Eukaryotic Microbiology: The Blogs Are Coming

Despite my desire to keep updating the blog with course information, which has been an abject failure in regards to updates, I still want to post when I can. To be fair, I'm behind on grading, so I can't in good conscience be blogging about the course when assignments need to be graded.

 However, I'll take a minute to make this brief post. It's getting to my favorite time of year: Student Blog Posts at Traveling Small with a Nucleus! I know for many students this is a writing assignment they truly enjoy. (Of course I'm sure some students do not like this assignment, but I have yet to hear from them.)
S0, I invite you to check out some previous students posts in the interim. The majority are quite good and there are some real gems in there. It's possible TSw/aN may be invaded with some organisms lacking a nucleus too. I'll keep you posted. 
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Eukaryotic Micro: Week 3 the last of the fungi

So this week (actually last week) we covered what is probably the last unit concerning fungi: Cryptococcus neoformans. The first two weeks covered two ascomycetes, Candida albicans and Fusaruim species, and now we move over to the basidiomycetes, otherwise known as 'if I asked you to draw a fungus this is what you would draw'.

 The primary research papers were:
This is an interesting point in the semester. Upon completion of this week, we are ~25% of the way through the semester and exactly 25% of the way through the 12 modules. This is the point where students have completed the short writing assignment four times now, so hopefully they are comfortable with what I am looking for. I lay out the guidelines on day 1, and then model what I expect. There are two difficulties. 1: Getting students to explain a dataset of their choosing such that someone would walk away knowing what was done, what it showed, and most importantly be able to ask informed questions about the data set. Students are reasonably good at explaining the data after a week or two, but struggle to give enough experimental information such that you would know how the data was obtained. 2: Identifying limitations with the data set. This is in fact difficult, but it is an important skill to foster if we really want people who are critical thinkers. I ask them that their limitation answers the question 'how does this affect the authors' conclusions or interpretations?' This latter issue usually takes a couple more weeks to get better at for most of the class.
Cryptococcus neoformans normal and titan cells
It's also interesting because Cryptococcus follows up the ascomycetes we already discussed extremely well. Like Candida albicans, C. neoformans is a budding yeast, which is distinct from Fusarium, which although more closely related to C. albicans, is a filamentous fungus. However, like Fusarium, C. neoformans forms dikaryotic filaments during sexual reproduction and grows in a filamentous form during asexual spore production. 

I like these two papers (this is the first year I've used the Gerstein paper) because they deal with different aspects of development/differentiation in different ways.  The Gerstein paper is focused on a role titan cells play using primarily genomic approaches; the Huang paper is focused on spore formation and development using classical genetic approaches.

 Gerstein et al ties in conceptually with the Selmecki and Ma papers from the Candida and fusarium modules respectively. All are centered on the acquisition of additional genetic information and the outcomes of this. I'm certain creationists always talk about the inability for an organism to acquire new 'information'. Well here are three independent examples.

 Huang et al ties in, slightly, with the Lui paper from the Fusarium module by dealing with cellular differentiation and development. This is something we will come back to in the future frequently and is a biological concept I think is often underappreciated in microbes.  

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Eukaryotic Microbiology: Two Weeks In

Have the second week of Eukaryotic Microbiology in the books (except for some residual grading to complete). So far we've covered Candida albicans and two Fusarium spp. Last week was C. albicans and we discussed two papers. (I'm not including the review articles students read at the beginning of each week.):
And this week was Fusarium and we discussed:

While I planned to discuss each week individually, these end up going well together, plus this is a difficult time of the semester for me guaranteeing I can't write as frequently as I would like.

So we stay within the ascomycota for the first two weeks. Some things that I wanted to emphasize in class and that came from the students:
  • What does it mean to be 'wild-type'? This came up with regards to a 'wild-type' genomic sequence. Is the CFTR mutation (the allele that causes cystic fibrosis when homozygous) a mutant genotype? Does the fact that in the caucasian population a cystic fibrosis causing mutation in the CFTR gene occurs with a frequency of 0.025 make a difference? What about the allele that causes sickle cell anemia when homozygous? People who have a 'mutant' allele and a 'normal' allele are more resistant to malaria, so this mutation is potentially beneficial.
  • How is phenotypic diversity generated in asexual organisms? This is an important question because sexual reproduction is promotes phenotypic diversity in many eukaryotes. However, there are significant issues associated with sexual reproduction that prevents using it as a simple explanation for phenotypic diversity.
  • How do organisms adapt to their environment and is a pathogen really any different from any other organism (short answer is 'no')?
  • How do duplicated genes evolve? This (Lui paper) goes hand-in-hand with the Candida Selmecki et al paper and the Fusarium Ma paper.
We'll be revisiting many of these issues throughout the semester. We spend a fair amount of time dealing with specific aspects of the papers, but I try to highlight some of these broader issues. This week we tackle Cryptococcus neoformans and will highlight at least one of the above issues again.
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First Eukaryotic Microbiology Classes

Since we do not start classes until after Labor Day, the first week represents a Wednesday, Friday week for my writing intensive Eukaryotic Microbiology course. I designed the course to run on a M/W/F cycle, so this first W/F week might seem problematic, but it is not. In fact, it works out extremely well.

 The first day of class (today), involves discussing what is going on in the course and going over the syllabus. Similar to last year, in the first class I try getting students involved by having describe their goals and defining plagiarism. This year I focused the first lecture on the structure of the course and less time going in detail on the syllabus. It was only partially successful because I didn't get through the course set up but got through most of it. Luckily there's time to finish on Friday.

