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My paper about protein complexes in bacteria is officially published - check it out in PLoS Computational Biology.

Essentially a picture of how many species are not E. coli (except those at the bottom - many of them are E. coli). Click to zoom in or get more context in the linked paper.

The one-sentence summary is as follows: bacteria are different from each other. OK, so that isn't really a mind-blowing observation. Microbiologists tend to think of conserved features in a monolithic way when they're working with multiple species, though, to the point that a ribosome from E. coli is considered more or less equivalent to a ribosome from a Bacillus species or even something exotic like Thermus thermophilus.

Our point here was to show that E. coli protein complexes, on average, don't appear to provide a solid model of those complexes present in other bacterial species. The really well conserved protein complexes tend to be those critical to Life As We Know It (i.e., RNA polymerases), but even among some complexes crucial to one species' existence, a closely-related species may lack one piece of the larger complex.* There's a broader biocomplexity issue here, too: we can't work under the assumption that protein complex components will be consistently co-conserved. Two species may appear to contain a complex of similar function, but one species may put the complex together in a different way than the other.

In some cases, complex parts may even be replaced with those more appropriate to the needs of the species. That's a broader discussion than we attempt in this manuscript but it's an ongoing course of study.

*Lack is a strong word here. If a strain or species doesn't need a certain protein (that is, it's not essential) and it doesn't have any obvious inability to maintain growth in its own environmental niche, does it really lack the protein?

Academic job searches in two dimensions

More than a few faculty members have reacted sympathetically when I've mentioned the problem of dual academic careers to them. It's hard enough for one person with extensive education to find a job: they may be exceedingly qualified for a set of jobs but only a few of those positions may be open at a time, whether we include non-academic career options or not. Adding another experienced scholar into that mix complicates the situation further. There's a reason this is called the two-body problem and not the two-body paradise of unending joy and tranquility.

How can we make it easier for couples to find jobs when they have very specific requirements?
Location is a major limiting factor. The availability of job information is likely an even larger factor: that majority of available jobs aren't posted publicly. Even among educational institutions, many of which must announce job openings, faculty participate in distinct but invisible hiring networks governed by status and prestige.

We'll have to work with what we have.

Pairs in places. Some job search sites offer a geographic view.

As far as job searches go, I've found the following sites:

• The HERC Dual Career search tool. I think I've mentioned this one on the blog before. It only covers the US but includes a variety of institutions and job types.
• HigherEdJobs dual career search.  Like the HERC site, this one primarily searches the US, but includes Canada and potentially even beyond, though I haven't found any postings there yet. The listings appear to include a number of older posts - I saw some dating from 2012 - which could be a useful feature if you're trying to find out who could be hiring rather than just who is hiring right now.
• Inside Higher Ed Careers dual career search. This search tool claims to search anywhere in the world but that may be an exaggeration.
• EURAXESS. Not exclusively a career search site (it does have a search tool for research jobs in Europe, though the selection may be limited depending upon the field) but also a guide for academics who might like to work in Europe.
• Many universities have dual-career assistance programs, sometimes through HERC. The programs may be referred to using other terms, i.e. Partner Opportunities. Most of them offer job search assistance to the partners of prospective hires, though one of you needs to get that far first! 

A variety of projects dedicated to this task exist (i.e., TANDEM) but they don't appear to have been organized with job seekers in mind. 

One of my recent side projects involves extending the dual-career search concept to jobs worldwide. I'm trying to make it into a meta-search by using publicly available job posts, though the major issue with this approach is variability in post format. Many of them don't even explicitly list geographic locations and a subset even list false-positive locations (a job may be headquartered in London but work may occur somewhere else). There will be some guesswork to do.

Communication across the tree of life

A squid, the traditional model of quorum sensing. It glows because the Vibrio inside it glow. They have to stay coordinated to glow. Quorum sensing lets that happen. More details and photo source here.

I was looking for a recent review of quorum sensing this morning - specifically in the context of bacterial communication across domains of life - and found this minireview in Frontiers in Plant Science. It isn't about plants. Rather, it uses Pseudomonas aeruginosa interactions with human cells as an example of inter-kingdom interactions* and the resulting behavior changes. It's not the most comprehensive review of inter-kingdom quorum sensing out there (this 2008 review by Hughes and Sperandio comes to mind, plus I'm sure there's more recent reviews I haven't found yet**) but it covers some important developments from the past few years.

