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May 31st: Graaf-Reinet, South Africa & Camdeboo National Park
Packing up a frozen frost covered tent is never fun. Winter has seemed to set in a bit earlier here. I again had a short drive of about 140km (86 miles) to the town of Graaf-Reinet. The town itself is set inside Camdeboo National Park, and is one of the oldest European settlements in South Africa. It is filled with tons of old colonial Dutch buildings. It is a town I would call quaint, despite its population of over 30,000 people; it often seems more like a town of a few thousand. I got settled in at an equally quaint home turned guesthouse called El Jardin. An extremely nice old Afrikaaner couple, Terrence and Nita, ran it. The rest of the day was spent running errands to the post office, grocery store, permit office, and bookshop, where I found a cheap used copy of a Drakensberg hiking book for later adventures. The book I was looking for, and subsequently purchased was recommended to me by one of the guys in the Eastern Cape Hiking Club I met in Lesotho titled Around Africa on My Bicycle. Riaan Manser of South Africa wrote it about his amazing adventure circumnavigating the entire continent of Africa on his bicycle by himself. The book made an excellent companion on the many lonely nights of camping, and I highly recommend reading it. I also took the time in town to stop by a couple museums as the town has a good bit of history, both recent and ancient. I first went to Die Ou Biblioteek (The Old Library) museum. Half of this museum is home to a nice sized fossil collection of dinosaur bones found in the area. The other half features exhibits on Robert Sobukwe, a native of Graaf-Reinet, and one of the founding members of the Pan Africanist Congress (PAC). The second museum I stopped by was Die Ou Residensie (The Old Residency) museum. This was basically a large collection of old photographs, old cameras, and also a rather huge collection of old guns. My interests lay in the photographs but I enjoyed the rest as well. When I came back to the guesthouse Nita told me Terrence had gone to the boys club and would be home at 6:30 and that she would have tea and homemade rusks for me in the morning before I left to go hiking for the day. Nita was also kind enough to pass along the recipe before the end of my stay, so I thought I would share it with everyone.
Nita's Delicious Homemade All-bran Buttermilk Rusks
Melt 500g (17.5 oz.) Margarine.
Add 2 cups of brown sugar; 2 cups buttermilk; 2 eggs beaten;
1kg.(2.2 lbs.) self rising flour; 1 dessertspoon (2tsp.) of baking powder;
5 cups all-bran.
Mix well.
Place in greased baking pan.
Bake 1 hour at 150°C (302°F).
Cut into squares.
Dry out in oven at 60°C (140°F) for 1 hour
June 1st: Graaf-Reinet, South Africa & Camdeboo National Park
After a quick cup of rooibos tea, a few rusks, and an orange I was off to the trailhead on the south end of town. The first part of the Eerstefontein Trail, just over 2½ km, takes you between the north ridge of Spandaukop and the south ridge of the Valley of Desolation. From there I would head in a huge loop of about 9½ km skirting my way along the edge of the Valley of Desolation, and then through the open plains of the Karoo, and back between the two ridges. I set off from the trailhead about a quarter to eight at a brisk pace to warm myself up in the chilly morning air, taking in all the splendid scenery. It often felt as if the dinosaurs had left, but all the plants remained. I had just wound my way between the two ridges and had followed the trail down into the thicker, denser foliage when I first heard the odd noises up ahead. The sound was a mix between a grunt and a bark, similar to the sound of a choking dog about to vomit. I was perplexed, a bit tense, and excited. There are no large predators that could eat me here, but the park is home to a plethora of other animals, one of which I was determined to find out about very soon.
I proceeded slowly unsure of what I might find around the next bend. To my surprise it was three large Kudu, and to their surprise, a human, which caused them to go crashing through the shrubs next to me. The distinctive calls soon became commonplace as they were always one step ahead of me for the first half of the trail. It was a whole new experience getting to encounter the African wildlife on foot, I got a small taste in Dwesa, but here in Camdeboo I felt much more at ease with the lack of big predators, and the wide open spaces. The animals were much more skittish when approached on foot and my only glimpses tended to be their rear ends while in flight of the unknown intruder, me. The only creature I managed to sneak up on was a roosting owl that seemed just as startled as I as I unknowingly approached him. On foot you get a much better sense of scale as well, not only with the kudu, ostrich, zebra, and steenbok, but of the landscape as well.
At around the halfway point the signage became less frequent and the trail began to thin. I kept following it until it was just a whisper of a line in the tall grass. From the right angle it appeared to be a trail, but as I soon found out these were often more deceptive than informative. The signs were also poorly thought out from the beginning, using green signs on a green pole that was roughly the height of the surrounding green grass. The hike soon turned into a huge game of I, Spy. The game began easy enough in the open rocks and grass where I could easily climb atop a large rock and pick out the next sign or cairn telling me where to go. I would then bushwhack a few hundred feet, and repeat, until I made it to the sign. The trail ebbed and flowed with a few hundred feet of good trail that either ended abruptly or split into four or five other games trails. I continued with this way finding for about 2km or so until I hit the bottom of the valley I was in. The next 2-3km was crossing the flat arid valley floor. The signs seemed to all but disappear, and there were no more rocks to get a good view. I was fortunately not lost at all, as I could clearly see the two ridges I needed to get back between; it was just a matter of getting there. I was able to pick a sign out here and there with the help of my camera lens and binoculars, but in the in-between I went with the good old fashion walkabout. These misadventures often led me to find interesting animals and places, with the most notable being stumbling upon a past nesting area of an ostrich. The ground was littered with bits of ostrich eggshell, which were astonishingly hard and thick. I finally came across a dried up riverbed as I was nearing the ridge I needed to cross. Remembering my previous crossing of the same dried up riverbed on the way in led me back to the trail I need to be on, so I could get out easily. I carried on through the heat of the midday sun the remaining 2½km back to the car feeling quite beat as I added plenty of time and distance to the original mapped trail with all my extracurricular activities to find my way.
The previous night Nita had also tipped me off about the basaar happening at the large Dutch reform church at the center of town were a bunch of people bring homemade food to sell to raise money for the church. I had worked up a mighty appetite and hadn't had a good home-cooked meal in a few weeks. Nita had recommended the pancakes. I left the trail with pancakes on the mind and made the short drive to the church. It was packed with people but I managed to spot the sign that read pankoek and bee lined it to the stand. It turned out their pancakes are more or less crepes, regardless I wolfed down three of them and a can of ginger beer, which provided me with the energy I needed to finish off my day. From there I soldiered on, driving up the road to the top of the Valley of Desolation to stumble my way through another 1½km of much better marked trail to get to the overlook point of both the entire town of Graaf-Reinet and the Valley of Desolations huge dolomite pillars. I then made my way back to the guesthouse feeling accomplished after hiking somewhere between 17-20km. I was in desperate need of a soak and found relief in the tub as I washed away the day of hiking from my skin. As I scrubbed away with soap I could taste the salt in the bathwater from my sweat and I felt more like an olive in brine then a man in a tub.
Mountain Zebra National Park/ Camdeboo National Park from Jordan Bierma on Vimeo.


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subset: commoncrawl


text:
Lupe Fiasco & Nick Javas' "Packers Vs Bears" Pepsi Max [NFL Promos]
Lupe Fiasco & Nick Javas "Packers vs. Bears" Pepsi Max NFL Audible Program. s/o to Andrew
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Content © 2019 Liberated Awakening. All Rights Reserved.
Meeting in Stillness with Moni
- When:
- 7:30pm - 9pm PST Wednesday, February 20 2019
- Where:
3835 West 37th Ave, Vancouver, BC, Canada
- Submitted by:
- Daniel on Mar 16, 2008
- Description:
Meeting in Stillness with Moni until 9pm
The Living Room ~ Map - 3835 West 37th Ave, Vancouver, BC, Canada
(1½ blocks west of Dunbar Street)
No need to pre-register ~ just show up!
Moni begins sitting in silence at 7pm ~
You are welcome to join.
Meeting format ~ We begin in silence together (about 20 minutes), followed by a talk, and space for questions and sharing, and sometimes an inner body guided meditation. We end with silence.


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The latest remarkable auction is none other than the legendary computer role playing game hybrid, Shadowkeep, from 1984, by Trillium, for the Apple II, which sold for $529.00 (with free shipping). Trillium was best known for their high quality text and graphics adventures, like Amazon and Dragonworld, that featured solid parsers and excellent graphics, and were typically written in partnership with a famous author. Shadowkeep was something of a departure for the company as it was essentially a lushly illustrated role playing game that had a text-based interface. As for this game's famous author connection, Alan Dean Foster created a companion book for the game with the same title whose existence was advertised prominently on the game box (actually, the company's usual thick multi-fold folder) cover.
