escape scrutiny despite their leading role in enabling Wall Street to peddle junk to investors around the globe. The firms, despite a glaring conflict of interest, slapped their supposedly gold-plated triple-A rating on all sorts of housing-related dross ginned up by investment banks – all in the name of expanding their lucrative fee streams.
Gross, calling this arrangement "sordid" and "nonsensical," notes that his colleague Paul McCulley likens the rating agencies to the guy who hands out fake ID's at a drinking party.
And as drunk as the ratings agencies acted in the housing bubble, this was hardly the first time, he adds.
"Their warnings were more than tardy when it came to the Enrons and the Worldcoms of ten years past," Gross writes, "and most recently their blind faith in sovereign solvency has led to egregious excess in Greece and their southern neighbors."
As they did in the subprime bust, the rating agencies have belatedly started to come to their senses. They have recently downgraded hard-hit nations such as Spain, Portugal and Greece.
But it's no small feat to catch up to market sentiment after pretending festering problems aren't there. The downgrades played a role in some vicious selloffs that swept markets over the past weeks and prompted a spokesman for one European official to Discussion amp; Vendor Fake Id Fakeidvendors p8aYU, "Who is Standard & Poor's by the way?"
The European Central Bank even went so far as to downgrade the rating agencies, so as to prevent a collapse of the Greek banking system.
But even a rising tide of sentiment against the rating agencies won't put them out of business, Gross warns. He says a wrongheaded regulatory framework means that banks, insurers and pension funds need to pay attention to their comments.
What's more, many observers say regulators themselves need the rating agencies, for all their many flaws – and it's hard to come up with a workable alternative.
"Like vampires in the dead of the night they will outlast us all," Gross writes. "Those looking to profit at their expense, however, will dismiss them."


text:
GE Service Protection Plus Extended 4 Year In Home Service Warranty Because you have purchased from an authorized dealer, all of your products come with the full manufacturer's warranty. It can be costly to repair/replace your product after the warranty expires so Abt Electronics is pleased to offer you an extended warranty backed by two of the largest companies in the US. This extended service is good anywhere in the US, Puerto Rico, US. Virgin Islands. The service is completely insured, has no deductible charges, covers parts which not even the manufacturers do (i.e. remote controls and more), has a no lemon policy, and no trip charges on TVs.
Please note:
This Extended Service Policies are not provided for Products used in a Commercial Applications. Warranties/Extended Service plans can only be sold to customers purchasing products in our retail store or on our website.
GE/Zurich online warranties do not apply to Abt Electronics warranties purchased in house. This warranty can be purchased for products recently bought on the Abt.com website. For extended warranty inquiries regarding a recent Abt Electronics retail store purchase, please call our sales department at 1-888-228-5800 x1.
Fill out the form below to request Better Price for this product.
Enter your zip code for a more accurate delivery estimate.

subset: commoncrawl


text:
ESSEX JUNCTION — Francis Bronson of Bridport has been a regular at the annual Vermont Farm Show for 30 years, and this week attended the 82nd annual fair at the Champlain Valley Expo in Essex Junction even though he has left dairy farming.
While looking over some of the show's more than 150 agricultural exhibits, Bronson said he was impressed by some of the technological advances on display.
"There's some innovative thought processes on some of this stuff," he said. "I wish some of it was around when I was still going."


text:
This scientific visualization depicts a flight through the galaxies in the Hubble eXtreme Deep Field (XDF). Using measured and estimated distances for approximately three thousand galaxies, astronomers and visualizers constructed a three-dimensional model of the XDF galaxy distribution. The camera traverses more than thirteen billion light-years of space. For cinematic reasons, the exceedingly vast distances in the 3D model have been greatly compressed.
Visualization: NASA, ESA, and F. Summers, L. Frattare, T. Davis, Z. Levay, and G. Bacon (STScI);
Science: G. Illingworth, P. Oesch, and D. Magee (University of California, Santa Cruz)
Publication: September 25, 2012


text:
Soybean rust reached farther north in 2006 than ever reported and inundated coastal areas from South Carolina to Virginia. Despite it's dramatic late season activity, the dreaded disease did little damage to the 2006 U.S. soybean crop.
By the time rust reached west central Indiana, the most northerly infections reported, all the full season crop was harvested and frost had taken out double-crop beans. The heaviest infestations in 2006 were along the Atlantic Coast and to the west along the Mississippi River.
Researchers in Indiana report the rust spores that infected soybeans in that state likely came from Louisiana in late September, but did not develop until later in the season — too late to be a factor in the 2006 crop.
Whether the more northerly migration of the disease in 2006 means more trouble for 2007 is open for debate. In the winter of 2005-2006, rust over-wintered as far north as Montgomery, Ala., on kudzu. Yet, Alabama was one of the least affected states by rust in 2006.
Likewise, very little over-wintering rust was found in Louisiana, yet that state seemed to be the hot-spot for growth and movement of rust.
By first frost, Louisiana, Arkansas, Tennessee, Missouri, Illinois and Indiana had reported rust. All these cases moved northward from Louisiana, generally following the Mississippi River. Only in southern Mississippi and Louisiana did the disease get to soybeans in time to be a problem.
Even in these states, spraying for rust was at a minimal level.
Similar spread of the disease occurred in the Carolina's and into south-central Virginia. Only in a few cases in South Carolina was one fungicide spray recommended for rust management. Though widely reported in Georgia, the disease did not create major problems in 2006, likely due to an extended drought in major soybean producing areas of the state.
By harvest time, rust was much more prevalent in the Carolinas than it was in Georgia.
e-mail: email@example.com


text:
Brian Knight, SQL Server MVP, MCITP, MCDBA, is the owner and founder of Pragmatic Works. He is also the co-founder of SQLServerCentral.com, BIDN.com and SQLShare.com. He runs the local SQL Server users group in Jacksonville (JSSUG). Brian is a contributing columnist at several technical magazines and does regular webcasts at PragmaticWorks.com. He is the author of more than a dozen SQL Server books. Brian has spoken at conferences like PASS, SQL Connections and TechEd and many Code Camps. His blog can be found at http://www.bidn.com.
SQL Bits was started by a group of individuals that are passionate about the SQL Server product suite.
There is a breadth of knowledge in the SQL Community that will benefit everyone in the community.
We want to spread that knowledge.
We all work with the SQL community, some of us for many years. We have a range of skills and experiences from being given the MVP award by Microsoft, organising user groups and speaking at events around the world.
Copyright © 2019 SQLBits Limited. All Rights Reserved.


text:
Feel bored with your digital camera? Try this lens and filter with 18 different effects. You can add special effects on your photos without Photoshop. There are 18 special effects in 2 turrets, special lens and filter.
Simple to install this lens onto your Olympus SLR. But a lens with a difference! Once fitted you now have access to different and crazy special effects and filters without any software installed. Cunningly designed like an old school telephone just dial in your preferred special effect and behold the Holga craziness that follows! This Holga Kit is available in a choice of Black, Yellow, Pink, Gold or Green. The full list of special effects/filters are listed on the Specs page.


text:
View Itinerary
The Mountain Lodges Trek was amazing. I loved everything about it. It was far and away one of the best travel experiences I've ever had. In conversations with other trekkers who had used other outfitters, it was clear that we Wildland folks were far better informed and prepared than anyone else because of their in-depth pre-trip information package.
- Josh Johns, MD


