text:
306 SW 9th St
Newport, OR 97365
Keep your cars radiator in working order and let us fix it!READ MORE
You can count on us to solve even the most difficult problem.LEARN MORE
Since 2017, Northwest Allpro Auto Clinic has provided motorists in the Newport, OR 97365 area with comprehensive auto repair services that include 4x4 Services, Alignment, Batteries, Brakes, Car & Truck Care, Counter Services, Domestic Cars & Trucks, Electrical Services, Electronic Services, Engine & Transmission, Engine Maintenance, General Services, Heating and Cooling Services, Hoses, Import Cars & Trucks, Machine Shop, Miscellaneous Services, Radiator Services, Tires, Towing, Transmission Services and Undercar Services. Our goal is to offer the best services available at affordable prices to get you back on the road in no time.
Northwest Allpro Auto Clinic also provides Radiator Repair and Replacement. We also perform cleaning of heat exchangers, cooling systems, radiators on boats, equipment, and machinery. Northwest Allpro Auto Clinic installs hitches and trailer brake control modules. We even provide towing, so call Northwest Allpro Auto Clinic at 541-265-5225 for all your needs!
You'll be hard-pressed to find the same level of expertise that our auto mechanic specialists provide! We have years of experience working on both domestic and import vehicles of all makes and models and can work with you to find cost-effective repair and maintenance alternatives.
For a high-performing vehicle, trust Northwest Allpro Auto Clinic with your car, truck, or van. To learn more about the services we offer, please contact us by phone at 541-265-5225 or send us an email. We also invite you to learn more about our dedication to you, our valued customer.
If I could only rate them higher, I would. Pat (owner/operator) went above and beyond for me when I had a pretty traumatic breakdown of one vehicle, and another ran off the road. Not only was his work prompt, excellent, and affordable, he went FAR ABOVE AND BEYOND what could be expected of him when dealing with my insurance company. Over the course of several months of the insurance company losing paperwork and reciepts, even after I had paid him cash out of pocket, Pat went to bat for me and resent and re-emailed everything i requested of him to make sure I got paid. He's got my business for life and I would recommend anyone to him.
This guy knows his stuff! Go here first, you won't be sorry. A+ customer service!
Absolutely wonderful. My car drives and sounds like new. Great guy, great service couldn't ask for more.
Diagnosis spot on, repair completed quickly and efficiently great price these guys are GREAT. Got a problem? Use them!
I had my Scion repaired here and Pat did an excellent job!! My husband's pick-up is going in next!! Professional, courteous, honest!! I have already recommended him to 3 people who need car repair!!
Great shop! Patrick fit my fiance's car and mine in same day and was very helpful. Knowledgeable and professional and just generally a good dude to do business with.
These guys are awesome ! We were on vacation, about two hours from home when our Subaru WRX began overheating and the owner was able to get our car in ASAP and diagnose the issue and get it fixed within a matter of hours. We are so thankful for your service !
Go check out this nice clean auto shop with of course "the auto channel" and coffee in the lobby. Guys are stopping in to talk cars ... Pat will give you the honest happy deal, and the smile and price to go with it. This company is going to be the best place to take a car, if you're tired of the dealership run around!!
This place was a life saver! Pat and his crew were amazing and knew exactly what was wrong with our truck. We were hundreds of miles away from home and had the smallest electrical issue and they were able to figure it out. They are smart and knowledgeable and we would highly recommend these guys for anyone who lives there or happens to have car issues on their travels. 5 out of 5!!
Friendly knowledgeable techs ... had weird noise coming from my engine. They check it out and quickly had me back on the road. Would highly recommend ... will go again!
According to recent studies, 5 percent of all motor vehicle fatalities are clearly caused by automobile maintenance neglect.
The cooling system should be completely flushed and refilled about every 24 months. The level, condition, and concentration of coolant should be checked. (A 50/50 mix of anti-freeze and water is usually recommended.)
Never remove the radiator cap until the engine has thoroughly cooled. The tightness and condition of drive belts, clamps and hoses should be checked by a pro.
Change your oil and oil filter as specified in your manual, or more often (every 3,000 miles) if you make frequent short jaunts, extended trips with lots of luggage or tow a trailer.
Replace other filters (air, fuel, PCV, etc.) as recommended, or more often in dusty conditions. Get engine drivability problems (hard stops, rough idling, stalling, diminished power, etc.) corrected at a good shop.
A dirty windshield causes eye fatigue and can pose a safety hazard. Replace worn blades and get plenty of windshield washer solvent.
Have your tires rotated about every 5,000 miles. Check tire pressures once a month; let the tires cool down first. Don't forget your spare and be sure your jack is in good condition.
Check your owner's manual to find out what fuel octane rating your car's engine needs then buy it.
Keep your tires inflated to the proper levels. Under-inflated tires make it harder for your car to move down the road, which means your engine uses more fuel to maintain speed.
Lighten the load. Heavier vehicles use more fuel, so clean out unnecessary weight in the passenger compartment or trunk before you hit the road.
Use the A/C sparingly. The air conditioner puts extra load on the engine forcing more fuel to be used.
Keep your windows closed. Wide-open windows, especially at highway speeds, increase aerodynamic drag and the result is up to a 10% decrease in fuel economy.
Avoid long idling. If you anticipate being stopped for more than one minute, shut off the car. Contrary to popular belief, restarting the car uses less fuel than letting it idle.
Stay within posted speed limits. The faster you drive, the more fuel you use. For example, driving at 65 miles per hour (mph) rather than 55 mph, increases fuel consumption by 20 percent.
Use cruise control. Using cruise control on highway trips can help you maintain a constant speed and, in most cases, reduce your fuel consumption.
Keep your engine tuned. A fouled spark plug or plugged/restricted fuel injector can reduce fuel efficiency as much as 30 percent.
Inspect the engine's belts regularly. Look for cracks or missing sections or segments. Worn belts will affect the engine performance.
Have the fuel filter changed every 10,000 miles to prevent rust, dirt and other impurities from entering the fuel system.
Change the transmission fluid and filter every 15,000 to 18,000 miles. This will protect the precision-crafted components of the transmission/transaxle.
Inspect the suspension system regularly. This will extend the life of the vehicle's tires.


text:
Representations have included software providers, pharmaceutical, medical device and other life sciences companies, health systems and other health care .
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subset: commoncrawl


text:
The whole world is a stage. Well, okay, Shakespeare, but I believe that the reverse is also true — the stage is the whole world. At least, this statement is especially true when I am performing on a stage in a play. My whole world changes to the stage. The stage is all I can think about. On stage my awareness must become focused on every second of the performance, to try to make sure the scene as appealing to the audience as possible. But with my thoughts centered on myself, I, like any other actor, begin to question my capability as a performer.
This problem of confidence became more apparent in a recent production of Talley's Folly in which I stared. In this play, there are only two actors throughout the entire show. There are no set changes and no breaks for the actors to go back stage for a breather. We, Vicki and I, were up there on stage for the whole performance that lasts for an hour and a half. I didn't mind all the lines involved, but what really killed me hard was the rehearsal. Vicki and I had to spill out our blood and energy to the director usually for up to four hours, every night, for three months. It felt like … oh … a rhinoceros was pounding his freshly polished horn right into my gut. Of course, he wouldn't take the horn out but he would ever so gently twist the horn to cause as much pain as rhinocerosly possible. Please understand there is no "intentional" connection between the rhinoceros and the director. Let's just say there are scars and leave it at that.
Now back to this problem, I consider myself a rather confident person; I mean, I have been performing plays all through high school. I sing at concerts, I emcee at talent shows, and I sometimes cartwheel at circuses. I perform for people and I love it and need it. Being up there on a stage is very thrilling and even essential. However, with this play, Talley's Folly, I could not be the same high school actor I used to be. I had to change into this serious college actor. This was not my chance to be Tin Man again. As rehearsals progressed, I started saying, "Whoa! Shouldn't there be some more graduale transition before I dip my green-horned feet into the college acting pool." Although I had great preparation in high school with an amazing director, this part of playing Matt Friedman, of course the part shares my name, was extremely more involved than any other part I had ever done. Let me be honest, this part and play was probably one of the toughest things I have ever done.
I began to feel very unqualified for the position. Don't get me wrong I was giving it the most I had, but it hurt tremendously. One does not take on rhinos when you an eighteen-lanky-year-old. The few friends I had around campus encouraged me. Some would say, "You'll get it." But, this provided false comfort. Sure, I'll get it, but when and how well will I get it? At that time the pressure this part brought to me was extinguishing my life. I couldn't wait till the whole play would be over, then I'd have my life back. I kept on wishing that the future would be now. My whole world became engrossed in the play and even more so my complete inadequacy at completing this part with the hopes of getting a throw-a-way "good job" from the director. The play was no longer play, but work, hard work. Mind you, it wasn't hell, but the director did breathe fire through those rhinocerous nostrils.
After a tiring rehearsal one night, I was lying on my bed trying to relax when the phone rang. I collected what energy I had and answered. The voice sounded vaguely familiar, but I couldn't tell.
"Hi Matt! How is everything? This is your uncle Larry." LARRY!
First I should explain. It's not common for Larry to call me, its long-distance from New York City and, second, he is a busy, successful actor-musician-director on Broadway.
"Wow! Larry, why did you call?"
He said he had heard about this grand production of a play I'm in and wanted to wish me to break a leg (we were opening in two nights). I told him I needed as much leg-breaking as he could muster for me because I was having a difficult time with this part. For once, I believed my stress was understood. Of course, he never said, "Well, this is what you should do." He just told me about how he was doing and where he was going with his life. Larry has had a great amount of success with his move to New York City. He worked in the great Broadway productions of Cats, Les Miserables, and others. But now he found himself at a time to reevaluate what he wanted out of life. Unfortunately, he was facing a problem; he was getting caught in a downward spiral. He kept thinking that here he was a kid from a small town in Kansas trying to make it in New York City. What chance did he even have at becoming successful? But these he also started asking the other question, what chance would he have if he kept thinking he didn't have a chance. He said he was trying his best to avoid that self-doubting downward spiral that starts out innocent enough but soon enough becomes a real whirlpool of despair. He said he ought to be going, but one more time he wished me to break a leg.
When the shock of him calling started to fad away, I began to realize what he had said. Oh my! I had started down this spiral of despair and I had never realized that I wasn't giving myself the chance to shine. My problem wasn't me so much as it was my constant self-doubting evaluation of myself. Why did I need to do this? I love acting. I enjoy the thrill of the stage and the challenge of creating a character that will make the audience fall in love. Looking at myself was the thing that was tearing me apart. Looking to what good I could do will build me up.
I went to practice the next day with this new found courage and finally felt like I was playing with the whole world in the play. I enjoyed every minute of the performances, not as Matt Sutton the actor, but as Matt Friedman, the lovelable romantic hoping for a kiss from Sally Talley. In my absence of courage I found despair; in the presence of courage I found joy. Thanks Larry.
written for English 111-14 on Oct. 24, 1995 with only slight revision
For more about Larry, see him on Broadway.


