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When you start with AWeber, your first month is free.

Pricing is based on the number of subscribers, hence my point above about managing your total subscriber count.

Try AWeber

>> Check our price comparison section at the end of this article to see how AWeber stacks up against GetResponse and MailChimp.

Simon Grabowski founded GetResponse in 1998. It now serves more than 350,000 customers in 182 countries and is available in 21 different languages. They market themselves as the world’s easiest email marketing platform .

world’s easiest email marketing platform

Signing up to GetResponse is as simple as AWeber. The main difference is that GetResponse doesn’t take your credit card details up front. They contact you towards the end of your 30-day free trial to arrange payments.

GetResponse offers three types of messages:

When you want to create a Newsletter you have a couple of options:

Both of these are useful and easy to use.

Pro Tip: Make sure you have selected your correct campaign before creating a newsletter. I’ve been caught out with this a couple of times.

Whichever option you choose there is a standard screen that appears first where you write your email subject line and choose your email address.

And then below that are some optional distribution settings:

Analyze: Share:

We’ll come back to the A/B Testing shortly…

1) Drag and Drop Email Editor

Like AWeber, there are over 600 pre-designed templates that you can choose from when you create a newsletter. The templates are arranged via industry to help your selection:

If you don’t see anything you like, you can order a template from the GetResponse designers.

Alternatively, you can start from scratch to build and save your unique template. There are many blank templates to work with:

Starting from scratch can be a good idea. You get to choose exactly how much design goes into it. It is very easy to drag and drop the different elements into your message:

GetResponse also has a ‘mobile screen view’ so you can see how your design will appear on a smaller screen:

2) HTML Source Editor

The HTML Source Editor lets you switch between HTML Source and WYSIWYG. Unless you really want to code a HTML message, then stick with the latter.

The WYSIWYG Editor is similar to the one we saw in AWeber. Its ‘word processor like’ functions make it easy to format your message in no time.

When you’ve finished creating your newsletter, you can either save it as a draft or move to the next stage.

Remember the A/B Test option from before?

This is where you get to configure your test.

You can choose one A/B test type from:

Subject Line

Bannon veered from James “Mad Dog” ­Mattis — the retired four-star general whom Trump had nominated as secretary of Defense — to the looming appointment of Michael Flynn as national-security adviser. “He’s fine. He’s not Jim Mattis and he’s not John Kelly … but he’s fine. He just needs the right staff around him.” Still, Bannon averred: “When you take out all the Never Trump guys who signed all those letters and all the neocons who got us in all these wars … it’s not a deep bench.” Bannon said he’d tried to push John Bolton, the famously hawkish diplomat, for the job as national-security adviser. Bolton was an Ailes favorite, too.

“He’s a bomb thrower,” said Ailes. “And a strange little fucker. But you need him. Who else is good on Israel? Flynn is a little nutty on Iran. Tillerson just knows oil.”

“Bolton’s mustache is a problem,” snorted Bannon. “Trump doesn’t think he looks the part. You know Bolton is an acquired taste.”

“Well, he got in trouble because he got in a fight in a hotel one night and chased some woman.”

“If I told Trump that,” Bannon said slyly, “he might have the job.”

Bannon was curiously able to embrace Trump while at the same time suggesting he did not take him entirely seriously. Great numbers of people, he believed, were suddenly receptive to a new message — the world needs borders — and Trump had become the platform for that message.

“Does he get it?” asked Ailes suddenly, looking intently at Bannon. Did Trump get where history had put him?

Bannon took a sip of water. “He gets it,” he said, after hesitating for perhaps a beat too long. “Or he gets what he gets.”

Pivoting from Trump himself, Bannon plunged on with the Trump agenda. “Day one we’re moving the U.S. Embassy to Jerusalem . Netanyahu’s all-in. Sheldon” — Adelson, the casino billionaire and far-right Israel defender — “is all-in. We know where we’re heading on this … Let Jordan take the West Bank, let Egypt take Gaza. Let them deal with it. Or sink trying.”

“Where’s Donald on this?” asked Ailes, the clear implication being that Bannon was far out ahead of his benefactor.

“He’s totally onboard.”

“I wouldn’t give Donald too much to think about,” said an amused Ailes.

Bannon snorted. “Too much, too little — doesn’t necessarily change things.”