text:
- Research article
- Open Access
Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models
© Tøndel et al.; licensee BioMed Central Ltd. 2012
- Received: 20 December 2011
- Accepted: 20 June 2012
- Published: 20 July 2012
Statistical approaches to describing the behaviour, including the complex relationships between input parameters and model outputs, of nonlinear dynamic models (referred to as metamodelling) are gaining more and more acceptance as a means for sensitivity analysis and to reduce computational demand. Understanding such input-output maps is necessary for efficient model construction and validation. Multi-way metamodelling provides the opportunity to retain the block-wise structure of the temporal data typically generated by dynamic models throughout the analysis. Furthermore, a cluster-based approach to regional metamodelling allows description of highly nonlinear input-output relationships, revealing additional patterns of covariation.
By presenting the N-way Hierarchical Cluster-based Partial Least Squares Regression (N-way HC-PLSR) method, we here combine multi-way analysis with regional cluster-based metamodelling, together making a powerful methodology for extensive exploration of the input-output maps of complex dynamic models. We illustrate the potential of the N-way HC-PLSR by applying it both to predict model outputs as functions of the input parameters, and in the inverse direction (predicting input parameters from the model outputs), to analyse the behaviour of a dynamic model of the mammalian circadian clock. Our results display a more complete cartography of how variation in input parameters is reflected in the temporal behaviour of multiple model outputs than has been previously reported.
Our results indicated that the N-way HC-PLSR metamodelling provides a gain in insight into which parameters that are related to a specific model output behaviour, as well as variations in the model sensitivity to certain input parameters across the model output space. Moreover, the N-way approach allows a more transparent and detailed exploration of the temporal dimension of complex dynamic models, compared to alternative 2-way methods.
- Parameter-phenotype map
- Dynamic models
- Metamodelling
- N-way Partial Least Squares Regression
- Hierarchical analysis
- Cluster-analysis
- Input-output relationships
- Circadian clock
Dynamic models in systems biology as well as in other fields become increasingly complex as more detailed knowledge is incorporated. The massive presence of nonlinear relationships between their high-dimensional parameter- and solution- spaces is a key characteristic of such systems. Moreover, dynamic models typically generate multidimensional blocks of temporal data. Clearly it is very challenging to obtain a comprehensive overview of the behavioural repertoires of such models across the high-dimensional input parameter space, including the sensitivity of the model output to changes in the various input parameters, as well as interactions between input parameters and correlation patterns between model outputs. For dynamic model construction and validation, sound handling of such information is crucial. Since most of the existing methods for parameter estimation and sensitivity analysis are appropriate only for systems of relatively low output dimensionality and typically focus on one output variable at a time 12, a generic methodology for analysis of model behaviour that is able to handle the entire range of model complexities and give a comprehensive overview of the relationships between the input parameters and all model outputs, is sorely needed.
Statistical approaches are gaining acceptance as a means for analysis of input-output relationships of complex dynamic models 2345678910, and statistical emulation of dynamic models (metamodelling 11) has been demonstrated to be a useful tool both for speeding up computations 12 and as a basis for sensitivity analysis 2313 and uncertainty assessment 141516. Multi-way (N-way) methods have previously been shown to be effective for data integration in e.g. systems biology 1718. We therefore hypothesise that N-way approaches will be especially advantageous for metamodelling of dynamic models due to the capability of integrating temporal data from several output state variables simultaneously while retaining the information about which state trajectory that corresponds to which state variable throughout the analysis (with 2-way methods, this information is lost when concatenating the trajectories for the different state variables prior to the analysis). Consequently, a more detailed exploration of the temporal dimension of dynamic models is possible. This is important in order to obtain a comprehensive overview of how variation in the input parameters is manifested in the model output. Moreover, methods utilising several model outputs simultaneously have already been demonstrated to reduce the model sloppiness by imposing more constraints on the system 5.
Traditionally, metamodelling is carried out in the causal direction, predicting model outputs as functions of the input parameters using e.g. regression methods. Application of metamodelling in the reverse direction is, however, also of potential interest 5. The two modelling directions can be understood as extensions of the classical/inverse calibration modelling 22. Accordingly, we refer to the causal direction as classical metamodelling, and the reverse direction as inverse metamodelling, and in our application of the N-way HC-PLSR we demonstrate how their combination provides more detailed insight into the complexity of the mapping between input parameters and model outputs. Inverse metamodelling may also facilitate fitting of nonlinear models to large amounts of experimental data. Given that the results from the computations can be substituted with relevant experimentally measured data or quantities calculated from measurements, these can be used to predict corresponding parameters. Moreover, combinations of classical and inverse metamodelling can identify the key metrics to measure in order to validate the models. A more comprehensive introduction to the multivariate metamodelling methodology is given in Additional file 1: Section S1.
As long as they handle high-dimensional data with nonlinear relationships and yield interpretable representations, a wide variety of statistical methods can be effectively used for multivariate metamodelling. We have recently shown that multivariate metamodelling based on PLSR and our nonlinear extension HC-PLSR 8 provides good approximations of the input-output mappings 8 as well as informative insight into complex interaction patterns between parameters 9 of advanced nonlinear dynamic models. PLSR can use multiple response variables simultaneously and utilise inter-correlations between them for model stabilisation. PLSR analysis has been shown to effectively reveal covariation patterns in large and complex data sets, and extract correlations between possibly noisy and partially redundant input variables and outputs 6. The success of PLSR in the context of sensitivity analysis and for constraining input parameter values from dynamic model outputs has also been demonstrated by Sobie et al. 56. Highly nonlinear input-output structures may, however, be difficult to model adequately with linear models such as PLSR, even with polynomial extensions. To confront these problems, HC-PLSR was introduced 8. Heterogeneity in model sensitivity to certain parameters between various regions in the parameter space of a dynamic model of the mouse ventricular myocyte was identified by HC-PLSR-based sensitivity analysis in 9. Similarly, zooming into different regions of the state variable behavioural domain provides the opportunity to identify regions where the relationship between certain parameters and the model output is less ambiguous, indicating that these parameters are especially important for defining a specific type of temporal model behaviour. In cases where variation in the input parameters can be directly related to genotypic variations, this may provide valuable information about how a specific genotype can be of particular importance for the manifestation of certain phenotypic characteristics.
Here, we combine three different aspects of multivariate metamodelling: 1) Description of highly nonlinear input-output relationships by regional metamodelling, 2) NPLSR, allowing a retention of a tensor data structure throughout the analysis and 3) Inverse metamodelling in addition to the classical approach, providing more confident conclusions and a more comprehensive model overview. Moreover, particularly complex details are pursued by more detailed metamodelling of individual outputs and their relationships to the varied input parameters. Altogether, this provides a powerful, robust and efficient approach to exploration of the behavioural repertoire of complex dynamic models.
We illustrate our methodology by an application to a complex dynamic model of the mammalian circadian clock developed by Leloup and Goldbeter 24, which is a well-established and validated model. Models of circadian rhythms have e.g. been used for identifying mechanisms of chronotolerance and chronoefficacy for anticancer drugs 25. The dynamic model we analyse describes circadian oscillations of cellular activity in conditions of continuous darkness, and consists of 16 coupled ordinary differential equations (ODEs) describing the dynamics of three genes through intertwined positive and negative feedback loops. By combining the classical and inverse approaches of the N-way HC-PLSR, we capture several interesting parts of the present complex input-output relationships, which are difficult to deduce directly from the model's differential equations.
In silico data set
Description and range of the parameters varied in the mammalian circadian clock model simulations
Parameter name
Minimum value
Level step size
Maximum value
v mB
Maximum rate of Bmal1 mRNA degradation
v mC
Maximum rate of Cry mRNA degradation
v mP
Maximum rate of Per mRNA degradation
v dPCN
Maximum rate of degradation of nuclear phosphorylated Per-Cry complex
v dIN
Maximum rate of degradation of nuclear Per-Cry-Clock-Bmal1 complex
k 1
Rate constant for entry of the Per-Cry complex into the nucleus
k 3
nM-1 h-1
Rate constant for the formation of the Per-Cry complex
k 5
Rate constant for entry of the Bmal1 protein into the nucleus
k 7
nM-1 h-1
Rate constant for the formation of the inactive Per-Cry-Clock-Bmal1 complex
A separate test set based on 8192 parameter combinations found by random Monte Carlo sampling 2728 within the same parameter levels as used in the calibration set was also generated, resulting in 8125 converging simulations.
Results from the N-way HC-PLSR metamodelling of the mammalian circadian clock model
The low percentage explained Y - variance (Additional file 1: Section S2, Figure S3) showed that the global metamodelling was inadequate in both the classical and inverse direction. However, before proceeding to improved, regional metamodelling, the dominating relationships and patterns between the 9 input parameters and the 16 output state trajectories of the mammalian circadian clock model were assessed using the global approximations.
Input-output map characteristics revealed by the global classical and inverse metamodels
The dominating input-output covariation patterns
Description of the mammalian circadian clock model state variables
State variable name
Concentration of Per mRNA
Concentration of non-phosphorylated Bmal1 protein in the nucleus
Concentration of Cry mRNA
Concentration of Bmal1 mRNA
Concentration of non-phosphorylated Per protein in the cytosol
Concentration of phosphorylated Per protein in the cytosol
Concentration of non-phosphorylated Per-Cry protein complex in the cytosol
Concentration of non-phosphorylated Cry protein in the cytosol
Concentration of phosphorylated Cry protein in the cytosol
Concentration of phosphorylated Per-Cry protein complex in the cytosol
Concentration of non-phosphorylated Per-Cry protein complex in the nucleus
Concentration of phosphorylated Per-Cry protein complex in the nucleus
Concentration of inactive complex between Per-Cry and Clock-Bmal1 in the nucleus
Concentration of non-phosphorylated Bmal1 protein in the cytosol
Concentration of phosphorylated Bmal1 protein in the cytosol
Concentration of phosphorylated Bmal1 protein in the nucleus
Within the parameter space analysed here, the parameters v mB (maximum rate of Bmal1 mRNA degradation), v mC (maximum rate of Cry mRNA degradation) and v mP (maximum rate of Per mRNA degradation) had the highest correlation to the circadian clock state variables along the first three global NPLSR factors, both in the inverse (Figure 3A) and classical (Figure 3B) metamodelling. The same over-all covariation patterns could be seen both in the inverse and the classical global metamodelling, since the orthogonal design in the parameters used in the global metamodelling has no dominant covariance directions, and both the inverse and classical NPLSR metamodels will consequently be dominated by the covariance structures of the state variables (but restricted to the parameter design). The input parameter v mB were e.g. negatively correlated with the state variables B C , B N , B NP and B CP in the NPLSR factor space, which was not surprising since these state variables represent the dynamics of the phosphorylated and non-phosphorylated protein Bmal1 concentrations in the cytosol and nucleus 24. Similarly, v mP was negatively correlated with the state variables M P (dynamics of Per mRNA) and P CP (dynamics of phosphorylated Per protein concentration in the cytosol), while v mC was negatively correlated with M C (dynamics of Cry mRNA) and PC CP (dynamics of phosphorylated Per-Cry complex in the cytosol). These patterns were all in concordance with our intuition of the mammalian circadian clock model.
Prediction results from the global inverse metamodelling
Prediction results from the global classical metamodelling
The results from the test set prediction of the state variable trajectories from the input parameters in the classical metamodelling shown in Figure 4B, indicated that the temporal behaviour of the following state variables could be predicted with high accuracy from the input parameters using global NPLSR: B N , M C , M B , B C , B CP and B NP , while the prediction error was especially high for the state variables P C , PC C , C C , PC N , PC NP and I N .
Separately analysed output space regions in the hierarchical cluster-based metamodelling
To facilitate comparison, it was decided to use the same grouping (clustering) of the observations in both classical and inverse metamodelling. The state variable NPLSR X-factors from the inverse metamodelling were more directly related to the state variable behaviour than the Y-factors representing the state variables in the classical metamodelling due to the asymmetric nature of the NPLSR models (defined primarily based on the X-scores, not the Y-scores). The inverse metamodelling was therefore carried out first, i.e. the clustering of the 8135 calibration set observations was carried out on the inverse metamodelling X-factors obtained from the output state trajectories (TOutput,A,Inverse, see Figure 2), thereby ensuring that the clusters represented different model behaviours. The same clusters were then also used in the classical N-way HC-PLSR metamodelling. This was chosen due to that clustering on the X-factors or the predicted Y-factors () in the classical metamodelling (as would be a more traditional procedure) would both make the clustering more related to the designed parameter combinations instead of the state variable behaviour, since the predicted Y-factors were here predicted as linear combinations of the X-factors that are related to the parameter combinations (see Additional file 1, equation S12c). Predicted Y-factors would have to be used in the clustering instead of the Y-factors directly calculated from the state variable data in the classical metamodelling, since otherwise the classification of new observations (for which state variable data are not available) would not be possible on variables equivalent to those used to cluster the calibration set observations.
Parameter ranges and mean values for the observations in the six N-way HC-PLSR clusters
v mB (nMh-1)
v mC (nMh-1)
v mP (nMh-1)
v dPCN (nMh-1)
v dIN (nMh-1)
k 1 (h-1)
k 3 (nM-1 h-1)
k 5 (h-1)
k 7 (nM-1 h-1)
0.02-0.23 (0.11)
0.95-1.12 (0.99)
1.14-2.09 (1.65)
0.99-1.14 (1.06)
0.08-1.52 (0.81)
0.08-1.52 (0.78)
0.08-1.52 (0.82)
0.27-0.41 (0.34)
0.05-0.95 (0.49)
0.02-0.28 (0.11)
0.95-1.54 (1.26)
0.98-1.14 (1.02)
0.99-1.14 (1.06)
0.08-1.52 (0.77)
0.08-1.52 (0.78)
0.08-1.52 (0.83)
0.27-0.41 (0.34)
0.05-0.95 (0.49)
0.02-0.07 (0.04)
1.03-1.54 (1.32)
1.14-2.09 (1.65)
0.99-1.14 (1.07)
0.08-1.52 (0.80)
0.08-1.52 (0.81)
0.08-1.52 (0.77)
0.27-0.41 (0.32)
0.05-0.95 (0.51)
0.07-0.33 (0.18)
0.95-1.54 (1.25)
1.14-2.09 (1.70)
0.99-1.14 (1.07)
0.08-1.52 (0.80)
0.08-1.52 (0.79)
0.08-1.52 (0.81)
0.27-0.41 (0.34)
0.05-0.95 (0.50)
0.12-0.38 (0.27)
0.95-1.54 (1.29)
0.98-1.30 (1.09)
0.99-1.14 (1.06)
0.08-1.52 (0.80)
0.08-1.52 (0.82)
0.08-1.52 (0.79)
0.27-0.41 (0.34)
0.05-0.95 (0.50)
0.23-0.38 (0.33)
0.95-1.54 (1.29)
1.14-2.09 (1.69)
0.99-1.14 (1.07)
0.08-1.52 (0.80)
0.08-1.52 (0.80)
0.08-1.52 (0.79)
0.27-0.41 (0.35)
0.05-0.95 (0.51)
Additional input-output map characteristics revealed by the regional classical and inverse metamodelling
Prediction results from the hierarchical inverse metamodelling
The three parameters v dPCN (maximum rate of degradation of nuclear phosphorylated Per-Cry complex), v dIN (maximum rate of degradation of nuclear Per-Cry-Clock-Bmal1 complex) and k 7 (rate constant for the formation of the inactive Per-Cry-Clock-Bmal1 complex) were predicted with low accuracy also in the inverse hierarchical metamodelling. This indicated that either the circadian clock model was relatively insensitive to variations in these input parameters, or our metamodelling was not able to describe the complex relationships between these parameters and the model outputs. This has been assessed in more detail below.
Prediction results from the hierarchical classical metamodelling
Several of the circadian clock state variable trajectories could be predicted with considerably higher accuracy in classical metamodelling using N-way HC-PLSR compared to the global NPLSR (Figure 8B). Only the state variables PC C (concentration of the Per-Cry protein complex in the cytosol), PC N (concentration of the Per-Cry protein complex in the nucleus) and I N (concentration of the inactive complex between Per-Cry and Clock-Bmal1 in the nucleus) were predicted with low accuracy (R2-values below 0.4), indicating that the metamodelling with N-way HC-PLSR was able to capture the main features of the input-output mappings for most of the 16 circadian clock state variables.
Figure 7 indicated that the data for the three above mentioned state variables contained extreme outliers (especially in Cluster 1 and 2), which may have caused the low prediction accuracy for these state variables. Both for the calibration set and the test set, being an outlier seemed to be closely associated to having the lowest level of the parameter v mP , but this was not itself sufficient to generate extreme values of these three state variables.
Detailed interpretation of the revealed model sensitivity patterns
As shown in Figure 10, the temporal behaviours of the state variables PC C and PC N were predicted with higher accuracy by all regional metamodels except regional model 1 and 2 (corresponding to clusters containing outliers for these state variables), compared to the global NPLSR. However, the state variable I N was predicted with very low accuracy in all the regional metamodels, and the parameters v dPCN , v dIN and k 7 could not be well predicted in any of the regional inverse metamodels (Figure 9). Two of the parameters that were predicted with low accuracy, v dIN and k 7 , appeared in the differential equation corresponding to the state variable I N . Hence, the low prediction accuracy was probably due to an insufficiently described mapping between I N and these parameters in the N-way HC-PLSR.