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Design Brief
We know you've been cooped up in-doors for the past few challenges, so it is time to take you outside and smell the roses. This weeks challenge is what's considered the outdoor room, the Garden. The Garden compliments the home by being an extension of the interior and may provide as a sanctuary.
You have 17 hours to complete this challenge. You have a budget of just $7,500 to go with your 8 hours of handy man time.The episode begins with a surprise for Deankie and Tiara: this week, they were helped out by none other than last season's winning team, Phil and Iva! I can't emphasize enough how priceless this help is. Not only are Phil and Iva the overall winners from last year, but they were actually the winners for the garden challenge last season! They live on a boat, and have a real knack for all things nature, so this is very much their forte. D & T got real lucky on this one!
But not all the teams are feeling the love this week. Ines and Jennifer found it hard to see eye-to-eye, which led to a few tears shed between them. Per and Nara also couldn't really get along this week, but it looked like blood was going to be shed instead of tears.
Adam and Adrian were saved from elimination last week, and seemed more determined than ever to win the competition. I LOVED the fact that they used wood on the walls - definitely going to steal that idea for my future home (not that I'm planning to have much of a garden...).
Sarah and Brian went with a Balinese theme garden, which I have a soft spot for. They got a lot of flak for placing a fountain in the middle of the garden, but I really didn't see a problem with it (although Adrian was onto something when he said it'd be dangerous for kids).
Yvette and Sonia did their chic thang with the garden, which I thought was actually pretty charming. I could see my girlfriends and I sipping lemonade and having SATC-style brunches there. Per and Nara had a real "party hard" garden that also, interestingly, had a little play area for little ones. According to Per, kids need a safe spot to play in while mama sleeps off a hangover, LOL.
Onto the results!
First of all, let's get the winners out of the way:
Way to go, Adam and Adrian! Can't keep a good man (or good men) down. They totally deserved this win. This goes to show that people can really surprise you if you give them a second chance.
Now, let's see who's in the bottom two:
|Aiyo, the hanging plants.
Well, things are definitely going to get tougher next week with only five teams remaining in the competition. Who are your favorites for the win??


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Johnny's got the number one late nite talk-show in town, but recently he's been in a bit of a ratings slump. He brings on super-hot porn star Missy Martinez to try and shake things up. When that still doesn't please his bosses, he decides to go out in style by banging his hot guest right there on live TV. Collapse
Available formats
Niche & Categories


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Step 1Add rice to hot skillet set over medium heat. Stir with wooden spoon and shake pan to keep rice from getting scorched. When rice is lightly toasted, 2 to 3 minutes, remove from heat and cool before using.
Step 1Soak gulaman in 3 cups water 1 hour. Drain.
Step 2Combine buko juice and coconut milk in saucepan and add gulaman. Bring to boil over medium heat, stirring constantly, until gulaman is completely dissolved, 15 minutes. Remove 1/2 cup liquid and whisk in yolks. Return yolk mixture to pan with sugar and cook, stirring until mixture thickens into a light custard, 3 to 4 minutes.
Step 3Mix shredded buko (if using macapuno, rinse excess syrup before mixing with condensed milk) with condensed milk and spoon into individual cups. Ladle slightly cool mixture into cups. Chill completely until set, 2 hours. When cool, serve with toasted rice.


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< Back To Programs
Tuscaloosa Outpatient Program
6001 12th Avenue East
Tuscaloosa, AL 35405
Driving Directions
The Tuscaloosa Outpatient Program is designed for males and females, ages 12 to 18, who are at risk for, or are actively experiencing substance use problems. Referrals are accepted from the court, school systems, and other community agencies; however, participants do not have to be referred from such sources. A call to the Program to schedule a time for screening and assessment is all that is required. Following the assessment, recommendations by the therapist to the client and his / her family concerning the appropriate level of care will be discussed.
The Program offers ASAM Level I outpatient treatment. Level I encompasses organized, non-residential services designed to treat the individual's level of clinical severity and to help the individual achieve permanent changes in his or her alcohol and/or drug-using behavior and mental functioning. To accomplish this, services must address major lifestyle, attitudinal, and behavioral issues that have the potential to undermine the goals of treatment or inhibit the individual's ability to cope with major life tasks without the non-medical use of alcohol or other drugs.
For more information, contact us at:
Email: firstname.lastname@example.com
Phone: (205) 344-6483
Toll Free: (877) 765-4967


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Join the Monterey County Business Council for our 22nd Anniversary Gala & Annual Economic Vitality Awards!
Friday, May 5, 2017
Hyatt Regency Monterey
1 Old Golf Course Road
Monterey, CA
6:00 PM Reception
7:00 PM Dinner & Program
$175 per person
Black tie optional
We hope you will join the business, education and government communities in our region.
During the event, MCBC will also be recognizing organizations that have contributed to the economic vitality, growth and preservation of our community
Baby Mesclun Greens, Apple, Blue Cheese, Candied Spiced Pecans, Red Balsamic Vinaigrette
Grilled Filet Mignon, Merlot Demi-Glace, Smoked Cheddar Sour Cream Mash with Seasonal Vegetables
Goat Cheese Ravioli with Roasted Eggplant, Roasted Peppers and Pine Nut
Apple Caramel Cheesecake, Caramel Sauce, Spiced Whipped Cream
MCBC wishes to thank our generous Presenting Sponsor
MCBC thanks our Executive Sponsor
Special thanks to the County of Monterey for its ongoing support of the Monterey County Business Council's efforts for economic development and education.
1 Old Golf Course Road
Monterey, CA


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Co-Founder (m/f)
- Vollzeit
- Mit Berufserfahrung
Company Information
Hype Ventures is an e-commerce incubator focussing on fast scaling products with a high potential to go viral mainly in the beauty and health sectors, while also following a clear strategy to quickly expand to other industries. The Hype Ventures founder team consists of highly experienced entrepreneurs, (ex) strategy consultants and CEOs who all built at least one successful startup during their careers. Offices are located in Berlin and San Francisco.
Job description
As Co-Founder and Managing Director of one of our ventures you will set up operations, build the team, and scale up to win the markets. You will have the chance to quickly expand internationally and shape the company from an early stage. At the same time, you will be given advice and help by highly motivated and talented people who will support you in building up the company. The role of Co-Founder offers a unique chance to take ownership and responsibility for the growth of the business, driving marketing, sourcing, HR and retail sales.
● Pro-active and definite hands on attitude
● Strong analytical skills, a well structured approach to work, and high attention to detail
● Passion for start-ups and e-commerce
● You are enthusiastic, a self-starter and result oriented
● A university degree or proven entrepreneurial experience
● You are fluent in English and German. Knowledge of other languages is a plus
● At least one year of relevant work experience
What we offer
You will gain the broadest experience and be fully responsible to successfully build one of our ventures. We offer an attractive equity package and compensation. The office is based in Berlin.
Please send an email to email@example.com for more information or to apply.


