Super-tasty Romesco Sauce

I don’t remember how I came across the idea of romesco. It certainly could have been when I was in Spain. But in any case I decided that I wanted to make some. This recipe is one that I’ve been working on for a number of years. I’ve made many variations of it and DSC_6932you can too. Try including different kinds of peppers, different spices, maybe different sources of oil (I tried bacon grease once instead of olive oil- YUM!). As with any sauce like this there are endless varieties and there is no one true recipe since even the most authentic recipe from Tarragona, I’m sure, is challenged by neighboring areas as being ‘not the true way’ of making it.

It goes great with chips, crackers, sliced baguette, or as a topping or accompaniment to an entree. It is hearty and flavorful and can be as spicy as you’d like to make it.

Romesco Sauce 

  • 2 red, yellow, or orange bell peppers
  • 1 head (10-15 cloves) garlic, sliced
  • 2 t fennel seeds *
  • 1.5 t ground cumin
  • 2 t chili powder
  • 1.5 t salt
  • 5 T olive oil
  • 3/4 cup sun-dried tomatoes
  • 2.5 cups toasted whole almonds
  • zest of 1/2 lemon

Heat oven to 400° F. Place bell peppers on a baking sheet in the oven. Allow to roast for 20-25 minutes until black spots form on the skin. Remove from oven. I put them in a glass container with a sealable top covering and let them cool for 15 minutes. You can then remove the stem and seeds easily. The skin can also be removed but I haven’t found it necessary.

Spread almonds on a baking sheet and place in oven for 10-15 minutes. Toward the end check to make sure they’re not burning. Remove from oven as soon as they start to brown (which is a little hard to see on almonds- just don’t let them burn). Remove from oven and let cool.

Meanwhile, heat olive oil in medium sized pan over medium heat. Add all but 2-5 cloves of the garlic (the more you keep untoasted, the spicier the resulting sauce will be), fennel, cumin, chili powder, and salt. Slowly toast the garlic until just browned then remove the pan from heat and let cool for 10 minutes or so.

Combine all ingredients (adding sun-dried tomatoes and lemon zest) in a food processor DSC_6931with a blade**. Process until it forms a slightly chunky paste. Adjust consistency with more olive oil or water to thin, or bread crumbs to thicken. Adjust salt to taste.

* fennel seeds are my own addition and probably not traditional. However, they lend a wonderful licorice flavor that I really enjoy. If you omit them I would simply add a bit more cumin and chili powder.

** I find a food processor works best here. However, you can use a stick blender- that just requires a bit more work. A standard upright blender doesn’t seem to work well since the mixture is too paste-like to mix down to the blades.

The Grad Circus

This comic is an homage to my time in graduate school- stimulating and frustrating, a time for development, exploration, and maturation. Pretty much 5 years for me. It’s not strictly autobiographical, though it certainly contains elements from my experience (and my foibles). There were plenty of ways to waste time. On the other hand, if you consider your time as ‘productive’ only when you have your nose to the grindstone you will not end up learning much. Here are some of the things that I didn’t include:

  • I can remember my advisor unlocking the door to HIS office, a bit early in the morning, and finding me playing a first-person shooter on his computer. Ummmm….. oops?
  • Going ‘bowling’ with excess liquid nitrogen. We would take a dewar flask full of the stuff and roll it down the hall. Not much point, but very cool to see.
  • I once scored an entire half of a gigantic sheet cake as ‘free food’. It was left over from a retirement party somewhere in the med school. The admin from our department tipped me off and I retrieved it. Much feasting was had on that day!
  • Many a late night was spent in the lab playing video games with my best friend across the hall. We’d be yelling back and forth at 3 AM then one of us would have to stop the game to collect a sample for a time point.
  • I once found a broken VCR in a dumpster, fixed it, and then sold it for $50- which is like $500 to a grad student. I then proceeded to collect every piece of broken equipment and electronics I could get my hands on- storing it in the lab. I built a rocking platform (retailed at about $600) and repaired several pieces of lab equipment with my haul. However, it really ended up taking up a lot of space.

Graduate school was a great time and one with a lot of freedom that came with the stress. I was lucky to have a great advisor who supported me even when he thought I was goofing off (he did not support me IN the goofing off- he supported me in my career despite that I was goofing off) and a bunch of great friends who I still keep in touch with. There was so much potential in that time- the potential is still there in my career, I just need to take more time to recognize it. And maybe score some more free food too.

