According to the World Health Organization:
"World TB Day raises awareness about the global epidemic of tuberculosis (TB) and efforts to eliminate the disease. One-third of the world's population is currently infected with TB. The Stop TB Partnership, a network of organizations and countries fighting TB, organizes the Day to highlight the scope of the disease and how to prevent and cure it.

The annual event on 24 March marks the day in 1882 when Dr Robert Koch detected the cause of tuberculosis, the TB bacillus. This was a first step towards diagnosing and curing tuberculosis. WHO is working to cut TB prevalence rates and deaths by half by 2015."

For more information on TB, check out the CDC page:  http://www.cdc.gov/tb/default.htm 

Author
Stavana Strutz is a doctoral candidate in the Parmesan lab at the University of Texas at Austin studying disease ecology and evolution.

Due to some evolutionary or ecological question we’re trying to answer, we’d like to know whether this overlap is what we’d expect under some null hypothesis.  For the sake of example, we’ll talk about the “niche identity” test of Warren et al. 2008.  In this case, we are asking whether the occurrence points from two species are effectively drawn from the same distribution of environmental variables.  If that is the case, then whatever overlap we see between our real species should be statistically indistinguishable from the overlap we would see under that null hypothesis.  But how do we test that idea quantitatively?

In the case of good old parametric statistics, we would do that by comparing our empirical measurement to a parametric estimate of the overlap expected between two species (i.e., we would say "if the null hypothesis is true, we would expect an overlap of 0.5 with a standard deviation of .05", or something like that).  That would be fine if we could accurately make a parametric estimate of the expected distribution of overlaps under that null hypothesis, i.e., we need to be able to specify a mean and variance for expected overlap under that null hypothesis.  How do we do that?  Well, unfortunately, in our case we can’t.  For one thing we simply can’t state that null in a manner that makes it possible for us to put numbers on those expectations.  For another, standard parametric statistics mostly require the assumption that the distribution of expected measurements under the null hypothesis meets some criteria, the most frequent being that the distribution is normal.  In many cases we don’t know whether or not that’s true, but in the case of ENM overlaps we know it’s probably not true most of the time.  Overlap metrics are bound between 0 and 1, and if the null hypothesis generates expectations that are near one of those extremes, the distribution of expected overlaps is highly unlikely to be even approximately normal.  There can also be (and this is based on experience), multiple peaks in those null distributions, and a whole lot of skew and kurtosis as well.  So a specification of our null based on a normal distribution would be a poor description of our actual expectations under the null hypothesis, and as a result any statistical test based on parametric stats would be untrustworthy.  I have occasionally been asked whether it’s okay to do t-tests or other parametric tests on niche overlap statistics, and, for the reasons I’ve just listed, I feel that the answer has to be a resounding “no”.

So what’s the alternative?  Luckily, it’s actually quite easy.  It’s just a little less familiar to most people than parametric stats are, and requires us to think very precisely about the ideas we’re trying to test.  In our case, what we need to do is to find some way to estimate the distribution of overlaps expected between a pair of species using this landscape and these sample sizes if they were effectively drawn from the same distribution of environments.  What would that imply?  Well, if each of these sets of points were drawn from the same distribution, we should be able to generate overlap values similar to our empirical measurement by repeating that process.  So that’s exactly what we do!

We take all of the points for these two species and we throw them in a big pool.  Then we randomly pull out points for two species from that pool, keeping the sample sizes consistent with our empirical data.  Then we build ENMs for those sets of points and measure overlaps between them.  That gives us a single estimate of expected overlaps under the null hypothesis.  So now we’ve got our empirical estimate (red) and one realization of the null hypothesis (blue)

All right, so it looks like based on that one draw from the null distribution, our empirical overlap is a lot lower than you’d expect.  But how much confidence can we have in this conclusion can we have based on one single draw from the null distribution?  Not very much.  Let’s do it a bunch more times and make a histogram:

All right, now we see that, in 100 draws from that null distribution, we never once drew an overlap value that was as low as the actual value that we get from our empirical data.  This is pretty strong evidence that, whatever process generated our empirical data, it doesn’t look much like the process that generated that null distribution, and based on this evidence we can statistically reject that null hypothesis.  But how do we put a number on that?  Easy!  All we need to do is figure out what the percentile in that distribution is that corresponds to our empirical measurement.  In this case our empirical value is lower than the lowest number in our null distribution.  That being the case, we can’t specify exactly what the probability of getting our empirical result is, only that it’s lower than the lowest value we obtained, so it’s p < (whatever that number is).  Since we did 100 iterations of that null hypothesis, the resolution of our null distribution is 1/100 = .01.  Given our resolution, that means p is between 0 and .01 or, as we normally phrase it, p < .01.  If we’d done 500 simulation runs and our empirical value was still lower than our lowest value, it would be p < 1/500, or p < .0002.  If we’d done 500 runs and found that our empirical value was between the lowest value and the second lowest value, we would know that .0002 < p < .0004, although typically we just report these things as p < .0004.  Basically the placement of our empirical value in the distribution of expected values from our null hypothesis is an estimate of the probability of getting that value if that hypothesis were true.  This is exactly how hypothesis testing works in parametric statistics, the only difference being that in our case we generated the null distribution from simulations rather than specifying it mathematically.

So there you go!  We now have a nonparametric test of our hypothesis.  All we had to do was (1) figure out precisely what our null hypothesis was, (2) devise a way to generate the expected statistics if that hypothesis were true, (3) generate a bunch of replicate realizations of that null hypothesis to get an expected distribution under that null, and (4) compare our empirical observations to that distribution.  Although this approach is certainly less easy than simply plugging your data into Excel and doing a t-test or whatnot, there are many strengths to the Monte Carlo approach. For instance, we can use this approach to test pretty much any hypothesis that we can simulate – as long as we can produce summary statistics from a simulation that are comparable to our empirical data, we can test the probability of observing our empirical data under the set of assumptions that went into that simulated data.  It also means we don’t have to make assumptions about the distributions that we’re trying to test – by generating those distributions directly and comparing our empirical results to those distributions, we manage to step around many of the assumptions that can be problematic for parametric statistics.

The chief difficulty in applying this method is in steps 2 and 3 above – we have to be able to explicitly state our null hypothesis, and we have to be able to generate the distribution of expected measurements under that null.  Honestly, though, I think this is actually one of the greatest strengths of Monte Carlo methods: while this process may be more intensive than sticking our data into some plug-and-chug stats package, it requires us to think very carefully about what precisely our null hypothesis means, and what it means to reject it.  It requires more work, but more importantly it requires a more thorough understanding of our own data and hypotheses.

I'm posting a more optimistic post this week, rejoicing in the fact that malaria has still not returned to the United States!  Malaria used to be endemic throughout portions of what is now the US in the 19th and first half of the 20th century. Occasionally,  mysterious infections are contracted here but are extremely rare. The CDC MMWR Surveillance Summary for 2010 just came out and only 2 out of 1,691 cases were "cryptic" in origin within the United States. Autochthonous cases still pop up, at least 76 cases from 1957-1994, so it isn't necessarily gone but has yet to reestablish itself.

Malaria is thought to have entered North America via European colonists and African slaves in the 16th and 17th centuries. From 1946 to 1951 the Centers for Disease Control sprayed DDT and eliminated the parasite. Since then, cases of locally acquired malaria have been documented in legal immigrants, the homeless [in Houston, Texas], and migrant workers from endemic areas living in substandard housing. Anopheline mosquitoes, the primary vectors, prefer to feed in the evening and night when the host is most likely sleeping and not slapping them. Good housing in the US is thought to greatly decrease all sorts of vector-borne diseases that otherwise would be much more rampant.

And no Parasaturdays post would be complete without a little background on the biology of the organism:

Monsters Inside Me was an Animal Planet series that ran from 2009-2010. It's a little dramatic but there are some cool computer generated animations of parasite life cycles. More videos can be found on the Animal Planet website if you feel like being very disturbed:  
http://animal.discovery.com/videos/monsters-inside-me/
More information on malaria can be found at the CDC website:  http://www.cdc.gov/malaria/about/history/ 

Zucker, J. (1996). Changing patterns of autochthonous malaria transmission in the United States: a review of recent outbreaks. Emerg Infect Dis, 2, 37–43.

Author
Stavana Strutz is a doctoral candidate in the Parmesan lab studying disease ecology and evolution.

Generally, N. fowleri is a free-living, freshwater, bacteria consuming amoeboid that transforms into cysts when it gets too hot or dry. However, it also feasts on central nervous tissue if given the chance, unlike 29 other Naegleria species. People tend to get infected when swimming in freshwater bodies of water when water gets inhaled into the nose. The mobile double flagellated stage can be induced to form if the trophozoite stage is washed with dilute salt solution or placed in distilled water. I suspect that this is the evolutionary escape stage triggered by inhospitable environmental conditions. I also wonder how this phenomenon would play out with neti pot use since the nasal wash is generally a mild saline. Does transformation into a more mobile stage make it more likely to migrate further into your sinuses? Or is the trophozoite stage simply being phagocytosed by structural cells lining the nasal epithelium called sustentacular cells as suggested by Visvesvara thus allowing the amoeba to travel even further inside your head? A further complication is that the amoeba has been found living in the throats and noses of healthy individuals. What makes it pathogenic? 