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Philosophical Kanye made an appearance on Ellen this week, and he didn't disappoint.
After working to pry more than three words out of the rapper, Ellen quizzed him about his kids and whether he regretted anything he'd ever tweeted. The money question though, was asking Yeezy about his appeal to Mark Zuckerberg for $1 billion in funding.
Ellen asked Kanye West to give her 'one example' of the ideas he had, 'because maybe someone watching will give you the money.'
And it's pretty clear she quickly realised that you should never, ever ask Kanye West to give you just one of his ideas. It all ended up in a rambling, nearly unbroken, nearly seven minute speech.
In what probably seemed like an eternity for audience members, West touched on key points such as Michael Jackson, the Renaissance period, glass ceilings, Walt Disney, and clothes.
Kanye on Ellen may have been the most uncomfortable thing I have ever seen.
— Tani Erdmann (@TannerStechnij) May 20, 2016
Kanye West on Ellen..my head still hurts
— Jamal Jackson (@jamaljackson__) May 20, 2016
He went on to say:
I don't give a fuck how much you sold, if you playing on radio. Are you connecting? Picasso is dead. Steve Jobs is dead. Walt Disney is dead. Name somebody living that you can name in the same breath as them. Don't tell me about being likable.
We got a hundred years here. We're one race, the human race, one civilization. We're a blip in the existence of the universe! And we constantly try to pull each other down! Not doing things to help each other. That's my point. It's like, I'm shaking talking about it.
I know it's daytime TV, but I feel that I can make a difference while I'm here. I feel that I can make things better through my skill set. I'm an artist, and I feel that I can make things better through my skill set.
Ellen during kanye's rant pic.twitter.com/BtQy1TBl2G
— kdesf (@WHITNEYH0UST0N) May 20, 2016
Pretty much through the whole thing, Ellen – who hasn't spoken for what seem like an eternity – is sitting motionless.
Kanye finally ends it with: "I'm sorry, daytime television. I'm sorry for the realness."
And Ellen sums it up pretty perfectly with, "Well it's not daytime television anymore," before cutting to commercial.
If your ears can handle it, watch a snippet of Kanye's rant here:


text: Visualization of complex biological datasets is essential to understand complementary spects of biology in big data era. In addition, analyzing of multiple datasets enables to understand biologcal processes deeply and accurately. Multiple datasets produce multiple analysis results, and these overlappings are usually visualized in Venn diagram. bioplots is a tiny R package that generates a heatmap to visualize overlappings instead of using Venn diagram.


text:
Some ideas on athletics and RB — 5/5/2010
I cannot go tonight to the forum on athletics. One of my kids is being recognized for some of his achievements, and I am proud to go with him and clap for what he has done.
But it means I will miss the forum, so here are my ideas, fwiw:
1. The job name should be changed to "Dean of Athletics."
2. All athletics, phys ed, intra-mural and interscholastic, should be provided on the basis of a stated plan that is fully integrated into the EDUCATIONAL mission of RBHS — with a mission, objectives, strategy, tactics, etc.
3. The principal purpose of all athletics at RBHS should be analogous to its educational effort: As the academics teach kids how to learn, athletics should teach kids how to stay fit for the rest of their lives.
4. In the midst of the worst obesity epidemic in recorded history, which results mainly from sedentary habits and high-caloric diets, RBHS should dedicate itself to graduating physically fit students who have learned how to practice lifetime fitness.
5. Just as academics meets students where they are and leads them to the extent of their ability through honors classes to AP level work, so should athletics lead students to the full expression of their talents and abilities.
6. All of the team and Booster fundraising should be unified so there is one goal, one method of raising funds and one set of books that is well-kept, open and available. The School has recently said Boosters are an independent group, which is false and misleading. Boosters should be in charge of all of the fundraising, do it once for all athletics per year and maintain itself as an Illinois 501c(3), or appropriate equivalent, on an open and transparent basis.
I hope everyone has a great discussion tonight.


text:
Subscribe To Woman In Coma Gives Birth To Healthy Baby Updates
I've already subscribed
A woman named Melissa Carlton who has been in a coma for two months gave birth to a healthy baby boy. Delivered via C-section, the little boy was named Nathaniel West. The baby, who weighed five pounds nine ounces at birth, was not expected to survive, nor the mother. Yet both are still alive and recovering. Since the birth, Carlton has responded to the baby with eye contact and is able to nurse with assistance.
The whole incident began when Carlton went in for surgery ten weeks ago. She was to have a benign tumor on her brain removed, but she had a seizure during the surgery. As a result, it damaged part of her brain and sent her into the coma. Melissa Carlton is on the road to recovery, however, and her new husband Brian has been nothing but supportive to the newest member of his family, as well as his recovering wife. The family of Carlton is very grateful for Brian and say they are very thankful for the way he has handled the situation...
"This is a story of joy and it's not lost on us that this is a story about Brian and his love...He has taken care of our daughter in a way that parents can only hope for, and that's a credit for Melissa because Melissa chose him."
Things have been looking up for the family as Melissa makes the slow road to recovery. CBS News is saying she touched her husband's face recently and hugged him for the first time since she's gone into the coma. Medically, a coma means a period of unconsciousness lasting longer than six hours where the victim can best be described as "comatose".This means when unconscious they cannot be woken up, nor will they respond to stimuli. They may also be prone to making involuntary actions.
Can you imagine having a baby in a coma? Much credit to everyone involved who made something that sounded like a bad tabloid headline a real heartwarming event. In a situation where literally everything could have gone wrong, everything went right in the most impossible and best way imaginable.


text:
HELLO! I am not back from the dead, although it kinda seems like it around here. I took a pause…to work a bunch, to eat a bunch and, of course, to cook a bunch! What has changed?
The Changes
- I'm a little healthier {I only ate 3 giant muffins this week and am no longer scared of a treadmill…sorta.}
- I drink more water {not sure why I told you that}
- My job title has changed twice {yes, twice in one week, actually}
- I have a new fire for cooking and sharing with you {I've got to start blogging again because I almost blew up the microwave yesterday}
October is almost over, and there are 6 beautiful cans of pumpkin puree in my pantry, beckoning to be molded into a decadent, delicious creation. I have an assortment of organic squash and such to go crazy with, too. And, if you've poked around this space at all, I love to take a recipe (or 4 or 5) and merge them and healthify them, based on which ingredients are already in my kitchen. This pumpkin bar is a lazy woman's approach to a traditional pumpkin pie. And I love all of that delicious, sweet filling but the store-bought crusts leave much to be desired. The shortbread crust and the nutty taste throughout more than make up for the quick, tastier version of a pie. Let's go!
Nutty Pumpkin Bars
Sitting on an almond shortbread crust, these Fall treats are sure to please even your nuttiest neighbors and loved ones during the season. Serve warm with English breakfast tea or chilled for a sweet snack. The best part? All original needs for cream are replaced with Coconut Almond milk, which gives an essence of tropical coconut to an already complex, warm pumpkin flavor. Party on your tongue! And, not, there not much to look at, but my job is making tasty stuff. You can pipe some buttercream or cream cheese frosting on them for pretty effect, if you like!
Makes 18 cupcake-sized bars or one 9X13 pan
Almond Shortbread:
1/2 cup almond slivers
1 cup (130 grams) all purpose flour
1/4 teaspoon kosher salt
1/2 cup (113 grams) unsalted butter, room temperature
1/2 cup (110 grams) light brown sugar
1/2 teaspoon pure vanilla extract
Step One: Line/grease 18 muffin cups or a 9X13 pan. Preheat oven to 350 degrees. In a food processor, chop the almonds to a ground meal, then add flour and salt and blend. Set aside.
Step Two: In a mixer (or using your hand and a spoon), cream butter and brown sugar for 2-3 minutes, or until fluffy. Add vanilla and blend. Then, add flour mixture and blend into a dough.
Step Three: Press dough into bottom of muffin cups or baking pan. Bake for 15 minutes, or until semi-firm to the touch like a soft cookie (not doughy). While waiting for it to bake, make the pumpkin filling.
Pumpkin Filling:
2 large eggs
1 – 15 ounce can (425 grams) pure pumpkin (2 cups)
3/4 cup (165 grams) light brown sugar
1/2 teaspoon pure vanilla extract
1 teaspoon ground cinnamon
1/2 teaspoon ground ginger
1/8 teaspoon ground cloves
1/4 teaspoon kosher salt
1 1/2 cups (360 ml) coconut almond milk
Step Four: In the mixer, blend eggs. Add pumpkin, sugar, vanilla, and all spices and mix until blended. Lastly, add the milk and blend until smooth.
Step Five: When the shortbread is semi-firm, distribute pumpkin filling into the cups (3/4 full). Bake for 20 minutes, or until tops are slightly golden and pumpkin is firm to the touch (not runny or jiggly). Let cool for 5 minutes before running knife around the edges before removing from the pan(s).
Serve warm or chilled. Ice cream on top doesn't hurt a thing.
Adapted from Source: Joy of Baking