As usual, here's my question: where do phages fit into the picture? If we conclude, as Holm and Vikström do, that this particular bacteria-host relationship would be quite different without quorum sensing molecules, the next logical step is to examine factors in the environment with other impacts on quorum sensing (and AHL production, specifically). Those factors certainly include other bacterial cells but they also include phages. There is clearly a precedent for studying phage-host interactions as they occur in quorum sensing: E. coli has been found to reduce the number of potential lambda phage receptors on its surface in response to AHL levels and quorum sensing genes have even been found in a phage infecting C. difficile. It's time to include the viral component of the microbiome in our quorum sensing models.

*It's surveillance rather than a two-way conversation, unless you consider phagocytosis to be a good metaphor for small talk.
**I should probably mention this 2009 Ng and Bassler review, too, because of Bonnie Bassler.

I was getting ready to write something* to replace Biopython's SwissProt module today as it can't parse Uniprot entries yet. Just as I was starting to do so, I found BioServices, which appears to plug some of the holes in Biopython with regards to services like Uniprot and KEGG. These services all have REST APIs so they aren't difficult to retrieve individual entries from. Writing a new parser for each service, though, is tedious and I'm glad there's an off-the-shelf solution.

One caveat: the Uniprot search function is billed as "a bit unstable". I haven't been able to get it to complete any searches successfully. BioServices may be more useful if a list of accession IDs is already available, though that defeats the purpose for me.

Additional caveat: the REST API does most of the work already in accessing Uniprot, so if you're willing to get your hands dirty then you can access the database contents easily with BeautifulSoup and the right urls. A simple proteome query may look like this:

"http://www.uniprot.org/proteomes/?query=" + your_query_here + "&fil=reference%3Ayes&sort=score"

*And in the end, I wrote it myself anyway.

Overcome thoroughly the manner of working like extinguishing fire

Not a catchy slogan but free recipes are always nice.

I read this BBC magazine piece about North Korean propaganda slogans this evening. It triggered two questions:
1. Where's the inflection point between "North Korea as convenient source of surreal entertainment" and "North Korea as source of genuine misery and an extant totalitarian state"?

2. Wouldn't propaganda slogans serve as excellent candidates for Markov tomfoolery?

I only have an answer for the second question. The answer is, of course, Yes.

I started off using the BBC's translated list of NK slogans and generated new ones in a very basic fashion. A few examples follow:

Let the whole nation join efforts to open up a broad avenue to independent reunification in this year that marks the 70th anniversaries of national resurrection and legendary hero of the proclivity to imports and ensure domestic production of coal and cement to the death!

Let us fiercely raise the sports wind across the Party Central Committee headed by the Moranbong Band to bring about a great golden age of construction by thoroughly applying the spirit of the office of the dignified powerful Paektusan nation.

Let us glorify the Kumsusan Palace of the Songun revolution and the 25th anniversary of the Workers' Party of Korea as the buglers of the country prospers; the less books we read, the later the country in all aspects as the major fronts of building a thriving socialist country.

OK, so these are virtually indistinguishable from the original slogans. It isn't a large text sample, but perhaps more importantly, there's noticeable consistency (about a third of the ~300 slogans begin with "Let us").  It isn't surprising: propaganda is intended to be simple, inspirational, and accessible. It doesn't usually vary from the basic idea of inspiration plus action equals solidarity.

With that it mind, I should add additional slogans to the training set. They should ideally include material from a variety of movements and political entities. This means that the final product will make less contextual sense, but hey, the goal here is to generate content in the absence of context.

Here's one more option for generating plots in R: plotrix.

It can create some novel things like nested bar graphs and can search for empty space in plots.

There are a number of examples here. The 3D plots are probably best avoided but many of these examples include features not present in ggplot2.

Continuing the music adventure from yesterday, today's genre is Crossover Jazz.
Spotify doesn't use this genre annotation for any specific artist but Wikipedia tells me the term could apply to everyone from Al Jarreau to Spyro Gyra. In some ways, it's an early or more moderate form of Smooth Jazz (as in Kenny G, though he's a crossover jazz musician as well).

I'm going to start off with Gerald Albright and Chuck Loeb, then go from there.

How is it making me feel?
Most of the time I feel like I'm on the Weather Channel during a calm day. Even when it's improvisational, this music tends to feel restrained and commercial, like it's trying to avoid being obtrusive. This effect leaves me feeling restless.

What was memorable?
Gerald Albright - Slam Dunk was one of the few tracks I've heard so far today which sounded like the Wikipedia genre description.