 Although I don't get too deep into the syllabus (the students can read), I do cover grades and how they are determined because this is an issue that cuts to the heart of many students. One thing I started doing last year in another class, is determining the course GPA. That is the GPA for the course, determined by the student grades. The last two years my Eukaryotic Microbiology course had a GPA of 3.0 and 2.7. I'm pretty happy with these GPAs overall, it means I am not giving out a ton of A's but the GPA is higher than one might expect for an introductory class (this is not an introductory class and is taken primarily by seniors in the major with a smattering of graduate students).

 The class is generally set up as follows:

  • Monday: I give a standard lecture introducing the students to an organism and the relevant topics for the week.
  • Wednesday: Students present primary research papers. However, they don't actually present the paper, I have them answer some specific questions:
    • A. What question is the paper addressing and why do we care?
    • B. Which conclusion do you think is the most interesting/important and why?
    • C. Pick one figure that you think best supports your favorite conclusion and explain in detail how the data support the conclusion.
    • D. What are the limitations of the data?
    • E. Why are the conclusions important?
Questions A and E are only answered by the presenters, which is why they are struck out. These questions are derived from a document by Little and Parker (no longer available online, but they were at the University of Arizona). I like these because they focus the students in on a specific aspect of the paper, their favorite conclusion, which may be completely different than the press release or authors' overall conclusion.

 The students then focus in on the data that supports that conclusions and not the entirety of the paper. Essentially, I do not want the presenters to reiterate the paper to the class, everyone is required to read the papers so there is no need to reiterate them.

 The most difficult part is finding limitations or some issue(s) with the data/interpretation of data. I think students are trained to accept the literature and not rigorously go after the authors and their arguments, which, is in a nutshell, how science works. This one takes time and experience to get good at. Even excellent papers can have issues and I think one of my jobs is to get students comfortable with finding issues.

 One thing I haven't told the students about is that the presenters have to give a 30 second elevator talk about the paper. I started doing this several years ago and I think it is extremely important. Basically, if you were an author and someone in the grocery store asked you about your work, how do you explain cogently and succinctly such that they are impressed and glad their tax dollars are supporting the work.
  • Friday: Discussion of things. This varies markedly and is dependent on the students. I have a discussion board for them to ask questions, raise issues, provide feedback, etc. I do not post to these boards unless things are going off the rails and try to keep it a student oriented discussion board. (Once a prof posts a comment, all additional comments cease in my experience.)
On Friday, the second day of class, I will model what I expect out of the student presentations. We are reading "Complementary Adhesin Function in C. albicans Biofilm Formation' by Nobile et al. I also provided a review article 'Adhesion in Candida spp.' by Paula Sundstrom.

 I will give a 30" elevator talk and then give an oral presentation that covers answers to the above questions A-E. Students are required to provide written responses to questions B-D to get us started.

 Starting Monday we really kick into gear, although we will stay with Candida albicans. FYI the topics we are covering are drug resistance and host environmental adaptation.

 My goal is to keep blogging about the course throughout the semester.
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How to Study: Repetition is good (which is why I post this every year)

With the onset of a new semester and a new crop of students having arrived or shortly arriving at college, here are some words of advice from someone who had to learn to study the hard way...

Here is an advice column for students looking for some techniques to improve their study habits. I am not an expert in learning, but I am an expert in being a college student with no fucking idea how to study and had to figure it out over the course of a year or two. I was one of those students who didn't have to do much to maintain an A/B average in high school. Although I was exposed to study skills and habits while in high school, none of it stuck because I really didn't need to study to do reasonably well. So here is what I learned that worked for me. If you have your own successful techniques, please feel free to add them in the comments.

Learning is an active process, it requires energy. It may not be as physically taxing as a 45 minute work out, but then again you may not be doing it right. What I discovered is that I learn when I do things, when I engage the material, when I'm an active participant. If it's a couple of days before the big exam and you're wondering to yourself 'What's the best way I can study? I know, I'll take some time to search online and get some tips.' Well, if this is you, you're fucked or at least I don't have anything for you. Come back after your upcoming exam, my advice might help you for the next exam. Right now, you are in cram mode, so you better start cramming and not wasting your time reading blogs. I will admit that cramming works, to a degree. Cramming is a short term solution, getting enough material under you belt to survive or even succeed at the exam. But it's a long-term problem. Are you really in college to survive exams and classes? That was really high school wasn't it? Cramming is problematic because the material is never actually learned, it may come up again on the final, it will likely be important next semester or the semester after that in your more advanced classes. Learning and cramming both take energy, but the former is far less stressful and provides both short-term and long-term gains.

Step 1. Find an environment to study in. Ultimately this became at my desk in the bedroom of my apartment. I also kept my stereo close by set at police notification level. I learned quickly that I could ignore the music, but sounds from the street, from the kitchen/dining/living room area, or from anywhere outside my room were distracting. To this day, when I'm working on grants or papers and do not want to be disturbed, I  close my office door and crank up some music. Although I am a chaotic person by nature, my desk was neat and organized. I needed a place to work comfortably and that was it. My textbooks and notebooks were stacked in/on some milk crates I used for shelves. (These of course were the store bought kind of 'milk crates' not the easily available sturdy and inexpensive milk crates available behind 7/11s, like the one across the street of my apartment. Although if they were the illicit version, which they weren't, they would have been returned when I moved to go to graduate school.)

$0.69 for 3 in 1989
Step 2. Get a bunch of notebooks. I used spiral bound notebooks available for next to nothing at drug stores. Of course these notebooks will have absurd cover designs or pictures you would never in a million years gravitate towards (see picture of my Molecular Biology notebook). That's not the point. The point is what's inside the notebook, and that will be gold. I mixed up the designs on the notebooks I bought so I could easily identify which one I wanted. The alternative is to be flipping through them wondering if this is the black notebook Im looking for. Get one notebook for every class you take (except maybe for the golf/tennis/etc classes). Any class that has a lecture has its own notebook. No cheating by getting a three-subject notebook. Also, get a couple of additional notebooks.

