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The BMW 523i is driven by a respectable 2.2-litre 170bhp six-cylinder engine (M52TU) produces 147mph maximum speed and 190bhp power, featured an aluminum alloy block with Double-VANOS (variable intake AND exhaust cam timing) for additional improvements in torque delivery, economy and emissions. The BMW 523i also features DSC (Dynamic Stability Control' system) which include Brake Standby, Rain Brake Support, Hill Start Assist, Fading Compensation and Soft Stop.
This BMW 523i Owner Manual – Minor Repair and Maintenance Guide - is divided into sections as follows:
Initial Overview
Controls in Detail
Operation and Maintenance
Minor Repairs, included in this section: toolkit, wiper blades, lights and bulbs, remote control for auxiliary heater, wheel-changing, thiefproof wheel studs, battery and fuses.
Technical Data
Get and download the BMW 523i Owner Manual – Minor Repair and Maintenance Guide – here (free download PDF file manual)


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Lou Williams Right Knee Injury Report: Lou Williams to a sprained right knee in the second quarter on game Atlanta hawks Lose 89-94 Brooklyn Nets – Friday January 18, 2013. Lou Williams leave the game in a wheelchair and not return.
Lou Williams Injury at 0.45 minutes of This Highlights
Lou Williams Right Knee Injury Status: He's day-to-day for now, but this could be a long-term injury. He will be tested further Friday and Saturday, the Hawks' official site reports.

subset: commoncrawl


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Whitney Houston was remembered last night at an emotional tribute attended by her daughter Bobbi Kristina.
Mother Cissy was noticeably absent from We Will Always Love You: A Grammy Salute to Whitney Houston, but stars such as Usher, Halle Berry, Celine Dion and Britney Spears all took to the stage to share their memories of the late icon, who passed away in February.
Britney revealed that she first scored a record deal when she sang the star's hit 'I Have Nothing' for label execs, and Berry said she "never, ever stopped loving" her.
In an introductory speech, the Oscar winner said: "She inspired a generation of little girls and women to believe in their own dream and to know that they had within themselves the greatest gift of all. I was one of those little girls who then became a woman who never ever, ever, stopped loving Whitney Houston."
Bobbi Kristina, who was recently seen in a promo from her forthcoming reality show revealing that she and Nick Gordon are engaged, was sitting on the front row alongside him and sister-in-law Pat Houston.
Look at a handful of snaps from the occasion below.
PHOTOS: Bobbi Kristina attends Whitney tribute


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Back in 2005, to attend the Game Fair at Blenheim Palace in Oxfordshire, Kate called upon shoe designer Penelope Chilvers for these country-appropriate boots. The proved to be the perfect piece of kit for a spot of horsing around on the grass, as well as for posing for photos for the press.
Shop: Our edit of the best knee-boots


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Good Morning everyone. On this Tuesday morning, most of us can rest easy: Just a quick restart at 5 AM PDT, and we'll be good to get back in the game. There a few servers, however, that will have to deal with an extended maintenance period, lasting until 11 AM PDT. You can see the full list of those servers here.
For players on those servers, and those of us who can't sneak in some WoW time at school or work, I bring you the perfect solution: A roundup of some of the best news and analysis from the past week here at WoW Insider. Dig in, and keep it here throughout the day for more breaking news, guides, and analysis.
- Starting your day with a crossword puzzle can be both fun and mentally stimulating. How about giving this WoW-themed one a try?
- Here's a friendly reminder from Nexus Prince Haramad, just in case you forgot last week: be sure to get your gems for July!
- There are an amazing amount of actions that annoy Allison.
- Here's an interesting world first: A multiboxer makes Gladiator in the Arenas.
- If being Myspace friends with a Murloc isn't enough, how about buying your own?
- Blizzard COO Paul Sams is calling out Microsoft on the subject of PC gaming.
- Not only is the Blizzard Authenticator sold out, but some people who made it in under the wire are having trouble actually getting their order.
- Will we ever see the Scourge Invasion event again? Kisirani is hinting that we might finally get a return engagement.
- With tank shortages reaching critical levels almost everywhere, now's a good time to discuss how to build and keep a good tanking corps. Be sure to check part 2 as well.
- The 4th of July was this past week, and nothing says 4th of July like good old fashioned... copyright infringement?
- Is buying your way through tier 6 raid content an insult to those who work hard to take down bosses on their own, or is a win-win situation for rich casuals and guilds in need of reagent and repair money? Join the discussion.
If that's not enough for you, we have fresh Wrath of the Lich King and Worldwide Invitational news waiting after the break.
The Worldwide Invitational is fading into memory, but there's still plenty of leftover analysis and discussion to be had. Here's a few stray pieces of news and analysis to keep you going until the next big set of news.
- Our WoW Insider Paris team got a chance to sit down with Xfire and shoot the breeze about everything from Death Knights to the innate sexiness of Gnomes.
- If you haven't had a chance to listen yet, check out the WoW Insider Show's WWI episode, live from Paris. Well, sort of from Paris. Turpster was there, at least.
- In case you were wondering about the in-game pet that WWI attendees are getting: It's Tyrael, and I'm jealous.
- Casual? Not sure why you should be excited about any of the WWI news? Let Robin Torres enlighten you.
WotLK's probably still at least a few months away, but there was some pretty exciting news about it this week anyway:
- Beta signups are open. If you haven't signed up yet, get on it! Be sure to check out the FAQ if you're unsure of something.
- WoTLK Collector's Edition hasn't technically been announced yet, but we may have a shot of the box anyway.
- While we're at it, here's a possible shot of the normal WoTLK box, too.
- Matthew Rossi discusses what's ahead for Shamans in WotLK.
- Join Scattered Shots for a discussion on what might be up with pet talent trees.
- So how should the dev team go about fixing Mages? Arcane Brilliance weighs in.
- Just in case you were a little afraid, don't worry. We won't have to pay too much to get our flying mounts airborne again in Northrend.


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Church Car parking
Description: It is Sunday, you have arrive to church and you realize all the parking spots is taken except for one. Try to park your car in the designated parking spot as quickly as you can, but avoid damaging other cars or walls.
dailyplaygames Uploaded 12/04/2012
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A big step forward for both companies
Buy what you already own
Join the NASDAQ Community today and get free, instant access to portfolios, stock ratings, real-time alerts, and more!
Diversified healthcare giant Johnson & Johnson (
) on Wednesday caught a big upgrade from analysts at Raymond
The firm said it boosted its rating on JNJ from "Market Perform"
to "Outperform." The move comes following J&J's announcement
late Tuesday that its proposed $19.7 billion cash/stock acquisition
of Swiss medical device maker Synthes has received regulatory
In pursuant to the deal, J&J's Janssen unit will begin
repurchasing up to $12.9 billion of JNJ's common stock.
Johnson & Johnson shares rose $1.32, or +2.1%, in premarket
trading Wednesday.
The Bottom Line
We began recommending shares of Johnson & Johnson (
) on Apr.27, when the stock was trading at $64.75. The company has
a 3.87% dividend yield, based on last night's closing stock price
of $63.08.
Johnson & Johnson (
) is a "Recommended" dividend stock, holding a Dividend.com DARS™
Rating of 3.5 out of 5 stars.
Be sure to visit our complete recommended list of the
Best Dividend Stocks
, as well as a detailed explanation of
our ratings system here
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
Created by Dividend.com
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text:
Longs Peak Council
Boy Scouts of America
ScoutMail Policies
General Guidelines
Scoutmail must be directly related to Scouting in our Council/Districts
Send information most recipients would want to know about, and target the area / program level.
Use sparingly for non-trivial information.
Ask council webmaster if you're not sure about the kinds of things we should send.
Council Approval (District webmasters - ask your DE when in doubt)
Verify information / dates are correct before sending.
Check your email to see if someone else has already sent the info.
Keep messages short - reference packet, web page or mailings for details.
Follow YP (first name + last initial only, no contact info).
Follow member privacy policies (permission for contact info).
Do not send repeated reminders about the same event (one early/initially and one before deadline OK).
Avoid giving the impression of spam.
Use normal sentence capitalization (ALL CAPS is rejected by some spam filters).
More Details
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Roundtable, topics, what to bring
FOS, Popcorn Kickoffs
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<< Back to Longs Peak Council home page
Last Updated Wednesday, 17-Oct-2012 07:45:34 CDT


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The American Lung Association in Pennsylvania hosts its fifth annual Fight For Air Climb at Hersheypark Stadium, 100 Hersheypark Drive, Hershey, on Sunday.
Participants, both teams and individuals, will climb more than 2,000 steps.
Check-in begins at 11:30 a.m. and the climb begins at 1 p.m.
The American Lung Association in Pennsylvania works to save lives by improving lung health and preventing lung disease through education, advocacy and research. For more information about lung health and air quality in Pennsylvania, visit www.lunginfo.org.
All proceeds from the event support lung disease prevention, research and advocacy programs.


