Always 2011 오직 그대만 (O-jik Geu-dae-man)

http://www.imdb.com/title/tt2082180/

A love story centered on ex-boxer Chul-min and a blind telemarketer Jung-hwa.

Ji-Sub So ... Cheol-min
Hyo-ju Han ... Jeong-hwa

http://www.hancinema.net/korean_movie_Always.php

The brave man

"The brave man is not he who does not feel afraid, but he who conquers that fear."
--Nelson Mandela

Ten Simple Rules for Starting a Company

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002439

Rule 1: A Great Product or Service Alone Is Not Enough to Make a Successful Business
Rule 2: Business Is Part Art, Part Science
Rule 3: Define to Yourself and Others Why You Are Starting a Company and Be Prepared for Those Reasons to Change
Rule 4: Decide What of Yourself You Are Willing to Put into the Company
Rule 5: Get Professional Business Help Early
Rule 6: Understand the Legal, Ethical, and Regulatory Environment Thoroughly
Rule 7: Establish a Relationship with Your Academic Institution at the Outset
Rule 8: Realistically Define the Value of Your Business
Rule 9: Think about Conflict of Interest Every Day
Rule 10: Decide Responsibilities and Equity Share before You Start

How to make an origami birthday pop-up card.

http://www.youtube.com/watch?v=QxsJ5M_uQKI

Divisions of the Brain

http://faculty.washington.edu/chudler/phylo.html

The brain can be separated into phylogenetic (through evolution) and embryological (through development) divisions. Below are two tables that show how the brain can be divided - do not get caught up in the terminology - these are just names for specific areas of the brain. "Divisions of the Nervous System" discusses the functions of many of these areas.

Telencephalon (red), Diencephalon (blue), Mesencephalon / Midbrain (grey), Metencephalon (green), Myelencephalon (yellow)

http://senselab.med.yale.edu/neurondb/ndbRegions.aspx?sr=1 

Subdivision	General Region	Specific Region	Neurons
Forebrain	Archicortex	Dentate	Dentate granule cell
		Hippocampus	CA1 pyramidal neuron
			CA1 oriens alveus interneuron
			CA3 pyramidal neuron
	Basal Ganglia	Neostriatum	Neostriatal cholinergic interneuron
			Neostriatal spiny neuron
		Substantia Nigra	Nigral dopaminergic cell
	Diencephalon	Retina	Retinal photoreceptor
			Retinal ganglion cell
			Retinal bipolar cell
		Thalamus	Thalamic relay neuron
			Thalamic reticular neuron
	Neocortex	Visual & Motor	Neocortical pyramidal neuron: deep
			Neocortical pyramidal neuron: superficial
			Neocortical basket cell
	Olfactory Epithelium	Olfactory Epithelium	Olfactory receptor neuron
	Olfactory Bulb	Olfactory Bulb	Olfactory bulb mitral cell
			Olfactory bulb granule cell
			Olfactory bulb periglomerular cell
	Paleocortex	Olfactory Cortex	Olfactory cortex pyramidal neuron
			Olfactory cortex interneuron: superficial
			Olfactory cortex interneuron: deep
Mesencephalon
Metencephalon	Cerebellum	Cerebellum	Cerebellar granule cell
			Cerebellar purkinje cell
	Inner Ear	Cochlea	Hair cell (auditory)
		Vestibular Organ	Hair cell (vestibular)
	Cochlear Nucleus	Anterior Ventral CN	CN bushy cell
		Posterior Ventral CN	CN octopus cell
		Dorsal CN	CN pyramidal (fusiform) cell
Myelencephalon
Spinal Cord	Segment	Ventral Horn	Spinal motor neuron
			Spinal Ia interneuron

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

Primary division of theneural tube	Secondary subdivision	Final segments in a human adult
Prosencephalon	Telencephalon Diencephalon	The human cortex (both hemispheres), Caudate, Putamen, Globus pallidus (pallidum) Thalamus, hypothalamus, subthalamus, epithalamus, subthalamic nuclei
Mesencephalon	Mesencephalon	Mesencephalon (Midbrain), Substantia nigra pars compacta (SNc),Substantia nigra pars reticulata (SNr)
Rhombencephalon	Metencephalon Myelencephalon	Pons and cerebellum Medulla

MARQ: an online tool to mine GEO for experiments with similar or opposite gene expression signatures.

