ZOMG, this is really ridiculous: I'm trying to Google multiple reaction monitoring (MRM) proteomics to gather resources and references for a workshop talk I'm giving in a couple of weeks. Searching for "mrm proteomics" brings up a neverending list of permutations of this ONE company's information (including every page on their website individually, as well as two different blogs, one Blogspot and one WordPress, that have the same entries) (I refuse to link to them since that'll just feed their Google beast). You don't even get to the freaking Wikipedia page for it until, like, the third page of the Google results! Sheesh people, I know it's important to get your company's information about there, but that's just obvious game of the Google search algorithm. They must have hired some "get your website in the top of search results!" service. It's just kinda lame.
I spent the day at the NIH Regional Grant Seminar in Indianapolis, and it has been a mix of useful stuff and other things that are too beginner-y for me. Not like I am some kind of NIH expert, but because of the great public service provided by the blogworld via e.g. Drugmonkey and Physioprof and the mentorship of my senior colleagues and advocates, I am already pretty clued in to the basics of the NIH operational model, so sessions describing how peer review happens and what is the job of a PO vs. an SRO aren't all that useful to me in particular. The most interesting part for me is to get to ask questions of the people who are usually just names on policy documents or the internet, and see what kind of a response they have to my and others' questions. Dr. Sally Rockey has had some good sessions and engages fully in each answer she gives.
Something I hope to probe more about tomorrow is the NIH's projected path towards putting their "money where their mouth is" about improving the pipeline issues created by our country's current model for grad school and postdoctoral training, something that this blogosphere is very familiar with. It's clear that they can tell there is a problem, and the data illustrate the problems in various ways:
- abysmal representation of African Americans in the investigator pool, both as applicants and as funded
- loss of women PIs at the R01 renewal stage (as described in this paper by Pohlhaus et al)
- huge reliance on postdoctoral "trainees"--who aren't really getting a training experience towards becoming independent--as research staff on R grants
A dissatisfying element is a tendency to invoke the "well, we can't mandate that institutions do X or Y" about things that it would be HARD to do (like, politically or financially hard)--even though, clearly, NIH and government policy DO mandate plenty of things about how institutions need to operate in order to get their money (ORI, IRB, IACUC). I totally understand that it is their job to toe the line and keep on message; but it seems like a very convenient reason why some things "can't" be changed in ways that might address the diversity issues.
For example, employee-style benefits including family coverage health insurance and family leave for graduate students and postdocs are "allowable costs" on F and T grants; but NIH's official position is that it can only be covered by these grants IF other graduate students and postdocs at the institution get the same options. I know there are plenty of institutions that do NOT provide adequate coverage of these things for any of their grad students or postdocs. At my own institution, I know that the family coverage purchasable by grad students is so expensive, and so poor with respect to cost-coverage ratio, that graduate students often have to put their children on Medicaid (which they easily qualify for!!). This is terrible! And what a barrier this puts up to anyone considering having children while in graduate school--especially women. It's not reasonable to assume that everyone in graduate school or postdoctoral training will have a partner who has a "better" job with "real" insurance. It really should be the responsibility of the training program to provide reasonable, livable coverage for cost-of-living, which in this country, includes health insurance. For postdocs, at many institutions (including mine) they get dropped from their benefits programs as soon as they obtain independent funding through e.g. a K99 or F32 award. What a disincentive to apply, yet putting trainees between a rock and a hard place since independent funding is their currency towards a faculty position, and it has a strong likelihood, again, of affecting trainees (especially women) with families who need to maintain their family insurance coverage.
These kinds of situations seem like a perfect opportunity for NIH to connect the dots between some of the various problems with the training process in the biomedical workforce, to not just allow but provide this coverage to any trainee who obtains funding regardless of what their institution does for others. For one, this would have an immediate impact on and incentive for graduate and postdoc trainees to apply for funding (and for PIs who care about this kind of thing to apply for training grants). For two, NIH could make it a part of the review criteria (either merit review or programmatic decision-making review) to evaluate trainee support at the institution (e.g. insurance subsidies, availability of family leave and benefits), and that institutions that provide poor support for their grad students and postdocs will get dinged for Environment and that this will factor into funding decisions. In principle, it would be cost-neutral, since those costs are already allowed to be budgeted into the "institutional allowance" amount (although, that amount isn't actually enough to cover 'real' insurance either; but it's closer than nothing). Maybe it would provide the nudge that institutions need to make them realize that these things actually do matter, both to the trainees and to NIH. NIH has the power to shift this paradigm, just like it did with new investigator funding rates--they just need to take it on.