These things are evidence of evil
Since you're at the drug store already, get some pens and pencils. I love pens, but despise cheap ass ball point pens. You'll be using these a lot, so get pens/pencils you are comfortable with. Make sure you get a variety of colors. I survived with black, blue, and red, but there is a veritable palate of colored inks now. Get what you love or at least can tolerate. I prefer mechanical pencils, but if you get classic ones, you better kick in for a decent pencil sharpener or two. Also grab some highlighters also in assorted colors.

Step 3. Do the readings strategically. Chapter 3 is covered Wednesday? Read it through by Tuesday night. That isn't very strategic is it? The strategy is to skim read the text. Get a sense of what's in there and what will be the likely topics and points for the upcoming lecture. You don't need to be more than familiar with the material. (In the case of labs, this is not true. You must be intimately aware of the material, because you will be using that information in the lab. Hell, there may even be a quiz on the lab manual!)

Step 4. Go to class. Although you probably couldn't pass a quiz on the readings material, the vocabulary is familiar. Now you already know a bit about the upcoming lecture. Gather up your pens and pencils and one of the extra notebooks. Leave your textbooks at home, along with the highlighters, and other notebooks. You don't need much.

Get to class on time and get a good seat. In large classes, I recommend a seat near to the front and in the middle where the professor can actually see you. Why? Psychology that's why. Take two students doing equally well, one student the professor recognizes, even if there is no name associated with the face, and one student the professor has barely, if ever, seen. If both come to discuss an issue regarding an examination or writing assignment, which one will have at least a sub-conscious advantage?

Open your notebook to page 1 get out a couple of writing implements and get ready. If the professor has handouts or, god forbid, print outs of the slides, then definitely pick them up, but DONT use them during the lecture (with rare exception). Your job is to take a shit ton of notes. Don't worry about neatness and perfection, just get the stuff written down. Write down the points on the slides, the drawings, incorporate what the professor is saying. The very act of writing things down is helping you learn the material! 'But we have the slide print outs, so why write stuff down?' you ask. In my more youthful days I would have responded with 'Because we didn't have the material presented to us, so stop being so fucking needy.' But in my dotage I think an example is better. What is another name for a television? Did 'idiot box' spring to mind? There's a reason for that. Some people watch tons of TV, these are not inherently the most educated people in the world. My mother loved to watch soap operas during the 70s, hours of soap operas. She was not an expert in social interactions because of this nor was she an expert story teller, she just watched a lot of soap operas. This is one of the biggest impediments to learning, fucking handouts. Remember I said learning was an active process. Lectures are not television. You should be doing something not just watching. The problem with handouts is that it facilitates the TV watching mentality. There are reasons to hand out the notes, which is why you are collecting them, but wait until later to use them. For now, take a shit ton of notes.  Do not be tempted to put notes in the margin of the print outs, you bought the cheap ass notebook, so use it. (Plus you'll want a pristine copy of those hand outs for later.) So, you were in class sitting in a strategic location, you took a shit ton of notes, now what? Go to your next class and repeat using the same notebook.

Notes on chromosomal
melting temps.
Step 5. THE MOST IMPORTANT STEP: The next day. So you went to all your classes, even the ones you think are boring, and you took a bunch of notes, even on things you think you know already. Now what? Hang out with friends, watch TV, play some PlayStation, then go to bed and the next day go to all your other classes. At some point on this second day, you need to carve out some studying time. When depends on your schedule. I did this in the mid-late-afternoon, because I was generally done with classes then. Go to your studying environment, get out your notes from yesterday, one of the fresh notebooks that will be specific for a specific class, any handouts, and your textbook. Now you will rewrite your notes in a more organized and legible manner. As you rewrite, you will refer to the text for additional points, and in your class-specific notebook you can either incorporate the textbook material or simple refer to the page numbers/figure numbers. Either redraw or cut out the handout figures you need and add them to your notebook. This could take as long as the original lecture, but probably won't. Regardless, you are now learning some serious material. The act of rewriting helps embed the information into your memory, by organizing the material in a manner that works for you (which is probably like it was presented) you are thinking about the material in total not simply one fact after another. You are also reading the text in a more in depth way, which is easier because you already skimmed it and went to the lecture. Do this for those boring easy classes too. It helps maintain good study habits and instead of simply learning the material, you'll own it. Another benefit is that if you do this, you will know before the next lecture what material you may not understand. This gives you a ton of time to meet with your colleagues, TAs, professor to get things straight.

Step 6. When you finish going through the crappy notes, rip out the page(s) and throw them away. You don't need them anymore because they are rewritten and you'll feel good about the progress you made.

I won't guarantee these steps will improve your grade, but I do guarantee that they will improve your understanding and knowledge of the material.

Additional thoughts:

A. Write in your textbooks, at least highlight important information. I used different colored highlighters for different purposes. Red was for definitions, blue was for what I thought were key concepts, green was for things referring to my class notebook. Will writing in your textbook reduce its value when you resell it? Well hopefully you will not resell it. Having that chemistry textbook could come in handy when you need to revisit something you forgot in your molecular biology class. If you absolutely do not want the book, why buy it in the first place? Probably you could borrow one from a colleague or use the library.

B. Scheduling. You need to prepare ahead of time when things are getting done. If you don't, you will almost certainly get behind or not have enough time. If you want to go to that party or game, you may need to start rewriting your notes earlier than normal to make sure you have enough time to finish before going out. Also, there will be several big assignments due for other classes throughout the semester, you'll need to be prepared for catching up on those notes you couldn't rewrite the day after class. (Don't get more than a class or two behind or you'll defeat the purpose of rewriting.)