Vazquez, M., Nogales-Cadenas, R., Arroyo, J., Botías, P., García, R., Carazo, J. M., Tirado, F., Pascual-Montano, A. Carmona-Saez, P. 2010, `MARQ: an online tool to mine GEO for experiments with similar or opposite gene expression signatures.' , Nucleic acids research (Web Server issue). doi:10.1093/nar/gkq476. [pubmed]

The enormous amount of data available in public gene expression repositories such as Gene Expression Omnibus (GEO) offers an inestimable resource to explore gene expression programs across several organisms and conditions. This information can be used to discover experiments that induce similar or opposite gene expression patterns to a given query, which in turn may lead to the discovery of new relationships among diseases, drugs or pathways, as well as the generation of new hypotheses. In this work, we present MARQ, a web-based application that allows researchers to compare a query set of genes, e.g. a set of over- and under-expressed genes, against a signature database built from GEO datasets for different organisms and platforms. MARQ offers an easy-to-use and integrated environment to mine GEO, in order to identify conditions that induce similar or opposite gene expression patterns to a given experimental condition. MARQ also includes additional functionalities for the exploration of the results, including a meta-analysis pipeline to find genes that are differentially expressed across different experiments. The application is freely available at http://marq.dacya.ucm.es.

GEM-TREND - Gene Expression Data Mining Toward Relevant Network Discovery

http://cgs.pharm.kyoto-u.ac.jp/services/network/

DNA microarray technology provides us with a first step toward the goal of uncovering gene functions on a genomic scale. In the last few years, vast amounts of gene expression data were collected, and much of these data are available in public databases, such as the Gene Expression Omnibus (GEO). To date, most researchers have been manually retrieving data from databases through web browsers using accession numbers (IDs) or keywords, but gene expression patterns are not considered when retrieving data from such databases. The data retrieved using keywords or IDs is usually limited by experimental conditions such as microarray platform, reagent, and cell type.

GEM-TREND (Gene Expression Data Mining Toward Relevant Network Discovery) was developed to retrieve gene expression data from GEO by comparing gene-expression signature of queries with those of GEO gene expression data and to provide network visualization. The comparison methods are based on the nonparametric, rank-based pattern matching approach of Lamb et al. (Science 2006) with the additional calculation of statistical significance. Retrieved gene expression data can then be viewed as a co-expression network with gene ontology (GO) annotation where genes and annotations are dynamically linked to external data repositories.

GEM-TREND provides a new way of data retrieval that is not using keywords (e.g., gene annotation, pre-computed profile characteristics) or IDs, and results are not restricted by experimental conditions. It is expected to support knowledge discovery.

Amazon Mechanical Turk (MTurk)

The Amazon Mechanical Turk (MTurk) is a crowdsourcing Internet marketplace that enables computer programmers (known as Requesters) to co-ordinate the use of human intelligence to perform tasks that computers are unable to do yet. It is one of the suites of Amazon Web Services. The Requesters are able to post tasks known as HITs (Human Intelligence Tasks), such as choosing the best among several photographs of a store-front, writing product descriptions, or identifying performers on music CDs. Workers (called Providers in Mechanical Turk's Terms of Service) can then browse among existing tasks and complete them for a monetary payment set by the Requester. To place HITs, the requesting programs use an open Application Programming Interface, or the more limited MTurk Requester site.^[1] Requestors are restricted to US-based entities. ^[2]

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

Apache Jena - Welcome to Jena - Java framework for building Semantic Web applications

incubator.apache.org/jena/


Welcome to the Apache Jena project! Jena is a Java framework for building Semantic Web applications. Jena provides a collection of tools and Java libraries to help you to develop semantic web and linked-data apps, tools and servers.
The Jena Framework includes:

• an API for reading, processing and writing RDF data in XML, N-triples and Turtle formats;
• an ontology API for handling OWL and RDFS ontologies;
• a rule-based inference engine for reasoning with RDF and OWL data sources;
• stores to allow large numbers of RDF triples to be efficiently stored on disk;
• a query engine compliant with the latest SPARQL specification
• servers to allow RDF data to be published to other applications using a variety of protocols, including SPARQL

Gwap - make games with a purpose

http://www.gwap.com/gwap/about/

When you play a game at Gwap, you aren't just having fun. You're helping the world become a better place. By playing our games, you're training computers to solve problems for humans all over the world.

http://sulab.org/gsoc/#idea12

The bravest

"The bravest are surely those who have the clearest vision of what is before them, glory and danger alike, and yet notwithstanding, go out and meet it."
--Thucydides

I see your beauty

I offer you peace. I offer you love. I offer you friendship. I see your beauty. I hear your need. I feel your feelings. My wisdom flows from the Highest Source. I salute that Source in you. Let us work together for unity and love. ~ Mahatma Gandhi

Proteosome inhibitor - bortezomib - a multiple myeloma drug

http://www.nature.com/nature/journal/v480/n7377_supp/full/480S40a.html

But two classes of drug that became available in the past decade — proteasome inhibitors and immunomodulators — have been far more effective than any previous drugs against this form of cancer. Just three of these new drugs have so far been licensed — a proteasome inhibitor called bortezomib and two immunomodulatory drugs, thalidomide and its cousin lenalidomide — with additional drugs currently in clinical trials.

