text:
This article describes how to re-enroll Symantec Encryption Desktop (previously PGP Desktop) for Windows clients.
For information on how to re-enroll Symantec Encryption Desktop for Mac clients, please see the following document:
Enrollment is the binding of a computer with Symantec Encryption Desktop client software to a Symantec Encryption Management Server (SEMS - formerly known as PGP Universal Server). After a client is bound to the server, it receives feature policy information from the Symantec Encryption Management Server.
It is occasionally necessary to re-enroll a Symantec Encryption Desktop Client computer. Re-enrolling a client often assists in troubleshooting when enrollment fails with an error or when users are placed in the incorrect policy. There are a few different methods to attempt this, each with their own limitations and risks. Please make sure you understand the consequences of doing each step before you attempt it.
Some of the reasons for re-enrolling a client:
- Enrollment fails
- Enrollment succeeds but there are Symantec Encryption Desktop errors
- Symantec Encryption Desktop settings during enrollment are incorrect
- Symantec Drive Encryption (formerly known as PGP Whole Disk Encryption) did not start because of policy or attribute misconfiguration
- If you are having a problem enrolling a client or if PGP Desktop is not acting as expected
To re-enroll a Symantec Encryption Desktop client:
- Click the Symantec Encryption Desktop Tray icon in your system tray and select Exit PGP Services.
- Navigate to %APPDATA%\PGP Corporation\PGP\ and delete the PGPPrefs.xml and PGPPolicy.xml files.
This deletes the preferences file and allows you to start with new settings.
- Restart the services by clicking Start > All Programs > Startup > PGPtray
The Symantec Enrollment Assistant will start up and begin the re-enrollment process.
Imported Document Id
Legacy ID
Terms of use for this information are found in Legal Notices.
Terms of use for this information are found in Legal Notices.


text:
By Mike Lehman, TTU Sports Information Coordinator
RICHMOND, Ky. – A career night from senior Jeremiah Samarrippas and a late lead fell just short for the Tennessee Tech men's basketball team as the Eastern Kentucky Colonels rallied at home to defeat the Golden Eagles in Richmond, 74-67.
Despite leading for most of the contest, the Tech squad was plagued late by foul trouble and a hot-shooting Glenn Cosey. Eastern Kentucky grabbed the lead with just over five minutes to play and did not relinquish it, despite the best efforts of the Golden Eagle players.
Point guard Jeremiah Samarrippas was nothing short of brilliant Thursday night, tying a career-high with 22 points while dishing out seven assists. He shot an incredible 8-of-11 from the field, including 3-of-5 from downtown, to lead all scorers.
The Tech defense played well most of the night, particularly when guarding Cosey, holding the senior to just two first-half points. Struggling to score from outside, the Colonels moved their game to the inside, scoring 36 points in the paint thanks in large part to foul trouble late for senior Dennis Ogbe and junior Dwan Caldwell.
With the game being called tight in the paint for the Golden Eagles, Ogbe wound up fouling out and Caldwell finished the contest with four fouls. Tech had no choice but to concentrate on perimeter defense and try to limit the paint touches for the Colonels.
Ogbe finished the game with 10 points and five rebounds, sinking all six of his free throw attempts. Junior Javon McKay also tallied 10 points, connecting on 4-of-7 shot attempts from the field. Guard Josiah Moore tacked on 12 points, hitting two 3-pointers while shooting 4-of-9 overall.
As a team, the Golden Eagles played particularly well on the offensive side of the ball, shooting 50 percent from the floor while hitting 46.7 percent of their attempts from beyond the arc. Tech also converted 16 of its 21 free throw attempts.
On the boards, Tech held the advantage as well, grabbing 29 overall and nine off the offensive glass. The Colonels were held to 20 rebounds in the contest.
Even with the Tech defense smothering him, Cosey started hitting shots in the second half, including three treys with men in his face. He finished with 20 points on the night, as did fellow Colonel Tarius Johnson.
The Golden Eagles will wrap up the 2014 regular season on Saturday with a 6:30 p.m. CT contest at Morehead State. The game marks the second half of a doubleheader with the Tech women's basketball squad which will play at 4:15 p.m.

subset: commoncrawl


text:
Truck Driver - Owner-Operator Tanker Truck Driver - Owner-Operator
Schneider's Tanker Choice program allows Owner-Operators to choose their own freight 24/7 from one of the largest tanker freight pools in the industry. This program allows you to take control of your business and bottom line by choosing loads that best meet your business needs and operating characteristics by having complete visibility to revenue - no after-load deductions. You're in charge - no brokers or dispatch to call for loads - you pick, click and go.
Eligible CDL Drivers:
- Current owner-operators
- Experienced truck drivers interested in leasing or purchasing a truck
Compensation & Cost Savings for Trucks - Owner-Operators are eligible for:
- Per-load revenue
- $5,000 lease-on incentive
- Weekly settlements for consistent cash flow
- Fuel discounts
- Paid lease orientation
- Schneider will also install a pump and compressor at no charge
- Schneider Purchase Power Program® - Leverage Schneider's buying power to save thousands in business costs such as fuel, tractors, tires, insurance and maintenance.
- No-charge trailer usage
- No-charge in-cab communications device and usage
- Access to Schneider facilities with free and safe parking, free showers, free laundry facilities and cafeteria services.
Owner-Operator Qualifications:
- Valid Class A Commercial Driver's License (CDL)
- Live within 100 miles of Keasbey, NJ or Paulsboro, NJ
- Minimum 6 months experience
- Ability to park tanker trailer at a predetermined service location
- Tanker, Hazmat, and TWIC card (Transportation Worker Identification Card) endorsement required at time of lease
More Reasons Owner-Operators Choose Schneider's Tanker Choice:
No dispatch. No agent.
The Tanker Choice option gives you total control of your business. Pick your own loads from Schneider's industry-leading freight base. Choose when and where you want to run, and when you want to get home.
"I enjoy being able to pick and choose where I want to go and look at the loads and revenue. It gives me a chance to have more of a home life and also run my own business." -- William McKindra
Lease with one of the top trucking companies in New Jersey. Call 800-285-3273 for more information!
Job Owner-Operator
Schedule Full-time
Sign On Bonus 5000


