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Tuesday, January 26, 2016

Sunday, January 17, 2016

Repost: Characterization of the infant noggin channel, Oral1

Ahhh, memories of Wee-Une and the late noughties...


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Introduction: The recent progression of the household Padawan to solids has provided a unique opportunity for the extensive study of the face-localized infant channel, Oral1. Although the existence of Oral1 has been known for some time, it has yet to be subjected to systematic study using modern investigative techniques.

Methods: By necessity, single channel currents were recorded from the infant noggin using the noggin-attached configuration, as excised outside-out patch studies from the whole noggin are understandably restricted under NIH guidelines.

Results: Gating behaviour of Oral1 was constant but sporadic, with the channel exhibiting constitutive activity in the absence of exogenously applied agonists. Nevertheless, both open probability and mean open time were markedly increased by the presence of the well-characterised growth-regulating factor, SpOoN. Contrary to our hopes, Oral1 did not display strong inward rectification. Indeed, much to the chagrin of myself and colleague, outward flux was often of an amplitude not that much smaller than the sizable inward flux.

And, apparently, it gets worse before it gets better
Figure 1. Oral1 visualised through a 4 mpx digital capture device at 1X magnification.

Regarding permeability, activation of the Oral1 evoked a highly milk selective conductance at first, but with prolonged activation appeared to switch to a more-or-less nonselective configuration permeable to peas and squash (Figure 1). Thus, Oral1 represents another example of a channel that can alter its permeability in seconds in a manner likely to be physiologically relevant to our shopping list. However, it appears that the channel is only weakly permeable to sweet potato, in the presence of which was observed a strong outwardly rectifying profile indicative of permeant block.

Conclusion: These data have substantially enhanced our understanding of the Oral1 selectivity filter, although in the absence of a successful attempt to crystallize Oral1, a precise structure of the channel pore is not yet available for us to draw more informative conclusions regarding the relationship between structure and function for this channel. Regardless, on the basis of the evidence presented in this paper, it is reasonable to predict that things are going to get progressively messier in the coming months.

[Originally posted 4th Jan 2009]

Monday, June 15, 2015

#DistractinglySexy

Women in the laboratory claim yet another innocent victim


Thursday, April 23, 2015

You remember that time ...?

... when candidates could simply provide a list of potential referees and contact details along with their CV/resume?

We need to get back to that time. Because, once again, I find myself seriously lamenting the current trend of requiring initial applications to include recommendation letters up front. Students should lament this, also, as the quality of their references can only be undermined by a trend that makes it immensely onerous for faculty to supply substantive and tailored letters without a significant sacrifice of time. Letters that, in a substantial number of cases, will never be read because the application is triaged before it's deemed appropriate to do so.

What's particularly perverse is that we, as faculty, still tend to hold some value in graduate school recommendation letters that have been tailored for the position and institution. This isn't a terribly consistent view to hold if we're allowing our admins to blanket bomb our colleagues for letters to the point that producing such letters, possibly as many as ten for a single student, renders it a real challenge to meet this ideal.

What's worse is that on top of a faculty member having to submit an order of magnitude more recommendation letters than are ever likely to be read, we are also expected to sign up to a veritable multitude of third party websites* in order to fill out tedious and agonizingly superficial tick box forms.

It's a scandal I tell you. A scandal!

/ humbug



* "Sorry, the password you have chosen is unacceptable. It must include at least one number, upper and lower case letter, a special character that isn't !@#$%& or *, and a fourth dynasty Egyptian hieroglyph"



Wednesday, April 15, 2015

Normalizing productivity and impact to lab size...