In multiple myeloma, plasma cells reproduce furiously, creating piles of damaged proteins that must be cleared from the cell. Protein complexes called proteasomes normally remove them, aided by enzymes that slice up the amino-acid chains. But in myeloma cells, this proteasome, known as 26S, can barely keep up with demand, suggesting a weakness that could be exploited. In 1993, Alfred Goldberg, a cell biologist at Harvard Medical School in Boston, Massachusetts, created the first proteasome inhibitor, MG132. It worked by interfering with protein clearing. Myeloma cells, which are already awash with damaged proteins, proved particularly vulnerable to MG132 and would suffocate in their own waste protein.

http://www.ncbi.nlm.nih.gov/pubmed/19114008

BMC Bioinformatics. 2008 Dec 29;9:559.

WGCNA: an R package for weighted correlation network analysis.

Langfelder P, Horvath S.

Source

Department of Human Genetics and Department of Biostatistics, University of California, Los Angeles, CA 90095, USA. Peter.Langfelder@gmail.com

Abstract

BACKGROUND:

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial.

RESULTS:

The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings.

CONCLUSION:

The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

prodrome

In medicine, a prodrome is an early symptom (or set of symptoms) that might indicate the start of a disease before specific symptoms occur. It is derived from the Greek word prodromos or precursor.

Translational bioinformatics

Russ Altman
http://rbaltman.wordpress.com/2012/03/22/translational-bioinformatics-2012-year-in-review/

http://www.amia.org/jointsummits2012/tbi-call-for-participations

The American Medical Informatics Association (AMIA) is pleased to invite submissions for the 2012 Summit on Translational Bioinformatics (TBI), which will be held on March 19th-21st 2012 at the Parc 55 Hotel San Francisco, CA. The Summit will be part of the Joint Summits on Translational Science and will be immediately followed by the Summit on Clinical Research Informatics (CRI) at the same venue on March 21st – 23rd.


translational bioinformatics annual review 2012 slides
https://files.me.com/russbaltman/1kmqvt

Crystallography tools

CCP4 exists to produce and support a world-leading, integrated suite of programs that allows researchers to determine macromolecular structures by X-ray crystallography, and other biophysical techniques. CCP4 aims to develop and support the development of cutting edge approaches to experimental determination and analysis of protein structure, and integrate these approaches into the suite. CCP4 is a community based resource that supports the widest possible researcher community, embracing academic, not for profit, and for profit research. CCP4 aims to play a key role in the education and training of scientists in experimental structural biology. It encourages the wide dissemination of new ideas, techniques and practice.


http://www.ccp4.ac.uk/

Crystallographic Object-Oriented Toolkit


 Coot is for macromolecular model building, model completion and validation, particularly suitable for protein modelling using X-ray data.
Coot displays maps and models and allows model manipulations such as idealization, real space refinement, manual rotation/translation, rigid-body fitting, ligand search, solvation, mutations, rotamers, Ramachandran plots, skeletonization, non-crystallographic symmetry and more.

http://www.biop.ox.ac.uk/coot/

Happiness

"Happiness does not depend on outward things, but on the way we see them."
--Leo Tolstoy

Age of Conquerors Out of Sync error when playing recorded games

Run the game from the Program Files (x86) directory

C:\Program Files (x86)\Microsoft Games\Age of Empires II\Age2_x1

and not

C:\Program Files\Microsoft Games\Age of Empires II\Age2_x1

Get recorded games from

http://www.aoczone.net/viewtopic.php?f=199&t=70124


or due to wrong AOC version ... patch 1.0c?

Tip: You can check the version of the game by clicking on the banner / flag at the top center of the screen in the game's main menu

RNA editing study under intense scrutiny

http://www.nature.com/news/rna-editing-study-under-intense-scrutiny-1.10217?WT.ec_id=NEWS-20120320

Today, the three groups estimate that up to 94% of the putative RNA-editing sites identified in Cheung’s paper are wrong. The groups, which worked independently, say that multiple sources of error contributed to the original paper’s overestimate of ‘RNA–DNA differences’ (RDDs). Other researchers had previously criticized Cheung's findings⁵ (see 'Estimates of errors').