text:
UTRADER Review
ULTRADER – Broker/Platform – Binary options – Opinions/Testimonials/Reviews Utrader platform was established in 2011 on the initiative of Day Dream Investment Ltd, with its registered office in Latvia, which owns… moreRead Full Review
|Website URL
|Latvia, Riga
|Support Types
|chat, online, e-mail
|Trading Platform
|Minimum 1st Deposit
|Minimum Trade Amount
|Maximum Trade Amount
|Up to 85%
|Free Demo Account
|Open Demo
|Account Types
|Start, Silver, Gold, Platinum, VIP
|Deposit Methods
|credit card, bank transfer, Skrill (Moneybookers), Neteller
|Withdrawal Methods
|credit card, bank transfer, Skrill (Moneybookers), Neteller
|Trading Methods
|classical binary, turbo, long term, pairs, one touch
|Number of Assets
|over 90
|Expiry Times
|30 sec - 1 year
|Option Types
|classical binary, turbo, long term, pairs, one touch
|Trading Currency
|Account Currency
|$, euro, pound, pln
|US Traders Allowed
|Mobile Trading
|Tablet Trading
|Overall Score
Full Review
ULTRADER – Broker/Platform – Binary options – Opinions/Testimonials/Reviews
Utrader platform was established in 2011 on the initiative of Day Dream Investment Ltd, with its registered office in Latvia, which owns the broker. We can say that UTRADER is one of the fastest-growing binary option companies in Europe. Year by year the enterprise has been making considerable progress to make sure the clients have an opportunity to cooperate with the best experts on the market. Utrader's head office is located in Riga, Latvia.
UTRADER – Basic information:
Minimum deposit: To start investing on Utrader's platform, you need to pay in at least 300 dollars. In the first transaction you must spend at least 20 dollars.
Financial instruments: Currencies, Goods, Indices, Shares. There are two types of commodities: soft and hard. The soft commodities are for instance sugar or coffee while hard ones are platinum, gold or silver. The commodity market is considered one of the most diverse markets in terms of goods it offers. Foreign exchange market, in turn, is very dynamic and exciting. You can invest in any indices, such as Dow Jones, NASADAQ or S&P 500. What is important – you can get considerable profits in no time. Additionally, there are various expiration time options so that you can choose the time, starting from an hour and ending up with a week. What is more, you can invest in the same indices in various options. As far as shares are concerned, they are proposed on the basis of prices of most important international and local companies. Importantly the profit or loss are established in advance, thanks to which you can avoid unpleasant surprises. The following types of options are at your disposal: high/low – classic, minute, long-term, ladder option, boundary, as well as pair and one touch.
Demo account: you may choose to open a demo account. The account will be active after your have deposited cash. By using the demo account, you can trade with virtual cash, not your real capital. Thanks to this account, you can test various transactions on a real platform. For beginners, the demo account is a perfect choice because you have an opportunity to gain experience and acquire knowledge without a risk of investing real cash. The amount of your deposit will remain unchanged. When you are ready, you can use this cash and make real investments.
UTRADER – Main advantages:
Thanks to Utrader, you can trade 24 hours a day and throughout the week. This means that you can invest anytime you feel like to, as there are no limits. The decision is entirely yours. You can make transactions in the middle of night, after work and you control the situation at all times.
It is evident that Utrader prioritizes customer service. Every user is approached on a case-by-case basis. In addition, the company provides you with an access to exclusive market information prepared by the best financial analysts. This sort of information will help you make profitable transactions. If you have any doubts, the customer service personnel will be happy to help you and dispel any doubts. Utrader makes sure new users are not overburdened with loads of new knowledge and is always ready to support you. Experts will surely give you a piece of advice to help you increase profits. You can contact them via chat, e-mail, phone or skype.
On the company's website you will find a section called "education". The personnel of the company assure that every investor enters the market with a basic scope of knowledge. After all knowledge is a key to success. The website also shares an e-book which will provide you will basic information on the business and binary options. You can also use the glossary. Now binary options will no longer be a secret. Advanced users will find descriptions of various strategies. There are also video tutorials and articles dedicated to binary option trading.
Utrader platform is also available on your smartphone and tablet. Thanks to the application, you can invest any time of the day or night and there are not limits. Transactions are now at hand. You can be sure you will not miss any valuable investments.
The platform is user-friendly and very simple, even for beginners. What is more you can choose your language to navigate it, that is Russian, English, Dutch and more. You can open a new account fast and smoothly. First, you need to choose one out of four accounts: Start, Silver, Gold or VIP. The higher the status, the more likely you are to receive better and more valuable bonuses. Plus you can also receive other extras, such as account manager, additional training materials and withdrawal priority. To open the account, you can use euro, dollar or ruble. Maximum profits may reach even 85%, which is a lot.
I do hope that our review has encouraged you to open a demo account and start learning and gaining experience in the field of binary options. We are sure that cooperation with Utrader will increase your profits. By investing on Utrader platform, you can educate yourself and be provided with support around the clock. Do not wait any longer, start creating effective investment strategies.
Utrader summary:
Wide range of assets
Fast withdrawals (up to 24 hours)
Customer service (e-mail, skype, phone, live chat)
Various currencies
Access to DEMO account
High profit (90-650%)
Training materials
Min. deposit – 200 USD
Min. transaction – 20 USD
Min. withdrawal – 25 USD
Training materials – glossary, e-book, guidebooks, online tutorials, training videos.
Currencies – USD, EUR, PLN, RUB, GBP
Profit – 90%, up to 650% in mobile variant
Number of assets – over 100
Types of assets – shares, currencies, goods, indexes
Option forms – High/low, one touch, long-term options (up to 9 months), turbo options (30-300 sec.)
Deposit options – credit card, bank transfer, Skrill (Moneybookers), Neteller
Withdrawal options – credit card, ban transfer, Skrill (Moneybookers), Neteller
Expiration time – 30 sec, 60 sec, 2 min, 5 min, 15 min, 30 min, 1 hour, 24 hours, 1 week, up to 9 months
Deposit/Withdrawal fee – once a month free, then 12 dollars
UTRADER – types of account:
Start account – min. deposit is 300-1000$, free training materials (e-books, online tutorials, educational videos, etc.), analytical materials.
Silver account – min. deposit is 1001-5000$, personal financial analyst, 2 investment strategies.
Gold account – min. deposit is 5001-25000$, personal financial analyst, 4 investment strategies, personal account overview prepared by an analyst (once a week for 30 minutes)
Platinum account – min. deposit is 25001-50000$, 4 investment strategies, personal account overview prepared by an analyst (once a week for 2 hours), quarterly investment plan.
VIP account – min. deposit from 50001$,4 investment strategies, securing instrument, unlimited account overview by an financial analyst, quarterly or yearly investment plan.
Transaction platform on UTRADER
The broker uses a transaction platform called Spot Option thanks to which you do not need to install any software. The platform is available online as well as on a smartphone.
The platform is available for investors 24 hours a day and you can access it 7 days a week.
How to open UTRADER account:
You can register on UTRADER fast and smoothly. First, you must choose 1 out of 5 available accounts, register on your account, and then pay in a minimum amount adapted to the type of account you have chosen (min. deposit ranges from 300$ to 50 000$). Next, you can start binary options trading.
UTRADER – Withdrawals
The broker does not charge for withdrawals if you do it once a month. If you choose to withdraw cash more often, the fee is 12$. Deposits and withdrawals can be made by means of the bank transfer, credit card, Moneynet, Skrill, Neteller, OkPay.
Visit Broker
Leave a Comment
You must be logged in to post a comment.