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Cut Big Data Down to Small Data and Save Big Bucks
Big Data was one of the buzziest buzzwords of 2012, and is buzzing only a little less in 2013. An awful lot of money is being flung at Big Data. That's nice for companies that have an unlimited supply of cash, which yours probably does not. Let's face it: Big Data is useful if you're trying to determine what happened to the universe 17 milliseconds after the Big Bang. Big Data is useful in a business sense for Wal~Mart, which handles over one million customer transactions per hour. Big Data is also fine for the U.S. government, which owns six of the world's 10 biggest supercomputers. Now let's look at your business. How many supercomputers do you own? Probably none. So instead of seeing how Big your Data can be, you are probably better off cutting your data down to size. Let's look at some ways of doing that — and how you'll save a Big Bunch of Bucks in the process.
It's easy to say, "storage has gotten cheap." True. Your cost to store data, per Gigabyte, is lower than it has ever been, and keeps on dropping. But just having data around is meaningless. Sorting it, correlating it, and analyzing it is still costly, especially when it comes to human time at the end of the process, which is still needed to see un-obvious correlations between different data points that machines can not yet spot. The human doing that correlation might be you, and don't you have enough work to do already without adding to the load?
Freelance CTO Mark A. Herschberg says, "In many cases a company can gain insights from 'small data.' Instead of culling through gigs of data take a sample. This can be as simple as picking data from a half dozen days throughout the year or picking every 1000th row of data. If you can get a sample of even a few thousand or tens of thousands of rows of data you can drop it into excel and do some basic data analysis on them. This can be done with just a few SQL statements and a few hours of time."
Anne Rozinat, co-founder of Fluxicon, suggests learning about Process Mining, and has kindly given us two links to videos (Video One; Video Two) that explain the concept better than we can here in a few words. The point here is that process mining, done correctly, only requires generic PCs or servers that you already have, not specialized machines or costly supercomputer time. This technique is absolutely worth checking out if you are analyzing processes rather than discrete event or item characteristics.
Josh Farkas, founder of Cubicle Ninjas, says, "I feel that the shift from big data to small data in consumable chunks has already occurred, and that small businesses may have the upper hand.
"The enterprise has long had the data advantage, but the birth of small business software as a service was a game-changer. Many small businesses manage this data better because of the more robust web functionality available today.
"Touch interfaces have actually helped this trend too. Interfaces are expected to be clear, intuitive, and a pleasure to interact with. Because of this, big data can be refined more elegantly to just your most pressing bits."
David Smith, VP of Marketing and Community for Revolution Analytics, contributes these suggestions:
- Start with flat files. There are definite performance and administration benefits to working with a database, Hadoop, or other data management systems, but you'd be surprised at the insights you can find in a simple comma-separated data file exported from your web server or CRM system. These flat files can still be very large (millions or even billions of rows), but that doesn't mean they're unmanageable.
- Use open source tools: There are many excellent tools available that work with flat ASCII files available free of charge. From simple tools like "grep" to more powerful data-processing languages, many of your data-processing needs can be met with open-source solutions.
- Predictive modeling with R: If you're looking to predict the future rather than just summarize the past, the R language is also open-source and includes every predictive modeling algorithm you'd ever need.
- Get some help: While these approaches are powerful and inexpensive, they're also not for the faint-of-heart: think coding, not pointing-and-clicking. You'll need someone with the necessary expertise to help, but fortunately there now many graduate programs in Data Science which provide new jobseekers with skills in data management, open source tools and predictive analytics
"On the downside," David notes, "while these ad-hoc approaches will give you useful information about your business at a snapshot in time, they don't necessary lend themselves to automated production systems that can provide a real-time dashboard for your business. But even these simple steps can give SMBs enough guidance to improve their bottom lines and upgrade to production-ready Big Data systems."
And finally, we have this statement from Rachel Delacour, CEO and cofounder of BIME:
- No doubt, Big Data has become an overused term to lump together too many technologies, tools and terabytes that defy easy categorization or simply evoke images of the information deluge. But 2013 will make one thing abundantly clear: Every single business has to cope with this new data-driven world in which data is a currency, an asset and a liability. It all depends on how you tackle it and turn it into something immediately valuable. Instead of falling for analysts and vendors who tout the really big thing, more organizations will start thinking small data in 2013, particularly if they are an SMB.Here's why: Big Data starts with a single consumer, a single sensor, a single transaction or a single click. It all flows from here, and the sooner a company picks up on those tremors, the better their read on the earthquake that may be coming. So the best way to think about Big Data is to think about its sources. Don't become paralyzed by the size of the entire datasphere and instead focus on three simple questions: What types of data do I have? What new types of data can I access thanks to the Net? And what new questions can I keep asking to improve my business? Asking them is almost free. Tools in the cloud let every small explore data and get answers, fast and affordable.
46 experts responded to my online query, "Can cutting 'big data' down to 'small data' save money?" They all agreed that Big Data can easily get out of hand, and that there are many ways to cut big data down to a size that is affordable and useful for even the smallest business. Today we've looked at a small selection of the answers we got. Please stay tuned to Cheap Computing. We'll run more responses to our "Big Data" question in the near future, and will also be asking our experts many more questions about how you can save money on your home and business IT needs.


text: Blog Pella Products: proudly made in America and sold locally in Salt Lake City, UT June 18, 2015 While we do our best to serve Salt Lake City customers with top-of-the-line window and door products that convey the most value, we don't talk often about where our products come from. Last night on ABC News they broadcast a segment of their "Made in America" on World News Tonight where the focal point was our factory in Pella, Iowa and our team members who make those products for you. If you missed it when it first ran you can watch the segment below: ABC US News | World News We are very proud to provide you the best products from the best, most capable workers in the world. They pour every ounce of pride they have into the products we sell in Salt Lake City. Contact us today if you'd like to learn more about how we can help you save money and improve the exterior of your home with a quality, American-made product! Back to Blog


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How long does it take for payout when I sell my Bitcoin through HappyCoins?
The payout procedure to your bank account runs every working day at 12:00 for all European banks. For Bitcoin received and confirmed on working days before 12:00 CET, funds will be available on Dutch bank accounts the same day and on most other European bank accounts within 1-3 working days.


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Mastervolt's parent company, Power Products LLC, aims to offer you a continuously growing portfolio of powerful brands. We are therefore constantly looking for new companies to strengthen our market position, and increase our range of electrical components and power management systems. If you are interested in our brands and latest acquisitions, you can also browse or download the catalogues of the various brands.
For over 30 years, Ancor has been the industry leader for quality tinned copper Marine Grade™ wire and cable. Ancor's comprehensive product line includes wires and cables, terminals and connectors, wire management products, and hand tools. Ancor designs products to the highest standards that meet UL, ABYC, NMMA and USCG standards.
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BEP offers a varied range of battery switches, bus bars, fuses and fuse holders for safe and reliable operations within its Pro Installer series. BEP's innovative culture and engineering excellence is focused on simplifying the installation and optimizing the user experience with thoroughly tested products that will thrive in any harsh environment.
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For 10 years, the groundbreaking Digital Switching technology from CZone has revolutionized the control and management of electrical systems on boats, RVs and emergency vehicles. CZone provides complete control and monitoring of all electrical circuits through dedicated touch screens, iPad, wireless remote, or through the screen of one of CZone's integration partners. CZone provides improved performance, safety, and reliability.
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Since 1972, Marinco power inlets and cords have set the industry standard. They are built to withstand the harshest marine environments. Marinco has continued to expand their product line to offer a complete range of shore power cords from 16 to 100 amps, as well as a broad range of cord adapters. Marinco is also a leading supplier of wipers, spotlights and horns to the marine industry.
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ProMariner™ was founded more than 35 years ago with one premise: to make boating more enjoyable. ProMariner pioneered a complete line of fully automatic on-board marine battery chargers, and continues to bring innovation to marine battery chargers today. The passion and dedication to quality is what has made ProMariner the market leader in on board marine battery chargers.
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Sometimes parents need a little help from the experts, so a local group, Mothers of Youth (MOY), brings in guest speakers to talk to them. The next one is author Cheryl Dellasega, who'll discuss teen-age bullying and how to stop girls from hurting each other.
MOY is composed of moms from Centreville, Chantilly, Herndon and Oakton, and it presents speakers, four times a year, to audiences ranging from 30-200 people.
"Our goal is to help parents of teens and pre-teens communicate better with them — and actually enjoy them," said Patsy Mangas, one of MOY's founders. "Sometimes, you just get bogged down, and people like this come in and give you insights about what's going on in teen-agers' minds."
DELLASEGA, AUTHOR of "Surviving Ophelia" and "Girl Wars," will help mothers find effective solutions to girls' cruel behaviors. She'll speak Wednesday, Oct. 1, at 10 a.m. in the Sully District Governmental Center at 4900 Stonecroft Blvd., off Route 28 and Westfields Boulevard, in Chantilly. Cost is $10, and a light breakfast will be provided. RSVP by Sept. 26 to email@example.com.
Dellasega, 49, of Hershey, Pa., is married and the mother of two boys, 18 and 23, and a girl, 19. She's an associate professor in the College of Medicine at Penn State University and a nurse practitioner with experience working with family issues. Her book, "Surviving Ophelia," describes how she and 67 other mothers survived their daughters' adolescences.
"That book prompted my interest into relationships that girls have with each other because they can be very hurtful and friendships ended," she said. "That led me to really start talking to girls and interviewing teachers, therapists, mothers and fathers about girl relationships."
Dellasega zeroed in on solutions and strategies to make things better, and "Girl Wars" contains 12 strategies to help girls develop healthy relationships with each other, from an early age. And if hurtful relationships do develop, she tells how parents can empower their daughters to deal with them or, if necessary, become involved, themselves.
SHE SAID THE problem seems to peak in middle school and can continue on into high school. She advises parents to "really focus on helping their daughters develop [healthy] relationship styles in middle adolescence. Said Dellasega: "If a girl becomes either a victim or a bully, it's something she could continue on in."
And she tells girls that they can change their behavior at any time and learn a better way of behaving that'll stand them in good stead throughout their lives. Said Dellasega: "So many times I hear, 'Well, that's the way girls are.' But it isn't — and they can change."