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Date: 6 years ago
Runtime: 0:57
Views: 12,545
All Comments
would be a pleasure doing business with you both ;-)
very hot. need a sock licker?
nice wish i work with you guys
very hot
you, in the blue shirt... lets see you get your ass licked and blowing a load all over your belly, or your friends face....
I wil be sucked by this shawed man
with all the watchers....nice to see you dressed for the occasion .


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Associate Professor of Chemistry and Biochemistry
255 Druckenmiller Hall
Current teaching schedule available on the public course finder.
Chemical tools to target, alter and understand glycosylation
Glycans, the oligosaccharide chains of sugars attached to many proteins and lipids, cover the surfaces of all cells, where they are crucial for myriad biological functions. In some pathogenic bacteria, glycans are essential for locomotion, and without the appropriate sugars on flagella these bacteria cannot colonize the gastrointestinal tract of their hosts. In pathogenic fungi, glycans are essential components of the cell wall, which provides a primary mode of defense against host immune recognition. Finally, in humans, glycans are responsible for mediating protein-protein interactions, and aberrant protein glycosylation often tracks closely with disease. Glycans are thus interesting targets for selective perturbation and analysis. Because glycans are refractory to study by most conventional methods (e.g. genetics), the use of chemical approaches with small molecule inhibitors and chemical reporters, in combination with other technologies, is necessary to perturb and analyze these biomolecules. The broad goals of the Dube lab are to chemically target unique glycans found on opportunistic pathogens for therapeutic and diagnostic purposes, and to create tools to better understand the role of glycosylation in human disease.
Students who conduct research in the Dube lab will be exposed to a variety of extremely powerful chemical and biological techniques, including organic synthesis, molecular cloning, protein expression and purification, and in vitro kinetics. If you're interested in conducting research in the Dube lab, please set up an appointment with Professor Dube to discuss this possibility. Current projects in the lab are outlined below.
I. Chemical tools to understand and target Helicobacter pylori glycosylation. The pathogenic bacterium Helicobacter pylori is the leading cause of duodenal ulcers and gastric cancer worldwide. Unfortunately, existing antibiotics no longer effectively eradicate H. pylori infection and cure these ailments. The development of new treatments will be greatly aided by insights into the pathogenesis of H. pylori. Virulence of H. pylori appears to be directly linked to the pathogen's ability to glycosylate proteins: H. pylori's flagellin proteins are heavily glycosylated with the unusual nine-carbon sugar pseudaminic acid, and this modification is absolutely essential for H. pylori to synthesize functional flagella and colonize the host's stomach. Although H. pylori's glycans are linked to pathogenesis and are targets of therapeutic intervention, H. pylori's glycome remains poorly understood. We are taking a metabolic labeling-based approach to discover novel H. pylori glycoproteins and to target H. pylori based on its unique glycans.
II. Utilizing the Golgi two-hybrid assay to detect the interactions of glycosylated proteins. During her postdoctoral work with Dr. Jennifer Kohler, Prof. Dube developed a two-hybrid assay that is capable of analyzing glycosylated proteins. We are using this assay to study glycoproteins of interest, in particular those associated with human diseases. Current efforts are focused on delineating the interactions of a glycan-processing enzyme implicated in breast cancer.
V. Tra* and D. H. Dube, "Chemical tools to detect and target Helicobacter pylori's glycoproteins" In Glycoscience: Biology and Medicine, T. Endo, P. Seeberger, G. Hart, C-H Wong, N. Taniguchi, ed., Springer (Japan), 2014
V. Tra* and D. H. Dube, "Glycans in pathogenic bacteria -- potential for targeted covalent therapeutics and imaging agents," Chem. Commun., 2014, 50, 4659-4673
K. Champasa*, S. A. Longwell*, A. M. Eldridge, E. A. Stemmler and D. H. Dube, "Targeted identification of glycosylated proteins in the gastric pathogen Helicobacter pylori", Mol. Cell. Proteomics, 2013, 12, 2588-2586.
P. Kaewsapsak*, O. Esonu*, and D. H. Dube, "Recruiting the host's immune system to target Helicobacter pylori's surface glycans", ChemBioChem, 2013, 14, 721-726
S. A. Longwell* and D. H. Dube, "Deciphering the bacterial glycocode: recent advances in bacterial glycoproteomics", Curr. Opin. Chem. Biol., 2013, 17, 41-48
D. H. Dube. "Metabolic labeling of bacterial glycans with chemical reporters." In Bacterial glycomics: Current research, technology, and applications. Twine, S. M., ed. Horizon Scientific Press, Norfolk (UK), 2012
D. H. Dube†, B. Li†, E. J. Greenblatt, S. Nimer, A. K. Raymond* and J. J. Kohler, "A two-hybrid assay to study interactions within the secretory pathway", PLOS One, 2010, 5, e15648. [† These authors contributed equally to this work.]
D. H. Dube, K. Champasa* and B. Wang*, "Chemical tools to discover and target bacterial glycoproteins",Chem. Commun., 2011, 47, 87-101.
P. V. Chang, D. H. Dube and C. R. Bertozzi, "A strategy for the selective imaging of glycans using caged metabolic precursors", J. Am. Chem. Soc., 2010, 132, 9516-9518.
M. B. Koenigs*, E. A. Richardson* and D. H. Dube, "Metabolic profiling of Helicobacter pylori glycosylation",Mol. BioSyst., 2009, 5, 909-912.
D. H. Dube, C. L. De Graffenried and J. J. Kohler, "Regulating cell surface glycosylation with a small-molecule switch", Meth. Enzymol., 2006, 415, 213-229.
D. H. Dube†, J. A. Prescher†, C. N. Quang* and C. R. Bertozzi, "Probing mucin-type O-linked glycosylation in living animals", Proc. Nat'l Acad. Sci. USA, 2006, 103, 4819-4824. [†These authors contributed equally to this work.]
D. H. Dube and C. R. Bertozzi, "Glycans in cancer and inflammation: potential for therapeutics and diagnostics", Nat. Rev. Drug Disc., 2005, 4, 477-488.
J. A. Prescher†, D. H. Dube†, and C. R. Bertozzi, "Chemical remodeling of cell surfaces in living animals"Nature, 2004, 430, 873-877. [†These authors contributed equally to this work.]
D. H. Dube and C. R. Bertozzi, "Metabolic oligosaccharide engineering as a tool for glycobiology", Curr. Opin. Chem. Biol., 2003, 7, 1-10.
S. J. Luchansky, H. C. Hang, E. Saxon, J. R. Grunwell, C. Yu, D. H. Dube and C. R. Bertozzi, "Constructing azide-labeled cell surfaces using polysaccharide biosynthetic pathways", Meth. Enzymol., 2003, 362, 249-272.
*indicates undergraduate co-author
When she is not trying to "cure cancer" (as her Mom proudly broadcasts to the world), she spends as much time playing outdoors as possible. You can often find her rock climbing, backpacking, skiing, or running, all on the same day. On special occasions, you can find her underwater exploring a coral reef. Danielle is hugely fond of coral reefs, as they were both her first outdoor playground and the first place that she discovered her passion for science.