text:
all below is posted in utter seriousness for the sake of intelligent discussion. no corny beef.
do you think that this one element of reggae culture, which you keep citing as the reason all of this is "wrong", that element being the "exclusivity" factor for sound system, is really still relevant in 2012 with regards to tunes made in the 1970s?
and further, do you think it of greater importance than (and thus trumping the facts that) these tunes were already RELEASED, and NEVER rarefied by any purposeful intention of their creators? the fact that they have become "soundman's cards" as you say, is a defacto occurrence. do you find this defacto distinction to be of more importance than the fact that artists & producers want their music to be available and heard? a simple YES or NO will do.
you're ignoring the fact that all of this jabber about "exclusivity" and "soundman's cards" is totally irrelevant to the artists & producers. i assure you the only time it was a factor for these folks is when these records were brand new and soundsystem play was, in addition to entertainment, a vehicle to promote the sales of records (something you always conveniently ignore).
the fact that 30 years later they are some "soundman weapon" for you and your buddies is relevant only to you and your buddies, it doesn't go beyond that.
i would bet every cent that i have that if clive or norman or trevor read this thread their response would only be "no man caan tell mi dat mi tune caan release!"
i understand your passion for soundsystem but your relentless clinging to this one aspect of the culture as paramount to all other concerns (even above those of the creators of the music you apparently love) is comical.
i also want to say again something i've said before which is that, i'm sorry to burst your bubble, but record collectors and their radio shows, and youtube channels, and selecting down at the local pub, etc. does not constitute for me, the continuation or existence of the culture you constantly refer to (coxsone, duke reid, 1970s sound system, etc.)
the time of sound system you refer to is functionally over, and even if and when it wasn't, as i've said 100 times over, producers & artists gave records to sounds as a means of promotion for their works. promotion for sales and recognition.
so to be extremely blunt and black & white about it, NO, I DO NOT CARE ABOUT "SOUNDMAN'S RECORD BOX" and RECORD COLLECTORS. but guess what, i'm a record collector myself, my entire house is filled with records and i own tons of rare records, many of which i've spent serious loot on. but that's my choice, and i never hold it against the fact these records were meant to be available, heard, released.
lastly i want to point out that while you keep harping on this idea of "targeting the killers" and saying it like you're exposing some secret insidious agenda, i will remind you that our mission statement from day 1 is to RE-RELEASE HIGH QUALITY AND RAREFIED MUSIC. so yes, that is exactly what we are doing, targeting rare and killer tunes that we like, and re-releasing them as best as we can.
finally, if you think i am getting rich off this than you know EVEN LESS about the business than i already think you do. and anyone who does know about the business is having a good laugh at you endlessly repeating that.


text:
Send Results to This Email:
Street Address:
Street Address:
(Daily News/Andrew J. Breig)
The grass-covered hill that forms the roof of Beale Street Landing was an immediate hit with riverfront visitors Tuesday, Sept. 24, as the first construction fences came down on the site.
"We're finally far enough along in construction that we feel like it's safe enough to take down the fencing in certain areas," said Dorchelle Spence, vice president of the Riverfront Development Corp., which manages the landing and 11 other riverfront parks, including Mud Island, for the city of Memphis.
So Tuesday afternoon, construction crews took down the fences and encouraged those coming to the park to go where they haven't been able to go until now.
"We took down the fence on the south side of the building to allow access to the grass roof and the patio," Spence said. "We took down the fencing on the east side to allow runners and walkers to run through the loading zone and not have to go onto Riverside Drive."
The fencing is still up for the newest focuses for construction crews, construction of two "islands" that are part of the terraced approach to the river's edge.
Construction is underway on the landing's playground and another one of the islands that are part of the park setting, including terraces that will allow visitors access to the river's edge.
"One of them is a play area for children 10 and under," Spence said. "The other is going to be a Wi-Fi zone for picnics and gatherings. Of course there will be the grass terraces that will take you down to the river level."
The landing building has been the site of special catered events for several months now, but there is still no contract for a restaurant tenant in the building with regular hours. The building houses the ticket office and souvenir shop for the Memphis Queen daily excursion boats, and the landing serves as the dock for the American Queen and other overnight river cruise ships when the river's level is higher.
The Mississippi River at Memphis was at -5.54 feet Tuesday, according to the National Weather Service. The river was at -9.86 feet a year ago this month, which was much closer to the all-time low reading at the Memphis river gauge of -10.7 feet in July of 1988.
Copyright 1995 -
by The Daily News Publishing Co. Inc. - All Rights Reserved