In order to reveal the sensitivity patterns for the state variable I N , a separate sensitivity analysis of the relationship between I N and the circadian clock input parameters was carried out using 2-way second order polynomial HC-PLSR analogous to the analysis presented in 8, but with the parameter ranges given in Table 1. This showed that in order to be well-predicted, the state variable I N had to be logarithmised prior to the analysis. This might explain why this state variable trajectory could not be well described together with the other state variables in the N-way HC-PLSR. The results are given in Additional file 1: Section S3.1, and indicated that the parameters having the largest effects on the I N state trajectory were v mB , v mC , v mP , v dIN , k 1 and k 7 . Several interactions between these input parameters were also identified.
The parameter k 7 (rate constant for the formation of the inactive Per-Cry-Clock-Bmal1 complex) was also involved in the differential equations representing the dynamics of the concentration of non-phosphorylated Bmal1 protein in the nucleus (state variable B N ) 24 and the concentration of the non-phosphorylated Per-Cry protein complex in the nucleus (state variable PC N ), in addition to I N . PC N was not well described by the classical metamodelling, but B N was predicted with high accuracy from the parameters (Figure 8B and Figure 10). Hence, the low prediction accuracy for k 7 indicated that the state variable B N was relatively insensitive to the parameter k 7 according to our analysis (within the analysed parameter range), even though the differential equation for this state variable involved k 7 . This was confirmed by the plot of the model sensitivities estimated from the regional metamodels shown in Figure 11 (only in regional model 3 a sensitivity was identified, but this was also low), as well as the separate sensitivity analysis for the state variable B N described in Additional file 1: Section S3.2 (carried out in the same way as for I N ). This illustrates how a combination of a classical and inverse metamodelling can provide more confident conclusions about model behaviour and sensitivity patterns.
The third parameter that could not be constrained from the state variable data was v dPCN (maximum rate of degradation of nuclear phosphorylated Per-Cry complex), which was only involved in the differential equation describing the dynamics of the concentration of the phosphorylated Per-Cry complex in the nucleus (state variable PC NP ). This state variable was predicted with an R2-value of approximately 0.7 in the classical metamodelling, which is not particularly low. Thus our results indicated a low sensitivity of PC NP to the rate of degradation of the corresponding protein. This result was confirmed by the results shown in Figure 11 and the separate sensitivity analysis for the state variable PC NP described in Additional file 1: Section S3.3. This was not straight-forward to explain, and calls for a more comprehensive analysis of the relationship between the state variable PC NP and the input parameter v dPCN . Possible explanations might be that our analysis did not cover the relevant range for this parameter, causing the model sensitivity to this parameter not to be detected, or that its input-output relationship is very complex.
As seen from Figure 11, the parameter v dPCN seemed to have negative impact on the state variable P C (concentration of non-phosphorylated Per protein in the cytosol) in regional NPLSR model 2, even though neither this parameter nor the state variable PC NP (concentration of the phosphorylated Per-Cry complex in the nucleus, for which this parameter was involved in the differential equation), appeared in the differential equation representing P C . Both P C and PC NP were related to the Per protein, though. In this region of the state variable space, the effect of v dPCN on P C was either more pronounced, or the relationship between these variables was less complex and therefore more visible in the analysis. As seen from Figure 7C, Cluster 2 was characterised by high values of several of the circadian clock state variables (especially M P , P C , P CP , PC C , PC N , PC NP and I N ).
The input parameter k 3 (rate constant for the formation of the Per-Cry complex) had a large negative effect on C CP that was visible only in Cluster 5 (Figure 11). This could be explained by the fact that k 3 was involved in the equation for C C , which was part of the differential equation for C CP . Furthermore, v dIN (maximum rate of degradation of nuclear Per-Cry-Clock-Bmal1 complex) seemed to have a slight negative effect on PC NP in Cluster 1. This result was not easily deducible from the equation structure of the circadian clock model, and could not be detected in the global metamodelling. Cluster 1 was characterised by e.g. an especially large spread in the values of the state variable C C .
The parameter k 7 (rate constant for the formation of the inactive Per-Cry-Clock-Bmal1 complex) seemed to have a positive effect on the state variable C C (non-phosphorylated Cry protein in the cytosol) in regional metamodel 1. However, since the inactive Per-Cry-Clock-Bmal1 complex represses the Per and Cry genes in the nucleus, a positive effect of k 7 on C C seemed unlikely. An additional sensitivity analysis was therefore carried out by adding eight simulations to the data set, keeping all parameters except k 7 constant at the mean values found for Cluster 1. The results are shown in Additional file 1: Section S3.4, and indicated that increasing k 7 resulted in a very small decrease in C C and C CP , had a clear positive effect on I N (as expected from the differential equation for I N ), and a negative effect on PC N and PC NP . In order to try to explain the positive effect of k 7 on C C seen in Cluster 1, a separate 2-way PLSR-based sensitivity analysis was therefore also carried out for the state variable C C in Cluster 1 (see Additional file 1: Section S3.4). The effects of v mB , v mC , v mP and k 3 on C C indicated for Cluster 1 were also manifested in the 2-way PLSR analysis, but a positive main effect of k 7 was not confirmed. However, several interaction terms involving k 7 seemed to have effects on C C , such as the interaction between v mP and k 7 (which had a positive effect). Since cross-terms between the input parameters were not included in the N-way PLSR analysis, confounding of these interaction effects with the main effect of k 7 may explain the positive sensitivity to k 7 indicated by the N-way PLSR. The indication of a positive effect of k 7 on C C could also have been caused by other sources of uncertainties in the NPLSR analysis.
Analogous to the increased prediction accuracy obtained for the two parameters k 1 and k 3 , model sensitivity to these parameters could be revealed in several local regions of the state variable space (Figure 11), even though this was not evident from the global metamodelling (Figure 5). According to Figure 11, the model seemed to be insensitive to these parameters in the region represented by Cluster 6. However, the inverse metamodelling indicated that also in Cluster 6 these parameters could be predicted with a much higher accuracy than by the global NPLSR metamodel, indicating model sensitivity to these parameters also in this region of the state variable space. This illustrates the importance of carrying out both classical and inverse metamodelling to gain a more detailed insight into the sensitivity patterns of a complex model.
The main traditional approach to analysis of input-output relationships has been to use aggregated outputs derived from the state trajectories, representing the dynamics of the state variables. For instance, in their original publication of the mammalian circadian clock model 24, the authors employed a sensitivity analysis of only one aggregated output – the circadian clock period– a very important trait, but too aggregated to give sufficient overview of the entire model behaviour. Multivariate metamodelling has, at least in principle, the capacity to reveal the relationships between all input parameters and all model outputs simultaneously. This has here been illustrated for the nine input parameters assumed to be most interesting for the mammalian circadian clock and the 16 state variables of the model, where the generated N-way metamodels allowed flexible quantitative input-output regressions yielding informative graphical insight into the main underlying input-output map characteristics. In our example N=3, but the analysis can be extended to more than three modes.
Our analysis confirmed the main conclusions from the original classical sensitivity analysis of the circadian clock period carried out by Leloup and Goldbeter 24, namely that the mammalian circadian clock model was highly sensitive to parameters related to synthesis and degradation of the protein Bmal1 and its mRNA. However, our analysis improved the overview of the input-output relationships on which the circadian clock period is based. The main patterns found in our previous analysis of the same model, using conventional (2-way) PLSR 7, were also confirmed in the global NPLSR metamodelling. However, the present cluster-based N-way analysis revealed additional aspects of the input-output relationships, for example the negative effect of increasing v dPCN on the state variable P C in the part of the model output space defined by Cluster 2. Hence, the N-way HC-PLSR-based metamodelling worked as intended in this illustration example. In the example used here, the focus was on oscillating state variables. Other types of behaviour of nonlinear dynamic systems such as multiple steady states could potentially lead to additional nonlinearities in the input-output mapping, probably increasing the gain of using a cluster-based approach compared to a global analysis.
An alternative to using NPLSR would be to unfold the state variable trajectory array by concatenating all the trajectory data into one 2-way matrix and use 2-way HC-PLSR to analyse the data. However, the information about related trajectories for different state variables would then be left unused, leading e.g. to loss of the opportunity to visualise covariance structures. In order to evaluate the gain of keeping the 3-way structure in the data, the same analysis was carried out using 2-way HC-PLSR on unfolded state trajectory data as well as on aggregated outputs calculated from the state trajectories. The clustering results from these analyses (shown in Additional file 1: Section S4) indicated that the increased resolution achieved using N-way HC-PLSR could not be achieved when the state trajectory array was unfolded into two dimensions or transformed into aggregated outputs, due to a less reasonable clustering of the observations. Hence, using the NPLSR factors seems to enable identification of more relevant clusters in which to carry out regional metamodelling. The global parameter prediction accuracies obtained were comparable to those obtained with the global inverse N-way PLSR. However, in the hierarchical metamodelling, neither the unfolded state trajectories nor the aggregated outputs could predict the circadian clock parameters with as high accuracy (on average) with 2-way HC-PLSR as with the N-way HC-PLSR.
In contrast to the results obtained using N-way HC-PLSR, our previously published metamodelling of each of the circadian clock state variables separately 8 showed that all circadian clock state variables could be predicted with high accuracy from the parameters (within the parameter space analysed in that publication, which was slightly different in the present analysis). However, there is a clear gain of using a common metamodel for all state variables in terms of obtaining overview of the input-output relationships as well as covariance patterns between the state variables. Nevertheless, as demonstrated here, a separate analysis of the input-output relationships for insufficiently described state variables should accompany this type of analysis in order to gain a more complete insight into the input-output relationships. This was illustrated in our application example for e.g. the state variable I N , which had to be logarithmised and analysed separately in order for its relationships to the input parameters to be adequately described.
In NPLSR, relations between model outputs and input parameters are easily interpretable through plots of the loadings, in contrast to results produced e.g. by genetic algorithms which are often more difficult to interpret (although the latter can also handle multiple outputs). Moreover, due to the decomposition of the data into estimated latent variables, NPLSR can provide efficient dimension reduction possibilities in high-dimensional systems. However, since the NPLSR models presented here used a high number of factors to explain the input-output covariance, the dimension reduction possibilities of NPLSR may not have been fully utilised. This was caused by the differences in the time-to-peak for the different state variables, which the NPLSR uses many factors to describe. Hence, a more careful pre-processing of the data would probably result in NPLSR models using fewer factors, perhaps through shift correction as described by Westad and Martens 29. Work is in progress on testing whether this allows the NPLSR models to use fewer factors while still keeping the same predictive ability. However, even when using relatively many factors, the NPLSR models still enable great dimension reduction possibilities.
Parameter value variances in the calibration and test set and the six N-way HC-PLSR clusters
Data set
v mB
v mC
v mP
v dPCN
v dIN
k 1
k 3
k 5
k 7
Calibr. set
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster 6
Test set
Since the selection of data subsets in N-way HC-PLSR is based on fuzzy clustering, no prior knowledge about the structure of the data is needed. Hence, this method automatically detects regions of different model behaviour. The number of clusters to use in the hierarchical metamodelling was here specified in advance, based on exploration of the predictive ability of metamodels of varying complexity. However, using instead an optimisation algorithm to find the optimal number of clusters would make semi-automatic exploration of input-output relationships of computational models possible.
The N-way HC-PLSR method presented here provides the opportunity to improve both prediction accuracy and analytical insight by identification of regional subsets of the data within which the relationships between input parameters and model outputs are more transparent than in a global regression analysis. This was exemplified by the model sensitivity to the two parameters k 1 and k 3 that was detected in the regional analysis but not in the global metamodelling.
Our results also indicate that analysing all state trajectories simultaneously using N-way methodology is more effective for identification of different behavioural domains for a system and regions where input-output mappings can be predicted with higher accuracy, than unfolding the state trajectory array into two dimensions or transforming state trajectories into aggregated outputs prior to the analysis. This is due to a more reasonable clustering of the observations. Moreover, application of the method for metamodelling in both the classical and the inverse direction represents a more comprehensive approach to the analysis of complex relationships between the model inputs and the temporal behaviour of the outputs, and allows more confident conclusions. Our results showed that the mammalian circadian clock model was highly sensitive to parameters related to the protein Bmal1, as previously found by Leloup and Golbeter 24, but in addition our approach revealed also more complex sensitivity patterns of the model.
Based on these results, we believe that the presented N-way HC-PLSR method will be instrumental for effective construction and validation of complex models. Due to its efficient handling of N-way data structures, demonstrated here in the analysis of the temporal model behaviour, we hypothesise that N-way HC-PLSR will be an especially useful tool for multivariate metamodelling of spatiotemporal models, a large future application area.
Generation of the in silico data set
A model of the mammalian circadian clock developed by Leloup and Goldbeter 24 was used to estimate the circadian oscillations of cellular activity in conditions of continuous darkness. The model consists of 16 coupled differential equations with state variables describing the dynamics of three key genes (Bmal1, Per and Cry), including their mRNA level, nonphosphorylated and phosporylated proteins as well as protein complexes. The model contains intertwined positive and negative feedback loops driving the circadian oscillations. A curated CellML implementation 303132 of the model was downloaded from http://models.cellml.org. The integration was carried out in SUNDIALS 2.3 33 using a wrapper for PySundials (http://pysundials.sourceforge.net) in the same way as in 8.
The parameter combinations in the calibration set were generated using an Optimised Multi-level Binary Replacement (OMBR) Design 26 of 9 variables with 8 equally spaced levels each (Table 1). This resulted in 8192 simulations with the circadian clock model. The ranges of each parameter are given in Table 1, and were found by an initial range-finding experiment published in 7. A full factorial design of 9 variables ´a 8 levels would require 89>134 million runs. Hence, the OMBR design was chosen, in order to explore the effects of as many parameters and parameter values as possible. In the OMBR design method, the values of a original parameters are replaced by multi-bit binary representations, and the binary factor bits are then combined in a fractional factorial design according to a chosen confounding pattern. Thereby drastically reduced experimental designs are obtained, yet maintaining reasonable coverage of the parameter space. The OMBR design has been compared to central composite designs and semi-random designs, and has been shown to give good predictive ability 7.
For each parameter combination the resulting differential equation model was solved from the original initial conditions (see 24) until convergence to a stable limit cycle. The test for convergence was done as follows: First the system was solved with rootfinding for variable M B to extract two complete cycles. Convergence of the cycle period was tested by requiring that the period difference relative to the mean of the periods for the two cycles should be less than 0.001. Convergence to synchronous oscillations was tested by (i) interpolating all 16 state variables at 200 equally spaced time points for each cycle, (ii) linearly transforming each state variable such that the minimum and maximum values of each cycle was 0 and 1, respectively, and (iii) requiring that the sum of absolute difference between the two cycles across all the 3200 interpolated time points should be less than 0.0001.
The data set resulting from the simulations of the mammalian circadian clock consisted of sampled values for one period (here 200 timesteps) of 16 state variables (corresponding to the 16 differential equations in the model), for the set of 8192 combinations of values for the nine varied input parameters. This gave a 3-way array of 8192x16x200 data points. A description of the mammalian circadian clock model state variables is given in Table 2. Due to the wide parameter ranges used, 57 (0.7%) of the 8192 simulations did not result in a stable limit cycle. These simulations were omitted in the following analysis. The parameters and state variables were mean-centred and standardised globally in the calibration set prior to the metamodelling.
A separate test set based on 8192 parameter combinations found by random Monte Carlo sampling 2728 within the same parameter levels as used in the calibration set was used. This resulted in 8125 converging test set simulations. In the test set, the variables were pre-processed in the same way as for the calibration set, using the global calibration set means and standard deviations.
