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Motorcycle Gloves
Klim Quest Short Gore-Tex Glove | Black or Gray
Ride in comfort and control with Klim Quest Short Gore-Tex Men's Glove. Waterproof and breathable with enhanced grip and feel, these riding gloves feature leather construction, hard knuckle and palm slider protection. Enjoy advanced tactility, less fatigue and improved confidence with the Quest Short Gore-Tex Glove by Klim. Learn More$179.99 -
Gyde-Powered by Gerbing 12V Heated Men's Vanguard Glove
Gyde-Powered by Gerbing 12V Heated Men's Vanguard Glove is the global leader in wearable powersport technology. Engineered with an advanced interconnect system, Gyde garments plug directly into the electrical system on your motorcycle, snowmobile, ATV, boat or even airplane providing a continual source of power to help you maintain warmth in any condition even at high speeds. Designed using a combination of patented Microwire™ heating technologies in conjunction with the electrical system on your vehicle enable Gyde to put heat where others can't and makes for the world's warmest and longest lasting 12 volt platform, specifically designed to combat temperature and speed.
Learn More$229.99 -
Gyde-Powered by Gerbing 12V Heated Men's Hero Glove
This Gyde-Powered by Gerbing 12V Heated Men's Glove is leading the charge in powersport apparel technology. Via an advanced interconnect system, these protective garments are designed to plug directly into the electrical system on your motorbike, ATV, boat, snowmobile or even your AIRPLANE; providing continuous power to help you maintain warmth even when you're flying in a deep freeze! Using a unique combination of Gyde's patented Microwire™ heating technologies and your vehicles electrical system, this heated glove keeps you the warmest with the longest lasting 12V platform specially engineered to hang even at your wicked speeds.
Learn More$199.99 -
Gyde-Powered by Gerbing 7V Heated Three Finger Mitts
The Gyde-Powered by Gerbing 7V Heated Three Finger Mitts are constructed out of a lightweight stretchable material that will help your range of motion, and increase your comfort, and contain a patented Microwire heat technology. Learn More$249.99 -
Klim Dakar Glove
The Klim Dakar Glove finishes off an already dynamic series of off-roading apparel with durable and functional hand protection for those riders who like to push it to the limit. This enhanced version of hand protection has long been a favorite of some of the most talented moto-athletes, proving great performance wherever your ride takes you.
Learn More$49.99 -
Klim Short Induction Glove
The Klim Induction Glove provides maximum airflow in combination with durability and comfort. This is a warm weather, short, perforated leather adventure glove to battle heat and fatigue. Learn More$139.99 -
Klim Induction Glove
The Klim Induction Glove provides maximum airflow in combination with durability and comfort. This is a warm weather, gauntlet, perforated leather adventure glove to battle heat and fatigue. Learn More$159.99 -
Klim GORE-TEX Fusion Winter Glove
The Klim GORE-TEX Fusion Winter Glove allows the rider to choose between warmth or grip, by simply placing the internal flap above or below the hand. Learn More$139.99 -
Klim Summit Glove
The Klim Summit Glove is designed for riding in severe terrain where dexterity, control and protection is paramount. Gore-Tex technology is guaranteed to keep you dry while Gore Grip provides superior tactility in the leather fingers and palm. The Summit also features Poron® SR™ backed foam on knuckles for exceptional impact protection. Putting them on is a cinch with the entry assist pull-on loop and an eject tab aids quick removal. Designed to be worn with liners for increased warmth or without for cooler riding temperatures. Treat yourself to unrivaled feel and control with these high caliber summit gloves from Klim! Learn More$159.99 -
Klim Inversion Pro Glove
The Klim Inversion Pro glove provides impact and abrasion protection with durability and cold weather comfort. Completely windproof GORE Windstopper material ensures your hands are protected from nasty wind chills while impact absorbing PORON SR foam protects knuckles and finger backs. The palms, fingers and knuckle overlays are made of sturdy goat skin to ensure toughness while also offering excellent dexterity. Available in small through XXXL sizes and in black or gray colors. Pull on a pair of Inversion Pro gloves, grab a handful of throttle and enjoy the ride! Learn More$89.99 -
Klim Quest Men's Glove | Long
Everybody loves an adventure, and yours is going to be a lot better with the men's Klim Quest Glove. These gloves come ready with GORE-TEX® + GORE GRIP—not an Finnish black metal band, but a technology that maximizes the tactility of these gloves. Waterproof and breathable, the Quest Glove from Klim goes the distance year after year. Learn More$229.99 -
Klim Rambler Men's Glove
Classic styling and ready for action, the Klim Rambler Glove just plain works in every condition. From grabbing an axe to twisting a throttle—not both at the same time, unless you're a serious badass—the Rambler will be your go to glove for all of your adventuring needs. Learn More$69.99 -
Klim Glove Liner 4.0 Insulated
The Klim Glove Liner 4.0 Insulated has been designed for ultimate cold weather riding with all the features to turn an ordinary riding glove into an insulated glove. Combining 80 grams of 3M™ THINSULATE™ platinum insulation with a leather palm for superior slip resistance, this glove is sure to elevate any glove to the next teir of cold weather protection. Use the Klim Glove Liner 4.0 Insulated for ideal protection from the elements, recommended use with any Klim Gore-Tex riding glove.
Learn More$54.99 -
Klim Mojave Glove | Gray, Orange & Blue
The KLIM Mojave Glove is designed for hot weather riding with a combination of comfort and protection. Built with comfort foam knuckles, touch screen compatible wrap fingertips and silicone grips, this glove is perfect for a motorcyclist looking for both protection and comfort. The KLIM Mojave Glove is full of features that will appeal to adventure riders wanting protection, comfort and versitility.
Learn More$39.99 -
Klim Adventure Glove Short
The Klim Adventure Glove protects your hands with the latest armor and superior comfort during wet and cold weather riding. Available in black or brown leather with outstanding GORE-TEX waterproof and breathable technology. GORE GRIP finger construction secures the internal layers of the Adventure glove to provide a direct feel for unparalleled tactility and dexterity. Poron XRD protective foam is impact absorbing and ready for action on the back of each finger and in the palm sliding pad to keep you safe. Ceramic printed knuckles on the Adventure gloves are made of 3D silicone for comfortable and effective impact protection. Velcro closures provide a secure fit and entry assist loops are another handy feature to make life a little easier. Each of your hands has 27 bones that make operating your motorcycle possible, keep them comfortable, dry and protected with the Adventure glove from Klim!
Learn More$169.99 -
Klim Dakar Pro Glove
Note: This product is no longer able to be ordered in the USA but is available in Canada. Please use the toggle button on the upper right of your screen to switch to our Canadian website. Prices will be converted to Canadian Dollars.
The Klim Dakar Pro glove provides protection and comfort in an armored leather glove designed for riders who prefer to take less than perfectly manicured roads. Poron XRD inserts on each finger and the knuckles safeguards against impacts while remaining comfortable and flexible. Integrated stretch panels on these well equipped Dakar Pro's conform to your hand while also having a little give to increase comfort. High-quality leather construction stands up to off-road abuse while perforated zones maximize air flow to keep your hands cool. Ride with confidence and in comfort in the dirt with the Dakar Pro gloves from the apparel experts at Klim! Learn More$89.99 -
Klim Adventure Motorcycle Glove, Long
The Klim Adventure Glove Long offers full gauntlet protection and superior comfort during wet and cold weather motorcycle fun. The full leather construction is available in black or brown with GORE-TEX waterproof and breathable technology that also incorporates GORE GRIP. The fingers of the Adventure glove provide a secure feel throughout the material as the layers are secured to each other to provide outstanding tactility and dexterity. Impact absorbing Poron XRD protective foam is ready for action on the back of each finger and in the palm sliding pad should you ever need it. Ceramic printed knuckles are made of 3D silicone for comfortable and effective impact protection.
Pull the Adventure gloves on with ease thanks to an entry assist loop and adjust the fit with dual Velcro closures, one on the wrists and the other around the gauntlet collar. The gauntlet is designed to be worn either inside or outside of the sleeve depending on your preference. There are 27 bones in each of your hands, keep them comfortable, dry and protected on motorcycle excursions with the Klim Adventure long glove!
$229.99 -
Macna LeatherGel
Leather Conditioner in 200ml tube. Learn More$18.25 $6.95 -
Macna Men's Summer Street R Leather Sport Motorcycle Glove
Track-ready glove with Kevlar reinforced leather Learn More$149.99 $62.95 -
Macna Men's Summer Borasco Leather Motorcycle Glove
Macna's Borasco summer motorcycle glove for men features soft leather and a Bemberg silk liner for warm weather comfort.
Learn More$122.50 $55.95 -
Macna Women's Summer Lane Leather Motorcycle Glove
Macna's Lane motorcycle glove for women is a full-coverage summer glove made from soft goat leather. Learn More$89.99 $40.95 -
Macna Women's Summer Cata Waterproof Motorcycle Glove
Macna's Cata motorcycle glove is a waterproof 3-season glove for women that features a combination of soft leather and man made materials, as well as semi-hard knuckle armor for exceptional comfort and protection. Learn More$94.00 $41.95 -
Oxford RainSeal Handlebar Muffs for Motorcycles
These hand covers mount to your handlebars to keep your hands warm & dry even in the coldest riding weather! A pouch on the inside allows you to add Oxford Electric Hothands for added warmth! Learn More$49.95 -
Macna Men's Cold Weather Electrically-Heated Proton Waterproof Leather & Textile Glove with Waterproof Adjustable Controller
With an incredible fit, motorcycle specific features and build quality, the Electrically-Heated Proton Waterproof Leather & Textile Glove by Macna is a great motorcycle glove that is also heated. Learn More$289.99 $124.95 -
R&G Leather Deluxe Motorcycle Gloves
Protect your hands in style and comfort with R&G Deluxe Motorcycle Gloves. Learn More$49.55 -
Macna Men's Summer Talik 2 Leather Motorcycle Glove
Macna's Talik 2 summer motorcycle glove for men features soft, perforated leather and a mesh liner for warm weather comfort.