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Chris Caswell as 'Sarah,' Paul Ugalde as 'Richard', and Kari Buckley as 'Mandy' in Vermont Stage Company's production of 'Time Stands Still.' / Lindsay Raymondjack Photography
If you go:
WHAT: Donald Margulies Time Stands Still, by Vermont Stage
WHEN: 7:30 p.m. Wednesdays-Saturdays, 2 p.m. Saturdays-Sundays, through Feb. 17
WHERE: FlynnSpace, Burlington
TICKETS: $24.30-$32.50. 862-1497, www.vtstage.org
James is a war correspondent who doesnt want to revisit his traumatic experiences. Sarah is a war photographer who cant let her experiences go. Theyre a couple bound by war and divided by war, united by love and driven apart by love.
Time Stands Still opened Wednesday at Vermont Stage, and the play by Pulitzer Prize-winner Donald Margulies takes on those two big all is fair in ... topics and makes us think about love and war in ways we rarely want to think about them. That uncomfortable intimacy, plus the quartet of strong performances by the productions all-Vermont cast, is exactly what makes it compelling.
Sarah (portrayed by Chris Caswell) literally cant forget her war experiences; theyre constantly with her, in the form of serious injuries she sustained from a roadside bomb. James (Robert Harte) tries to welcome her back to their New York City apartment and return to some sense of normalcy. But for two people making their livings reporting on horrors in far-flung, devastated nations their friend Richard calls them the Sid and Nancy of journalism normalcy is an elusive concept.
Richard (Paul Ugalde), a photo editor, does what he can to further the couples careers while trying to make Sarah feel welcome upon her return. He starts on the wrong foot, however, when the middle-aged magazine man shows up with his new girlfriend, Mandy (Kari Buckley), with an emphasis on the word girl shes young, naïve and chatty, everything the worldly, hard-nosed Sarah is not.
As with most good plays, however, characters who start at opposite ends of the spectrum eventually meet somewhere in the middle. Mandy becomes a voice of reason when the seemingly strong Sarah and James show signs of wear once they finally remove their emotional calluses and dig deep inside themselves. Mandy sees nothing but joy in life while Sarah and James are constantly surrounded by pain; they need to understand the perspectives of each other before they can become complete people.
The cast members deliver those heavy themes with plenty of their own joy and pain. Caswell is an enormously versatile and talented actress who, because of those qualities, is often put in quirky character roles. Director Gregory Ramos gives her a chance to shine as a leading lady, and Caswell seizes the opportunity with a performance that slowly chips away at Sarahs crusty exterior and shows her to be a warm, caring, bitingly funny woman with a lot of baggage to handle.
Harte, a veteran of the New York and Vermont theater scenes, makes James wounded confusion as authentic as Caswell does Sarahs deep-rooted anger. They make a fascinating couple, one thats as genuine and vivid as those images of war they regularly bring home.
Ugalde gives Richard the requisite nice-guy touches but isnt afraid to get in the trenches when he sees that Sarah and James need seriously tough love. Buckley, who Ramos directed while she was a student in the University of Vermont Department of Theatre, is delightful as the chipper Mandy, bringing humor and fresh energy to a production that initially appears it might be too bleak to enjoy.
Scenic designer Blair Mielnik, who has worked on out-of-state sets as well as at the Weston Playhouse, creates an inviting apartment scene complete with faux-brick walls, multi-paned windows and a complete kitchen with a functioning sink. Mielnik focuses on the details, too, right down to a stray film negative atop a banged-up filing cabinet in a corner of the room that reminds the audience just where Sarahs heart lies.
Time Stands Still is powerful and painful, but in the end it becomes something unexpected: It becomes, almost despite itself, sweet and tender. Maybe love cant eradicate war, and maybe love cant win, but it certainly puts up a good fight.
Contact Brent Hallenbeck at 660-1844 or firstname.lastname@example.com. Follow Brent on Twitter at www.twitter.com/BrentHallenbeck.


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Tag Archives: sequel trilogy
Ghosts of Crucible – SWBW #10
Podcast: Play in new window | Download
Review this Episode: Star Wars: Crucible
Star Wars Bookworms is a monthly podcast but we have gone above and beyond this month releasing a second episode with (maybe) a third on the way! In this episode we are joined by Expanded Universe expert Geralyn Bray, a blogger over at the fansite Roqoo Depot. At the top of the show we give her a short interview to learn more about her favorite things in Star Wars.
Force ghosts, Force ghosts everywhere! We also discuss the Episode VII rumors that both Ian McDiarmid and Ewan McGregor may return to the Star Wars movies. As Force ghosts, clones, holocron projections? Will McGregor get a stand alone Kenobi movie?
Should Star Wars Mirror the Marvel Multiverse? I Say No
* The opinions in this article are my own and may not reflect the opinions of others at The Star Wars Report… especially Mark's
If you haven't read Brian's great article about the potential of a Star Wars multiverse you should go check it out at Tosche Station. My post here is actually a response to his post in a way. My post isn't meant to be an attack on his (we are friends) merely a look at things from a different perspective. I don't hate what he lays out as a plan for the future of the Star Wars Expanded Universe, and if that is the way things went I could accept it, but it's not what I hope to see. Continue reading
A Whole New Discussion- TWL Discussion Video
Podcast: Play in new window | Download
Check out Karl and Jason's first joint discussion video as they sit down to talk (face to face) about the news surrounding Harrison Ford's possible return for the Sequel Trilogy as well as the news about Michael Arndt taking on the job as the screenwriter for Episode VII.


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LACBC Bike Prom
Time to get footloose for a totally gnarly bike prom!
Did you skip your actual prom? Are you glad you did or wish you hadn't? Do you long for the Eighties? Or do you wonder what they were like AND what an '80s prom even means?
Well, now's your chance to do it up right... on your bicycle!
Join LACBC as we get down with the RAD sounds of DJs Kenefunkport, Skramble, and Starskee Suavé. There will be: Dancing! A raffle with prizes from Swrve/VeloLove, Golden Saddle Cyclery, Orange 20, Flying Pigeon, C.I.C.L.E., Kill Radio, the Bike Oven, and more! A photobooth! Bike valet! And the crowning of a Bike Prom King and Queen! '80s prom attire is strongly encouraged.
Good Girl Dinette will offer a 15% discount to folks attending the Bike Prom if you want to get dinner beforehand; just show your Eventbrite ticket or LACBC membership card on Saturday, February 23. The Wicked Kitchen food truck will be serving at Bike Prom. Theo Chocolate will also be selling some chocolate treats, and Peddler's Creamery will be supplying the ice cream.
LACBC Bike Prom
When: Saturday, February 23; 8 PM - 12 AM
Where: American Legion Hall Post 206 - 227 N. Ave. 55, Los Angeles, 90042 (4 blocks west of the Highland Park Gold Line Station) - map
Purchase your tickets at the door.
Ticket prices are $10 for LACBC members and $15 for nonmembers.
Interested in volunteering on our Prom Committee? Email firstname.lastname@example.com


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Workers and Unemployed Action
|Workers and Unemployed Action
|Séamus Healy
|56 Queen Street,
County Tipperary
United Ireland[1]
|Political position
|Dáil Éireann
1 / 166
|Local government
1 / 949
|Politics of the Republic of Ireland
Political parties
Politics of Northern Ireland
Political parties
Workers and Unemployed Action (WUA) is an Irish political party based in Clonmel in South County Tipperary, set up in 1985 by Séamus Healy. WUA has one Teachta Dála (TD) and has endorsed and seen a number of its members elected to the South Tipperary County Council, Tipperary County Council, and Clonmel Borough Council.[2]
The organisation was set up in response to lack of employment and the economic situation in the South Tipperary area. Both Séamus Healy and his brother Paddy Healy had both been involved in the socialist group, the League for a Workers Republic.[citation needed]
In the 2000 Tipperary South by-election, Séamus Healy was elected to the Dáil for Tipperary South, officially running as an independent candidate. At the 2002 general election, Healy was re-elected. Phil Prendergast was elected mayor of Clonmel in 2003.
In 2007, Prendergast left and joined the Labour Party after being headhunted to stand at the 2007 general election against Healy, but neither of them were elected.[3] However, Prendergast was nominated to run for the Seanad and was elected. The party was recognised by the Dáil registrar of political parties in September 2008.[4]
The party was involved in discussions with other left wing groups about a nationwide alliance at the 2009 local elections, which were not successful.[5]
At the 2009 local elections, Martin Henzey was returned on Carrick-on-Suir Town Council, while Séamus Healy, Pat English, Billy Shoer and Theresa Ryan were elected to Clonmel Borough Council. Healy and English were elected to South Tipperary County Council, for the Clonmel electoral area.[6]
The party joined the United Left Alliance which was founded in November 2010, and fielded Séamus Healy in Tipperary South at the 2011 general election. Séamus Healy was the first deputy elected for South Tipperary at the 2011 general election.
As a result of Healy's election to the 31st Dáil, Billy Shoer was co-opted to South Tipperary Country Council and Helena McGee was co-opted to Clonmel Borough Council.
In October 2012 the WUA left the United Left Alliance following disagreements with the Socialist Party and People Before Profit Alliance over the tax affairs of Independent TD Mick Wallace.[7]
Pat English was elected from the Clonmel Electoral Area to Tipperary County Council in the county council election held on 23 May 2014.[8]
- ^ https://wuag.wordpress.com/about/constitution-and-rules/
- ^ "Fianna Fáil set to suffer over hospital funding freeze". Irish Examiner. 4 June 2004.
- ^ South Tipperary Election 2007 www.rte.ie
- ^ Registrar of Political Parties (26 September 2008). "Electoral Acts 1992 and 2001 Registration of Political Parties" (PDF). Iris Oifigiúil (Claremorris: Government Publications) (78): 1167.
- ^ Socialist Party proposals for Local Election Socialist Alliance Rejected Socialist Party Press Release, Indymedia website (4 December 2008)
- ^ Election Results 2009 - How Ireland Voted, Irish Times, 9 June 2009
- ^ "Seamus Healy withdraws from United Left Alliance over Wallace frustrations". RTÉ News. 2 October 2012. Retrieved 2 October 2012.
- ^ Local Election Results for Tipperary County Council Irish Times. Retrieved: 9 June 2014.