text:
This is not an offer for credit and should be used for estimation purposes only based on the information you provided. You should not base your decision on this estimate alone. Title, registration, tax and other fees, and personal circumstances such as employment status and personal credit history, were not considered in the calculations.
Transaxle - Differential with helical gears
Gear selection Forward-Reverse
Six, 8-volt deep cycle
Charger - 48 VDC PowerWise QE, 120 VAC, UL/CSA
Overall (w/o roof) - 52.0 in. (132 cm) (top of steering wheel)
Overall (w/roof - 80 in. (203 cm) (top of canopy)
Without Batteries - 844 lb. (382 kg)
Curb – 1,210 lb. (549 kg)
Service - Rear wheel mechanical self-adjusting drum
Parking - Self-compensating, single point engagement
Electric Blue
Flame Red
Forest Green
Inferno Red
Metallic Charcoal
Monster Green
Patriot Blue
Cost for some colors may vary


text:
Phoenix Racing team sold, new owner to bring driver, sponsor
James Finch has sold Phoenix Racing and the new owner will take over the management of the team in September, the longtime NASCAR team owner said Wednesday.
Finch would not name the buyer, whom he has said is new to Sprint Cup racing. He has repeatedly not confirmed reports that Harry Scott of Turner Scott Motorsports is the potential buyer.
"They bought all the assets — the shop and all," Finch said in a phone interview. "And all the employees, that was the main thing, they'll keep them.
"That was one of our goals and that's what happened. … I'm going to manage it for five or six races up to Richmond (in mid-September)."
The new buyer has a driver and sponsor for the team, but Finch said the change likely won't be made until September.
AJ Allmendinger, who has driven the majority of the races for the team this year, will be in the No. 51 Cup car next week at Indianapolis Motor Speedway and Finch said he could be in the car at least through the Sept. 1 race at Atlanta Motor Speedway.
Phoenix Racing has competed in NASCAR for 25 years in the Nationwide and the Cup series.
The team, based in Spartanburg, S.C., has had one Cup win — with Brad Keselowski in April 2009 at Talladega Superspeedway.
It also has 13 Nationwide wins in 539 starts.


text:
Suramerayamajjapamadatthana veramani sikkhapadam samadiyami.
Here's one translation: I observe the precept of abstaining from intoxicants that cloud the mind and cause carelessness.
Here's another: I undertake the precept to refrain from intoxicating drinks and drugs which lead to carelessness.
And there are lots of similar translations to be found.
But here's a detailed rebuttal to those types of translations, along with an alternative translation that supports moderate drinking:
So which is it? To abstain from the action of drinking these intoxicants? Or to abstain from the condition of being intoxicated?Kare wrote:The best way of translating this sentence, is to start from the end.
samadiyami = I undertake
sikkhapadam = the training precept
veramani = of abstaining from
Now for the long compound:
This is a compound made up from sura + meraya + majja + pamada + thana
sura and meraya are two different alcoholic drinks. Sura may be a kind of beer, and meraya maybe some kind of cider. Anyway, both are alcoholic.
majja = either intoxication or intoxicant drink
pamada = indolence, carelessness, negligence, intoxication
majja and pamada are practically synonyms here
now for the last member of the compound: thana. This word means "condition".
So, suramerayamajjappamadatthana is literally "beer-cider-carelessness-intoxication-condition".
In order to make this into a more idiomatic English, we have to start from the end: "the condition of intoxication and carelessness caused by beer and cider"
So what then does the precept say? It says: I undertake the training precept of abstaining from the condition of intoxication and carelessness caused by beer and cider (or, alcoholic drinks).
This is the literal meaning of the precept. Not to abstain from the drinks, but to abstain from the condition of intoxication.
Or is it impossible to know for certain? Is anyone's Pali good enough to answer with authority?


text:
Bethany Lutheran College together with Bethany Lutheran Theological Seminary announce the 45th annual Bjarne Wollan Teigen Reformation Lectures to be held on October 25 and 26, 2012. The Lecture Series will explore the topic of Bible translations.
Dr. Cameron MacKenzie of Concordia Lutheran Theological Seminary, Ft. Wayne, Indiana, will speak on the history of Bible translations, especially the work of Martin Luther and the King James Version.
Pres. Paul Wendland of Wisconsin Lutheran Seminary, Mequon, Wisconsin, will address the various challenges posed in translating, "word for word" (formal equivalency), and "thought for thought" (functional equivalency).
Formal reaction and discussion of the two lectures will follow the second essay presentation.
On Thursday, October 25 at 4:30 p.m. in Trinity Chapel the choirs of Bethany Lutheran College will participate in the annual Reformation Choral Vespers. The Bethany Concert Choir and the Handbell Choir are under the direction of Dennis Marzolf and the Mary Martha Singers are directed by Ann Fredrickson. The public is cordially invited to attend this special service of the Word and music.
For registration information, please call the Bethany Lutheran Theological Seminary at 507-344-7354.