Learn More$96.99 $44.95 -
Macna Women's All-Season Candy Waterproof Motorcycle Glove
Macna Women's "Candy" summer motorcycle soft goat leather riding gloves Learn More$99.99 $42.95 -
Macna Women's Summer Jewel Leather Motorcycle Glove
Macna Women's "Jewel" summer motorcycle soft goat leather riding gloves Learn More$80.99 $28.95 -
Macna Men's Summer Vortex Leather Sport Motorcycle Glove
A sporty, full-featured glove with hard knuckle armor. Learn More$129.99 $50.95 -
Macna Men's Summer Scalpel Leather Motorcycle Glove
Macna's Scalpel summer motorcycle glove for men is a sporty glove that will keep you looking and feeling cool. The Scalpel features soft goatskin leather with perforations in the fingers, base of the thumb, and top of the hand for warm weather comfort.
Learn More$96.99 $44.95 -
Macna Men's Summer Equator Leather & Mesh Motorcycle Glove
Macna's Equator summer motorcycle glove for men features soft, goatskin leather and mesh panels in the wrist, fingers and top of the hand for warm weather comfort.
Learn More$96.00 $42.95


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Vindication is rarely as swift or complete as that likely awaiting the Disney execs who passed on M. Night Shyamalan's latest effort "Lady in the Water." Disney's misgivings were well founded, as Shyamalan has followed "The Village" with another disappointment -- a ponderous, self-indulgent bedtime tale.
Vindication is rarely as swift or complete as that likely awaiting the Disney execs who passed on M. Night Shyamalan's latest effort "Lady in the Water." After Disney balked, the director carted the project to Burbank neighbor Warner Bros., then lambasted his former studio for a lack of vision in a tie-in, tell-some book. Disney's misgivings were well founded, as Shyamalan has followed "The Village" with another disappointment — a ponderous, self-indulgent bedtime tale. Awkwardly positioned, this gloomy gothic fantasy falls well short of horror, leaving grim theatrical prospects beyond whatever curiosity the filmmaker's reputation and the mini-controversy can scare up.
Although Shyamalan indicates in the storybook-style animated opening sequence that the story is derived from ancient myth, his perplexing creation stimulates a nagging sense that he's simply making it up as he goes along. (This is apparently the case, as the production notes say the idea "began as an impromptu bedtime story for his two young daughters.") Among their complaints, Disney execs reportedly warned Shyamalan about presenting a clueless film critic as a supporting player, though compared with the pic's other transgressions, that one is harmless. The character appears designed to exact a measure of revenge against those who slighted "The Village" and to inoculate "Lady in the Water" against whatever barbs are hurled its way. That rather gratuitous plot point, however, coupled with the writer-director-producer's expansion of his traditional Hitchcockian cameos into a pivotal role, does flavor the film with a distracting hint of self-absorption. If Shyamalan's earlier works hinged on a clever twist or big surprise (think "The Sixth Sense" and "Unbreakable," and, less successfully, "The Village"), "Lady" telegraphs its intentions from the outset. The lady in question, Story ("Village" star Bryce Dallas Howard), is a "Narf" — a sea nymph from The Blue World who has taken up residence in the swimming pool of the Cove apartments, which no one will confuse with "Melrose Place." The complex is home to an eclectic group of misfits, beginning with Cleveland Heep (Paul Giamatti), its emotionally wounded superintendent, who discovers Story and takes it upon himself to help the naked nymph fulfill her mission and return home, a task that will require the aid of various tenants. Throughout, she's in danger of attack by another Blue World visitor, the Scrunt, a werewolf-like creature that bears a passing resemblance to the "beasts" in "The Village." Story is alternately fearful and full of other-worldly serenity, forcing Cleveland to elicit clues regarding the myth and how best to assist her from Young-Soon (Cindy Cheung), whose Korean-speaking mother is well versed in Narf lore. Others helping decipher the scheme include the aforementioned critic (Bob Balaban); a would-be writer (Shyamalan) and his sister (Sarita Choudhury); and a wordsmith (Jeffrey Wright) who excels at crossword puzzles. Shyamalan's script is its own kind of puzzle, albeit one that never connects and generally handcuffs the large cast. Giamatti is appropriately schlubby and dour as the Everyman thrust into the organizing role, while Howard can never get much beyond a vacant, beatific stare. Only Cheung brings much vitality to the proceedings, and that's by jabbering away in fast-talking Pidgin English that's mostly silly but to some might border on offensive. Nor does it help that Balaban's arrogant critic keeps discussing movie conventions and cliches in a film that builds toward an unsatisfying and abrupt climax. Reminding the audience where movies fall flat hardly seems advisable. Tech credits similarly fail to impress, including the fleetingly glimpsed creature effects, which, given the marginal level of suspense, hardly seem to justify a PG-13 rating. What's most acutely lacking, though, is magic equal to the movie's fairy-tale underpinnings — starting with the enticing, childlike notion of something mysterious living in the swimming pool. Establishing a sense of fantasy, beyond James Newton Howard's hard-working score, might help people accept that a sort-of mermaid has moved in next door. Instead, much of the action is confined to Story taking refuge in Cleveland's depressing flat, and the narrative proves a bit too intense for younger tykes yet neither exciting nor engaging enough to galvanize adults. By publicly harpooning Disney, Shyamalan has rendered a kind of service — illustrating how filmmakers can lose perspective on passion projects. Whatever the rationale behind the criticism, however, after using "Lady in the Water" to tuck in the kids, it should have been tucked away.Lady in the Water
A Warner Bros. release presented in association with Legendary Pictures of a Blinding Edge Pictures production. Produced by M. Night Shyamalan, Sam Mercer. Directed, written by M. Night Shyamalan.
Camera (Technicolor), Christopher Doyle; editor, Barbara Tulliver; music, James Newton Howard; music supervisor, Susan Jacobs; production designer, Martin Childs; art directors, Christina Wilson, Stefan Dechant; set decorator, Larry Dias; costume designer, Betsy Heimann; sound (Dolby Digital/DTS/SDDS), Chris Munro; supervising sound editor, Frank Eulner; visual effects supervisor, Edward Hirsch; creatures designer, Crash McCreery; associate producers, Jose L. Rodriguez, John Rusk; assistant director, Rusk; second unit director, Brick Mason; casting, Douglas Aibel. Reviewed at Warner Bros. studios, Burbank, Cal., July 6, 2006. MPAA Rating: PG-13. Running time: 110 MIN.
Cleveland Heep - Paul Giamatti
Story - Bryce Dallas Howard
Mr. Dury - Jeffrey Wright
Harry Farber - Bob Balaban
Anna Ran - Sarita Choudhury
Young-Soon Choi - Cindy Cheung
Vick Ran - M. Night Shyamalan
Reggie - Freddy Rodriguez
Mr. Leeds - Bill Irwin
Mrs. Bell - Mary Beth Hurt

subset: commoncrawl


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Welcome to the Trader-X blog!
things i recommend:
please read before asking questions:
Sunday, November 19, 2006
NTGR - 111706; 30-minute chart
Great post by X below on "Chasing success". I could not agree more.
I took an interesting trade in NTGR on Friday. It had a few "red flags" - the entry was above the OR low, and as X would say that 1st bar was crazy wide. But, the 1st bar also closed weak and below the 50% mark of its range, and it left a long upper-tail (bearish). The 2nd bar was a narrow-range, inside bar, and a "hanging man" type candle. And the 5MA was declining and serving as overhead resistance.
I entered on a break of the 2nd bar low. The target was the Fibonacci extension of the previous day's high to the OR low, and it was hit two bars later.
trader-x, stocks, fibonacci, trading, ntgr
13 comments:
- estocastica said...
Awesome trade -- I was just in the middle of writing up a "Day Trades That Got Away" post about NTGR! I passed becasue the 5MA wasn't quite close enough for me. Otherwise, I thought it was beautiful.
- Sami said...
i have a question or two about exits.
Do you always exit at the Fib target? or would you consider trailing a stop above/below resistance/support? or on the opposite end of key candles much like your initial stop?
If you do not hit a Fib target, but the stock closes weak, would you hold overnight? or always close the trade at the end of the day to avoid next day gaps?
thanks bunches. - 2:27 PM
- Trader-X said...
estocastica - I passed it up for the same reason. It reminds me on ILMN on Monday. I need to re-think "close" as opposed to touching.
Good trade. - 3:06 PM
- Trader-X said...
sami, not speaking for Tom C. but I usually close at the Fibonacci extension. Sometimes I take 1/2, and trail a stop. But I always close something as it usually reverses at that point.
I never, never, never hold overnight. To me that is just gambling (unless of course you are swing trading, but don't confuse the two!). - 3:08 PM
- Will said...
X and esto,
After helping myself to a heaping portion of the X-patented 5EMA on 30min bars, I too quickly ran into the horseshoe / hand grenade quandry (i.e. just what is close enough?)...
My personal solution was to throw a 1% envelope around the EMA, which works great for me. YMMV, of course. I've an annotated chart of NTGR with it, but I don't think it's kosher to post a picture or do "a href" in someone else's comments, so here's the link to the pic to copy/paste if ya'll are interested: www.dummyspots.com/wp-content/
Best wishes and still follow both of your sites daily. Cheers. - 5:54 PM
- Trader-X said...
Will, I don't mind at all...thanks for sharing.
Here is Will's link. - 6:08 PM
- Trader-X said...
will, I also added you to my blogroll...
dummyspots.com - 6:14 PM
- Sami said...