Study Estimated rate of false positives Possible sources of error Lin, et al.¹ 89% mapping errors, genetic variation, gene duplications Pickrell, et al.² 88-94% sequencing errors, mapping errors, genetic variation Kleinman & Majewski³ 68-90% sequencing errors, mapping errors, genotyping errors Schrider, et al.⁵ >90% gene duplications

All three groups say that the work is riddled with errors that arose during the process of sequencing RNA fragments and mapping the reads back to their corresponding stretches of DNA. Cheung's group sequenced RNA using machines, sold by Illumina of San Diego, California, that read the sequences of short fragments of genetic material.

“These sequencing techniques are great, but you’ve really got to get to know and understand the biases and potential errors that accompany them,” says Joseph Pickrell, an evolutionary genomicist at Harvard Medical School in Boston, Mass., who co-authored the comment with Pritchard.

Pan-Pacific Personnel

Pan-Pacific Personnel Icn.
www.panpacificpersonnel.com/

Placement of bilingual individuals

A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments

http://bioinformatics.oxfordjournals.org/content/24/3/374.full

Motivation: The proliferation of public data repositories creates a need for meta-analysis methods to efficiently evaluate, integrate and validate related datasets produced by independent groups. A t-based approach has been proposed to integrate effect size from multiple studies by modeling both intra- and between-study variation. Recently, a non-parametric ‘rank product’ method, which is derived based on biological reasoning of fold-change criteria, has been applied to directly combine multiple datasets into one meta study. Fisher's Inverse χ² method, which only depends on P-values from individual analyses of each dataset, has been used in a couple of medical studies. While these methods address the question from different angles, it is not clear how they compare with each other.

Results: We comparatively evaluate the three methods; t-based hierarchical modeling, rank products and Fisher's Inverse χ² test with P-values from either the t-based or the rank product method. A simulation study shows that the rank product method, in general, has higher sensitivity and selectivity than the t-based method in both individual and meta-analysis, especially in the setting of small sample size and/or large between-study variation. Not surprisingly, Fisher's χ² method highly depends on the method used in the individual analysis. Application to real datasets demonstrates that meta-analysis achieves more reliable identification than an individual analysis, and rank products are more robust in gene ranking, which leads to a much higher reproducibility among independent studies. Though t-based meta-analysis greatly improves over the individual analysis, it suffers from a potentially large amount of false positives when P-values serve as threshold. We conclude that careful meta-analysis is a powerful tool for integrating multiple array studies.

Contact: fxhong@jimmy.harvard.edu

Supplementary information: Supplementary data are available at Bioinformatics online.                   

Drug research: Plug the real brain drain

Why have so many trials failed, and what should be done better? A drug may be effective and still fail in a trial. One reason is that companies often look for the most broadly applicable drug — for example, 'for all stroke patients' — but disease conditions often differ among patients, resulting in huge variations in treatment responses. Another problem with past trials was that the often crude clinical endpoints missed small but meaningful treatment effects, such as improvements in hand, leg or bladder function. With novel approaches, we can do better.

Neuroscience faculties and medical centres must work together to establish research consortia and networks that unite basic and clinical scientists. On a smaller scale, retreats with select groups of experts from both sides are inexpensive and can jump-start a field. Already, studies of spinal-cord injury are more focused now that the two sides are communicating — some basic researchers have begun using clinical criteria for functional improvement.

In 2011, a report commissioned by the European Brain Council found that, in terms of health-care costs and lost productivity, brain disorders are a greater socio-economic burden than cancer, cardiovascular diseases and diabetes combined¹. Yet in 2005, research funding for cancer and neurological diseases was roughly equal (see 'Costs and research funding in Europe'). More than half of that total comprised private funding; now that drug companies have shifted focus, cancer funding is likely to eclipse that of neuroscience.

http://www.nature.com/nature/journal/v483/n7389/full/483267a.html

corpus callosotomy

In June 1979, in a procedure that lasted nearly 10 hours, doctors created a firebreak to contain Vicki's seizures by slicing through her corpus callosum, the bundle of neuronal fibres connecting the two sides of her brain. This drastic procedure, called a corpus callosotomy, disconnects the two sides of the neocortex, the home of language, conscious thought and movement control. Vicki's supermarket predicament was the consequence of a brain that behaved in some ways as if it were two separate minds.