C. Turn off your phone. You can survive an hour or two without reading all those awesome texts and tweets coming in. A 30 second distraction actually amounts to much longer, because it takes time to get back to where you were before you were distracted. Every time you break focus, you are back to a more superficial level of learning and it takes some time to get back to that deeper level.

D. When it's test time, you'll find it much easier to study. The material is already there in your mind because you've been through it at least twice already. You may have to pull an occasional all-nighter, but it will be different than the cramming you did previously.

Notes for a recently submitted grant
from a relevant paper.
E. For the record, I still use these techniques to prepare grants and papers (see photo). I do a lot of background reading and have notebooks dedicated to taking notes on the papers, complete with different colored pens. This allows me to make connections and think about the material in a much deeper way than I would be able to otherwise. Same for seminars I attend, I bring a notebook.


With those words of advice,
Good luck and have a great semester!

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Why I'm Marching for Science

Saturday April 22nd 2017 is Earth Day. It is also the day chosen for the March for Science. I was thinking about traveling to Washington DC to march, but will most likely be marching in Minnesota in the Twin Cities march (@ScienceMarchMN).

 There are numerous reasons why I'm marching. The most obvious reason for me is that science has been under a constant attack by republicans the last few decades, and these attacks reached a tipping point for me in the last election. Anti-science viewpoints do not know party lines but democrats have not been a constant opponent of science, disparaging scientists, scientific agencies, education, etc. If the previous two statements have your face turning red and mental spittle flying, then there's little point in reading further even though what I want to express in this essay is not specifically a republican/democrat comparison.

 I want to suggest that if, as a nation, we have decided that science is the enemy, that we don't need to hear from any more experts, that we make our own reality, then its time to close up shop. We can unequivacolly state that we no longer want a more perfect union, we do not want to promote the general welfare or provide for the common defense, nor are we interested in our posterity. We can still have a government that makes, executes, and interprets law, but let's not kid ourselves that we care about the mission statement outlined in the Preamble of the Constitution of the USA.

 Why do I say this? It seems that I'm linking our system of government and science and if so is that appropriate. I think it is. As a student of public education, a few decades removed, I remember learning about the Enlightenment and how Enlightenment ideas and thinking influenced the founding fathers. For those who may have forgotten, these ideas were a focus on reason, logic, science (as it existed at that time), etc. as a way to improve our lot in the universe. Enlightenment principles led to things like the Preamble. Don't take my word for it, you can read what the framers thought by reading The Federalist Papers.

 Our government was founded on the idea of rationale policy and governance, on the use of logic and honest debate to inform decision making, on science to explain what was true and what was not. If we have decided as a nation that allegiance to party is more important than making informed decisions, then why waste time pretending we care about our system of government anymore?

 I've spent some amount of time thinking about why this concept is so difficult and anathema to many people. I think in part it is because when we think about 'logic' or 'rationality' or 'science', we think these are simple terms easy to understand. Kind of like we all went to school, so we all feel like we understand a teacher's job. Let's take the example of 'logic', that should be easy. The fact is many books have been devoted to this topic and many more will be. When I say logic, probably a lot of people think along the lines of:
If A=B and B=C, then A=C
This is true and this is logic, but it's logic at its simplest.

 How about another example many people are familiar with that may not be so simple.
You see a train coming down the line and it is going to hit and kill five people, you can flip the switch so the train goes onto another track, but it will kill one person there. What do you do?
Simple? How about...
You see a train coming down the line and it is going to hit and kill five felons, you can flip the switch so the train goes onto another track, but it will kill one person there. What do you do?
Simple? How about...
You see a train coming down the line and it is going to hit and kill five white girls aged 14-15 but they are felons, you can flip the switch so the train goes onto another track, but it will kill one person there. What do you do?
Simple? How about...
You see a train coming down the line and it is going to hit and kill five elderly people, you can flip the switch so the train goes onto another track, but it will kill one child there. What do you do?
We can go on and on. Some people will not flip the switch in the first case, because that would in fact make them the murderer of one person, although they saved five. Others would say they'd flip the switch in heartbeat in that example, but then what happens in the latter examples. I say the first case is a logical decision because the needs of the many (5 people) outweigh the needs of the few (1 person). In this case, I'm making it a simple numbers game. In the subsequent examples, I'm including information that may impact the value of the people in question such that the raw numbers may not matter as much. Speaking of value, what if in case 2, I point out they were non-violent felons, would your choice change?

Let's get back to science. Science requires logic, but it is more than logic as science requires observation, making inferences based on these observations (aka hypotheses), conducting experiments, and then interpretation of the data, and repeat. Both making inferences and interpreting data requires logic but also requires creativity and imagination of possibilities.

Our legislators need to write laws to accomplish the mission statement described in the Preamble. The world is a dynamic place as is society so we constantly need new laws. For example, we need a new budget every year, there are problems that need to be solved, etc. How are laws written? This is where science plays an important role.

Let's say people who lived in communities in the 1950s downwind of nuclear test sites got cancer at a much higher rate than people living elsewhere. First, this is only discovered serendipitously because doctors in these areas see more cases of cancer than they expected based on their training (that's because by and large people don't move too far). One possibility is that the training was shoddy, so the doctors call colleagues who work elsewhere and learn that their colleagues find cancer at a rate they learned about in medical school. This seems like a real problem, so now some epidemiologists are called in to look more closely at the situation and they find that after controlling for other factors, like diet, age, etc. people downwind of nuclear testing sites are in fact much more likely to come down with cancer. Congress is mobilized, there's a problem that needs solving.