Much like with Penguin Software having to change their name to Polarium after Penguin the book publisher took notice, Trillium ended up having to change their name to Telarium after Trillium Press got on their case. That's why today, Trillium versions of the games are worth more than the later Telarium versions, though most releases were otherwise identical. I believe I personally have the complete Telarium Apple II version of Shadowkeep along with the paperback novel, though I'll have to verify if in fact instead it's the Trillium version. In any case, the typical pricing for Shadowkeep has been in the ~$250 range, so for this latest game auction to go for double that is indeed impressive, and is probably due in part to the completeness of the example.
Shadowkeep is also notorious for a few other reasons. First, is an incredibly robust copy protection scheme. Second, is that all of the known Apple II images/ROMs on the Web have been altered. You see, if you play directly on the game disks rather that making play disks, the game is irrecoverably altered. That's right, once you play on the originals, there's no going back to its original state, ever. I have yet to check if my disks are in fact intact or have been played on, and thus, altered. Finally, there's the question of other versions outside of the Apple II version. The Commodore 64 and IBM PC versions were at least ANNOUNCED, and there have been occasional sightings that would make Bigfoot hunters proud, but there's still no credible evidence that those versions of the game were ever actually released.
Yes, I've been looking for a while the PC version but never found it :(
Strange because the screenshot of games magazinz is not the Apple version (My guess c64)
Anyway, always cool that you talk about the ancestor of RPG on computer...
I just checked and I have the Telarium version, so there was at least a small secondary run after the Trillium release. As you can tell from the materials in the boxed copy from the eBay auction though, they were already going through the Telarium transition at the time of Shadowkeep's release.
Maybe I'll take a break from heavy duty book writing tonight and give my copy of Shadowkeep a spin and see what's doing...
Bill, I have a killer idea for your next book: Bill's Videogame Collector and Price Guide.
Bill, I have a killer idea for your next book: Bill's Videogame Collector and Price Guide.
there was a time I wanted to do something liek this with ARCADE machines, I started keep ing track of Ebay auctions, and all the Majore sales across the country (still some going on).. but man... its so hard. And the rare stuff seldom sells, so how do you price it "fair"? and you cant base a price on wht one collector paid.. what if he got a deal? or worse, paid to much?
hate to say it, but I think a book on brain surgery would be easier :) cool idea none the less.
Well, CLok, maybe you should go in with us, particularly if you have the stat/number crunching aspect down.
Well, CLok, maybe you should go in with us, particularly if you have the stat/number crunching aspect down.
ehhe Im a talker, not a do'er I love spreading my evil ideas in chat forums (well.. thats an overstatment, I dont post much of anywhere else). You guys amaze me with the ability to write books. I tried ot write a short story, think i was at about 200 pages (err 100 if you think both sides), not so amazing as it took me like 7 years to get that much, and sadly it wasnt very good, much like games, its really hard to write something that doesnt sound like its a copy of something else.
As far as a book like that, I actually think there is a market for it, but alot of people are not going to like to see there pristen copy of "the SIMS" is worthless :) it wpould take alot fo work, that I know, and its a pretty nitch market, and I can see alot of backlash with "fair" prices. Not to mention.. Ultima with Cloth map, without, with original cloth map, with cheaper cloth map... its a task that is quite exstensive. the upside is when pricing.. you cant be wrong :) only off.
A shadowkeep question... Why did this game get a book? as its rare (or so it seems) it wasnt a huge sales success (just a guess), why was a book wrote? has anybody read the book? is it good?
A shadowkeep question... Why did this game get a book? as its rare (or so it seems) it wasnt a huge sales success (just a guess), why was a book wrote? has anybody read the book? is it good?
The book was actually written more or less in conjunction with the game, so the game technically didn't come first. That's why it's advertised like that on the cover of the game ("Now a Novel by" and then "ALAN DEAN FOSTER" below it). I believe the background is is that Alan Dean Foster was inspired by the relative originality of the game world to write the book. It's probably more a name/money thing, but still, certainly something unique for the time. Like I had mentioned, Trillium/Telarium had strong relationships with well known authors, like Michael Crichton with the game Amazon, among others. It was basically a two part strategy by Trillium to compete with Infocom. Instead of Infocom's talented, but unknown game authors, they would one, use a famous author to headline their games, and two, offer up nice graphics along with a quality parser. Some of their games even featured mini-game elements along the way. Of Infocom's competition, I'd say they were the most successful at distinguishing themselves, but obviously the company's fortunes turned by the mid-80's much like Infocom's when that style of game fell out of commercial favor (and I personally didn't care as much for some of the later games, like Nine Princes in Amber and Perry Mason).
I do have the book, an original paperback from back in the day. It's on my list, but I have yet to read it. It's one of those books that's available everywhere used and for pretty cheap too, so it must have had a healthy print run.
I did open up my used copy of Shadowkeep the game last night too, but got distracted testing out a Dick Smith Wizzard (an Australian CreatiVision). Anyway, as I mentioned, my copy of Shadowkeep has the Telarium branding on the cover, but everything inside (just the two double sided disks and the manual in mine) retained the original Trillium branding. I guess making the change on the exterior was enough to appease the Trillium publishing company.
As for writing a collector's book with monetary values, I'm no longer into that idea. I think a book is too quickly out of date. I think a much better idea is to create a phone and Web app with regular updates. In fact, I think that would be an awesome business idea and could extend well beyond videogame--a living database of the used value of items. I imagine you could get a tremendous start if you were able to mine eBay's public data, among other auction sites, and averaging things out.\
In any case, I'm booked with other book projects through at least the first week in October.
I think it might work, and could be a profitable thing since it would require regular updates and new editions. I'm thinking primarily of something like the ones I used to get for comic books. I guess if we didn't want to put the time in to do a comprehensive book, we could just focus on the most valuable or rarest items. Maybe it could be based around platforms, so you'd get info on how and what to collect for the Apple II vs. IBM PC, etc. The hard part for me would be tracking all the various ebay auctions to get a fair sense of what something is worth. You'd have to by necessity track many, many auctions over an extended period of time to arrive at that. I'm assuming there is probably already tracking software of some sort out there for that purpose, so it might not actually be difficult.


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Califone 3064AV Multimedia Stereo Headphones are lightweight and streamlined, making them a popular choice for reading groups, computer work and music lessons. The headphones come with two 3.5mm mini plugs that fit most media players and are Mac and Windows compatible. Cushioned, leather ear cups fit around the ear and block out ambient noise, allowing students to keep volume at safer levels. The electret microphone sits on a flexible gooseneck arm and picks up sounds from any direction. The Califone 3064AV Multimedia Stereo Headphones are backed by a 90-day warranty. Meets the latest requirements for PARCC Common Core testing.
Key Features
- Electret microphone sits on a flexible gooseneck arm
- Lightweight
- Comes with padded leatherette ear cups and adjustable headband for a comfortable fit
- Compatible with all computer formats
- Backed by a 90-day warranty
- Type: Stereo
- Ear Cushion: Padded leatherette
- Plug Size: 3.5mm
- Warranty: 90 days
- Warranty: 90 days
- Assembly Required: No
- Lead Time: Ships within 2 - 3 business days
- Shipping Method: Ground
- Weight: 1.00 pounds


text:
Alhambra Police arrested a man who is accused of lewd and lascivious acts with a child under 14, Alhambra Police officials said.
Victor Hugo Flores, 46, of Alhambra, was arrested Thursday at 900 S. Fremont Avenue., Sgt. Gerald Johnson said.
Police began investigating Flores in July 2012 when they received a Suspected Child Abuse Report.
"We had contact with the victim, but the case went cold for a while," Johnson said. "The victim approached us a few weeks ago with additional information and evidence and talked to the Detective Bureau."
The case was reopened and detectives obtained an arrest warrant for Flores on June 20. He was arrested without incident.
Johnson did not release Flores' relationship with the female victim who is now older than 14 years.
Flores is scheduled to appear July 24 at Alhambra Municipal Court and is being held in lieu of $1,145,000 bail, according to Los Angeles County Sheriff's booking records.


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94 N. Main Street, Jasper, GA - (706) 253-2457
Lou Chastain
Mr. Jimmy (Lou) Chastain, age 53 of Jasper, Ga., passed Tuesday morning, April 20, 2010 at Piedmont Mountainside Hospital.
Survivors include his wife, Tracey Chastain, Jasper; and daughter, Mallory Chastain, Jasper.
Mr. Chastain was a retired employee of the Georgia Department of Transportation where he was the district engineer covering Pickens, Cherokee, Gilmer and Fannin counties.
The funeral service was held Thursday, April 22 at 2 p.m. from Jasper First Baptist Church with the Rev. Dr. Jimmy Lewis officiating. Interment followed in Lake Forest Memorial Park. The family did accept flowers or donations may be made to Jasper First Baptist Building Fund. Pallbearers were: Blane Jordan, Frank Martin, Mike Westbrook, Wes King, Joe James and Mike Thomason. Cagle Funeral Home in charge of arrangements.
