JASPAR (http://jaspar.genereg.net)

JASPAR (http://jaspar.genereg.net) is the leading open-access database of matrix profiles describing the DNA-binding patterns of transcription factors (TFs) and other proteins interacting with DNA in a sequence-specific manner.

http://www.ncbi.nlm.nih.gov/pubmed/19906716

Departures (おくりびと, Okuribito?)

Departures (おくりびと, Okuribito?) is a 2008 Japanese film by Yōjirō Takita. It won the Academy Award for Best Foreign Language Film at the 2009 Oscars and has earned $61,010,217 in Japan as of April 12, 2009.[1]

http://en.wikipedia.org/wiki/Departures_(film)

http://www.imdb.com/title/tt1069238/

cancer data, plant-pathogen metabolic reconstruction

Targeted metabolic reconstruction: a novel approach for the characterization of plant–pathogen interactions
Andrés Pinzón, Luis M. Rodriguez-R, Andrés González, Adriana Bernal and Silvia Restrepo

Corresponding author. Andrés Pinzón, Laboratorio de Micología y Fitopatología, Universidad de los Andes, Bogotá, Colombia. Tel: +57 1 3394949; ext. 2777; E-mail: am.pinzon196@uniandes.edu.co

http://bib.oxfordjournals.org/cgi/content/abstract/bbq009v1?papetoc


Online resources of cancer data: barriers, benefits and lessons
Emanuela Gadaleta, Nicholas R. Lemoine and Claude Chelala

http://bib.oxfordjournals.org/cgi/content/abstract/bbq010v1?papetoc

- The Cancer Genome Atlas (TCGA) - http://cancergenome.nih.gov
- International Cancer Genome Consortium (ICGC) - http://www.icgc.org/
- BioMart - http://www.biomart.org
- Pancreatic Expression database - http://www.pancreasexpression.org
- Cancer Biomedical Informatics Grid (caBIG) - http://cabig.nci.nih.gov
- The Lymphoma Enterprise Architecture Data-system (LEAD) - http://umlmodelbrowser.nci.nih.gov/umlmodelbrowser/
- Repository of Molecular Brain Neoplasia Data (REMBRANDT) - http://rembrandt.nci.nih.gov
- ONcology Information eXchange (ONIX) - http://www.ncri-onix.org.uk
- Oncomine - http://www.oncomine.org
- ArrayExpress - http://www.ebi.ac.uk/arrayexpressas/ae
- Gene Expression Omnibus - http://www.ncbi.nlm.nih.gov/geo/

Need some link checker to see which links are still alive and dead

BioCyc SAQP - web-based SQL query interface
http://biocyc.org/query.shtml

Java don't support multiple inheritance

multiple inheritance, ie. invalid class cat extends animal,feline,fourLegs

but you can still do
class cat extends animal implements feline, fourLegs

http://java.sys-con.com/node/37748
http://csis.pace.edu/~bergin/patterns/multipleinheritance.html

Bacteria Nitrogen Fixation

- Convert N2 gas to NH4+ ammonium
- symbiotic N2 fixing bacteria lives in the plant root nodules (anaerobic) in poor N2 conditions
- bacterias (eg. leguminous, Rhizobia - symbiotic, cyanobacteria - free living) give plants with fixed N2 and plants give bacterias with nutrients and carbs.

nodules provide anaerobic conditions by:
- low gas permeability
- leghemoglobin

plant gives:
- nodule
- leghemoglobin - oxygen transport
- nutrients

bacteria gives:
- fixed N2 via Nitrogenase enzyme
- heme part of hemoglobin

Nitrogenase rxn:
N2 + 8Fdred + 8H+ + 16 ATP 2NH3 + H2 + 16 ADP + 16Pi
by redox rxn in Fe protein and reducing MoFe (iron-molybdenum) protein.


Rhizobial genes participating in nodule formation
= nodulation (nod) genes

Summary of infection process / Establishing the symbiotic relationship

1. FLAV (flavonoid) is a chemo-attractant that attracts bacterias to secrete Nod D genes.
2. Nod D genes bind to promoter of Nod A,B,C genes
3. Nod factor LCO (LipoChitin Oligosacharides) is expressed
4. LCO binds to lectin receptors in plant root hair
5. bacteria attaches to cell wall of root hair
6. root hair curls due to Nod factors
7. cell wall degrades and bacteria have access to plant plasma membranes
8. Golgi vesicles fuse and extends plasma membrane to form the infection thread towards the root cortex
9. cells deep in root cortex differentiate and divide - initiates nodule formation
10. infection thread branches and moves towards developing nodules
11. bacteria enclosed in plasma membrane are release into nodule
12. bacteria starts to differentiate and divide and becomes bacteroids - N2 fixing symbiont organelles in root epithelial cells
13. nodule elongates and differentiates, including vascular system surrounds bacteroids so it's possible to transfer nutrients and fixed N2
14. layers of cells form to prevent O2 from entering.
15. Nitrogenase enzyme and leghemoglobin are synthesized

Park Bom::. You And I Lyric

http://fulllyricseng.wordpress.com/2009/10/29/park-bom-you-and-i-lyric/

No matter what happens
Even when the sky is falling down
I’ll promise you
That I’ll never let you go

Oh~~~Oh~~Oh~~~oh~~Oh~~~oh~~Yeah~~~

You naega sseureojilddae
Jeoldae heundeullimeopsi
Ganghan nunbicheuro
Myeotbeonigo nal ileukyeojweo

And you, na himae gyeoulddae
Seulpeumeul byeolang kkeutkkaji ddo akkimeopsi
Chajawa du son japeun geudaeyegae

[Chorus]

Nan haejoongae eopneundae
Chorahan najiman
Oneul geudae wihae i norae booleoyo
Tonight geudaeye du noonae
Geu miso dwiae nalwihae gamchweowatdeon
Apeumiboyeoyo

You and I together
It just feels so right
Ibyuliran maleulhaedo
Geu nuga mweorahaedo nan geudael jikilgae

You and I together
Nae du soneul nochijima
Annyoungiran maleun haedo
Naegae i saesangeun ojik neo hanagiae

You maneun sarangcheoreom
Oori sarang yeokshi jogeumssik byunhagaetjyo
Hajiman jaebal seulpeo malayo
Oraen chinhan chingu cheoreom
Namaneul mideulsuitgae gidaelsuitgae
I promise you that I’ll be right here, baby

[Chorus]
Nan haejoongae eopneundae
Chorahan najiman
Oneul geudae wihae i norae booleoyo
Tonight geudaeye du noonae
Geu miso dwiae nalwihae gamchweowatdeon
Apeumiboyeoyo

You and I together
Nae du soneul nochijima
Annyoungiran maleun haedo
Naegae i saesangeun ojik neo hanagiae

Waeroun bami chajaolddaen
Na salmyeosi nooneul gamayo
Geudaeye soomgyeoli nal aneulddae
Mueotdo duryeopji anjyo
E saesang geu eoddeon nugudo
Geudaereul daeshin halsueopjyo
You are the only one
And I’ll be there for you, baby

You and I together
It just feels so right
Ibyeoliran maleulhaedo
Geu nuga mweorahaedo nan geudael jikilgae

You and I together
Nae du soneul nochijima
Annyoungiran maleunhaedo
Naegae i saesangeun ojik neo hanagiae

Just you and I
Forever and ever

****Translation****

No matter what happens
Even when the sky is falling down
I’ll promise you
That I’ll never let you go

Oh~~~Oh~~Oh~~~oh~~Oh~~~oh~~Yeah~~~

You, When I fell
you held me back up with an unfaltering gaze

And You, through those sad times
held my hands till the end of the world

[Chorus]
I might be a shabby person who has never done anything for you
But today, I am singing this song just for you
Tonight, within those two eyes and a smile
I can see the pains from protecting me
You and I together. It’s just feels so right
Even though i bid you goodbye, to me this world is just you
You and I together, don’t ever let go of my hands
even though i bid you goodbye, to me this world is just you

Our love has changed a bit by bit just like others
But don’t be sad
Hopefully I will be someone who you can trust like an old friend
and someone you can lean onto
I promise you that I’m be right here baby

[Chorus]
I might be a shabby person who has never done anything for you
But today, I am singing this song just for you
Tonight, within those two eyes
and smile I can see the pains from protecting me
You and I together. It’s just feels so right
Even though i bid you goodbye, to me this world is just you
You and I together, don’t ever let go of my hands
even though i bid you goodbye, to me this world is just you

I close my eyes lightly whenever I feel lonely again
I no longer fear when your breath holds me
No one in the world can replace you
You are the only one in I’ll be there for you baby

You and I together, It’s just feels so right
Even though i bid you goodbye, to me this world is just you
You and I together, don’t ever let go my hands
even though i bid you goodbye, to me this world is just you

Just you and I
Forever and ever..