I'm currently lecturing in introductory organic chemistry; the head of the course is a very experienced outstanding teacher who (literally) wrote the book, and has won many awards for his excellent teaching. He started the course for the first few weeks of lecture. I'm covering lecture for now, and he will pick it up again later in the course (about a month and a half from now).
I messed up carbocation rearrangement today, right at the beginning of lecture. I lost 'em; they were VERY restless and chattery and obviously like "This lady does not know what she is talking about!" It actually turned out that the mistake I made was a very instructive example of a common misconception about the topic that students have, and the ensuing discussion of where I had gone wrong cleared up a lot of questions they had and got everybody to a much better understanding of the topic than if I'd just gotten it right the first time through.
BUT I am pretty sure that it is still going to kill their perception of me as someone they can trust to teach them. Every time I get anything wrong it's like another nail in my "I'm a younger female who doesn't really 'look' like a professor" coffin. I don't have any room to make mistakes, because I'm already having to prove to them in the first place that I can teach them about chemistry.
It's like a Catch-22; I end up sacrificing my dignity in a way that helps them LEARN more, but it might end up adversely affecting their learning overall if it makes them think I can't help them understand things. And it also shows up in their memories as a huge looming perception, so when it comes to instructor evaluation time... what do YOU think happens?
...postdocs who are pregnant do not have to live in fear of letting anyone know until it's unavoidably obvious. And then don't have to have their jobs held over their heads once it is. Where recent PhD grads never have to feel anxiety about whether their offers for postdoc or other positions will dissolve if they let it slip that they are, or would like to soon become, pregnant (a fear my sister recently experienced). Where postdocs on the TT market (or any other job market for that matter) don't have to feel fear, stress and hopelessness about it, either.
THAT is one thing that could help improve the diversity of the biomedical research workforce. But what will/could the NIH do about it? I know what each and every one of us can do individually about it: work on our inbuilt and resource-motivated biases. But systemically? Hell if I know--brainstorming here...
I am so proud of my visiting high school student (who joined us through a cooperation with Project Exploration and GERI during the summers of 2009 and 2010) Genesis Galan, who won a $25,000 college scholarship!!! Way to go Genesis!
Enzymes are the chemical engines and machinery of life. They are the catalysts that move things around inside our cells, that break apart proteins and other molecules to either recycle them or get rid of them out of the body. They are the machines that read, translate, replicate and repair our DNA. They are proteins themselves: big long amino acid polymers that glob up on themselves to give three-dimensional shapes with velcro-like molecular surfaces and little sticky pockets for other molecules. In an enzyme, those sticky pockets can do more than just stick (i.e. bind) to another molecule--they can perform chemical reactions on those other molecules. They do this by bringing those molecules so close in space either to other molecules they also bind into their sticky pockets, or to their own amino acid chemical groups, that the atoms in the molecules just can't help but interact and form (or break) bonds with each other. Think of static-y balloons: rub them on your hair, then hold them juuuuust close enough to the wall and they POP on over to stick. Or of how even though you didn't like your college roommate very much at first, you spent enough time in close proximity and eventually bonded and became friends. These kinds of forces happen at the atomic scale, bringing molecules together that otherwise either would not ever get close enough together to react, or who don't really WANT to react but can't help it when they get stuck together in a sticky pocket and stay there together for a long enough time. For molecules, even a few milliseconds can be long enough.
The molecules that enzymes are bringing into sticky pockets and forcing to become friends with themselves or others are called their "substrates." The stuff that gets formed in the sticky pocket is called the "product." How much a given substrate "likes" an enzyme (and vice versa) is governed by the chemical properties of the molecule and how well they are compatible with the sticky pocket. A key aspect of this is that in order to be a good enzyme, a good catalyst, there has to be just the right balance between how much the substrate and product stick to the sticky pocket: if either one of them sticks too much, the enzyme has trouble kicking them out to move on to the next molecule of substrate and therefore can only do the reaction once. How fast the enzyme can go through this process (the "rate" of the reaction) is thus also governed by the stickiness, but additionally by the amount of time it takes for molecules of the substrate to randomly bump into the enzyme when they are both floating around in a solution where at any given time, they may be very, very far away from each other (think trying to find another human in a vacant building vs. a crowded airport). This seems pretty straightforward as a conceptual illustration, but how do we actually quantify this? How do we figure out how fast an enzyme goes with a given substrate, how well a substrate sticks to an enzyme, how well its product sticks to the enzyme, and how to relate any of this to what we want to know about the world and how it works? A fascinating new translation (from the original German) of the paper by Michaelis and Menten that is considered to be the first quantitative description of all of this, along with some commentary and historical contextualization, was published this week in the journal Biochemistry.