text:
FARMINGTON — Police announced this morning they have arrested two men in connection with the double shooting Friday. One of the suspects arrested was one of the men who suffered a gunshot wound during the incident on Meetinghouse Hill Road.
Police said the investigation into the incident is progressing and more arrests are immiment. �
Last night, officers from Farmington, State Police, and Somersworth arrested: Stephen Roy, 24, of 4 Lords Court in Somersworth, charging him with robbery and conspiracy to commit robbery. He is currently being held on $100,000 cash bail and was scheduled to be arraigned at 11:30 a.m. Wednesday at 7th Circuit Court, Rochester Division. Roy was one of the two men shot during the incident on Meetinghouse Hill Road. �
Also last night, officers from Berwick, Maine, detained: Joshua E. Dionne, 27, of 34 Market St., Apartment B, North Berwick, Maine, on a parole violation and held at York County Jail. A warrant is forthcoming today charging him with being a fugitive from justice. Once he is charged as a fugitive from justice, he will be arraigned and await extradition back to New Hampshire where he will face charges of robbery and conspiracy to commit robbery. �
A third suspect has been identified and an arrest warrant is forthcoming. However, the department is withholding the suspect's identification until that person is taken into custody. �
Investigators from the Farmington Police Department and New Hampshire State Police are actively investigating the shooting but will not comment further on the circumstances leading up to the shooting or the status of the investigation except to say that it is progressing. Additional details will be released when it is determined that releasing this information will not jeopardize or interfere with the investigation. �
If anyone has information about this incident, please contact Det. Brian Driscoll at 755-2731 x105 or information can be given anonymously by calling the department's Crimeline at 755-9797. �
On Tuesday, Farmington police said the two shootings reported Friday appear to be linked to an incident at a residence on Meetinghouse Hill Road.
The Police Department said they continue to actively investigate circumstances that led to two men — a 21-year-old and a 24-year-old — sustaining gunshot wounds at 316 Meetinghouse Hill Road.
One victim was treated at York Hospital in York, Maine, for a gunshot wound to the head Friday afternoon before checking out hours later. The other victim was originally treated at Portsmouth Regional Hospital for a gunshot wound to the chest before he was transferred to Brigham and Women's Hospital in Boston, where he is currently being treated, according to a police press release.
The injuries are considered to be non-life threatening at this time.
Farmington police say they were notified of the incident on Saturday around 4 p.m. though the incident occurred on Friday, Jan. 11. Police Chief Kevin Willey confirmed the incident was originally thought to have occurred in Barrington, though he wouldn't say how investigators first received that false information.
Following interviews, it was determined the crime occurred in Farmington and the department then requested assistance from the Strafford County Regional Tactical Operations Unit, which executed a search warrant at the Meetinghouse Hill property early Sunday morning without incident.
Willey noted the man at York Hospital "at this point, is not cooperating" with officers. The Portsmouth Herald reported the man at Portsmouth Regional originally told officials he was shot while riding a four-wheeler vehicle near power lines in Barrington.
Willey said the next steps in the investigation will include finding witnesses to interview and gathering evidence.
"We're currently investigating and attempting to locate and interview any additional witnesses," he said. "…There are a number of steps including obtaining and analyzing forensic evidence, witness interviews and then we'll see where the investigation takes us from there."
Willey noted gun crime in Farmington is not common. The most recent shooting incident was reported last summer when a Farmington man accidentally killed himself while cleaning a loaded gun and falling down a flight of stairs in his apartment complex. Before that, Willey said there was an incident 18 months ago but the crime remains unsolved due to an uncooperative alleged victim in the case.
He added authorities are releasing few details in the current Meetinghouse Hill case and said he was not ready to release what kind of firearm or ammunition was used in the incident.
No suspects are in custody at this time and the investigation is moving forward with assistance from the New Hampshire State Police and the Strafford County Attorney's office, according to Farmington police.
County Attorney Tom Velardi said officials are hoping members of the public will step forward with more information on the incident.
"We want to understand what happened and make sure the community stays safe," Velardi said. "Right now, there are questions that need to be answered."
He noted as his office continues to collaborate with Farmington police, it is unclear what criminal charges, if any, would come out of this event.
"It's too early to tell," he said. "One of the things we're trying to determine is really what exactly happened and if it was criminal … We're really starting from scratch here."
Those with more information are urged to contact Farmington police at 603-755-2731 ext. 105 for Detective Brian Driscoll or to call the anonymous department tip line at 603-755-9797.


