Go see the British Library. It's free, and they have a great collection of illuminated manuscripts, Da Vinci sketches, etc. My wife and I really enjoyed it, as it's a well-hidden gem. Really enjoyed seeing a copy of the Magna Carta.
Try password safe. Choose one strong password to encrypt (via twofish) the entire data base, then choose strong random passwords for everything within. Only one password to store in memory that way.
It can run on a USB key (no registry entries), making it very portable. You can right-click entries to (1) surf to the selected logon page, (2) auto-fill username and password, and (3) hit submit, making surfing nearly as easy as the built-in firefox password manager, but much more secure. Of course, it has all the standard features, like auto-generating random passwords, database search, categories/subcategories/etc. My wife and I both use it and are pretty satisfied.
In the related links, you can find non-windows implementations, making it very portable.
Here are the ICSI results. Results are from a PC behind a bog-standard Linksys WRT-54g, for what it's worth.
Not my field, but I see Direct TCP access to remote DNS servers (port 53) is allowed. I'll leave it to the networking experts to pick through the rest of the report.
If you can't control the rat population, why can't you work on rat-proofing the cables, i.e., putting them in steel conduit? Am I missing something obvious, like regulatory considerations? Surely protecting the cables once and for all would be cheaper than frequently replacing them plus working on population control?
Please don't confuse philosophy and physics. They are two separate fields. The physics here is suggesting that the Universe might behave. Plato was commenting on the difference between human perception and reality.
Careful, though. There's a reason the degree is called a "Ph.D. / Doctor of Philosophy": the modern physical sciences descend from natural philosophy. Indeed, Newton, Leibniz, and Descartes were all active philosophers as well as mathematicians and physicists. Even today, there's a great deal of intersection between philosophy, mathematics, and physics. (e.g, model theory, Gödel, examining group theory to get new ideas on theoretical physics, which tie into modern philosophy on the observable universe, etc.)
I am just wondering if they have a way to stop the process if they need to... Ah well. Good work
There has been recent work to treat autoimmune diseases by "erasing" the immune system's "memory" (e.g., memory B cells) by attacking the marrow with chemotherapy, then reseeding the system with harvested haematopoietic stem cells. Here's an example I find after a fast search. Of course, it leaves the patient with 0 immune system while it regenerates from the stem cells, and I'd imagine you'd have to redo all your vaccinations, etc., but I suppose that could do the trick. -- Paul
I've wasted more than enough time with this recently: Java risk: http://domination.sf.net. The implementation seems to be pretty good, it has some basic AI players, and can be played over a network. Good, classic strategy game, without the overhead of sorting out all the little army pieces. -- Paul
Without the risk of discrimination, increased genetic testing could be a boon to both consumers and insurers. The earlier we know about a condition, the less expensive and more effective it is to treat, with likely a higher quality of life. Genetic testing would allow us to better assess who to monitor to attain this early detection. Moreover, with increased knowledge of risk factor, a patient could choose lifestyle changes that are preventative. (Even cheaper for insurers and further improved quality of life for patients!)
Take skin cancer: if you know you lack a key tumor suppressor gene that makes you more sensitive to UV damage, you'll be much more likely to use sunscreen and avoid peak sunlight hours (lifestyle/preventative); you'll also know to keep closer tabs on your freckles and moles for melanoma (monitoring).
With a level, non-discriminatory playing field, both patients and insurers benefit from the knowledge, rather than just insurers who want to drop any patient they can. -- Paul
Really, if it is *that much* of a concern, quit buying from a third party vendor. License a spec, rent a manufacturing facility, put some people to work, and create your own Cisco Certified Uber Network Gear eXtreme, Uncle Sam Edition
By the article, Cisco has no direct sales--only gold/silver partners who they claim to train train themselves. However, some of the counterfeit equipment was purchases through gold/silver partners. -- Paul
This could be used in a whole host of areas where object-object interactions are important, but testing for those interactions can be very expensive. (e.g., molecular dynamics, agent-based biology modeling) One method we've used to solve these issues are interaction potentials. (the object-object interactions are through gradients of the potentials that can be scaled linearly with the number of objects if cleverly constructed.) However, I'm intrigued at using these data structures as an alternative approach, although I wonder if you'd get the savings if the objects were spaced more uniformly.
Do you have any publications you can point me towards? Thanks! -- Paul
I know, I know. You could probably just data-mine the DNA itself to figure out individual identities. In the future, if you ever go to another site and put it a few genes (for whatever purpose) that get linked to your real identity, you will be screwed. But hey, how's that any different than data mining Netflix?
Well, if somebody finds my Netflix data, they may find out my most secret movie preferences. If insurance companies or employers link me to my DNA and discover a genetic pre-disposition to brain cancer or a debilitating disease, I'll never get health insurance again, and the misfortune will probably extend to any offspring as well. And would anybody hire you (and again, your children) if you have a genetic pre-disposition to MS or some other debilitating condition? Prospective employers are already googling for damaging Facebook information; just wait until genes enter the mix!