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SAN ANTONIO, Texas – The South Dakota State women's soccer team closed out the UTSA Tournament with a scoreless tie against Sam Houston State. As a team the Jackrabbits tallied 27 shots, 12 of those were on goal. The last time SDSU recorded 27 shots in a game was back in 2010. They posted 41 total shots against Centenary on Halloween of 2010. In their game against Lamar on Friday they collected 17 shots. Due to the heat both teams rotated the majority of their players in and out. Sam Houston State brought in 11 players off the bench, while South Dakota State subbed in six players. The Jackrabbits leave San Anotino with a win and a tie. They scored three goals and allowed zero. As a team they outshot Lamar and Sam Houston State by a combined total of 44-8. The Jacks are home next week as they host Nevada and Northern Illinois in the Jackrabbit Invitational. South Dakota will be the 4th team in the tournament but will not play SDSU.
Comment Disclaimer: The views and opinions expressed in the comments section of DakotaRadioGroup.com are solely those of the comment author and do not necessarily reflect the views and opinions held by DakotaRadioGroup.com.


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Wanted teenager arrested after standoff in Hollywood
16-year-old boy wanted for armed carjacking
Published On: Apr 23 2013 11:11:26 PM EDT Updated On: Apr 24 2013 12:48:07 PM EDT
The Broward Sheriff's Office arrested a 16-year-old boy wanted for armed carjacking after a three hour standoff.
A Hollywood police officer saw the teenager outside 2424 Wily Street Tuesday evening.
The Broward Sheriff's Office SWAT unit used tear gas to force the teenager out of his home. He was arrested without incident.
The teenager was accused of carjacking a Fort Lauderdale parking enforcement officer on April 7. Her car and radio were stolen.
Police had identified the teenager and Trevante Simms as suspects. Simms was later shot and killed at a house part in Miramar.


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No big surprise that you aren't gonna see Taylor Swift nude any time soon. In lieu of any real nudie pics I hunted down some of the best fakes of her from Celeb Defamer. Their are some really well done ones of her there. I had to scale these two pretty significantly to fit in the post too.
You can find hundreds of thousands of celebrity fakes at the biggest site of it's type – Celeb Defamer. You can finally see all your favorite female celebrities nude. Score yourself a membership and let your celebrity fantasy come true.
Get instant access to Celeb Defamer for just $2.95!


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- Filed Under
NEW ORLEANS - Parked on a folding chair in the far end of a somber Arizona Cardinals locker room, a subdued Tyrann Mathieu spoke as he looked down to the floor.
Sure, the former LSU star and New Orleans product had just corralled his first NFL interception in his return to his beloved South Louisiana, but he had also just walked off the field after a loss at the Mercedes-Benz Superdome.
"We got beat," Mathieu said, almost as if he was saying, "What, do you want me to be happy?" following the New Orleans Saints' 31-7 victory. ...


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After writing about the break from schedules, last week I got back into a running schedule. Once road racing ended, I am turning my focus to the track. I did this last year, and did one mile track race in June. This year, I've got 2 1 mile track races scheduled, 1 for May, 1 for June. My year-round focus is not the track - I just don't have that fast-twitch speed. I started training last week. And on Tuesday, I had my first time trial of the year.
It had been raining for a couple of days, and there was some wind, but we were going to do the workout anyways, which was going to include an 800m (2 lap) time trial. While I won't ever compete in an 800m race, the idea was that this would jump start my mile training. Last year, I did it in 2:46, and this year we were hoping for 2:40. Plus, it would in theory make the mile not seem as painful, at least not as fast. So we did our usual 2.5 mile warmup, and then threw in a few 200s, just to warm up the legs (both in 40 seconds) before rolling into the 800. I did have some nervousness about this, and I usually don't get nervous for track workouts. But this was going to be so hard and fast. Sarah told me to tuck in behind her, and we were off. Now, she is a 36 10k runner - this pace is not all out for her. Which is good, because she made it look effortless, and I could just hang on behind her. We went thought the first lap in 80 (1:20), right on pace, but I was doubting my ability to hang onto this. With 300m to go, it just felt so uncomfortable and hard. I ended up finishing in 2:44, all out of breath and that feeling of blood in my throat. You just don't get that feeling in the longer races, but this was a sprint. We took a few easy laps of VERY slow jogging, and then continued the workout with 3x400. These were descending: 88, 85, 84. The first one felt more like a trot after the 800, but by the second 2, I could feel the 800 from earlier. And it just kept raining. It felt as if it would let up every other interval, and then start raining again. A couple of easy laps, and then 4x200m. The first 3 were in 39, and then she said to go all out for the last one - 38! I had never run a 200 that fast. Last year, it took me a while to run 39, and this year, i was doing it consistently in week 2 of training. All promising. It poured during our 2.5 mile cooldown, and we were just soaked, muddy, drenched, you name it. But it was also exhilarating. I so rarely get to do the hard workouts with a partner, and this made it a lot of fun. During the easy parts, we chatted about grad school (we are in the same program - she just defended her dissertation), running, teaching, etc. The workout was a great confidence booster. Even though I didn't hit that 2:40, I'm still chipping away at it. These shorter distances require a different skill set, and I'm just in experienced there. But I am looking forward to getting in more practice at it!
You. Are. Fast! That's awesome that you have someone speedy to train with. Great workout!
Great and tough workout! Bet you hit that 2:40 earlier rather than later.
Run on everyone.....................Steve
You make that sound like a lot of fun!
Nice job running in circles
Nice workout! I hate track workouts at the start of the session, but when I am done, it is the best feeling! Good luck on the track this summer!!
Great workout! I've been enjoying doing speed work lately too. I can't wait to read these race reports for the mile
"Anything that gets your blood racing is probably worth doing" - Hunter S. Thompson
This sounds crazy intense. Just about right for you.
Carpe Currendum
Very nice workout. 800 is a really tough distance to race - far too long to sprint but way to short to cruise.
+1 running_eng. Key is to break up the second lap into 4x100, trying to accelerate each one.
Attitude is everything
Great workout! Please continue to post about your speedwork, it's fun to read about!
Nice, very nice!


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November 25, 2015
Eufuala is one of the teams in class 5A Region 2. The Tigers made the playoffs but are coming off what they would call a sub-par 6-5 season.
Head Coach Dan Klages says the team returned to the weight room right after their season ended. And, he thinks his guys have stayed focused in the off-season.
"After going 6-5, that's not good enough in Eufaula," Klages said. "We need to go to work. Right now, I feel good about the way we are physically. What I want to see with this bunch is for them to come together as a football team. Instead of being a collection of individual players."
Eufaula opens up at Dothan on August 28th.
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Cristina Patterson paints on a teeny, tiny canvas, a contact lens. The Los Angeles-based artist has designed and hand-painted special-effect ocular looks for more than 300 films and television shows, including Angelina Jolie's eyes in 'Maleficent.'
Source: Bloomberg, June 23 2014
Category: Film
Please use the comments to demonstrate your own ignorance, unfamiliarity with empirical data and lack of respect for scientific knowledge. Be sure to create straw men and argue against things I have neither said nor implied. If you could repeat previously discredited memes or steer the conversation into irrelevant, off topic discussions, it would be appreciated. Lastly, kindly forgo all civility in your discourse . . . you are, after all, anonymous.