X, thanks for the reply. My objective is neither to day-trade nor swing trade. I am happily busy running my small software development business during trading hours (and nights, and weekends), and frankly my portfolio is larger than i can manage to day-trade.
I've been managing my portfolio based on fundamentals and macro-economics. I would like to supplement that with sound technical analysis to reduce volatility and possibly enhance performance. For example, i would like to exit positions when medium-term trends move against me, and to add to positions when overcoming resistance levels.
For me, i am finding that the best way to really learn technical analysis is to trade intra day charts. It is very hard to convince people to buy at new highs. But watching a streaming chart where a position gaps up, consolidates on low volume, and then breaks above OR high, finding support at MA, that just drives the point home. It is like a switch flipped in my head after i started following intra-day streaming charts. I trade a small portfolio based on intra-day charts so that i can learn with real money and emotions.
I am applying the knowledge from the intra-day charts to my real portfolio using daily and weekly time-frames. So far the results have been good. It is like i've been blind all my life and just opened my eyes for the first time.
Sorry for the long comment... and thanks much for the blog. I've learned more from your blog and Alphatrends than all the books i've read combined. - 7:38 PM
- estocastica said...
Great stuff Will! Also, thanks again for you comments on my blog regarding the additional Fib series on the OR bar.
- Trader-X said...
estocastica - please link to that comment you were talking about...
- estocastica said...
I intended to link it with my original post but forgot. Will's comment is the third down of the following post:
Additional Fib - 9:02 PM
- Trader-X said...
wow... yet another timely post. I, too, had questions about 'close' to the 5ema. Right now I'm waiting for candles to touch... but the 1% envelope sounds like it makes sense. Although... I've seen bars come close to the 5ema and fail. Usually when it's touching and all the other stars are aligned - it'll move. Thanks for reading my mind (you, Will and Esto) and I'll test the envelope technique to see how effective it is.


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Lies and Fearmongering
David Parker manages both with this press release.
The National Government has given its clearest signal yet that superannuation entitlements will be cut in the future, Labour Associate Finance spokesman David Parker says.
Fearmonger No 1
A reminder. John Key has said he will resign his seat in Parliament if he cuts superannuation entitlements. But not even that will stop Labour trying to prey on the insecurity of retired or near retired persons.
"The pensions of tomorrow need to be protected today. National said before and during the election they would continue with payments to the Super Fund, but have now resiled from this."
And if Parker reads the announcement from Michael Cullen, when the Fund was established, he will see that Cullen explicitly said that contributions are funded out of surpluses and would be suspended temporarily when there is no surplus.
Not even Dr Cullen was foolish enough to advocate borrowing money to save money.
"Mr English argues that he's not prepared to borrow to fund the investment in the Super Fund, but he's already done that to pay for his tax cuts – an astounding third of which go to the top three per cent of income earners," David Parker says.
Then we have the lie – the claimed borrowing for tax cuts. The extra tax cuts by National were fully paid for by reduced expeniture on KiwiSaver. This is a fact. Parker knows this. He just hopes if you repeat a lie enough times, people will believe it.
"Those tax cuts were not just unfair, but they are a substantial cause of the structural deficit New Zealand now faces and are behind the Government's plan to now cut investment in the Super Fund.
And he lies again. National's tax cuts are considerably less foregone revenue that the reduction in KiwSaver subsidisies. The structural deficit would be worse, not better, if National tax cuts and KiwiSaver changes had not been made. This is indisputable. The quantum of each is known and the foregone revenue from tax cuts is les than the reduction in KiwiSaver subsidies.
Everytime you hear Labour talk about borrowing for tax cuts – they are lying. They are desperate to have people beleive it, but it is not true, as National reduced expenditure by a greater amount than the tax cuts to pay for it.
If Labour were honest they would campaign on how National reduced KiwiSaver subsidies for tax cuts, and debate the merits of that. Of course they don't want to, as anyone economically literate now concedes the KiwiSaver subsidies were far too generous.


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These tasty mini bites of cheese on toast are perfect for parties as a canapé, or as a starter or snack
Mini Welsh rarebits
2 tsp English mustard powder
6 tbsp Duchy Originals from Waitrose Organic Golden Ale
50g butter
1 tsp Worcestershire sauce, or to taste
350g Caerphilly cheese, grated
2 medium Waitrose British Blacktail Free Range Egg yolks
6 slices Waitrose 3 Malts Sourdough With Mixed Seeds
For the pear and walnut relish:
1 Perfectly Ripe Conference Pear
2 tbsp lemon juice
50g walnut pieces, finely chopped
25g pack fresh flat-leaf parsley, chopped
1. Mix the mustard with a little ale to make a paste in the base of a pan. Stir in the rest of the ale, the butter and Worcestershire sauce over a gentle heat and cook for about 5 minutes, until the butter has melted.
2. Add the cheese and cook for a further 5 minutes, stirring occasionally and being careful that the mixture doesn't boil, until melted and smooth. Season, then remove from the heat and allow to cool slightly. Beat the yolks into the just-warm but still melted cheese.
3. Preheat the grill to high and toast the bread lightly on both sides, then leave to cool on the grill rack.
4. Spread the cheese mixture over the cooled toast and grill for 3-4 minutes or until bubbling and golden. Coarsely grate the pear and immediately toss with the lemon juice. Stir in the walnuts and parsley. Cut each toast into 4 slices and serve with the relish, ready to spoon on top.
- Preparation time: 10 minutes, plus cooling time
- Cooking time: 10 minutes
- Total time: 20 minutes, plus cooling time
Makes: about 24
Nutritional Info
Typical values per serving:
Energy |
531.368kJ 127kcals |
Fat | 8.8g |
Saturated Fat | 4.3g |
Carbohydrate | 6.7g |
Sugars | 1.4g |
Salt | 0.4g |
5.3g protein, 1.1g fibre
Average user rating Based on 7 ratings


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Genome-wide prediction of discrete traits using Bayesian regressions and machine learning. | |
Jump to Full Text | |
MedLine Citation:
PMID: 21329522 Owner: NLM Status: MEDLINE |
BACKGROUND: Genomic selection has gained much attention and the main goal is to increase the predictive accuracy and the genetic gain in livestock using dense marker information. Most methods dealing with the large p (number of covariates) small n (number of observations) problem have dealt only with continuous traits, but there are many important traits in livestock that are recorded in a discrete fashion (e.g. pregnancy outcome, disease resistance). It is necessary to evaluate alternatives to analyze discrete traits in a genome-wide prediction context. METHODS: This study shows two threshold versions of Bayesian regressions (Bayes A and Bayesian LASSO) and two machine learning algorithms (boosting and random forest) to analyze discrete traits in a genome-wide prediction context. These methods were evaluated using simulated and field data to predict yet-to-be observed records. Performances were compared based on the models' predictive ability. RESULTS: The simulation showed that machine learning had some advantages over Bayesian regressions when a small number of QTL regulated the trait under pure additivity. However, differences were small and disappeared with a large number of QTL. Bayesian threshold LASSO and boosting achieved the highest accuracies, whereas Random Forest presented the highest classification performance. Random Forest was the most consistent method in detecting resistant and susceptible animals, phi correlation was up to 81% greater than Bayesian regressions. Random Forest outperformed other methods in correctly classifying resistant and susceptible animals in the two pure swine lines evaluated. Boosting and Bayes A were more accurate with crossbred data. CONCLUSIONS: The results of this study suggest that the best method for genome-wide prediction may depend on the genetic basis of the population analyzed. All methods were less accurate at correctly classifying intermediate animals than extreme animals. Among the different alternatives proposed to analyze discrete traits, machine-learning showed some advantages over Bayesian regressions. Boosting with a pseudo Huber loss function showed high accuracy, whereas Random Forest produced more consistent results and an interesting predictive ability. Nonetheless, the best method may be case-dependent and a initial evaluation of different methods is recommended to deal with a particular problem. |
Oscar González-Recio; Selma Forni |
Publication Detail:
Type: Journal Article Date: 2011-02-17 |
Journal Detail:
Title: Genetics, selection, evolution : GSE Volume: 43 ISSN: 1297-9686 ISO Abbreviation: Genet. Sel. Evol. Publication Date: 2011 |
Date Detail:
Created Date: 2011-03-14 Completed Date: 2011-05-10 Revised Date: 2013-06-30 |
Medline Journal Info:
Nlm Unique ID: 9114088 Medline TA: Genet Sel Evol Country: France |
Other Details:
Languages: eng Pagination: 7 Citation Subset: IM |
INIA, Ctra La Coruña km 7,5, 28040 Madrid, Spain. email@example.com |
Export Citation:
APA/MLA Format Download EndNote Download BibTex |
MeSH Terms | |
Algorithms* Animals Artificial Intelligence Bayes Theorem Computer Simulation Genome-Wide Association Study* Humans Plants / genetics Polymorphism, Single Nucleotide Quantitative Trait Loci |
Comments/Corrections |
Full Text | |
Journal Information Journal ID (nlm-ta): Genet Sel Evol Journal ID (iso-abbrev): Genet. Sel. Evol ISSN: 0999-193X ISSN: 1297-9686 Publisher: BioMed Central |
Article Information Download PDF Copyright ©2011 González-Recio and Forni; licensee BioMed Central Ltd. open-access: Received Day: 31 Month: 5 Year: 2010 Accepted Day: 17 Month: 2 Year: 2011 collection publication date: Year: 2011 Electronic publication date: Day: 17 Month: 2 Year: 2011 Volume: 43 Issue: 1 First Page: 7 Last Page: 7 ID: 3400433 Publisher Id: 1297-9686-43-7 PubMed Id: 21329522 DOI: 10.1186/1297-9686-43-7 |
Genome-wide prediction of discrete traits using bayesian regressions and machine learning | |
Oscar González-Recio1 | Email: email@example.com |
Selma Forni2 | Email: firstname.lastname@example.com |
1INIA. Ctra La Coruña km 7.5, 28040 Madrid. Spain |
2Genus Plc, 100 Bluegrass Commons Blvd. Ste 2200. Hendersonville, TN, USA |
The availability of thousands of markers from high throughput genotyping platforms offers an exciting prospect to predict the outcome of complex traits in animal breeding using genomic information (the so-called genomic selection) and in personalized medicine. Besides production and other functional traits, genomic selection offers a novel challenge for discovering genetic variants affecting important diseases in humans, plants and livestock, and also for breeding resistant individuals to improve farm profitability.