 

GradCircus_RedPenBlackPen

On a different note I’m pretty proud of this comic. Some of my previous ideas and concepts have been pretty complicated (see here and here). I’ve thought that I should do an outline sketch first then do a final version with more care. Both of these previous cases I started sketching and just decided to keep it as is. They’re pretty good considering. With this one I actually did sketch out an idea:

 

Initial pen sketch of ideas for the grad circus

Initial pen sketch of ideas for the grad circus

then did a pencil outline of the final

Pencil outlines for the final grad circus. Didn't include details or text.

Pencil outlines for the final grad circus. Didn’t include details or text.

then traced with pen and filled in text and details.

My wife was impressed but said, “maybe for your next one you should do something about a guy that has a full time job and three kids.” Funny. This process did end up taking me about 5 hours (and a few cramped hands) of late evening and weekend time to complete giving me new respect for cartoonists, especially Bil Keane (and now Jeff Keane), whose simple comics look so ‘easy’. These maps are hard to draw!

 

 

 

 

ThrowWayBack Thursday

Yes. Chickens are easy targets. As are all birds, they are descended from the rulers of the Earth, the dinosaurs. Yes, the lowly chicken. And turkey. And robin. Note that they were probably NOT descended from velociraptors (as implied by my comic), but were from the theropods, of which velociraptor is a member. Enjoy!

ThrowbackThursdayComic

Academic Rejection Training

Following on my previous post about methods to deal with the inevitable, frequent, and necessary instances of academic rejection you’ll face in your career I drew this comic to provide some helpful advice on ways to train for proposal writing. Since the review process generally takes months (well, the delay from the time of submission to the time that you find out is months- not the actual review itself) it’s good to work yourself up to this level slowly. You don’t want to sprain anything in the long haul getting to the proposal rejection stage.

ThreeQuickWays

Dealing with Academic Rejection

Funny, it feels like I’ve written about exactly this topic before…

I got rejected today, academically speaking*. Again. I was actually pretty surprised at how

"Not Discussed", again

“Not Discussed”, again

nonplussed I was about the whole thing. I’ve gotten mostly immune to the being rejected- at least for grant proposals and paper submissions. It certainly could be a function of my current mid-career, fairly stable status as a scientist. That tends to lend you a lot of buffer to deal with the frequent, inevitable, and variably-sized rejections that come as part of the job. However, I’ve also got a few ideas about advice to deal with rejection (some of which I’ve shared previously).

Upon rejection:

  1. Take a deep, full breath: No, it won’t help materially- but it’ll help you feel better about things. Also look at beautiful flowers, treat yourself to a donut, listen to a favorite song, give yourself something positive. Take a break and give yourself a little distance.
  2. Put things in perspective: Run down Maslow’s hierarchy of needs. How you doing on there? I’ll bet you’ve got the bottom layers of the pyramid totally covered. You’re all over that. And it’s unlikely that this one rejection will cause you to slip on this pyramid thing.
  3. Recognize your privilege: In a global (and likely local) perspective you are extremely privileged just to be at this level of the game. You are a researcher/academic/student and get to do interesting, fun, rewarding, and challenging stuff every day. And somebody pays you to do that.
  4. Remember: science is ALL about failure. If you’re not failing, you’re not doing it right. Learn from your failures and rejections. Yes, reviewers didn’t get you. But that means that you need to do a better job of grabbing their attention and convincing them the next time.
  5. Recognize the reality: You are dealing with peer review, which is arbitrary and capricious. Given the abysmal levels of research funding and the numbers of papers being submitted to journals it is the case that many good proposals get rejected. The system works, but only poorly and only sometimes. And when everyone is scraping for money it gets worse.
  6. Evaluate: How do YOU feel about the proposal/submission: forget what the reviewers said, forget the rejection and try to put yourself in the role of reviewer.
    This is YOU on the steps of the NIH in 6 months! Winning!

    This is YOU on the steps of the NIH in 6 months! Winning!

    Would YOU be impressed? Would YOU fund you? If the answer is ‘no’ or ‘maybe’ then you need to reevaluate and figure out how to make it into something that you WOULD or decide if it’s something you should let go.

  7. Make plans: Take what you know and plan the next step. What needs to be done and what’s a reasonable timeline to accomplish this. This step can be really helpful in terms of helping you feel better about the rejection. Instead of wallowing in the rejection you’re taking ACTION. And that can’t be a bad thing. It may be helpful to have a writing/training montage to go along with this since that makes things more fun and go much faster. Let me suggest as the theme to Rocky as a start.