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While Dad's deep sea fishing, Mum wants nothing more than poolside bliss. The kids want to escape the constraints and boredom of rules and routine. Auntie Cath and Grandma just want flavoursome holiday food lovingly prepared by someone other than themselves.
Pleasing a travelling tribe is no easy feat. Handling a suitcase full of differing holiday wishes can be overwhelming. Over time, and after a lot of traveller feedback, we've found some resorts definitely do it better than others
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A Scalable Algorithm to Explore the Gibbs Energy Landscape of Genome-scale Metabolic Networks
Abstract – The integration of various types of genomic data into predictive models of biological networks is one of the main challenges currently faced by computational biology. Constraint-based models in particular play a key role in the attempt to obtain a quantitative understanding of cellular metabolism at genome scale. In essence, their goal is to frame the metabolic capabilities of an organism based on minimal assumptions that describe the steady states of the underlying reaction network via suitable stoichiometric constraints, specifically mass balance and energy balance (i.e. thermodynamic feasibility). The implementation of these requirements to generate viable configurations of reaction fluxes and/or to test given flux profiles for thermodynamic feasibility can however prove to be computationally intensive. We propose here a fast and scalable stoichiometry-based method to explore the Gibbs energy landscape of a biochemical network at steady state. The method is applied to the problem of reconstructing the Gibbs energy landscape underlying metabolic activity in the human red blood cell, and to that of identifying and removing thermodynamically infeasible reaction cycles in the Escherichia coli metabolic network (iAF1260). In the former case, we produce consistent predictions for chemical potentials (or log-concentrations) of intracellular metabolites; in the latter, we identify a restricted set of loops (23 in total) in the periplasmic and cytoplasmic core as the origin of thermodynamic infeasibility in a large sample () of flux configurations generated randomly and compatibly with the prior information available on reaction reversibility.
Author Summary – The operation of biological systems is constrained under all circumstances by the laws of physics. Thermodynamics, in particular, dictates preferential directions in which biochemical reactions should flow at stationarity. When applied to cellular reaction systems (like metabolic networks), it favors the emergence of some (thermodynamically feasible) ways to organize the flow of matter while prohibiting others. The development of detailed predictive models for the biochemical activity of a cell relies on the possibility to integrate the laws of thermodynamics in genome-scale reconstructions of cellular metabolic networks. In this work we have devised an efficient relaxation algorithm to implement thermodynamic constraints in genome-scale models. Besides allowing to check for thermodynamic feasibility of reaction flow configurations, it is also capable of providing information on other relevant physico-chemical quantities. We have applied it to two cellular metabolic networks of different complexity, namely that of human red blood cells and that of the bacterium Escherichia coli. In the former case, we have obtained predictions for the intracellular chemical state (in terms of metabolite concentrations and reaction free energies) consistent with empirical knowledge; in the latter, we have effectively corrected thermodynamically infeasible flux configurations.
pacs:Valid PACS appear here
Constraint-based models of cellular metabolism are important tools to analyze and predict the chemical activity and response to perturbations of cells without relying on kinetic details that are often unavailable. In such frameworks, the metabolic capabilities of a cell are inferred from the overall configuration space compatible with minimal physico-chemical constraints describing the non-equilibrium steady state of the underlying reaction network. First, feasible reaction flux vectors need to satisfy mass-balance conditions. Then, according to the second law of thermodynamics, in an open chemical network at steady state and constant temperature and pressure the direction of each reaction should ensure a decrease in Gibbs energy. Thermodynamic consistency of flux configurations satisfying mass-balance alone is in general not guaranteed due to the presence of infeasible cycles book ; beard1 ; beard2 , even if reaction reversibility is pre-assigned based on careful estimations of chemical potentials in physiologic conditions alberty (a procedure that was recently extended to genome-scale fleming ; hatz ). Besides the flux organization, several other aspects involved in the analysis of genome-scale metabolic networks hinge directly on the explicit inclusion of thermodynamic constraints into the models, like the estimation of metabolite concentrations or the identification of reactions subject to regulation kummel2 .
Much work has been concerned with implementing thermodynamic constraints in genome-scale models of metabolism. The removal of thermodynamic inconsistencies was proved to be useful in estimating concentrations and affinities besides fluxes in Flux-Balance-Analysis kau ; palrev , whose goal is to identify mass-balanced flux configurations maximizing a pre-determined physiological objective function sauer ; biomass . This has been achieved for instance by building mixed integer-linear or non-linear optimization problems that minimize the Euclidean distance of concentration levels from experimentally known values hoppe , or ensure the absence of cycles in the resulting flux pattern tbmfa ; cobrall ; hepat . On the other hand, information on feasible Gibbs energy ranges can be retrieved by exploiting the patterns of reaction interconnections encoded in the stoichiometry to narrow the experimental bounds kummel2 . These procedures may however require reliable prior thermodynamic information on metabolites and/or reactions, a type of knowledge that is often unavailable. An important lesson of this kind of approaches is that the key input for the thermodynamic profiling of a reaction network is often provided by the stoichiometric matrix beardstoc .
The scalability of algorithms to solve mixed integer-linear (or non-linear) programming problems may become an issue when the underlying network size is large or when one is interested in sampling the solution space (for both free energies and fluxes) rather than focusing on a potentially small set of configurations (e.g. optima). Luckily, however, solutions to computationally hard problems can often be generated efficiently with the help of heuristic algorithms based on simple local rules. The use of message-passing algorithms to characterize the high-dimensional volume of the solution space of FBA models BMC (with a convex, continuous solution space) or to solve large combinatorial constraint-satisfaction problems KSAT (with a discrete and possibly fragmented solution space) is an example of the success of this kind of strategy.
Our goal in this paper is to obtain information about the landscape of Gibbs free energies compatible with a given vector of reaction directions by following a route that allows to use all stoichiometric information via heuristics inspired by perceptron learning. In a nutshell, the method we propose consists in exploiting the network's structure to iteratively build up correlations between the chemical potentials of the reacting species starting from a seed of empirical biochemical knowledge, until a thermodynamically consistent profile is achieved. The resulting algorithm is completely scalable and can be employed for different purposes, like checking the feasibility of flux configurations, identifying and removing infeasible cycles, estimating reaction affinities, and obtaining bounds for (log-)concentrations and free energies of formation. In the following, we describe the method in detail, providing a mathematical proof of convergence as well as theoretical arguments highlighting the main idea behind the procedure. As applications, we focus on two metabolic networks of rather different complexity. First, we shall obtain a detailed reconstruction of the Gibbs energy landscape underlying metabolic activity in the human red blood cell (hRBC) starting from the flux maps obtained in palsson ; andrea . Then, the metabolic network of Escherichia coli, iAF1260 bigcoli , will be analyzed to eliminate infeasible cycles from randomly generated flux configurations.
Materials and Methods
The cellular systems analyzed in this study are (i) the model of the hRBC metabolism developed inWiback:2002fk and discussed in palsson , and (ii) the reconstructed metabolic network of the bacterium Escherichia coli iAF1260, presented in bigcoli . The former consists of 35 intracellular reactions among metabolites subject to uptake fluxes. The latter includes reactions among metabolites. The basic information extracted from these models is the stoichiometric matrix (with and the number of metabolites and reactions, respectively), denoted below as . For Escherichia coli, we will consider the inner matrix of periplasmic and cytoplasmic reactions without repetitions, which consists of reactions among chemical species, once periplasmic and cytoplasmic metabolites are distinguished. According to the reversibility assignment given in bigcoli , of the processes above are unidirectional, with being reversible. The biochemical data we shall refer to or make use of include the standard free energies of formation of metabolites, given in kummel , where they are computed at , , and ionic strenght , according to the prescriptions of alberty . The estimated intracellular concentration ranges for the hRBC were extracted from the Bionumbers database Milo:2010fk and refer to measurements in different settings. It is worth to notice that the experimental errors on such values reflect the intrinsic uncertainty due to statistical cell-to-cell fluctuations (since measurements of concentration levels are usually carried out by averaging over numbers of cells ranging from to ) and analytical error. The stoichiometric matrix and thermodynamic potentials employed for the analysis of the hRBC are respectively available as Supporting Datasets S1 and S2.