Every cloud has a silver lining

Every cloud has a silver lining means that you should never feel hopeless because difficult times always lead to better days. Example: "What am I going to do? My girlfriend has left me again!" Reply: "Don't worry. It will be all right. Every cloud has a silver lining."

http://www.goenglish.com/EveryCloudHasASilverLining.asp

Probability Uncertainty

decision theory - probability theory + utility theory (depends on my preference eg. represent how much time to spend waiting for flight)
- agent is rational when it chooses the action with the maximum expected utility taken over all results of actions

atomic event - a complete specification of the world about which the agent is uncertain, ie the rows of the truth table, setting all random variables
- mutually exclusive: at most one is true,
- exhaustive: at least one is true

disjunction rule?
P(a V b) = P(a) + P(b) - P(a ^ b)

Sum(P(A)) = 1
P(True) = 1
P(False) = 0

random values - assignment of values to random variables

unconditional probability P(A), prior, marginal, probability that A will appear in the absence of any other info

conditional probability P(effect|cause), posterior, new information (cause) can change probability of (effect)
eg P(a|b) = P(a ^ b) / P(b) , P(b) = denominator = normalization constant alpha

product rule: P(a ^ b) = P(a|b)P(b) = P(b|a)P(a)
also P(a^b^c) = P(a^b|c)P(c) = P(a|b^c)P(b^c)

independence:
if a and b are independent then P(a^b) = P(a)P(b)
P(a|b) = P(a)

objectivity - objectively observable, known
subjectivity - based on own degree of belief
eg. betting game

joint probability = table of all possible probability values
eg. P(pits in all spots, breeze in 3 places)

General idea: compute distribution on query variable by fixing evidence
variables and summing over hidden variables (H)
H = X - Y -E
eg. P(~cavity|toothache)
X = everything (toothache, cavity, catch)
Y = query (cavity)
E = evidence (toothache)

problems: exponential time and space complexities, how to fill in the numbers for all

conditionally independent:
A is conditionally independent of B given C:
P(A|B,C) = P(A|C) # throw B out from evidence
P(B|A,C) = P(B|C) # throw A out from evidence
P(A,B|C) = P(A|C) * P(B|C) # can't throw A and B because they are in the query

Baye's rule: for diagnosis, derived from product rule: P(a,b) = P(a|b)P(b) = P(b|a)P(a)
So P(a|b) = P(b|a)P(a)/P(b) = alpha * P(b|a)P(a)
P(cause|effect) = P(effect|cause)P(cause)/P(effect)

naive bayes - Baye's rule and conditional independence
P(Cause,Effect1, ... ,Effectn) = P(Cause) πiP(Effecti|Cause)

Probability is a rigorous formalism for uncertain knowledge
• Joint probability distribution specifies probability
of every atomic event
• Queries can be answered by summing over
atomic events
• For nontrivial domains, we must find a way to
reduce the joint size
• Independence and conditional independence
provide the tools
•

inference by enumeration using conditional independence

Plant genomics

Special Issue:
Arabidopsis: A rich harvest 10 years after completion
of the genome sequence.
The year 2010 marks the 10th anniversary of the publication of the fully assembled Arabidopsis nuclear genome DNA sequence and on this occasion, we at The Plant Journal wish to offer an opportunity to look back and highlight some of the numerous accomplishments in plant science, and beyond, that the availability of the first plant nuclear genome DNA sequence has spawned.

We would like to thank all of those who have contributed to achieving the highest possible scientific standard for the articles in this Special Issue and hope you, our Readers, enjoy it.

Peter McCourt and Christoph Benning

Click here to browse the table of contents or click on the links below to access the full paper directly:
Editorial
Arabidopsis: A rich harvest 10 years after completion of the genome sequence
Peter McCourt, Christoph Benning

Original Articles
The development of Arabidopsis as a model plant
Maarten Koornneef, David Meinke

Shotguns and SNPs: how fast and cheap sequencing is revolutionizing plant biology
Steven D. Rounsley, Robert L. Last

Linking genotype to phenotype using the Arabidopsis unimutant collection
Ronan C. O'Malley, Joseph R. Ecker

Small RNAs – secrets and surprises of the genome
Xuemei Chen

Embryogenesis – the humble beginnings of plant life
Ive De Smet, Steffen Lau, Ulrike Mayer, Gerd Jürgens

Arabidopsis seed secrets unravelled after a decade of genetic and omics-driven research
Helen North, Sébastien Baud, Isabelle Debeaujon, Christian Dubos, Bertrand Dubreucq, Philippe Grappin, Marc Jullien, Loïc Lepiniec, Annie Marion-Poll, Martine Miquel, Loïc Rajjou, Jean-Marc Routaboul, Michel Caboche

Light signal transduction: an infinite spectrum of possibilities
Joanne Chory

Getting to the root of plant biology: impact of the Arabidopsis genome sequence on root research
Philip N. Benfey, Malcolm Bennett, John Schiefelbein

Seasonal and developmental timing of flowering
Richard Amasino

The flowering of Arabidopsis flower development
Vivian F. Irish

The ubiquitin-proteasome system regulates plant hormone signaling
Aaron Santner, Mark Estelle

Research on plant abiotic stress responses in the post-genome era: past, present and future
Takashi Hirayama, Kazuo Shinozaki

Arabidopsis and the plant immune system
Marc T. Nishimura, Jeffery L. Dangl

Arabidopsis and primary photosynthetic metabolism – more than the icing on the cake
Mark Stitt, John Lunn, Björn Usadel

Lipid biochemists salute the genome
James G. Wallis, John Browse

Arabidopsis – a powerful model system for plant cell wall research
Aaron H. Liepman, Raymond Wightman, Naomi Geshi, Simon R. Turner, Henrik Vibe Scheller

Special Issue Podcast The Special Issue is also accompanied by a podcast – a conversation between Christoph Benning, Co-Editor of this Special Issue and Professor of Biochemistry and Molecular Biology at Michigan State University in East Lansing Michigan , Caroline Dean, Project Leader at the John Innes Centre in Norwich in England, Elliot Meyerowitz, George W. Beadle Professor of Biology at the California Institute of Technology in Pasadena, California, and Chris Somerville, Director of the Energy Biosciences Institute in Berkeley, California.

Python convert String to List

>>> l=eval('[1,[2,4],3]')
>>> l
[1, [2, 4], 3]
>>> l[0]
1
>>> l[1]
[2, 4]

Beer

http://theoatmeal.com/comics/beer -- TheOatMeal

Orthogonal

Orthogonal:
- perpendicular, 90 degree angle, X
- It is also widely used to describe conditions that are contradictory, or opposite, rather than in parallel or in sync with each other.

Transactions

http://xml.sys-con.com/node/43755

two-phase commit: prepare phase and commit phase

Programming in R

http://manuals.bioinformatics.ucr.edu/home/programming-in-r
http://www.r-cookbook.com/node/2
http://cran.r-project.org/doc/manuals/R-intro.html
http://www.statmethods.net/interface/io.html
http://biocodenv.com/wordpress/?p=15 http://biocodenv.com/wordpress/?p=15

Sample script: refillsMisses.R
{{{
#!/usr/bin/Rscript

args <- c('1min_interval_data_clean.txt', '1min_interval_refillsMisses.txt', '1min_interval_refillsMisses.png', 'mcf')
#args <- c('z', '1min_interval_refillsMisses.txt', 'z.png', 'perl')

infile <- args[1]
outfile <- args[2]
outplot <- args[3]
filter <- args[4]

# read in data from infile
inFrame <- data.frame(read.table(infile, header=1))
inFrame <- subset(inFrame, PROG==filter)

# calculations between select columns
miss <- inFrame['DATA_CACHE_MISSES']
acc <- inFrame['DATA_CACHE_ACCESSES']
ret <- inFrame['RETIRED_INSTRUCTIONS']
ref <- inFrame["DATA_CACHE_REFILLS.ALL"]
prog <- inFrame['PROG']

refMiss <- data.frame(ref-miss)
colnames(refMiss) <- "RefillsMinusMiss"

missAcc <- data.frame(miss/acc*refMiss)
colnames(missAcc) <- 'MissDivAccessMultDiff'

# create output data frame with calculated columns
outFrame <- data.frame(inFrame, refMiss, missAcc)

# write to outfile
write.table(outFrame, file=outfile, row.names=FALSE, quote=FALSE)

# plot
png(outplot)
missAccMat <- as.matrix(missAcc)[,1]
missMat <- as.matrix(miss)[,1]
retMat <- as.matrix(ret)[,1]

#http://www.astrostatistics.psu.edu/su09/lecturenotes/clus2.html
#scatterplot3d(missAccMat, missMat, retMat)

plot(missAccMat, retMat, col='red', pch=15)
}}}

# open plotting window
op <- par(mfrow=c(2, 2))

# draw histogram
hist(x)

Filtering without subset, subset seems to modify the input matrix
Is this what you want?

> tt <- matrix(1:20, ncol = 4)
> tt
[,1] [,2] [,3] [,4]
[1,] 1 6 11 16
[2,] 2 7 12 17
[3,] 3 8 13 18
[4,] 4 9 14 19
[5,] 5 10 15 20
> tt[tt[,1] < 3, ]
[,1] [,2] [,3] [,4]
[1,] 1 6 11 16
[2,] 2 7 12 17

OS questions

http://www.techinterviews.com/operating-system-questions

The Bets

http://www.docstoc.com/docs/26993860/Artificial-Reasoning 30/85 - The Bets
Bets on B (5/8) - you bet $5 and bookmarker bets $3
Bets on ~B (3/4) - you bet $6 and bookmarker bets $2

You suffer a net loss both ways. See Pay Off Matrix.

So it needs to be P(B) = P(~B)

or if a and b are mutually exclusive then P(avb) = P(a) + P(b)

so subjective bliefs (betting rates) should follow the basic rules of probability so that there's no sure-loss

http://en.wikipedia.org/wiki/Bayes%27_theorem
P(A|B) = \frac{P(B | A)\, P(A)}{P(B)}.
P(A|B) = (P(B|A)*P(A))/P(B)

Gene prioritization tools

http://homes.esat.kuleuven.be/~bioiuser/gpp/tools.php

Origami Bijou

http://www.origamibijou.com/

E: Sub-process /usr/bin/dpkg returned an error code (1)

https://bugs.launchpad.net/ubuntu/+source/nvidia-common/+bug/331675


Workaround:

(cd /usr/bin/; sudo mv nvidia-detector nvidia-detector2)

Then finish installing nvidia-common

(cd /usr/bin/; sudo mv nvidia-detector2 nvidia-detector)

Should work. The problem looks like that it tries to execute the python script before the python libraries are installed!


