ridgesize running error, sda1 space out

I run 10 jobs on ridgesize, with debuging output to text file.  These text files run up to 450G in size, and take all spaces in /tmp/ in /dev/sda1/.

foreach on dataframe, large data frame cause memory collapse

https://stackoverflow.com/questions/29828710/parallel-processing-in-r-for-a-data-frame

Caution:
The following foreach run on a large data frame (cellmap.org) cause memory collpased on my MacPro, presumably to due to multiple call of the large data frame.

registerDoMC(4) #intel i7 has 4 cores
tb.gin.lenient$ordered_pairs =NA

total = length(tb.gin.lenient[,1]);
x = foreach( i = 1:50, .combine=rbind ) %dopar% {
 pairs = tb.gin.lenient[i , c("ORF1", "ORF2")];
 pairs;
 if ( is.na(pairs[1]) | is.na(pairs[2])) {
  tb.gin.lenient$ordered_pairs[i] = "NA_found";
 } else {
  ordered_pairs = sort(pairs);
  tb.gin.lenient$ordered_pairs[i] =  paste( ordered_pairs[1], ordered_pairs[2], sep="_");
 }
  tb.gin.lenient[i,,drop=FALSE]
}

bio233 20150929Tue review of exam 1

bio233 20150929Tue review of exam 1

go over exam 1

make up exercises for part 2. Group exercise.

A few student sat alone, but I asked them to join a group nearby, they were welcomed by the groups.
In one group, students were explaining how to do unit conversions and scale conversions.

In another group, students were exchanging answers, and they were trying to gaming the test as a group. The group that were gaming the system quickly got all the correct answers. Unfortunately, they have missed the point of the entire makeup practice.

The students clearly enjoyed the group work to redo the exam.

bio125, exercises, cancer, April 16, 2015

Section 1,

UCSC genome browser.
Problem: Some students changed the setting of the display control and was confused by the display.

Section 2,

Go through many questions.

Problem in math percentage calculation. 500ml 0.5%, how much do we need? I wrote on the board
500x0.5% = 2.5 which confused many students. They think I skipped some steps.  Another student
explained it as  0.5% = 0.5mL / 100mL = x / 500mL, and find out x. This made the students said that now they got it.

flow cytometer, sample probes tend be clogged by yeast cell clumps.

When yeast cells were too dense, the sample probes of Calibur tend to be clogged. Presumably because chained yeast cells clumps.

online problem sets

Careful checking of online activity log show that some students did not download the required R codes, but was able to get 100 points on the quiz. This indicates that they took advantage of the multiple attempts, and systematically tested the answers.

This did not align with the intent of assisting student learning.

bio233, student project presentations

From 2-5pm. There were 12 presentations in bio233 today. This seems to be the best learning experience that I have had for a long time. Some students really spent time on their research and exploration.

Some students present a research paper, some presented a research proposal. For the research proposal, the research plan lacks molecular detail and quantitative rigorousness. (I should provide two rubic next time. One on paper and one on proposal. My 2011 and 2012 research proposal presentations worked much better because I had more time to lead the students worked on their research project along the semester.)

Short presentations often do not touch details. When student discuss experiments in details, they often ran overtime.

One student did not prepare an ppt presentation. She indeed scripted her talk. I made PPT an explicit requirement in the syllabus.

bio233 chapter 2, microscopy, intro to microbe world

Preparation: Plastic sheets, pencils, dark markers,
Microscope

=> About 20 minute,  plastic sheets drawing exercises for contrast, group project
one bacterial cell drawn with pencils and
one cell drawn with dark markers
 for contrast activities.
Most groups drew very nice pictures.


=> Went through slides and used clicker based questions.

A students asked what should be remembered.

Lessons:

• I forget to share PPT screen after the video showing.
• YouTube live-recording does not allow 'pen' feature on PPT.
• Bamboo pen does not go to the Monitor window. When I synchronized my desktop and the monitor window, I lost my screensharing windows. 
• I had trouble of locating of my live-event control window. I had two of them opening at the same time for a while. 

FYE advsing, comparative women's study, fall 2014, FYE advising problem

Comparative women's study, fall 2014. Instructor does not take first year students.

Lessons learned, grant writing

Prediction and supporting evidence should be presented side by side.

bio233 exam 2,

part one is online.

part two is in paper, including MEGA exercise. Some students use MEGA5 and cannot open MAS file that I generated using MEGA6. Some students did not download MEGA to their own computer.

The lac operon question is ambiguous, I need to improve the clarity of this question.

bio233, chapter 10, bacterial genetics

I was not clear on transformation, transfection and transduction. I need to summarize a table for this.
The table is:
Transformation: Transfer DNA fragments into cells.
Transfection: Transfer virus DNA into cells
Transduction: Transfer DNA via phages into cells


R factor

Cis-trans complementation.

bio233, phylogeny lab on west nile virus

MEGA6 is over 350M and took extremely long time to download over Spelman wireless. I had to put them on flash drive and distribute it around.

Many students work out the phylogeny tree on their own.  Some even high-fived afterwards.

Once students started to work on the computer lab, it is hard to get their attention back. 

Bio233, reflection on microbes in environments.

This lab should be done at the start of the semester. Students then can use these microbes for Gram stain, and scale of the microbe exercises.

mortality rate change at the first time point, bug fix

When I estimated the mortality rates from simulated network aging data, the first time points always have a very low value.  This abnormality bugged me because it contradicts the theoretic assumption on the initial stage, but I thought it is due to the boundary effect and did not investigate it for several weeks. Also, I thought there may be some differences between theoretical predictions and empirical data. I went over the code today and found out that it is due to typo in the code.  After correction, the initial stage of mortality rate change fit extremely well to the network aging model.
Lesson learned: I should be more confident about my theoretic models and should pay more attention to abnormality during numerical works.

My learned lessons in mathematical biology (in progress)

• The key of modeling in biology is how to model the phenotype. 
• If both functions can be expanded into similar first few terms of Taylor series, they can approximate each other. This is very useful to exchange binomial form and the exponential form. 
• Initial values are very important for numerical fitting. 
• The gnls function in R often looks for local optima around the initial values. For poor data, gnls can even output the initial values as fitting results.