In one crucial way, however, Vicki was better than her pre-surgery self. She was no longer racked by epileptic seizures that were so severe they had made her life close to unbearable.

The idea of dichotomous consciousness captivated the public, and was greatly exaggerated in the notion of the 'creative right brain'. But further testing with split-brain patients gave a more-nuanced picture. The brain isn't like a computer, with specific sections of hardware charged with specific tasks. It's more like a network of computers connected by very big, busy broadband cables. The connectivity between active brain regions is turning out to be just as important, if not more so, than the operation of the distinct parts.

Severing the corpus callosum was first used as a treatment for severe epilepsy in the 1940s, on a group of 26 people in Rochester, New York. The aim was to limit the electrical storm of the seizure to one side of the brain. At first, it didn't seem to work. But in 1962, one patient showed significant improvement. Although the procedure never became a favoured treatment strategy — it's invasive, risky, and drugs can ease symptoms in many people — in the decades since it nevertheless became a technique of last resort for treating intractable epilepsy.

http://www.nature.com/news/the-split-brain-a-tale-of-two-halves-1.10213

Odds ratio

 In genetic case-control association studies the odds ratio (OR) typically represents the ratio of the odds of disease if allele A is carried compared to if allele B is carried. If all else is equal, genetic loci with a higher OR are more informative for disease prediction

http://www.genomesunzipped.org/2010/09/getting-even-with-the-odds-ratio.php

Cooking books

Tropp's Modern Art
Pleasures of the Vietnamese Table
The Thousand Recipe Chinese Cookbook

genome informatics

http://www.google-melange.com/gsoc/org/google/gsoc2012/genomeinformatics

The Genome Informatics group is organizing the joint efforts of Galaxy, GBrowse, GMOD, JBrowse (fast genome browser implemented almost entirely in JavaScript), Reactome, Wormbase, and ProtEco (unifies web access to information and tools about the biology of E. coli, its bacteriophages, plasmids, and mobile genetic elements).

We would like to know who you are and how you think. Incorporate the following into your application:

§  Your information

§  Name, email, and website (optional)

§  Brief background: education and relevant work experience

§  Your programming interests and strengths

§  What are your languages of choice?

§  Any prior experience with open source development?

§  Your interest and background in biology or bioinformatics

§  Any prior exposure to biology or bioinformatics?

§  Your ideas for a project (an original idea or one expanded from our Ideas Page)

§  Provide as much detail as possible

§  Strong applicants include an implementation plan and timeline (hint!)

§  Refer to and link to other projects or products that illustrate your ideas

§  Identify possible hurdles and questions that will require more research/planning

§  What can you bring to the team?

http://www.google-melange.com/gsoc/org/google/gsoc2012/obf

The OBF is a nonprofit volunteer run organization focused on supporting open source programming in bioinformatics. It acts as an umbrella organization for the BioPerl, BioPython, BioJava, BioRuby, BioSQL, and BioLib projects, and organizes conferences and workshops to promote and support open-source bioinformatics.

Intro to Biostatistics

http://cran.r-project.org/doc/contrib/Krijnen-IntroBioInfStatistics.pdf


Applied Statistics for Bioinformatics using R
Wim P. Krijnen
November 10, 2009

Statistical hypothesis testing consists of hypotheses, distributional assump-
tions, and decisions (conclusions). The hypotheses pertain to the outcome
of a biological experiment and are always formulated in terms of population
values of parameters. Statistically, the outcomes of experiments are seen as
realizations of random variables. The latter are assumed to have a certain
suitable distribution which is seen as a statistical model for outcomes of an
experiment. Then a statistic is formulated (e.g. a t-value) which is treated
both as a function of the random variables and as a function of the data
values. By comparing the distribution of the statistic with the value of the
statistic, the p-value is computed and compared to the level of significance.
A large p-value indicates that the model fits the data well and that the as-
sumptions as well as the null-hypothesis are correct with large probability.
However, a low p-value indicates, under the validity of the distributional as-
sumptions, that the outcome of the experiment is so unlikely that this causes
a sufficient amount of doubt to the researcher to reject the null hypothesis.

> dat <- matrix(c(5,5,5,5),2,byrow=TRUE)
> chisq.test(dat)

Pearson’s Chi-squared test with Yates’ continuity correction

data: dat

X-squared = 0.2, df = 1, p-value = 0.6547

Since the p-value is larger than the significance level, the null hypothesis of
independence is not rejected.

Suppose that for another cutoff value we obtain 8 true positives (tp), 2

false positives (fp), 8 true negatives (tn), and 2 false negatives (fn). Then

testing independence yields the following.