Here's where science and politics separate. There are many issues and repercussions involved here. A law could ban nuclear testing within 50 miles of downwind communities. Is that sufficient distance? We could change the law to make it 500 miles, is that even possible? A law could ban above ground testing, would that prevent the problem? It would if the problem was aerosolized radioactive compounds, but not if the cancer was due radioactive compounds seeping into the water supply. Or we could attack the epidemiologists for being in the pocket of big-antinuke with an agenda trying to get rid of jobs on nuclear testing facilities.

Look at what happened with the smoking-lung cancer science. It took decades and hundreds of thousands of lives before the cigarette companies agreed that smoking causes cancer. The list can go on and on. 

Legislators have a difficult job and we should elect the best and brightest and bravest. If based on scientific studies, it is shown that a new yet inexpensive chemical used on farms kills shrimp, what does a legislator do? If they propose a law banning the chemical, an entire industry and all the employees of said company are out of work. If you don't ban the chemical, the shrimpers on the coasts will be devastated. Maybe you propose banning the use of the chemical near estuaries near the coasts, but this puts farmers in those regions at a disadvantage to those who are not near these areas. Here's where bravery comes in, is a legislator in Iowa going to consider the shrimpers, the industrialists, or the farmers? What about the Mississippi legislators?

 Science can tell us much, but it cannot necessarily tell us policy. In the shrimp example, science will also tell us that shrimp are a primary food source of many fish and ultimately mammals. If the shrimp fail, the fish fail and the whales fail, if the fish fail the seals fail and the sharks fail. The impact is not limited to shrimp. Of course if farm costs go up, food prices go up which means less spending on other areas across the country. Making policy is difficult, there are very few, if any, truly 'everybody wins' scenarios. The question I expect politicians to be able to answer is 'why did you vote the way you did?' I expect a politician to explain the pros and cons of their choices and if they can't articulate both the good and bad and back those claims up with data, then what good are they? We might as well make decision based on our guts or 'common sense' whatever that means.

 This is why I march for science. Many politicians are ideologues, chickenshit, or both. The worst are the chickenshit ideologues, which represents much of the republican caucus. These are the folks who want a specific policy to go through, kind of a 'we are going to throw the switch on the train track' regardless of the scenario. When scientists point out that there are two people on the track now and 50 young adults on the other track, politicians attack the scientists or the data. Companies that can make money off switches that change tracks can pay 'scientists' to suggest the possibility that maybe there aren't 50 people on the other track because its tough to tell the difference between 47 and 50 and therefore any other number. I 'March for Science' because the legislators in Congress applied for and advocated for a difficult job and they are deciding en masse they don't want to do their job or at least not be held accountable for what they do. They are often not the best nor the brightest and are generally not the bravest. They are too scared to make a decision that may hurt their constituents even if it benefits the greater good, so they lie, obfuscate, and attack those who point out the problems in order to get reelected. A brave legislator tells the farmer that they voted to ban the chemical because it adversely effects shrimpers and so many others; an honest legislator tries to offset the hit the farmer takes through tax credits or other mechanisms. Our legislators* do not.

 So as a citizen of the USA, a father, and a scientist, I will march because science is essential for the government to make effective policy and to be held accountable for that policy. I march because I want our representatives to be honest, I want them to be accountable to the people who voted for them and able to defend decisions that in the short term may hurt their constituents. If you are too scared to do the job, do something else. Most people are reasonable and if you explain why a hurt is happening and can explain how you are dealing with that hurt, you will get much in return. When you act like your constituents are petulant children, you can't be surprised when they act that way.

 *there are a few, very few, exceptions.
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Myths of Evolution: I

I 'belong' to a Creation-Evolution debate group on Facebook. Probably 'follow' is a better word than belong. My reason for joining was to see what arguments were given from the other side in the hope that I would see some nuanced discussion/rationales for creationism. Of course, what I see is the general troll-level argument you get on any website. Essentially the Ken Hamm approach to science and biology. It being the internet, I am not surprised and don't generally participate (hence the 'follow' as opposed to 'belong'). Hell, I'll even admit to providing troll-master-level responses to some creationist posts, albeit with you know data and shit.

My ultimate goal in joining this group was to hear from the other side and potentially use that as a jumping off point for some posts where I could provide some information for others who may have the same questions or be thinking along the same lines. Because the Facebook posts are essentially the equivalent of 30 year olds living in their parents' basement complaining about Lady Gaga's stomach during the Super Bowl halftime show, this goal hasn't come to fruition from my end. Still I saw this recent post which made me cringe from the potential waste of a young mind. Also, it reminded me of the culpability of teachers and scientists who teach a linear version of the history of life. Here's the post and associated picture (the picture is fairly well known):
It seems finding evidence against evolution is child's play. One of my friends told his young daughter that some people believed we evolved from apes, and her immediate reply was, why aren't there still ape-men today?