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"You are about to embark upon the Great Crusade, toward which we have striven these many months. The eyes of the world are upon you."
-- General Dwight D. Eisenhower, Order of the Day, June 6, 1944
June 6th, 2004 marks the 60th anniversary of the greatest day in modern history, the Allied invasion of Normandy. Avalon Hill will mark that anniversary with a commemorative new release: Axis & Allies D-Day. This game uses much of what makes Axis & Allies great in a whole new way. In this series of ten columns, we will give you a pre-release look inside this Larry Harris-designed game.
As we work through the next ten columns, you should keep one mantra in mind. As you evaluate what's different about this game, always remember the following:
Axis & Allies is strategic, Axis & Allies D-Day is tactical.
The core game is the essence of global strategy. You control the wartime economies and military deployments of superpowers that can clash all over the world. This epic scale is what makes Axis & Allies the greatest of all World War II games.
D-Day, on the other hand, is not like this at all. It focuses on an area 100 miles on a side, smaller than most single spaces on the A&A map. Individual units represent actual forces that fought on the beaches and in the towns. Every important occupied village is seen on the map, and all of them can matter in the course of a game.
This is a different sort of game. If you get your mind in the right place, you will see that what Axis & Allies does on a global scale, it can do just as well in the fields of France -- but with a very different feel.
A Personal Feel
D-Day looks and feels different from Axis & Allies. When R&D started working on the Axis & Allies line, two packages came in from Larry Harris. The first was the basis for what became the new Avalon Hill edition of Axis & Allies, on store shelves now. (If you want to read more about that design process, you can read my last series of columns.) The second was the first draft of D-Day. It had planes on the box top, suggesting it would be similar to Axis & Allies Pacific, which also had a plane on it. That assumption couldn't have been more wrong.
Above is Lisa Hanson's box design for D-Day. Tommy Lee Edwards' cover illustration shows a few American infantrymen crashing the beach. They look resolute, but in serious danger. The men on the cover would become the symbols of the game. They weren't faceless legions, they were people whose lives were put in jeopardy for the sake of freedom.
In this game, every unit has a designator stating which historical military unit it represents, such as the 101st Airborne Division. When you lose a unit, you do not get it back. Ever.
Consider the effect of that in Axis & Allies. Imagine your side started the game with 10 tanks. As you lost them in combat, you couldn't build more of them. What would you do? Would you save them from combat? Would you try to get everything out of them you could? Would you even build the last one until you had to?
In D-Day, you get a certain number of units and no more. Some start in play, and others come in as reinforcements. You can never buy more units. In fact, you never buy anything at all.
A Shorter, Tighter Game
Because there is no economic activity in this game, you have only the troops at hand to make your victory happen. Worse, you must do it in a set amount of time. The Allies must launch their invasion and defeat the German forces within a limited number of turns or the Wehrmacht wins. The game can be played in less than two hours, meaning that if you have an evening free, you conceivably could play a tournament format, switching back and forth between playing the Axis and the Allies.
The game is for two or three players. The Axis is always played by one player. The Allies can be played by one player, or with one person as the Americans and one as the British and Canadians. That's right, the heroes of the Great White North make their first Axis & Allies appearance in this game.
The rulebook is tight. All the rules for the game are crammed into 24 pages. There's a lot in those pages, however, all of which I'll try to cover in the next nine columns.
The rulebook is flavorful, too, as you can tell from the quote that begins this column. It's salted with quotes from the leaders who participated in the activities around D-Day: Eisenhower, Rommel, Pétain, Churchill, De Gaulle. Whereas in Axis & Allies you represent all the decision-makers who control economies and deployments, in D-Day you represent these military leaders. Your decisions may mirror theirs, or you may approach them a different way. It's your call, for ill or for good.
What's In the Box
D-Day has a rulebook, map board, battle board, six-sided dice, and plastic pieces, just like Axis & Allies. That's where the similarity ends.
One of the pieces is the blockhouse, a brand new piece for Axis & Allies. A blockhouse is a machine-gun and artillery emplacement pointed at the ocean. It has a devastating effect in the game, which I'll discuss in a couple weeks.
Other than a single turn marker, there aren't any counters or tokens in the box. You don't retain control of territories after you leave them, so there are no control markers. Because each piece represents a historical unit, there are no counters on which to stack units. Similarly, there are no Industrial Production Certificates in the game. Instead of spending freely, you have to make do with the units you have.
D-Day also marks the first official invasion of cards into this great wargame line. I'll spend a lot of pixels on the cards in an upcoming column, but for now I'll just tell you that they're not like any cards you'll find in any other games by Wizards of the Coast. They are crucial to the game, and they will let you play four different games out of the same box.
There's a new, bigger type of card in the game, too: the reinforcement chart. Each side gets one. From the beginning of the game, you'll know what pieces you have to use. How you deploy those pieces is up to you.
In the columns for the revised version of Axis & Allies, we kept the map under wraps until near the end of the series. Not this time. Next column, I'll show you France like you've never seen it before.
Catch up on any previews you missed!
Mike Selinker has been playing, designing, developing, and just plain loving games of every variety for many, many years. He is a gamer in the very best sense of the word. Mike lives in Seattle.


