Steve Jobs

http://www.wired.com/gadgetlab/2011/10/steve-jobs-through-the-years-2/?pid=2412&viewall=true

“Remembering that I'll be dead soon is the most important tool I've ever encountered to help me make the big choices in life. Almost everything — all external expectations, all pride, all fear of embarrassment or failure — these things just fall away in the face of death, leaving only what is truly important. Remembering that you are going to die is the best way I know to avoid the trap of thinking you have something to lose. You are already naked. There is no reason not to follow your heart.” — Steve Jobs, at a Stanford University commencement ceremony in 2005.

“People don’t know what they want until you show it to them.”

“'I was in the parking lot [after the lecture], with the key in the car,” Jobs said. “I thought to myself, If this is my last night on earth, would I rather spend it at a business meeting or with this woman? I ran across the parking lot, asked her if she'd have dinner with me. She said yes, we walked into town and we've been together ever since.''

SWAN (Semantic Web Applications in Neuromedicine)

Welcome to the SWAN project!

SWAN (Semantic Web Applications in Neuromedicine) is a Web-based collaborative program that aims to organize and annotate scientific knowledge about Alzheimer disease (AD) and other neurodegenerative disorders. Its goal is to facilitate the formation, development and testing of hypotheses about the disease. The ultimate goal of this project is to create tools and resources to manage the evolving universe of data and information about AD in such a way that researchers can easily comprehend their larger context ("what hypothesis does this support or contradict?"), compare and contrast hypotheses ("where do these two hypotheses agree and disagree?"), identify unanswered questions and synthesize concepts and data into ever more comprehensive and useful hypotheses and treatment targets for this disease.

DISCO - DISCOvery

NIF Interoperation Capabilities
What is DISCO?

DISCO is an information integration approach designed to facilitate interoperation among Internet resources. It consists of a set of tools and services that allows resource providers who maintain information to share it with automated systems such as NIF. NIF is then able to “harvest” the information and keep those sets of information up-to-date.
How is this accomplished?

By using a series of files and/or scripts which are then placed in the root directory of the resource developer’s resource. (NIF can also host the files on its servers and crawl for changes there.) Once the files of the resource providers are in place, and DISCO is notified, the DISCO server can then recognize and "consume" the information shared, providing machine understandable information to NIF Integrator Servers (also known as Aggregators) about your resource.

http://www.springerlink.com/content/c613m0l225p072g5/fulltext.html

DOMEO

DOMEO - Document Metadata Organizer

Ciccarese P, Ocana M, Clark, T. DOMEO: a web-based tool for semantic annotation of online documents. Paper at Bio-Ontologies 2011, Vienna, Austria. Accepted

So highlight text in the web (eg. Pubmed article) and hit Annotate. Loads ontology data when annotating as well. Also lets you share annotations.

http://code.google.com/p/domeo/

http://www.slideshare.net/paolociccarese/swan-annotation-framework-text-mining

PLoS Computational Biology Guidelines for Reviewers

Research articles modeling aspects of biological systems should demonstrate both scientific novelty and profound new biological insights. Research articles describing improved or routine methods, models, software, and databases will not be considered by PLoS Computational Biology, and may be more appropriate for PLoS ONE.

To be considered for publication in PLoS Computational Biology, any given manuscript must satisfy the following criteria:

* Originality
* High importance to researchers in computational biology
* Significant biological insight and general interest to life scientists
* Rigorous methodology
* Substantial evidence for its conclusions

Manuscripts also must be well written to ensure clear and effective presentation of the work and key findings.

The best possible review of a Research Article would answer the following questions:

* What are the main claims of the paper and how significant are they? Is this paper important in its discipline?
* Have the authors provided adequate proof for their claims?
* Are these claims novel? If not, please specify papers that weaken the claims of originality of this one.
* Would additional work improve the paper? How much better would the paper be if this work were performed and how difficult would it be to do this work?
* Are the claims properly placed in the context of the previous literature? Have the authors treated the literature fairly?
* Do the data and analyses support the claims? If not, what other evidence is required?
* Are original data deposited in appropriate repositories and accession/version numbers provided for genes, proteins, mutants, diseases, etc.?
* Does the study conform to any relevant guidelines such as CONSORT, MIAME, QUORUM, STROBE, and the Fort Lauderdale agreement?
* Are details of the methodology sufficient to allow the experiments to be reproduced?
* Is any software created by the authors freely available?
* PLoS Computational Biology encourages authors to publish detailed protocols and algorithms as supporting information online. Do any particular methods used in the manuscript warrant such treatment?
* Is the manuscript well organized and written clearly enough to be accessible to non-specialists? Would you recommend the author seek the services of a professional science writer?*
* Have any parts of the paper been published elsewhere? Are there any copyright issues associated with this that conflict with the PLoS license?*
* Does the paper use standardized scientific nomenclature and abbreviations? If not, are these explained at the first usage?