The statistical treatment of the genetic basis of these traits is not straightforward because multiple genes, gene by gene interactions and gene by environment interactions underlie most complex traits and diseases. Capturing all marker signals is currently challenging. Besides the large p small n problem, the statistical treatment of the categorical nature of a trait may increase parameterization. So far, methods dealing with genome-assisted evaluations have focused on traits expressed or recorded in a continuous and Gaussian manner [1-3]. However, other traits (e.g. disease, survival) are generally recorded in a binary or few-classed manner (e.g. healthy or sick, number of occurrences, status). Most methods dealing with genome-assisted evaluations may be extended in a relatively well known manner to analyze categorical traits [4-6]. A larger amount and various types of genomic information (e.g. single nucleotide polymorphisms, copy number variants or DNA sequencing) for several species are likely to be available in the future. Using this large amount of data may be highly informative, yet quite challenging for current methods from the point of view of computation efficiency. Genome-wide association studies (GWAS) and genomic selection methods must be adapted to cope with these challenges.
Machine-learning is becoming more and more popular to deal with the difficulties stated above, and has been previously applied in GWAS in humans [7] and livestock [8-10]. Machine-learning methods aim at improving a predictive performance measure by repeated observation of experiences. They are model specification free, and may capture hidden information from large databases. This is appealing in a genomic information context in which multiple and complex relationships between genes exist. The ensemble methods, such as Random Forest (RF) algorithms [11] and boosting [12], are the most appealing alternatives to analyze complex discrete traits using dense genomic markers information, and have been previously applied in GWAS for human diseases [13,14]. They may provide a measurement of the importance of each marker on a given trait and good predictive performance. Boosting has been previously applied in a genomic selection context for regression problems using the L2 loss function [8]. RF and boosting do not require specification of the mode of inheritance and hence may account for non-additive effects. Further, they are fast algorithms, even when handling a large amount of covariates and interactions, and can be applied to both classification and regression problems.
The objective of this study was to present the threshold extension of two Bayesian regression methods that are used in genome-assisted evaluations (Bayes A and Bayesian LASSO), a boosting algorithm for discrete traits, to describe more thoroughly the RF alternative to deal with discrete traits in a genome-wide prediction context, and to apply them to both simulated and real data to compare their predictive ability.
Let y ={yi} be a vector of phenotypes recorded in a binary fashion (0/1) from n animals genotyped for p markers X = {xi}. Four different methods were applied: two linear regressions using a Bayesian framework, and two machine-learning ensemble algorithms.
A threshold version of Bayes A (TBA) model was proposed here, which is an extension of the Bayesian regression proposed by Meuwissen et al. [1]. The traditional threshold model [4] postulates that there is an underlying random variable, called liability (λ) that follows a continuous distribution, and that the observed dichotomy is the result of the position of the liability with respect to a fixed threshold (t):
phenotype={0 if λ <t1 if λ≥t |
The liability is taken as the response variable. The proposed modification consists of the linear regression of the single nucleotide polymorphism (SNP) coefficients on a liability variable with Gaussian distribution. The TBA can be described as follows:
λ=μ1+Xb+e |
where, λ is the underlying liability variable vector for y, μ is the population mean, 1 is a column vector (n×1) of ones; b = {bj} corresponds to the vector for the regression coefficient estimates of the p markers or SNP assumed normally and independently distributed a priori as N(0, σj2), where σj2 is an unknown variance associated with marker j. The prior distribution of σj2 is assumed to be distributed as the scaled inverse chi-square σj2~υjsj2χυj−1, with υj = 4 and sj2=0.002. Elements of the incidence matrix X, of order n × p, may be set up as for different additive, dominant or epistatic models. In the more practical scenario, it takes values -1, 0 or 1 for marker genotypes aa, Aa and AA, respectively. The residuals (e) are assumed to be distributed as N(0, σe2), with residual variance σe2=1, as stated above. As in a regular threshold model, two parameters have to be set fixed (e.g. threshold and the residual variance are set to zero and one, respectively) since these parameters are not identifiable in a liability model.
This method can be solved via the Gibbs sampler described in Meuwissen et al. [1], with the simple incorporation of the data augmentation algorithm to sample the individual liabilities from their corresponding truncated normal distribution as described in Tanner and Wong [15]. The joint posterior distribution of the n liabilities is:
Prob(λ|μ,b,t)=∏i=1n{Φ[t−(μ+xib)]σe}1−yi{1−Φ[t−(μ+xib)]σe}yi |
The Bayesian LASSO described by Park and Casella [16] and its version for genomic selection detailed in de los Campos et al. [17] can also be extended to discrete traits [18]. As stated in the previous model, the response variable is a liability response (λ) that follows a continuous distribution. The Bayesian threshold LASSO (BTL) can be solved as:
λ=μ1+Xβ+e, |
where λ is the vector of liabilities for all individuals, μ is the population mean, 1 is a column vector (n × 1) of ones; β∧ are the LASSO estimates with their respective incidence matrix X as described for model TBA. As a modeling choice, e was considered the vector of independently and identically distributed residuals, as e∼N(0,σe2). In accordance with tradition, we fixed the threshold to be 0 and the residual variance to be 1 as described for model TBA; alternate choices result in the same model.
In a fully Bayesian context, the LASSO estimates (β∧) can be interpreted as posterior modes estimates when the regression parameters have independent and identical double-exponential priors [19]. Park and Casella [16] have proposed a conditional Laplace prior specification for the LASSO estimates of the form:
p(β|σe2)=∏j=1pγ2σe2e−γ|βj|/σe2, |
where σe2 is the residual variance, and γ is a parameter controlling the shrinkage of the distribution. Inferences about γ may be done in different ways [16]. To follow the Bayesian specifications, a gamma prior is proposed here for γ2, with known rate (r) and shape (δ) hyper-parameters, as described by de los Campos et al. [17]. Samples from posterior distributions of those estimates may be drawn from the Gibbs sampling algorithm described in de los Campos et al. [17], with the corresponding data augmentation algorithm for liabilities, as described for TBA.
Gradient boosting may be classified as an ensemble method [20]. This algorithm combines different predictors in a sequential manner with some shrinkage on them [12] and performs variable selection. Gradient boosting forms a "committee" of predictors with potentially greater predictive ability than that of any of the individual predictors in the form:
y=μ+∑m=1Mvhm(y;X) |
Each predictor (hm(y; X) for m ∈ (1, M)) is applied consecutively to the residual from the committee formed by the previous ones. This algorithm can be calculated using importance sampling learning ensembles as follows:
(Initialization): Given data (y, X), let the prediction of phenotypes be F0 = μ, with μ being the population mean.
Then, for m in {1 to M}, with M being large, calculate the loss function (L) for (yi,Fm−1(xi)+h(yi;xi,jm))
where jm is the SNP (only one SNP is selected at each iteration) that minimizes ∑i=1nL(yi,Fm−1(xi)+h(yi;xi,jm)) at iteration m, h(yi; xi, jm) is the prediction of the observation using SNP j at the current iteration, Fm-1(xi) is the updated prediction at the previous iteration and L(·) is a given loss function. The updated prediction at each iteration m may be expressed as Fm(xi) = Fm-1(xi)+v·h(yi; xi, jm) with v being some shrinkage factor that, without loss of generality, can be assumed constant and small (0<v <1), but it may be optimized to balance predictive ability and computation time.
Therefore, after the initialization, the algorithm flows as follows:
Step 1: Compute residuals as rm=y−∑i=0m−1v⋅Fm−1(xi), and fit the weak learner for each SNP j (j ∈{1,..., p}) to current residuals, where ν was set to 0.01.
Step 2: Select SNP j, where j=argminj∑i=1nL(yi,Fm−1(xi)+h(yi;xi,jm)), i.e. the SNP minimizing the loss function.
Step 3. Update predictions as Fm(xi) = Fm-1(xi)+ν·h(yi; xi, jm), (i∈{1,..., n}), where h(yi; xi, jm) is the estimate for individual i obtained by regressing the current residual (ri) at iteration m on its genotype for the SNP selected in step 2.
Step 4: Increase the iteration index m by 1, and repeat steps 2-4 until a convergence criterion is reached.
Here, we used ordinary least square regression as predictor h(y; X) and two different loss functions: the L2 loss function (L2B), which is a quadratic error term in the form (yi-Fm (yi; xi, jm))2, and a pseudo-Huber loss function (LhB) in the form log[cosh(yi−Fm(yi;xi,jm))]. The pseudo Huber loss function is a priori more appealing for discrete traits because it is continuous, differentiable, greater than or equal to the logit loss function and overcomes the disadvantage of the squared loss by becoming more linear when (yi-Fm(yi; xi, jm)) tends to infinite. The choice of the number of iterations, M, is a model comparison problem which may be overcome in many different ways [12,20]. Here, a cross-validation design was used as described in González-Recio et al. [8]. More details on the gradient boosting can be found in Freund and Schaphire [21], Friedman [12] and González-Recio et al. [8].
Random Forest can be viewed as a machine learning ensemble algorithm and was first proposed by Breiman [11]. It is massively non-parametric, robust to over-fitting and able to capture complex interaction structures in the data, which may alleviate the problems of analyzing genome-wide data. This algorithm constructs many decision trees on bootstrapped samples of the data set, averaging each estimate to make final predictions. This strategy, called bagging [22], reduces error prediction by a factor of the number of trees.