N. fowleri infection becomes a threat when temperatures increase and water levels decrease. Right now, we are still in the middle of a drought…and this summer will be prime time for increased brain eating amoeba caused meningitis. Normally, individuals come down with the disease after swimming in poorly chlorinated pools and freshwater bodies of water. The cyst stage can survive temperatures of 51-65°C or 123-149°F while the amoeboid stages can tolerate 10-50°C. Additionally, amoeba cultured at 30°C and 37°C were more pathogenic than those grown at 23°C and 44°C [in mice]. And just in case you didn’t know, 37°C equals 98.6°F. It makes sense from an ecological viewpoint that an organism grown and adapted to temperatures similar to mammalian host temperatures will be more pathogenic. When the water is the same temperature as your body, beware.

Yesterday at the party I didn’t know what to tell my friend, I assumed that effective water sterilization took place at water treatment plants and the chlorine concentration used was enough to kill the cyst, flagellated, and trophozoite life stages. In fact according to Jonckheere’s 1975 paper, “viable Naegleria cysts are not to be found in clean water where low concentrations of chlorine are constantly maintained.” Visvesvara also reports that chlorine levels at 1 ppm are enough to kill the amoeba. It seems that either the water treatment plants aren’t doing their job or the water is getting contaminated after leaving the plant. The Louisiana State Department of Health has already attributed these deaths to “improper use of neti pots” as opposed to the failure of local water treatment plants to insure safe water. They have also stated that tap water is safe for drinking but not for nose irrigation. I didn’t know that the only safe use of tap water was for drinking, I would have thought it safe for other hygienic purposes as well. This more or less seems like a legal move to me, a way to shift the blame away from themselves and to neti pot users.

Until this question yesterday, I never would have second guessed using tap water for nose irrigation. Should we also not rinse contact lenses or eyes when they come in contact with chemicals with tap water for fear of Acanthamoeba spp. infecting our corneas? I would be concerned if my water treatment plants were unable to kill N. fowleri, what other pathogens are slipping by and into the water supply?

But back to the question at hand? Should my friend use tap water in her neti pot? I would say definitely yes if it has been boiled or passed through a filter with an absolute pore size of 1 micron or smaller (not nominal pore size). I would also say yes if the weather has been cool and wet. However, I can’t say yes if both a drought and high temperatures are co-occurring. So the safest course of action is to follow the CDC guidelines (http://www.cdc.gov/parasites/naegleria/faqs.html). And hey, it might be worth it to buy a fancy water filter, you’ll not only be protecting yourself from amoeboid meningoencephalitis but also Giardia, Cryptosporidium, lead, and many other contaminants. 

John, D.T. (1982). Primary Amebic Meningoencephalitis and the Biology of Naegleria Fowleri. Annual Review of Microbiology, 36, 101–123.

De Jonckheere, J. & van de Voorde, H. (1976). Differences in destruction of cysts of pathogenic and nonpathogenic Naegleria and Acanthamoeba by  chlorine. Applied and Environmental Microbiology, 31, 294–297.

Visvesvara, G.S., Moura, H. & Schuster, F.L. (2007). Pathogenic and opportunistic free-living amoebae: Acanthamoeba spp., Balamuthia mandrillaris, Naegleria fowleri, and Sappinia diploidea. FEMS Immunology & Medical Microbiology, 50, 1–26.

Author
Stavana Strutz is a doctoral candidate studying disease ecology and evolution  in the Parmesan lab at the University of Texas at Austin. 

This virus belongs to the same family of viruses as those that cause West Nile and yellow fever (there is also a band named Yellow Fever). Unfortunately, there is no vaccine for dengue like there is for yellow fever. When I traveled to Brazil in 2007 I received the yellow fever vaccine. 

So why am I discussing dengue fever today? I recently stumbled across Google dengue trends: http://www.google.org/denguetrends/  

I couldn't help but notice the close proximity of dengue cases in northern Mexico to southern Texas. This is a whole new means of disease surveillance being termed "webidemiology." While the Google trends do not represent medically diagnosed cases, they do fit medical data well, at least in the case of Flu. 