text:
It's more than just saying hello.
Ahhhhh, the front desk staff. They greet the members and employees by name each day. They keep the schedule of classes straight and promptly answer members' questions with a cheery and helpful tone. They resolve members' problems authoritatively. They are eager to please and want to ensure that your business is successful.
What, you say? Not my front desk staff? Well, if your front desk staff doesn't resemble the above, maybe you should listen up because good front desk etiquette is a big key to member retention.
"Many times the new member will not get connected because they aren't in a league or they don't take classes," says Pamela Koch general manager at Le Club in Milwaukee, WI. "They are a lone workout person. It really is up to the employees to grab that person and make them feel welcome. If that person doesn't feel connected to the club whether through the members or staff, they will leave."
The front desk staff not only helps in member retention, but they can help in creating a friendly, communicative culture in the club by warmly greeting the internal staff each day, a seemingly small act that pays off big, says Sandy Coffman, president and owner, Programming for Profit, a training and consulting firm in Bradenton, FL.
"That front desk person can set the scene for the rest of the club," Coffman says. "The whole staff comes into the club through the front desk. That front desk person can keep the attitude and whole mood of the club on a positive note."
To maintain good front desk etiquette, club owners must invest in training for their front desk staffs.
"I don't know that you can afford not to have good front desk training," says Coffman.
Koch's club has comprehensive training for all of the staff, including the front desk staff. During the training, the club emphasizes the basic service philosophy of the club, which is that members should be treated as royally as guests. In fact, the club's philosophy is that staff call members by name, have a warm welcome and a fond farewell, and have a "magic moment" during the member's visit, Koch says.
Here are some things to remember in training front desk staff:
Implement a comprehensive training program. Amazingly, some health clubs don't have much of a training program for the front desk staff. They may show staff how to run the computer and the phone system and they may tell them to greet each member, but they don't go over how to greet the member and how to have good phone etiquette.
Le Club has a week of mandatory training on operations of the club and learning the club's philosophy. Then, new staff members have up to three months to complete the second phase of training, which is to study the member concierge manual and take a test on it. The manual takes the front desk staff member through how to greet and say goodbye to members, how to register members, how to do credit transactions, invoicing and more. The manual also discusses phone etiquette, giving the staff member several scripts to practice everything from what greeting to use when first answering the phone to what to say when making a tennis court or massage reservation.
Hire for personality. "The first thing I tell my manager when I consult in that area is that you have to hire a good personality because a good personality is recognizable in the first seven seconds," Coffman says. "That starts with a smile. So many people don't hire for this. If you hire someone that doesn't smile readily, often and large, and is very comfortable with direct eye contact, then where are you going with the training program? You are already in the hole."
Koch says that she looks for personality first in every employee.
"They have to have a servant's heart," Koch says. "They have to be willing to be available to that person at all times, full force."
Each potential Le Club employee must take a personality test before being hired. In addition, Koch says she can get a feel for an applicant's personality by asking him or her to describe how they've handled certain scenarios in the past. Koch also looks at an employee's communication skills to ensure they can communicate effectively. The final skill she looks for is the technical skill in using the computer, taking money and other front desk responsibilities.
"We feel the technical aspect can always come, but the service attitude, they either have it or they learn it," Koch says.
Train staff in how to greet people. "This is the biggest mistake," Coffman says. "They (club owners) take for granted that everyone has professional communication skills."
Front desk staff must be shown how to say hello, call people by name, introduce themselves, and how to shake hands. In addition, they need to be taught how to use proper voice inflection in person and on the phone.
"Phone etiquette has to be part of that," Coffman says. "People can hear your smile over the phone."
Too many people talk too fast when answering the phone, she says. Staff should be trained not only in what to say when answering the phone but also in how to speak clearly, slowly and cheerfully but professionally. After all, voice inflection is 38 percent of the message while what is actually said is only 7 percent of the message.
Train staff in how to use the computer to properly greet members. Most clubs have sophisticated software check-in programs that show the front desk staff the member's name, birthday, date person joined and more. The front desk staff should be aware of what to look for on the screen to make the greeting more personal, such as wishing him or her a happy birthday or congratulating him or her for a full year of membership.
Train staff in how to say goodbye. Saying goodbye may seem simple enough, but if not done properly or not done at all, it can leave a bad impression with a member or visitor. If done properly, it can be an opportune time to make a connection with a member or guest.
"Eighty percent of business comes from word of mouth," Coffman says. The last experience a member has at a club will have a tremendous influence, she says. So, the goodbye should be pleasant and meaningful, and it should provide an opening for a member to ask a question or for the staff member to remind the member about an upcoming class or event at the facility.
Train for body language. Body language is 55 percent of the first impression, says Coffman. The staff needs to be shown how to look enthusiastic and display energy when receiving a guest.
Ensure staff can multitask. The front desk is a busy place. Often the reception staff will have to greet and say goodbye to members and guests, swipe cards, run the computer, answer the phone, handle questions, make reservations for tennis or racquetball courts, and handle member complaints. That's why it's important that when hiring front desk staffers, club owners test them on their multitasking abilities.
Teach the front desk staff about what is happening in all areas of the club. The front desk is the first place members go with their questions. For that reason, the front desk staff must know what is going on in every area of the club. They must know about the newest class, league information and new hires.
Empower your front desk staff. If the front desk staff members are truly professionals, then they must be empowered to make decisions, Coffman says. While following guidelines and policies is important, the front desk staff must be able to make decisions on the spot since members and guests usually come to them when they have an issue with the facility.
"The front desk person has to be empowered to make those decisions so they don't have to check with the manager on everything," says Coffman.
Keep the front desk for the front desk staff. The only people that should be behind the front desk should be the front desk employees. That space is the front desk staff's workspace and having other staff behind the desk is an invasion of that workspace, says Coffman.
Offer employee evaluations. Training should be a continual process with follow up and measurements for performance. There should be an incentive program, a reward program and their paycheck should be based on how they are responding to the training, says Coffman. The training should have measurement standards and those should be turned into incentives and bonus pay based on performance.
"That shows that their performance is valued," says Coffman. "Everyone wants to be recognized for their performance."
In the end, it's just important to keep reminding front desk staff of the importance of their jobs and to give them the training that shows that importance. Not enough clubs do this, Coffman and Koch, agree.
"They [club owners] take for granted that everyone knows this already," Coffman says. Besides, she says, sometimes even owners and managers don't possess these skills of etiquette.
Unfortunately, that can lead the front desk to focus simply on checking in members rather than providing the personal service and proper etiquette required to retain members.
"That's a constant battle for us," Koch says. "We have to keep reminding the front desk, especially during the busy times. It's hard to greet every person and give them a fond farewell."
Bundling services is a norm.
Everybody loves a deal. Buy five personal training sessions and get a 10 percent discount or buy 10 sessions and get a 15 percent discount. Who wouldn't like that? Bundling or packaging services has been around for years, but for some, bundling now is a no brainer.
"It's a way for us to try to encourage people to purchase different items and increase our volume," says Joseph Barone, director of the Boca West Country Club in Boca Raton, FL. The Boca West Country Club bundles tennis and golf lessons, swimming lessons, personal training session and massage and facial sessions, as well as a few other areas in the spa. Barone estimates that 95 percent of what is sold at the club is sold through discounted packages and his members love it for the savings it offers them and for the convenience it offers them.
Barone has been in south Florida for 15 years and has been at the Boca West Country Club for nine years. If you haven't started bundling yet, here's what Barone's experience has taught him about packaging.
Make it too hard to resist. Almost universally, anytime anything is discounted people tend to purchase more. That's a marketing advantage.
"Anyone would be interested in buying more if it will be less in the long run," Barone says.
Avoid custom-tailored packages. While some members may want you to custom tailor packages just for them, the accounting nightmares that could result may not be worth it. Barone's club discourages custom tailoring, but occasionally if a service of a similar cost is requested, the club will substitute it as long as it doesn't compromise the profit margin.
Packaging doesn't have to be an accounting nightmare. While custom-tailoring can be an accounting nightmare, set packages that can be programmed into a computerized accounting program are easy to work with. As long as there aren't variations from the set packages each time a transaction occurs, then tracking the packages can be pain free.
Sell it to the staff. Some of your professional staff may have an issue with packaging because they see it cutting into their commissions. After all, you'll have to make up the percentage lost somewhere, whether it's out of the professional staff member's commission or your profit.
"Somewhere you are taking the hit with the discount," says Barone. However, packaging usually increases volume, and if you can get it across to staff that in the long run they'll make more money, then selling the packaged services concept to them will be easier.
Consider no expiration dates. Depending on the demographics of your members, you may want to consider not putting expiration dates on your packages. For clubs such as Barone's where a large majority of the members live outside the state during parts of the year, expiration dates can inhibit the purchase of packages. Members who return from spending the summer in New Jersey may not appreciate that the package they purchased in March expired in August before they could use it all.
Barone's club doesn't have expirations dates on the packages, but it does have limited times during which the packages can be purchased. For example, a Mother's Day special may only be available for purchase in May, but it can be used with no expiration date.
Be in it for the long term. Once you start discounting, it's difficult to go in the other direction, says Barone. You can't take away the discounts that members have come to rely on. In fact, you may just be creating a condition where every time you turn around, someone will be looking for a discount, he says.