Thelma McDonald
Thelma Marie ?Cooter? McDonald, age 72, of Murrayville, Ga. died April 8, 2010 at her home.
Ms. McDonald was a native of Pickens County, Ga. who had lived in the Murrayville area for the past 10 years. She was a former Forsyth County resident. She was a retired convenience store clerk and was a member of Marble Hill Baptist Church in Pickens County.
She was the daughter of the late Robert and Mattie Lee Fields Carney and was also preceded in death by a son, Timothy Wayne Jones; brother, Leon Carney; sister, Vera Mae; and grandson, Robert Lee Jones.
Ms. McDonald is survived by daughters, Debra Gillespie, Cumming, Ga., Connie Sue Jones, Cleveland, Ga., and Angela Jones, Murrayville, Ga.; son, Anthony Jones, Murrayville, Ga.; and sister, Jean Forrester, Bowling Green, Fl. Ten grandchildren, seven great-grandchildren and a large number of other relatives and friends also survive. Banister Funeral Home of Dahlonega was in charge of the arrangements.
Ruth Mitchell
Ruth Hamilton Mitchell, age 85 of Big Canoe, Ga., died Tuesday, April 20, 2010 at Piedmont Mountainside Hospital.
She was a retired registered nurse and member of Trinity Evangelical Church.
Survivors include her son and daughter-in-law, Barry and Jana Mitchell; and daughter and son-in-law, Beverly and Dane Vaughn, Jasper. Five grandchildren, eight great-grandchildren and several nieces and nephews also survive.
The funeral service was held at 1 p.m. Thursday, April 22 from the Chapel of South Canton Funeral Home with the Rev. Jeff Falkowski officiating. Interment followed in Arlington Memorial Park.
James Burson
James Leland Burson of Jasper, Ga. went home to be with the Lord on April 21, 2010. A Smyrna, Ga. native, James held a Bachelor?s degree in Biology and Master?s degree in Physiology from Emory University. Prior to retiring in 2001, he worked in environmental health and safety with Merck & Company (in Wilson, N.C. and Albany, Ga.). He also held positions in environmental and safety-related at Georgia Tech, Georgia Power Co. and Lockheed-Georgia Co. during his working years.
He loved the outdoors all his life and he loved to teach and share all the wonders of nature. He was an AT Thru-Hiker in 1959, and continued to walk sections of the trails all through the years. After retirement he became active with the Friends of Amicalola Falls State Park. He loved working with the groups that came there; as well as the Appalachian Trail Celebration and Backpacking Clinic held there each spring. He served on the Friends of Georgia State Parks & Historic Sites Board of Directors. He was a Master Gardener. He was a lifelong learner; always volunteering and giving of himself.
He was married to his wife, Barbara Lesley Burson, a Smyrna native, for 48 years. He cherished Barbara and their daughters, Leslie Ann Burson, Leigh Ann Burson, Laurie Burson Carter; his son-in-law, Robert Carter; and his wonderful grandchildren, Ella Mary Carter (8), Benjamin Joseph Burson (6) and Samuel Robert Carter (3).
He is survived by his brother-in-law and sister, Charles and Ann Pittard; nephews, Tommy and Davey Pittard and their families; his brother-in-law and sister-in-law, Charles and Paula Lesley; nephews, Chad, Alec and Brett Lesley and their families.
He faithfully walked with and served the Lord all his days. He was most loved and cherished. Many hearts grieve his passing today.
The funeral service was held at 10 a.m. Saturday at Foothills Community Church, 13724 Hwy. 53, Marble Hill, Ga 30148 with the Rev. John Reale officiating. Interment followed at 1:30 at Cheatham Hills Memorial Park. In lieu of flowers, contributions may be made to the Foothills Community Church Building Fund or to Friends of Amicalola State Park, 418 Amicalola Falls Lodge Road, Dawsonville, Ga. 30534.
Bobby Wright
Mr. Bobby Joe Wright, age 64 of Talking Rock, Ga., passed away April 23, 2010 at his residence.
Survivors include his sister, Helen Jordan; niece, Susan Mosley; nephew, Chris Mosley; and aunt and uncle, Bertie and Carl Owenby, Talking Rock. Several cousins also survive.
Bobby Joe was cremated. Interment was at Sunrise Memorial Gardens in Jasper. Cagle Funeral Home in charge or arrangements.
Lucille Caylor
Mrs. Lucille Caylor, age 72 of Raymore, Mo., and formerly of Jasper, passed Monday, April 19, 2010 at her residence.
Survivors include her daughter and son-in-law, Susie and Larry Bouman, Raymore; brother and sister-in-law, James and Edna Chastain; sister, Retha Lathem, Pikeville, Tn.; and grandchildren, Amy Wisner and Jonathan Rice. Five great-grandchildren also survive.
The funeral service was held Friday, April 23 at 2 p.m. from the Chapel of Cagle Funeral Home with the Revs. Randy Cloer, Paul Cannaverde and Huey Holloway officiating. Interment followed in Sunrise Memorial Gardens.
Ruby Stone
Mrs. Ruby Stone, age 96 of Jasper, passed Tuesday morning, April 20, 2010 at Grandview Health Care in Jasper.
Survivors include her sons and daughters-in-law, James E. and Diane Stone and Robert and Judy Stone, all of Jasper, and Larry and Vickie Stone, Florida; daughter, Annette McClanahan, Maryland; and brother, Thomas Courson, Marietta. Seven grandchildren, eight great-grandchildren and five great-great-grandchildren also survive.
The funeral service was held Friday, April 23 at 11 a.m. from the Chapel of Cagle Funeral Home with the Rev. Larry Phillips officiating.


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text:
April Fools' Day is celebrated on 1 April in both the Muggle and wizarding world. It is celebrated by playing jokes and pranks on others.[1]
Known April Fools' Day pranksEdit
- 1979: Caractacus Phelps dropped over eight-hundred Stink Pellets off the roof of the Daily Prophet office in Diagon Alley, causing aproxmiately five-hundred cases of Stinkitus.[2]
- Before 1994: A witch sneaks up behind Alastor Moody and says "Boo!", causing an over-reaction on his part.[3]
Behind the scenesEdit
- Fred and George Weasley were born on 1 April, most likely as a reference to this Day, as the twins are notorious pranksters.
- The video game review site IGN is well known for making hoax videos on April Fools' Day. In 2011, their hoax was Harry Potter themed: a fake television spin-off called The Aurors.
- In Chapter 2 of Harry Potter and the Order of the Phoenix, when Aunt Petunia confirmed that Dementors really existed, it was stated that "Uncle Vernon looked from Aunt Petunia to Dudley to Harry as if hoping somebody was going to shout 'April Fool!'"
- Harry Potter and the Goblet of Fire (First mentioned)
- Harry Potter and the Order of the Phoenix (Mentioned only)
Notes and referencesEdit
- ↑ April Fools' Day at Wikipedia
- ↑ Harry Potter and the Chamber of Secrets (video game)
- ↑ Harry Potter and the Goblet of Fire - Chapter 15 (Beauxbatons and Durmstrang)
Community content is available under CC-BY-SA unless otherwise noted.


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Kent unconcerned by Hall of Fame vote total
SAN FRANCISCO -- Jeff Kent likes being part of the Hall of Fame chatter. Just don't ask him to participate in it.
Named on 87 of the 571 ballots cast by Hall of Fame voters, Kent's 15.2 percentage fell far short of the 75 percent needed for induction. Since this represented his first time on the ballot, he'll have as many as 14 more chances -- assuming his vote total never dips below 5 percent -- to gain election.
SAN FRANCISCO -- Jeff Kent likes being part of the Hall of Fame chatter. Just don't ask him to participate in it.
Named on 87 of the 571 ballots cast by Hall of Fame voters, Kent's 15.2 percentage fell far short of the 75 percent needed for induction. Since this represented his first time on the ballot, he'll have as many as 14 more chances -- assuming his vote total never dips below 5 percent -- to gain election.
During his 17-year Major League career, Kent avoided introspective, self-analytical discussions about his stature in the game. But he was more than happy to prompt dialogue among fans and media. And there was plenty of it, given his status as one of the most prolific offensive second basemen ever.
"I've always appreciated the fact that I've been part of arguments, debates and articles about my career and the things I've done," Kent said Wednesday in a telephone interview. "That's what's great about baseball. I don't know if anybody's truly correct. But there are always good arguments in the game. I'm in a sense grateful that people consider it and take note of it."
Kent's Hall of Fame vote total could spark a fresh debate. Some might consider it shockingly low. Then again, maybe Kent was fortunate to receive as many votes as he did. The influx of qualified first-time candidates, including electees Greg Maddux, Tom Glavine and Frank Thomas, along with impressive holdovers from previous ballots like Jack Morris and Craig Biggio, could have limited the vote for Kent and others.