I’m not saying you (or I) can do all of these in a short time. This process can take time- and sometimes distance. And, yes, I do realize that some of this advice is a little in the vein of the famous Stuart Smalley. But, gosh darn it, you ARE smart enough.

stuart_smalley

*For those interested, I submitted an R01 proposal to the NIH last February. It was reviewed at the NIH study section on Monday and Tuesday. The results of this review were updated in the NIH submission/tracking system, eRA commons, just this morning. I won’t know why the proposal was ‘not discussed’ for probably a week or so, when they post the summary of reviewers’ written comments. But for now I know that it was not discussed at the section and thus will not be funded.

At this point I’ve submitted something like 8 R01-level proposals as a PI or co-PI. I’ve been ‘Not Discussed’ on 7 of those. On the eight I got a score, but it was pretty much the lowest score you can get. Given that NIH pay lines are something around 10% I figure that one of the next 2 proposals I submit will be funded. Right? But I’ve been successful with internal funding, collaborations, and working on large center projects that have come to the lab- so I really can’t complain.

How to know when to let go

This post is a story in two parts. The first part is about the most in-depth peer review I think I’ve ever gotten. The second deals with making the decision to pull the plug on a project.

Part 1: In which Reviewer 3 is very thorough, and right.

Sometimes Reviewer 3 (that anonymous peer reviewer who consistently causes problems) is right on the money. To extend some of the work I’ve done I’ve done to predict problematic functions of proteins I started a new effort about 2 years ago now. It went really slowly at first and I’ve never put a huge amount of effort in to it, but I thought it had real promise. Essentially it was based on gathering up examples of a functional family that

"At last we meet, reviewer 3, if that is indeed your real name"

“At last we meet, reviewer 3, if that is indeed your real name”

could be used in a machine learning-type approach. The functional family (in this case I chose E3 ubiquitin ligases) is problematic in that there are functionally similar proteins that show little or no sequence similarity by traditional BLAST search. Anyway, using a somewhat innovative approach we developed a model that could predict these kinds of proteins (which are important bacterial virulence effectors) pretty well (much better than BLAST). We wrote up the results and sent it off for an easy publication.

Of course, that’s not the end of the story. The paper was rejected from PLoS One, based on in-depth comments from reviewer 1 (hereafter referred to as Reviewer 3, because). As part of the paper submission we had included supplemental data, enough to replicate our findings, as should be the case*. Generally this kind of data isn’t scrutinized very closely (if at all) by reviewers. This case was different. Reviewer 3 is a functional prediction researcher of some kind (anonymous, so I don’t really know) and their lab is set up to look at these kinds of problems- though probably not from the bacterial pathogenesis angle judging from a few of the comments. So Reviewer 3’s critique can be summed up in their own words:

I see the presented paper as a typical example of computational “solutions” (often based on machine-learning) that produce reasonable numbers on artificial test data, but completely fail in solving the underlying biologic problem in real science.

Ouch. Harsh. And partly true. They are actually wrong about that point from one angle (the work solves a real problem- see Part 2, below) but right from another angle (that problem had apparently already been solved, at least practically speaking). They went on, “my workgroup performed a small experiment to show that a simple classifier based on sequence similarity and protein domains can perform at least as well as <my method> for the envisioned task.” In the review they then present an analysis they did on my supplemental data in which they simply searched for existing Pfam domains that were associated with ubiquitin ligase function. Their analysis, indeed, shows that just searching Reviewer3Attackfor these four known domains could predict this function as well or better than my method. This is interesting because it’s the first time that I can remember where a reviewer has gone in to the supplemental data to do an analysis for the review. This is not a problem at all- in fact, it’s a very good thing. Although I’m disappointed to have my paper rejected I was happy that a knowledgeable and thorough peer reviewer had done due diligence and exposed this, somewhat gaping, hole in my approach/results. It’s worth noting that the other reviewer identified himself, was very knowledgeable and favorable to the paper- just missing this point because it’s fairly specific and wrong, at least in a particular kind of way that I detail below.

So, that’s it right? Game over. Take my toys and go home (or to another more pressing project). Well, maybe or maybe not.