In the early 1900s, a Canadian woman named Maud Leonora Menten went to Germany to work with Leonor Michaelis at the University of Berlin. By this time in scientific history, Michaelis and Menten knew that enzymes could perform chemical reactions. They knew approximately that this stickiness of pocket for substrate was a factor in determining the fundamental properties of the reaction (things like its rate and the mechanism by which it occurred). Catalyzed themselves by work from Victor Henri, they hypothesized that there would be a quantitative relationship between the amount of enzyme around compared to the concentration (or density of molecules floating in the solution--trying to find each other in the vacant building or the crowded airport)--but nobody had ever analyzed this properly before. Henri had the basic idea right, but had forgotten to take some key factors into account (things like changes to enzyme products that just happen on their own without the enzyme being involved, and how much an enzyme depends on having the right pH balance in order to work properly) that made it too hard for him to figure it out. In just that one year, Menten worked with Michaelis to set up experiments to test this and to properly control those other aspects that Henri didn't, and collected the data to write the paper describing analyses and equations that transformed the way scientists thought about enzyme function and provided the foundation on which modern biochemistry is built.
All of this was done without even knowing how much enzyme they were working with, just diluting some kind of preparation of it (which, Johnson and Goody point out, Michaelis and Menten didn't even describe in the paper) in different proportions to substrate (which was sucrose) and without having modern molecular analysis tools available. The "readout" (i.e. detection method) of the enzyme activity that Menten used was the optical rotation of the solution she was working with: how much the solution "twisted" some polarized light that was passed through it. The enzyme, invertase, was breaking apart sucrose into fructose and glucose, and removing the bond between them had an effect on the optical rotation. She had to handle the solutions just right and put them in conditions that would minimize the unrelated conversion of the glucose to make sure that those effects wouldn't complicate the analysis. With that scrappy, deeply thought-out experiment, she and Michaelis are a reminder about how much you can figure out with very few resources. This piece of history also illustrates how much discovery of the un'seeable' can come from the basic function of human exploration, even without fancy machines and lots of pre-determined knowledge about a system.
One thing their models don't account for is how enzymes probably ACTUALLY function inside cells, where concentrations and densities of proteins and other molecules are extremely high and dynamic, relative to when they are free floating in solutions. The interior of a cell is like an obstacle course, with strings of stuff and big protein chunks in glommed up 'complexes' everywhere you turn. Molecular distances are measured in Angstroms, where one Angstrom is 1/10th of a nanometer. A typical enzyme protein is, on average, about 4-5 nanometers in diameter (40-50 Angstroms) when its amino acid polymer chain is all velcroed up on itself (i.e. the protein is 'folded'). An enzyme can be sequestered over in one region of a cell (which is about a picoliter, or a billionth of a liter, for a human white blood cell), thousands of nanometers away from its substrates and with protein after protein in between them. Having an obstacle course of proteins between you and your substrate is a lot different from only having a bunch of tiny water molecules filling that distance. Proteins do not get out of your way easily the way water molecules do--however, they do sometimes actively facilitate your travels to bring you to your substrate (through their own enzymatic machinery functioning as kinetic motors). Also, you might just end up next door to some other substrate that you don't like that much, that doesn't stick very well in your sticky pocket, but hey, you're hanging around in the same locale and what the heck. You might crawl from there to some other molecule of another substrate in a complex next door, without ever letting completely go and floating away (the way the traditional understanding of enzyme reaction rates assumes is happening).