text:
Recent Changes at the AHR
There have been some recent changes at the American Historical Review. Of course, the basic mission and content of the flagship journal of the AHA are unchanged. Our goal is to publish the most interesting and original historical scholarship being produced today, and to offer the widest selection possible of book reviews of current historical literature. The venerable scholarly article remains our primary format for scholarship. And there is absolutely no reason to think this will change. Indeed, as I argued in an earlier Perspectives piece ("The Golden Age of the Article Is Now," September 2006), with digital publishing and archiving, articles now enjoy a range of distribution and ease of access that makes them more valuable than ever before, both for those who write them and for those who read them.
Even in a world where change for the sake of change seems the rule, there is no reason why a successful scholarly journal should seek to alter its course. We have not done so. But some changes, leaving the core of what we do untouched, have been undertaken. I would like briefly to discuss each of them.
Featured Reviews
One of the goals of the AHR is to offer historians new scholarship and reviews that address broad concerns, beyond those that define their particular fields, periods, and subject areas. In keeping with this goal, we have decided to reintroduce the section "Featured Reviews," which last appeared in the journal in 1996. It is no secret that most readers of the journal turn first to the book reviews in their own fields. And with the explosive growth of scholarship and increasing specialization, this is entirely understandable. How else can scholars keep up with the burgeoning historical literature? But there are some books that deserve the attention of a wider range of historians, even if their main appeal may be to specialists. We intend to make works of this sort the subject of our featured reviews. I am not speaking primarily about works of synthesis or books on theory or method (although such books might be included). Books for the "Featured Reviews" section should, because of their range, their methodological innovation, the boldness of their claims, or the novelty of the research, have something to offer to a larger number of historians. The editors, in consultation with members of the board of editors and others, will select the books to be reviewed in this section. Because these reviews are longer than the 800 words we normally allot reviewers, the number of books we are able to review is quite limited—not more than four or five an issue. Our hope, however, is that reviewers will take advantage of the more generous word limit and submit reviews that not only assess the scholarly merits of a book but also discuss the larger issues and concerns it raises. Inevitably, many worthy books will not be accorded this treatment. Undoubtedly, too, there will be cases where the books we choose will, in retrospect, prove to be less important, interesting, or otherwise appealing than we had thought. The gain, in my estimation, outweighs the risk, for we can only profit from greater awareness of the concerns and methods that cut across our profession. In any case, we will do everything we can to ensure that the books we choose for featured reviews are deserving of this special attention.
AHR Conversation
In the December 2006 issue of the AHR, we inaugurated what we hope will be an annual feature of the journal: the AHR Conversation. Inspired by a similar feature in our sister publication, the Journal of American History, it is intended to present readers with an exchange of views among a range of historians on a theme of importance and interest across the profession. Last year, six historians took part in a prolonged online discussion on the subject of "Transnational History." This year's topic is "Religious Identity and Violence." Discussions to be published as AHR Conversations are taking place as I write, over the summer months, and then will be lightly edited and annotated for publication in the December issue. In contrast to articles, which are usually deeply researched, rigorously evaluated, and extensively edited, the nature of these discussions preserves the liveliness of a dialogue, challenges participants to speak out of the comfort zone of their own specialties, and encourages them to respond to the interventions of historians with very different orientations. While our AHR Conversations will not necessarily represent original contributions to scholarship, they will, we hope, present readers with an all too rare example of historians from vastly different fields talking to each other about common concerns. We encourage readers' suggestions for future topics.
The University of Chicago Press
Since the late 1960s, the American Historical Review has been published by the AHA. And during the intervening decades, there has not been any compelling reason for us to consider changing an arrangement whereby we have essentially been self-published. As a membership-based subscription journal, we had little need for marketing. As one of the leading academic journals in the United States, if not the world, neither had we much need to promote our publication to libraries and other institutional subscribers. In recent years, however, it has become increasingly apparent that the status quo could be preserved only at a very steep cost. In the new digital environment, there was the threat of eroding membership subscriptions, as members got used to accessing the journal digitally through their institutions' library services and other databases. And as institutions began to view the digital version of the AHR as the primary means of disseminating the journal to their students and faculty, the number of subscriptions to the print edition declined notably. Finally, rapidly evolving digital technology meant that our current arrangement with the History Cooperative, which has been constrained by limited technical and financial resources, put us at risk of being left behind with antiquated or inadequate Web-based capacity.
For all of these reasons, we decided to explore the possibility of striking up a partnership with a major university press. Several presses were considered. More than 18 months ago, we began serious discussions with the University of Chicago Press, the largest academic press in North America, and a press with deep experience in journal publishing. In June 2007, after many rounds of negotiations involving editorial and staff personnel in Bloomington and especially the vigorous and expert participation of AHA Executive Director Arnita Jones and Assistant Director Robert Townsend, a contract was signed and approved by the AHA Council.
We are confident that this move is in the best interests of the AHR, the AHA, and the journal's readers and subscribers. In particular, we are looking for UCP's help in two areas. First, the press has impressive marketing experience and personnel, which will enable us to maintain and perhaps even expand our distribution, especially in international markets. Second, UCP continues to invest in the latest digital technology, allowing journal content to be presented in the most up-to-date, reader-friendly, and searchable fashion. Finally, because of UCP's distinguished record of scholarly publications, we are convinced that this arrangement will in no way threaten the integrity and autonomy of the AHR. This last point needs stressing: in all of our discussions and negotiations with the University of Chicago Press, we have received repeated assurances that the press has no intention of interfering with our editorial process. The editorial offices of the AHR will remain in Bloomington, under the authority of the American Historical Association. The only change that readers will notice—aside from the addition of the name and logo of the University of Chicago Press to the AHR's cover and spine—will be a more attractive and searchable digital version of the journal's content. The October 2007 issue should be the first to appear under this new arrangement with UCP.
So some things have changed at the AHR, it is true. But more things have stayed the same—chief among them our commitment to the best, the most interesting, and the most innovative historical scholarship. Send us your best work!
—Robert A. Schneider is the editor of the American Historical Review. He can be reached at email@example.com.
Tags: AHA Activities
Please read our commenting and letters policy before submitting.