Part 2: In which I take a close look at Reviewer 3’s points and try to rescue my paper

One of the hardest things to learn is how to leave something that you’ve put considerable investment into and move on to more productive pastures. This is true in relationships, investments, and, certainly, academia. I don’t want to just drop this two year project (albeit, not two solid years) without taking a close look to see if there’s something I can do to rescue it. Without going into the details of specific points Reviewer 3 made I’ll tell you about my thought process on this topic.

So, first. One real problem here is that the Pfam models Reviewer 3 used were constructed from the examples I was using. That means that their approach is circular: the Pfam model can identify the examples of E3 ubiquitin ligases because it was built from those same examples. They note that four different Pfam models can describe most of the examples I used. From the analysis that I did in the paper and then again following Reviewer 3’s comments, I found that these models do not cross-predict, whereas my model does. That is, my single model can predict the same as these four different individual models. These facts both mean that Reviewer 3’s critique is not exactly on the mark- my method does some good stuff that Pfam/BLAST can’t do. Unfortunately, neither of these facts makes my method any more practically useful. That is, if you want to predict E3 ubiquitin ligase function you can use Pfam domains to do so.

Which leads me to the second point of possible rescue. Reviewer 3’s analysis, and my subsequent re-analysis to check to make sure they were correct, identified around 30 proteins that are known ubiquitin ligases but which do not have one of these four Pfam domains. These are false negative predictions, by the Pfam method. Using my method these are all predicted to be ubiquitin ligases with pretty good accuracy. This is a definite good point then to my method, meaning that my method can correctly identify those known ligases that don’t have known domains. There! I have something useful that I can publish, right? Well, not so fast. I was interested in seeing what Pfam domains might be in those proteins other than the four ligase domains so I looked more closely. Unfortunately what I found was that these proteins all had a couple of other domains that were specific to E3 ubiquitin ligases but that Reviewer 3 didn’t notice. Sigh. So that means that all the examples in my E3 ubiquitin ligase dataset can be correctly identified by around 6 Pfam domains, again rendering my method essentially useless, though not incorrect. It is worth noting that it is certainly possible that my method would be much better at identification of new E3 ligases that don’t fall into these 6 ‘families’ – but I don’t have any such examples, so I don’t really know and can’t demonstrate this in the paper.

So where does this leave me? I have a method that is sound, but solves a problem that may not have been needed to be solved (as Reviewer 3 pointed out, sort of). I would very much like to publish this paper since I, and several other people, have spent a fair amount of time on it. But I’m left a bit empty-handed. Here are the three paths I can see to publication:

  1. Experimental validation. I make some novel predictions with my method and then have a collaborator validate them. Great idea but this would take a lot of time and effort and luck to pull off. Of course, if it worked it would demonstrate the method’s utility very solidly. Not going to happen right now I think.
  2. Biological insight. I make some novel observations given my model that point out interesting biology underpinning bacterial/viral E3 ubiquitin ligases. This might be possible, and I have a little bit of it in the paper already. However, I think I’d need something solid and maybe experimentally validated to really push this forward.
  3. Another function. Demonstrate that the general approach works on another functional group- one that actually is a good target for this kind of thing. This is something I think I have (another functional group) and I just need to do some checking to really make sure first (like running Pfam on it, duh.) I can then leave the ubiquitin ligase stuff in there as my development example and then apply the method to this ‘real’ problem. This is most likely what I’ll do here (assuming that the new example function I have actually is a good one) since it requires the least amount of work.

So, full disclosure: I didn’t know when I started writing this post this morning what I was going to do with this paper and had pretty much written it off. But now I’m thinking that there may be a relatively easy path to publication with option 3 above. If my new example doesn’t pan out I may very well have to completely abandon this project and move on. But if it does work then I’ll have a nice little story requiring a minimum of (extra) effort.

As a punchline to this story- I’ve written a grant using this project as a fairly key piece of preliminary data. That grant is being reviewed today- as I write. As I described above, there’s nothing wrong with the method- and it actually fits nicely (still) to demonstrate what I needed it to for the grant. However, if the grant is funded then I’ll have actual real money to work on this and that will open other options up for this project. Here’s hoping. If the grant is funded I’ve decided I’ll establish a regular blog post to cover it, hopefully going from start to (successfully renewed) finish on my first R01. So, again, here’s hoping.

*Supplemental data in a scientific manuscript is the figures, tables, and other kinds of data files that either can’t be included in the main text because of size (no one wants to read 20 pages of gene listings in a paper- though I have seen stuff like this) or because the information is felt to be non-central to the main story and better left for more interested readers.