Problem:
run-parts: executing /etc/kernel/header_postinst.d/nvidia-common
Traceback (most recent call last):
File "/usr/bin/nvidia-detector", line 2, in
import NvidiaDetector
ImportError: No module named NvidiaDetector
run-parts: /etc/kernel/header_postinst.d/nvidia-common exited with return code 1
Failed to process /etc/kernel/header_postinst.d at /var/lib/dpkg/info/linux-headers-2.6.28-8-generic.postinst line 110.
dpkg: error processing linux-headers-2.6.28-8-generic (--configure):
subprocess post-installation script returned error exit status 2
dpkg: dependency problems prevent configuration of linux-headers-generic:
linux-headers-generic depends on linux-headers-2.6.28-8-generic; however:
Package linux-headers-2.6.28-8-generic is not configured yet.
dpkg: error processing linux-headers-generic (--configure):
dependency problems - leaving unconfigured
were encountered while processing:
linux-image-2.6.28-8-generic
linux-restricted-modules-2.6.28-8-generic
linux-image-generic
linux-restricted-modules-generic
linux-generic
linux-image
linux-headers-2.6.28-8-generic
linux-headers-generic
E: Sub-process /usr/bin/dpkg returned an error code (1)

Workopolis - Top 10 interview mistakes

http://www.youtube.com/watch?v=U9rn5kuTpHw
* don't be late
* give a professional appearance
* proper body language
* give complete and concise answers
* turn off electronics

Keep in mind that you want to focus on what you can do for the company, not what they are going to do for you.

Networking tips
http://money.cnn.com/2010/03/19/news/economy/shyness_jobs_interviews.fortune/index.htm?source=cnn_bin&hpt=Sbin

TLRs and innate immunity

http://en.wikipedia.org/wiki/Toll-like_receptor

Toll-like receptors (TLRs) are a class of proteins that play a key role in the innate immune system. They are single membrane-spanning non-catalytic receptors that recognize structurally conserved molecules derived from microbes. Once these microbes have breached physical barriers such as the skin or intestinal tract mucosa, they are recognized by TLRs which activates immune cell responses.

More than 100 years ago, Richard Pfeiffer, a student of Robert Koch, coined the term "endotoxin" to describe a substance produced by Gram-negative bacteria that could provoke fever and shock in experimental animals.

Beautiful

Beautiful

zooey deschanel

Natalie Portman

Han Hyo-joo (한효주)

소이현 / So Yi Hyun (So Yi Hyeon) (Gloria 2010)

Lee Min Jung 이민정 

Drama: Midas, Wonderful Radio (aka Love on Air) (OST Bitter) (2012)

Kwai Lun-mei (桂纶镁)

http://en.wikipedia.org/wiki/Kwai_Lun-mei

Lee Yeon-hee
http://en.wikipedia.org/wiki/Lee_Yeon_Hee


Son-Ye-Jin
http://en.wikipedia.org/wiki/Son_Ye-jin


Ryoko Hirosue - Summers Snow, Okuribito
http://www.ryoko-hirosue.org/

Alice Cullen (Ashley Greene) - Twilight - New Moon

Lee Ji Ah (The Legend, Beethoven Virus, The_Legend)

Téa Leoni

Stephanie Sun (singer)

why do nice guys finish last

http://www.themodernman.com/nice_guys.html

in short, forget about them -- sayonara

bonjour solitaire / solitary



-fin.

Sub-process /usr/bin/dpkg returned an error code

Error:
files list file for package `linux-image-2.6.24-19-generic' is missing final newline
Errors were encountered while processing:
/var/cache/apt/archives/linux-image-2.6.24-19-generic_2.6.24-19.36_i386.deb
Processing was halted because there were too many errors.
E: Sub-process /usr/bin/dpkg returned an error code (1)

Fix:
https://lists.ubuntu.com/archives/ubuntu-users/2007-January/104520.html

# move the info dir containing corrupted data out of the
# way, and create a new 'info' dir
cd /var/lib/dpkg
sudo mv info info.bak
sudo mkdir info

# reinstall the affected package, in my case this was
sudo apt-get --reinstall install libgtk1.2

# this will print lots of warnings about missing list
# files for the dependencies, which is obvious.
# now move the newly generated files in 'info/' to the
# 'info.bak/' directory containing info for all packages
# this will replace the information for the package
# we just installed
sudo mv info/* info.bak/

# and now get things back to where they were
sudo rm -rf info

K-dramas to watch

To watch:

http://www.mysoju.com/nine-tailed-fox/

A Love To Kill OST - Lee Soo Young

alien stone

i maybe a stone, tough on the inside and out and yet as time goes on, i too crumble a bit at a time, i am just a stone after all ...

feels like an alien too, like you don't belong in this world, probably cuz i've been immersed with too much fantasy, anime, dramas lol.

pessimist and optimist

A pessimist sees the difficulty in every opportunity;
an optimist sees the opportunity in every difficulty.
—Sir Winston Churchill

Light and Plant Development

1. (9 marks total) These questions pertain to phytochrome and plant development

(a) (2 marks) What is meant by the “photoreversibility” of phytochrome?
Phytochrome exists in 2 interconvertible forms: Pr and Pfr
Conversion of Pr (inactive form) to Pfr (active form) when red light is applied and the process is reversed when far red light is applied


(b) (3 marks) Indicate and explain 3 events involved in the signal transduction pathway leading to active Pfr upon red light illumination (eg. some of the structural and other changes to phytochrome)
Assembly of phytochrome which is a dimer composed of chromophore pigment + polypeptide
chromophore undergoes cis to trans isomerazation when red light is absorbed
Phy protein also undergoes photoreversible conformational change when light is absorbed, autophosphorylation at the Ser in hinge region to expose the PAS domain repeats that contains nuclear localization signals that directs the active form of Pfr into nucleus (regulates gene expression)
His-kinase-related domain mediates autophosphorylation
small amounts of phytochrome remains in the cytosol which mediates rapid responses

(c) (2 marks) What modifying enzymes subsequently affect the stability or activity of the physiologically active form (Pfr) and how do they act?
When Pfr is autophosphorylated at the Ser in the hinge region, it’s less active and less stable form, so phosphatase PAPP5 dephosphorylates the phytochrome making it fully active and stable
On the other hand, a kinase does the reverse process, it phosporylates Pfr to make Pfr less active and less stable so that it can be degraded by proteasome

(d) (2 marks) Once Pfr is activated indicate some processes at the cellular level that are triggered when Pfr remains in the cytosol (Hint: these are changes that mediate rapid responses, the answer is not changes in gene expression. You can indicate the types of responses that are triggered as part of your answer).
- when Pfr is activated, rapid responses (due to ion fluxes) at the cellular level are triggered which cause relatively rapid turgor changes (cell volume changes – swelling / shrinking), organelle movements, stem elongation in pigweeds and in sun plants as shade avoidance response

At equilibrium:
-Saturating red light
85% Pfr
15% Pr
-Saturating far red light
97% Pr
3% Pfr

functional domains of phytochrome:
1. N-terminal half
- Chromophore-binding bilin lyase domain
- PHY domain – stablizes phytochrome in Pfr form
2. Hinge region
- Separates N- from C-terminus; role in conversion: Pr Pfr (active form)
3. PAS domain repeats
- Mediates dimerization & interactions with downstream effector proteins
- Contains NLS (nuclear localization signals) – when exposed, NLSs direct
active form of phytochrome (Pfr) into nucleus
4. His-kinase-related domain
- Mediates autophosphorylation
- Important for stopping phytochrome responses

fluence = amount of light

Law of Reciprocity (VLFR - very low fluence response & LFR)
- Response can be induced by brief pulses of light, provided that the total
amount of light energy adds up to the required fluence

HIR - high irridiance (fluence rate) response
– Need long exposure to light of high irradiance
– Not photoreversible
– Reciprocity does not apply
• i.e. cannot provide dimmer light for a longer time
• Dependent on fluence rate NOT fluence
- eg. induction of flowering, production of ethylene, enlargement of cotyledons, inhibition of hypocotyl elongation

daylight = more red light
canopy and soil = more far-red light

sun plants exhibit shade avoidance in canopies (shade = far-red light only passes through, leaves filters out red-light) - Stem elongates to lift leaves towards light, so in shade, lots of Far-red light, so Pfr is converted to Pr so there's low Pfr:Ptotal ratio when stem elongates

shade-tolerant plants don't do shade avoidance

high Pfr:Ptotal (lots of light) promotes seed germination, optimal growth conditions

photomorphogenesis - driven by changes in gene expression (eg. MYB, LHCB), slower, long term process, convert etiolated (seedling grown in dark, soil - long hypocotyl, apical hook, no chlorophyll) to de-etiolated

in the dark, it's in inactive form, Pr
when red light is present, it is converted to Pfr

How phytochrome was discovered: seeds germinated when exposed to red-light and inhibited when exposed to far-red light (petri dish with seeds), R/FR/R = germinate, the last one determines

Galaxy

Galaxy
http://galaxy.psu.edu/

Galaxy allows you to do analyses you cannot do anywhere else without the need to install or download anything. You can analyze multiple alignemnts, compare genomic annotations, profile metagenomic samples and much much more...