These factors and differences play a major role in actual biological enzyme catalysis, and add so much complexity to the system that these simple Michaelis-Menten models break down. It's analogous to the difference between Newton's Law of Gravity and General Relativity. The rates could be either faster or slower than you would expect from how sticky the enzyme pocket is for the substrate (faster because of proximity increases for the substrate, slower because of more chances of sticking to something else nearby including inhibitory, grabby product, and not being released to find another molecule of substrate), and we don't have good ways of measuring these effects to any accuracy yet. This will be the next challenge for biochemistry, to update this model and incorporate all of these complex molecular interactions into a fuller, integrated picture of how these machines work inside the cell. This is, to put it simply, a loooooong way off. But we need to remember as modern scientists how much we can do when we make the best possible assumptions we can with all of the information we have available, are rigorous about defining and remembering those assumptions, and not be afraid to ask these kinds of questions just because they are hard.
Menten didn't stay in Germany--she went to the University of Chicago to complete a PhD degree (because Canada didn't allow women to get PhD degrees at the time--way to go, dudes). She went on to publish other work and be very successful as a faculty member at the University of Pittsburgh--where, surprise surprise, she wasn't promoted to full professor until she was 70 years old and had been there for 26 years. From all accounts available, she was a fascinating person, researcher, doctor and painter, and was multifaceted and accomplished in all aspects of her life. Also interestingly, I don't see any discussion of her family or marital status in the sources I've trolled through (admittedly briefly)--it's nice to know that a woman's accomplishments can be discussed independently of whether or not she had a husband and children. I wonder why I never knew Menten was a woman before now. I'll make sure to tell my students about her.
Johnson, K., & Goody, R. (2011). The Original Michaelis Constant: Translation of the 1913 Michaelis–Menten Paper Biochemistry, 50 (39), 8264-8269 DOI: 10.1021/bi201284u
is like a bunch of bratty teenagers. Every time (every TIME!) we're really getting rolling, a bunch of people are up to speed, we're about the launch off on a bunch of paper-completing experiments...
it's like a beaver dam where little leaks start springing everywhere and everyone has to drop what they are doing and run around trying to stick their fingers in the holes.
And by the time we get it figured out (>$5K later), all the momentum is lost and it is like starting over. Then, the cycle begins again.
I finally can take some time, real time, to just sit down and think about writing this. Of course, this isn't until the day of the actual deadline and practically the 11th hour (almost literally), because due to my job and living situation (and, sorry, baby), it is a luxury to find some time to think. Normally by this time of the evening I am too tired and my brain is too scrambled, but I think I can make this work tonight.
Here are our good lady's questions (as submitted by her readers), and my responses below:
1. It seems to me that often women don't have as strong professional networks as men - the kind that gets built over shared interests (sports or drinking). People seem to gravitate towards others like them. What specific advice do you have for establishing and maintaining network with men as well as other women?
As a grad student and postdoc, it was socializing at every social event possible, and sending brazen emails to anybody I wanted to talk to, even some very big name people, with no regard for my relative nobodyness. The worst response I ever got was none, and mostly people were very helpful, so I guess it worked!
Now as a PI it requires much more effort, and involves some drinking, but not as much as when I was a student. The things that have worked for me so far as a PI:
- Going to conferences and workshops alone, or with my lab group (but trying to leave them their own time to hang out without me). Asking lots of questions in seminars and at conference talks and discussion panels. Just generally getting my voice and opinion heard in large settings, and when you're a "girl" people remember you for that, apparently.
(Actually, the best networking at a conference I have done so far was when my sister also attended, and the two of us hung out the whole time and were enough of a novelty item that a bunch of people remembered us. Subsequently, some of them remembered me and noticed my science, too, which was cool.)
- Inviting people who I would like to meet to be speakers in my local seminar series. Always signing up to meet with the visiting speakers others have invited whenever I can possibly make room for it in my schedule. Pushing myself past my (surprising, given that I can be kind of gregarious) introverted tendencies to chat to people and be friendly. When all else fails, tell them about the latest crazy idea I have for something cool, or talk NIH shop.
- For maintenance: try to pick some smallish society or meeting, or sub-meeting of a big society, to make an annual trip and bring my group (or as many of us as we can afford). The conference with my sister turned into this for me: I see the same group of leaders in the organization, as well as other successful people whose work is relevant to mine, since this organization crosscuts a wide range of fields but centers on a philosophy of approach. Many of them now remember who I am, and notice when I pass across their radar screens, and some even keep a watch out for how I am doing. This is one of the best ways to build a meaningful and helpful network. It's like having a whole crew of senior folks out there in your corner, wanting the best for you, and ready to help you with advice (mentors at large!).