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High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
High-throughput sequencing of DNA fragments is used in a range of quantitative assays. A common feature between these assays is that they sequence large amounts of DNA fragments that reflect, for example, a biological system's repertoire of RNA molecules (RNA-Seq [1,2]) or the DNA or RNA interaction regions of nucleotide binding molecules (ChIP-Seq [3], HITS-CLIP [4]). Typically, these reads are assigned to a class based on their mapping to a common region of the target genome, where each class represents a target transcript, in the case of RNA-Seq, or a binding region, in the case of ChIP-Seq. An important summary statistic is the number of reads in a class; for RNA-Seq, this read count has been found to be (to good approximation) linearly related to the abundance of the target transcript [2]. Interest lies in comparing read counts between different biological conditions. In the simplest case, the comparison is done separately, class by class. We will use the term gene synonymously to class, even though a class may also refer to, for example, a transcription factor binding site, or even a barcode [5].
We would like to use statistical testing to decide whether, for a given gene, an observed difference in read counts is significant, that is, whether it is greater than what would be expected just due to natural random variation.
If reads were independently sampled from a population with given, fixed fractions of genes, the read counts would follow a multinomial distribution, which can be approximated by the Poisson distribution.
Consequently, the Poisson distribution has been used to test for differential expression [6,7]. The Poisson distribution has a single parameter, which is uniquely determined by its mean; its variance and all other properties follow from it; in particular, the variance is equal to the mean. However, it has been noted [1,8] that the assumption of Poisson distribution is too restrictive: it predicts smaller variations than what is seen in the data. Therefore, the resulting statistical test does not control type-I error (the probability of false discoveries) as advertised. We show instances for this later, in the Discussion.
To address this so-called overdispersion problem, it has been proposed to model count data with negative binomial (NB) distributions [9], and this approach is used in the edgeR package for analysis of SAGE and RNA-Seq [8,10]. The NB distribution has parameters, which are uniquely determined by mean μ and variance σ2. However, the number of replicates in data sets of interest is often too small to estimate both parameters, mean and variance, reliably for each gene. For edgeR, Robinson and Smyth assumed [11] that mean and variance are related by σ2 = μ + αμ2, with a single proportionality constant α that is the same throughout the experiment and that can be estimated from the data. Hence, only one parameter needs to be estimated for each gene, allowing application to experiments with small numbers of replicates.
In this paper, we extend this model by allowing more general, data-driven relationships of variance and mean, provide an effective algorithm for fitting the model to data, and show that it provides better fits (Section Model). As a result, more balanced selection of differentially expressed genes throughout the dynamic range of the data can be obtained (Section Testing for differential expression). We demonstrate the method by applying it to four data sets (Section Applications) and discuss how it compares to alternative approaches (Section Conclusions).
Results and Discussion
We assume that the number of reads in sample j that are assigned to gene i can be modeled by a negative binomial (NB) distribution,
which has two parameters, the mean μij and the variance . The read counts Kij are non-negative integers. The probabilities of the distribution are given in Supplementary Note A. (All Supplementary Notes are in Additional file 1.) The NB distribution is commonly used to model count data when overdispersion is present [12].
Additional file 1. Supplement. Contains all Supplementary Notes and Supplementary Figures.
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In practice, we do not know the parameters μij and , and we need to estimate them from the data. Typically, the number of replicates is small, and further modelling assumptions need to be made in order to obtain useful estimates. In this paper, we develop a method that is based on the following three assumptions.
First, the mean parameter μij, that is, the expectation value of the observed counts for gene i in sample j, is the product of a condition-dependent per-gene value qi, ρ(j) (where ρ(j) is the experimental condition of sample j) and a size factor sj,
qi,ρ(j) is proportional to the expectation value of the true (but unknown) concentration of fragments from gene i under condition ρ(j). The size factor sj represents the coverage, or sampling depth, of library j, and we will use the term common scale for quantities, such as qi, ρ(j), that are adjusted for coverage by dividing by sj.
Second, the variance is the sum of a shot noise term and a raw variance term,
Third, we assume that the per-gene raw variance parameter vi, ρ is a smooth function of qi, ρ,
This assumption is needed because the number of replicates is typically too low to get a precise estimate of the variance for gene i from just the data available for this gene. This assumption allows us to pool the data from genes with similar expression strength for the purpose of variance estimation.
The decomposition of the variance in Equation (3) is motivated by the following hierarchical model: We assume that the actual concentration of fragments from gene i in sample j is proportional to a random variable Rij, such that the rate that fragments from gene i are sequenced is sjrij. For each gene i and all samples j of condition ρ, the Rij are i.i.d. with mean qiρ and variance viρ. Thus, the count value Kij, conditioned on Rij = rij, is Poisson distributed with rate sjrij. The marginal distribution of Kij - when allowing for variation in Rij - has the mean μij and (according to the law of total variance) the variance given in Equation (3). Furthermore, if the higher moments of Rij are modeled according to a gamma distribution, the marginal distribution of Kij is NB (see, for example, [12], Section 4.2.2).
We now describe how the model can be fitted to data. The data are an n × m table of counts, kij, where i = 1,..., n indexes the genes, and j = 1,..., m indexes the samples. The model has three sets of parameters:
(i) m size factors sj; the expectation values of all counts from sample j are proportional to sj.
(ii) for each experimental condition ρ, n expression strength parameters qiρ; they reflect the expected abundance of fragments from gene i under condition ρ, that is, expectation values of counts for gene i are proportional to qiρ.
(iii) The smooth functions vρ : ℝ+ → ℝ+; for each condition ρ, vρ models the dependence of the raw variance viρ on the expected mean qiρ.
The purpose of the size factors sj is to render counts from different samples, which may have been sequenced to different depths, comparable. Hence, the ratios (Kij)/(Kij' ) of expected counts for the same gene i in different samples j and j' should be equal to the size ratio sj/sj' if gene i is not differentially expressed or samples j and j' are replicates. The total number of reads, Σi kij, may seem to be a good measure of sequencing depth and hence a reasonable choice for sj. Experience with real data, however, shows this not always to be the case, because a few highly and differentially expressed genes may have strong influence on the total read count, causing the ratio of total read counts not to be a good estimate for the ratio of expected counts.
Hence, to estimate the size factors, we take the median of the ratios of observed counts. Generalizing the procedure just outlined to the case of more than two samples, we use:
The denominator of this expression can be interpreted as a pseudo-reference sample obtained by taking the geometric mean across samples. Thus, each size factor estimate is computed as the median of the ratios of the j-th sample's counts to those of the pseudo-reference. (Note: While this manuscript was under review, Robinson and Oshlack [13] suggested a similar method.)
To estimate qiρ, we use the average of the counts from the samples j corresponding to condition ρ, transformed to the common scale:
where mρ is the number of replicates of condition ρ and the sum runs over these replicates. the functions vρ, we first calculate sample variances on the common scale
and define
In Supplementary Note B in Additional file 1 we show that wiρ - ziρ is an unbiased estimator for the raw variance parameter viρ of Equation (3).
However, for small numbers of replicates, mρ, as is typically the case in applications, the values wiρ are highly variable, and wiρ - ziρ would not be a useful variance estimator for statistical inference. Instead, we use local regression [14] on the graph to obtain a smooth function wρ(q), with
as our estimate for the raw variance.
Some attention is needed to avoid estimation biases in the local regression. wiρ is a sum of squared random variables, and the residuals are skewed. Following References [15], Chapter 8 and [14], Section 9.1.2, we use a generalized linear model of the gamma family for the local regression, using the implementation in the locfit package [16].
Testing for differential expression
Suppose that we have mA replicate samples for biological condition A and mB samples for condition B. For each gene i, we would like to weigh the evidence in the data for differential expression of that gene between the two conditions. In particular, we would like to test the null hypothesis qiA = qiB, where qiA is the expression strength parameter for the samples of condition A, and qiB for condition B. To this end, we define, as test statistic, the total counts in each condition,
and their overall sum KiS = KiA + KiB. From the error model described in the previous Section, we show below that - under the null hypothesis - we can compute the probabilities of the events KiA = a and KiB = b for any pair of numbers a and b. We denote this probability by p(a, b). The P value of a pair of observed count sums (kiA, kiB) is then the sum of all probabilities less or equal to p(kiA, kiB), given that the overall sum is kiS:
The variables a and b in the above sums take the values 0,..., kiS. The approach presented so far follows that of Robinson and Smyth [11] and is analogous to that taken by other conditioned tests, such as Fisher's exact test. (See Reference [17], Chapter 3 for a discussion of the merits of conditioning in tests.)
Computation of p(a, b). First, assume that, under the null hypothesis, counts from different samples are independent. Then, p(a, b) = Pr(KiA = a) Pr(KiB = b). The problem thus is computing the probability of the event KiA = a, and, analogously, of KiB = b. The random variable KiA is the sum of mA
NB-distributed random variables. We approximate its distribution by a NB distribution whose parameters we obtain from those of the Kij. To this end, we first compute the pooled mean estimate from the counts of both conditions,
which accounts for the fact that the null hypothesis stipulates that qiA = qiB. The summed mean and variance for condition A are
Supplementary Note C in Additional file 1 describes how the distribution parameters of the NB for KiA can be determined from and . (To avoid bias, we do not match the moments directly, but instead match a different pair of distribution statistics.) The parameters of KiB are obtained analogously.
Supplementary Note D in Additional file 1 explains how we evaluate the sums in Equation (11).
Data sets
We present results based on the following data sets:
RNA-Seq in fly embryos
B. Wilczynski, Y.-H. Liu, N. Delhomme and E. Furlong have conducted RNA-Seq experiments in fly embryos and kindly shared part of their data with us ahead of publication. In each sample of this data set, a gene was engineered to be over-expressed, and we compare two biological replicates each of two such conditions, in the following denoted as 'A' and 'B'.
Tag-Seq of neural stem cells
Engström et al. [18] performed Tag-Seq [19] for tissue cultures of neural cells, including four from glioblastoma-derived neural stem-cells ('GNS') and two from non-cancerous neural stem ('NS') cells. As each tissue culture was derived from a different subject and so has a different genotype, these data show high variability.
RNA-Seq of yeast
Nagalakshmi et al. [1] performed RNA-Seq on replicates of Saccharomyces cerevisiae cultures. They tested two library preparation protocols, dT and RH, and obtained three sequencing runs for each protocol, such that for the first run of each protocol, they had one further technical replicate (same culture, replicated library preparation) and one further biological replicate (different culture).
ChIP-Seq of HapMap samples