N-way HC-PLSR
Our previously published method for nonlinear metamodelling, HC-PLSR 8, has here been extended to enable use of N-way data by using NPLSR 1934, giving N-way HC-PLSR. HC-PLSR 8 includes regional analysis using subsets of the original data set generated by fuzzy C-means (FCM) clustering 35363738 (see Additional file 1: Section S1).
In N-way HC-PLSR, a global NPLSR model comprising all observations is first generated, and FCM clustering (using Euclidian distance) on a chosen number of first mode (see Figure 1) factors (scores) of the global NPLSR X-factors (or alternatively, Y-factors) is used to separate the observations into groups within which regional NPLSR models are made. The FCM fuzzifier parameter was here chosen equal to 2. To prevent possibly unstable regression models due to a small number of calibration observations, we post-processed the clustering with the requirement that each cluster should contain at least ten observations. Smaller clusters (and their associated observations) were regarded as outliers, and not included in the subsequent regional regression analysis (but were still included in the global regression analysis).
The optimal number of factors to use in the global and regional NPLSR models, respectively, was here chosen according to the minimum cross-validated mean squared error (MSE) of prediction of the response array Y, with the extra requirement that each included component accounts for at least 1% of the total cross-validated Y-variance, in the same way as in 8. Here, 10-fold cross-validation where the ten segments were randomly chosen was used both in the global and the regional NPLS regressions. These ten segments were successively kept out of the NPLSR calibration, and predicted using an NPLSR model based on the remaining observations. NPLSR is described in Additional file 1: Section S1, and the MATLAB® N-way Toolbox v.3.11 34 was used here.
In our N-way HC-PLSR implementation, Linear Discriminant Analysis (LDA) 39, Quadratic Discriminant Analysis (QDA) 40 (the MATLAB® function "classify" from the Statistics Toolbox™ v7.6) or Naive Bayes classification (the MATLAB® function "NaiveBayes" from the Statistics Toolbox™ v7.6) can be used for classification of new observations to be predicted (based on predicted NPLSR factors for new observations). The implementation contains two options for prediction: 1) Prediction using the local regression model calibrated in the most probable cluster, and 2) Prediction using a weighted sum of the local regression models, using the estimated cluster membership values as weights. The N-way HC-PLSR was carried out in MATLAB® 41 Version 7.13 (R2011b), using in-house code which can be obtained from the authors upon request.
Classical and inverse metamodelling of the mammalian circadian clock model
The 3-way array of state variable data (observations x outputs x time points) was first used as regressor in a test set validated N-way HC-PLSR using the parameter combinations as response-variables (inverse metamodelling). To complement this analysis, analogous classical metamodelling with N-way HC-PLSR was carried out, where the parameter combinations were used as regressor variables to predict the 3-way state trajectory array. The calibration- and test sets calculated with the mammalian circadian clock model described above were used. The methodology is illustrated in Figure 2. The most probable local regression model was chosen for prediction, since this gave higher prediction accuracy than using a weighted sum of the local models. No clusters containing less than ten observations were identified, so all calibration set observations were included in the regional regression analysis.
In the inverse metamodelling, the clustering (and classification in the prediction stage) of the observations was based on the global predicted first mode X - factors, the X - scores ( in Additional file 1: Section S1), representing the state variable trajectory data. All factors found to explain a significant amount of the cross-validated calibration set Y-variation were included. The number of clusters to use was chosen by evaluating the ability to constrain (i.e. correctly predict) the input parameters from the model output in the inverse metamodelling, using from 1 to 20 clusters within the calibration set. The calibration set observations were treated as "new observations" (see Figure 2) here, that is, the same procedure as for the test set observations was used in the prediction stage. Using cross-validation to find the optimal number of clusters, as was done to find the optimal number of factors for the NPLSR models, would be too time consuming given the size of the datasets used here. The mean correlation coefficient (R2)-values for the prediction of the parameters were used as selection criterion, and using 6 clusters was considered optimal in order to balance metamodel complexity against predictive ability.
The same clusters were also used for the classical metamodelling, since the X-factors in the inverse metamodelling are more directly related to the model output state variables than the Y-factors from the classical metamodelling (a PLSR model is asymmetric). However, for the classification in the prediction stage of the classical metamodelling to be relevant, was predicted from the first mode predicted Y-factors, the Y-scores (calculated using equation S12c in Additional file 1), since in this case the Y-factors represent the state variable trajectories. This was done using second order polynomial Ordinary Least Squares (OLS) regression (including square terms and cross-terms), calibrated using calibration set from the classical metamodelling (* C A (see Additional file 1, eq. S12c)) as regressors and calibration set T XA,NWay from the inverse metamodelling () as response variables. Hence, , where is calibrated using polynomial OLS regression, and includes the calculation of from . The test set observations were then classified based on , according to the clusters found in the inverse metamodelling. See Additional file 1, sections S1.6 and S1.7 for a more comprehensive description of this methodology, including all predicting equations for test set observations.
QDA was chosen instead of LDA and Naive Bayes classification in this study, since LDA assumes the covariance matrix to be equal for all classes and Naive Bayes classification assumes that the presence of a particular feature of a class is unrelated to the presence of any other feature. In QDA, these assumptions are not made.
In the classical metamodelling, the sensitivity of each state variable to variations in the different parameters was estimated as the product of the second mode X-factors and the transpose of the second mode Y-factors (also called X- and Y- loadings), using the optimal number of NPLSR factors for the corresponding NPLSR model. The NPLSR loadings calculated here were not orthogonal. Work is currently in progress to assess the effects of using non-orthogonal loadings to estimate the model sensitivities.
Additional sensitivity analyses
Some of the input-output relationships were not well described by the N-way HC-PLSR. Additional separate sensitivity analyses were therefore carried out for some of the state variables using 2-way second order polynomial HC-PLSR with the parameters and their cross-terms and second order terms as regressors and the state trajectories as response variables, analogous to the analysis presented in 8. The regressors were mean-centred and standardised prior to the HC-PLSR, while the state trajectories were only centred. Some of the state trajectories were logarithmised prior to the regression analysis.
The same clusters as in the N-way HC-PLSR described above were used. QDA 40 on predicted PLSR Y- scores (see Additional file 1, eq. S7d) were used for classification, and all PLS components (PCs) explaining a significant amount of the cross-validated Y-variance were included. Both in the global and regional regression analyses the optimal number of PLS components to use was found by 10-fold cross-validation. In the regional regression models, the matrices of cross-terms and second order terms were deflated with respect to the variation described by the first order terms (in an OLS regression) in order to better separate the effects of nonlinear terms and first order terms. The HC-PLSR was carried out in MATLAB® 41 Version 7.13 (R2011b), using in-house code that can be obtained from the authors upon request.
Method benchmarking
For comparison, the inverse metamodelling was carried out using 2-way HC-PLSR where the 3-way state variable array was unfolded by concatenating the time series for all state variables, as well as by using aggregated outputs representing the state variable trajectories. The following aggregated outputs were derived from the state trajectories: period of oscillation, bottom, peak, time-to-bottom and time-to-peak for each state variable curve (see Additional file 1: Section S4). This resulted in 65 aggregated outputs. Both parameters, state variables and aggregated outputs were mean-centred and standardised prior to the HC-PLSR.
The number of PLS components to use in the PLSR models was chosen based on the percent explained cross-validated Y-variance as described in 8 and in the same way as for the N-way HC-PLSR, clustering and classification were done on the global X- scores of all PCs explaining a significant amount of the cross-validation variance. The same settings as described in 8 were used for the HC-PLSR, except that QDA 40 (the MATLAB® function "classify" from the Statistics Toolbox™ v7.6) was used for classification as in the N-way HC-PLSR. The same number of clusters as in the N-way HC-PLSR (6 clusters) was used also in the 2-way HC-PLSR analyses.
This study was supported by the National Program for Research in Functional Genomics in Norway (FUGE) (RCN grant no. NFR151924/S10) and by the Norwegian eScience program (eVITA) (RCN grant no. NFR178901/V30). Rasmus Bro is thanked for providing us with the newest version of The N-way Toolbox for MATLAB.
Authors' Affiliations
- Lillacci G, Khammash M: Parameter Estimation and Model Selection in Computational Biology. PLoS Comput Biol 2010, 6: e1000696. 10.1371/journal.pcbi.1000696View ArticleGoogle Scholar
- Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S: Global Sensitivity Analysis: The Primer. Chichester: Wiley-Interscience; 2008.Google Scholar
- Saltelli A, Chan K, Scott EM: Sensitivity Analysis. 1st edition. Chichester: Wiley; 2000.Google Scholar
- Santner TJ, Williams BJ, Notz W: The design and analysis of computer experiments. New York, USA: Springer; 2003.View ArticleGoogle Scholar
- Sarkar AX, Sobie EA: Regression Analysis for Constraining Free Parameters in Electrophysiological Models of Cardiac Cells. PLoS Comput Biol 2010, 6: e1000914. 10.1371/journal.pcbi.1000914View ArticleGoogle Scholar
- Sobie EA: Parameter sensitivity analysis in electrophysiological models using multivariable regression. Biophys J 2009, 96: 1264-1274. 10.1016/j.bpj.2008.10.056View ArticleGoogle Scholar
- Tøndel K, Gjuvsland AB, Måge I, Martens H: Screening design for computer experiments: Metamodelling of a deterministic mathematical model of the mammalian circadian clock. J Chemometr 2010, 24: 738-747. 10.1002/cem.1363View ArticleGoogle Scholar
- Tøndel K, Indahl UG, Gjuvsland AB, Vik JO, Hunter P, Omholt SW, Martens H: Hierarchical Cluster-based Partial Least Squares Regression is an efficient tool for metamodelling of nonlinear dynamic models. BMC Syst Biol 2011, 5: 90. 10.1186/1752-0509-5-90View ArticleGoogle Scholar
- Tøndel K, Vik JO, Martens H, Indahl UG, Smith N, Omholt SW: Hierarchical Multivariate Regression-based Sensitivity Analysis Reveals Complex Parameter Interaction Patterns in Dynamic Models. SubmittedGoogle Scholar
- Vik JO, Gjuvsland AB, Li L, Tøndel K, Niederer SA, Smith N, Hunter PJ, Omholt SW: Genotype-phenotype map characteristics of an in silico heart cell. Front Physio 2011, 2: 106.Google Scholar
- Kleijnen JPC: Design and Analysis of Simulation Experiments. 1st edition. New York, USA: Springer; 2007.Google Scholar
- Conti S, O'Hagan A: Bayesian emulation of complex multi-output and dynamic computer models. J Stat Plan Infer 2010, 140: 640-651. 10.1016/j.jspi.2009.08.006View ArticleGoogle Scholar
- Campbell K, McKay MD, Williams BJ: Sensitivity analysis when model outputs are functions. Reliab Eng Syst Safe 2006, 91: 1468-1472. 10.1016/j.ress.2005.11.049View ArticleGoogle Scholar
- Cacuci DG: Sensitivity and Uncertainty Analysis: Theory v.1: Theory Vol 1. 1st edition. Boca Raton: Chapman and Hall/CRC; 2003.View ArticleGoogle Scholar
- Cacuci DG, Ionescu-Bujor M, Navon IM: Sensitivity and Uncertainty Analysis: Applications to Large-scale Systems Vol 2. 1st edition. Boca Raton: CRC Press; 2005.View ArticleGoogle Scholar
- Ayyub BM, Klir GJ: Uncertainty modeling and analysis in engineering and the sciences. Boca Raton: Chapman & Hall/CRC; 2006.View ArticleGoogle Scholar
- Yener B, Acar E, Aguis P, Bennett K, Vandenberg S, Plopper G: Multiway modeling and analysis in stem cell systems biology. BMC Syst Biol 2008, 2: 63. 10.1186/1752-0509-2-63View ArticleGoogle Scholar
- Conesa A, Prats-Montalbán JM, Tarazona S, Nueda MJ, Ferrer A: A multiway approach to data integration in systems biology based on Tucker3 and N-PLS. Chemometr Intell Lab In Press.Google Scholar
- Bro R: Multiway calibration. Multilinear PLS. J Chemometr 1996, 10: 47-61. 10.1002/(SICI)1099-128X(199601)10:1<47::AID-CEM400>3.0.CO;2-CView ArticleGoogle Scholar
- Krishnaiah PR: Multivariate Analysis. New York: Academic Press Inc; 1967.Google Scholar
- Wold S, Martens H, Wold H: The multivariate calibration method in chemistry solved by the PLS method. In Lecture notes in Mathematics, Matrix Pencils. Heidelberg: Springer; 1983:286-293.Google Scholar
- Martens H, Næs T: Multivariate calibration. Chichester, UK: John Wiley and Sons; 1989.Google Scholar
- Martens H, Martens M: Multivariate Analysis of Quality: An Introduction. 1st edition. Chichester: Wiley; 2001.Google Scholar
- Leloup J-C, Goldbeter A: Modeling the mammalian circadian clock: Sensitivity analysis and multiplicity of oscillatory mechanisms. J Theor Biol 2004, 230: 541-562. 10.1016/j.jtbi.2004.04.040View ArticleGoogle Scholar
- Altinok A, Lévi F, Goldbeter A: Identifying mechanisms of chronotolerance and chronoefficacy for the anticancer drugs 5-fluorouracil and oxaliplatin by computational modeling. Eur J Pharm Sci 2009, 36: 20-38. 10.1016/j.ejps.2008.10.024View ArticleGoogle Scholar
- Martens H, Måge I, Tøndel K, Isaeva J, Høy M, Sæbø S: Multi-level Binary Replacement (MBR) design for computer experiments in high-dimensional nonlinear systems. J Chemometr 2010, 24: 748-756. 10.1002/cem.1366View ArticleGoogle Scholar
- Kalos MH, Whitlock PA: Monte Carlo Methods Volume 1: Basics. 1st edition. New York, USA: Wiley; 1986.View ArticleGoogle Scholar
- Liu JS: Monte Carlo Strategies in Scientific Computing. New York, USA: Springer; 2008.Google Scholar
- Westad F, Martens H: Shift and intensity modeling in spectroscopy–general concept and applications. Chemometr Intell Lab 1999, 45: 361-370. 10.1016/S0169-7439(98)00144-0View ArticleGoogle Scholar
- Lloyd CM, Halstead MDB, Nielsen PF: CellML: its future, present and past. Prog Biophys Mol Bio 2004, 85: 433-450. 10.1016/j.pbiomolbio.2004.01.004View ArticleGoogle Scholar
- Cuellar AA, Lloyd CM, Nielsen PF, Bullivant DP, Nickerson DP, Hunter PJ: An Overview of CellML 1.1, a Biological Model Description Language. SIMULATION 2003, 79: 740-747. 10.1177/0037549703040939View ArticleGoogle Scholar
- Lloyd CM, Lawson JR, Hunter PJ, Nielsen PF: The CellML Model Repository. Bioinformatics 2008, 24: 2122-2123. 10.1093/bioinformatics/btn390View ArticleGoogle Scholar
- Hindmarsh AC, Brown PN, Grant KE, Lee SL, Serban R, Shumaker DE, Woodward CS: SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers. ACM Trans Math Softw 2005, 31: 363-396. 10.1145/1089014.1089020View ArticleGoogle Scholar
- Andersson CA, Bro R: The N-way Toolbox for MATLAB. Chemometr Intell Lab 2000, 52: 1-4. 10.1016/S0169-7439(00)00071-XView ArticleGoogle Scholar
- Bezdek JC: Pattern Recognition with Fuzzy Objective Function Algorithms. Norwell: Kluwer Academic Publishers; 1981.View ArticleGoogle Scholar
- Berget I, Mevik B-H, Næs T: New modifications and applications of fuzzy C-means methodology. Comput Stat Data Anal 2008, 52: 2403-2418. 10.1016/j.csda.2007.10.020View ArticleGoogle Scholar
- Næs T, Isaksson T: Splitting of calibration data by cluster analysis. J Chemometr 1991, 5: 49-65. 10.1002/cem.1180050106View ArticleGoogle Scholar
- Næs T, Kubberød E, Sivertsen H: Identifying and interpreting market segments using conjoint analysis. Food Qual Prefer 2001, 12: 133-143. 10.1016/S0950-3293(00)00039-2View ArticleGoogle Scholar
- McLachlan GJ: Discriminant Analysis and Statistical Pattern Recognition. 1st edition. Hoboken: Wiley-Interscience; 1992.View ArticleGoogle Scholar
- Hastie T, Tibshirani R, Friedman JH: The Elements of Statistical Learning. New York, US: Springer; 2003. Corrected edition.Google Scholar
- The MathWorks, Inc.: MATLAB®, v. 7.13. 2011.Google Scholar
- Martens H, Veflingstad S, Plahte E, Martens M, Bertrand D, Omholt S: The genotype-phenotype relationship in multicellular pattern-generating models - the neglected role of pattern descriptors. BMC Syst Biol 2009, 3: 87. 10.1186/1752-0509-3-87View ArticleGoogle Scholar
- Isaeva J, Sæbø S, Wyller JA, Nhek S, Martens H: Fast and comprehensive fitting of complex mathematical models to massive amounts of empirical data. Chemometr Intell Lab In press.Google Scholar
- Isaeva J, Martens M, Sæbø S, Wyller JA, Martens H: The modelome of line curvature: many nonlinear models approximated by a single bi-linear metamodel with verbal profiling. Physica D 2012, 241: 877-889. 10.1016/j.physd.2012.02.002View ArticleGoogle Scholar
- Tenenhaus M, Vinzi VE, Chatelin Y-M, Lauro C: PLS path modeling. Comput Stat Data Anal 2005, 48: 159-205. 10.1016/j.csda.2004.03.005View ArticleGoogle Scholar
- Pearson K: On lines and planes of closest fit to systems of points in space. Philos Mag 1901, 2: 559-572. 10.1080/14786440109462720View ArticleGoogle Scholar
- Jolliffe IT: Principal Component Analysis. 2nd edition. New York, US: Springer; 2002.Google Scholar
- Jolliffe IT: A note on the use of principal components in regression. J Roy Stat Soc C-App 1982, 31: 300-303.Google Scholar
- Hoerl AE, Kennard RW: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 1970, 12: 55-67. 10.1080/00401706.1970.10488634View ArticleGoogle Scholar
- Tibshirani R: Regression Shrinkage and Selection via the Lasso. J Roy Stat Soc B Met 1996, 58: 267-288.Google Scholar
- Zou H, Hastie T: Regularization and variable selection via the Elastic Net. J Roy Stat Soc B 2005, 67: 301-320. 10.1111/j.1467-9868.2005.00503.xView ArticleGoogle Scholar
- de Jong S: SIMPLS: an alternative approach to partial least squares regression. Chemometr Intell Lab 1993, 18: 251-263. 10.1016/0169-7439(93)85002-XView ArticleGoogle Scholar
- Martens H: Non-linear multivariate dynamics modelled by PLSR. In Proceedings of the 6th International Conference on Partial Least Squares and Related Methods. Beijing: Publishing House of Electronics Industry; 2009:139-144.Google Scholar
- Berglund A, Wold S: INLR, implicit non-linear latent variable regression. J Chemometr 1997, 11: 141-156. 10.1002/(SICI)1099-128X(199703)11:2<141::AID-CEM461>3.0.CO;2-2View ArticleGoogle Scholar
- Gath I, Geva AB: Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 1989, 11: 773-780. 10.1109/34.192473View ArticleGoogle Scholar
- Frigui H, Krishnapuram R: A robust competitive clustering algorithm with applications in computer vision. IEEE Trans Pattern Anal Mach Intell 1999, 21: 450-465. 10.1109/34.765656View ArticleGoogle Scholar
- Bro R: PARAFAC. Tutorial and applications. Chemometr Intell Lab 1997, 38: 149-171. 10.1016/S0169-7439(97)00032-4View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