Sadly, if the story is true a father lost a teachable moment and my experience is that those don't come around as often as you might expect. Anyway let's break this down:
  1.  'It seems finding evidence against evolution is child's play.' Sure, it seems like finding evidence is child's play, but reality doesn't work like that. It seems like the earth is flat. I bet if you ask an uneducated child to draw the earth, they would not draw a sphere unless they were taught is was a sphere previously. It seems like a volume of water wouldn't be larger when it was frozen, but I wouldn't but a full glass of water in the freezer if I were you. It seems like the sun moves across the sky not that the earth is spinning beneath it. Actually finding examples that discount the 'seems' approach to understanding the universe is in fact child's play.
  2. 'some people believed we evolved from apes' Hell, let's go all the way and say some people currently believe we evolved from an ape-like ancestor. It's not a past tense kind of thing. Some people believe a male was specifically created by a god ~6000 years ago and a woman was cloned from his rib (apparently without a Y chromosome). I would be more comfortable if I could write it as 'Some people believed a male was specifically created by a god ~6000 years ago and a woman was cloned from his rib.' I left off the snarky part because this is now a historical comment and I'm not going to call out people who didn't have the benefit of current knowledge.
  3. 'her immediate reply was, why aren't there still ape-men today?' I don't have a problem with this question being asked, although I highly expect that either the question wasn't asked this way or that the set up was different (shorter version: I believe the entire story is a lie). The fact that the daughter said 'ape-men' when the premise never uses the term suggests it is fabricated or at least embellished. Regardless, let's say she asked this question or one very similar. This should not be the end of the story but the beginning of the story. As a father, teacher, even simply a member of the human species, I would redirect her and use it as one of those infrequent teachable moments. For example, 'that's a great question, but maybe we should back up and ask why some people believe that'. This of course requires some honesty and openness on the part of the father, which based on the post is not apparent. Essentially if we want to obtain more knowledge about the universe as it exists, we need to ask for evidence and to evaluate it. In the absence of time or energy, we should in fact defer to experts who have had the time and energy to ask for, evaluate, and potential obtain the evidence.
Second, the picture is objectively factually wrong. There are m(b)illions of humans (shown on the right). Of course the default human is a white man, because of course it is. However, there are not millions of the ape-like ancestor on the right, because they are all dead. The picture suggests we evolved from modern species still in existence, which is not how evolution works. This is why you don't get to have Sunday dinner with your great great great grandmother, she's dead. But you can have Sunday dinner with you cousin 5 times removed.

Here is a more accurate picture:
 (Tangent: there are not millions of chimpanzees left, there's much less than a million, there are not a million of all the non-human apes (chimps, gorillas, bonobos, orangutans) combined on the planet, this is a sad.)
Third, there is blame to lay at the feet of scientists, teachers, publishers, etc who at least subconsciously promote the viewpoint shown in the first picture. For example, a google search for 'evolution' reveals a majority of pictures like:
From the Front Range Forum
Searching for 'diversity of life' shows most pictures similar to this one:
Evolution of Life
The issue is that evolution in these panels is shown as a linear pathway from one form to another culminating in humans. We are amazing organisms, but are we any more amazing than a moss, which can harness the light of the sun to pull carbon dioxide out of the air to make food? Are we so much better than bacteria that can breathe rocks? We are not the culmination of evolution, all other life is at at least as evolved as we are. A strong argument can be made that most organisms are more evolved than we are as they have much shorter generation times and thus reproduce faster than we do.
The rebuttal picture I included is much more accurate because it shows that modern chimpanzees and humans evolved from a common ancestor. Based on general looks, the common ancestor may compare better to the chimpanzee, but both the chimpanzee and human are as distantly related to this common ancestor as you and your sibling are to your great grandmother.  (In fact, we can make that  picture more accurate by including another branch coming off the lineage leading to the chimpanzees that ends at the bonobos.

 If you're going to discuss evolution and try to explain it to others, please use branching trees, especially those that do not implicitly suggests humans are the most evolved (at the pinnacle of a tree or at the edge). Here's a great example of a tree showing human evolution:
A less egocentric view of evolution
(I know humans are at the top, but notice the y-axis is a timeline. The things at the top still exist, the chimpanzees and humans, the organisms further down are extinct.)
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Welcome to the 4th Reich part 1.

I've been perusing the whitehouse.gov site and this is something I've seen


Making Our Military Strong Again?!?!

 What the fuck does that even mean?

 Here are some facts, albeit not 'alternative facts' aka not shit I make up.

 Here's how much of our discretionary budget goes to the military:
https://www.nationalpriorities.org/campaigns/military-spending-united-states/

Over half of the discretionary budget goes to the military, although it's more than this graph shows because I think you should include Veteran's benefits (6%) into this figure at the very least. So at least 60% of all discretionary funding goes to the military. (No mention of unfunded wars in this graph.)

 How does the US stack up against the planet earth?
From Fake News aka people who disagree with Der Furher
Well not the planet earth, but the next 13 highest spending countries don't spend as much on the military as we do COMBINED! You might notice that France, UK, Japan, Germany, South Korea etc. are our allies. With Trump being in you might want to throw Russia in as an ally.
President Trump will end the defense sequester and submit a new budget to Congress outlining a plan to rebuild our military. We will provide our military leaders with the means to plan for our future defense needs.
This quote is right from whitehouse.gov. So I'm guessing Trump needs more money for the military. I wonder why, is it to have shiny new tanks driving down Pennsylvania Ave for the State of the Union? We spend more than China by roughly 6x! This raises the question, if we spend so much of our fucking money on the military, but (in Trump's world) the military is so fucking horrible, why in the world would we throw more money at them? They'll just fucking waste it right?

 I'm wondering if Trump and company will bring up 'data' like we have fewer ships in the navy than in 1860. If so, I'll pit one destroyer against the entire 1860 US navy.

 Maybe our military needs more money, I want to know why. I want a justification, because the numbers I see tell me different. 
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Reviewing grants for NIH vs NSF: a comparison

During my career, I have reviewed grant proposals for both the National Institutes of Health (NIH) and the National Science Foundation (NSF). The standard NIH research proposal is called the R01, generally giving 5 years of funding to a research lab; the standard NSF proposal generally gives 3 years of support to a research lab. By and large, an NIH award will provide more funding than a NSF award on a per year basis.