$ hg clone http://bitbucket.org/galaxy/galaxy-dist/

Fix this error by using virtualenv

> "/var/lib/python-support/python2.5/sqlalchemy/databases/sqlite.py",
> line 343, in reflecttable
> >> raise
> exceptions.NoSuchTableError(table.name)
> >> NoSuchTableError: migrate_version

http://pypi.python.org/pypi/virtualenv

$ virtualenv --not-site-packages ENV
$ export PATH=$HOME/ENV/bin:$PATH
$ ./run.sh

point your browser to
http://127.0.0.1:8080
http://localhost:8080/

BIRCH and bioLegato

BIRCH is a resource for molecular biology, consisting of software and databases

Most of the programs and databases of BIRCH have been unified through bioLegato. bioLegato is best thought of as a program that runs other programs. As new programs are added to BIRCH, they appear as menu items in bioLegato. bioLegato takes care of all the "behind the scenes" tasks, such as interconverting file formats, allowing the user to concentrate on the science.

http://home.cc.umanitoba.ca/~psgendb/quickintro/quickintro.html

CMPT 470 - Web-Based Info Syst

http://www.cs.sfu.ca/CC/470/qyang/lectures.htm

Criminology

http://www.prenhall.com/saferstein/


Criminalistics: An Introduction to Forensic Science, 9/e
Richard Saferstein

Glossary

Algor mortis Postmortem changes that cause a body to lose heat.

Autopsy The medical dissection and examination of a body in order to determine the cause of death.

Expert Witness An individual whom the court determines to possess knowledge relevant to the trial that is not expected of the average layperson.

Livor Mortis The medical condition that occurs after death and results in the settling of blood in areas of the body closest to the ground.

Locard’s Exchange Principle Whenever two objects come into contact with one another, there is exchange of materials between them.

Rigor Mortis The medical condition that occurs after death and results in the stiffening of muscle mass. The rigidity of the body gradually disappears 24 hours after death.

Python WSGI web test

Web Test
http://pythonpaste.org/webtest/

Werkzeug
http://werkzeug.pocoo.org/documentation/0.6/test.html

LIMS - laboratory information management system

http://en.wikipedia.org/wiki/Laboratory_information_management_system

description logics - dl, alc

Description logics
• Formalisms for expressing concepts, their attributes (or
associated roles), and the relationships between them.
• Can be regarded as providing a KR system based on a
structured representation of knowledge.

Description Logic: (p138) - Ch 9
Three types of non-logical symbols:
• atomic concepts:
Dog, Teenager, GraduateStudent
We include a distinguished concept: Thing
• roles: (all are atomic)
:Age, :Parent, :AreaOfStudy
• constants:
johnSmith, chair128
Four types of logical symbols:
• punctuation: [, ], (, )
• positive integers: 1, 2, 3, ...
• concept-forming operators: ALL, EXISTS, FILLS, AND
• connectives: =, =, and →

[AND Company
For example: [EXISTS 7 :Director]
[ALL :Manager [AND Woman
“a company with at least 7 directors,
whose managers are all women with [FILLS :Degree phD]]]
PhDs, and whose min salary is $24/hr” [FILLS :MinSalary $24.00/hour]]

([AND Surgeon Female] = Doctor) is not valid.

C= is subsumed-by

But it is entailed by a KB that contains
(Surgeon = [AND Specialist [FILLS :Specialty surgery]])
(Specialist C= Doctor)

computing subsumption, that is, determining
whether or not KB = (d subsumed-by e) - 'no negation of alpha query)
assumptions:
- KB is acyclic
- d is atomic, only appears once in LHS
- replace
Under these assumptions, it is sufficient to do the following:
• normalization: using the definitions in the KB, put d and e into a special
normal form, d′ and e′
• structure matching: determine if each part of e′ is matched by a part of d′.
- In other words, for every part of the more general concept,
there must be a corresponding part in the more specific one.
p153. [and person [fills :age 27]

computing satisfaction: To determine if KB = (c → e), we use the following procedure:
1. find the most specific concept d such that KB = (c → d)
2. determine whether or not KB = (d C= e), as before.
joe->person , canCorp, joe is manager of cancorp, manager of cancorp is canadian, so you get joe->canadian

computing classification:
- Positioning a new atom in a taxonomy is called classification
- wine example, Wine is at the top root node, then there's white-very-dry-bordeaux-wine

The Logic ALC
Main components:
• Concepts: classes of individuals
• Roles: binary relations between individuals
• Complex concepts using constructors
• Define terminology: TBox
• Give assertions: ABox
Examples:
• Concept names: Person, Female
• Role names: ParentOf, HasHusband
• Individual names: John, Mary

Assertion C(a) (in B&L it's a->C)

MotherWithoutDaughter = Mother ∀ParentOf.¬Female
(P erson M ale)(John)

A Tableaux Algorithm for ALC (Attributive Concept Language with Complements, more expressive DLs)
• Try to prove concept satisfiability by constructing a model.
• A tableau is a graph representing such a model.
• A set of tableaux expansion rules is used to construct the tableau.
• Either a model is constructed or there is an obvious contradiction.
• If tree T contains a clash the concept C is unsatisfiable.

Unfolded: expand every concept name occurring in C

Open Data

http://radar.oreilly.com/2010/03/truly-open-data.html

open source practices to making data available to the public

http://agbt.org/about.html

The 11th annual Advances in Genome Biology and Technology (AGBT) meeting will be held in Marco Island, Florida, from February 24-27, 2010. The AGBT meeting is now widely regarded as the premier scientific forum for capturing information about the latest advances in new DNA sequencing technologies and their applications to diverse areas in biology and biomedical research. The meeting will have daytime plenary sessions that feature keynote speakers, additional invited speakers, and abstract-selected talks.

http://twitter.com/bffo

Hematology and Oncology

http://answers.google.com/answers/threadview?id=509511

Hematology-Oncology(Heme/Onc) usually refers to the department
that sees patients with blood and platelet disorders and cancers that
are treated with a non-surgical therapy, such as bone marrow
transplant, stem cell transplant, pheresis, or chemotherapy. (Think
?liquid? therapy when differentiating heme/onc from oncology) Types of
cancer typically seen in a heme/onc department would be leukemias,
lymphomas, Hodgkins, non-Hodgkins, multiple myelomas and immunological
disorders. So, an oncologist is not a hematologist, or vice versa. But
a hematologist oncologist is a hematologist that specializes in the
diseases mentioned above. A hematologist oncologist would not treat
operable cancers such as prostate cancer.

Oncology usually refers a department that sees cancers that require
surgical treatment such as ovarian cancers, throat and digestive
system cancers, thyroid cancer, etc. Patients would be seen by an
oncologist or a surgeon with experience in cancer surgery. Some
doctors have received special training, or have experience with a
certain kind of medicine, and may see and treat patients themselves
without referring them to a specialist.


Because disorders and diseases seen in heme/onc often overlap, it is
more effective to have hematologists working closely with oncologists.
Many patients see both ?heme? and ?onc? doctors during the course of
their therapy. A breast cancer patient, for example, may be treated
with a bone marrow transplant or stem cell transplant, and her
oncologist would work in conjunction with the hematologist. Another
benefit to doctors and patients is the heme/onc clinic is equipped
with special microscopes and often have their own lab, which enables
the doctors and medical technologists to make rapid diagnosis, or
monitor patients quickly and efficiently. Hematologists and
oncologists are well trained specialists that have the skills to
identify cells under the microscope that general practice doctors
often don?t posses.

Hallmarks of Cancer

http://teachercenter.insidecancer.org/browse/Hallmarks%20of%20Cancer/

Hallmarks of Cancer

o Image:Hallmarks, Overview
Hallmarks, Overview
Cancer is a disease that affects people of all nationalities and age groups and all cancers start with mutations in one cell.
o Image:Hallmarks, Growing uncontrollably
Hallmarks, Growing uncontrollably
Professor Robert Weinberg explains that cancer cells have to learn how to grow in the absence of growth stimulatory signals that normal cells require from their environment.
o Image:Hallmarks, Evading death
Hallmarks, Evading death
Professor Robert Weinberg discusses how cancer cells have to learn how to avoid the process of programmed cell death known as apoptosis carried out in normal cells.
o Image:Hallmarks, Processing nutrients
Hallmarks, Processing nutrients
Professor Robert Weinberg explains how cancer cells have to learn how to become angiogenic, that is to say attract blood vessels to grow into the tumor mass.
o Image:Hallmarks, Becoming immortal
Hallmarks, Becoming immortal
Professor Robert Weinberg explains how normal cells can only double a certain limited number of times; and cancer cells have to learn how to proliferate indefinitely, i.e, they have to become immortalized.
o Image:Hallmarks, Invading tissues
Hallmarks, Invading tissues
Professor Robert Weinberg, explains that cancer cells have to learn how to invade and metastasize.
o Image:Hallmarks, Avoiding detection
Hallmarks, Avoiding detection
Bruce Stillman, Ph.D. is president and chief executive officer of Cold Spring Harbor Laboratory, explains that there are two adaptive immune responses, and those immune responses adapt to changes in cells in our body whether they be by infection or other.
o Image:Hallmarks, Promoting mutations
Hallmarks, Promoting mutations
Bruce Stillman, Ph.D., president of Cold Spring Harbor Laboratory, explains that genomic instability is a characteristic of cancer cells.

P-glycoprotein

http://www.rcsb.org/pdb/static.do?p=education_discussion/molecule_of_the_month/current_month.html

they pump toxins away from the cell, including cancer drugs, so they make them less useful, and this so cancers produce more P-glycoproteins to do just this.

so we are now trying to find ways to block P-glycoprotein from ejecting the drugs from the cells.