> dat <- matrix(c(8,2,2,8),2,byrow=TRUE)

> chisq.test(dat)

Pearson’s Chi-squared test with Yates’ continuity correction

data: dat

X-squared = 5, df = 1, p-value = 0.02535

Since the p-value is smaller than the significance level, the null hypothesis of

independence is rejected.

Example 2. In the year 1866 Mendel observed in large number of exper-

iments frequencies of characteristics of different kinds of seed and their off-

spring. In particular, this yielded the frequencies 5474, 1850 the seed shape

of ornamental sweet peas. A crossing of B and b yields off spring BB, Bb and

bb with probability 0.25, 0.50, 0.25. Since Mendel could not distinguish Bb

from BB, his observations theoretically occur with probability 0.75 (BB and

Bb) and 0.25 (bb). To test the null hypothesis H0 : (π1 , π2 ) = (0.75, 0.25)

against H1 : (π1 , π2 ) = (0.75, 0.25), we use the chi-squared test6 , as follows.

> pi <- c(0.75,0.25)

> x <-c(5474, 1850)

> chisq.test(x, p=pi)

Chi-squared test for given probabilities

data: x

X-squared = 0.2629, df = 1, p-value = 0.6081

From the p-value 0.6081, we do not reject the null hypothesis.

The null-hypothesis of the Fisher test is that the odds ratio equals 1 and
the alternative hypothesis that it differs from 1. Suppose that the frequencies

of significant oncogenes for Chromosome 1 equals f11 = 300 out of a total of

f12 = 500 and for the genome f21 = 3000 out of f22 = 6000. The hypothesis

that the odd ratio equals one can now be tested as follows.

> dat <- matrix(c(300,500,3000,6000),2,byrow=TRUE)

> fisher.test(dat)

Fisher’s Exact Test for Count Data

data: dat

p-value = 0.01912

alternative hypothesis: true odds ratio is not equal to 1

95 percent confidence interval:

1.029519 1.396922

sample estimates:

odds ratio

1.199960

Since the p-value is smaller than the significance level, the null hypothesis

of odds ratio equal to one is rejected. There are more significant oncogenes

in Chromosome 1 compared to that in the genome. 

Puffiness Under Eyes

http://www.eyecare123.com/puffiness_under_eyes.htm

Rinse your face in cold water when you wake up or when puffy eyes attack anytime of the day to constrict blood vessels and reduce swelling.

Avoid foods high in saturated fat and consume more fibre in your diet. Regularity helps move toxins out of the body, which helps control puffiness.

Get enough sleep. If you don't, the skin surrounding your eyes is guaranteed to swell. If you're out late Saturday night, log in a few extra hours on Sunday morning. You must get 8 hours sleep daily.

Sleep on your back with your head elevated by two pillows. This allows fluid to drain overnight instead of collecting under your eyes, causing puffiness. 

Kiss

"I would rather have had one breath of her hair, one kiss from her mouth, one touch of her hand, than eternity without it. One."
--City of Angels

Google doodle - Akira Yoshizawa's 101st birthday

https://www.google.ca/webhp?hl=en

"We're celebrating one of the all-time great origami artists - Akira Yoshizawa - with a logo folded by Robert Lang!" tweeted Marissa Mayer, vice president of location and local services at Google.

org-mode

http://orgmode.org/
- an Emacs Mode for Notes, Planning, and Authoring

Creativity

“If you’re not prepared to be wrong, you’ll never come up with anything original.” — Ken Robinson

Ken Robinson says schools kill creativity

Cortical layers

http://www.rikenresearch.riken.jp/eng/frontline/5900

Layer 1 is the outermost layer

Neuroanatomy 101

http://resenv.media.mit.edu/classes/MAS965/week04/Neuroanatomy101.ppt

To infinity

"Finite to fail, but infinite to venture."
--Emily Dickinson

What employers are really looking for: The top in-demand skills and degrees

http://www.workopolis.com/content/advice/article/1991-what-employers-are-really-looking-for-the-top-in-demand-skills-and-degrees

Peter Harris

• Ability to work in a team
• Leadership
• Communication skills (written)
• Problem-solving skill
• Strong work ethic

• Ability to work in a team structure
• Ability to verbally communicate with persons inside and outside the organization
• Ability to make decisions and solve problems
• Ability to obtain and process information
• Ability to plan, organize and prioritize work
• Ability to analyze quantitative data
• Technical knowledge related to the job
• Proficiency with computer software program
• Ability to create and/or edit written reports
• Ability to sell or influence others

Genome Reference Consortium GRCm38, UCSC version mm10

We are pleased to announce the release of the latest Genome Browser for the December 2011 Mouse genome assembly. The Mus musculus genome assembly (Genome Reference Consortium GRCm38, UCSC version mm10) was produced by the Mouse Genome Reference Consortium.