Algorithm to compute chemical potentials
According to the second law of thermodynamics, in an open system at constant temperature and pressure the Gibbs energy (where , , are respectively the energy, volume and entropy of the system) never increases spontaneously. This means that the direction ( for forward, for backward) of every chemical reaction occurring in the system should be opposite to the Gibbs energy change induced by reaction , i.e.
The equality holds if reaction is in equilibrium. Denoting by the stoichiometric coefficient of reactant in reaction , with the standard sign convention to distinguish substrates () from products (), the vector of 's for a well-mixed system can be written in terms of the chemical potentials (where is the Gibbs energy per mole of species ) as (see e.g. book , Ch. 1)
Given flux vectors such that (i.e. steady-state flux configurations), equation (2) implies that , i.e. that the 'loop law' holds. The Gibbs energy landscape reconstruction problem consists, given a vector of reaction directions (), in generating vectors that satisfy the system of linear inequalities
Note that the solution space of (3) for fixed directions is convex (while non-convexity can arise if directions are allowed to vary). Relaxation methods (see e.g. LP , Ch. 12, or gof ) are among the most effective procedures to find solutions of systems such as (3). These techniques date back at least to the Jacobi method for solving systems of linear equalities, and were extended to inequalities in the 1950's agmon ; motz . In essence, they are iterative methods in which variables are updated so that at every iteration one of the violated inequalities is fixed. While this readjusts the entire vector without a guarantee that constraints that were previously satisfied will be broken, convergence to a solution (if it exists) is guaranteed if the update step is chosen wisely. We shall employ a relaxation algorithm known as MinOver, which was developed in the context of neural network learning mez , and has been employed, in a slightly modified form jstat , to explore the space of flux states compatible with minimal stability constraints à la Von Neumann pnas ; kyoto . Figure 1 displays a flowchart of the procedure for the present case.
One starts from a 'trial' probability distribution of chemical potential vectors. Its role for the moment is simply that of initializing the algorithm, which is done by generating a random vector under . For simplicity, one may think that , with prescribed distributions , e.g. uniform over a given interval: in this case each initial is selected randomly and independently from its trial distribution . On the other hand, might contain prior biochemical information, e.g. by being a uniform distribution centered around the known experimental values of and of sufficiently large width to span several orders of magnitude in concentrations. (The precise construction of for our case studies is discussed below.) The algorithm is based on the following steps:
Generate a chemical potential vector from .
Compute from (3) and (i.e., is the index of the least satisfied constraint).
If then is a thermodynamically consistent chemical potential vector for ; exit (or go to 1 to obtain a different solution).
If , update as
(where is a constant and is the -th column of matrix ), go to 2 and iterate.
As is generally true in MinOver schemes, the reinforcement term in (4) drives the gradual adjustment of chemical potentials by ensuring that, at every iteration, the least satisfied constraint (labeled ) is improved. Convergence to a solution, if one exists, is guaranteed for any . To see it, suppose that a vector exists, such that
with a constant (in other words, is a solution of (3)). From Eq. (4), the chemical potential vector at the -th iteration step, , satisfies
where is the value taken by at step . Similarly, one finds
with . As a consequence,
However by the Cauchy-Swartz inequality , and an upper bound for the number of steps can be obtained from which to leading orders in and gives
where , and are constants proportional to . Hence starting from an initial random vector of chemical potentials the algorithm is able to obtain a new vector ensuring that (3) is satisfied. Re-initializing the algorithm from a different random vector sampled from allows to retrieve another solution and in turn explore the space of 's satisfying (3).
In essence, the final outcome of multiple (random) initializations of the above algorithm is a set of correlated probability distributions for the 's (at odds with the , which was assumed to be a product measure, so that no correlations were present initially). The algorithmic origin of such interdependencies can be understood considering that chemical potentials are updated dynamically through a series of reinforcement steps of the form . It follows that the final value of can be written as , where is the trial chemical potential sampled from (i.e. the initial value of ) and is an index which is updated (increased or decreased by one according to the sign of the reaction) each time reaction tries to invert. The connected correlations between chemical potentials (where is an average over all possible choices of the initial conditions) can thus be decomposed as
where is the variance of and if and otherwise (so that ). In the Gaussian approximation for , the leading term (of ) in the above sum is
Therefore to leading order, the dynamics tends to correlate (resp. anti-correlate) the chemical potentials of metabolites typically appearing on the same (resp. opposite) side of the reaction equations. In a sense, the above scheme allows to modify by building up correlations between chemical potentials according to the interconnections encoded in . Note that, at odds with the method proposed in kummel2 , the resulting 's can exceed the initial bounds defined by .
If there is no prior information on the direction of some reactions (e.g. because they are putatively reversible), the corresponding constraints (3) are formally absent, as if . However, the above method still allows to retrieve information about the chemical potential of a metabolite involved in them, provided it is not employed in reversible processes only, in which case its clearly is never updated.
Finally, some observations are in order about the solution space of (3), which in general has the form of an unbounded cone passing trough the origin. If one is interested in uniform sampling the space of 's making thermodynamically feasible, boundedness is an essential precondition. The simplest way to obtain a bounded solution space consist in clamping some 's, i.e. in keeping them fixed at definite values throughout the updating. Note that fixing some 's is also crucial to set a scale for chemical potentials. The same effect can be achieved by assigning hard ranges of variability for potentials in the form of bounds like (e.g. according to experimental or biochemical information). Such inequalities can simply be added to the list of thermodynamic constraints (3). Alternatively, one may add other types of global, non-homogeneous constraints bearing a physical justification. For instance, if uptake fluxes are included in , the chemical potentials of the external metabolites should be fixed; or, volume constraints for the feasible ranges of concentrations could be added if the standard free energies of formation are known. Once this is done, the system (3) becomes inhomogeneous and its solution space is formed by the union of a convex polyhedron and a cone; boundedness can be achieved if and only if the cone shrinks to the null vector, which occurs if the related homogeneous system of equations has no solutions apart from the trivial one, solodov . In synthesis, one can obtain a bounded solution space for (3) by clamping the chemical potential of a sufficient number of metabolites to ensure that the homogeneous system of equalities associated to the inhomogeneous system of inequalities obtained by clamping some 's in (3) admits the null vector as its only solution.
A number of interesting theoretical and computational questions arise at this stage, regarding e.g. the minimal amount of prior information on chemical potentials needed to bound the solution space of (3), or computationally efficient and scalable methods to obtain uniform sampling (besides Monte Carlo, which may be infeasible at high dimensions as suggested by the "curse of dimensionality", see e.g. Schellenberger:2009fk ). To our knowledge, there is no mathematical proof that MinOver schemes are capable of sampling a bounded solution space uniformly, although low dimensional tests suggest that this might indeed be the case pnas . Our goal in the present paper, rather than uniformly sampling the space of chemical potentials granting feasibility, is that of exploring feasible configurations "close" to the prior biochemical information we have injected. To quantify this idea, we have explicitly compared the solutions obtained via the above procedure with those retrieved from the minimization of a cost function (the average Euclidean distance between the prior and the solution) and by a standard relaxation method (see Results: Exploring the Gibbs energy landscape of the hRBC metabolic network). The heuristics we present indeed turns out to roughly minimize , with the advantage of being considerably faster than a penalty method. In addition, it allows to access refined information on the Gibbs energy landscape (i.e. compute chemical potentials and Gibbs energy changes) even when the initialization is complemented by a noisy or inconsistent biochemical prior, since the algorithm correctly identifies and gradually removes inconsistencies. The solutions thus obtained identify a restricted and statistically well-behaved set of chemical potentials with physiological significance. We are currently unable to go beyond this point. In addition, we shall see that by the same method one can verify the thermodynamic consistency of, and eventually adjust, specific flux configurations of biochemical reaction networks.
Algorithm to identify and remove loops