Until good privacy protections and anti-discriminatory legislation are in place, we're talking about a whole different level of risk. -- Paul
How about selling 10-year t-shirts for $15-$20 each, with $10-$15 to EFF? I know I'd buy one, and I'm sure the demand would result in a much larger contribution to EFF than a single grab box, etc. -- Paul
This isn't my area, but my Ph.D. is in applied and computational math, and I've spent a great deal of time solving first-order hyperbolic problems where characteristics cross. (In my context, level set methods where the zero contours can split and/or merge.)
For a hyperbolic problem like this, you'll want to be careful. Since the waves have variable propagation speeds, there's a possibility for shock formation. (characteristics can cross) Think of Burger's equation as a nice, tangible first-order analog. In such a case, it will be important to choose a numerical method that satisfies some kind of entropy condition to handle the shock. Similar things have been encountered in level set methods, where you solve an equation of the form ft
+ V |grad(f)| = 0, where V is the variable speed of an interface that's represented as the zero contour of f.
As for visualization, you'll probably want to check out the "industry standards" Matlab and Mathematica. You could plot the time evolution of level surfaces of your wave equation, for instance. As for other softare, I'd generally advise pulling together what you can find at netlib, although more cutting-edge stuff may require you to roll your own C/C++ or FORTRAN. But any of that stuff will be faster than running in Matlab or Mathematica, and it will take a whole lot less memory.
Don't forget that the gap needs to be bridged from both sides: while it will indeed take some cultural changes in the medical community to use computational / predictive tools in choosing therapy, it will also require cultural changes in the modeling community to facilitate this. Furthermore, doctors' trust in computational tools must be earned by a well-validated track record of results by the mathematical / engineering community. Interestingly, these cultural changes are underway and can already be observed.
My primary field of research is developing computational tools for modeling cancer progression and angiogenesis, primarily using a PDE point of view where I model nutrient transport within the body and uptake by tumor cells, some simple biomechanics, the degradation and remodeling of the extracellular matrix by the tumor, and the resulting motion of the tumor boundary within the tumor. In fact, this was my dissertation topic just a little over a month ago; the interested reader can see my publications here and some animations of cancer simulations here.
In the several years I've been doing this work, I've seen interesting changes on both sides of the aisle. The mathematical models of cancer have grown in sophistication and realism at an incredible speed. Five or six years ago, models would only examine a single, isolated aspect of cancer growing in homogeneous tissues that were more idealized than even simulated in vitro petri dishes; today, they model many aspects of cancer and the interaction between those aspects. Several years ago, the models were little more than interesting mathematical objects with simplified, spherical solutions that weren't very interesting outside the mathematical community; today, we're simulating complex tumor shapes in fairly realistic tissues, and the results are shedding light on current problems in cancer biology that are otherwise difficult to understand.
Several years ago, it was difficult to even get doctors, oncologists, and others to even look at our research (in our field in general). Today, we're building a track record of results that makes the work easier to trust. Mathematicians and engineers are also realizing the need to acquire the "vocabulary" and biological background necessary to communicate with doctors and biologists, and they're making moves to bridge the gap and collaborate. In the meantime, more cancer biologists are realizing that it takes more than studying isolated cells to understand cancer systems, and they're reaching out to mathematicians to model these complex systems.
The result: very rich and exciting collaborations between doctors and mathematicians to develop helpful predictive tools. My group (at the UT Health Science Center in Houston, with the M.D. Anderson Cancer Center) is doing exciting joint work with oncologists, biologists, mathematicians, and engineers to combine experiments with well-calibrated models of glioblastoma, an aggressive form of brain cancer. Sandy Anderson and Vito Quarnata are doing similar joint mathematical/biological work on breast cancer at Vanderbilt and the University of Dundee, and their work has been featured on slashdot before.
So, it really requires growth toward collaboration from both sides, but fortunately, the need for this has been recognized by both communities and is occurring as we speak. It's a very exciting time in cancer systems biology and computational / predictive oncology! -- Paul
A lot of the comments here seem to miss one important point of free TV broadcasts: emergency broadcasts.
It's easy to forget this when we have instant access to CNN, Weather Underground, etc. online, but many rely upon TV for their weather forcasts and news (even if in dumbed-down form). During storms, many rely upon TV to get thunderstorm, tornado, and hurricane warnings and information. This is especially true of those with lower income. TV is a convenient, ubiquitous method of disseminating emergeny information.
If analog TV were elimated without providing this conversion assistance, millions of the most (economically) vulnerable Americans would lose a primary source of emergency information. It's a matter of public safey, and I think it's one of the major arguments in favor of providing assistance. -- Paul
I'm a PhD student in math, and I have no idea why anyone would want to give a student a calculator. Much less a graphing calculator. It's fine as a means of removing tedium, but students need to do a lot of tedious things once or twice. In the calculus class I teach, I can't think of a single aspect of the class that would be improved by having a calculator.
I'm also a Ph.D. student in math (defending my dissertation next month), and I've found the exact opposite to be true. There's no better way to develop a deeper understanding of something than to play with it. As regards calculus and functions, this means plotting functions, composing them, zooming in on them, adding them, differentiating them, multiplying them, etc. This is especially relevant with polar and parametric equations, which can take some time to get the hang of.
The newer calculators even let you play with systems of differential equations and trace out solutions, flow lines, etc. What a great way to learn to visualize otherwise abstract concepts! If students would just sit and play with equations and see what the solutions would look like, they would have a much better grasp of what to expect when they encounter something new. Otherwise, it can tend to be a matter of memorizing a cook book of solution techniques.