GRCm38 includes approximately 2.6 Gb of sequence and is considered to be "essentially complete". The assembly includes chromosomes 1-19, X, Y, M (mitochondrial DNA) and chr*_random (unlocalized) and chrUn_* (unplaced clone contigs). For information about the process used to assemble this version, please see the GRC website.

Bulk downloads of the sequence and annotation data are available via the Genome Browser FTP server or Downloads page.

The Mouse browser annotation tracks were generated by UCSC and collaborators worldwide. See the Credits page for a detailed list of the organizations and individuals who contributed to the success of this release.

Poster Genius

http://www.postergenius.com/cms/index.php

Create your best scientific postereasily, in less than 10 minuteswith PosterGenius™

Real Friend

"A real friend is one who walks in when the rest of the world walks out."
--Walter Winchell

Neurons and support cells

http://www.siumed.edu/~dking2/ssb/neuron.htm

Although glial cells vastly outnumber nerve cells (approx. 10:1 glia to neurons), nerve cells are so large, including the total volume of all their dendrites and axons, that most of the cellular volume of the brain consists of nerve cells.

http://www2.jogjabelajar.org/modul/how/b/brain/brain.htm

Glia

http://pubpages.unh.edu/~jel/images/brain_glia.jpg

Firefox crash - GLXtest process failed

Notes: GLXtest process failed (exited with status 1): GLX version older than the required 1.3

Update Firebug?
$ sudo apt-get install firebug

Old Firefox releases

http://releases.mozilla.org/pub/mozilla.org/firefox/releases/

Zymeworks and Merck

http://www.xconomy.com/seattle/2011/08/29/zymeworks-snags-187m-deal-with-merck-to-discover-two-pronged-antibodies/

Zymeworks is announcing today it has secured a partnership with Whitehouse Station, NJ-based Merck (NYSE: MRK) to develop new antibody drugs for cancer and autoimmune diseases that are engineered to hit two or more targets on cells instead of just one. In exchange for helping Merck create these so-called “bispecific” antibodies, Zymeworks is getting an undisclosed cash fee upfront, plus milestone payments, which could be worth as much as $187 million over time if drugs from the partnership reach certain goals. Merck will have exclusive worldwide rights to sell drugs from the partnership and Zymeworks will get tiered royalties on product sales if any materialize.

Stand like a rock

"In matters of style, swim with the current; in matters of principle, stand like a rock."
--Thomas Jefferson

Age of Conquerors - Combat Tips

http://www.youtube.com/watch?v=CoHlZAbCc8c

http://www.cheatchannel.com/files/age2thec.htm

http://www.youtube.com/watch?v=at1EJnbOECI

http://www.youtube.com/watch?v=jJnvjjb9UVs


http://www.youtube.com/watch?v=a5ozesE4tV0&feature=related


Boom
http://www.youtube.com/watch?v=U-uQdNYCdAA&feature=watch-vrec

AOC Counters

http://aoknewskool.forumakers.com/t5-counter-table-units

Counter Table Units

 by rated_R_ on Wed Jul 28, 2010 10:48 am

Countertable

*Note: ( ) means: works, but not really considered a counter.

If your are attacked by... counter with...
Barracks units and their counters:
Champion line Archers, scorpions, cavalry archers, bombard towers, hand cannoneers, Saracen mamelukes, Aztect jaguar warriors, Byzantine cataphracts, (paladins)

Halberdier line Champions, archers, cavalry archers, scorpions, bombard towers, keep line, hand cannoneers, Saracen mamelukes, Aztect jaguar warriors, Byzantine cataphracts

Eagle warriors Champions, bombard towers, hand cannoneers, Saracen mamelukes, Aztec jaguar warriors, Byzantine cataphracts, (Goth huskarls, paladins)

Archery range units and their counters:
Archers Skirmishers, siege onagers, bombard towers, bombard cannons, keep line, paladin line, eagle warriors, Goth huskarls
Skirmishers Anything except archers, monks and Korean war wagons

Cavalry archers Skirmishers, siege onagers, towers, eagle warriors, Goth huskarls

Hand cannoneers Paladin line, archers, cavalry archers, siege onagers, towers, (scorpions, skirmishers)