The algorithm just discussed generates chemical potential vectors given a thermodynamically feasible vector of reaction directions. A generic assignment of reaction directions, however, could be such that the system (3) has no solutions apart from the trivial one. In accordance with the Farkas-Minkowski lemma solodov this happens if and only if there is at least one infeasible loop, i.e. if there is a set of intracellular reactions for which positive constants exist such that
In presence of a loop, relaxation methods (MinOver included) do not converge, just because the least satisfied constraint moves along the loop causing the iteration to cycle indefinitely. More precisely, in presence of a loop the MinOver dynamics becomes periodic or almost periodic. For a periodic dynamics so that, using (4), for any we have
Comparing this with (12) it can be gathered that reactions updated over a period define a loop of length (setting to the number of times constraint has been updated). This suggests a simple way to correct configurations of reaction directions that are thermodynamically infeasible to start with:
While running the algorithm to compute chemical potentials for a large number of iteration steps , keep track of the last say least unsatisfied constraints, i.e. store for , with a reasonably large number (e.g. ), and count the number of different reactions appearing in the series.
Search, within such subset of reactions, for a loop of length by looking for solutions to equation (12) with for reactions only, for all -uples, starting from and increasing .
If a loop is found, change the direction of one of its reversible reactions chosen with uniform probability.
In the Results section we shall use this heuristics to spot loops and eliminate them in all of the infeasible configurations we shall generate for a large metabolic network (E. coli iAF 1260). For this network, it turns out that in all runs and that accounting for short loops (of length up to ) suffices to correct all randomly-generated configurations we tested. This is rather important since, in principle, step (ii) of the above procedure could be exponential in .
A computer code implementing the algorithms to compute chemical potentials and identify and remove infeasible loops is downloadable from http://chimera.roma1.infn.it/SYSBIO/
Exploring the Gibbs energy landscape of the hRBC metabolic network
As a first application, we have employed the MinOver scheme outlined above to analyze the thermodynamic landscape of the hRBC metabolic network. As a starting point, we have considered the flux configurations obtained in palsson and andrea respectively by Monte Carlo sampling of the solution space of mass balance equations (MBE) and by MinOver sampling of states compatible with Von Neumann's constraints (VNC). In brief, MBE describe steady-state fluxes in terms of Kirchhoff-type laws enforced at metabolite nodes of the network as . In such a scenario, intracellular concentrations are clamped. VNC, instead, 'soften' the mass-balance equations by requiring that, for intracellular metabolites, . In the underlying steady state intracellular concentrations can grow in time if flux vectors allowing for it exist. Once the nutrient availability is set, VNC define a self-consistent flux problem where the system selects how much of the nutrients to use and, eventually, which metabolites are globally produced. For intracellular metabolites, VNC correspond to a stability requirement since, in a dynamical setup, a violation of one of the inequalities implies the existence of a metabolite whose total amount used as input in metabolic processes exceeds the total amount returned as output (see e.g. gale ; Martino:2005mi ). VNC are also closely linked to the metabolite producibility problem introduced in ib00 . We shall thus make use of the reaction direction vectors obtained in palsson ; andrea for the hRBC. In summary:
according to palsson , the net flux of all reactions is in the forward direction, except PGI, R5PI and ApK, which are found to operate bidirectionally;
according to andrea , the net flux of all reactions is in the forward direction, except R5PI (which is found to be operating bidirectionally), and PGI and ApK (which are found to have a net backward flux).
As a first step, we tested the thermodynamic feasibility of these direction assignments, solving (3) by starting from the vector . A solution is found for both MBE and VNC assignments. Both sets of assignments then turn out to be (expectedly) thermodynamically feasible. In this case, however, it is the emerging thermodynamic landscape that is of interest to us. As the initial distribution of chemical potentials we selected a product of independent uniform distributions . The for compounds with known empirical bounds on concentrations were centered at a value computed from the Gibbs energy of formation and the estimated intracellular concentration under the hypothesis of a dilute solution, i.e. at
and were taken to span two standard deviations in concentrations. The for metabolites whose concentration estimates were unavailable (namely 6PGL, RL5P, X5P, R5P, S7P) were taken to be centered again at (14) but with , and were assumed to span four orders of magnitude uniformly in the chemical potential scale. Finally, the chemical potential of water was clamped.
Results for the estimated concentrations and Gibbs energy changes (computed from (2) using the final chemical potential vectors) are showcased in Figures 2 and 3, respectively.
Note that several of the bounds encoded in (black markers) indicate that the Gibbs energy change in a reaction is positive and in contrast with the reaction direction assignment from the flux problem. Specifically this happens for GAPDH, PGK, PGM, LDH, G6PDH, TA and PNPase, due either to the actual experimental estimates for the affinities or to the initial uncertainty we place on concentrations. Such bounds turn out to be altered by MinOver in a direction compatible with direction assignments based on mass balance, as final affinities display significant changes with respect to the picture embedded in . At the same time, we observe that in some cases the fluctuations of do exceed the initial boxes defined by , leading to an estimate for the concentration range also for metabolites whose level has not been experimentally probed (6PGL, RL5P, X5P, R5P, S7P). Our predictions for the levels of (1,3)-diphosphoglycerate ((1,3)-DPG), 2-phosphoglycerate (2PG) and phosphoenolpyruvate (PEP) slightly differ from the experimental estimates. This is most likely a consequence of the fact that we are forcing the phosphoglycerate kinase (PGK) and the glyceraldehyde phosphate dehydrogenase (GAPDH) reactions in the forward direction, in agreement with the steady state direction assignments for glycolysis, even if the experimental values would classify them as reversible. In addition, we obtain levels of key metabolites like ATP and inorganic phosphate that differ slightly from experimental estimates, while our predictions for ADP and AMP fail under the MBE and VNC direction assignments, respectively. On one hand this could be due to errors in the prior information on standard free energies but on the other hand, precise experimental estimates of the levels of such highly interchanging metabolites might be difficult to achieve.
We can now quantify the extent to which the solutions we generate are "close" to the prior. In Figure 4 we compare the Gibbs energy changes obtained for the hRBC using MBE directions in three different ways: (a) the MinOver algorithm introduced here, with update given by (4); (b) a penalty method defined by the update rule
where and are constants and the sum extends over all 's such that ; (c) a standard relaxation method defined by the update rule
In short, algorithm (b) minimizes the Euclidean distance between the prior and the solution under the thermodynamic constraint, which is enforced through the term proportional to . The relaxation method (c) simply corresponds to a particular choice of the constant appearing in (4): in specific, the step size is required to be proportional to the amount by which the least satisfied constraint is violated. One sees that the penalty method and MinOver produce almost identical solutions. Measuring explicitly the average Euclidean distance between the prior and the solution (the average being taken over the choice of the priors), one indeed finds (penalty method) versus (MinOver), while for the relaxation method (c). Note that the gap between the performance of MinOver and that of the penalty method (, corresponding to a relative error on of just over 1%) provides a very rough estimate of the average distance between the solutions obtained by MinOver and those obtained by cost function minimization, and is much smaller than the spread of the initial configurations of chemical potentials, which in this case is about . Comparing running times for this case, moreover, one sees that MinOver is about times faster than the penalty method ( versus roughly seconds), while the standard relaxation method is even faster (about seconds).
Identifying and removing thermodynamically infeasible loops in the Escherichia coli metabolic network model iAF1260
The application that we have just discussed shows that the algorithm we present can provide information on the Gibbs energy landscape, even correcting inconsistent input knowledge. We shall now employ the procedure outlined in the Materials and Methods section to efficiently identify and eliminate loops from thermodynamically infeasible flux configurations of the reconstructed metabolic network of Escherichia coli iAF1260 bigcoli . We shall focus specifically on the periplasmic and cytoplasmic core of the network, which presents the advantage that the cycles identified here are independent of the transport and environment selections. The network includes reactions among chemical species.