Of course, there are times when the calculator can be a hinderance. In particular, the built-in symbolic differentiation and integration can become a crutch. (On the other hand, it's a great way to check your answers.) However, most of the associated problems can easily be dealt with by properly writing your curriculum. (e.g., giving calculator-free exams to test differentiation knowledge, splitting them into two-part exams (without calculator, then with calculator), giving weekly 5-minute self-quizzes, etc.)
At the end of the day, a graphing calculator is just another tool that can be used to help or hinder education. How it goes depends on a combination of student motivation and the leadership and guidance they receive from their professors and teaching assistants. (i.e., you) -- Paul
Some of my colleagues (e.g., Vittorio Cristini) have been modeling the potential benefits of nanoparticle drug delivery for a couple of years now. As has been known for some time (e.g., see papers from R.K. Jain), the blood vessels that grow to supply tumors with nutrients (the tumor-induced neo-vasculature) are different than regular, non-pathological vessels. They tend to be more tortuous and leaky, with larger holes than regular vessels.
This is where the nanoparticles come in: one can design nanoparticles that encapsulate cancer drugs in particles that are too large to exit normal blood vessels but can pass through the leakier, tumor-induced blood vessels. This naturally targets cancerous tissues.
However, there are other issues to consider. Due to the high pressure inside tumors (due to the rapid proliferation of cells within a confined area, among other factors), along with the leaky vessels, blood flow can be very poor inside a tumor, and so while the drug may be targeted toward and delivered to the tumor, it may not actually penetrate very far into the tumor. Some great work has been done by Steven McDougall,
Sandy Anderson, and
Mark Chaplain in this area. In particular, look at their DATIA (dynamic adaptive tumour-induced angiogenesis) papers.
One way around this (suggested by R.K. Jain and Vittorio Cristini, among others) is to use targeted anti-angiogenic therapy to prune out the worse blood vessels and improve flow within the tumors, thereby also improving drug delivery and penetration.
Lastly, on the therapeutic aspect of blocking up tumor blood vessels with the nanoparticles, the work we've done (see this paper, which will appear in the Journal of Theoretical Biology soon), indiscriminately cutting off the nutrient supply to a tumor can increase tumor invasiveness by increasing morphological (shape) instability. (See some of the animations here.) So ironically, while more tumor cells may be killed, those that remain may spread farther and initiate new tumors. Given that hypoxic tumor cells are more likely to be resilient to further treatment (e.g., hypoxic breast cancer cells), this is a problem worth keeping in mind when planning anti-angiogenic therapy.
If you're interested in these topics, please do check out the paper above. (You can also download it at my website without any special memberships.) Even if you don't like it, we have a lot of references you may find handy. -- Paul
I've been hoping that eventually it will be possible to run a complete simulation of clinical research protocols long before any research participants are recruited.
In fact, that's one of the main goals that we have in mathematical modeling of cancer: to make the term computational oncology a meaningful one.
Right now, there are some pretty decent models of angiogenesis, tumor growth, cell population dynamics with and without stem cells, tissue stress, and tumor microenvironment, and they're both producing previously-observed behavior and predicted interesting things to test.
The next natural step is to start coupling these models together, and such work is already under way. (See my website, for example.) This is a challenging problem, however, because it involves behavior at a scales ranging over several orders of magnitude. (i.e., it's a multiscale problem.) Another major aspect is to learn to better calibrate the models with in vitro and in vivo data. I'll be spending a few years of a post-doc on those types of issues.
However, we (not just us, but the field in general) already have models that can make good qualitative predictions that can help describe unusual cancer behavior. For instance, it's been known for a long time that angiogenesis (the development of new blood vessels at the behest of a growing tumor) is necessary for a tumor to grow beyond a certain size and is often involved in metastasis. This lead to the great idea of developing anti-angiogenic drugs that target and prevent this process. However, the results have been mixed, and in some cases, the therapy can worsen tumor invasion. Recent work with mathematical models has shown that hypoxia can lead to nonuniform growth and shape instabilities, leading, in turn, to invasive fingering and even tumor fragmentation. (i.e., greater invasion).
If a relatively simple tumor model can help predict and explain this type of behavior, imagine what a complete model, combined with a patient's MRI imagery, sequenced genome, etc., can do!:-)
This post has gotten too long, but let me just say that we would also like to see it go this way. -- Paul
So, while the mathematical model of growth might represent some predictive value, it certainly will not effectively model new developments, such as the above, when they are found.
There's still plenty of value to be found in higher-scale models. (e.g., how the tumor as a whole interacts with the microenvironment, how proliferation-induced pressure turns off the vasculature and prevents drug delivery, how oxygen and glucose delivery throughout the tumor and the microenvironment affects the patterning of hypoxic and necrotic cells, which, in turn, affects angiogenesis and matrix degradation) Cancer is a multiscale problem, with interaction between all the scales. Focusing on one scale alone (molecular or tissue-scale) likely will not solve the entire problem.