[I've edited the data to include a model small town grocer lab (now the red data point), and to correct the calculations for Figure 4, which were nonsense on the first post]

The following relates to some recent posts over at DM's blog with regard to an essay penned by Andrew D. Hollenbach in ASBMBTODAY. Dr. Hollenbach makes a point in his essay of running a small laboratory, and this got me thinking about productivity as a function of lab size. It's not my intent here to venture an opinion on the pros and cons of productivity metrics &c, but I'd like to post some preliminary figures I rustled up in which I've attempted to graphically present the two main metrics of productivity as a function of laboratory size for a number of heavy hitting labs in neuroscience and cancer (this is a small sample size at this point, and a not remotely randomized one at that, so... pinch of salt &c). I've included a data point based on the available information about Dr. Hollenbach's lab, in addition to a point representing a model "riff raff" lab with one PI, one postdoc, and an annual productivity of one paper (IF ~3.5), which is shown by the red dot.

I know that these metrics have been measured against lab funding before, but I'm not sure I've seen them measured against laboratory labor force directly. Laboratory sizes were ascertained purely based on lab websites and include only grad students, postdocs and postdoc-like research staff (no lab managers, techs, or IT staff*). Clearly, there's no telling what kind of turnover has been going on year by year over the period of analysis, so I'm taking the lazy approach of assuming lab sizes are not changing in absolute number and composition over this time frame (but, again, salty, salty, pinchy, pinchy &c). Only senior/corresponding author publications are counted. 5-year journal Impact Factors were used where possible, with 2013 numbers used when a 5-yr average was not available.

I restricted my analysis to relatively recent productivity over a 3 yr period between the beginning of 2012 and the end of 2014**.

Here it all is...

Figure 1. Displays the relationship between the number of publications per year, for the three years of 2012-2014, and the number of active research personnel in the laboratories sampled. (The red datum represents a model small town grocer outfit of 1 PI and 1 postdoc, with a productivity of 1 paper/yr and an IF average of 3.5)


Figure 2. Mean yearly publication output for the three years of 2012-2014 normalized to the number of active research personnel in the laboratories sampled. (The red datum represents a model small town grocer outfit of 1 PI and 1 postdoc, with a productivity of 1 paper/yr and an IF average of 3.5)



Figure 3. Shows the relationship between lab personnel number and the mean Impact Factor of the journals in which articles were published in the years 2012-2014. (The red datum represents a model small town grocer outfit of 1 PI and 1 postdoc, with a productivity of 1 paper/yr and an IF average of 3.5)

Figure 4. Here I've normalized the average impact for a given year (sum total of journal IFs for all pubs over 3 yrs divided by 3***) to the number of active research personnel in the laboratory. (The red datum represents a model small town grocer outfit of 1 PI and 1 postdoc, with a productivity of 1 paper/yr and an IF average of 3.5)

Figure 5. I thought I'd add in a chart showing the relationship between the impact/lab member/yr and the lab size just for good measure. (The red datum represents a model small town grocer outfit of 1 PI and 1 postdoc, with a productivity of 1 paper/yr and an IF average of 3.5)



* One can argue for the inclusion of these, but for this preliminary analysis at least, I've chosen to exclude them
** Why only 3 yrs, why not 5? - I'll certainly bulk out the numbers in terms of years and laboratories covered later, in the meantime this whole exercise was really just a quick break from end of semester marking.
*** This chart was updated after a facepalm moment over the original cell equation. I think this is more informative.

Saturday, March 28, 2015

Life on the Rusty Edge of Pharmacology II...

Well, dang, we ended up getting a pretty good handle on what The Other is, and how it relates to This and That after all!

So that's nice.



Friday, October 31, 2014

Life on the Rusty Edge of Pharmacology...

So, my working hypothesis was that This happened because of That.
So I applied a drug reported to inhibit That with the prediction that it would consequently inhibit This.
The drug meant to inhibit That actually ended up causing more of This, suggesting that This probably isn't strictly about That after all, and that the That-inhibiting drug is likely doing a little of The Other...
... which opens up a new and exciting avenue of investigation as we move forward to unravel the nature of The Other and its relationship to This and That*.




* Nah, I'm kidding, nobody is going to fund that study. That data is probably going to be buried until it's unearthed millennia from now by aliens picking over the bones and assorted detritus of human civilization.