Characteristically, Kent sounded unlikely to spend time squeezing significance from the voting.
"I didn't have any expectations, so I really don't have any reaction personally," he said. "... I'll continue to ride the wave of not overthinking it, because I can't control it, and we'll see where it lands."
Rich Aurilia, Kent's double-play partner at shortstop when both played for the Giants, expected greater support for his ex-teammate.
"I thought he'd get about 30-35 percent of the vote," said Aurilia, a Giants analyst for Comcast SportsNet Bay Area. But, Aurilia added, "There was just such great talent on the ballot this year."
Kent's credentials compared favorably to virtually anybody's on the ballot.
He's the all-time leader among second basemen with 351 home runs, 1,389 RBIs, a .509 slugging percentage and 508 doubles accumulated while playing that position (his overall career totals include 377 homers, 1,518 RBIs, a .500 slugging percentage and 560 doubles). Kent's .866 OPS (on-base plus slugging percentage) as a second baseman ranks third behind Jackie Robinson's .905 and still-active Chase Utley's .876.
After being traded from the Blue Jays to the Mets late in the 1992 season, Kent performed capably for the Mets for 4 1/2 seasons, batting .279. But he didn't establish himself as a formidable run producer until 1997, his first year with the Giants after being traded from Cleveland. Kent amassed 29 homers and 121 RBIs, beginning a nine-year stretch in which he hit .296 while annually averaging 28 homers and 110 RBIs with a .365 on-base percentage and a .529 slugging percentage. He won four Silver Slugger awards and ranked among the top 20 in Most Valuable Player balloting seven times in that span, capturing the trophy in 2000 with San Francisco.
Kent's offensive contributions from 2003-04 with the Astros and from 2005-08 with the Dodgers indicated that he was a legitimate hitter on his own merit and not dependent on Barry Bonds, who batted in front of or behind Kent during most of their years together in San Francisco.
"I think what Jeff meant to the game of baseball as a second baseman may be appreciated more as time goes by and some of these so-called 'no-brainer' Hall of Famers get in in the upcoming years," Aurilia said. "I think his [vote total] should increase, hopefully dramatically. I'd put him up against anybody all time at the position. He's going to be right there, head-to-head, with them."
Chris Haft is a reporter for MLB.com. Read his blog, Haft-Baked Ideas, and follow him on Twitter at @sfgiantsbeat.


text:
We're told David is seeking treatment for alcohol and other issues -- but not drugs. Arquette partied at Beacher's Madhouse at the Roosevelt Hotel on New Year's Eve before checking into a live-in facility.
A source close to David says he is doing it because it's "the right thing to do for his family."
UPDATE: Through her rep, Courteney Cox tells TMZ, "I really admire David and his choice to take charge and better his life. I love and support him."
See also
- 2010's Most Shocking Splits!
- Courteney Cox: I'm Not Divorcing David Arquette
- David Arquette: I Had Dinner with Courteney Last Night
More Today in TMZ History


text:
- Research article
- Open Access
The functional landscape of mouse gene expression
- Wen Zhang†1, 2,
- Quaid D Morris†1, 3,
- Richard Chang1,
- Ofer Shai3,
- Malina A Bakowski1,
- Nicholas Mitsakakis1,
- Naveed Mohammad1,
- Mark D Robinson1,
- Ralph Zirngibl2,
- Eszter Somogyi2,
- Nancy Laurin2,
- Eftekhar Eftekharpour4,
- Eric Sat5,
- Jörg Grigull1,
- Qun Pan1,
- Wen-Tao Peng1,
- Nevan Krogan1, 2,
- Jack Greenblatt1, 2,
- Michael Fehlings4, 6,
- Derek van der Kooy2,
- Jane Aubin2,
- Benoit G Bruneau2, 7,
- Janet Rossant2, 5,
- Benjamin J Blencowe1, 2,
- Brendan J Frey3 and
- Timothy R Hughes1, 2Email author
© Zhang et al.; licensee BioMed Central Ltd. 2004
- Received: 1 September 2004
- Accepted: 18 October 2004
- Published: 6 December 2004
Large-scale quantitative analysis of transcriptional co-expression has been used to dissect regulatory networks and to predict the functions of new genes discovered by genome sequencing in model organisms such as yeast. Although the idea that tissue-specific expression is indicative of gene function in mammals is widely accepted, it has not been objectively tested nor compared with the related but distinct strategy of correlating gene co-expression as a means to predict gene function.
We generated microarray expression data for nearly 40,000 known and predicted mRNAs in 55 mouse tissues, using custom-built oligonucleotide arrays. We show that quantitative transcriptional co-expression is a powerful predictor of gene function. Hundreds of functional categories, as defined by Gene Ontology 'Biological Processes', are associated with characteristic expression patterns across all tissues, including categories that bear no overt relationship to the tissue of origin. In contrast, simple tissue-specific restriction of expression is a poor predictor of which genes are in which functional categories. As an example, the highly conserved mouse gene PWP1 is widely expressed across different tissues but is co-expressed with many RNA-processing genes; we show that the uncharacterized yeast homolog of PWP1 is required for rRNA biogenesis.
We conclude that 'functional genomics' strategies based on quantitative transcriptional co-expression will be as fruitful in mammals as they have been in simpler organisms, and that transcriptional control of mammalian physiology is more modular than is generally appreciated. Our data and analyses provide a public resource for mammalian functional genomics.
- Additional Data File
- Ribosome Biogenesis
- Unannotated Gene
- Predict Gene Function
- Fred Hutchinson Cancer Research Center
Tissue-specific gene expression has traditionally been viewed as a predictor of tissue-specific function: for example, genes specifically expressed in the eye are likely to be involved in vision. But microarray analysis in model organisms such as yeast and Caenorhabditis elegans has established that coordinate transcriptional regulation of functionally related genes occurs on a broader scale than was previously recognized, encompassing at least half of all cellular processes in yeast [1–5]. Consequently, gene expression patterns can be used to predict gene functions, thereby providing a starting point for the directed and systematic experimental characterization of novel genes [1–10]. As an example, it was observed in yeast that a group of more than 200 genes involved primarily in RNA processing and ribosome biogenesis is transcriptionally co-regulated, in addition to being constitutively expressed at some level [11]. Application of statistical inference methods led to the prediction that the uncharacterized genes in this co-regulated group were likely to be involved in RNA processing and/or ribosome biogenesis [5, 9]. Subsequent experimental analysis using yeast mutants validated that many of these predictions were in fact accurate [9].
To date, this approach has only been extensively applied to relatively simple model organisms such as yeast and C. elegans. Its general utility in mammals has not yet been established with respect to the proportion of either genes or functional categories to which it can be effectively applied. Nor has it been formally examined how the use of quantitative transcriptional co-expression for inference of gene function compares to the more traditional approach of inferring functions on the basis of tissue-specific transcription. The extent and precision of hypotheses regarding gene functions that can be drawn from expression analysis in mammals is an important and timely question, given the current absence of knowledge of the physiological functions of at least half of all mammalian genes. Given that distinct and coordinate expression of a group of functionally related genes implies an underlying pathway-specific transcriptional regulatory mechanism, identification of such instances would also represent a step towards delineating mammalian transcriptional networks.
Here, in order to demarcate the general utility of using gene expression patterns to infer mammalian gene functions, and to use this information to begin characterizing genes discovered by sequencing the mouse genome [12], we used custom-built DNA oligonucleotide microarrays to generate an expression data set for nearly 40,000 known and predicted mouse mRNAs across 55 diverse tissues. Several criteria show that these data are reliable and consistent with other information about gene expression and tissue function. Cross-validation results from machine-learning algorithms show that patterns of gene co-expression within many functional categories are 'learnable' and distinct from patterns of other categories, thus proving that many functional categories are transcriptionally co-expressed and likely to be co-regulated. In contrast, tissue-specificity alone is a comparatively poor predictor of gene function, illustrating the importance of quantitative gene expression measurements. To exemplify this, we functionally characterized the highly conserved gene PWP1, which is widely expressed. PWP1 is co-expressed with many RNA-processing genes in mouse, and we show that its yeast homolog is required for rRNA biogenesis. The data and the associated analyses in this paper will be invaluable for directing experimental characterization of gene functions in mammals, as well as for dissecting the mammalian transcriptional regulatory hierarchy.
