 the optimal threshold might have been obtained if our test networks had an average degree in the same range of the DREAM4 networks. Furthermore, we simulated data with networks generated with the ER algorithm, which have significantly different topology than those used in DREAM4. Also, note that the performances on the DREAM4 benchmarks are much more sensitive to the value of t then we observed in the tests of our in silico data. Obviously this is due to the fact that we used a large ensemble (1000 networks) over which the performances were averaged, but it also indicates that the DREAM4 benchmarks consist of a set of networks with widely varying topologies.

Discussion
We described an algorithm to infer gene regulatory networks from expression data, that proved to be effective by best performing at the DREAM4 In Silico Network challenge in the 100-gene networks sub-challenge. The proposed technique combines the advantages of the standard score in highlighting the deviation from the mean after a gene knockout, with the downranking algorithm that reduces the confidence initially predicted to unnecessary feed-forward edges.
Our algorithm is substantially different from the techniques used by the best performer teams of previous DREAM In Silico Network challenges. In particular, for DREAM2 the winning  approach was fitting ordinary differential equation (ODE) models [27,30]; for DREAM3, instead, the best method was based mainly on finding significant deviations from wild type in knockout data (so using the same primary source of information of our algorithm), but also applied ODE models on the time series for additional predictions [31].
To see how methods based on ODEs would perform on the DREAM4 data, we analyzed them with one of the best performer algorithm [27] for the DREAM2 In Silico Network challenges. The predictions of this algorithm on the DREAM4 100-gene networks was very poor (average AUC(ROC) = 0.5722, AUC(PvsR) = 0.0313). Note that in DREAM2 there was no noise added to the in silico data, while for DREAM4 both biological and experimental noise were present. Since the internal noise is propagated through gene relationships, its effect on large networks make sophisticated models (like ODEs) much less reliable than our method based on simple cause-effect logic and graph inspection.
Further increases of the performance of our algorithm may be obtained by studying the possible relationships between the selected threshold t and other parameters, like the network average degree and size, the noise on the knockout data, and so on. Moreover, while the second step of our algorithm improves the inference of the so-called cascade motif [32], it should be possible to reduce also the systematic errors in the prediction of other network motifs (e.g. the fan-in and fan-out errors [32]). Finally, also the rest of the available data from the DREAM challenges (knockdowns, time series, multifactorial) may be used to refine the network prediction, but the gain would probably be small, as already shown by the DREAM3 best performer [31].
It has become unambiguously clear that systematic perturbations (e.g. experimental gene knockouts) are needed to establish the directed structure of GNs. However, systematic single-gene knockouts imply experimental requirements which are unrealistic and these experiments infeasible (and unethical) for many organisms. It is unlikely that data such as considered here will become available from real experiments. Fortunately, ''systems genetics'' experiments may provide an alternative. In systems genetics experiments a population under study is genotyped and gene expression profiled are simultaneously collected (possibly even including metabolomics and proteomics data [33]). It has been demonstrated that causal links in GNs can be elucidated based on these data (see [34,35] for reviews). Genetic polymorphisms, naturally present in the populations, act as genetic perturbations: if the gene activity of a gene B is affected by a polymorphism inside another gene A, this is highly indicative for a causal effect A?B. In fact Liu et al. [34] proposed a very similar strategy as the one outlined in this paper: first creating a causal influence network (but based on systems genetics data instead of knockout data like is done here) and subsequent sparsification of this network to retain only the edges corresponding to direct causal influences. In that approach each edge in the initial network was statistically tested for being supported by the data, while we were here not able to do so based on the data considered here. Down-ranking edges based on our simple graphical inspection is very useful in the context of systems genetics data as it will provide the sparsest network supporting the causal influences. This then allows methods like the one of Liu et al. approach to statistically identify the networks best supported by the data by adding edges, rather than removing edges from the causal influence network. Heuristic model search algorithms are strongly dependent on a good initial guess in the network space: we argue that networks which result from the algorithm described in this paper will provide a better initial guess than the initial causal influence network, as GNs are known to be sparse. In this sense, the resulting networks from our approach here should not be seen as the final prediction, but rather as inputs to more sophisticated methods involving thorough statistical testing. Nevertheless, as evidenced by its winning performance over 18 other participating teams in the DREAM4, this method can be considered state-of-the-art on its own.

Author Contributions
Conceived and designed the experiments: AdlF. Performed the experiments: AP. Analyzed the data: AP NS. Contributed reagents/materials/ analysis tools: AP NS. Wrote the paper: AP NS AdlF.You will need a head for heights to visit Slieve League; at 600 meters (2,000 feet), these are the fifth-tallest sea cliffs in Europe. What makes them so special is that they are also some of the most accessible ones. Nearly three times higher than the Cliffs of Moher in County Clare, they offer gasp-worthy views of the swirling waters of the Atlantic Ocean. On a clear day you can see as far as County Sligo or the coast of County Mayo. Only experienced walkers should tackle One Man’s Pass, a narrow trail that reaches the highest point, but there is a network of easier trails for visitors of any ability to enjoy.
At Slieve League/Sliabh Liag, these 600-meter (2,000-foot)-high sea cliffs are among the highest in Europe. The nervewracking drive up to the carpark, with a sheer drop on one side, is enough of a challenge for most people, but it's worth the panic for the views from the top. Only experienced and brave hikers might want to venture to the summit along One Man’s Pass, with its sheer drops and narrow ledges. The reward is far-reaching views as far as the strand at Malin Beg. Or book onto a (seasonal) boat trip from Teelin to see the cliffs from the sea.Episode 018: So it’s just stickers on the wall?
This week’s episode has three parts. First we talk about knowing what customers are truly thinking. Second, we talk about we as designers look for in our customers. Third, it’s all about measuring things so you know how and when something can and should get fixed.
Available here and on iTunes. Follow us on Twitter and Facebook, too. Or follow Justin directly on Twitter.
Podcast: Play in new window | DownloadChervil is native to Europe and Asia but is naturalized in parts of eastern North America. The Romans, who brought it to Britain, cooked and ate both the foliage and the skinny, white taproots. Chervil is used to season poultry, seafood, young spring vegetables (such as carrots), soups, and sauces. The herb is also rich in many antioxidants, vitamins, including vitamin A, C, E, and phenolic antioxidants like lutein and cryptoxanthin. Chervil has had various uses in folk medicine.I rarely watch the news but have been following it a bit now with all the floods and heavy rains going on here in Scandinavia, and yesterday my eyes caught an article in the newspaper about vegetables that can be bought in the supermarkets are full of poison/chemicals (not that that was a news-flash to me) so I’ve got really inspired to write a post about how to grow your own vegetables.
The garden in the images belong to me and my neighbor who is also a very dear friend of mine.
(Click on the pictures to see them better/larger, and then ‘back’ to get back to the post again).
~Enjoy the article!
First you need to find a spot in your garden where you want to grow the vegetables and then simply start digging, remove rocks and weeds etc. It doesn’t have to be deep because you will fill the spot with soil afterwards.
As you can see there’s some work to do when digging and making space for the garden.
When you have done the digging, the next thing you need to do is to get some soil.
Me and my friend bought 6 large sacks (50 liters each) for only 200 Norwegian kroner, which is about $30 and that’s plenty of soil and will be enough for a garden with the size we have. (Although we had some soil left from last year too).
In addition to the soil, we also bought organic fertilizer (but you don’t necessary need that) because we created the veggie garden a few weeks later than normal because of all the rain.
The organic fertilizer was about 300 Norwegian kroner, which is about $47.
Fill the veggie garden with the soil and mix some of the fertilizer into the soil. (You need very little, be sure to read the