“What has he gotten himself into with the Russians?” pressed Ailes.

“Mostly,” said Bannon, “he went to Russia and he thought he was going to meet Putin. But Putin couldn’t give a shit about him. So he’s kept trying.”

Again, as though setting the issue of Trump aside — merely a large and peculiar presence to both be thankful for and to have to abide — Bannon, in the role he had conceived for himself, the auteur of the Trump presidency, charged forward. The real enemy, he said, was China. China was the first front in a new Cold War.

“China’s everything. Nothing else matters. We don’t get China right, we don’t get anything right. This whole thing is very simple. China is where Nazi Germany was in 1929 to 1930. The Chinese, like the Germans, are the most rational people in the world, until they’re not. And they’re gonna flip like Germany in the ’30s. You’re going to have a hypernationalist state, and once that happens, you can’t put the genie back in the bottle.”

First and foremost is that the authors are apparently unaware of a result proved long ago that gives necessary conditions on the kinetic network alone for the absence of Turing instabilities. Thus the violation of any of these conditions can give rise to a Turing instability. The result appears in a paper by Othmer in 1980, entitled 'Synchronized and differentiated modes of cellular dynamics', which appeared in Dynamics of Synergetic Systems, H. Haken ed. The theorem goes as follows (σ(A) is the spectrum of A.

Let D be diagonal with Dj {greater than or equal to} 0. In order that σ(K − μD) ⊂ LHP for all such D and all μ ∈ [0, ∞),it is necessary that

• σ(K) ⊂ LHP

• σ(K[i1, i2, · · ·, ip]) ⊂ LHP for all pth

-order submatrices of K, where 1 {less than or equal to} p {less than or equal to} n − 1.

The result could alter the search for Turing instabilities described in Appendix 1 quite dramatically, since one could first categorize the networks that have a sub-network that is unstable when severed from the full network at the steady state of the full system, and only then determine the pattern of diffusion coefficients that produce different types of instabilities. This eliminates the need to examine all RH determinants over the entire range of wave numbers. Notice that the theorem does not require that the fully-isolated sub-network be unstable on its own, though this is allowable, but rather that the Jacobian of the terms affecting the sub-network have one or more eigenvalues in the RHP.

A second remark is that much of Appendix 1 and most of Appendix 2 contains material readily available in the literature, and could be eliminated. It would be better to replace this material with a precise formulation of the problem the authors are addressing. For example, they implicitly assume that all diffusible species must satisfy homogeneous Neumann boundary conditions, but this means that the results don't apply to common situations in pattern formation, such as the production of a morphogen at the boundary of the domain.

We thank the editor for the positive assessment of our work and for highlighting the scientific impact of our findings. To emphasize the novel conclusions regarding the components’ diffusivities pointed out by the editor, we have changed the title to “High-throughput mathematical analysis identifies Turing networks for patterning with equally diffusing signals” and improved the Abstract in a similar direction.

As outlined in more detail below, the approach that we used to search for Turing patterning systems already took the theoretical results mentioned by reviewer #2 into account. We have now clarified the details of our approach in Appendix 1.

We thank reviewer #1 for the insightful comments. Indeed, the relation of our findings with “local activation, long range inhibition” (LALI) models was not sufficiently detailed in the previous version of our manuscript. We agree that a thorough discussion of the underlying pattern forming mechanisms is of central interest to a broad readership. We have therefore significantly extended our analysis and now provide an additional section comprising 18 pages of simulations and discussion (Appendix 3) to address the role of LALI in Type II/III networks.

We are grateful to the reviewer for these excellent remarks and the valuable input. We agree with the reviewer that it is important to relate our findings to the LALI mechanism originally proposed by Meinhardt and Gierer. We therefore wrote a new section (Appendix 3) to investigate with additional analyses and numerical simulations whether the dynamics of Type II/III networks fit with the classical description of pattern formation by LALI. This analysis indicates that the mechanism underlying pattern formation is different from the concept of shortrange activation and long-range inhibition based on differential diffusivity. We have therefore corrected the relevant sentences in our manuscript and now state that our analysis identified systems that are different from models of “short-range activation and long-range inhibition based on differential diffusivity”.

