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Mycology Symposium, Day 1

When I’m not too busy raging at skuptaloids online, I enjoy molecular biology and mycology, the study of fungi. Towards those ends, I’m visiting the Duke Symposium in Celebration of Mycology and Mycologists. I was only able to attend a few afternoon lectures on the first day of this conference, but am enjoying it greatly! Some of the lectures I attended:

“Glycoengineered yeast: from platform to product”

A completely qualitative assesment of the information storage in various biochemical media. You can see why I have a huge crush on glycans. Souce is "Emerging Glycomics Technologies" by Turnbull and Feild 2007; click for lynkz

Discussed the engineering considerations is convincing yeasts to produce biochemicals – for example, drugs. A major challenge is in glycosylation, the addition of complex sugars to proteins. Glycochemistry is very interesting to me; it is still very much a biochemical frontier.

“Membrane lipids and fungal virulence”

Glucosylceramides in fungi and humans are different, with fungal compounds featuring an unsaturated site and a methyl side group. Humans and fungi also have slightly different enzyme active sites to deal with these differences, suggesting that drugs can be developed to target the active sites in fungal pathogens without disrupting human biochemistry. The drug candidates discussed actually have analogs in commercial fungicides. View full article »

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I love graphs – my eyes quickly glaze over at a table of numeric data, but a graph, used correctly, can quickly and easily tell the whole story.

‘Used correctly’ is the key phrase – for all their power, graphs are infamously easy to bungle, and when used incorrectly they can misinform – or lie outright.

I’m going to look at an example that touches on a few graphical and statistical concepts near and dear to my heart, as well as carbon geochemistry.

Fig. 1: An image from C3Headlines; the 3 C's are "Climate, Conservative, Consumer". Oh, and the article is titled "The Left/Liberal Bizarro, Anti-Science Hyperbole Continues". It sure would be tragic if they made obvious n00b mistakes after using such language. Click for link!

Coming from an article on the website C3Headlines, this image claims that carbon dioxide concentrations have ‘Linear, Not Exponential Growth’. thereby ‘expos[ing] the lunacy of typical left/liberal/progressive/Democrat anti-science’, The author has reached this conclusion by graphing January CO2 levels* and fitting a linear trendline to them.

Already this is a warning sign – the comparisons the author makes are entirely qualitative, apparently  based up on eyeballing the graph. However, trend lines are created by a statistical process called a linear regression, which comes with a caveat: it will fit a trend line to ANY data given to it, linear or nonlinear. Fortunately, there are also ways of evaluating how good a trend line is. View full article »

dry ice in occupied durham

And what,

you might be asking yourselves,

have they been doing all these recent months instead of writing high-octane science friction and science fact here on the intarwubs?

Frozen carbon dioxide turns directly into a gas. How sublime! The dry ice is so cold that it causes water vapor in the air to condense, forming a fog.

Answer: All sorts of zany things! During a recent Really Free Market hosted by Occupy Durham, I had the opportunity to do another chemistry show.  Like the demonstration in my CO2 Problems video, I used soapy water and phenol red pH indicator to help illustrate the properties of frozen carbon dioxide. The color change is particularly dramatic, and is a good tie-in to the environmental effects of CO2. The greenhouse effect seems harder to demonstrate effectively – if anyone has a good way of demonstrating the idea, let me know!

“]

dry ice and phenol red, bubblin' away... { pix courtesy of Specious }

One thing I showed in this demo which wasn’t in CO2 Problems is the strange noises that dry ice makes in response to metal. If you try to cut a piece of dry ice with a knife, or press a paperclip into it, the ice will make a horrible screeching shriek. It’s most dramatic if you put a larger chunk of dry ice into a metal pot – it will scream and skitter around! My explanation? The warm, thermally conductive metal speeds up the sublimation of CO2 near its edge; the expanding gas pushes the metal away briefly and then the pressure buildup dissipates, bringing the metal back in contact with the ice. This oscillation makes the screeching noise. Try it out yourself and see if you think I’m right!

temperature aNOMalies

If you are new to climate science, you might be wondering what, exactly, this ‘temperature anomaly’ thing is that you keep hearing about. I know I was a bit confused at first! This post explains the concept, using a real-world example.

Absolute temperatures (yearly averaged) from two sites in the UK: one urban (St. James Park, green) and one rural (Rothamsted, red). Although the urban site is consistently warmer, the two sites show the same warming trend. But is there a way to compare them directly? Data from Jones et al. 2008, kindly provided by Dr. Jones.