text:
What we offer
Discover a wealth of courses, on-site training programs, and online learning opportunities offered by certified trainers and industry professionals.
E-Learning courses
We offer customizable, up-to-date e-learning courses in 18 different languages for up to 10,000 trainers, gym staff, and customers.
Home courses
A certified Technogym personal trainer will teach your selected course from the comfort of your own home.
For Trainers
The courses are designed to increase the trainers' knowledge and understanding of Technogym's equipment in order to allow them to support, motivate and encourage their customers.
Individual Training Courses
Get one-on-one training from a Wellness Institute Master Trainer or on-site trainer with a solid professional background and track record.
Group Training Courses
Courses can be entirely customized to meet your facility's requirements, and consist of group training sessions at various levels for staff members and trainers alike.
For Business Operators
Get the most out of your facility and increase your revenue with our range of business services.
Business Consultancy
We offer customized Business Consulting programs that make use of the most successful software applications, including Wellness System, Contact Manager, and Club 2.0.
Club 2.0
Club 2.0 is an innovative way to organize your business activities by categorizing your club's users based on their aspirations (i.e. discovering what truly motivates different people to work out).


text:
Offshore Energy Research Association of Nova Scotia (OERA) is an independent, not-for-profit organization that funds and facilitates collaborative offshore energy and environmental research and development including examination of renewable energy resources and their interaction with the marine environment. Our mission is to lead environmental, renewable and geoscience energy research that enables the sustainable development of Nova Scotia energy resources through strategic partnerships with academic, government and industry.
OERA was previously known as Offshore Energy Environmental Research (OEER) and Offshore Energy Technical Research (OETR). In April 2012, OEER and OETR amalgamated to become Offshore Energy Research Association of Nova Scotia (OERA), the Nova Scotian leader of research in marine petroleum exploration, development and production and renewable energy technologies.
OERA's members are St. Francis Xavier University, Acadia University, Cape Breton University, Saint Mary's University, Dalhousie University, Nova Scotia Community College and the Nova Scotia Department of Energy.
OERA funds research in the following areas:
The OERA has undertaken considerable research on the viability of energy exploration on Nova Scotia's offshore. The result is a comprehensive Play Fairway Analysis and Geoscience Data Package Program.Learn More About Our Offshore Energy Research Program
Nova Scotia is home to an incredible natural energy resource, the Bay of Fundy. With the world's highest tides, it's been identified as potentially the best tidal power generation site in North America.Learn More About Our Marine Renewable Energy Research
We have conducted a number research projects dedicated to increasing our knowledge on the potential impacts of seismic exploration on marine invertebrates.Learn About Our Marine Sound Research Projects
Create a few Pages, don't forget to try out the Portfolio template and Traditional Blog template using category filters! Templates can be selected from the right side of the page editor.