Stable units and their counters:

Hussar line Halberdier line, champion line, paladin line, camel line, massed scorpions, towers, cavalry archers, Turk jannisaries, Saracen mamelukes, Byzantine cataphracts, (hand cannoneers, eagle warriors, Persian elephants, Teutonic knights)
Paladin line Halberdier line, camel line, bombard towers, Saracen mamelukes, (Persian elephants)
Camel line Halberdier line, towers, massed scorpions, Saracen mamelukes, Teutonic knights, (eagle warriors, champions)

Siege workshop units and their counters:

Rams > Anything except archery
Onagers > Anything hand to hand
Scorpions > Onager line, ram line, paladins, eagle warriors, towers, Unique Unit archery, Goth huskarls

Monk counters:
Monks Anything fast or with range

Unique units and their counters:
Aztec jaguar warrior Archers, cavalry archers, hand cannoneers, towers, scorpions, Saracen mameluke, Byzantine cataphracts, (paladins)
Briton longbowman Siege onagers, paladins, eagle warriors, Goth huskarls, Korean war wagon

Byzantine cataphract Paladins, camel line, bombard towers, Saracen mameluke, Mongol mangudai

Celtic woad raider Paladin, massed scorps, towers, cavalry archers, hand cannoneers, Unique Unit archery, Aztect jaguar warriors, Byzantine cataphracts, Teutonic knights, Saracen mameluke

Chinese chu ko nu Skirmishers, onagers, eagle warriors, paladins, towers, Goth huskarls, British longbowmen, Korean war wagons
Frankish throwing axeman Paladins, onager line, archers, hand cannoneers, cavalry archers, towers, scorpions, Byzantine cataphracts, Persian war elephants, (Saracen mamelukes, Teutonic knights)

Goth huskarl Champion line, hand cannoneers, paladins, bombard towers, Aztec jaguar warrior, Byzantine cataphracts, Saracen mamelukes, Frankish throwing axemen

Hun tarkan Paladins, camel line, halberdier line, cavalry archers, Unique Unit archery, Goth huskarl

Japanese samurai Archers, cavalry archers, hand cannoneers, towers, scorpions, Frankish throwing axemen, Saracen mameluke

Korean war wagon Bombard towers, siege onagers, camel line, skirmishers, trebuchets

Mayan plumed archer Skirmishers, eagle warriors, paladins, onager line, Goth huskarls, Briton longbowmen, Korean war wagons
Mongol mangudai Skirmishers, archers, massed onagers, towers, eagle warriors, Goth huskarls, Korean war wagons

Persian war elephant Halberdiers, monks, very massed scorpions, bombard towers, Saracen mamelukes

Saracen mameluke Onagers, massed scorpions, archers, towers, Mongol mangudai

Spanish conquistador Towers, archers, cavalry archers, onager line

Teutonic knight Scorpions, archers, cavalry archers, hand cannoneers, towers

Turkish janissary Archers, cavalry archers, paladins, onager line, massed scorpions, towers

Viking berserker Archers, cavalry archers, scorpions, hand cannoneers, towers, Japanese samurai, Frankish throwing axemen,
Saracen mamelukes, Teutonic knights, Persian war elephants

Retina - a brain tissue

In vertebrate embryonic development, the retina and the optic nerve originate as outgrowths of the developing brain, so the retina is considered part of the central nervous system (CNS) and is actually brain tissue.^[1] It is the only part of the CNS that can be visualized non-invasively.

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

http://www.nature.com/neuro/journal/v15/n3/full/nn.3032.html

Brain circuits are assembled from a large variety of morphologically and functionally diverse cell types. It is not known how the intermingled cell types of an individual adult brain region differ in their expressed genomes. Here we describe an atlas of cell type transcriptomes in one brain region, the mouse retina. We found that each adult cell type expressed a specific set of genes, including a unique set of transcription factors, forming a 'barcode' for cell identity. Cell type transcriptomes carried enough information to categorize cells into morphological classes and types. Several genes that were specifically expressed in particular retinal circuit elements, such as inhibitory neuron types, are associated with eye diseases. The resource described here allows gene expression to be compared across adult retinal cell types, experimenting with specific transcription factors to differentiate stem or somatic cells to retinal cell types, and predicting cellular targets of newly discovered disease-associated genes.

Love and desire

"Love: The irresistible desire to be irresistibly desired."
--Mark Twain

Love is a war

"Love does not begin and end the way we seem to think it does. Love is a battle, love is a war; love is a growing up."
--James Baldwin