A RF algorithm aimed at genome-wide prediction is described next, in a more extensive manner than the previous methods, as this is the first time that this algorithm is used in a genomic breeding value prediction context:
Let y (n × 1) be the data vector consisting of discrete observations for the outcome of a given trait, and X = {xi} where xi is a (p × 1) vector representing the genotype of each animal (0, 1 or 2) for p SNP, to which T decision trees are built (see classification and regression tree theory e.g. [20]). Note that main SNP effects, SNP interactions, environmental factors or combinations thereof may be also included in xi. This ensemble can be described as an additive expansion of the form:
y=μ+∑t=1Tctht(y;X) |
Each tree (ht(y; X) for t∈(1, T)) is distinct from any other in the ensemble as it is constructed from n samples from the original data set selected at random with replacement, and at each node only a small group of SNP are randomly selected to create the splitting rule. Each tree is grown to the largest extent possible until all the terminal nodes are maximally homogeneous. Then, ct is some shrinkage factor averaging the trees. The trees are independent identically distributed random vectors, each of them casting a unit vote for the most popular outcome of the disease at a given combination of SNP genotypes.
Each tree minimizes the average loss function of the bootstrapped data, and is constructed using a heuristic approach as follows:
1. First, bootstrapped samples from the whole data set are drawn with replacement so that realization (yi, xi) may appear several times or not at all in the bootstrapped set Ψ(t) t = (1,..., T).
2. Then, draw mtry out of p SNP markers at random, and select the SNP j, j∈(1,..., mtry), where
j=argminjL(y,ht(X)), |
with L(y, ht(X)) being a certain loss function. i.e. SNP j is the one that minimizes a given loss function at the current node, and is selected in this step. The algorithm takes a fresh look at the data that have arrived at each node and evaluate all possible splits. Many loss functions can be chosen (e.g. logit function, squared loss function, misclassification rate, entropy, Gini index, ...). The behavior of a given loss function may depend on the nature of the problem. The squared loss function is popular for continuous response variables, and the logit function for categorical responses.
3. Split the node in two child nodes according to SNP j genotype that one individual may or may not have (e.g. individuals with the risk allele will pass to a child node, and the remaining animals will pass to the other child node).
4. Repeat steps 2-3 until a minimum node size is reached (usually <5). The predicted value of the genotype xi is the majority vote for the outcome at the terminal nodes (for regression problems, it is the average phenotype of the individuals in the node).
Finally, a large amount of trees are constructed repeating steps 1-4 to grow a random forest. The forest may be stopped when the generalization error averaged across the out of bag samples (see section below) have converged. Convergence may be visually tested but it may also be determined using traditional methods for convergence testing of Monte Carlo Markov chains.
Final predictions can be made by averaging the values predicted at each tree to obtain a probability of being susceptible. In a naïve 0 = non-susceptible/1 = susceptible scenario, individuals with probability <0.5 may be considered as non-susceptible. To predict observations of new individuals, their marker genotypes are passed down each tree, and the estimate of the corresponding terminal nodes is assigned to the new individual in each tree. The predictions of each tree in the RF algorithm are averaged for each animal to compute the final prediction.
There are two main aspects that can be tuned in random forest: the first one is the number of SNP or covariates sampled at random for each node (mtry). Generalized cross-validation strategies can be used to optimize mtry. In high dimensional problems such as GWAS, Goldstein et al. [23] have suggested mtry to be fixed to >0.1 p. The algorithm may speed up for smaller mtry values. Nonetheless, cross-validation can be used to determine the best value of mtry for each trait, although at an expense of increasing computation time. Genetic background may influence the behavior of this tuning parameter. The second aspect is the criterion to select the best SNP to split the node. As commented above, different criteria may be used and the best choice may depend on the nature of the problem. Entropy theory seems the most appealing to evaluate genomic information on discrete traits (as concluded from pilot studies, results not shown). Other loss functions such as the L1-loss function or the misclassification rate could be implemented in an easy manner. Without loss of generality we show how to implement the entropy theory in the node splitting decision. The information gain (IG) for each covariate s drawn at random in a given node was calculated as described in Long et al. [9]:
Suppose there are Nk+ individuals with genotype k (k ∈ {0, 1, 2}) at each SNP covariate xj showing y = 1 (e.g. presence of disease) at such node, and Nk− individuals with the same genotype with y = 0 (e.g. absence of disease). The information gain for each covariate xj can be calculated as:
IG(xj)=H(Pr(Y))−∑k=12(∑C=+,−NkCN(−∑C=+,−NkCNklog2NkCNk)) |
where Nk=Nk++Nk−, and H(Pr(y))=−∑y∈APr(y)log2Pr(y) is the entropy of the probability distribution of y, and A is the set of all states that y can take ({0,1}). The SNP covariate with the highest IG at each node is used to split the node into two new child nodes, each one containing the individuals from the parent node with the risk or the non-risk allele, respectively.
There are two features involved in the RF algorithm that deserve further attention: the out of bag samples, and the variable importance.
The out of bag data (OOB) is an interesting feature of RF. Each tree is grown using a bootstrapped sample of the data, which leaves roughly one third of the observations out because some animals will appear more than once and others will not appear at all. The samples that do not appear are called the OOB samples. The OOB acts as a tuning/validation set at each tree and is almost identical to a n-fold cross validation, removing the need for a set aside test or tune test. Tuning of parameters can be done along the RF using the OOB, and generalization error can be calculated as the error rate of the OOB [11,24].
RF may use the OOB to provide an importance measure of predictor variables (SNP or environmental effects). The relative variable importance (VI) is estimated as follows. After each tree is constructed, the OOB are passed down the tree and the prediction accuracy of disease outcome is calculated using the chosen criterion (e.g. misclassification rate, L2 loss function). Then, genotypes for the pth SNP are permuted in the OOB, and the accuracy for the permuted SNP is again calculated. The relative importance is calculated as the difference between these prediction accuracies (that of the original OOB and that of the OOB with the permuted variable). This step is repeated for each covariate (SNP) and the decrease of accuracy is averaged over all trees in the random forest. The variable importance may be expressed as a percentage of the accuracy obtained with the most important SNP, and provides insight in the level of association of the SNP with the disease. The SNP with higher VI may be of interest for prediction of trait susceptibility (e.g. disease resistance, low fertility) at low marker density, candidate gene studies or gene expression studies.
Our own java code has been developed for implementing RF for categorical or continuous traits under a genome-wide prediction context, and is available upon request to the authors.
Simulated and field data sets were used for the model comparisons. Description of these data is given next.
QMSim software [25] was run to simulate a population of thousands of animals genotyped for roughly 10,000 markers. First, 1000 historical generations were generated in a population with effective size decreasing from 1400 to 400 to mimic a bottleneck, in order to produce a realistic level of LD for the platform used in the simulation. At this point, 40 generations were generated to achieve a population size of 21,000 animals. Then, 20,000 females and 300 males from the last historical population were selected as founders, followed by 15 generations of selection for estimated breeding values from best linear unbiased predictions and random matings. During these generations, replacement ratio were set at 0.83 and 0.45 for males and females, respectively. A random sample of 2500 animals in generations 11 to 14 was used as training set, while the whole generation 15 was used as testing set (1500 animals). Phenotypes were simulated as a Gaussian distribution with heritability equal to 0.25. Then, the phenotype of the animals was coded as 0 or 1 depending on whether their simulated phenotype was below or above, respectively, of the population average (using only generation from 11 to 14), which creates a discrete scenario for the phenotypes.
A genome was simulated with 30 chromosomes 100 cM long. Two scenarios with different numbers of QTL were simulated. In the first, three QTL were randomly located along each chromosome with effects sampled from a gamma distribution. This generated 90 QTL affecting the trait that still segregated in the training population. A second scenario with 33 QTL per chromosome was also simulated with a total of 1000 QTL having some effect on the trait and following a traditional infinitesimal model specification.
Then, 9990 bi-allelic markers were uniformly distributed along the genome and coded as 0, 1 or 2, regarding the number of copies of the most frequent allele. Simulation was performed to obtain a linkage disequilibrium close to 0.33 (squared correlation of the alleles at two consecutive loci). Ten replicates were analyzed, and the mean and standard deviations are presented.
A field data set was used here to illustrate the behavior of the methods in classification problems applied to genome-wide prediction of disease resistance in pigs. In this study we used one of the most important congenital diseases in pig industry as response variable: scrotal hernia (SH). Most affected individuals cannot feed effectively and consequently growth is affected [26]. This leads to higher feed costs, slower throughput, lack of product uniformity and consequent loss in income. In a nucleus breeding population, such individuals cannot be considered for use as breeding stock and effectively end up as culls. Heritability estimates around 0.30 and prevalence between 1% have been reported previously for this trait [27,28].
Data were provided by PIC North America, a Genus Plc company. The data set contained records of scrotal hernia incidence (score 0 or 1) in 2768 animals from three different lines. Animals from two purebred lines (A and B) were born in elite genetic nuclei, where environmental conditions were better controlled and risk of infections was lower. Animals from a crossbred line (C), from line A and other lines not used in this study, were born in commercial herds. Selection emphasis in line A was placed on reproduction and lean growth efficiency. Line B has been selected mainly for reproductive traits. Selection against scrotal hernia was equally emphasized in both lines A and B. The prevalence of the disease ranged between 1 and 2% in all lines. Genotypes of all animals with phenotypic records were obtained for 6742 SNP located in different genomic regions identified as candidate regions in previous studies [29,30]. A comprehensive scan under the available marker density was performed with all chromosomes being covered. After genotype editing following Ziegler et al. [31], 5302 SNP were retained, and all 923 total animals from line A, 919 from line B and 700 from line C were used. Fifty per cent of animals in the data set of each line were affected with scrotal hernia. For each individual and main effect for SNP jth, we defined two covariates xj1 and xj2, with xj1=1 if the genotype was aa (0, otherwise), and xj2=1 if the genotype was AA (0, otherwise).