This reminded me that, yes, we have dengue in the United States which may be surprising to most people. Dengue has been found in Texas since 1980 and recently emerged again in Florida in 2011. Public health experts in Florida are already wondering when it will show up again this year, many think dengue will rear its ugly flaviviral head in March. Dengue has been found in 5 southern Texas counties which isn’t surprising since northern Mexico does seem to be somewhat endemic for the pathogen. 

For a long time I was under the impression that tropical diseases didn't happen in the USA, either because we were too far north and/or too "clean" and too developed. But tropical diseases have showed up in what is presently the United States for quite some time. Yellow fever actually made a large proportion of Philadelphia sick in a raging epidemic back in 1793, then our nation’s capital, while West Nile has spread across the country ever since its introduction in New York in 1999. I think there are tons of interesting and more-or-less unknown parasites here that may or may not be on the rise.

But I digress, back to dengue! It is a vector-borne virus spread by mosquitoes, primarily vectored by Aedes aegypti, the yellow fever mosquito. Notice the lyre shape on its thorax and white banded joints. It will fly inside to bite you and is sneakier than the next mosquito I'm going to tell you about, Aedes albopictus, the extremely invasive Asian tiger mosquito.  

A. albopictus was introduced to the United States via tires at a port in Houston in 1985 and will swarm you to get some blood. They also prefer to feed with their hind legs raised, which always reminds me of the elitist pinky finger raising affectation that occurs while drinking tea. There is a solid white stripe running down their thoraxes as opposed to the whimsical lyre on the thoraxes of A. aegypti.

Basically, if a black and white striped mosquito lands on you, you should kill it as quickly as possible! Also, now that we are finally receiving rain, make sure to empty out containers holding standing water. This not only decreases your already nearly zero risk of catching dengue [in the US] but also lowers your risk of contracting the recently introduced West Nile Virus and any other strange pathogens spread by mosquitoes. Remember that mosquitoes are more or less syringes with wings.  

And on a final note, it's pronounced "den-ghee" not "den-gay" or the lazy "dheng."

Author
Stavana Strutz is a doctoral candidate in the Parmesan lab at the University of Texas studying disease ecology and evolution.

Lately, I have been working on a piece of art on Alligator Gar,  Atractosteus spatula, that lead me to write on this subject. Gar are a fascinating fish, rightfully respected by both anglers and scientists.

From Wiki:   "the gar (or garpike) is strictly applied to members of the Lepisosteidae, a family including seven living species of fish in two genera that inhabit fresh, brackish, and occasionally marine, waters of eastern North America, Central America, and the Caribbean islands."

The reason I bring up this group of fish is that, yes, it is possible to drown them which surprises a lot of fish-laypersons. Its just such a counter intuitive idea. You can drown any fish in the right conditions, but gar are a bit special. The deal is that gar have a special circulatory system centered around their swim bladder. The swim bladder in fishes is closely related, homologous, to lungs in terrestrial vertebrates.  Scientists think that the first lungs, which would have been simple sacs connected to the gut that allowed the organism to gulp air under oxygen-poor conditions, evolved into the lungs of today's terrestrial vertebrates and some fish (e.g. lungfish, gar, and bichir). So gar use this quasi-lung when they gulp air and supplement the oxygen obtained through their gills.  Gar are often found in slack turbid water, and seen sticking their toothy mugs out of the water to gulp air to allow gas exchange in their swim bladders. When dissolved oxygen is low enough in water, all fish would effectively drown, but gar would be among the first without this ability because their gills aren't as efficient as most other fishes.  These critters are often found in oxygen poor waters, so this ability to breath from air no doubt comes in handy. When using gill nets, these guys are among the first to perish if left in too long.

Here's that Gyotaku art print that I recently put up on inkedanimal.com.

A quick node to the awesomeness of this animal.  It's the largest freshwater predator in at least North America.

Is this why scientists are constantly "socializing"?  

The following articles details how fruit flies besieged with parasite wasp eggs will selectively seek out alcoholic food. Alcohol consumption greatly decreases wasp larvae survival by causing their guts to shoot out of their anuses, this was nicely phrased by Carl Zimmer over at the NY Times.

http://www.nytimes.com/2012/02/21/science/tipsy-fruit-flies-on-a-mission-to-kill-parasites.html

I again apologize for the abrupt nature of this post. I, myself, am besieged with grading immunology exams. Next week I promise to write something meaty about parasites and distributions!

Author
Stavana Strutz is a doctoral candidate in the Parmesan lab studying disease ecology and evolution.