Expression analysis of mouse XM gene sequences
In order to generate an extensive survey of mammalian gene expression, we analyzed mRNA abundance in 55 mouse tissues using custom-designed microarrays of 60-mer oligonucleotides [13] corresponding to 41,699 known and predicted mRNAs identified in the draft mouse genome sequence using gene-finding programs [12, 14] (NCBI 'XM' sequences; approximately 39,309 are unique; for further details, see the Materials and methods section). Tissue collection was a collaborative effort among several labs in the Toronto area, each with expertise in distinct areas of physiology; consequently, the mouse tissues we analyzed were obtained from several different strains of mice which are typically used to study specific organs and cell types of interest (additional cell lines and fractionated cells from animals were also analyzed, but the results are not included here because the data appear to bear little relationship to the tissue of origin of the cells examined). Since it has previously been established that there is a high correlation in expression of orthologous genes between mice and humans [15], large variations in tissue-specific expression should not occur between individuals within the same species, although we cannot rule out subtle strain-specific differences. To maximize the fidelity of measurements, unamplified cDNA from at least 1 μg of polyA-purified mRNA was hybridized to each array, with fluor-reversed duplicates performed in each case. For most organs this required pooling RNA from multiple animals; for example, more than 50 mice were required to obtain sufficient prostate mRNA. Consequently, potential variations due to parameters such as circadian rhythms or individual dissections should have been minimized by averaging over multiple animals.
All hybridizations were performed in duplicate. Data processing and normalization are described in detail in the Materials and methods. The data were processed so that each measurement reflects the abundance of each transcript in each tissue relative to the median expression across all 55 tissues; although the microarray spot intensities were used to determine which genes were detected as expressed (see below), the figures herein show the normalized, arcsinh-transformed and median-subtracted data, which for convenience we refer to as ratios. All of the data, together with tables detailing correspondence to genes in other cDNA and EST databases, annotations and other features of the encoded proteins, probe sequences, and other files used in our analyses below, are available as Additional data files and without restriction on our website [16].
Validation of expression data
Third, our array data are consistent with RT-PCR analysis. We tested for expected tissue-specific expression of 107 genes (a mixture of characterized and uncharacterized) in 18 selected tissues. In this analysis a single primer pair was tested for each gene. (It is possible that the predicted exon structures for many of the poorly characterized XM genes are incorrect: there was a clear correspondence between whether a product was obtained and whether there was an EST or cDNA in the public databases, which would indicate correct gene structures – see Materials and methods.) Among the 55 primer pairs that could result in amplification, 53 (96%) gave a correct-product size in the tissue(s) expected on the basis of our array data, and 47 (85%) produced amplification most strongly or exclusively in the expected tissue(s) (Figure 2b and data not shown). Although RT-PCR is semi-quantitative, there is an obvious correspondence between the left and right panels in Figure 2b, confirming that our microarray measurements are largely consistent with a more conventional expression analysis method.
Fourth, in the analyses detailed in the following sections, we show that the annotations of genes expressed preferentially in each tissue correspond in many cases to known physiological functions of the tissue, further confirming the accuracy of the dissections and the microarray measurements. Moreover, sets of functionally related genes were often observed to display uniform expression profiles, a result that is highly unlikely to occur by chance.
Definition of 21,622 confidently detected transcripts
Correspondence between gene and tissue function
To examine the relationships among tissues and gene functions, we asked whether genes carrying specific Gene Ontology 'Biological Process' (GO-BP) categories, which reflect the physiological function of a gene, were preferentially expressed in each of the tissue samples, using a statistical test (Wilcoxon-Mann-Whitney; WMW). A selection of the WMW scores are shown in Figure 4b, and expression patterns of all genes in all GO-BP categories can be seen in the Additional data files and at the Toronto gene expressions website [19]. This analysis revealed that the preferentially expressed GO-BP categories typically reflected known functions of the tissue, sometimes with surprising resolution. For example, while the category 'synaptic transmission' scored highly in all neuronal tissues, 'learning and memory' was highest in cortex and striatum; 'locomotor behavior' was highest in cortex, midbrain, and spinal cord; 'response to temperature', in the trigeminal nucleus of the brainstem; and 'neurogenesis', in both adult central nervous system and embryonic heads (Figure 4d). While the WMW test may not have captured all of the categories relevant to each brain tissue, this finding does illustrate that our data contain differential expression of genes involved in distinct high-level neural functions. Further investigation of several tissue-associated GO-BP categories that were initially unanticipated revealed that they are easily rationalized; for instance, lung, bladder, skin, and intestines all express immune-related categories, presumably because they are exposed to the environment and infiltrated by immune cells (see for example, [20]).
Correspondence between gene function and transcriptional co-expression
We also asked more generally whether groups of co-expressed transcripts were associated with specific GO-BP categories. Figure 4c shows that this is indeed the case: any given 'cluster' of genes with correlated expression levels is more likely than not to be associated with a local enrichment of one or a few annotation categories, and manual analysis suggests that tissue-specific expression often reflects the known physiological role(s) of the tissues in which the genes are expressed (examples are shown in Figure 4d). False-discovery rate analysis (see the Materials and methods section) confirmed that over 58% of the 21,622 genes were co-regulated with a set of genes significantly enriched for at least one GO-BP category. For the 7,387 GO-BP annotated genes, over 66% were co-expressed with a set of genes significantly enriched for at least one GO-BP category; in over 25% of these instances, the most significant category was one of its existing annotations. Random permutation analysis (that is, repeating the analysis with randomized gene identities) established a false discovery rate [22] of less than 1% for these analyses (see Materials and methods for details). Hence, quantitative co-expression of functionally related genes appears to be a general phenomenon in mammals.
Using transcriptional co-expression to predict mouse gene functions
It stands to reason that a gene expressed in a specific tissue is likely to be functioning in that tissue. Therefore, we next asked how accurately mammalian gene functions can be predicted on the basis of gene expression profiles. There are many anecdotal examples in which the tissue-specific or cell-type-specific expression of a gene has been used to aid in discovering its function, and this approach has been advocated in previous analyses of mouse tissue expression data (see for example, [15]). Our data indicate that the expression of most mouse genes shows some degree of tissue restriction, but most of the genes are not expressed in a highly tissue-specific manner (Figure 3b). Furthermore, most tissues express genes from multiple functional categories (Figure 4b), and genes from many functional categories are expressed across many tissues (Figure 5), which could make it difficult to distinguish genes in these categories on the basis of expression in one or a few tissues. In addition, defining tissue specificity involves drawing thresholds to form lists, rather than using the quantitative expression information directly to draw functional inferences.
An alternative strategy is to generate functional predictions on the basis of transcriptional co-expression [23, 24], which we show (above) often reflects gene function (Figure 5). This approach utilizes quantitative measurements and places no restriction on tissue-specificity, allowing all expressed genes to be treated equally in the analysis. Furthermore, the use of quantitative co-expression allows the application of sophisticated computational tools that have been optimized for the general problem of classification on the basis of features within a data matrix [25]. We examined the extent to which this approach is effective for our data, and we show (below) that it yields almost universally superior predictions of gene function in comparison to using information regarding simple tissue specificity or tissue restriction.
In this analysis, we used support vector machines (SVMs) [26]. An SVM is a machine-learning algorithm (a computer program) that has previously been shown to work well for the prediction of gene functions in yeast on the basis of microarray expression data [25] but which has not, to our knowledge, been used extensively to predict gene functions from mammalian expression-profiling data. The theory and implementation of SVMs have been described elsewhere in detail [25, 26]. Briefly, an SVM outputs a 'discriminant value' for each gene in each category, and this value reflects relative confidence that the gene is in the category in question. The SVM considers each functional category separately, and the discriminant value is assigned on the basis of where the gene lies relative to other genes within the 'gene expression space' (for example, analysis of 55 samples results in 55 different coordinates). If the gene lies in a region where there is a high proportion of genes that are known to be in the category in question, this will lead to a high discriminant value. SVMs are conceptually related to clustering analysis in the sense that the discriminant values are derived from similarity among expression profiles. But in clustering analysis, genes are grouped solely on the basis of their expression levels; in contrast, SVMs use the known classifications (that is, knowledge regarding which genes are in the category and which are not) in order to map the initial gene expression space into a one-dimensional space (the discriminant values) in which the two classes are optimally distinguished.
Importantly, the discriminant values output by an SVM can be processed to obtain an estimate of the probability that the prediction for each gene in each category is correct (that is, an estimate of precision), on the basis of how well previously annotated genes in the given category can be distinguished from previously annotated genes that are not in the category. This is accomplished by a three-fold cross-validation strategy, in which the analysis is run three times, each time with a different one-third of the annotations masked so that the SVM algorithm does not know whether or not they are in the category when it is assigning discriminant values. Any given discriminant value is then converted to a precision value by simply asking what proportion of the masked genes with discriminant values above the given discriminant value really are in the category in question. The proportion of known genes in the category that are identified by the SVM as being in the category is also obtained at each discriminant value, and is referred to as recall. For all subsequent analyses we used precision and recall as our primary measures of success.