Analyses within each line were performed leaving out the 15% youngest individuals, as testing set. The raw phenotype was used as dependent variable in a control case design. Note that systematic effects were not included as covariates for simplicity, although any covariate may be included in the algorithms without loss of generality. The predicted susceptibilities of animals in the testing set were the percentages of trees in a random forest that a given animal was considered as affected.
Performance of the models was based on predictive ability to correctly predict genetic susceptibility in the testing sets. The true genetic susceptibilities of individuals in the simulated data set are known. However, true genetic merits are unknown in the field data case. Therefore, predictive ability was evaluated in a different manner in the field data, as described below.
The true genetic susceptibilities were obtained from the simulations and followed a Gaussian distribution, whereas distributions of predicted susceptibilities were dependent on the model used. A Gaussian distribution was assumed for Bayesian regressions and an unknown distribution bounded between 0 and 1, representing the probability of individual i to be susceptible, for machine learning methods. Pearson's correlations were calculated between true and predicted genetic susceptibility merit for each model and simulated scenario.
In addition, the area (AUC) under the receiving operating characteristic curve was calculated for each model in each simulation. This curve is a graphical plot of the sensitivity, or true vs. false positive rate (1 − specificity) for a binary classifier system as its discrimination threshold changes [32]. The AUC can be used as a model comparison criterion and can be interpreted as the probability that a given classifier assigns a higher score to a positive example than to a negative one, when the positive and negative examples are randomly picked. Individuals with a true genetic susceptibility above or below the population average were assumed positive or negative cases, respectively. Models with higher values of AUC are desirable and are considered more robust.
True genetic susceptibilities of individuals in the field data are unknown. Instead, estimated breeding values (EBV) for SH susceptibility obtained from routine genetic evaluation using the BLUP method [33] were assumed as the true genetic values. Routine evaluations included 6.9 million animals in the pedigree and approximately 2.3 million records of SH. The effects of line, litter, farm, and month of birth nested into farm were included in the threshold animal model used in the analyses. This may indeed be a crude approximation because EBV were calculated under a linear model with strong assumptions of linearity, additivity, non migration or non selection, although millions of records and animals are used in these genetic evaluations and the accuracy ranged between 0.50 and 0.96 for 95% of the EBV. To minimize the issue of this approximation, animals were classified as susceptible or non-susceptible. Non-susceptible animals were those in the lower α percentile of the EBV distribution in each line, whereas those in the upper (1-α) percentile were considered as susceptible (α ∈ {5,10,25,50}). Lower values of α selected the more extreme animals, thus a smaller approximation error is expected.
Predicted accuracy was calculated between these EBV (y) and predictions (y^) in the testing set from methods TBA, BTL, RF, L2B or LhB. The predictive accuracy was estimated using misclassification rate, the phi coefficient correlation, sensitivity and specificity.
The phi coefficient correlation is the equivalent to the Pearson's product moment correlation for binary variables. It can be calculated as
rφ=p(y^=1|y=1)−p(y^=1)p(y=1)p(y^=1)p(y=1)p(y^=0)p(y=0) |
This coefficient may be not robust enough under certain circumstances such as those in which the categories are extremely uneven. Under these circumstances rϕ has a maximum absolute value determined by the distribution of y^ and y.
Sensitivity and specificity for a given classifier may be computed as
Sensitivity=number of TNnumber of TN+number of FP, |
Specificity=number of TPnumber of TP+number of FN |
Sensitivity measures the proportion of healthy animals that are identified as not being affected (TN = true negatives), whereas specificity measures the proportion of affected animals that are correctly identified as such (TP = true positives). Values of sensitivity and specificity closer to 1 are preferred. Specificity and sensitivity are more informative than raw rate of misclassification, as the latter does not differentiate if misclassification is on true healthy or true affected animals.
Furthermore, all animals in the respective testing sets were used to calculate the AUC statistic, described above, for each method within a line. Animals with SH were considered as positive examples, whereas animals without SH were considered negative examples. As stated before, AUC measures predictive ability and may be considered as a model comparison criterion. Higher AUC values are desirable, as mentioned above.
Table 1 shows the average predictive ability (standard deviations in parentheses) across replicates, measured as Pearson correlation, between true and predicted genomic values, and also using the AUC statistic for each model on each simulated data set. Machine-learning methods showed higher averaged accuracy in the simulated data set than Bayesian regression, although with a large standard deviation across replicates. Smaller differences between Bayesian regressions and machine-learning were found in the simulated scenario with 1000 QTL. TBA and L2B were the methods showing poorest accuracy (0.26 ± 0.10 and 0.24 ± 0.04, respectively) in the scenarios with 90 and 1000 QTL, respectively. The boosting algorithm, both L2B and LhB, achieved the highest averaged accuracy (0.37-0.41) in the simulated data set with a smaller number of QTL. In contrast, methods BTL and LhB showed better predictive ability in the 1000 QTL scenario, 0.35 ± 0.04 and 0.34 ± 0.06, respectively. Differences between methods within replicates were in accordance with the averages shown in Table 1, although standard deviations between methods across replicates were large. The AUC ranged between 0.61-0.66 for Bayesian regression and between 0.63 and 0.70 for machine-learning methods. Although similar values were found for all methods, RF showed higher and preferable classification performance according to this parameter (0.70 ± 0.07 for 90 QTL and 0.69 ± 0.04 for 1000 QTL). It is not possible to draw clear conclusions on the preferred method according to the number of QTL affecting the trait, in light of the results from the simulations. Nonetheless, there is a slightly better behavior of machine-learning on traits with a small number of genomic regions affecting the outcome of the trait. Previous studies have also shown good performance of boosting in dealing with different continuous traits in real data [8].
Bayesian regression showed larger Pearson correlations than ensemble algorithms in the scenario with a larger number of QTL. Method BTL achieved the largest Pearson correlation (0.38), followed by TBA and LhB (0.33). Method RF showed the smallest Pearson correlation (0.22) in this simulated scenario and the largest AUC (0.72). This suggests that RF ranked individuals less accurately than other methods when a large number of QTL affects additively the trait, but the method is more accurate than other methods at discerning between healthy and affected individuals.
It must be pointed out that the simulated scenarios are purely additive and other more realistic scenarios with a more complex interaction between genes and biological pathways might provide different results.
The three data sets had a disease occurrence of 50%. The relative predictive importance obtained with RF for each SNP covariate xjl in each line is plotted in Figure 1. Many more SNP were identified as predictors of SH in line A than in line B and C, suggesting that many more genomic regions may be associated to SH in line A than in line B or C. Lines B and C showed few genomic regions with a large relative importance variable associated to the genetic resistance to SH. Thirty seven, four and six SNP had a larger relative variable importance than 50% in lines A, B and C, respectively. The odds ratio of SNP with VI > 50% ranged from 1.41 to 2.17 in line A, from 2.56 to 3.03 in line B and from 1.86 to 2.50 in line C, suggesting a considerable risk of being susceptible to SH of those animals carrying the unfavorable alleles. The SNP with the largest importance estimate (VI = 100%) in line C had also the maximum VI in line B, but had a VI < 21% in line A. These results suggest that the genetic variants presented in line B and C in this genomic region provide a relatively larger predictive ability of SH than genetic variants in the same genomic region in line A. The relative VI of the most important SNP in line A was lower than 2% in lines B and C, although other SNP in LD with those may have been detected in these lines. Fifty, 44 and 48 markers with VI greater than 99.5 percentile were found in lines A, B and C, respectively. Most represented chromosomes were SSC4, SSC7, SSC14 and SSC17 in line A, SSC1, SSC2, SSC6 and X chromosome in line B, and SSC8 in line C. Validation of these results and conclusions about their role in genetic or biological pathways should be performed on different populations and studies.
Tables 2, 3 and 4 show the predictive accuracy of each method within lines A, B and C, respectively. RF had an equal or better predictive accuracy in the pure lines at α = 0.05, 0.25 and 0.50, than the rest of methods used in this study. Only L2B achieved a larger phi correlation (1.00) than RF (0.75) in line B at α = 0.05, and BTL showed higher accuracy at α = 0.10 in the purebred lines. Misclassification rate and sensitivity + specificity followed similar trends. RF and L2B were the most accurate at correctly detecting the most extreme animals in lines A and B, respectively, i.e. lower misclassification, and larger rϕ, sensitivity and specificity were achieved at α = 0.05. RF and L2B achieved misclassification = 0, rϕ = 1, sensitivity = 1 and specificity = 1 at α = 0.05 in lines A and B, respectively, which means a perfect classification of the most extreme animals. At this α level, TBA and BTL showed misclassification = 17%, rϕ = 0.71 in line A and misclassification = 14%, rϕ = 0.75 in line B, and were either less sensitive or specific than RF and L2B. RF outperformed BTL at α = 0.05, 0.25 and 0.50 in lines A and B, whereas TBL achieved better predictive accuracy at α = 0.10. RF doubled the rϕ obtained with TBA at α = 0.50 in line A, and was 12% larger in Line B.
None of the methods was clearly preferred in the crossbred (line C), where similar phi correlations were found for RF, TBA and boosting, with larger robustness for LhB at α < 0.50. No differences were found between RF, TBA and LhB to correctly detect most extreme animals in the crossbred line. The Huber loss function was more robust than the squared sum of residuals at analyzing binary traits, in accordance with its resemblance with the L1 loss function.