In fact, developing a good tissue-scale model is a natural step toward creating a multiscale model, where molecular- and cell-scale dynamics affect the growth parameters that govern tissue-scale behavior. (Similarly to how the behavior of individual molecules leads to things that can be averaged at larger scales, like heat, viscosity, etc.) First, you fix the parameters and neglect the small-scale dynamics to figure out the large-scale behavior. Then, you model the small-scale dynamics and learn how to couple them to the previously-fixed parameters.
On a tangent, I also worked with "Sandy" Anderson in a different U.S.-Scottish collaboration. He's a really great guy, as well as Mark Chaplain and Steven McDougall. And Sandy is a pretty incredible cook!:-) -- Paul
Nature is replete with examples where scale matters. Insect-scale airfoils don't work particularly well. Jumbo jet-scale insects wouldn't fly, either. At the molecular level, flagella give great propulsion in fluids, but the same wouldn't hold at the macroscopic level.
The same is true in biology. I remember having read a study done at NASA on the effect of iron nanoparticles in lungs. (Alas, I can't seem to find the link anymore.) They concluded that at the nano scale, the iron particles could escape the normal protections and remain in the lungs (in the interstitium and cells themselves), where they could collect and have a toxic effect, including diminished lung function. (The test rats became lethargic, etc.) All this at exposure levels that wouldn't be considered toxic at other scales.
I've seen similar research on sunscreen. Zinc oxide particles are great protecting at UVA and UVB. However, at large scale, they're quite visible and hard to blend in. Make them smaller, and that problem goes away, but they get absorbed deeper into the skin. Make them smaller still, and it's quite possible that they'll be absorbed into the cells themselves, leading to new potential health effects. (e.g., does zinc oxide become carcinogenic when they remain in the cells for too long? Does the motion into the cells increase the likelihood of reactive oxygen species (free radicals) accumlating inside the cells, rather than outside?)
I'm not a biochemist or a biologist (I'm a biomathematician), so I don't have the answers to these questions. But it's clear that scale really does matter, and it needs to be considered. Is the danger overhyped? Possibly, or maybe not. That's why it needs to be studied. But it's going to be important to understand these effects when we move from the low levels that occur naturally to the high levels that will occur in human-made materials and products. -- Paul
I've often wondered this myself. What is the reward for developing open source software? If companies can come in and use open source components in their own creation in a way that they make money without violating licenses, but at the same time aren't obligated to give anything back to the community, where's the motivation for new developers to go open source? Not everybody operates with an altruistic "I'm giving back to the community" motivation.
True, open source contributions may work against your future earning potential. On the other hand, it can also help build it in a number of ways. In my case, I'm not a formally-trained programmer. I learned C++ on my own out of books and trial/error for my scientific research. As such, I didn't have a lot of confidence as a programmer.
Starting an open source project helped me to gain valuable feedback that improved my programming skills in a way I could never have done on my own. I also got a helpful confidence boost--I'm no longer ashamed of my coding, or scared of letting others see it. This has been liberating, and has helped me to improve as a collaborator. In my case, the improved skillset gained through open source contributions will most certainly add to my future earnings potential.
For those who already have all their skills and couldn't possibly gain from feedback (whoever that may be), open source could be viewed as the equivalent of pro bono work done by lawyers. Lawyers often do pro bono work to help the poor, etc., and possibly to keep certain skills sharp on things they may not do on a day-to-day basis. For a programmer, open source gives the opportunity to practice something new or out of the daily grind and get valuable feedback on it. Or to work on a larger project that they wouldn't have time for otherwise.
And then as mentioned above, there's the resume aspect. When I was applying for an NSF postdoc fellowship (still underway), I was asked for "synergistic activity": ways you contribute to the maths/science/engineering community or education beyond your normal duties. Being able to say "lead author of a project used in undergraduate education and industrial and academic research in North America, South America, Europe, Asia, and Australia" was certainly a boost, considering many graduate students can only claim making better handouts for their classes or the occasional presentation.
I accidentally posted my comment (meant to click preview) without inserting my formatting tags. Please disregard and read this instead. Sorry!! -- Paul
This may go against the grain here, but I replace my desktop drive about every 12-18 months. As I see it, here are the benefits of doing so:
+1) The drive still has decent resale value at that point, particularly if you sell on a computer forum and not on ebay. This helps reduce the cost of the hard drive update.
+2) Drive capacities are increasing quickly while costs continue to decline. This reduces the cost of the upgrade.
+3) Replacing before the warranty period is up means that the likelihood of experiencing a hard drive failure is low.
+4) While WinXP is a lot better than Win9x, it still doesn't hurt to do a fresh reinstall every 12-18 months. A hard disk replacement is the perfect timing for this.
Of course, there are some valid counter-arguments to these points:
-1) Security. (i.e., somebody could recover your private data.) I run Darik's Boot 'n' Nuke a few times, so I'm not terribly concerned about this. After running such a program, the odds of somebody successfully recovering data on a home budget are pretty low.
-2) You may be replacing too often. Well, I can't do much about that. But good drives don't cost much more than $100-$150 these days. A little peace of mind is worth something, and the regular size/speed upgrades are a nice bonus.
-3) This is no substitute for backups. I completely agree, and make backups of my most critical data to remote servers.
-4) Perhaps this isn't necessary. Perhaps not, but a fresh format is a helpful after 18 months.