Predicted functions for unannotated genes are supported by sequence features
We next used these trained SVMs (Figure 6a) to predict functions for the 12,123 unannotated genes for which we detected expression in our data. The number of genes with at least one predicted function (that is, one GO-BP category) is shown in Figure 6b at varying precision thresholds (blue line). All of the predictions with precision above 15% are listed in the Additional data files. To make the outputs easier to peruse manually, we grouped 587 GO categories into 231 'superGO' categories, by combining categories that resulted in the same set of predicted genes and that were manually verified to be physiologically related. Figure 6b (red line) confirms that the number of unannotated genes that are predicted to have some function by an SVM with 'superGO' categories are similar to those with the original GO categories, although the number of categories has been compressed.
While some predictions correspond to expression in a single tissue (for example, the 56 genes predicted in 'vision' were predominantly expressed in the eye), such cases were unusual. Rather, most of the predictions were based on expression in multiple functionally related tissues (for example, the five genes predicted in 'regulation of cell migration' were characterized primarily by high expression in colon, large intestine, and small intestine) or more complex patterns (for example, genes predicted in 'CNS/brain development' were preferentially expressed in all adult neural tissues as well as in embryonic heads). Many predictions were found to be in categories related to the cell cycle and RNA processing. These genes tended to be expressed constitutively, but were most highly expressed in embryonic tissues, presumably because of rapid cell growth during development. However, many other predictions relate to neural functions, the immune response, muscle contraction, small-molecule metabolism, and other aspects of adult physiology. All of the individual predictions are provided in a table in the Additional data files, together with the expected precision and other information regarding the gene and the encoded protein, and these can be sorted by gene or by functional category.
Among the 1,092 unannotated genes, 488 (45%) have no overt sequence features suggesting physiological or biochemical function (that is, they have no similarity to previously characterized proteins or known functional domains; they are listed in Additional data files; and also see Materials and methods). Examination of the remaining 55% provided evidence that many of the predictions are likely to be correct. First, a handful of genes that were not annotated in our version of GO have in fact been characterized in the literature. For example, SVMs correctly predicted that phospholamban, the regulator of the Ca2+-ATPase in cardiac sarcoplasmic reticulum [27] is involved in 'muscle contraction or development'. Other genes are similar to characterized genes in other species: for example, the mouse homolog of the yeast 'Extra Spindle Poles' (ESP1) gene was predicted by SVM to function in 'mitotic cell cycle', 'cytokinesis', and 'microtubule based process', consistent with the function of its yeast counterpart [28].
Domains associated with genes predicted to function in specific biological processes
Predicted function
Enriched domain
Description of domain
Proportion of genes with this domain
-log10 significance (P)
Chromosome organization or DNA packaging
HMG (high mobility group) box
Pregnancy/embryo implantation
Somatotropin hormone family
Acyl-CoA/fatty acid/peroxisome
Short-chain alcohol dehydrogenase
RNA/ribosome metabolism/processing
RNA recognition motif
Carboxylic acid/amine metabolism
Enoyl-CoA hydratase/isomerase family
Humoral immune response
The function of this domain is unknown
The function of this domain is unknown
RNA-nucleus import/export
U1 snRNP-specific protein C
Microtubule-based process
Chromosome-segregation ATPases
Comparisons among data sets for predicting gene functions
Although there was a significant correlation among the three different mouse tissue-specific data sets compared in Figure 2a, there were also many cases in which the three data sets disagreed in their assessment of relative abundance of individual genes in different tissues (Figure 2a and data not shown). We reasoned that the SVM cross-validation analysis could provide an objective measure of the quality of the different data sets: since random measurements lead to very poor predictions (Figure 6a), any errors in the data would tend to degrade the precision and recall values. While our manuscript was in preparation, an additional data set was released by Su et al., the authors of reference [15]. Their newer data [30] include measurements of 36,182 known and predicted genes over 61 tissues, measured in duplicate using custom-built Affymetrix arrays, and are thus similar in scope to our data set. Figure 6c shows a comparison between cross-validation results from running SVMs on the three data sets: ours, that of Su et al. [30], and that of Bono et al. [17], with each restricted to the 13 tissues and 1,800 genes common to all three, and the same GO-BP annotations used for all three data sets. Figure 6c shows that, although our data fare slightly better, the power of our data set and that of Su et al. [30] for predicting GO-BP categories are comparable. This confirms that distinct and coordinate regulation of many mammalian functional pathways is authentic because it is observed in two independent data sets.
Comparison of tissue-specificity with co-expression for predicting gene functions
We used two different approaches to ask how well tissue specificity can predict the functional classes of genes, in comparison to co-expression. First, from our data we compiled three sets of lists: genes that are expressed in each of the 55 individual samples; genes that are expressed highest in each of the individual 55 samples and in groups of functionally related samples (for example, treating all neural tissues as a single group); and also genes that are expressed uniquely in individual samples. All of these lists (175 in total) are compiled in the Additional data files. For each of the 992 GO-BP categories, we assessed the precision and recall for each of these lists (that is, whether these lists can distinguish genes in the category from those not in the category), and then identified the best precision value and its associated recall value for that category. Figure 6d shows a histogram of the difference between SVM precision and tissue-specificity precision, at the same recall value, for each GO-BP category. The vast majority of data points are greater than zero (P < 10-76; two-sided pairwise t test), indicating that co-expression patterns can be used (by SVMs) to predict gene functions significantly better than tissue-specificity alone.
It is possible that improved results might be obtained by other ad hoc procedures for sorting the genes in different ways, or by more automated procedures for generating large numbers of lists. However, an alternative analysis suggests that this is unlikely: when we re-ran SVMs with the matrix of 1s and 0s indicating which gene is expressed (or not) in each tissue, rather than the matrix of quantitative expression values, the resulting predictions were inferior (dotted magenta line in Figure 6a). In theory, if any combination of on/off information about gene expression in different tissues was informative for identifying genes in any category, it would have been identified by the SVMs. The result we obtained indicates that the quantitative measurements contain critical information reflecting functions of genes that is not, for the most part, contained in the binary (expressed/not expressed) information.
Validation of predictions by de novofunctional analysis
Simultaneous gene discovery, network mapping, and functional inference
The data presented here and the resulting inferences for the physiological roles of mammalian genes significantly extend previous microarray-based analyses of mammalian gene expression [7, 15, 17, 21, 23, 24, 30]. First, the data support the notion that there are thousands of mouse genes that are not represented in current cDNA databases [12, 34–36]. Amongst all 21,622 confidently detected transcripts (Figure 4), 5,600 were not associated with a cDNA; 3,551 of these had EST but not cDNA support, indicating that many of them correspond to bona fide genes (Figure 2b). Moreover, inferences for the physiological roles of these transcripts can be obtained by analysis of quantitative expression levels across tissues; the 1,092 unannotated genes for which we made high-confidence predictions (Figure 7) contain 54 with no EST or cDNA support, and an additional 114 that have only EST support.
Second, our estimate that more than 58% of all transcripts are regulated together with genes in specific functional categories is much higher than previous estimates. One analysis, based on cursory analysis of early yeast and Xenopus expression data, suggested that only 5–10% of all genes fall within 'synexpression' groups [2]. Our results represent a minimum estimate of the correspondence between gene function and gene expression, because shortcomings in either the annotations or the data would tend to reduce these figures. Our results indicate that there are regulatory pathways that control many distinct biological processes in mammals, and that it is already possible to interpret the expression patterns of the majority of mammalian genes in a functionally meaningful way by comparing them to the patterns of the subset of genes that are already annotated. Moreover, it may be more straightforward than was previously anticipated to apply the same computational techniques to mammalian microarray data that are now being applied to identify 'network modules' and regulatory mechanisms in far simpler organisms, such as yeast (see for example, [37]). These potential applications represent an obvious future extension of the work presented here.
Third, while previous analyses using microarray expression data to predict gene functions [1–10] have focused on the fact that genes in large or general categories can be recognized (for example, ribosome biogenesis, translation, or proteolysis), we show quantitatively that this methodology is applicable to a much wider variety of functional categories, many of which are specific to higher organisms (for example, the category 'Pregnancy/embryo implantation' is specific to mammals; Figures 5,7).
Fourth, our analysis shows that the use of quantitative gene expression measurements to infer mammalian gene functions is more powerful than the traditional approach of using information on simple tissue specificity. Genes in many GO-BP categories were precisely identified by SVM using quantitative co-expression, but not on the basis of tissue specificity or tissue restriction (Figure 6a,6d). It appears from our results that genes in functional categories with more widespread expression (such as 'epidermal differentiation', 'regulation of cell migration', and 'apoptotic program'), categories corresponding to basic cellular functions (such as 'oxidative phosphorylation', 'RNA processing', and 'DNA replication'), and even categories that describe interactions among different cell types ('taxis', 'glycoprotein metabolism', 'cell-cell adhesion') can all be recognized and distinguished from genes in other categories on the sole basis of their coordinate expression across many tissues (Figures 5,7), even though they are expressed in many tissues (and in some cases all tissues, as in the case of mRNA splicing and other 'housekeeping' functions). PWP1 is an example of a gene that is widely expressed but has a pattern of expression that was predictive of its function (Figure 8).