1) We value the reviewer’s elegant interpretation of the Type II network shown in Figure 2c . As the reviewer points out, the final aspect of the pattern together with the larger value of D/c compared to D/c could be interpreted as a longer range of the inhibitor w compared to the activator v. However, we find that by just altering the magnitude of the rates associated with cycle c3 (without modifying the overall strength of c or D/c and D/c), the final aspect of the periodic patterns can change, such that v – surprisingly – appears to have a longer range than w ( Appendix 3—figure 2 ). Moreover, the definition of ranges in terms of ratios between diffusion and decay cannot be generalized to other Type II networks that lack negative self-regulatory feedbacks of the diffusible molecules (e.g. Appendix 3—figure 3 ) or where the minimum diffusion ratio d is defined by more complicated reaction terms (e.g. equation 31 in Appendix 3).

2) Similar to the case discussed above, the final aspect of the patterns in Type III networks can also change depending on the reaction kinetics and diffusion ratios, showing that the relationship between the range of v and w within the same network can vary ( Appendix 3—figure 4 ). Moreover, for other Type III networks, alternative ad hoc approximations of the effective ranges would need to be defined in order to interpret the systems within the LALI framework. In cases with equally diffusing reactants, such effective ranges appear to be just reformulations of the reaction kinetics that do not contribute to the identification of the general principles that underlie pattern formation.

3) To develop a general understanding of the pattern formation process in Type II/III networks, we have extended our work with simulations on a pair of cells inspired by Turing’s original analysis of pattern formation dynamics. Turing’s original simulation highlights that the role of differential diffusivity is not to limit the expansion of the activator but to destabilize the equilibrium state by maintaining an imbalance between reactants that drives a continuous deviation from equilibrium. Importantly, the reaction terms of a Turing system guarantee that the deviation happens simultaneously above and below the equilibrium state. In agreement with these observations, one-dimensional simulations like the one shown in Appendix 6—figure 8 reveal that the periodic patterns of Turing systems are formed with a simultaneous appearance of activation and inhibition peaks. The periodic patterns therefore do not reflect a longer range of the inhibitor to limit the auto-activation.

We propose instead that the role of immobile reactants in Type II and Type III networks is not to implement an effective difference in the ranges of local auto-activation and long-range inhibition, but rather to help the system to diverge from equilibrium, which in classic two-component Turing systems is achieved by differential diffusivity. We find that immobile factors help to destabilize the system since they are not subjected to the equilibrating effect of diffusion and can therefore fulfill a role as “capacitors” that integrate the effect of diffusing reactants to destabilize the reaction-diffusion system by quickly amplifying perturbations ( Appendix 3—figure 12 ).

We have made the appropriate changes in the title and the Abstract of our manuscript to highlight that our analysis refers to the lack of differential diffusivity of the mobile signalling molecules.

Our graph-theoretical formalism provides a mechanistic understanding of the requirements for stability and diffusion-driven instability in a network. Importantly and in contrast with purely algebraic or numeric approaches, this is independent of the network complexity or the number of nodes because the graph formalism allows to break down the network into cycles that have a clear and defined role in the dynamics of pattern formation. This provides an intuitive understanding of the requirements for the generation of Turing patterns in terms of feedback, cycles, and network topologies.

We now explain in more detail at the beginning of Appendix 2 that the graph-theoretical formalism allows to re-write the coefficients of the characteristic polynomial and therefore to derive the Routh-Hurwitz determinants in terms of cycles.

The reviewer is correct. The condition in Satnoianu 2000 is related to the requirement for a positive cycle. However, in our work we did not intend to recast in graph-theoretical form results from reaction network theory that have already been proven in another way. This is the reason why we did not pursue this line of inquiry, but the development of such graph-theoretical counterparts has been reviewed elsewhere (e.g. Radde 2010).

Radde N, Bar NS, M Banaji (2010). Graphical methods for analysing feedback in biological networks–A survey. International Journal of Systems Science. 41: 35-46.

We have now clarified the term “robust” according to the reviewer’s suggestion where appropriate.

We thank the reviewer for pointing out the value of our software. There are indeed networks that satisfy Turing instability conditions with only one diffusing component. We observe, however, that in such networks the de-stabilizing cycle encompasses only non-diffusible nodes, which causes the network to amplify any of the fluctuations in the initial conditions without a preferred wavelength. Similar behaviors have also been observed when there is a positive self-regulatory feedback on the non-diffusible node (Klika et al. 2012, White and Gilligan 1998). RDNets offers the possibility to filter out such “noise amplifiers”.