Any way around it, I acknowledge that this strategy is a bit more expensive than may be necessary, but it has served me well in the past six years +. I've only had one drive fail in the past, back when I let my drives go well beyond the warranty period. Of course, that drive was a total loss, with no recovery of value to apply to the new drive, and there were some non-recoverable files. In my opinion, preventing problems before they occur is preferable, and getting speed and capacity boosts are just icing on the cake. -- Paul
Slashdot Mirror
Go see the British Library. It's free, and they have a great collection of illuminated manuscripts, Da Vinci sketches, etc. My wife and I really enjoyed it, as it's a well-hidden gem. Really enjoyed seeing a copy of the Magna Carta.
But on the downside:
Personally, I'd rather trust a backed up encrypted database using a strong password, than an easy-to-lose scrap of paper.
Try password safe. Choose one strong password to encrypt (via twofish) the entire data base, then choose strong random passwords for everything within. Only one password to store in memory that way.
It can run on a USB key (no registry entries), making it very portable. You can right-click entries to (1) surf to the selected logon page, (2) auto-fill username and password, and (3) hit submit, making surfing nearly as easy as the built-in firefox password manager, but much more secure. Of course, it has all the standard features, like auto-generating random passwords, database search, categories/subcategories/etc. My wife and I both use it and are pretty satisfied.
In the related links, you can find non-windows implementations, making it very portable.
I hope this helps; good luck! -- Paul
Whoops! Good point. :-)
Here's the relevant line, then: Direct UDP access to remote DNS servers (port 53) is allowed.
Thanks -- Paul
Here are the ICSI results. Results are from a PC behind a bog-standard Linksys WRT-54g, for what it's worth.
Not my field, but I see Direct TCP access to remote DNS servers (port 53) is allowed. I'll leave it to the networking experts to pick through the rest of the report.
If you can't control the rat population, why can't you work on rat-proofing the cables, i.e., putting them in steel conduit? Am I missing something obvious, like regulatory considerations? Surely protecting the cables once and for all would be cheaper than frequently replacing them plus working on population control?
Careful, though. There's a reason the degree is called a "Ph.D. / Doctor of Philosophy": the modern physical sciences descend from natural philosophy. Indeed, Newton, Leibniz, and Descartes were all active philosophers as well as mathematicians and physicists. Even today, there's a great deal of intersection between philosophy, mathematics, and physics. (e.g, model theory, Gödel, examining group theory to get new ideas on theoretical physics, which tie into modern philosophy on the observable universe, etc.)
There has been recent work to treat autoimmune diseases by "erasing" the immune system's "memory" (e.g., memory B cells) by attacking the marrow with chemotherapy, then reseeding the system with harvested haematopoietic stem cells. Here's an example I find after a fast search. Of course, it leaves the patient with 0 immune system while it regenerates from the stem cells, and I'd imagine you'd have to redo all your vaccinations, etc., but I suppose that could do the trick. -- Paul
I've wasted more than enough time with this recently: Java risk: http://domination.sf.net. The implementation seems to be pretty good, it has some basic AI players, and can be played over a network. Good, classic strategy game, without the overhead of sorting out all the little army pieces. -- Paul
Isolating the haematopoietic stem cells would probably help, but I'm not sure how long they survive in extended culture conditions.
Without the risk of discrimination, increased genetic testing could be a boon to both consumers and insurers. The earlier we know about a condition, the less expensive and more effective it is to treat, with likely a higher quality of life. Genetic testing would allow us to better assess who to monitor to attain this early detection. Moreover, with increased knowledge of risk factor, a patient could choose lifestyle changes that are preventative. (Even cheaper for insurers and further improved quality of life for patients!)
Take skin cancer: if you know you lack a key tumor suppressor gene that makes you more sensitive to UV damage, you'll be much more likely to use sunscreen and avoid peak sunlight hours (lifestyle/preventative); you'll also know to keep closer tabs on your freckles and moles for melanoma (monitoring).
With a level, non-discriminatory playing field, both patients and insurers benefit from the knowledge, rather than just insurers who want to drop any patient they can. -- Paul
By the article, Cisco has no direct sales--only gold/silver partners who they claim to train train themselves. However, some of the counterfeit equipment was purchases through gold/silver partners. -- Paul
This could be used in a whole host of areas where object-object interactions are important, but testing for those interactions can be very expensive. (e.g., molecular dynamics, agent-based biology modeling) One method we've used to solve these issues are interaction potentials. (the object-object interactions are through gradients of the potentials that can be scaled linearly with the number of objects if cleverly constructed.) However, I'm intrigued at using these data structures as an alternative approach, although I wonder if you'd get the savings if the objects were spaced more uniformly.
Do you have any publications you can point me towards? Thanks! -- Paul
Well, if somebody finds my Netflix data, they may find out my most secret movie preferences. If insurance companies or employers link me to my DNA and discover a genetic pre-disposition to brain cancer or a debilitating disease, I'll never get health insurance again, and the misfortune will probably extend to any offspring as well. And would anybody hire you (and again, your children) if you have a genetic pre-disposition to MS or some other debilitating condition? Prospective employers are already googling for damaging Facebook information; just wait until genes enter the mix!