RF showed consistently larger AUC values than the other methods whichever line (Table 5), whereas a clear trend was not extracted from the AUC values of other methods. For instance, the boosting algorithms had larger AUC values (0.66-0.67) than Bayesian regression (0.62) in line C, but lower in line A (0.55-0.60 vs 0.64-0.65). This result also suggests that RF is less dependent on the choice of the threshold for classifying healthy and affected animals, providing larger stability to the classification.
The true genetic architecture of SH is obviously unknown and no conclusions on its relationship with the performance of the different methods can be extracted. There was no clear relationship between the preferred method and the number of relevant genomic regions identified in each line (Figure 1). The choice of the model to be used in genome-wide prediction of traits like SH may depend on the interest of the breeder. For instance, detection of susceptible animals was done more accurately in line A using RF, whereas the Bayesian regressions were preferred in line B. Thus, a different method may be desired depending on the objective of the breeding program. The model with higher sensitivity would be preferred in a breeding program aiming at eradicating a given disease or trait. In a specifity+sensitivity scenario, RF was the best method at α = 0.05, 0.25 and 0.50, and it also showed the larger AUC values, regardless of the line.
Results showed that RF had the lowest risk, among methods used here, of misclassifying animals for low-medium heritability discrete traits in all lines, although all methods had considerable misclassification risks at α = 0.50. However, in a disease resistance genome-assisted prediction context, for instance, we are mainly interested in correctly detecting the most susceptible or resistant animals (lower α values), and RF seemed to perform slightly better than the Bayesian regressions to detect susceptibility to SH in this population, mainly in line A. Note that the threshold versions presented here incorporate n liability variables to be estimated in the model, increasing the parameterization of the models, and therefore hampering their predictive ability.
Results from the analyses of the crossbred line were not conclusive, as different behaviors between methods were found for different α values. This may be explained by the larger genetic heterogeneity expected in line C which may not be captured with only 5000 markers.
A small number of animals was used in the testing set and only punctual estimates are given here. This may be important at low α levels with a smaller number of records. Uncertainty about these estimates may be reported from their posterior densities [34] in the case of Bayesian methods and using bootstrap or cross-validation strategies in the case of this version of RF [11]. Uncertainties are not reported in this study because this data set aims at serving just as an example of three different models applied to discrete traits in a genome-assisted prediction context without overloading the discussion. Furthermore, the preferred model may be case-specific.
The misclassification rate and the logit function were also used as splitting criteria in RF but with poorer predictive ability (results not shown). Here, hyperparameters were set as fixed, although it is possible to assign them a prior distribution for their estimation [35]. Nonetheless, a minor improvement on predictive ability is expected if the ad-hoc choice of the parameters is within a sensible range of values.
Two Bayesian regressions (TBA and BTL) and two machine-learning algorithms (RF and boosting) were proposed here to analyze discrete traits in a genome-wide prediction context. Machine-learning performed better than Bayesian regression with a small number of QTL with pure additive effects. RF seemed to outperform other methods in the field data sets, with better classification performance within and across data sets. It is an elegant method with an interesting predictive ability for studies on discrete traits using whole genome information. It is also easily interpretable as it is based on naïve decision rules. The boosting algorithms may achieve high predictive accuracy if a case-specific loss function is used, although it may be influenced by genetic architecture. Comparison between Bayesian regressions was dependent on the data set used, although the threshold version of the Bayesian LASSO seemed to be preferred to the threshold Bayes A.
RF and boosting do not need an inheritance specification model and may account for non-additive effects without increasing the number of covariates in the model or computing time. Results from this study showed some advantages in the use of machine learning to analyze discrete traits in genome-wide prediction, although model comparisons for specific case problems are encouraged.
The authors declare that they have no competing interests.
SF participated in the statistical analyses, discussion of results, got access and edited the data, coordinated the study and helped writing the manuscript. OGR participated in the statistical analyses and editing of the data, development of the software, discussion of the results and drafted the manuscript. Both authors read and approved the final manuscript and contributed equally to this study.
Several people contributed to the accomplishment of this study. The authors express their gratitude to Dr. Alan Mileham and his team at Genus Plc for working with DNA samples and genotyping, Dr. Nader Deeb and Dr. Matthew Cleveland for designing the experiment, Dr. David McLaren for revising the manuscript and Dr. Denny Funk for supporting the partnership between institutions. Authors thank J.A. Jiménez-Montero for help with the simulations.
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[Figure ID: F1] |
Figure 1
SNP covariate relative variable importance (VI) in each line using random forest algorithm. |
Accuracy (standard error across replicates in parentheses), measured as Pearson correlation between predicted and true genomic assisted values, and area under the operating characteristic curve for different methods and number of QTL
# QTL | TBA | BTL | RF | L2B | LhB | |
Pearson correlation | 90 | 0.26 (0.03) |
0.33 (0.04) |
0.36 (0.04) |
0.37 (0.07) |
0.411 (0.07) |
1000 | 0.32 (0.16) | 0.35 (0.01) | 0.30 (0.03) | 0.24 (0.01) | 0.34 (0.02) | |
0.65 (0.02) |
0.70 (0.02) |
0.65 (0.04) |
0.69 (0.03) |
1000 | 0.66 (0.01) | 0.66 (0.00) | 0.69 (0.01) | 0.63 (0.01) | 0.66 (0.01) |
1Higher value is desirable; the best value is in bold face; TBA = Threshold Bayes A, BTL = Bayesian Threshold LASSO, RF = Random Forest; L2B = L2-boosting algorithm, LhB = Lh-boosting algorithm
Specificity, sensitivity, phi correlation and misclassification rate for each model at detecting different α and (1-α) percentiles of extreme animals in the testing set within line A
Parameter | Method | α (number of records) | |||
0.05 (12) | 0.10 (79) | 0.25 (98) | 0.50 (138) | ||
Specificity1 | TBA | 1 | 0.71 | 0.58 | 0.56 |
LhB | 0.75 | 0.71 | 0.61 | 0.67 | |
Sensitivity1 | TBA | 0.75 | 0.58 | 0.58 | 0.56 |
LhB | 0.50 | 0.45 | 0.45 | 0.42 | |
Phi correlation1 | TBA | 0.71 | 0.24 | 0.16 | 0.13 |
LhB | 0.24 | 0.13 | 0.06 | 0.09 | |
Misclassification rate (%)2 | TBA | 17 | 39 | 42 | 43 |
LhB | 42 | 49 | 49 | 46 |
1Higher value is desirable; the best value for each percentile is in bold face;
2Lower value is desirable; the best value for each percentile is in bold face;
TBA = Threshold Bayes A, BTL = Bayesian Threshold LASSO, RF = Random Forest; L2B = L2-boosting algorithm, LhB = Lh-boosting algorithm
Specificity, sensitivity, phi correlation and misclassification rate for each model at detecting different α and (1-α) percentiles of extreme animals in the testing set within line B
Parameter | Method | α (number of records) | |||
0.05 (7) | 0.10 (25) | 0.25 (78) | 0.50 (137) | ||
Specificity1 | TBA | 0.75 | 0.86 | 0.74 | 0.75 |
LhB | 0.75 | 0.71 | 0.57 | 0.63 | |
Sensitivity1 | TBA | 1 | 0.95 | 0.64 | 0.58 |
LhB | 0.67 | 0.78 | 0.73 | 0.69 | |
Phi correlation1 | TBA | 0.75 | 0.80 | 0.34 | 0.34 |
LhB | 0.42 | 0.46 | 0.28 | 0.32 | |
Misclassification rate (%)2 | TBA | 14 | 8 | 35 | 34 |
LhB | 29 | 24 | 32 | 36 |
1Higher value is desirable; the best value for each percentile is in bold face;
2Lower value is desirable; the best value for each percentile is in bold face;
TBA = Threshold Bayes A, BTL = Bayesian Threshold LASSO, RF = Random Forest; L2B = L2-boosting algorithm, LhB = Lh-boosting algorithm
Specificity, sensitivity, phi correlation and misclassification rate for each model at detecting different α and (1-α) percentiles of extreme animals in the testing set within line C
Parameter | Method | α (number of records) | |||
0.05 (7) | 0.10 (24) | 0.25 (80) | 0.50 (104) | ||
Specificity1 | TBA | 1 | 0.50 | 0.64 | 0.71 |
LhB | 1 | 1 | 0.82 | 0.69 | |
Sensitivity1 | TBA | 0.33 | 0.30 | 0.54 | 0.53 |
LhB | 0.33 | 0.20 | 0.46 | 0.45 | |
Phi correlation1 | TBA | 0.26 | -0.16 | 0.17 | 0.24 |
LhB | 0.26 | 0.20 | 0.28 | 0.15 | |
Misclassification rate (%)2 | TBA | 57 | 67 | 43 | 38 |
LhB | 57 | 67 | 41 | 43 |
1Higher value is desirable; the best value for each percentile is in bold face;
2Lower value is desirable; the best value for each percentile is in bold face;
TBA = Threshold Bayes A, BTL = Bayesian Threshold LASSO, RF = Random Forest; L2B = L2-boosting algorithm, LhB = Lh-boosting algorithm
Area under the receiver operating characteristic curve1 for each model and breed line in the field pig data
TBA | BL | RF | L2B | LhB | |
Line A | 0.64 | 0.65 | 0.67 | 0.55 | 0.60 |
Line B | 0.70 | 0.69 | 0.73 | 0.60 | 0.72 |
Line C | 0.62 | 0.62 | 0.67 | 0.67 | 0.66 |
TBA = Threshold Bayes A; BTL = Bayesian Threshold LASSO; RF = Random Forest; L2B = L2-boosting algorithm; LhB = Lh-boosting algorithm
1Higher value is desirable; the best value for each line is in bold face
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A Report from Sarah Walker
The white puppy had been dumped on the street 3 days earlier. At the request of a friend that had been feeding her through her open window and dropping kibble out on the street, I was asked to foster "Parche" until they returned for the season. saying they would adopt her then… this was a huge request as I have 6 doggies of my own. I had no idea that in 3 days everything would change.