Klika V, Baker RE, Headon D, EA Gaffney (2012). The influence of receptor-mediated interactions on reaction-diffusion mechanisms of cellular self-organisation. Bull Math Biol 74: 935-957.

White KAJ, CA Gilligan (1998). Spatial heterogeneity in three species, plant–parasite–hyperparasite, systems. Philos Trans R Soc Lond B Biol Sci 353: 543-557.

We thank the reviewer for pointing out that the existence of an unstable subnetwork is sufficient for the existence of Turing patterns. In our analysis, we had already taken the implications of this theorem into account. We used the equivalent result from Cross 1978 (as explained in Othmer 1980) and another theorem proven by Kellog (theorem 4, page 174 in Kellog 1972; reviewed in Hershkowitz 1992) to characterize the conditions that determine stable Turing patterns. We now clarify this point in Appendix 1 (see “Step 5. Selecting unstable networks with diffusion”).

Cross GW (1978). Three types of matrix stability. Linear algebra and its applications. 20: 253- 263.

Kellogg RB (1972). On complex eigenvalues of M and P matrices. Numerische Mathematik. 19: 170-175.

Hershkowitz D (1992). Recent directions in matrix stability. Linear Algebra and its Applications. 171: 161-186

Considering the broad readership of , Appendix 1 and Appendix 2 are vital for an understanding of our findings by non-experts and additionally serve to define the notation of our analysis. Following the suggestion of the reviewer, we have now improved Appendix 1 by adding a precise formulation of the problem we are addressing, including the definition of boundary conditions. We restrict our analysis to zero flux boundary conditions because we are interested in deriving the analytical conditions required to form self-organizing spatial patterns in the absence of pre-existing asymmetries or external inputs.

Luciano Marcon

Xavier Diego

James Sharpe

Patrick Müller

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Now let's talk about what to do when the data isn't in the cache…

At a basic level, a proxy server is an intermediate piece of hardware/software that receives requests from clients and relays them to the backend origin servers. Typically, proxies are used to filter requests, log requests, or sometimes transform requests (by adding/removing headers, encrypting/decrypting, or compression).

Proxies are also immensely helpful when coordinating requests from multiple servers, providing opportunities to optimize request traffic from a system-wide perspective. One way to use a proxy to speed up data access is to collapse the same (or similar) requests together into one request, and then return the single result to the requesting clients. This is known as collapsed forwarding.

Imagine there is a request for the same data (let's call it littleB) across several nodes, and that piece of data is not in the cache. If that request is routed thought the proxy, then all of those requests can be collapsed into one, which means we only have to read littleB off disk once. (See Figure 1.14 .) There is some cost associated with this design, since each request can have slightly higher latency, and some requests may be slightly delayed to be grouped with similar ones. But it will improve performance in high load situations, particularly when that same data is requested over and over. This is similar to a cache, but instead of storing the data/document like a cache, it is optimizing the requests or calls for those documents and acting as a proxy for those clients.

In a LAN proxy, for example, the clients do not need their own IPs to connect to the Internet, and the LAN will collapse calls from the clients for the same content. It is easy to get confused here though, since many proxies are also caches (as it is a very logical place to put a cache), but not all caches act as proxies.

Another great way to use the proxy is to not just collapse requests for the same data, but also to collapse requests for data that is spatially close together in the origin store (consecutively on disk). Employing such a strategy maximizes data locality for the requests, which can result in decreased request latency. For example, let's say a bunch of nodes request parts of B: partB1, partB2, etc. We can set up our proxy to recognize the spatial locality of the individual requests, collapsing them into a single request and returning only bigB, greatly minimizing the reads from the data origin. (See Figure 1.15 .) This can make a really big difference in request time when you are randomly accessing across TBs of data! Proxies are especially helpful under high load situations, or when you have limited caching, since they can essentially batch several requests into one.

It is worth noting that you can use proxies and caches together, but generally it is best to put the cache in front of the proxy, for the same reason that it is best to let the faster runners start first in a crowded marathon race. This is because the cache is serving data from memory, it is very fast, and it doesn't mind multiple requests for the same result. But if the cache was located on the other side of the proxy server, then there would be additional latency with every request before the cache, and this could hinder performance.

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