Ouellette and Stein -- Changing of the guard

http://www.nature.com/nature/journal/v428/n6982/full/nj6982-584a.html

Blast in ubuntu

BLAST
http://www.ncbi.nlm.nih.gov/staff/tao/URLAPI/unix_setup.html

http://www.ncbi.nlm.nih.gov/staff/tao/URLAPI/blastdb.html

Fasta -> DB
For nucleotide: formatdb -i input_db -p F -o T
For protein: formatdb -i input_db -p T -o T

----refp_db----
>gi|113722133|ref|NP_055861.3| probable helicase senataxin [Homo sapiens]
MSTCCWCTPGGASTIDFLKRYASNTPSGEFQTADEDLCYCLECVAEYHKARDELPFLHEVLWELETLRLI
NHFEKSMKAEIGDDDELYIVDNNGEMPLFDITGQDFENKLRVPLLEILKYPYLLLHERVNELCVEALCRM
EQANCSFQVFDKHPGIYLFLVHPNEMVRRWAILTARNLGKVDRDDYYDLQEVLLCLFKVIELGLLESPDI
YTSSVLEKGKLILLPSHMYDTTNYKSYWLGICMLLTILEEQAMDSLLLGSDKQNDFMQSILHTMEREADD
DSVDPFWPALHCFMVILDRLGSKVWGQLMDPIVAFQTIINNASYNREIRHIRNSSVRTKLEPESYLDDMV
TCSQIVYNYNPEKTKKDSGWRTAICPDYCPNMYEEMETLASVLQSDIGQDMRVHNSTFLWFIPFVQSLMD
LKDLGVAYIAQVVNHLYSEVKEVLNQTDAVCDKVTEFFLLILVSVIELHRNKKCLHLLWVSSQQWVEAVV
KCAKLPTTAFTRSSEKSSGNCSKGTAMISSLSLHSMPSNSVQLAYVQLIRSLLKEGYQLGQQSLCKRFWD
KLNLFLRGNLSLGWQLTSQETHELQSCLKQIIRNIKFKAPPCNTFVDLTSACKISPASYNKEESEQMGKT
SRKDMHCLEASSPTFSKEPMKVQDSVLIKADNTIEGDNNEQNYIKDVKLEDHLLAGSCLKQSSKNIFTER
AEDQIKISTRKQKSVKEISSYTPKDCTSRNGPERGCDRGIIVSTRLLTDSSTDALEKVSTSNEDFSLKDD
ALAKTSKRKTKVQKDEICAKLSHVIKKQHRKSTLVDNTINLDENLTVSNIESFYSRKDTGVQKGDGFIHN
LSLDPSGVLDDKNGEQKSQNNVLPKEKQLKNEELVIFSFHENNCKIQEFHVDGKELIPFTEMTNASEKKS
SPFKDLMTVPESRDEEMSNSTSVIYSNLTREQAPDISPKSDTLTDSQIDRDLHKLSLLAQASVITFPSDS
PQNSSQLQRKVKEDKRCFTANQNNVGDTSRGQVIIISDSDDDDDERILSLEKLTKQDKICLEREHPEQHV
STVNSKEEKNPVKEEKTETLFQFEESDSQCFEFESSSEVFSVWQDHPDDNNSVQDGEKKCLAPIANTTNG
QGCTDYVSEVVKKGAEGIEEHTRPRSISVEEFCEIEVKKPKRKRSEKPMAEDPVRPSSSVRNEGQSDTNK
RDLVGNDFKSIDRRTSTPNSRIQRATTVSQKKSSKLCTCTEPIRKVPVSKTPKKTHSDAKKGQNRSSNYL
SCRTTPAIVPPKKFRQCPEPTSTAEKLGLKKGPRKAYELSQRSLDYVAQLRDHGKTVGVVDTRKKTKLIS
PQNLSVRNNKKLLTSQELQMQRQIRPKSQKNRRRLSDCESTDVKRAGSHTAQNSDIFVPESDRSDYNCTG
GTEVLANSNRKQLIKCMPSEPETIKAKHGSPATDDACPLNQCDSVVLNGTVPTNEVIVSTSEDPLGGGDP
TARHIEMAALKEGEPDSSSDAEEDNLFLTQNDPEDMDLCSQMENDNYKLIELIHGKDTVEVEEDSVSRPQ
LESLSGTKCKYKDCLETTKNQGEYCPKHSEVKAADEDVFRKPGLPPPASKPLRPTTKIFSSKSTSRIAGL
SKSLETSSALSPSLKNKSKGIQSILKVPQPVPLIAQKPVGEMKNSCNVLHPQSPNNSNRQGCKVPFGESK
YFPSSSPVNILLSSQSVSDTFVKEVLKWKYEMFLNFGQCGPPASLCQSISRPVPVRFHNYGDYFNVFFPL
MVLNTFETVAQEWLNSPNRENFYQLQVRKFPADYIKYWEFAVYLEECELAKQLYPKENDLVFLAPERINE
EKKDTERNDIQDLHEYHSGYVHKFRRTSVMRNGKTECYLSIQTQENFPANLNELVNCIVISSLVTTQRKL
KAMSLLGSRNQLARAVLNPNPMDFCTKDLLTTTSERIIAYLRDFNEDQKKAIETAYAMVKHSPSVAKICL
IHGPPGTGKSKTIVGLLYRLLTENQRKGHSDENSNAKIKQNRVLVCAPSNAAVDELMKKIILEFKEKCKD
KKNPLGNCGDINLVRLGPEKSINSEVLKFSLDSQVNHRMKKELPSHVQAMHKRKEFLDYQLDELSRQRAL
CRGGREIQRQELDENISKVSKERQELASKIKEVQGRPQKTQSIIILESHIICCTLSTSGGLLLESAFRGQ
GGVPFSCVIVDEAGQSCEIETLTPLIHRCNKLILVGDPKQLPPTVISMKAQEYGYDQSMMARFCRLLEEN
VEHNMISRLPILQLTVQYRMHPDICLFPSNYVYNRNLKTNRQTEAIRCSSDWPFQPYLVFDVGDGSERRD
NDSYINVQEIKLVMEIIKLIKDKRKDVSFRNIGIITHYKAQKTMIQKDLDKEFDRKGPAEVDTVDAFQGR
QKDCVIVTCVRANSIQGSIGFLASLQRLNVTITRAKYSLFILGHLRTLMENQHWNQLIQDAQKRGAIIKT
CDKNYRHDAVKILKLKPVLQRSLTHPPTIAPEGSRPQGGLPSSKLDSGFAKTSVAASLYHTPSDSKEITL
TVTSKDPERPPVHDQLQDPRLLKRMGIEVKGGIFLWDPQPSSPQHPGATPPTGEPGFPVVHQDLSHIQQP
AAVVAALSSHKPPVRGEPPAASPEASTCQSKCDDPEEELCHRREARAFSEGEQEKCGSETHHTRRNSRWD
KRTLEQEDSSSKKRKLL
>gi|187233964|gb|ACD01221.1| TP53 [Homo sapiens]
RAMAIYKQSQHMTEVVRRCPTNERCSDSDGLAPPQHLIR
>gi|119395734|ref|NP_000050.2| breast cancer type 2 susceptibility protein [Homo sapiens]
MPIGSKERPTFFEIFKTRCNKADLGPISLNWFEELSSEAPPYNSEPAEESEHKNNNYEPNLFKTPQRKPS
YNQLASTPIIFKEQGLTLPLYQSPVKELDKFKLDLGRNVPNSRHKSLRTVKTKMDQADDVSCPLLNSCLS
ESPVVLQCTHVTPQRDKSVVCGSLFHTPKFVKGRQTPKHISESLGAEVDPDMSWSSSLATPPTLSSTVLI
VRNEEASETVFPHDTTANVKSYFSNHDESLKKNDRFIASVTDSENTNQREAASHGFGKTSGNSFKVNSCK
DHIGKSMPNVLEDEVYETVVDTSEEDSFSLCFSKCRTKNLQKVRTSKTRKKIFHEANADECEKSKNQVKE
KYSFVSEVEPNDTDPLDSNVANQKPFESGSDKISKEVVPSLACEWSQLTLSGLNGAQMEKIPLLHISSCD
QNISEKDLLDTENKRKKDFLTSENSLPRISSLPKSEKPLNEETVVNKRDEEQHLESHTDCILAVKQAISG
TSPVASSFQGIKKSIFRIRESPKETFNASFSGHMTDPNFKKETEASESGLEIHTVCSQKEDSLCPNLIDN
GSWPATTTQNSVALKNAGLISTLKKKTNKFIYAIHDETSYKGKKIPKDQKSELINCSAQFEANAFEAPLT
FANADSGLLHSSVKRSCSQNDSEEPTLSLTSSFGTILRKCSRNETCSNNTVISQDLDYKEAKCNKEKLQL
FITPEADSLSCLQEGQCENDPKSKKVSDIKEEVLAAACHPVQHSKVEYSDTDFQSQKSLLYDHENASTLI
LTPTSKDVLSNLVMISRGKESYKMSDKLKGNNYESDVELTKNIPMEKNQDVCALNENYKNVELLPPEKYM
RVASPSRKVQFNQNTNLRVIQKNQEETTSISKITVNPDSEELFSDNENNFVFQVANERNNLALGNTKELH
ETDLTCVNEPIFKNSTMVLYGDTGDKQATQVSIKKDLVYVLAEENKNSVKQHIKMTLGQDLKSDISLNID
KIPEKNNDYMNKWAGLLGPISNHSFGGSFRTASNKEIKLSEHNIKKSKMFFKDIEEQYPTSLACVEIVNT
LALDNQKKLSKPQSINTVSAHLQSSVVVSDCKNSHITPQMLFSKQDFNSNHNLTPSQKAEITELSTILEE
SGSQFEFTQFRKPSYILQKSTFEVPENQMTILKTTSEECRDADLHVIMNAPSIGQVDSSKQFEGTVEIKR
KFAGLLKNDCNKSASGYLTDENEVGFRGFYSAHGTKLNVSTEALQKAVKLFSDIENISEETSAEVHPISL
SSSKCHDSVVSMFKIENHNDKTVSEKNNKCQLILQNNIEMTTGTFVEEITENYKRNTENEDNKYTAASRN
SHNLEFDGSDSSKNDTVCIHKDETDLLFTDQHNICLKLSGQFMKEGNTQIKEDLSDLTFLEVAKAQEACH
GNTSNKEQLTATKTEQNIKDFETSDTFFQTASGKNISVAKESFNKIVNFFDQKPEELHNFSLNSELHSDI
RKNKMDILSYEETDIVKHKILKESVPVGTGNQLVTFQGQPERDEKIKEPTLLGFHTASGKKVKIAKESLD
KVKNLFDEKEQGTSEITSFSHQWAKTLKYREACKDLELACETIEITAAPKCKEMQNSLNNDKNLVSIETV
VPPKLLSDNLCRQTENLKTSKSIFLKVKVHENVEKETAKSPATCYTNQSPYSVIENSALAFYTSCSRKTS
VSQTSLLEAKKWLREGIFDGQPERINTADYVGNYLYENNSNSTIAENDKNHLSEKQDTYLSNSSMSNSYS
YHSDEVYNDSGYLSKNKLDSGIEPVLKNVEDQKNTSFSKVISNVKDANAYPQTVNEDICVEELVTSSSPC
KNKNAAIKLSISNSNNFEVGPPAFRIASGKIVCVSHETIKKVKDIFTDSFSKVIKENNENKSKICQTKIM
AGCYEALDDSEDILHNSLDNDECSTHSHKVFADIQSEEILQHNQNMSGLEKVSKISPCDVSLETSDICKC
SIGKLHKSVSSANTCGIFSTASGKSVQVSDASLQNARQVFSEIEDSTKQVFSKVLFKSNEHSDQLTREEN
TAIRTPEHLISQKGFSYNVVNSSAFSGFSTASGKQVSILESSLHKVKGVLEEFDLIRTEHSLHYSPTSRQ
NVSKILPRVDKRNPEHCVNSEMEKTCSKEFKLSNNLNVEGGSSENNHSIKVSPYLSQFQQDKQQLVLGTK
VSLVENIHVLGKEQASPKNVKMEIGKTETFSDVPVKTNIEVCSTYSKDSENYFETEAVEIAKAFMEDDEL
TDSKLPSHATHSLFTCPENEEMVLSNSRIGKRRGEPLILVGEPSIKRNLLNEFDRIIENQEKSLKASKST
PDGTIKDRRLFMHHVSLEPITCVPFRTTKERQEIQNPNFTAPGQEFLSKSHLYEHLTLEKSSSNLAVSGH
PFYQVSATRNEKMRHLITTGRPTKVFVPPFKTKSHFHRVEQCVRNINLEENRQKQNIDGHGSDDSKNKIN
DNEIHQFNKNNSNQAAAVTFTKCEEEPLDLITSLQNARDIQDMRIKKKQRQRVFPQPGSLYLAKTSTLPR
ISLKAAVGGQVPSACSHKQLYTYGVSKHCIKINSKNAESFQFHTEDYFGKESLWTGKGIQLADGGWLIPS
NDGKAGKEEFYRALCDTPGVDPKLISRIWVYNHYRWIIWKLAAMECAFPKEFANRCLSPERVLLQLKYRY
DTEIDRSRRSAIKKIMERDDTAAKTLVLCVSDIISLSANISETSSNKTSSADTQKVAIIELTDGWYAVKA
QLDPPLLAVLKNGRLTVGQKIILHGAELVGSPDACTPLEAPESLMLKISANSTRPARWYTKLGFFPDPRP
FPLPLSSLFSDGGNVGCVDVIIQRAYPIQWMEKTSSGLYIFRNEREEEKEAAKYVEAQQKRLEALFTKIQ
EEFEEHEENTTKPYLPSRALTRQQVRALQDGAELYEAVKNAADPAYLEGYFSEEQLRALNNHRQMLNDKK
QAQIQLEIRKAMESAEQKEQGLSRDVTTVWKLRIVSYSKKEKDSVILSIWRPSSDLYSLLTEGKRYRIYH
LATSKSKSKSERANIQLAATKKTQYQQLPVSDEILFQIYQPREPLHFSKFLDPDFQPSCSEVDLIGFVVS
VVKKTGLAPFVYLSDECYNLLAIKFWIDLNEDIIKPHMLIAASNLQWRPESKSGLLTLFAGDFSVFSASP
KEGHFQETFNKMKNTVENIDILCNEAENKLMHILHANDPKWSTPTKDCTSGPYTAQIIPGTGNKLLMSSP
NCEIYYQSPLSLCMAKRKSVSTPVSAQMTSKSCKGEKEIDDQKNCKKRRALDFLSRLPLPPPVSPICTFV
SPAAQKAFQPPRSCGTKYETPIKKKELNSPQMTPFKKFNEISLLESNSIADEELALINTQALLSGSTGEK
QFISVSESTRTAPTSSEDYLRLKRRCTTSLIKEQESSQASTEECEKNKQDTITTKKYI
----refp_db-----