Cities tend to be warmer than their surrounding countrysides, a fact known as the urban heat island effect (UHI). This occasionally is offered as an alternative explanation for greenhouse warming, but it fails on closer inspection. We can use data from Jones et al. (2008) [PDF] to see one reason UHI can’t explain observed warming. One time series is from St. James Park, in the city of London; the other is from nearby Rothamsted, a rural site some tens of miles away. As you can see, the urban location is consistently about 2 C warmer; however, the warming is nearly identical at both sites (a strongly significant 0.03 deg C/year). Jones et al. note:

“… the evolution of the time series is almost identical. As for trends since 1961 all sites give similar values …  in terms of anomalies from a common base period, all sites would give similar values.”

This gives us a hint about what a temperature anomaly is: View full article »

NO SOPA! NO PIPA!

In solidarity with countless other sites (most of them with higher traffic  and cultural relevance  >_< ) TopOc is temporarily going offline for the 18th of January 2012 in protest of the Stop Online Piracy Act (SOPA) and the Protect IP Act (PIPA).

True Stories. Click for a non blackedout site.

If you are unfamiliar with these lovely bits of legislation, they would effectively mean the end of the internet as we know it. If someone posts a link on my site which supposedly violates copyright law, TopOc can disappear – for good. Given the notoriously itchy trigger finger on certain copyright holders, that should scare the pants off you.

Tor, an piece of anonymity software developed by the US Navy for use in repressive countries, would effectively be outlawed. Indeed, the proponents of SOPA and PIPA believe that it would be effective because it is based on censorship techniques which have been used effectively in Syria, China, and Uzbekistan.

Additionally, this legislation would seriously compromise internet security.

Perhaps most disturbingly, members of the US Congress have rejected expert testimony critical of SOPA and PIPA, deriding the critics as ‘nerds’. Considering the poor quality of testimony that they are willing to entertain, this is a real slap in the face.

What can you do? Call your Senators and Representatives! Tell them to keep the IntarTubes free!

Regularly scheduled programming will resume shortly, I swear. The next post will be about the concept of a temperature anomaly – stay tuned! Additionally, I apologize for the relative lack of citations; it is not in my nature to make unsupported assertions. But I got a late start and, well, most of my sources are also participating in the blackout so it would sort of be a moot point. 

UPDATE: …aaaaand we’re back. Thanks to everyone who participated; we’re making a difference!

Some people think that the existence of workarounds for the blackout is somehow a problem for it. On the contrary, that people are finding and using them is a further success of the action. When people use these hacks, it puts them in direct contact with the inner workings of the technology they depend on, and this understanding is as critical for maintaining internet freedom (and freedom in general) as our legal system. Every n00b who is introduced to caches or proxies by the blackout is a success for the world’s first cyber-strike, a success in addition to its influence on policymakers.

Back to writing…

i still exist!

Its true! Here I am!

So what is on the TopOc horizon for 2012?

  • More hard-hitting commentary!
  • More sassing of people who don’t understand graphs!
  • Updates on previous projects!
  • Audiovisual delights!
  • More sweet hax!
  • Fractals and fungaloids!
  • Pentagons and pentagrams!
  • More dry ice! (The shark puppet will also return.)

Here is a mushroom to tide you over while you wait…

It's like a fungal satellite dish!

TopOc is occupying Durham, for great win and/or lulz! One highlight on the horizon is a leet haxor skillshare – I want to show off the sweet alcohol stove I built! (via this video)

In the meantime, enjoy this pleasing image 🙂

Praying mantis.... or preying mantis??? Clearly the tyrannosaur of the insect world. Photo via Ildar Sagdejev; clix four phool.

Yay!

cnfusin rained and chas

Last time, we looked at a very simple atmospheric model known as the Lorenz equations, and saw it exhibit the ‘Butterfly Effect,’ in which even very small changes in initial conditions can dramatically effect which path the system takes. However, we also saw that the initial condition had a relatively small impact on the statistical properties of the system. Because climate is a statistical property of the earth system, asking
“How can we expect to predict future climate when we can’t predict the weather?”
is a lot like asking

“How can we claim to know the half-life of a radioactive element when we can’t predict when a given atom will decay?”

To those familiar with chaos, this shouldn’t come as a surprise. Lorenz didn’t just discover apparent disorder in his model, but a deeper, eerie structure lurking in the noise.

The Lorenz Attractor: wibbly-wobbly mess of the millenium. Three simulation runs (red, green, blue) are shown; they start close together but quickly spin off on different trajectories, demonstrating sensitivity to initial conditions. Nonetheless, the trajectories quickly converge on an intricate structure in the phase space, called an 'attractor'. The attractor doesn't vary with initial conditions, but is instead a feature of the Lorenz equations themselves. Image generated with code from TitanLab - click to check them out 🙂

You may remember that the Lorenz equations relate three variables (X, Y, Z), which vary over time. In the above image, I’ve plotted the evolution of three runs of the Lorenz model by putting a dot at each (X(t), Y(t), Z(t)) coordinate, at every time t in the given interval. The three runs start very close together in this three-dimensional ‘phase space’, but quickly diverge.