text:
|Home > Florida Then & Now > A Short History of Florida > Character Mapping Strategy
Character Mapping
This strategy helps students select and describe a character from a story, and then compare/contrast it to another character from either the same story or another.
After reading a story, the students choose a character they wish to describe in detail.
The students draw a picture of their character and/or write its name in the middle of a blank piece of paper.
- The students draw a short line outward from their picture for each description they attribute to their character.
- The students create a character map for two characters in their story to compare/contrast them, or take characters from two different stories to compare and contrast.
- The students share their character maps with the class.
Santa, C. (1993). Pegasus: Teacher implementation guide for grade 4. Dubuque, Iowa: Kendall/Hunt Publishing Company.
|Home > Florida Then & Now > A Short History of Florida > Character Mapping Strategy
Florida: A Social Studies Resource for Students and Teachers
Produced by the Florida Center for Instructional Technology,
College of Education, University of South Florida © 2002.


text:
I'm trying to perform at a high level and show either these coaches or those around the NFL what I can do.
(PRWEB) December 15, 2012
The most winning Quarterback in the history of Michigan State football might get the chance he has waited his whole life for this Sunday. Lynx Shreds Athlete Kirk Cousins might possibly be the Redskins starting quarterback this weekend due to RG3's right knee injury in last Sundays game vs. the Baltimore Ravens. RG3 sprained his right LCL in his knee when he was tackled by Haloti Ngata late in the 4th quarter. Once the injury happened, back up rookie quarterback Kirk Cousins took the field and threw a touchdown and ran in a two point conversion to tie up the game which lead to overtime. After the Redskins defense stopped the Ravens first possession they got the ball back and won the game with a field goal 31-28.
If Kirk has to start Sunday against the Cleveland Browns, teammates and coaches have no doubt in Cousins' ability to keep the Redskins' winning streak alive.
"My dad said to me for a long time when I was struggling to see if I was going to play at Michigan State, he said, 'Kirk, the cream always rises to the top. If you're good enough, in time, you'll get your shot. And if you're not, you won't,"' Cousins recalled. "I'm not worried about it in the sense that if I'm good enough, I'll be able to show people over time what I can do, and if I'm not, I won't."
Cousins is a Lynx Shreds Athlete along with several other current NFL and NBA stars that promote the clothing brands' lifestyle and vision. "The Lynx vision lives through pushing your friends/teammates and yourself to get better and go bigger and harder in every physical activity that you do. Putting in the work and time to improve at your sport and following your passion to the extreme. If Cousins gets the opportunity to start this Sunday he will do just that."
That vision is the heart and soul behind Lynx Shreds: "Lynx Shreds isn't just about the clothing but about what you are doing and where, but it's also who you are doing it with and giving it everything you've got. Pushing yourself and your friends to make each other the best you can possibly be. The company is creating apparel that is constantly changing to meet the needs of the hard-charging athletes such as Kirk doing what they love."
With this type of vision Lynx Shreds has, the brand produces MMA gear, along with a surf clothing, skate clothing, and other extreme sports style theme. If you or other athletes you know can relate to the Lynx Shreds vision, then Lynx apparel would be the perfect fit. Lynx is now running a christmas sale on their website listed below.