How the process works in general terms:
After an investigator(s) writes a proposal to either agency, the proposal is assigned to a study section or panel for review. The study section/panel is comprised of expert researchers in the general research area the proposal is about. Specific experts are recruited based on the specific proposals submitted such that there is at least one expert working in the area of each proposal. In general, reviewers receive a stack of 8-15 proposals to review. Reviewing takes a lot of time and energy with reviewers often referring to the literature to get up-to-date on specific topics. Ultimately, the reviewers all gather together in a room to discuss the proposals and make recommendations for which proposals get funded and which do not. At the NIH, reviewers have much more influence on which proposals obtain funding than NSF. In part, this is because NSF is legally bound to ensure funding is spread across the country and to different types of institutions, the NSF program officers have to superimpose the reviewer recommendations with these other criteria to make funding decisions. (To be clear, proposals NSF reviewers find to be fundamentally flawed are not funded simply to spread the wealth.)
A generic stack of grants to review
Once the initial reviews are written though, the process is fundamentally different between NIH and NSF. I believe the NSF model is profoundly better than the NIH model and I'll explain why using a specific rationale that I think is readily justified but also anecdotes, which I realize do not count as data and are therefore less reliable. (Full disclosure, I have reviewed grants for both institutions, have submitted proposals to both institutions, and have been funded by NIH but not NSF.)

What happens at NIH pre-meeting:
When you review a proposal you score it on a variety of criteria using a 1 - 10 point scale (1 being the best). You also give your proposal an overall score. For your stack of proposals, you are supposed to spread out your scores such that you don't give every proposal 1s across the board. A reviewer has to note both the strengths and weaknesses of the proposal for each of the criteria which is the basis of the review. Any given proposal is reviewed by 3 reviewers (sometimes more, but generally not). Once all the proposals are reviewed and scored, this information is sent to NIH and the information becomes available to the other reviewers. Thus, a reviewer cannot 'cheat' and see what the other reviewers think before writing their own critique.

For a given proposal titled 'XYZ' that is reviewed by reviewers Dr. 123, Dr. 456, and Dr. 789, a different proposal titled 'ABC' would be reviewed by Dr. 123, Dr. 045, and Dr. 232, and a third proposal titled 'JKL' is reviewed by Dr. 123, Dr. 045, and Dr. 789. The point here is that different groups of reviewers are reviewing different proposals. However, there is generally overlap of reviewers because they share similar expertise. Say the study section is on signal transduction in eukaryotic systems, there might be a group of 6 experts who work with mouse models, another 8 experts who work in fungal systems, and 6 more experts who work with Drosophila. So generally speaking, every proposal using mouse models (and likely other mammalian models) would be reviewed by 3 out of the 6 experts who work with mouse models. Say there are 15 proposals in the study section (out of 90) studying mammalian signal transduction, then you should see that these are being reviewed by a specific cohort of the entire study section. Same for the proposals using fungi as a model and proposals using invertebrates as a model.

What happens at NIH during the meeting:
At NIH, once all the proposals are scored, they are ranked with the lowest overall score (based on the 3 reviewers) being ranked first. Depending on the cohort your proposal falls into, this could work for or against you. Some reviewers score high (more likely to give 1s) than others. So one reviewer's 2.2 may be another reviewers 1.3, even if they are equally enthusiastic about their respective proposals. Based on the luck of the (reviewer) draw your proposal might be scored as the 10th best, but with a different draw that same proposal might be scored as the 1st (best) proposal for the entire study section. Here's the outcome of this situation, proposals are discussed in their rank order, so the lowest scoring (best) proposal is discussed first, second best discussed 2nd, third best discussed 3rd. Of the 90 or so proposals submitted only the top third is actually discussed by the entire group, the other two-thirds are 'triaged' (i.e. not discussed). Of those discussed only a handful are actually funded, 0-4.   As the group discusses a proposal the 'best' proposal is described to the entire panel most of whom have not read the proposal at least not in any depth. Usually the first page (the Specific Aims) is read by everyone, but generally not much else of the proposal. After the brief presentation where the reviewers go over their strengths and weaknesses, any member of the panel can ask questions or comment. If a reviewer gave a proposal a 1.3 but stated nothing but weaknesses, the question would inevitably arise 'why did you score this so high?' Once the discussion is complete, the three reviewers give revised scores (generally they change little and if so move towards the mean). The entire panel then enters their own score for the proposal, which is generally the average of the three reviewers. Then the panel moves on to the next grant.
NIH (FYI panels take place at a hotel not here)
How can this go wrong?
First, there is the psychological issue that the panelists know they are discussing the proposals from 'best' to 'worst'. Even though a reviewer may love their proposal, which was ranked 10th, it is not discussed until after nine other proposals. These reviewers may lower their ultimate score to reflect this, but the entire panel knows it was 10th and the reviewers are changing their scores to make it not be 10th, rightly or wrongly.

Confounding this issue is the majority of grants are solid good proposals that should be funded. That me rephrase that, the top 20 proposals (or so) in a study section are solid excellent proposals. Hell, you could take the top 10-15 and (in general terms) the scientific/impact difference between the top 10-15 proposals is negligible, yet only the top few have any chance of funding. This means that once you reach a certain point, funding is really a luck issue and nothing more. In fact, there have been suggestions of putting the top proposals into a lottery to determine funding. This is not a new problem. When I was trying to obtain my first R01, I submitted my last attempt at funding for one project (you had three attempts). Based on previous critiques and scores, I was confident of funding. However, one of the main parts of an Aim had been completed and published during the time I spent on the first and second submission. It would be stupid to propose doing published stuff, so I changed that Aim to focus on the follow up studies based off of what we had published. The third submission was triaged (it was actually discussed because I was a new investigator, but was scored in the triaged range), and the biggest issue was that I had reworked an Aim and 'we have not had a chance to fix it.' (Quote from the actual reviewer.) My program officer recommended I resubmit with a new title as a new proposal, which of course I did. This 'first' submission was funded and received one of the lowest (best) possible scores. My point is how arbitrary the system can be.