$ formatdb -i refp_db -p T -o T

----tp53.fa-----
>gi|187233964|gb|ACD01221.1| TP53 [Homo sapiens]
RAMAIYKQSQHMTEVVRRCPTNERCSDSDGLAPPQHLIR
----tp53.fa-----
$ blastall -p blastp -i tp53.fa -d refp_db

Fix broken packages

Probably mixed ubuntu distributions, ie jaunty and intrepid

Check
$ sudo vi /etc/apt/sources.list

and remove jaunty

$ sudo apt-get install autoremove
$ sudo apt-get -f install xine-ui vlc mplayer

$ sudo apt-get build-dep mplayer meconder

Quick programming reference

C++
http://www.stumbleupon.com/su/3dhSXA/www.sourcepole.com/sources/programming/cpp/cppqref.html

Perl
http://www.doulos.com/knowhow/perl/quick_start/

Writing scalable applications

http://msdn.microsoft.com/en-us/library/ms810434.aspx

http://code.google.com/appengine/articles/scaling/overview.html
* Minimizing work - Retrieve objects/entities by key, key name, or ID, Paging results, read / write sparsely
* Paging through large datasets
* Avoiding datastore contention
* Sharding counters
* Effective memcache

http://msdn.microsoft.com/en-us/magazine/cc163854.aspx#S4
Performance on the Data Tier
Tip 1—Return Multiple Resultsets
Tip 2—Paged Data Access
Tip 3—Connection Pooling
Tip 4—ASP.NET Cache API
Tip 5—Per-Request Caching
Tip 6—Background Processing
Tip 7—Page Output Caching and Proxy Servers
Tip 8—Run IIS 6.0 (If Only for Kernel Caching)
Tip 9—Use Gzip Compression
Tip 10—Server Control View State
Conclusion

http://www.javaperformancetuning.com/tips/design.shtml#REF2

J2EE Patterns
MVC http://java.sun.com/blueprints/patterns/MVC-detailed.html
http://java.sun.com/blueprints/patterns/catalog.html

Python TimeComplexity

http://wiki.python.org/moin/TimeComplexity

Pokemon Top abilities

http://www.smogon.com/smog/issue4/top_abilities

480 spd-def-a Lopunny normal wk: fighting
525 sa-a Lucario fight/steel wk: fire/fighting/ground
545 sa-sd Togekiss normal/flying wk: rock / elec / ice
480 sa/a Octillery water wk: grass / elec
540 hp-sp.def-sp.a Blissey normal wk: fighting

510 def-a Gliscor ground/flying wk: ice / water
525 a-def Torterra grass/ground
480 spd Froslass ice
525 sp-a Glaceon ice
600 Garchomp dragon/ground wk: dragon/ice, special
500 sp-a Wailord water
425 sp-a chimecho psychic

http://www.youtube.com/watch?v=aFUV6N9ZIys

Battle vs Synthia
http://www.youtube.com/watch?v=S6tBB1ksgLc&feature=fvsr

Types
http://www.smogon.com/dp/types/

HM Slave - Gyrados, Bibarel, Tropius, Skarmory
http://bulbapedia.bulbagarden.net/wiki/Appendix:HM_slave

Where To Find Good Rod In Pokemon Platinum?
U can find the good rod on route 209 east from hearthome city :}

Where do you get a Super Rod on Pokemon Platinum?
Find a fisherman in the Fight Area. You need to have the National Dex. It is at the northeast exit (top left).

POKEMON PLATINUM: HOW TO GET REMORAID?
You need to fish with the Good Rod on Route 212 South, Route 213, Route 222, Route 223, Route 224, Route 230, Pastoria City, Sunyshore City, or at the Pokemon League. It is common and should be easy to catch


Hustle increases the user's Attack stat by 50%, but lowers the Accuracy of the user's Physical moves by 20%. Special moves are unaffected by Hustle.

http://www.smogon.com/dp/pokemon/octillery


Sniper multiplies the base power of an attack 1.5× during a critical hit.

List of Pokemon with Unique Type combinations
http://bulbapedia.bulbagarden.net/wiki/List_of_Pok%C3%A9mon_with_unique_type_combinations

http://bulbapedia.bulbagarden.net/wiki/Happiness

Hidden Power?
However, in Platinum, there is a man in the Veilstone Game Corner Prize Exchange house that will tell the player the type of their Pokémon's Hidden Power. In HeartGold and SoulSilver, he is present in the Celadon Game Corner Prize Exchange house
http://bulbapedia.bulbagarden.net/wiki/Hidden_Power_(move)

Honey (wait 6 1/4 hours)
http://bulbapedia.bulbagarden.net/wiki/Honey
http://wiki.answers.com/Q/What_do_you_have_to_do_when_you_have_put_honey_on_a_tree_on_pokemon_diamond

Soft reset DS
heyy! heres how to SR a DS L + R + Select + Start and .... HOLD!!