Kasowski et al. [20] compared protein occupation of DNA regions between ten human individuals by ChIP-Seq. They compiled a list of regions for polymerase II and NF-κB, and counted, for each sample, the number of reads that mapped onto each region. The aim of the study was to investigate how much the regions' occupation differed between individuals.
Variance estimation
We start by demonstrating the variance estimation. Figure 1a shows the sample variances wiρ (Equation (7)) plotted against the means (Equation (6)) for condition A in the fly RNA-Seq data. Also shown is the local regression fit wρ(q) and the shot noise . In Figure 1b, we plotted the squared coefficient of variation (SCV), that is the ratio of the variance to the mean squared. In this plot, the distance between the orange and the purple line is the SCV of the noise due to biological sampling (cf. Equation (3)).
Figure 1. Dependence of the variance on the mean for condition A in the fly RNA-Seq data. (a) The scatter plot shows the common-scale sample variances (Equation (7)) plotted against the common-scale means (Equation (6)). The orange line is the fit w(q). The purple lines show the variance implied by the Poisson distribution for each of the two samples, that is, . The dashed orange line is the variance estimate used by edgeR. (b) Same data as in (a), with the y-axis rescaled to show the squared coefficient of variation (SCV), that is all quantities are divided by the square of the mean. In (b), the solid orange line incorporated the bias correction described in Supplementary Note C in Additional file 1. (The plot only shows SCV values in the range [0, 0.2]. For a zoom-out to the full range, see Supplementary Figure S9 in Additional file 1.)
The many data points in Figure 1b that lie far above the fitted orange curve may let the fit of the local regression appear poor. However, a strong skew of the residual distribution is to be expected. See Supplementary Note E in Additional file 1 for details and a discussion of diagnostics suitable to verify the fit.
In order to verify that DESeq maintains control of type-I error, we contrasted one of the replicates for condition A in the fly data against the other one, using for both samples the variance function estimated from the two replicates. Figure 2 shows the empirical cumulative distribution functions (ECDFs) of the P values obtained from this comparison. To control type-I error, the proportion of P values below a threshold α has to be ≤ α, that is, the ECDF curve (blue line) should not get above the diagonal (gray line). As the figure indicates, type-I error is controlled by edgeR and DESeq, but not by a Poisson-based χ2 test. The latter underestimates the variability of the data and would thus make many false positive rejections. In addition to this evaluation on real data, we also verified DESeq's type-I error control on simulated data that were generated from the error model described above; see Supplementary Note G in Additional file 1. Next, we contrasted the two A samples against the two B samples. Using the procedure described in the previous Section, we computed a P value for each gene. Figure 3 shows the obtained fold changes and P values. 12% of the P values were below 5%. Adjustment for multiple-testing with the procedure of Benjamini and Hochberg [21] yielded significant differential expression at false discovery rate (FDR) of 10% for 864 genes (of 17,605). These are marked in red in the figure. Figure 3 demonstrates how the ability to detect differential expression depends on overall counts. Specifically, the strong shot noise for low counts causes the testing procedure to call only very high fold changes significant. It can also be seen that, for counts below approximately 100, even a small increase in count levels reduces the impact of shot noise and hence the fold-change requirement, while at higher counts, when shot noise becomes unimportant (cf. Figure 1b), the fold-change cut-off depends only weakly on count level. These plots are helpful to guide experiment design: For weakly expressed genes, in the region where shot noise is important, power can be increased by deeper sequencing, while for the higher-count regime, increased power can only be achieved with further biological replicates.
Figure 2. Type-I error control. The panels show empirical cumulative distribution functions (ECDFs) for P values from a comparison of one replicate from condition A of the fly RNA-Seq data with the other one. No genes are truly differentially expressed, and the ECDF curves (blue) should remain below the diagonal (gray). Panel (a): top row corresponds to DESeq, middle row to edgeR and bottom row to a Poisson-based χ2 test. The right column shows the distributions for all genes, the left and middle columns show them separately for genes below and above a mean of 100. Panel (b) shows the same data, but zooms into the range of small P values. The plots indicate that edgeR and DESeq control type I error at (and in fact slightly below) the nominal rate, while the Poisson-based χ2 test fails to do so. edgeR has an excess of small P values for low counts: the blue line lies above the diagonal. This excess is, however, compensated by the method being more conservative for high counts. All methods show a point mass at p = 1, this is due to the discreteness of the data, whose effect is particularly evident at low counts.
Figure 3. Testing for differential expression between conditions A and B: Scatter plot of log2 ratio (fold change) versus mean. The red colour marks genes detected as differentially expressed at 10% false discovery rate when Benjamini-Hochberg multiple testing adjustment is used. The symbols at the upper and lower plot border indicate genes with very large or infinite log fold change. The corresponding volcano plot is shown in Supplementary Figure S8 in Additional file 2.
Comparison with edgeR
We also analyzed the data with edgeR (version 1.6.0; [8,10,11]). We ran edgeR with four different settings, namely in common-dispersion and in tagwise-dispersion mode, and either using the size factors as estimated by DESeq or taking the total numbers of sequenced reads. The results did not depend much on these choices, and here we report the results for tag-wise dispersion mode with DESeq-estimated size factors. (The R code required to reproduce all analyses, figures and numbers reported in this article is provided in Additional file 2; in addition, this supplement provides the results for the other settings of edgeR. The raw data can be found in Additional file 3.)
Additional file 2. Supplement II. PDF file presenting the source code of all the analyses presented in this paper, with comments, as a Sweave document.
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Additional file 3. Raw data. Tarball containing the raw data for the presented analyses.
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Going back to Figure 1 we see that edgeR's single-value dispersion estimate of the variance is lower than that of DESeq for weakly expressed genes and higher for strongly expressed genes. As a consequence, as we have seen in Figure 2edgeR is anti-conservative for lowly expressed genes. However, it compensates for this by being more conservative with strongly expressed genes, so that, on average, type-I error control is maintained.
Nevertheless, in a test between different conditions, this behavior can result in a bias in the list of discoveries; for the present data, as Figure 4 shows, weakly expressed genes seem to be overrepresented, while very few genes with high average level are called differentially expressed by edgeR. While overall the sensitivity of both methods seemed comparable (DESeq reported 864 hits, edgeR 1, 127 hits), DESeq produced results which were more balanced over the dynamic range.
Figure 4. Distribution of hits through the dynamic range. The density of common-scale mean values qi for all genes in the fly data (gray line, scaled down by a factor of seven), and for the hits reported by DESeq (red line) and by edgeR at a false discovery rate of 10% (dark blue line: with tag-wise dispersion estimation; light blue line: common dispersion mode).
Similar results were obtained with the neural stem cell data, a data set with a different biological background and different noise characteristics (see Supplementary Note F in Additional file 1). The flexibility of the variance estimation scheme presented in this work appears to offer real advantages over the existing methods across a range of applications.
Working without replicates
DESeq allows analysis of experiments with no biological replicates in one or even both of the conditions. While one may not want to draw strong conclusions from such an analysis, it may still be useful for exploration and hypothesis generation.
If replicates are available only for one of the conditions, one might choose to assume that the variance-mean dependence estimated from the data for that condition holds as well for the unreplicated one.
If neither condition has replicates, one can still perform an analysis based on the assumption that for most genes, there is no true differential expression, and that a valid mean-variance relationship can be estimated from treating the two samples as if they were replicates. A minority of differentially abundant genes will act as outliers; however, they will not have a severe impact on the gamma-family GLM fit, as the gamma distribution for low values of the shape parameter has a heavy right-hand tail. Some overestimation of the variance may be expected, which will make that approach conservative.
We performed such an analysis with the fly RNA-Seq and the neural cell Tag-Seq data, by restricting both data sets to only two samples, one from each condition. For the neural cell data, the estimated variance function was, as expected, somewhat above the two functions estimated from the GNS and NS replicates.
Using it to test for differential expression still found 269 hits at FDR = 10%, of which 202 were among the 612 hits from the more reliable analysis with all available samples. In the case of the fly RNA-Seq data, however, only 90 of the 862 hits (11%) were recovered (with two new hits). These observations are explained by the fact that in the neural cell data, the variability between replicates was not much smaller than between conditions, making the latter a usable surrogate for the former. On the other hand, for the fly data, the variability between replicates was much smaller than between the conditions, indicating that the replication provided important and otherwise not available information on the experimental variation in the data (see also next Section).
Variance-stabilizing transformation
Given a variance-mean dependence, a variance-stabilizing transformation (VST) is a monotonous mapping such that for the transformed values, the variance is (approximately) independent of the mean. Using the variance-mean dependence w(q) estimated by DESeq, a VST is given by
Applying the transformation τ to the common-scale count data, kij/sj, yields values whose variances are approximately the same throughout the dynamic range. One application of VST is sample clustering, as in Figure 5; such an approach is more straightforward than, say, defining a suitable distance metric on the untransformed count data, whose choice is not obvious, and may not be easy to combine with available clustering or classification algorithms (which tend to be designed for variables with similar distributional properties).
Figure 5. Sample clustering for the neural cell data of Kasowski et al. [18]. A common variance function was estimated for all samples and used to apply a variance-stabilizing transformation. The heat map shows a false colour representation of the Euclidean distance matrix (from dark blue for zero distance to orange for large distance), and the dendrogram represents a hierarchical clustering. Two GNS samples were derived from the same patient (marked with '(*)') and show the highest degree of similarity. The two other GNS samples (including one with atypically large cells, marked '(L)') are as dissimilar from the former as the two NS samples.
DESeq can also be used to analyze comparative ChIP-Seq assays. Kasowski et al. [20] analyzed transcription factor binding for HapMap individuals and counted for each sample how many reads mapped to pre-determined binding regions. We considered two individuals from their data set, HapMap IDs GM12878 and GM12891, for both of which at least four replicates had been done, and tested for differential occupation of the regions. The upper left two panels of Figure 6 which show comparisons within the same individual, indicate that type-I error was controlled by DESeq. No region was significant at 10% FDR using Benjamini-Hochberg adjustment. Differential occupation was found, however, when contrasting the two individuals, with 4,460 of 19,028 regions significant when only two replicates each were used and 8,442 when four replicates were used (upper right two panels).