Until good privacy protections and anti-discriminatory legislation are in place, we're talking about a whole different level of risk. -- Paul
How about selling 10-year t-shirts for $15-$20 each, with $10-$15 to EFF? I know I'd buy one, and I'm sure the demand would result in a much larger contribution to EFF than a single grab box, etc. -- Paul
This isn't my area, but my Ph.D. is in applied and computational math, and I've spent a great deal of time solving first-order hyperbolic problems where characteristics cross. (In my context, level set methods where the zero contours can split and/or merge.)
For a hyperbolic problem like this, you'll want to be careful. Since the waves have variable propagation speeds, there's a possibility for shock formation. (characteristics can cross) Think of Burger's equation as a nice, tangible first-order analog. In such a case, it will be important to choose a numerical method that satisfies some kind of entropy condition to handle the shock. Similar things have been encountered in level set methods, where you solve an equation of the form ft + V |grad(f)| = 0, where V is the variable speed of an interface that's represented as the zero contour of f.
Since second-order wave equations are so important in physics, you may want to check out the Journal of Computational Physics. You should probably also try the Journal of Scientific Computing.
As for visualization, you'll probably want to check out the "industry standards" Matlab and Mathematica. You could plot the time evolution of level surfaces of your wave equation, for instance. As for other softare, I'd generally advise pulling together what you can find at netlib, although more cutting-edge stuff may require you to roll your own C/C++ or FORTRAN. But any of that stuff will be faster than running in Matlab or Mathematica, and it will take a whole lot less memory.
Best of luck, and have fun! :-) -- Paul
Don't forget that the gap needs to be bridged from both sides: while it will indeed take some cultural changes in the medical community to use computational / predictive tools in choosing therapy, it will also require cultural changes in the modeling community to facilitate this. Furthermore, doctors' trust in computational tools must be earned by a well-validated track record of results by the mathematical / engineering community. Interestingly, these cultural changes are underway and can already be observed.
My primary field of research is developing computational tools for modeling cancer progression and angiogenesis, primarily using a PDE point of view where I model nutrient transport within the body and uptake by tumor cells, some simple biomechanics, the degradation and remodeling of the extracellular matrix by the tumor, and the resulting motion of the tumor boundary within the tumor. In fact, this was my dissertation topic just a little over a month ago; the interested reader can see my publications here and some animations of cancer simulations here.
In the several years I've been doing this work, I've seen interesting changes on both sides of the aisle. The mathematical models of cancer have grown in sophistication and realism at an incredible speed. Five or six years ago, models would only examine a single, isolated aspect of cancer growing in homogeneous tissues that were more idealized than even simulated in vitro petri dishes; today, they model many aspects of cancer and the interaction between those aspects. Several years ago, the models were little more than interesting mathematical objects with simplified, spherical solutions that weren't very interesting outside the mathematical community; today, we're simulating complex tumor shapes in fairly realistic tissues, and the results are shedding light on current problems in cancer biology that are otherwise difficult to understand.
Several years ago, it was difficult to even get doctors, oncologists, and others to even look at our research (in our field in general). Today, we're building a track record of results that makes the work easier to trust. Mathematicians and engineers are also realizing the need to acquire the "vocabulary" and biological background necessary to communicate with doctors and biologists, and they're making moves to bridge the gap and collaborate. In the meantime, more cancer biologists are realizing that it takes more than studying isolated cells to understand cancer systems, and they're reaching out to mathematicians to model these complex systems.
The result: very rich and exciting collaborations between doctors and mathematicians to develop helpful predictive tools. My group (at the UT Health Science Center in Houston, with the M.D. Anderson Cancer Center) is doing exciting joint work with oncologists, biologists, mathematicians, and engineers to combine experiments with well-calibrated models of glioblastoma, an aggressive form of brain cancer. Sandy Anderson and Vito Quarnata are doing similar joint mathematical/biological work on breast cancer at Vanderbilt and the University of Dundee, and their work has been featured on slashdot before.
So, it really requires growth toward collaboration from both sides, but fortunately, the need for this has been recognized by both communities and is occurring as we speak. It's a very exciting time in cancer systems biology and computational / predictive oncology! -- Paul
A lot of the comments here seem to miss one important point of free TV broadcasts: emergency broadcasts.
It's easy to forget this when we have instant access to CNN, Weather Underground, etc. online, but many rely upon TV for their weather forcasts and news (even if in dumbed-down form). During storms, many rely upon TV to get thunderstorm, tornado, and hurricane warnings and information. This is especially true of those with lower income. TV is a convenient, ubiquitous method of disseminating emergeny information.
If analog TV were elimated without providing this conversion assistance, millions of the most (economically) vulnerable Americans would lose a primary source of emergency information. It's a matter of public safey, and I think it's one of the major arguments in favor of providing assistance. -- Paul
I'm also a Ph.D. student in math (defending my dissertation next month), and I've found the exact opposite to be true. There's no better way to develop a deeper understanding of something than to play with it. As regards calculus and functions, this means plotting functions, composing them, zooming in on them, adding them, differentiating them, multiplying them, etc. This is especially relevant with polar and parametric equations, which can take some time to get the hang of.