Since we are not focusing on the reconstruction of the Gibbs energy landscape but simply on the existence of solutions of (3), a detailed biochemical prior is not needed. Therefore, for the present purposes we have taken to be product of identical uniform distributions. To begin with, we have fixed the direction of reactions that are putatively irreversible in the reconstructed network ( in total) and verified that this assignment is indeed thermodynamically feasible by finding a solution of (3) restricted to irreversible processes. Then we integrated the above assignments by fixing randomly and independently with equal probability the directions of the processes that are putatively reversible. A large ensemble of vectors ( instances) thus obtained was tested for thermodynamic feasibility. Note that by excluding the possibility that reactions are not operating we are considering a worst-case scenario in which all reactions bear a non-zero flux. Only about 1.5% of these configurations turned out to be thermodynamically feasible, i.e. free from loops. We focus on infeasible instances, for which no vector of chemical potentials was found to satisfy (3), applying to them the loop identification and removal protocol. Figure 5 shows the histogram of convergence times for the above procedure, i.e. the times required to verify that a given vector of flux directions is thermodynamically infeasible and to correct it.
On an Intel dual core at 3.06 GHz the average CPU time for convergence is of the order of a few seconds, while it exceeds 10 seconds in about 5% of the (random) instances. In the worst case within our ensemble, convergence time was around 100 seconds.
We have furthermore studied the set of loops that were thus identified and corrected. Quite remarkably this analysis revealed that thermodynamical infeasibility is related to the presence of a small set of cycles, 23 in total. These are reported in Table 1 and three of them are depicted explicitly in Figure 6.
Note that some of these cycles include a single reversible reaction. This implies that in order to ensure that such cycles will not be present in the final configuration it is necessary to fix the direction of the reactions SERt2rpp, GLUt2rpp, ACt2rpp, GLYCLTt2rpp, THRt2rpp, SUCOAS, PPAKr and PROt2rpp opposite to that shown in Table 1 (see the Supporting Table S1 for abbreviations). In turn, this is easily seen to impose a further constraint on the direction of the reversible fluxes CAt6pp and GLUABUTt7pp (via cycles of length 4). Finally we checked that excluding these loops guarantees thermodynamic feasibility of randomly generated flux configurations
Ideally, constraint-based models of metabolic activity allow to appraise the energetic potential of cells based on minimal constraints related to local mass-balance and thermodynamic feasibility rules, possibly complemented with optimization principles that can encode for functional constraints. As a result, the flow of matter in non-equilibrium steady states could be characterized in terms of the Gibbs energy change of reactions, which specifies the directionality of interconversions, and of the average number of turnovers per time per volume, i.e. the flux, without the need of detailed information on enzyme kinetics or transport mechanisms. Thermodynamic constraints, strongly linked to overall intracellular conditions like ionic strength and pH stock , are particularly subtle and rich of consequences. It has indeed been argued that the Gibbs energy landscape contains important regulatory information kummel2 . Reactions far from equilibrium are expected to be roughly insensitive to fluctuations in metabolite concentrations, so that they will be driven mostly by enzyme regulation. On the other hand, reactions close to equilibrium (i.e. with a net Gibbs energy change close to zero) bear a high sensitivity to variations in metabolite levels and are therefore unlikely targets for tight regulation. Besides, knowledge of reaction free energies (or more precisely of the chemical potentials of the metabolites involved) provides clues on metabolite levels which would be hard to obtain from mass-balance constraints only. Therefore, developing effective procedures to deal with the complexity of flux models encompassing both mass and energy constraints at genome scale is a central challenge for computational systems biology.
Many important steps have been taken recently to tackle it. At one level, thermodynamic feasibility can be translated into topologic constraints ('absence of loops') for the flux configuration emerging from mass-balance constraints expa . This suggests than an improvement in reversibility assignments (e.g. along the lines of kummel ) can be a key to ensure energy balance a priori in metabolic network reconstructions, with the caveat that the possibility that a reaction reverses can depend on the boundary conditions (e.g. the external supply of a certain metabolite) or on intracellular perturbations (e.g. a knockout causing the accumulation of an intermediate metabolite) hoppe . Another possibility consists in building mixed integer-linear constraint-based models that include thermodynamic requirements in the form of consensus rules (using information on standard Gibbs free energies) tbmfa or as additional constraints on metabolite levels (using information on measured intracellular concentrations) hoppe . Here we have followed a different though related route that in our view complements the approaches just described. The starting point is the fact that, given a flux configuration, thermodynamic constraints can be written as simple stoichiometric inequalities for the chemical potentials. Feasibility implies the existence of a vector of chemical potentials satisfying such system. We have presented an algorithm that is able to construct solutions starting from a possibly limited and noisy biochemical prior. Our approach differs substantially from previous methods in that it relies on modifying the structure of correlations between chemical potentials (after fixing some variables to set a scale) using the stoichiometric information on reaction connectivity to drive the updating process. In this sense, the important difference with kummel2 is that the prior information is used only to initialize the algorithm, and a large flexibility is allowed in deforming it until a viable solution is obtained.
The usefulness of the algorithm in the analysis of genome-scale networks has been tested in two different cases. For the metabolic network of the human red blood cell, our approach has proved capable of reconstructing the Gibbs energy landscape correcting inconsistent prior information. In turn, this has lead to predictions for intracellular metabolite levels. It is important to stress that the bounds on concentrations we have obtained (which vary rather heterogeneously across compounds) only reflect stoichiometric information. For the metabolic network of Escherichia coli, instead, we have focused on the problem of correcting thermodynamically infeasible flux states in the core formed by the periplasmic and cytoplasmic matrix. We have thoroughly analyzed a large ensemble of configurations of reaction directions, identifying the cycles responsible for thermodynamic inconsistency and correcting them in a very modest amount of CPU time. Quite intriguingly, we have related infeasibility to the existence of a relatively small number of short cycles in the flux configuration, whose removal suffices to ensure thermodynamic feasibility in worst-case flux configurations.
The main advantage of our method consists in our view in its efficient implementation. On the critical side, we point to two aspects that deserve further study. In first place, our tool requires flux configurations as inputs, i.e. it is still unable to produce thermodynamically feasible configurations of fluxes and chemical potentials starting from no previous reversibility hypothesis. However it may provide the basis of a more general procedure for the analysis of genome-scale metabolic networks that couples flux- and thermodynamic profiling, a challenging open problem in computational biology. Secondly, our method relies on prior biochemical information and it would be desirable for it to be effective even if much or most biochemical priors are unknown. As we pointed out, some information has to be injected into the problem for the sake of definiteness. The interesting question is therefore what is the minimum necessary prior needed to reconstruct the Gibbs energy landscape and how are predictions affected by restricted priors. Such problems are mathematical in nature and could bear a particularly high significance for modeling purposes.
We remark that the corrected flux configurations thus obtained, like the starting ones (which were drawn from a uniform product measure over reactions), are not guaranteed to be consistent with any steady state assumption. On the other hand, see Supporting Text S1, starting from a mass-balanced configuration one retrieves another mass-balanced configuration. Clearly, a method that directly generates thermodynamically steady state flux vectors and chemical potentials would be highly welcome. Nevertheless, we note that our analysis focuses on a worst-case scenario where all reactions bear a non-zero flux. In a more realistic case where reactions may be inactive it is reasonable to expect that the flux directions we generate will allow for a steady state. In turn, the identification of these loops might be weakly conditioned on the sample of flux assignments we employed. We can't rule out the possibility that assigning directions based e.g. on mass balance constraints provides a selection criterion for flux configurations that differs substantially from the uniform measure we employed. However we expect that our sample allows to correctly identify loops involving at most reversible reactions, being the number of distinct direction assignments in our sample. Only loops that include a larger number of reversible processes might therefore play a role in a differently selected set of direction assignments.
Acknowledgments – This work was supported by the DREAM Seed Project of the Italian Institute of Technology (IIT) and by the joint IIT/Sapienza Lab Nanomedicine . The IIT Platform "Computation" is gratefully acknowledged.
|Cycle ID
|SERt4pp NAt3pp SERt2rpp(R)
|NAt3pp GLUt4pp GLUt2rpp(R)
|NAt3pp ACt2rpp(R) ACt4pp
|NAt3pp GLYCLTt2rpp(R) GLYCLTt4pp
|PROt4pp PROt2rpp(R) NAt3pp
|HPYRRx TRSARr(R) HPYRI(R)
|CRNDt2rpp(R) CRNt2rpp(R) CRNt8pp
|ABUTt2pp GLUABUTt7pp(R) GLUt2rpp(R)
|NAt3pp THRt2rpp(R) THRt4pp
|PPAKr(R) ACCOAL PTA2
|ACt4pp CAt6pp(R) ACt2rpp(R) CA2t3pp
|CA2t3pp GLYCLTt2rpp(R) GLYCLTt4pp CAt6pp(R)
|SERt4pp CAt6pp(R) SERt2rpp(R) CA2t3pp
|GLUt4pp CAt6pp(R) CA2t3pp GLUt2rpp(R)
|CA2t3pp PROt2rpp(R) PROt4pp CAt6pp(R)
|THRt4pp CAt6pp(R) THRt2rpp(R) CA2t3pp
|ADK1(R) ACKr(R) ACS PTAr(R) PPKr(R)
|ADK3(R) ACKr(R) PTAr(R) NDPK1(R) PPKr(R) ACS
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