A strategy and resource for mouse functional genomics
Analysis of mutant phenotypes is one of the most powerful and definitive ways to study gene functions. Many of the predicted gene functions (see Figure 7 and the Additional data files) in turn predict specific mutant phenotypes; for example, mutation of genes predicted to function in 'vision' would be expected to display defects in sight or eye morphology, while mutation of those predicted to function in 'RNA processing/ribosome biogenesis' might be lethal embryonically, but with alterations in RNA profiles or ribosome content. We have already initiated efforts to validate predicted gene functions in animals: of the XM genes on our array, 2,917 are already represented in collections of publicly available gene-trap ES cell lines [38] (indicated on the right in Figure 7). It will become increasingly straightforward to test these predictions as RNA interference methods are refined and the collection of mapped mouse mutants expands [38–40]. All of our predictions are listed in the Additional data files and will provide guidance for future efforts in mammalian functional genomics and/or support for other functional studies.
In contrast to other available mouse tissue-specific data sets (such as those described in [30]), all of our data, as well as the SVM predictions, can be downloaded anonymously without restriction from the Additional data files or from our website [16] and can be freely copied, modified, and propagated. The oligonucleotide sequences are provided, so that copies of our array design can be obtained and modified by other labs, and our expression data can be mapped to any clone collection or updated sequence annotation by batch BLAST. Many other supporting files are provided, including GO annotations, maps to gene-trap collections, and genomic locations of the probes. To facilitate perusal of the data, we have also created a web tool [19] that displays subsets of the gene expression data together with functional information and SVM predictions. This tool currently supports queries originating with a gene of interest, a functional category of interest, or a region of the chromosome of interest, which may facilitate the use of gene expression patterns and predicted gene functions in identifying genes that confer mapped traits [41]. We anticipate expanding and refining this resource to mirror both additional data and updated annotations.
We have created an extensive mouse expression data set and asked whether quantitative gene expression patterns correspond to functional gene categories. Our major finding is that most tissues express many functional categories, consistent with the fact that they contain many different cell types performing many different functions, but that many different functional pathways are coordinately expressed in a quantitative manner across tissues such that many categories display one or more distinctive patterns. For example, embryonic heads contain many cell types, and consequently express genes in a variety of categories including 'CNS/brain development', 'M phase', 'skeletal development', and 'microtubule-based process', yet an SVM can distinguish genes in these categories because they are differentially regulated across all 55 tissues in a way that is characteristic for each functional category (Figures 5,7). The simplest explanation for this observation is that there are discrete factors or sets of factors that control each coordinately regulated pathway. We conclude that functional genomics strategies that rely on quantitative transcriptional co-expression will be as fruitful in mammals as they have been in simpler organisms, and that transcriptional control of mammalian physiology may be more modular than is generally appreciated.
Materials and methods
Mouse mRNA isolation
Mouse tissues were isolated from the following strains: ICR (whole brain, testis, skeletal muscle, heart, lung, liver, embryo at 15 days, embryo at 12.5 days, embryo at 9.5 days, mammary gland, placenta at 9.5 days and placenta at 12.5 days); CD1 (Charles River Laboratory, Wilmington, USA; cortex, cerebellum, striatum, hindbrain, midbrain, bone marrow, knee, teeth, mandible, calvaria, femur (bone marrow flushed diaphyses), tongue surface, snout, large intestine, thyroid, aorta, brown fat, lymph node, olfactory bulb, adrenal gland, prostate, digits, trachea, trigeminal nucleus); C3H (The Jackson Laboratory, Bar Harbor, USA; salivary gland, thymus, ovary, uterus, tongue, stomach, small intestine, spleen, colon, uterus, pancreas, epididymis, eye, bladder, skin); C57BL/6 (The Jackson Laboratory, Bar Harbor, USA; spinal cord); Black Swiss (NTac:NIHBS; Embryonic heads); and R1 (ES cells). With the exception of embryonic tissues and ES cells, tissues were harvested from 3–6 month-old mice. Following recommended University of Toronto protocols, mice were euthanized by barbiturate injection and tissues were dissected as quickly as possible (within 10 minutes), snap-frozen in liquid nitrogen, and preserved at -80°C until use. RNA was extracted using homogenization and Trizol reagent (Invitrogen, Carlsbad, USA) following the instructions from the manufacturer, and mRNA was purified as described previously [3].
Microarray probe design
A FASTA file of 42,192 known and predicted mRNAs (XM sequences) was obtained from Deanna Church at NCBI on July 9, 2002 and is posted as Additional data file 1. Interspersed repeats and low complexity DNA sequences were masked with Repeat-Masker [42]. The 500 nucleotides from the 3' end of each mRNA were extracted and 10 non-overlapping Tm-balanced probes were generated using PrimerX [43] with default settings. The most unique among the 10 was identified on the basis of having the highest ΔG difference between the first (identical) and second most significant BLAST hits among the 42,192 initial XM mRNAs. Then, 41,699 probe sequences (those for which probes could be designed using this procedure) were submitted for oligonucleotide microarray production (Agilent Technologies, Palo Alto, USA). These arrays are manufactured using an ink-jet process, in which oligonucleotides are synthesized on the array by direct deposition of phosphoramidites [13]. The specificity, sensitivity, and reproducibility of these 60-mer arrays has been described elsewhere in detail [13].
Among the probes on the array, 40,822 were unique; those that were not unique can be attributed primarily to gene duplications, predominantly pseudogenes of GAPDH, ribosomal proteins, and retrovirus-like sequences. To minimize the impact of redundancy on statistical analyses, we collapsed the data from 1,928 duplicated probes and XM sequences that were in these sequence families (including 100 probes duplicated between the two array designs) into 525 groups that shared identical probe sequence and/or were both annotated and regulated in the same way. We also mapped all of the XM sequences to the current version of the mouse genome (Build 32) and to three cDNA databases (UniGene, RefSeq, and Fantom II; see below) and identified 1,991 XM sequences in which XM sequences adjacent on the chromosome also mapped to the same cDNA; these were collapsed into 904 groups. The Additional data files include a table mapping the 41,699 probes against the 39,309 presumed distinct transcripts.
Labeling and hybridization
The mRNA (1–2 μg) was reverse-transcribed with random nonamer primers (1 μg per reaction) and T18VN (0.25 μg per reaction) to synthesize cDNA. The reaction contained a 1:1 mixture of 5-(3-aminoallyl) thymidine 5'-triphosphate (Sigma, St. Louis, USA) and thymidine triphosphate (TTP) in place of TTP alone. The cDNA products were bound to QIAquick PCR Purification columns (Qiagen, Hilden, Germany) following the manufacturer's instructions, washed three times with 80% ethanol, and eluted with water. Purified cDNA was reacted with N-hydroxy succinimide esters of Cy3 or Cy5 (Amersham Pharmacia Biotech, Piscataway, USA) following the manufacturer's instructions. Hydroxylamine-quenched Cy-labeled cDNAs were separated from free dye molecules using QIAquick columns. Mixed labeled cDNAs were added to hybridization buffer containing 1 M NaCl, 0.5% sodium sarcosine, 50 mM methyl ethane sulfonate (MES), pH 6.5, 33% formamide and 40 μg herring sperm DNA (Invitrogen, Carlsbad, USA). Hybridizations were carried out in a final volume of 0.5 ml injecting into an Agilent hybridization chamber at 42°C on a rotating platform in a hybridization oven (Robbins Scientific Corporation, Sunnyvale, USA) for 16–24 h. Slides were then washed (rocking for approximately 30 seconds in 6 × SSPE, 0.005% sarcosine, then rocking for approximately 30 seconds in 0.06 × SSPE) and scanned with a 4000A microarray scanner (Axon Instruments, Union City, USA). Hybridizations were performed in duplicate with fluor reversal: that is, each mRNA sample was examined in duplicate, once in the Cy3 channel and once in the Cy5 channel, on separate arrays. Each array was hybridized with two samples simultaneously, each from an individual tissue. Essentially identical results were obtained from single-channel data from the same mRNA sample analyzed on different arrays, which were distinct from individual channels on the same arrays analyzed with a different mRNA. The organization of the hybridizations, and the data for individual channels, are given in the Additional data files.