- I have a different network for being a woman in science at my institution, since we have an NSF ADVANCE grant, and they work hard to make connections between us. It mostly gets maintained by going to the functions regularly and trying to set up lunches as often as calendars and chaotic lives allow.
- Relatedly, in my own department, we have a lunch network where as many folks as possible go to lunch every Friday at a local brewpub. For intradepartmental networking, this has been invaluable, and also helped me feel very comfortable in a department of almost all male faculty.
2. Early on, what was your "Oh @!#$%" moment and how did you recover?
As many of you know from my blog's beginnings, I submitted (re- and re-re-submitted) a K99/R00 grant and eventually got it. I was completely convinced that the central approach proposed in the grant was totally new, that I was the first person to try it. I had searched high and low, yonder and non, with every permutation of every keyword you can think of, to support that presumption. Nothing had come up, and nobody countered me on it through all my talking about and grantwriting about the idea, and so I kept on assuming. Fast forward to the first year of being a PI, and we have finally gotten the proof-of-concept data and submitted to a journal. The reviewer comments: "This would be really cool if it hadn't already been done before: [CITATION (from FIVE YEARS PREVIOUS!)]"
I definitely felt like an idiot for days. I might have even cried a little. But then I sucked it up, reminded myself that pretty much nobody ever does anything truly new, and re-worked the frame to lower the hyperbole a bit and focus on what was innovative and useful about it, submitted it elsewhere (with this newfound citation included) and things went fine. This is just the way it goes--sometimes you got scooped a long time ago. It was kind of like when I was a little kid and learned about the structure of the atom, how it was a central nucleus with all those little electrons spinning around it in orbits, and thought "MY GOD!!!! IT'S LIKE THE SOLAR SYSTEM!! MAYBE WE ARE JUST SPECKS ON AN ELECTRON!!!!!!!@$1$@1$!$" and told my dad, and he said, "Yeah, some philosophers wondered about that a long, long time ago." I mean, of course I am a genius, but I guess so are a lot of other people, some of whom were born before I was.
3. How do you deal with female health issues (heavy periods and period pain that lasts for a week, heavy migraines that strike suddenly, etc.), when you are in a predominantly male environment?
Mostly I just don't talk about it. Or I just make faces and complain that I don't feel well. I'm kind of overly talkative about my health problems in general, so there's usually something else I can talk about (like my heart arrhythmia, nerve pain, or the weird ocular migraine that I got once). I used to talk about some possibly TMI pregnancy stuff to them (SORRY, babies, but the bodily issues are related to the question!), and they usually acted kind of weirded out, or just were matter of fact because most of them have wives and/or female relatives.
4. How do you balance "assertiveness" and "bitchiness" - in the sense that it's harder as a female (than a male) to "get away with" being protective of your time, stating your opinion, and so forth?
I have changed, from when I was an undergrad to when I was a postdoc, and again now that I am a PI, and it's an adaptation to my environment. I come from a very "nice" place, where everyone is "nice". I postdocced in a very NOT NICE place where many people were hostile, and many interactions were very competitive. There I learned to be NOT NICE and to hold my own and give people a piece of my mind. I just didn't really care if people thought I was a bitch, and secretly kind of enjoyed it when they did, because it was badass and I knew I was righteous.
But when I came to PI-U, I realized that things were different here. Going back to the "nice" model works a lot better here, and there wasn't much need to be badass most of the time. Here, I still try to state my opinion, speak up, ask for what I want and need, etc. But I tend to combine it with more self-deprecation and gentleness--not that this is the best way to do it everywhere, but it's how I have adapted, and it mostly suits this environment (departmentally). I think I could use a little bit of my inner badass back, because sometimes I think I am getting set aside or lowered in priority--but it's usually a balance of when it's worth it and when it would just make it worse. So, I'm saving it for when and if it's really worth it.
What does this mean? By "agencies," I presume they mean NIH and NSF are included. Sounds like some more you-know-what is gonna be hitting the fan in the coming months...
Okay, well, not exactly. But pretty freaking close. A "just accepted" article in the journal Analytical Chemistry from Aydogan Ozcan's group at UCLA describes a cellphone-based device for detecting and counting fluorescence-labeled cells (or other fluorescing particles) flowing past the cellphone camera.