The newer calculators even let you play with systems of differential equations and trace out solutions, flow lines, etc. What a great way to learn to visualize otherwise abstract concepts! If students would just sit and play with equations and see what the solutions would look like, they would have a much better grasp of what to expect when they encounter something new. Otherwise, it can tend to be a matter of memorizing a cook book of solution techniques.
Of course, there are times when the calculator can be a hinderance. In particular, the built-in symbolic differentiation and integration can become a crutch. (On the other hand, it's a great way to check your answers.) However, most of the associated problems can easily be dealt with by properly writing your curriculum. (e.g., giving calculator-free exams to test differentiation knowledge, splitting them into two-part exams (without calculator, then with calculator), giving weekly 5-minute self-quizzes, etc.)
At the end of the day, a graphing calculator is just another tool that can be used to help or hinder education. How it goes depends on a combination of student motivation and the leadership and guidance they receive from their professors and teaching assistants. (i.e., you) -- Paul
Some of my colleagues (e.g., Vittorio Cristini) have been modeling the potential benefits of nanoparticle drug delivery for a couple of years now. As has been known for some time (e.g., see papers from R.K. Jain), the blood vessels that grow to supply tumors with nutrients (the tumor-induced neo-vasculature) are different than regular, non-pathological vessels. They tend to be more tortuous and leaky, with larger holes than regular vessels.
This is where the nanoparticles come in: one can design nanoparticles that encapsulate cancer drugs in particles that are too large to exit normal blood vessels but can pass through the leakier, tumor-induced blood vessels. This naturally targets cancerous tissues.
However, there are other issues to consider. Due to the high pressure inside tumors (due to the rapid proliferation of cells within a confined area, among other factors), along with the leaky vessels, blood flow can be very poor inside a tumor, and so while the drug may be targeted toward and delivered to the tumor, it may not actually penetrate very far into the tumor. Some great work has been done by Steven McDougall, Sandy Anderson, and Mark Chaplain in this area. In particular, look at their DATIA (dynamic adaptive tumour-induced angiogenesis) papers.
One way around this (suggested by R.K. Jain and Vittorio Cristini, among others) is to use targeted anti-angiogenic therapy to prune out the worse blood vessels and improve flow within the tumors, thereby also improving drug delivery and penetration.
Lastly, on the therapeutic aspect of blocking up tumor blood vessels with the nanoparticles, the work we've done (see this paper, which will appear in the Journal of Theoretical Biology soon), indiscriminately cutting off the nutrient supply to a tumor can increase tumor invasiveness by increasing morphological (shape) instability. (See some of the animations here.) So ironically, while more tumor cells may be killed, those that remain may spread farther and initiate new tumors. Given that hypoxic tumor cells are more likely to be resilient to further treatment (e.g., hypoxic breast cancer cells), this is a problem worth keeping in mind when planning anti-angiogenic therapy.
If you're interested in these topics, please do check out the paper above. (You can also download it at my website without any special memberships.) Even if you don't like it, we have a lot of references you may find handy. -- Paul
In fact, that's one of the main goals that we have in mathematical modeling of cancer: to make the term computational oncology a meaningful one.
Right now, there are some pretty decent models of angiogenesis, tumor growth, cell population dynamics with and without stem cells, tissue stress, and tumor microenvironment, and they're both producing previously-observed behavior and predicted interesting things to test.
The next natural step is to start coupling these models together, and such work is already under way. (See my website, for example.) This is a challenging problem, however, because it involves behavior at a scales ranging over several orders of magnitude. (i.e., it's a multiscale problem.) Another major aspect is to learn to better calibrate the models with in vitro and in vivo data. I'll be spending a few years of a post-doc on those types of issues.
However, we (not just us, but the field in general) already have models that can make good qualitative predictions that can help describe unusual cancer behavior. For instance, it's been known for a long time that angiogenesis (the development of new blood vessels at the behest of a growing tumor) is necessary for a tumor to grow beyond a certain size and is often involved in metastasis. This lead to the great idea of developing anti-angiogenic drugs that target and prevent this process. However, the results have been mixed, and in some cases, the therapy can worsen tumor invasion. Recent work with mathematical models has shown that hypoxia can lead to nonuniform growth and shape instabilities, leading, in turn, to invasive fingering and even tumor fragmentation. (i.e., greater invasion).
If a relatively simple tumor model can help predict and explain this type of behavior, imagine what a complete model, combined with a patient's MRI imagery, sequenced genome, etc., can do! :-)
This post has gotten too long, but let me just say that we would also like to see it go this way. -- Paul
There's still plenty of value to be found in higher-scale models. (e.g., how the tumor as a whole interacts with the microenvironment, how proliferation-induced pressure turns off the vasculature and prevents drug delivery, how oxygen and glucose delivery throughout the tumor and the microenvironment affects the patterning of hypoxic and necrotic cells, which, in turn, affects angiogenesis and matrix degradation) Cancer is a multiscale problem, with interaction between all the scales. Focusing on one scale alone (molecular or tissue-scale) likely will not solve the entire problem.
In fact, developing a good tissue-scale model is a natural step toward creating a multiscale model, where molecular- and cell-scale dynamics affect the growth parameters that govern tissue-scale behavior. (Similarly to how the behavior of individual molecules leads to things that can be averaged at larger scales, like heat, viscosity, etc.) First, you fix the parameters and neglect the small-scale dynamics to figure out the large-scale behavior. Then, you model the small-scale dynamics and learn how to couple them to the previously-fixed parameters.