Image processing and normalization
TIFF images were quantitated with GenePix (Axon Instruments). Individual channels were spatially detrended (that is, overall correlations between spot intensity and position on the slide were removed) by high-pass filtering (see [44]) using 10% outliers. We applied variance stabilizing normalization (VSN) [45] using 25% of the genes to normalize all single channels to each other. We manually identified and removed measurements that were inconsistent between dye-swaps, by either removing data from residual artifacts apparent on microarray images or removing the higher of the two disparate intensity measurements (in order to minimize false-positive detections). Measurements were transformed to arcsinh values (which are similar to natural log values, but are defined for negative numbers which emerge from the VSN) and for each measurement the median across all arrays was subtracted to obtain relative expression ratios for each gene in each tissue compared to all tissues. Remaining inconsistencies between dye-swaps were addressed by removing the higher of any two measurements that differed by more than two arcsinh units (in order to further minimize false-positive detections). The dye-swap arcsinh values were then averaged between replicates and among multiple probes detecting the same sequence. Clustering and manual analysis indicated that ratios below zero were generally not biologically meaningful (and probably stem largely from measurement error among low-intensity spots); hence ratios below zero were set to zero for all analyses using median-subtracted arcsinh values (Figures 1, 2, 4, 5, 6, 7 and SVM analyses). Missing values (fewer than 0.01% of all data points) were set to zero. Median-subtracted arcsinh values correspond approximately to the following ratios (arcsinh = linear): 0 = 1/1; 1 = 2.7/1; 2 = 7.5/1; 3 = 20/1, 4 = 55/1; 5 = 155/1, 6 = 405/1.
Mouse GO-BP annotations were downloaded from the Gene Ontology website [46] and the European Bioinformatics Institute (EBI) [47] and both were mapped to XM gene sequences by sequence identity to the annotated source sequences. The full annotation database is on our website [16]. Fewer than 0.01% of these annotations were derived from gene expression (IEP code); we confirmed that removal of these genes had no appreciable impact on statistical analysis or the SVM analysis, and hence the use of these annotations to analyze gene expression is not circular. The Mouse Genome Informatics (MGI) annotations are reported to be manually compiled, whereas the EBI annotations include automated sequence-based annotations (for example, potassium channels are annotated as being in 'ion transport' and the mouse homolog of the yeast Tim8 protein, which is a translocase of the inner mitochondrial membrane, is annotated as being in 'mitochondrial translocation'). All GO-BP annotations were propagated up all possible edges of the GO graph. Redundant GO-BP categories were excluded. Categories with fewer than three genes among the 21,622 expressed genes were excluded from our analysis since they are not appropriate for the statistical tests we used, and those with more than 500 genes were excluded because they are not specific to distinct physiological processes.
False-discovery rate analysis
Each gene was associated with a co-regulated group consisting of the 50 annotated genes with the highest Pearson correlation coefficient relative to it. Annotation enrichment of this group in each GO-BP category was scored using the hypergeometric P value [48]. The minimum value of this score across all GO-BP categories was used as the measure for significant enrichment in any GO-BP category. P values were assigned to these measures using a permutation scheme on the gene labels. The statistical significance of the P values was evaluated using the Benjamini-Hochberg (BH) linear step-up procedure [22] to ensure a false discovery rate (FDR) of less than 1%. For annotated genes, a second measure was computed: the minimum among its annotated categories of the hypergeometric P values of its co-regulated group. A gene-specific permutation scheme associated P values with these scores and the FDR was also controlled at 1%.
Cluster diagonalization
Starting with an initial hierarchical clustering (agglomerative, average linkage, based on Pearson correlation coefficient), rows were divided into groups by removing a small number of links at the highest levels of the tree and grouping together all rows contained within the same disconnected subtree. Each row group was then associated with the column that contained the maximum expression value averaged over all the profiles in the group. The row groups were then sorted in increasing order of their associated column numbers.
Support vector machines
We used the SVM software package Gist [49] version 2.0.8 in Linux with parameter settings '-radial -zeromeanrow -diagfactor 0.5'. Precision was established by three-fold cross validation.
Identification of corresponding clones in cDNA and EST databases
We identified the closest corresponding mouse mRNAs in FANTOM II [50] (60,770 sequences); RefSeq [51] (16,601 sequences); UniGene [52] (87,495 sequences); and Ensembl [53] (32,911 sequences) using BLASTN with a threshold of E-60. We identified corresponding mouse mRNAs in dbEST [54] (3,939,961 sequences) using BLASTN with a threshold of E-20.
Identification of genes common to other microarray data and Spearman rank correlations
For Figure 2a, mRNA sequences were downloaded from [55] (for Su et al. data [15]) and [56] (for Bono et al. data [17]). The Su et al. [15] gene expression data were downloaded from [57] (9,977 sequences represented on the array) and the Bono et al. [17] data, from [58] (54,005 sequences represented on the array). The selected 41,699 NCBI mRNAs were used in a BLAST search against these two mRNA databases; a BLAST comparison between the two databases was also performed to retain only genes for which the closest sequence to each XM gene is also the closest sequence between the two other databases. All BLAST searches were performed with threshold E-60, and the best hit was selected for the multiple blast results. The 1,109 genes that have common hits in all the BLAST results and with gene expression data available were selected for the gene expression analysis. The 1,109 genes from all three datasets were normalized to make them comparable. To facilitate comparison, in the Bono et al. [17] dataset, each gene was median-centered in each tissue by subtracting its median expression value across all 13 common tissues. The Su et al. [15] data were arcsinh-transformed before median-centering. The data from the study described here that was used in the comparison was not zeroed, as it was in other analyses, and was median-centered using the median calculated only on the 13 common tissues, rather than all 55. The Spearman rank correlation coefficients of each pair of tissues among all three studies were transformed to Z-scores by multiplication by sqrt(1108) and then converted to P values using the cumulative probability density of a standard normal distribution.
For Figure 6c, an alternative mapping strategy was employed: our probe sequences, the Bono et al. [17] clone sequences, and the Su et al. [30] probe sequences were associated with 30,832 MGI sequences by mapping directly to corresponding MGI/GenBank sequences; 1,800 genes were identified in which a reciprocal best match between the probe sequences and the MGI sequence was identified in all three studies.
Primer pairs were designed to have a matching Tm (59°C) and sequences are listed in the Additional data files. RT-PCR assays were performed using the OneStep RT-PCR Kit (Qiagen). Reactions were performed in 25 μl volumes containing 0.5 ng polyA+ mRNA, 7.5 units porcine RNAguard (Amersham) and 300 pM each of the forward and reverse primers. After 30 rounds of amplification, the reaction products were separated on 2% agarose gels stained with ethidium bromide. Inverted black-and-white images of the gels were recorded using a Syngene gel documentation system and GeneSnap software (Synopics, Frederick, USA). In total, 107 primer pairs were tested. Of the 57 XM genes tested that corresponded to a known cDNA, 42 were among those that were amplified (74%). Of the 25 tested that corresponded to an EST but not to a known cDNA, 12 were amplified (48%). However, of the 25 tested that did not correspond to a cDNA or EST, only one was amplified (4%).
Identification of genes associated with gene traps
Six different gene-trap resources were searched to identify genes associated with gene trap ES cell lines. For BayGenomics [59], Centre for Modeling Human Disease (CMHD) [60], University of California Resource of Gene Trap Insertions [61], and Fred Hutchinson Cancer Research Center (FHCRC) [62], the gene-trap sequence tags were downloaded from the website and searched against the selected 41,699 mRNA sequences using BLASTN. For the German Genetrap Consortium (GGTC) [63] and Mammalian Functional Genomics Centre (MFGC) [64], the web-based BLAST servers were used to search the 41,699 mRNA sequences against their gene-trap sequence databases. The hits with lengths equal to or larger than 50 nucleotides, and identity equal to or larger than 98%, were considered to be associated with the gene-trap ES-cell lines.
RNA extraction, northern blotting, affinity purification, and mass spectrometry
The TetO7-PWP1 and isogenic wild-type control strains were created and analyzed as previously described for other essential yeast genes [9]. Briefly, strains were exposed to 10 μg/ml doxycycline (Sigma) for a total of 24 h before harvesting for RNA extraction. RNA extraction and northern blotting were performed using standard protocols and oligonucleotide probes as described previously [9]. TAP purification of Pwp1p was performed as previously described [9] using 1.3l culture volumes; gel-purified proteins were identified by MALDI-TOF mass spectrometry.
Additional data files
We thank David MacLennan, Bill Stanford, and Charlie Boone for helpful conversations, Li Zhang, Richard Hill, Tony Candeliere, Dominic Falconi, and Usha Bhargava for assistance in obtaining mouse tissues, Dawn Richards and Victoria Canadien at Affinium Pharmaceuticals for MALDI-MS, Deanna Church for assistance with XM sequences, and Shawna Hiley for proofreading the manuscript. This work was supported by grants to T.R.H. and B.J.B. from CIHR, Genome Canada, and the CFI. WZ was supported by a University of Toronto Open scholarship and Q.D.M. is supported by an NSERC postdoctoral award. M.G.F. is supported by the Krembil Chair in Neural Repair and Regeneration.
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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