I volunteer with a wonderful group of people that form JBAR. Jaltemba Bay Animal Rescue, and that is what we do, RESCUE! But our rescue station in La Colonia… IS FULL. So I thought Parche could stay with me for a little while.
Three days later at the end of the street market, I came around the corner and there were 2 very young puppies totally lost and disoriented. They had been dumped as well. I stopped and one ran under my car and laid down and wasn't going to move any time soon. Racing the engine didn't work either. But his sister was a little more friendly. I shut off the engine and decided to walk the short distance to my house and return with FOOD, and frontline. I could see they were covered with ticks. They were in bad shape and very very thin, but pretty dogs of a red tan with black muzzles. The female had beautiful eyeliner. Wish I could look so good!!! In 15 minutes of coaching and cooing, I had them back to my house and isolated into the laundry room away from all my dogs.
The clean up process is not nice but I will tell you that hours were spent on hundreds of ticks. their gums were "white". I called my vet in P.V. Dr. F. Aguilar and he told me what vitamins to get them on. Smoked Salmon and Rice from Alaska, adorned their puppy kibble. They ate like tomorrow would not come. But I only gave small amounts and had them wormed immediately.
I decided to name them Bonnie and Clyde! They howled all night. It rained hard that night but they were safe and dry in the laundry room. Next morning the gardener came to the door to find me and tell me that there was ANOTHER puppy at the back door. "No". I said. "No es possible!". There cowering in the corner of the outside door was the tinniest little brown puppy. TERRIFIED! Totally emaciated. He was all head and bones. His eyes were the brightest and so beautiful, but so scared. He was obviously the runt of the litter. He had heard his brother and sisters howls and found his way to my back door. In my estimation he did not have long to live. I think he knew it.
I carefully and gently picked him up and brought him to " the laundry room" where now it was his turn to get some VIP treatment. A dab of Frontline, good food but not toooooo much and a break from the world on a soft and clean blanket. I named him Billy the Kid.
It is now one month later their gums are pink, they are no longer anemic from all the ticks. They have doubled in size because they are fed separately and get to eat in peace. Their hair is as soft as can be from the vitamins and fish oils and they are loved. In return, they are loving and smart. Time for a wonderful home(s) for them.
Editor's Note: Sarah Walker has rescued and nursed many injured animals back to health so they could be adopted out to good homes. This story is about how Sarah's summer is going so far! This article was originally published in the July/August issue of the Jaltemba Bay Animal Rescue (JBAR) newsletter.


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Central & South AsiaBomb blast kills Indian police Fourteen police officers die after their vehicle hits a mine in eastern India. 16 Dec 2007 18:55 GMT Security officers in eastern India face a well-armed Naxalite force The attack follows an incident in neighbouring Chhattisgarh state on Saturday, where four Maoists were killed and several injured in a gun battle with police. More than 6,000 people - mostly police, government soldiers, landlords and civilians suspected to be spies - have been killed in violence. The Naxalites are active in six of India's 28 states - Andhra Pradesh, Jharkhand, Bihar, Karnataka, Orissa and Chattisgarh. Source: Agencies
The woman who washes the dead The story of two women - a Palestinian Bedouin and a Syrian refugee - as one prepares the other for burial. Refugees, Greece, Religion
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Media Theorised In our latest online series we showcase the key works of five big thinkers from around the world – Noam Chomsky, Edward Said, Marshall McLuhan, Roland Barthes and Stuart Hall - whose theories on the media will sharpen your critical tools when you next consume the news.
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- 2 cups butter, softened
- 1 cup sugar
- 2 teaspoons vanilla extract
- 4 cups all-purpose flour
- 60 walnut or pecan halves, toasted
- In a large bowl, cream butter and sugar until light and fluffy. Beat in vanilla. Gradually add flour and mix well. Drop by heaping tablespoonfuls into greased miniature muffin cups; flatten slightly. Press a walnut half into the center of each.
- Bake at 350° for 10-12 minutes or until edges are lightly browned. Cool for 2 minutes before removing from pans to wire racks. Yield: 5 dozen.
Originally published as English Tea Cakes in Best of Country Cookies
1999, p54
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Reviewed Mar. 29, 2012
"This is my go-to recipe for all my sibling's bake sales. It's so adaptable! Substituting the vanilla extract for lemon or orange and adding in lemon zest gives them a nice citrus punch. Or I subtract 1/2 cup of the flour and add in 1/2 a cup of cocoa powder for chocolate, or I just add in cinnamon, ginger, and cloves for spiced teacakes."
Reviewed Dec. 15, 2010
"easy and incredibly delicious. disappear fast. melt in your mouth!"


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WASHINGTON (AP) - The Bush administration assured Congress on Friday that it is aggressively working to deal with a rising tide of mortgage foreclosures in the country.
Officials, however, said homeowners threatened with the loss of their homes must do their part by actively seeking help to avert foreclosures.
Robert K. Steel, Treasury's undersecretary for domestic finance, and Brian D. Montgomery, an assistant secretary at the Department of Housing and Urban Development, said the administration has a comprehensive plan and is working with an industry group formed to
deal with the looming foreclosure crisis.
Many Democrats have been highly critical of the administration's efforts, contending that they are too dependent on the industry and offer too little in terms of government assistance to stem what could be an estimated 2 million home foreclosures in the coming two years.
But Steel argued that the administration is working through a new initiative at the Federal Housing Administration to offer FHA-insured loans to borrowers in danger of default and also through HOPE NOW, an industry alliance.
"Under the president's leadership, the administration is working diligently to help mitigate the impact of rising foreclosures on homeowners and the economy," Steel said in his prepared testimony.
Montgomery told the committee that the FHA program is available for homeowners to help them "weather personal financial crises and reinstate delinquent loans."
While the government reported Wednesday that the overall economy surged ahead at an annual rate of 3.9 percent in the July-September quarter, economists believe that the steepest downturn in housing in more than two decades, which has Wall Street worried because of lingering credit problems, will cut economic growth by half in the current quarter, raising concerns about a possible recession.
Estimates are that mortgage defaults could rise to 2 million or more in the coming two years in the subprime mortgage market, loans provided to borrowers with weak credit histories. Those loans are now resetting from low introductory rates to much higher rates which can add as much as $250 to $300 to the typical monthly mortgage payment.
(Copyright 2007 by The Associated Press. All Rights Reserved.)


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Music/Video Servers & MP3 Players From dedicated music servers to PCs for music to integrating your iPod - discuss everything about music servers here. |
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03-27-2008 | #1 |
Join Date: Feb 2007
Posts: 695
Canton's iPod Digital Sound Station
Canton has introduced a high-performance desktop audio system designed specifically for the iPod. The Canton DSS 303 Digital Sound Station brings the company's renowned loudspeaker driver technology and industrial design to a compact and stylish audio system that includes an integrated iPod dock and charger, FM/AM radio, clock, and auxiliary input for a complete desktop audio solution for the bedroom, dorm room, or office.
The DSS 303's high quality loudspeakers are designed and manufactured by Canton with the goal of achieving full dynamic range and extremely linear frequency response from 38 Hz to 25 kHz. The 4-inch aluminum cone woofer has been carefully matched to the enclosure and amplification circuitry to deliver exceptional bass performance and a smooth linear midrange sound. The 1-inch fabric dome tweeter is mounted on-axis with the woofer for optimal transparency and imaging. The internal amplification modules are capable of delivering up to 50 watts of power to each channel. The Canton DSS 303 carries Apple's "Made for iPod®" certification, and includes seven different adapters for nearly every iPod made that includes a docking connection. A 3.5mm rear-panel line-in connection is included to accommodate other players, such as the iPod shuffle, or those from other manufacturers. Additionally, the system features a rear panel USB port that can be connected to a PC to allow all synchronization and download functions to be performed with the iPod in the DSS 303 cradle. The Canton DSS 303 includes a high quality FM/AM tuner section, and a full-featured clock, along with sleep and wake-up alarm functions. It is available in high gloss white and black finishes that are matched to the finish of most iPod models. It features a discrete set of front panel controls, as well as a full-feature credit-card remote for control of the system as well as basic iPod functions. It has a suggested retail price of $499. |
03-27-2008 | #2 |
Super Member
Join Date: May 2007
Location: Florida
Posts: 2,422
Re: Canton's iPod Digital Sound Station
I got a chance to hear this @ CEDIA last year and it sounded great and had really good bass output for such a small unit. The remote has an LCD screen that really allows it to be small and highly versatile.
Ken Taraszka, MD Associate Editor HomeTheaterReview |
03-29-2008 | #3 |
Super Member
Join Date: May 2007
Location: Oklahoma (Go Sooners!!)
Posts: 1,243
Re: Canton's iPod Digital Sound Station
I personally haven't seen a need for something like this, I haven't even gotten the iPod dock for my Denon AVR.
I listen to my iPod when I'm driving using the USP connection on my truck radio or using ear buds while I'm mowing the yard. When I'm home listening to music I can just stream it from my Computer thru the Denon. I'm sure they appeal to a lot of people since there is thousands of different ones made. I guess something like this would work in my shop (but not at $499) but it would have to be tough enough to withstand the conditions in there, from hot to cold, dust & lots of electrical interference with the equipment running. Right now I just have a DeWalt work site radio out there. |
03-31-2008 | #4 |
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Join Date: May 2007
Location: White Plains, New York
Posts: 1,706
Re: Canton's iPod Digital Sound Station
Sounds another great product from Canton. Seems like we need a review. To see how it stacks up against the competition. And the Meridian F80.
keenyt, maybe you could shed some light on this, since I believe you have heard both? |
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