On a tangent, I also worked with "Sandy" Anderson in a different U.S.-Scottish collaboration. He's a really great guy, as well as Mark Chaplain and Steven McDougall. And Sandy is a pretty incredible cook! :-) -- Paul
Nature is replete with examples where scale matters. Insect-scale airfoils don't work particularly well. Jumbo jet-scale insects wouldn't fly, either. At the molecular level, flagella give great propulsion in fluids, but the same wouldn't hold at the macroscopic level.
The same is true in biology. I remember having read a study done at NASA on the effect of iron nanoparticles in lungs. (Alas, I can't seem to find the link anymore.) They concluded that at the nano scale, the iron particles could escape the normal protections and remain in the lungs (in the interstitium and cells themselves), where they could collect and have a toxic effect, including diminished lung function. (The test rats became lethargic, etc.) All this at exposure levels that wouldn't be considered toxic at other scales.
I've seen similar research on sunscreen. Zinc oxide particles are great protecting at UVA and UVB. However, at large scale, they're quite visible and hard to blend in. Make them smaller, and that problem goes away, but they get absorbed deeper into the skin. Make them smaller still, and it's quite possible that they'll be absorbed into the cells themselves, leading to new potential health effects. (e.g., does zinc oxide become carcinogenic when they remain in the cells for too long? Does the motion into the cells increase the likelihood of reactive oxygen species (free radicals) accumlating inside the cells, rather than outside?)
I'm not a biochemist or a biologist (I'm a biomathematician), so I don't have the answers to these questions. But it's clear that scale really does matter, and it needs to be considered. Is the danger overhyped? Possibly, or maybe not. That's why it needs to be studied. But it's going to be important to understand these effects when we move from the low levels that occur naturally to the high levels that will occur in human-made materials and products. -- Paul
True, open source contributions may work against your future earning potential. On the other hand, it can also help build it in a number of ways. In my case, I'm not a formally-trained programmer. I learned C++ on my own out of books and trial/error for my scientific research. As such, I didn't have a lot of confidence as a programmer.
Starting an open source project helped me to gain valuable feedback that improved my programming skills in a way I could never have done on my own. I also got a helpful confidence boost--I'm no longer ashamed of my coding, or scared of letting others see it. This has been liberating, and has helped me to improve as a collaborator. In my case, the improved skillset gained through open source contributions will most certainly add to my future earnings potential.
For those who already have all their skills and couldn't possibly gain from feedback (whoever that may be), open source could be viewed as the equivalent of pro bono work done by lawyers. Lawyers often do pro bono work to help the poor, etc., and possibly to keep certain skills sharp on things they may not do on a day-to-day basis. For a programmer, open source gives the opportunity to practice something new or out of the daily grind and get valuable feedback on it. Or to work on a larger project that they wouldn't have time for otherwise.
And then as mentioned above, there's the resume aspect. When I was applying for an NSF postdoc fellowship (still underway), I was asked for "synergistic activity": ways you contribute to the maths/science/engineering community or education beyond your normal duties. Being able to say "lead author of a project used in undergraduate education and industrial and academic research in North America, South America, Europe, Asia, and Australia" was certainly a boost, considering many graduate students can only claim making better handouts for their classes or the occasional presentation.
So, there's another perspective. ;-) -- Paul
I accidentally posted my comment (meant to click preview) without inserting my formatting tags. Please disregard and read this instead. Sorry!! -- Paul
This may go against the grain here, but I replace my desktop drive about every 12-18 months. As I see it, here are the benefits of doing so:
+1) The drive still has decent resale value at that point, particularly if you sell on a computer forum and not on ebay. This helps reduce the cost of the hard drive update.
+2) Drive capacities are increasing quickly while costs continue to decline. This reduces the cost of the upgrade.
+3) Replacing before the warranty period is up means that the likelihood of experiencing a hard drive failure is low.
+4) While WinXP is a lot better than Win9x, it still doesn't hurt to do a fresh reinstall every 12-18 months. A hard disk replacement is the perfect timing for this.
Of course, there are some valid counter-arguments to these points:
-1) Security. (i.e., somebody could recover your private data.) I run Darik's Boot 'n' Nuke a few times, so I'm not terribly concerned about this. After running such a program, the odds of somebody successfully recovering data on a home budget are pretty low.
-2) You may be replacing too often. Well, I can't do much about that. But good drives don't cost much more than $100-$150 these days. A little peace of mind is worth something, and the regular size/speed upgrades are a nice bonus.
-3) This is no substitute for backups. I completely agree, and make backups of my most critical data to remote servers.
-4) Perhaps this isn't necessary. Perhaps not, but a fresh format is a helpful after 18 months.
Any way around it, I acknowledge that this strategy is a bit more expensive than may be necessary, but it has served me well in the past six years +. I've only had one drive fail in the past, back when I let my drives go well beyond the warranty period. Of course, that drive was a total loss, with no recovery of value to apply to the new drive, and there were some non-recoverable files. In my opinion, preventing problems before they occur is preferable, and getting speed and capacity boosts are just icing on the cake. -- Paul