Figure 6. Application to ChIP-Seq data. Shown are ECDF curves for P values resulting from comparisons of Pol-II ChIP-Seq data between replicates of the same individual (first and second column) and between two different individuals (third and forth column). The upper row corresponds to an analysis with DESeq ('D'), the lower row to one based on Poisson GLMs ('P'). If no true differential occupation exists (that is, when comparing replicates), the ECDF (blue) should stay below the diagonal (gray), which corresponds to uniform P values. In the first column, two replicates from HapMap individual GM12878 (A1) were compared against two further replicates from the same individual (A2). Similarly, in the second column, two replicates from individual GM12891 (B1) were compared against two further replicates from the same individual (B2). For DESeq, no excess of low P values was seen, as expected when comparing replicates. In contrast, the Poisson GLM analysis produced strong enrichments of small P values; this is a reflection of overdispersion in the data, that is, the variance in the data was larger than what the Poisson GLM assumes (see also Section Choice of distribution). The third column compares two replicates from individual GM12878 (A1) against two from the other individual (B1). True occupation differences are expected, and both methods result in enrichment of small P values. The forth column shows the comparison of four replicates of GM12878 (A1 combined with A2) against four replicates of GM12891 (B1, B2); increased sample size leads to higher detection power and hence smaller P values.
Using an alternative approach, Kasowski et al. fitted generalized linear models (GLMs) of the Poisson family. This (lower row of Figure 6) resulted in an enrichment of small P values even for comparisons within the same individual, indicating that the variance was underestimated by the Poisson GLM, and literal use of the P values would lead to anti-conservative (overly optimistic) bias. Kasowski et al. addressed this and adjusted for the bias by using additional criteria for calling differential occupation.
Why is it necessary to develop new statistical methodology for sequence count data? If large numbers of replicates were available, questions of data distribution could be avoided by using non-parametric methods, such as rank-based or permutation tests. However, it is desirable (and possible) to consider experiments with smaller numbers of replicates per condition. In order to compare an observed difference with an expected random variation, we can improve our picture of the latter in two ways: first, we can use distribution families, such as normal, Poisson and negative binomial distributions, in order to determine the higher moments, and hence the tail behavior, of statistics for differential expression, based on observed low order moments such as mean and variance. Second, we can share information, for instance, distributional parameters, between genes, based on the notion that data from different genes follow similar patterns of variability. Here, we have described an instance of such an approach, and we will now discuss the choices we have made.
Choice of distribution
While for large counts, normal distributions might provide a good approximation of between-replicate variability, this is not the case for lower count values, whose discreteness and skewness mean that probability estimates computed from a normal approximation would be inadequate.
For the Poisson approximation, a key paper is the work by Marioni et al. [6], who studied the technical reproducibility of RNA-Seq. They extracted total RNA from two tissue samples, one from the liver and one from the kidneys of the same individual. From each RNA sample they took seven aliquots, prepared a library from each aliquot according to the protocol recommended by Illumina and sampled each library on one lane of a Solexa genome analyzer. For each gene, they then calculated the variance of the seven counts from the same tissue sample and found very good agreement with the variance predicted by a Poisson model. In line with our arguments in Section Model, Poisson shot noise is the minimum amount of variation to expect in a counting process. Thus, Marioni et al. concluded that the technical reproducibility of RNA-Seq is excellent, and that the variation between technical replicates is close to the shot noise limit. From this vantage point, Marioni et al. (and similarly Bullard et al. [22]) suggested to use the Poisson model (and Fisher's exact test, or a likelihood ratio test as an approximation to it) to test whether a gene is differentially expressed between their two samples. It is important to note that a rejection from such a test only informs us that the difference between the average counts in the two samples is larger than one would expect between technical replicates. Hence, we do not know whether this difference is due to the different tissue type, kidney instead of liver, or whether a difference of the same magnitude could have been found as well if one had compared two samples from different parts of the same liver, or from livers of two individuals.
Figure 1 shows that shot noise is only dominant for very low count values, while already for moderate counts, the effect of the biological variation between samples exceeds the shot noise by orders of magnitude.
This is confirmed by comparison of technical with biological replicates [1]. In Figure 7 we used DESeq to obtain variance estimates for the data of Nagalakshmi et al. [1]. The analysis indicates that the difference between technical replicates barely exceeds shot noise level, while biological replicates differ much more. Tests for differential expression that are based on a Poisson model, such as those discussed in References [6,7,20,22,23] should thus be interpreted with caution, as they may severely underestimate the effect of biological variability, in particular for highly expressed genes.
Figure 7. Noise estimates for the data of Nagalakshmi et al. [1]. The data allow assessment of technical variability (between library preparations from aliquots of the same yeast culture) and biological variability (between two independently grown cultures). The blue curves depict the squared coefficient of variation at the common scale, wρ(q)/q2 (see Equation (9)) for technical replicates, the red curves for biological replicates (solid lines, dT data set, dashed lines, RH data set). The data density is shown by the histogram in the top panel. The purple area marks the range of the shot noise for the range of size factors in the data set. One can see that the noise between technical replicates follows closely the shot noise limit, while the noise between biological replicates exceeds shot noise already for low count values.
Consequently, it is preferable to use a model that allows for overdispersion. While for the Poisson distribution, variance and mean are equal, the negative binomial distribution is a generalization that allow for the variance to be larger. The most advanced of the published methods using this distribution is likely edgeR [8]. DESeq owes its basic idea to edgeR, yet differs in several aspects.
Sharing of information between genes
First, we discovered that the use of total read counts as estimates of sequencing depth, and hence for the adjustment of observed counts between samples (as recommended by Robinson et al. [8] and others) may result in high apparent differences between replicates, and hence in poor power to detect true differences.
DESeq uses the more robust size estimate Equation (5); in fact, edgeR's power increases when it is supplied with those size estimates instead. (Note: While this paper was under review, edgeR was amended to use the method of Oshlack and Robinson [13].)
For small numbers of replicates as often encountered in practice, it is not possible to obtain simultaneously reliable estimates of the variance and mean parameters of the NB distribution. EdgeR addresses this problem by estimating a single common dispersion parameter. In our method, we make use of the possibility to estimate a more flexible, mean-dependent local regression. The amount of data available in typical experiments is large enough to allow for sufficiently precise local estimation of the dispersion. Over the large dynamic range that is typical for RNA-Seq, the raw SCV often appears to change noticeably, and taking this into account allows DESeq to avoid bias towards certain areas of the dynamic range in its differential-expression calls (see Figure 2 and 4).
This flexibility is the most substantial difference between DESeq and edgeR, as simulations show that edgeR and DESeq perform comparably if provided with artificial data with constant SCV (Supplementary Note G in Additional file 1). EdgeR attempts to make up for the rigidity of the single-parameter noise model by allowing for an adjustment of the model-based variance estimate with the per-gene empirical variance. An empirical Bayes procedure, similar to the one originally developed for the limma package [24-26], determines how to combine these two sources of information optimally. However, for typical low replicate numbers, this so-called tagwise dispersion mode seems to have little effect (Figure 4) or even reduces edgeR's power (Supplementary Note F in Additional file 1).
Third, we have suggested a simple and robust way of estimating the raw variance from the data. Robinson and Smyth [11] employed a technique they called quantile-adjusted conditional maximum likelihood to find an unbiased estimate for the raw SCV. The quantile adjustment refers to a rank-based procedure that modifies the data such that the data seem to stem from samples of equal library size. In DESeq, differing library sizes are simply addressed by linear scaling (Equations (2) and (3)), suggesting that quantile adjustment is an unnecessary complication. The price we pay for this is that we need to make the approximation that the sum of NB variables in Equation (10) is itself NB distributed. While it seems that neither the quantile adjustment nor our approximation pose reason for concern in practice, DESeq's approach is computationally faster and, perhaps, conceptually simpler.
Fourth, our approach provides useful diagnostics. Plots such as Supplementary Figure S3 in Additional file 2 are helpful to judge the reliability of the tests. In Figure 1b and 7, it is easy to see at which mean value biological variability dominates over shot noise; this information is valuable to decide whether the sequencing depth or the number of biological replicates is the limiting factor for detection power, and so helps in planning experiments. A heatmap as in Figure 5 is useful for data quality control.
Materials and methods
The R package DESeq
We implemented the method as a package for the statistical environment R [27] and distribute it within the Bioconductor project [28]. As input, it expects a table of count data. The data, as well as meta-data, such as sample and gene annotation, are managed with the S4 class CountDataSet, which is derived from eSet, Bioconductor's standard data type for table-like data. The package provides high-level functions to perform analyses such as shown in Section Application with only a few commands, allowing researchers with little knowledge of R to use it. This is demonstrated with examples in the documentation provided with the package (the package vignette). Furthermore, lower-level functions are supplied for advanced users who wish to deviate from the standard work flow. A typical calculation, such as the analyses shown in Section Applications, takes a few minutes of time on a personal computer.
All the analyses presented here have been performed with DESeq. Readers wishing to examine them in detail will find a Sweave document with the commented R code of the analysis code as Additional file 2 and the raw data in Additional file 3.
DESeq is available as a Bioconductor package from the Bioconductor repository [28] and from [36].
ChIP-Seq: (high-throughput) sequencing of immunoprecipitated chromatin; ECDF: empirical cumulative distribution function; FDR: false-discovery rate; GLM: generalized linear model; RNA-Seq: (high-throughput) sequencing of RNA; SCV: squared coefficient of variation; NB: negative-binomial (distribution); VST: variance-stabilizing transformation.
Authors' contributions
SA and WH developed the method and wrote the manuscript. SA implemented the method and performed the analyses.
We are grateful to Paul Bertone for sharing the neural stem cells data ahead of publication, and to Bartek Wilczyński, Ya-Hsin Liu, Nicolas Delhomme and Eileen Furlong likewise for sharing the fly RNA-Seq data. We thank Nicolas Delhomme and Julien Gagneur for helpful comments on the manuscript. S. An. has been partially funded by the European Union Research and Training Network 'Chromatin Plasticity'.
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Is there a way to display the current function in the statusbar? There are times where I'm jumping between parts of a file and it would be handy to know which function I'm currently working within.
If that's not currently available, how hard would it be to make a plugin that does this?


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