However, despite their different individual behaviors, these runs are confined to a structure in phase space, known as the Lorenz attractor – an attractor, because all trajectories converge on it, regardless of their initial conditions. If you perturb the system by bouncing it off the attractor, it quickly settles back into the same loops through phase space. Lorenz (1963) described it: View full article »

A companion article at ArkFab shares my thoughts on peer review in regards to this project and DIY/community/citizen science in general. 

At long last, the much-anticipated booklet, “CO2 Trouble: Ocean Acidification, Dr. Everett, and Congressional Science Standards” is available and approved for human consumption! Download and share HERE (or at Scribd HERE).

In this document, I have bundled, updated, and expanded my series of essays debunking the congressional testimony of Dr. John Everett regarding the environmental chemistry of carbon dioxide.

It has been designed to be a fairly short (less than 30 pages, including images, appendicies, etc.) and accessible read. It has been challenging but fun to write; I have had to learn a lot about GIMP, Python, Scribus, social networking, and of course ocean acidification to get to this point.

It was also very useful for me as an opportunity to go back through my earlier remarks and double-check my work. For example, I later realized that the documentation which Dr. Everett provides for his CO2 data in part two is ambiguous: Although the citation for the rate data is referred to as “Recent Global CO2”, the URL provided links to the longer record as measured at Mauna Loa Observatory. This confusion had led me in the past to make incorrect claims about some of the figures he presents. Ultimately it was inconsequential to my argument, but it was frustrating to have to deal with such ambiguities. On the other hand, this led me into comparing the Mauna Loa record with the global record (Appendix B) which was an interesting exercise.

In researching this project, I also came across new phenomena I wasn’t previously aware of. For example, while I was calculating historical rates of CO2 change, I ran though the 1000-year Law Dome record and saw this:

View full article »

Last time, we saw that some mathematical systems are so sensitive to initial conditions that even very small uncertainties in their initial state can snowball, causing even very similar states to evolve very differently. The equations describing fluid turbulence are examples of such a system; Lorenz’s discovery of extreme sensitivity to initial conditions ended hopes for long term weather forecasting. Because the state of the weather can only be known so well, the small errors and uncertainties will quickly build up, rendering weather simulations useless for looking more than a few days ahead of time.

But Lorenz’s discovery doesn’t have much impact on climate modelling, contrary to the claims of some climate skuptix. Climate is not weather, and modelling is not forecasting.

Weather refers to the state of the atmosphere at a particular time and place: What temperature is it? Is it raining? How hard is the wind blowing, and in which direction? Climate, on the other hand, is defined in terms of the statistical behavior of these quantities:

“Climate in a narrow sense is usually defined as the average weather, or more rigorously, as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. […] Climate change refers to a change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer. ” IPCC

Many climate skuptik talking points derive from confusing these two quantities, in much the same way that a gambler might win a few hands of poker and decide that they are on a roll.

Although it is generally not possible to predict a specific future state of the weather (there is no telling what temperature it will be in Oregon on December 21 2012), it is still possible to make statistical claims about the climate (it is very likely that Oregon’s December 2012 temperatures will be colder than its July 2012 temperatures). It is very likely that the reverse will be true in New Zealand. It is safe to conclude that precipitation will be more frequent in the Amazon than in the Sahara, even if you can’t tell exactly when and where that rain will fall.

In fact, Lorenz’s groundbreaking paper, ‘Deterministic Nonperiodic Flow’, would seem to endorse this sort of statistical approach to understanding fluid dynamics:

“Because instantaneous turbulent flow patterns are so irregular, attention is often confined to the statistics of turbulence, which, in contrast to the details of turbulence, often behave in a regular well-organized manner.” (Lorenz 1963)

Let’s take a closer look.

Fig. 1. Three solutions of the Lorenz equations, starting at virtually identical points. Although the solutions are similar at first, they rapidly decouple around T=12.

The Lorenz equations consist of three variables describing turbulent fluid flow (X,Y, and Z), and three controlling parameters (r, b, and s). The equations are differential equations, meaning that a variable is described in terms of how it changes over time- saying ‘Johnny is driving west at 60 miles per hour’ is a simple differential equation. In order to solve a DiffEq, you need an initial condition – “Johnny started in Chicago” is an initial condition; without knowing that, you can’t say where she will be after driving for three hours. View full article »