Second, people suck. On a study section I have served on (ad hoc) numerous times, there are two distinct factions based on the type of organism each faction studies. Some members of one of these factions would read and critique the high ranking proposals of the other faction in order to present 'issues' and 'faults' during the discussion session. This is completely valid if everything is equal, but this was done with the goal of diminishing the high ranking proposals of the other faction in order to increase the standing of proposals from their faction. (In other words it was not done to critically evaluate the science across the board but in a strategic way to help their colleagues and their field.)

Third, (and most importantly in my opinion) the scoring is done blind. Apart from the three reviewers, the rest of the panel scores the proposal in secret (again based on my discussions with panel members it is usually the average of the three reviewers). Once a proposal is scored it is not brought up or discussed again.

What happens at NSF pre-meeting:
It's pretty much the same as described above for NIH, however there is not a 1-10 scale but a qualitative scale (Excellent, Very good, Good, Not competitive). There are still 3 reviewers, there are still strengths and weaknesses, there are still cohorts based on areas of expertise. NSF proposals are broken up into two sections the 'intellectual merit' basically the science being proposed, and the 'broader impacts' basically how does this benefit society. Each of these sections is a critical part of the review, have an excellent intellectual merit, but no real broader impacts and your proposal is not scored well.

What happens at NSF during the meeting:
At the surface level, its similar to NIH. However, the proposals are not pre-ranked/pre-scored. The order of discussion is based on reviewer availability as some ad hocs call in. It's also based on the leadership of NSF some of whom may be interested in a specific area and want to sit in to hear the discussion in an area they are familiar with. (At NSF the program officers are practicing scientists who have taken a multi-year leave from their research institution to serve at NSF, the upper leadership are usually 'permanent' staff at NSF).
NSF headquarters (FYI panels take place here!)
Proposals are discussed by the reviewers and then a general discussion takes place. This discussion is more robust than what I have observed on NIH study sections. Once the discussion is done, the panel, not the reviewers, suggests a category to put the proposal in (again either Excellent, Very good, Good, Not competitive). The Very good and Good categories are further broken up into two groups I and II to distinguish the very very good and the not so good goods.) After the panelists make a recommendation, the reviewers can agree/disagree and another mini-discussion can ensue. Regardless, the proposal under consideration is placed on the board. (This is an excel spreadsheet projected on the wall.) We then move on to the next proposal.

A key difference is that once a proposal is placed, it can be discussed further. This is particularly important when two similar proposals get profoundly different rankings. We can then discuss why. At NSF proposals can move around a lot. Furthermore, everyone at the table has to agree on the categories and position within the categories of every proposal (we still decide on the most excellent, the 2nd most excellent, the 3rd most excellent, etc.). I may not agree with the ultimate position of every proposal on the board, but as a group we are in agreement.

How can this go wrong?
First, psychology still exists. If the reviewers score a proposal Excellent or Not competitive, as a panelist you are influenced by this. I did not read every proposal (although often I and the other panelists will read particularly contentious proposals at night before we meet the second day). Regardless, those initial critiques carry weight even if we know it happens and try to avoid it.

Second, people still and always will suck (#Trump2016). My most recent NSF full proposal was not funded and one of the reviews referred to me as 'she' and 'her' whereas the other reviewers referred to me as 'the investigator' or 'Dr. XYZ' (this is standard boilerplate when talking about the researcher). I'm not saying the reviewer was biased against me because they thought I was a woman, but its possible. This was the only time in close to two decades, I've read a such a condescending review that attempted to explain to my feeble girl-brain what science is and how it's done by 'real'-scientists. (Full disclosure, I'm not a woman and it doesn't matter anyway.) After talking with my program officer, my proposal was the one on the fence between funding and no funding, which unfortunately fell on the side on no funding. And here is why I think the NSF system us better...

Why the NSF system is better:
Regarding my unfunded NSF proposal: It is my fault it wasn't funded. I could complain about sexism and bias, but if my proposal had been slightly stronger the other reviewers would have gone to bat for me more and the panel would have placed my proposal higher and I would have been funded. This is not the case at NIH, one slightly not enthusiastic review can tank your proposal. I expect when my NIH proposal was dinged for getting too much done and rewriting an Aim the panel hadn't yet corrected, there was some brief discussion of this being not a reasonable critique (if the reviewer didn't actually say this out loud, it wouldn't be discussed period) and then the scores were adjusted somewhat. The reviewers who supported my proposal increased their scores slightly to show some semblance of reviewer cohesiveness and the reviewer who was an idiot decreased their score somewhat to 'fix' the BS critique and the panel scored to the mean, which amounted to triage. If the proposal had to be placed on a board and put in context with other proposals, then I doubt it would have been triaged and expect it would have been funded based on the score of the subsequent proposal.

In conclusion, I like the NSF system more because it is more transparent, accountable, and self-correcting.

Some potential confounding factors:
Success rates: There is really no difference in success rates between NIH and NSF, they both suck (#Trump2016) and essentially a lottery system of top proposals seems appropriate (although NSF has additional criteria that impact who gets awarded).

Number of grants: My experience is that there is really no difference when it comes to the meetings. Some NSF programs have a preproposal (essentially their triage step) and then review 30 or so full proposals, which is about the NIH study section full review. I'll point out that every preproposal is reviewed too, there is no 'it wasn't good enough to discuss' category.

Probably others I cannot think of now.

Also, I know none of this is #Trump2016's fault, but it is my go to hashtag to express contempt at the shortsightedness of one party (Republicans).
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