http://www.ploscompbiol.org/static/reviewerGuidelines.action

Oxford Journals
http://www.oxfordjournals.org/our_journals/nar/for_authors/msprep_database.html

p-value, q-value (FDR)

http://www.nonlinear.com/support/progenesis/samespots/faq/pq-values.aspx

For example, if there are 200 spots on a gel and we apply an ANOVA or t-test to each, then we would expect to get 10 false positives by chance alone. This is known as the multiple testing problem.

Another way to look at the difference is that a p-value of 0.05 implies that 5% of all tests will result in false positives. An FDR adjusted p-value (or q-value) of 0.05 implies that 5% of significant tests will result in false positives. The latter is clearly a far smaller quantity.

a p-value of 0.01 implies a 1% chance of false positives

To interpret the q-values, you need to look at the ordered list of q-values. There are 839 spots in this experiment. If we take spot 52 as an example, we see that it has a p-value of 0.01 and a q-value of 0.0141. Recall that a p-value of 0.01 implies a 1% chance of false positives, and so with 839 spots, we expect between 8 or 9 false positives, on average, i.e. 839*0.01 = 8.39. In this experiment, there are 52 spots with a value of 0.01 or less, and so 8 or 9 of these will be false positives. On the other hand, the q-value is a little greater at 0.0141, which means we should expect 1.41% of all the spots with q-value less than this to be false positives. This is a much better situation. We know that 52 spots have a q-value less than 0.0141 and so we should expect 52*0.0141 = 0.7332 false positives, i.e. less than one false positive. Just to reiterate, false positives according to p-values take all 839 values into account when determining how many false positives we should expect to see while q-values take into account only those tests with q-values less the threshold we choose.

Olver Sacks

http://www.nytimes.com/2010/11/14/books/review/APaul-t.html?pagewanted=all

The Mind's Eye
The Island of the Color­blind,”
“An Anthropologist on Mars,”
“The Man Who Mistook His Wife for a Hat”

Middle school science

http://www.middleschoolscience.com/life.htm

http://www.ontariosciencecentre.ca/school/curriculum/chart.asp

http://www.middleschoolscience.com/life.htm

Outdoor relay games

http://ubclts.com/files/Outdoor_Games_and_Relays_pt1.pdf

http://ubclts.com/files/Outdoor_Games_and_Relays_pt2.pdf

Pull up

http://nerdfitness.com/blog/2011/04/25/do-a-pull-up/

Inverted Rows
http://nerdfitness.com/blog/2009/11/06/inverted-row-are-you-missing-out-on-this-great-exercise/
http://www.youtube.com/watch?v=OYUxXMGVuuU

Back exercises
http://www.youtube.com/results?search_query=back+exercises+at+home&aq=1&oq=back+exerci

Common Latex mistakes

Using underscores eg. hello_world should be hello\_world
cutoff > 0.8 to $cutoff > 0.8$

Working with tables:
\usepackage{longtable}


\begin{center}
\begin{longtable}{|c|p{3cm}|p{6cm}|c|}
\caption{
\bf{my table title}} \\
%table information
\hline
1 & 2 & 3 & 4 \\ \hline
a & b & c & d \\ \hline
\end{longtable}
\begin{flushleft} my table caption
\end{flushleft}
\label{tab:label}
\end{center}

7 ways to improve your conversation skills

http://www.lifeoptimizer.org/2011/04/01/improve-conversation-skills/

1. Talk slowly
2. Hold more eye contact
3. Notice the details
4. Give unique compliments
5. Express your emotions
6. Offer interesting insights
7. Use the best words

Hemoglobin

Hemoglobin is also found outside red blood cells and their progenitor lines. Other cells that contain hemoglobin include the A9 dopaminergic neurons in the substantia nigra, macrophages, alveolar cells, and mesangial cells in the kidney. In these tissues, hemoglobin has a non-oxygen-carrying function as an antioxidant and a regulator of iron metabolism.[6]

Hemoglobin variants are a part of the normal embryonic and fetal development,

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