Pokemon tips / cheats
http://www.neoseeker.com/Games/cheats/DS/pokemon_platinum.html

Nature

Lonely: +attack, -defense
Brave: +attack, -speed
Adamant: +attack, -special attack
Naughty: +attack, -special defense
Bold: +defense, -attack
Relaxed: +defense, -defense
Impish: +defense, -special attack
Lax: +defense, -special defense
Timid: +speed, -attack
Hasty: +speed, -defense
Jolly: +speed, -special attack
Naive: +speed, -special defense
Modest: +special attack, -attack
Mild: +special attack, -defense
Quiet: +special attack, -speed
Rash: +special attack, -special defense
Calm: +special defense, -attack
Careful: +special defense, -special attack
Gentle: +special defense, -defense
Sassy: +special defense, -speed
Hardy: Neutral
Docile: Neutral
Serious: Neutral
Bashful: Neutral
Quirky: Neutral

obtain trade evolution pokemon
Obtain Trade-Evolution Pokemon
Pokemon such as Graveler, Haunter, Machoke, and Kadabra will only evolve when traded. You can use this trick to evolve these Pokemon using only a single DS.

Requirements:
Must have access to GTS
Must have Wi-Fi


1. Head to the GTS in Jubilife City with the Pokemon you want to evolve.

2. Deposit your Pokemon in the GTS. Request something that is IMPOSSIBLE or very rare to get, to decrease the chances of your Pokemon being traded. If your deposited Pokemon is traded, the trick will fail.

3. Seek for a Pokemon on the GTS and make any trade.

4. Once you finish the trade, withdraw the Pokemon you had deposited in the GTS. If you did the trick right, it should evolve as if it was traded.


You can do this trick with any Pokemon that is evolved through trading.

Japan: It's Not Funny Anymore

http://kotaku.com/5484581/japan-its-not-funny-anymore

First Order Logic - FOL

Propositional logic is declarative but not expressive, hence we have First Order Logic (FOL), also called Predicate Calculus

Has quantifiers, universal ∀x (usually use ->), existential ∃x (usually use ^)

∃x ∀y is not the same as ∀y ∃x

∃x ∀y Loves(x,y)
“There is a person who loves everyone in the world”
∀y ∃x Loves(x,y)
“Everyone in the world is loved by at least one person”

∀x Likes(x,IceCream) = ¬∃x ¬Likes(x,IceCream)

kinship domain:
object are people
Properties include gender and they are related by relations
such as parenthood, brotherhood,marriage
predicates: Male, Female (unary)
Parent,Sibling,Daughter,Son...
Function:Mother Father

{a/Shoot} <- substitution

Rule Based Reasoning

Entailment means that one thing follows from another:
KB ╞ α

Propositional Logic
--------------------

The proposition symbols P1, P2 etc are sentences
conjunction ^
disjunction V
implication (if-then) a -> b ≡ ~a V b // false iff a=T and b=F
biconditional a <-> b ≡ (a->b) ^ (b->a)

logically equivalent α ≡ ß iff α╞ β and β╞α

for:
1. (a ^ b) V (c V d)
2. (a V c V d) ^ (b V c V d)

We say we pick all the operand and operators after "a" except "^ b" since we are distributing over "^" in phrase "^ b" i.e. "a" operates on "V (c V d)" and "b" operates on the same phrase and the final operator is "^" which will go in the middle.

The same is true for the second example you mentioned...

1. (a ^ b) V (c ^ d) // Distribute over "^" in "a^b"
2. (a V (c ^ d)) ^ (b V (c ^ d)) // Expand
3. ((a V c)^(a V d)) ^ ((b V c)^(b V d)) // all operators outside parentheses are of type "^" so good to remove extra ones
4. (a V c) ^ (a V d) ^ (b V c) ^ (b V d)



A sentence is valid if it is true in all models (truth tables where α=True),

Validity is connected to inference via the Deduction Theorem:
KB ╞ α if and only if (KB ⇒ α) is valid

A sentence is satisfiable if it is true in some model

A sentence is unsatisfiable if it is true in no models

Satisfiability is connected to inference via the following:
KB ╞ α if and only if (KB ∧¬α) is unsatisfiable [] (empty clause)

Interpretation: any assignment of true and false to atoms

Rules of inference


Model checking
⌧truth table enumeration (always exponential in n)
⌧improved backtracking, e.g., Davis--Putnam-Logemann-Loveland (DPLL), Backtracking with constraint propagation, backjumping.
⌧heuristic search in model space (sound but incomplete)
e.g., min-conflicts-like hill-climbing algorithms

Resolution is sound

Resolution is NOT complete:
P and R entails P V R but you cannot infer P V R From (P and R) by resolution

Resolution is complete for refutation: adding (¬P) and (¬R) to (P and R) we can infer the empty clause. (proof by contradiction)

CNF = conjunctive normal form eg. (A V B) ^ C

( P ∧ ¬Q ) ∨ ( ¬R ∨ P ) ≡ ( P ∨ ¬ R ∨ P ) ∧ ( ¬ Q ∨ ¬R ∨ P ) ≡ ( P ∨ ¬R ), (¬Q ∨ ¬R ∨ P )

The set of support: those clauses coming from negation of the theorem or their decendents.

Horn clause: Eg C ^ (B -> A) ^ ( C ^ D -> B) NOT! (C V D -> B)

Forward chaining (data driven) - linear time
Idea: fire any rule whose premises are satisfied in the KB,
add its conclusion to the KB, until query is found

Backward chaining (goal driven) - linear time
Idea: work backwards from the query q:
to prove q by BC,
check if q is known already, or
prove by BC all premises of some rule concluding q
Avoid loops: check if new subgoal is already on the goal stack
Avoid repeated work: check if new subgoal
1. has already been proved true, or
2. has already failed


Efficient propositional inference:
- DPLL 1. early termination 2. purse symbol (same sign for everywhere, so either all nots or positives) 3. unit clause - clause with only a literal
- WalkSAT (incomplete), local search using randomness

Ch. 5 - Game Playing - games

Minimax
eg. chess, othello, backgammon (chance node due to Dice, use Expecti-minimax), tic-tac-toe, Grundy's game
- complete, optimal, like DFS: time complexity - O(b^m), space complexity O(bm)
- players take turn, max-min-max-min-max etc.

An Evaluation Function:
- Estimates how good the current board configuration is for a player.
- linear weighted sum of features eg. Eval(s) = w1f1(s) + w2f2(s) + ... + wnfn(s)
f1 = number of white queens - number of black queens

Ply - number of look-ahead levels

Aplha(max of children)-beta(min of children) pruning
- in practice alpha-beta pruning, often get O(b^(d/2)) rather than O(b^d) remember b^(1/2) = sqrt(b)


Expectiminimax - use weights that are linear due to average score, ie. Sum(prob_state*score), Dice has 21 (1+2+3+4+...+6 = n*(n+1)/2 = 6*7/2 http://polysum.tripod.com/) unique dice rolls (6-5 is the same as rolling 5-6)

Nintendo DS Games for the Brain

Brain Age Part 1 and 2 Bundle For Nintendo DS. Train Your Brain!

Professor Layton

Brain Challenge

Flash Focus

Big Brain Academy

Ultimate Brain Games

Brain Challenge

Left Brain Right Brain
---------------------

Others

http://www.mrbass.org/nintendoDS/games/

My Japanese Coach

Lunar Knights

Constraint Satisfaction Problems - CSP

• The constraint network model
– Variables, domains, constraints, constraint graph, solutions
• Examples:
– graph-coloring, 8-queen, cryptarithmetic, crossword puzzles, vision
problems,scheduling, design
• The search space and naive backtracking,
• The constraint graph (nodes=variables, edges=constraints, solution=assignment of value to variable such that constraint is not violated)
• Like DFS, go deep, called backtracking search
- state = assignment of values to variables while constraints are satisfied
- operator = assignment to next variable such that constraints are not violated
- goal = consistent assignment of all variables
- depends on variable ordering, so d={z,x,y} gives different tree when d={x,y,z}

Maybe:
• Consistency enforcing algorithms
– arc-consistency, AC-1,AC-3

Eg. map-coloring
variables - locations / countries / regions
values - colors in domain of red, green, blue
constraints - adjacent countries are colored differently
graph - nodes are variables (locations), edges are constraints so put an edge between two adjacent regions

Eg. 9x9 sudoku
variables - cells / squares that hold numbers, 81 variables for 9x9 problem
values - numbers in domain 1 to 9
constraints (27 constraints) -
a) each row has unique numbers ie all numbers from 1 to 9, 'Not-equal', AllDiff(a11, a12, a13, ..., a19) - 9 constraints
b) each col has unique numbers ie all numbers from 1 to 9, AllDiff(a11, a21, a22, ..., a29) - 9 constraints
c) each 3x3 grid (9 of them) sums contains all numbers from 1 to 9 - AllDiff(a11, a12, a13, a21, a22, a23, a31, a32, a33) 9 constraints

Eg. Four queen
variables - rows (x1, x2, x3, x4)
values - columns {1,2,3,4}
constraint ((4 choose 2) = 6 constraints)
- graph - (x1-x2), (x1-x3), (x1-x4), ..., (x4-x2), (x4-x3) (a box with a cross inside)
- so each variable constrains all other variables

Look-ahead schemes:
• Value ordering/pruning (choose a least restricting value),
• Variable ordering (choose the most constraining variable)
• Constraint propagation (take decision implications forward)

Heuristics:
1. Minimum Remaining Values (MRV) heuristic - choose the variable with the fewest legal values
2. Degree heuristic: MRV tie breaker, choose the variable with the most constraints on remaining variables
3. Given a variable, choose the least constraining value:
– the one that rules out the fewest values in the remaining variables
4. min-conflicts

Forward checking
- Idea: Keep track of remaining legal values for unassigned variables
Terminate search when any variable has no legal values
- constraint propagation

Arc-consistency (AC-3)
Arc ( X i ,X j ) is arc-consistent if for any value of X i there exist a matching (allowed) value of X j
Begin
1. For each a in Di if there is no value b in Dj that matches a then delete a from the Dj.
End.
X → Y is consistent iff
for every value x of X there is some allowed y
- detects error earlier than forward checking

• Time complexity: O(ed3)
• e = # edges, d = variable domain size)

Local search for CSPs - h(n) = min-conflicts
• Variable selection: randomly select any conflicted variable
• Value selection by min-conflicts heuristic:
– choose value that violates the fewest constraints
– i.e., hill-climb with h(n) = total number of violated constraints

WalkSAT - adds random walk to GSAT ( hill climbing )

Simulated Annealing
Theoretically, with a slow enough cooling schedule, this algorithm will find the optimal solution. But so will searching randomly.