This device fantastically uses LEDs pointing in at each other from each end of a cheap microfluidic chamber (i.e. a silicone-coated glass chip) that acts as both the channel for the cells to flow through and an optical waveguide (making the light waves from the LED oscillate and bounce around, kind of like how a fiberoptic works) that enables fluorescence excitation and emission, which gets sent through some cheap lenses and an optical filter (to isolate the fluorescence signal) and picked up by the cellphone's integrated camera.
To demonstrate that this could give useful biological information, they combined the device and phone with an external pump to flow liquid containing particles (either a suspension of fluorescent particles or diluted blood) past the camera. In the version where they analyzed blood, the blood had been dyed with a fluorescent stain that made the white blood cells glow in the LED setup (because the dye stains DNA, and red blood cells don't contain any DNA). Then they wrote some software that post-processed the movie images and counted the particles or cells.They were able to get pretty good detection and resolution of particles as small as 1 micrometer in diameter--which is smaller than most human cells (meaning that even smaller things, like bacteria, might be able to be detected and distinguished from each other).
Could this really be used to diagnose cancer? Sort of. White blood cell counts are a key metric by which leukemia and lymphoma (cancers of the blood) are diagnosed and monitored. This device was able to get white blood cell counts that were really close to those obtained with a fancy blood cell counter instrument. Of course, to do anything more than just count the number of easily stainable white blood cells, people with a lot of biomedical expertise will have to think of ways to apply this detector. For example, from the proof-of-concept experiments they showed, you wouldn't be able to tell what kind of blood cancer someone had, and you wouldn't even get very much information about the status of their blood cancer if they had started on a treatment. Even so, being able to do this accurately with something that costs $50 is revolutionary: most fluorescence-based detectors cost tens of thousands of dollars and require sensitive, fancy optics to work properly. Even the "cheap" versions are a few thousand dollars. This is some LEDS stuck onto a glass chunk with some plastic bits attached. And it works.
Why is this so amazing? Well, apart from that it is small and readable by an extremely common and widely available camera, it is also made of all low-cost parts, so it will be extremely accessible yet gives pretty sophisticated information (fluorescence image readings). Its applications are only limited to what kinds of dyes are available to specifically stain different things you might want to look for in cells or particles, and the ingenuity of the applier in figuring out what to stain with fluorescence and try to look for. There are fluorescence-type stains that can be applied to cancer cells to figure out more about the type, stage and response to treatment of the disease--and while not trivial to apply with this device, potentially within reasonable optimization reach. They can even use different cheapo plastic optical filters to look for different fluorescence colors (besides just the green fluorescence they show in the paper). Of course, if they want to try to look for more than one color at a time (called 'multiplexed' fluorescence), things might get complicated.That requires more than one LED and more than one optical filter, but, I imagine, isn't impossible.
Beyond just white blood cell counts, I can envision this being used as a readout technique for detecting circulating tumor cells (CTCs)--rare cells that are shed by other kinds of tumors into the blood stream. CTCs usually have different proteins and carbohydrates on their outer surfaces than blood cells do, so it's possible to either stain them with fluorescent dyes that don't stain the blood cells, or catch them using antibody grabbers in microfluidic chambers (very similar to the one used in this device) that latch onto those specific proteins and carbohydrates, but let all the other cells flow by. In that case, you could then just stain the captured cells with the same antibody grabbers (left free floating rather than attached to the chamber, and pre-labeled with a fluorescent dye) or some other generic dye for staining all cells or cell parts (like the nucleus).
It will need to be integrated with some kind of pump to be more universally useful, because not everybody has a syringe pump sitting at the back of a closet that they can just hook up and go. It'll also probably need some integrated sample well that can keep the sample 'agitated' (i.e. shaking) during the flow so the particles don't settle and clump up. However, these should be easy enough to figure out (maybe they can plug into the vibration unit on the phone to do the shaking?). They designed their counting software using a platform that is compatible with Android phones--so even though the phones they used in this study couldn't do any integrated processing, it's a very short jump to adapt this device to an Android phone or iPhone and have... (wait for it...) an app for that. lol. I am fascinated to see how this device gets further developed and used, and I plan to buy one for myself as soon as it's available.