text:
Where's Kevin?
We just noticed that Kevin Systrom, the cofounder of Instagram, has yet to post a single photo on the service in 2013.
Systrom used to post about 15 photos a month—so he wasn't prolific, but definitely a steady user of his own service. It may be coincidence, but his Instagram frequency declined sharply after Facebook closed on its acquisition of his company and the Instagram team started working out of Facebook headquarters in Menlo Park.
One explanation is just that Mark Zuckerberg is keeping his new hire busy. But Systrom did post one photo publicly to Facebook, a panoramic shot of the Alpine Meadows ski resort in Tahoe, using the Facebook iPhone app, in January.
"As you can imagine, Kevin's a busy guy," Facebook spokesperson Meredith Chin told us. "But of course he uses Instagram every day, even if he doesn't post. He likes and comments on photos all the time."


text:
Carrie Fisher, 50, has acted in 37 films and written four novels. Currently, she is starring in a one-woman show, Wishful Drinking, at Los Angeles's Geffen Playhouse. Previously, our correspondent went up against her father, Eddie Fisher, and her mother, Debbie Reynolds—the first time has interviewed every member of a Hollywood family.
George Wayne: You are one of those rare creatures in that concrete jungle called Hollywood. You really have no enemies. Everyone—and I mean everyone—loves Carrie Fisher.
Carrie Fisher: Oh, not so fast. I don't know who you've talked to. Certainly there are people who like me, but then there are those who don't know me who gossip about me. You can't believe the things I've heard.
What's one of the more outrageous rumors you've heard about yourself lately?
The most outrageous one of late is that I've slipped on heroin. The one great thing I did, in terms of living out here, is that I never found a heroin dealer.
Well, I am glad you brought up that subject. A friend of yours, a Republican operative by the name of R. Gregory Stevens, overdosed at your mansion.
This friend of mine had a very dangerous job. He ran presidential elections in very unstable countries, so for him to have died anywhere else was like Patton dying in a car accident. He did drugs. A lot of people were staying at my house, which is why he was sleeping with me. He came home around midnight, and he and I sat up and talked. He was very tired, and he then went to bed. When I got into bed, I put a pillow between us and I woke him up. We watched TV for a little bit. I was woken up in the morning by my friend Bruce Cohen because we were going to have tango lessons. I had planned to make soufflé that morning. And I went around the bed to wake up Greg and he was dead.
Oh my God!
And from the first moment I blamed myself. I thought I'd put the pillow on his face. I was in shock for months. I thought I had killed him because it had happened on my watch and I had failed to save him. They say his body was so worn out from drug use that if it hadn't happened that night it would've happened on another one.
And you live in this legendary Beverly Hills mansion, built in the 1930s, where Bette Davis lived, and where also the famous costume designer Edith Head lived.
Bette Davis lived here. Robert Armstrong built it. He played the filmmaker in King Kong.
There must be so many ghosts roaming those grounds.
No … no, but we had Greg for a while. I was totally convinced, which kind of made me happy, because I kept thinking, Please don't go.
What do you mean?
Lights would go on and off, and I had this toy machine, that when you touched it would say, "Fuck you! Eat shit! You're an asshole!" And it would go off in the night, by itself, in my closet. I was a nut for a year, and in that year I took drugs again.
Did you have an exorcist come over and clear the air?
My friends did, and after that things were fine. But you know, he didn't just die in his sleep; he died in mine. So it's still not gone, and I don't think it will ever be.
Well, now you're back at center stage again, as it were, with what is probably your Act V, this one-woman show!
And I'm singing in it, too, which I haven't done in 33 years. It's an unauthorized autobiography.
It's funny, your mother sat for an interview with me in 1997, your father in 1999. I'll never forget going to meet your mother at the Essex House hotel and walking into her room, and she was steaming her clothes over an ironing board!
That must have been a blip. She lives next door to me, to my compound, which I call "Kennecuntport."
And speaking of Kennecuntport, I found it very intriguing to learn that you had a very cute houseguest.
You mean James Blunt. Yes, he was here for about four months, and he ended up using my bathroom a lot because it has a piano in there and the acoustics are good.
And how did James Blunt pay the rent? I mean, he's cute, sensitive, tall, and pale.
I will never tell, and I don't know if I am that sort of landlady.
Oh please! If it was moi, I would be like, "All right, James, put down the lyric book and show me some bedroom judo."
Yeah, like on your knees. No, I have a little more class than that. I am a very discreet human when it comes to other people. I don't tell.
O.K., so something happened.
Absolutely not, but I did become his therapist. He was a soldier. This boy has seen awful stuff. Every time James hears fireworks or anything like that, his heart beats faster, and he gets "fight or flight." You know, he comes from a long line of soldiers dating back to the 10th century. He would tell me these horrible stories. He was a captain, a reconnaissance soldier. I became James's therapist. So it would have been unethical to sleep with my patient.
I would be remiss if I didn't ask how you ended up in Star Wars.
I slept with some nerd. I hope it was George.
You weren't sure?
No … I took too many drugs to remember.
And that famous metal swimsuit from the movie, do you own it?
No, but I will have to borrow it from George sometime when I am really depressed and I want to be even more depressed. I get along very well with George Lucas. He always comes to my birthday party. I also wear the wig from the movie in my show.
Your poor daughter, Billie … She is what, like 14 going on 24?
She's got my mouth and she's got my brains.
She should be best friends with Courtney Love's daughter.
She is. They are friends. Courtney lived next door to us; Frances stayed over the other night.
After an 11-month marriage to Paul Simon, all he left you with was one of his acoustic guitars.
And nine songs written about me.
And Dan Aykroyd, with whom you got cold feet at the altar.
Yeah, because he would have cheated on me for years and years. But it would have been funny. And he's adorable and we had a lovely whatever-the-hell-it-was. That was like a rebound from everything else.
And you've hinted that someone you like may move in with you.
And the funny thing is, since someone died here there have been more people living here than ever before. You kind of expect people to say, "So where did it happen?" No one has ever done that.
And what does your Dr. Feelgood have you on these days to keep your bipolar disorder at bay?
Oh, man, name it. I take Tegretol, Seroquel, Lamictal, Topamax.
But no Percocet.
No, darling, that's the bad news.
How many pills a day?
About seven.
I guess that's not bad.
Talk to my liver about that.
Is there a day that you can go without taking any pills?
No, I am a mental diabetic.
Well, as you have said in the past, "Instant gratification takes too long."
That's right.
And your new show is at the Geffen Playhouse. You and David Geffen are friends, and I know you don't like to talk about other people, but I am asking: Is it true, as the blogs say, that he is dating actor Seann William Scott?
I don't know, but I would doubt it. That's just the same as saying he married Keanu Reeves when they had never even met. I was actually with David at the Geffen when he ran into Keanu. He said to Keanu, "Was it as good for you as it was for me?" They had never even met, and all that stuff had come out about them being married.
Well, count your blessings, Carrie. The Force is with you.
George Wayne has been a Vanity Fair contributor since 1998.
Follow to get the latest news and analysis about the players in your inbox.See All Players