Tree structured CSPs (eg by cutset) can be solved in linear time (O(d^C*d^2) c = cut-set size

conditioning - instantiate a variable, prune its neighbours' domain

VLC does not support the audio or video format "undf"

VLC does not support the audio or video format "undf". Unfortunately there is no way for you to fix this.

http://ubuntuforums.org/showthread.php?t=1117283

$ sudo apt-get install ffmpeg libavcodec-unstripped-51

http://ubuntuforums.org/showthread.php?t=1103825

$ sudo apt-get install ffmpeg ubuntu-restricted-extras

Install FFmpeg and x264 on Ubuntu Hardy Heron 8.04 LTS
http://ubuntuforums.org/showpost.php?p=6963607&postcount=360

Compiling VLC
http://wiki.videolan.org/UnixCompile

Wikimapia

http://wikimapia.org

First-order-logic FOL

General rule:
Use ⇒ for ∀
Use ∧ for ∃

Every student loves some student.
∀ x ( Student(x)⇒∃ y ( Student(y)∧ Loves(x,y) ))

There is a student who is loved by every other student.
∃ x ( Student(x)∧∀ y ( Student(y)∧¬(x = y)⇒ Loves(y,x) ))

There is a student who is loved by every other student.
∃ x ( Student(x)∧∀ y ( Student(y)∧¬(x = y)⇒ Loves(y,x) ))

Bill takes either Analysis or Geometry (but not both)
Takes(Bill, Analysis)⇔¬ Takes(Bill, Geometry)

No student loves Bill.
¬∃ x ( Student(x)∧ Loves(x, Bill) )

Bill has at most one sister.
∀ x, y ( SisterOf(x, Bill)∧ SisterOf(y, Bill)⇒ x = y )

Bill has exactly one sister.
∃ x ( SisterOf(x, Bill)∧∀y ( SisterOf(y, Bill)⇒ x = y ))

Bill has at least two sisters.
∃ x, y ( SisterOf(x, Bill)∧ SisterOf(y, Bill)∧¬ (x = y) )

Only one student failed History.
∃ x ( Student(x)∧ Failed(x, History)∧∀y ( Student(y)∧ Failed(y, History)⇒ x = y ))

No student can fool all the other students.
¬∃ x ( Student(x)∧∀ y ( Student(y)∧¬ (x = y)⇒ Fools(x,y) ))

AI references

http://sli.ics.uci.edu/Classes/2009W

http://www.cc.gatech.edu/classes/AY2003/cs4600_fall/

http://www.ics.uci.edu/~smyth/courses/cs271/schedule.html

http://www.cs.sfu.ca/~hkhosrav/personal/310.html

http://www.cs.sfu.ca/~mori/courses/cmpt310/

http://www.cs.sfu.ca/CC/310/pwfong/

http://www.cs.unb.ca/profs/hzhang/CS4725/

http://www.earlham.edu/~peters/courses/log/transtip.htm

http://pages.cs.wisc.edu/~dyer/cs540/

http://www.ics.uci.edu/~smyth/courses/cs271/schedule.html

http://www.ics.uci.edu/~welling/teaching/271fall09/

Abscissic Acid (ABA)

Physiological effects
- promotes seed dormancy
- prevents seed germination

Seed germination is inhibited by ABA in antagonism with Gibberellin.

http://en.wikipedia.org/wiki/Abscisic_acid

Cytokinins (CK)

Cytokinins - promotes controlled cell division

Physiological effects:
- occurs to repair leaf abscission and wounds
- leaf senescence signals expression of CKs, and so leaf senescence is prevented and plant remains green
- promotes growth of shoots (lateral bud growth), some regulation of cell division in shoot apical meristem (SAM) (pg 25)
- suppresses growth of roots
- enhance de-etiolation (greening of plants), thylakoid formation, cotyledon expansion
- limited duration

Forms:
- zeatin (found in coconut milk), some kinetin (amino-purine), biosynthetic ipt gene

Experiment:
- excised root grow indefinitely
- excised shoot grow after adding coconut milk and herring sperm DNA with auxin
- so this means, the hormone CK suppresses root growth and is needed for shoot growth
- Infection by Agrobacterium tumefaciens -> crown gall tumours

Infection by Agrobacterium tumefaciens leads to uncontrolled cell division and tumour formation 'crown gall tumours'
- baterium's cell contain a circular Ti-DNA that contains T-DNA which encodes for cytokinin and auxin, this T-DNA integrates with the host DNA during transformation and starts infecting the wound

Cytokinin oxidase metabolizes cytokinin (irreversible degradation, produces adenine + alcohol)

When CK oxidase is over-expressed
- you get REDUCED SAM (shoot apical meristem)
- and INCREASED ROOT growth

Morphogenesis in cultured plants - AUXIN / CK flux:
- low auxin, high CK => formation of shoots
- high auxin, low CK => formation of roots
- med auxin, med CK => undifferentiated

4. (2 marks) Morphogenesis of cultured plant cells
CK:
- High auxin and low CK promotes root growth
- Low auxin and high CK promotes shoot growth
- Equal concentration of auxin and CK shows no differentiation

5. (1 mark) De-etiolation of seedlings (greening)
CK:
- CK enhances de-etiolation of seedlings
- CK promotes chloroplasts development by converting etioplasts in dark-growing seedlings to thylakoids
- CK promotes cotyledon expansion

6. (5 marks) Induction of alpha-amylase production in cereal aleurone layer cells
GA: In Ca2+ independent pathway
1. GA1 binds to membrane receptor in aluerone cell
2. GA-receptor complex binds to heterotrimeric G protein initiating Ca2+ independent and Ca2+ dependent signaling pathway
3. In Ca2+ independent pathway, the Ca2+ activates the F-protein, the F-protein diffuses to the nucleus
4. the SCF-ubiquitin ligase complex degrades the DELLA repressor
5. GAMYB gene is expressed and it binds to the alpha-amylase hydrolytic enzyme promoter
6. alpha-amylase enzymes are expressed and secreted from the aleurone cell for starch degradation in endosperm

Gibberellins (GA)

http://universe-review.ca/I10-22a-anatomy2.jpg


http://media-1.web.britannica.com/eb-media/97/5597-003-9A3253A5.gif

Gibberellins - promotes internode (between nodes) elongation

Gibberellins Physiological Effects
- promotes internode elongation (creating taller plants vs rosette/dwarf plants - cabbage, dandelions lack GA)
- promotes juvenile to mature stages eg. cone buds in conifers
- sex determination eg. +GA prevent anther development in corn
- promotes seed germination via reserve mobilization (alpha-amylase production) and phytochrome induced transcription of genes
- anatagonistic to ABA (Abscisic acid)
- GA biosynthesis occurs throughout the entire life cycle

Forms:
- GA1 (active form, has COOH at C6 and beta-OH at C3) encodes CPS, the first enzyme in GA biosynthesis
- GA3 commercial form
- mostly inactive form
- GA20 + OH + GA-3beta-hydrolase => GA1

Reserve mobilization by GA
1. GA secreted in embryo
2. GA reaches aleurone layer via scutellum
3. Aleurone layer secretes alpha-amylase (starch hydrolase)
4. Starch is broken down to simple sugars and is transported back to the embryo

Experiment:
- 5'GA1-GUS reporter show sites of GA biosynthesis
- GA1 encodes CPS, the first committed enzyme in GA biosynthesis.

Commercial applications of GA:
- increases stalk length that offsets fruit compaction
- promotes fruit development without pollination (parthenocarpy = virgin/seedless fruit)
- beer, GA promotes malting (start germination by water then quickly halt it by heat then development of color and flavour is produced by kilning) of barley
- sugar - GA increase sugar yield and internode length
- hasten cone production

Commercial applications of GA Inhibitors:
- ornaments - crysanthemums (sprayed with GA inhibitor, reduces internode elongation so they are small)
- prevents lodging (bending of stems to ground) by reducing stem length


GA induces alpha-amylase production via Ca2+ independent pathway.
1. GA binds to receptor
2. GA-receptor binds to G-protein
3. G-protein activates F-box protein
4. F-box protein binds to DELLA-domain repressor (which is degraded by SCF-ubiquitin ligase)
5. GAMYB gene expression is activated
6. GAMYB activates alpha-amylase expression

1. (1 mark) Reproduction in conifers
GA:
Influence the transition from juvenile to
mature stages e.g. induction of cone-
buds in conifers


2. (2 marks) Seed germination
GA:
- Promotes post-germinative mobilization of reserves in cereal
grains

- promotes light-sensitive germination mediated by phytochromes which are induced by transcription of genes encoding GA biosynthetic enzymes

3. (3 marks) Control of root growth
CK:
- CK suppresses root growth so plants that are CK deficient show root growth compared to wild-types
- CK suppresses the size and rate of cell division activity of roots
- bell-shaped curve (X axis = CK signaling, Y axis = Root growth rate):
a) wild-type shows supraoptimal levels of CK
b) Over-expression of CK oxidase decreases CK signaling concentration to optimum level and root growth occurs
c) Further decreasing CK signaling below the optimum amount causes decrease in root growth