text:
hi jenkerz
Hey you scottish twat, I came back to visit!
Happy birthday :)
You're a legend.
I think a pink panther would suit you better ;):
don't act like you can't afford it, I know selling your body pays pretty well
Come to California for a pint sometime, you beautiful Scot
hi red
Hello Mr. Not Red
did you got killed or something? Your profile is all red
What the fuck did you just fucking say about me, you little bitch? I'll have you know I graduated top of my class in the Navy Seals, and I've been involved in numerous secret raids on Al-Quaeda, and I have over 300 confirmed kills. I am trained in gorilla warfare and I'm the top sniper in the entire US armed forces. You are nothing to me but just another target. I will wipe you the fuck out with precision the likes of which has never been seen before on this Earth, mark my fucking words. You think you can get away with saying that shit to me over the Internet? Think again, fucker. As we speak I am contacting my secret network of spies across the USA and your IP is being traced right now so you better prepare for the storm, maggot. The storm that wipes out the pathetic little thing you call your life. You're fucking dead, kid. I can be anywhere, anytime, and I can kill you in over seven hundred ways, and that's just with my bare hands.
That would require 2x more energy... pfft.. im way too lazy for that!
Jangan doesnt approve!
Mmmm... its too long!
Pink Panther Legend? da faq!
Yeah, that's how I felt when I had to give up my visor position. Although, at times, I do miss it.. & thank you. We're doing a lot better.
Weird to see you blue Nick.. hope everything is alright.
There's no RL just AFK
Custom title enabled
Account Inactive
ex visor
Omega Male
RZ Godfather


text:
Jalapeno Jelly?! Yes, you read that right. For those of you who have not tried it, it's really much better than it sounds.
What you'll need:
12-15 medium to large jalapenos
2 packages liquid pectin
2 cups apple cider vinegar
6 cups sugar
First, stem and seed peppers. Place peppers into blender with 1 cup vinegar and run on chop setting until no large chunks remain.
Pour into large pot and add other cup of vinegar and sugar. Bring to a hard boil and boil for 10 minutes.
Add two packages of pectin and boil for one minute. Here's a handy trick I learned. Cut off the tops of your liquid pectin and stand them in a glass, or in a measuring cup. Because timing is so critical in jellies, this will really help!
Remove from heat, skim off any foam, and pour into jars, leaving 1/4" head space. Wipe rims, apply lids and rings. Process for 10 minutes in water bath.
Now that you have tons of it, what are you going to do with it? I prefer to eat this on wheat crackers with some cream cheese. Leave a comment below telling us how YOU like jalapeno jelly.
Don't forget about the GIVEAWAYS this week! Today is the last day to get your chance to win. (I will take entries until 11:59 p.m. EST Friday-please note the linky contest will end at 4 p.m. today) Here are the ways to get your entries in:
- One entry for joining our Facebook page
- One entry for linking up in our Linky Party blog post contest (AND you'll get entered to win an awesome cookbook, which you can also buy on Amazon--makes a great gift)
- One entry for commenting on one of my posts
- One entry for commenting on one of Ott, A's posts
- One entry for putting our "Canning Week" button on your blog (please comment and let me know you did)
- One entry for Tweeting on Twitter (please comment and let me know and use "canning" as a tag)


text:
Minimalist LEGO Versions of Your Favorite Cartoon Characters
Spotted by Super Punch, these clever (if perhaps, not entirely original) LEGO print advertisements by German agency Jung Von Matt are like a guessing game of sorts. Click through to see if you're able to correctly identify some of your favorite cartoon characters when they're presented in extremely minimalist minifig-form.
Donald Duck & Huey, Dewey, and Louie
Bonus, puppet-inspired ad:


text:
Barry Vogel: Home Performance Pioneer
This online-only article is a supplement to the Fall 2017 print edition of Home Energy Magazine.
September 03, 2017
Have you ever dreamed about buying a plot of land and building a cabin, a retreat, or even your year-round home? Barry Vogel, an Attorney, Counselor and Mediator, in Ukiah, California, has done this not once but three times. The last time was in 1986, when a friend told him about an acre of land for sale a quarter mile from downtown Ukiah, just a four-minute walk from his law office. Not wanting to miss out on this prime location, he and his wife made a full-price offer that very day. Choosing to live so close to work was a way of designing his life to avoid car commuting. Although that particular decision was carefully planned, his choice of Ukiah was more serendipitous. "Before I settled on Ukiah as a place to live, I wanted to check-out another town in Trinity County, " he says, "but I liked Ukiah so much I never left."
To read complete online articles, you need to sign up for an Online Subscription.
Once an order has been placed there is an automatic $10 processing fee that will be deducted with any cancellation.
The Home Energy Online articles are for personal use only and may not be printed for distribution. For permission to reprint, please send an e-mail to firstname.lastname@example.com.


text:
Product Description
Packed with versatile features, the SR22® is lightweight (just 17.5 ounces) and perfect for just about anything - plinking, target shooting and even small game hunting. It is easy to field strip, fun to shoot, and will run reliably with a wide variety of ammunition (not just high-velocity ammunition).
Additional Information
|Enable Recurring Profile


text:
Windows - Security
Kaspersky AntiVirus v7 0 1 32 Final (+ FRESH KEYS)
Download your torrents safely! And Anonymously!
Torrent info
Name:Kaspersky AntiVirus v7 0 1 32 Final (+ FRESH KEYS)
Infohash: 573F88C31C6769E8FFCDD2F230CD1E864AD4E420
Total Size: 26.36 MB
Magnet: Magnet Download
Seeds: 0
Leechers: 0
Stream: Watch Full Movie @ Movie4u
Last Updated: 2016-01-29 11:05:02 (Update Now)
Torrent added: 2008-06-27 15:55:24
Torrent Files List
kav7.keys.exe (Size: 26.36 MB) (Files: 4)
kav7.keys.exe105.50 KB
KeyViewer.exe278.50 KB
READ ME!!!.txt3.54 KB
Version 188.8.131.521 ( English ) 25.82 MB.exe25.98 MB
trackerleech seeds
Torrent description
Kaspersky Internet Security achieves a superb 98% detection rate in testing conducted by PC Pro experts. With a final score that included ease of use and value as well as effectiveness, Kaspersky Internet Security outshone all competition in winning best security suite.
Kaspersky Lab delivers the world's most immediate protection against IT security threats, including viruses, spyware, crimeware, hackers, phishing, and spam. Kaspersky Lab products provide superior detection rates and the industry's fastest outbreak response time for home users, SMBs, large enterprises and the mobile computing environment. Kaspersky technology is also used worldwide inside the products and services of the industry's leading IT security solution providers. Learn more at www.kaspersky.com. For the latest on antivirus, anti-spyware, anti-spam and other IT security issues and trends, visit www.viruslist.com.
Kaspersky Internet Security scored an impressive 9 out of 10 points in a security review carried out by Micro Mart. The score was based on performance, features, ease of use and price.
Windows and Vista Compatible
related torrents
Torrent namehealth leech seeds Size
Kaspersky AntiVirus v7 0 1 32 Final (+ FRESH KEYS)2 0 26.43 MB 0
Kaspersky AntiVirus v7 0 1 32 Final(FRESH KEYS 17 01)10 3 26.13 MB 0
Kaspersky AntiVirus v7 0 1 32 Final(FRESH KEYS 1700385I5 8 285.50 KB 0