text:
DISCLAIMER: Live feed is best viewed in Mozilla Firefox or Safari. To download Mozilla Firefox, click here. Live feed is unable to be displayed in Internet Explorer at this time.
The University of Southern Mississippi | 118 College Drive, Hattiesburg, MS 39406-0001 | 601.266.1000 | AA/EOE/ADAI
© 1995 - 2017 The University of Southern Mississippi. All rights reserved.
Accreditation | Legislative Updates | Privacy & Legal


text:
- Proceedings
- Open Access
Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks
- Jayasimha Reddy Katukuri1Email author,
- Ying Xie2,
- Vijay V Raghavan1 and
- Ashish Gupta1
© Katukuri et al; licensee BioMed Central Ltd. 2012
- Published: 11 June 2012
Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper.
- Support Vector Machine
- Semantic Type
- Link Prediction
- Biomedical Literature
- Concept Network
Text mining of biomedical literature is a research area that has attracted lot of attention in the last 5 to 10 years. Swanson [1] was one of the proponents of the hypotheses discovery from biomedical literature. As a result of his pioneering work in hypotheses discovery, Swanson discovered a novel connection between Raynaud's disease and fish oil by examining two disjoint biomedical literature sets [1]. The hypothesis of the beneficial effect of fish oil on Raynaud's disease was confirmed by an independent clinical trial two years later, which demonstrated the value of biomedical literature mining in scientific discovery. Swanson's hypothesizing model, the so called Swanson's ABC model, can be simply described as A relates to B, B relates to C, therefore A may relate to C, [2]. Ever since Swanson's discovery, a lot of research works have been carried out with the aim of automating and refining Swanson's ABC model [1, 3–8]. Nevertheless, most of these reported approaches are based on analyzing the retrieval result set for one or two initial topics provided as query by a user, instead of being able to scale up to the whole set of literature database for the purpose of discovering real, novel and cross-silo biomedical hypotheses.
In recent years, link discovery has been extensively studied on social networks such as those obtained from Facebook data and bibliographic databases maintained by DBLP. As an important problem of link mining, link discovery refers to the discovery of future links between objects (or nodes) that are not directly connected in the current snapshot of a given network. In [9], Őzgür and his colleagues applied link discovery technique to generate hypotheses on relationships between genes and vaccines. This work first extracted networks on gene-gene interactions and gene-vaccine interactions from literature with the help of gene and vaccine ontology; then analyzed the networks by computing different types of centrality measures for each node in the networks. Given its restricted focus on gene and vaccine relationships, this work by its nature was not designed for cross-silo biomedical discovery.
In order to address the challenge of large-scale cross-silo biomedical hypotheses discovery, in this paper, we first model a biomedical literature repository as a comprehensive network of biomedical concepts belonging to different semantic types. Then we extract such a large-scale concept network form Medline [10]. We further calculate a variety of topological and semantic features from the concept network and model the hypotheses discovery as a classification problem based on those features. Moreover, in order to automatically build the classification model for prediction, we take two snapshots of the concept networks corresponding to two consecutive time durations, such that a training data set can be formed based on a group of labeled concept pairs that are automatically extracted from the snapshots. We further extract multiple heterogeneous features for labeled concept pairs solely from the first snapshot of the concept network. The impact of those heterogeneous features on hypotheses discovery has been studied.
The rest of the paper will be organized as follows. In the Related work section, we briefly describe relevant works in biomedical hypotheses discovery and link mining. In the Hypotheses generation as supervised link discovery on biomedical concept network section, we formulate hypotheses generation from literature as link discovery in a concept network and further model the link discovery as a supervised learning process based on a set of topological and semantic features. In the Concept network creation and feature extraction using Map-Reduce framework section, we address the challenges of extracting large-scale concept networks from literature corpus. We also address the challenges involved in automatically generating labeled data and extracting heterogeneous features for a large number of labeled data using Map-Reduce framework. In the Experimental results section, we present experimental results. Finally, we conclude our paper with the Conclusions section.
Related work
Swanson's pioneering work in 1986 on biomedical hypotheses generation led to the discovery of the novel connection between Raynaud's disease and fish oil by examining two disjoint biomedical literature sets (Swanson [1]). In his follow-up work in 1990, Swanson suggested a trial-and-error search strategy, by which the ABC model guides a manual online search for identifying logically related non-interactive literature (Swanson [7]). By applying this strategy for biomedical literature analysis, Swanson discovered some other novel biomedical hypotheses, such as the implicit connection between the blood levels of Somatomedin C and dietary amino acids arginine (Swanson [7, 11]), and hidden link between the mineral magnesium and treating the medical problem causing migraine headaches (Swanson [7]).
Along with the advances in the text retrieval and mining techniques, researchers have made several efforts to partially automate Swanson's ABC model for hypotheses generation. Stegmann and Grohman proposed a way to guide a researcher to identify a set of promising B terms by conducting clustering analyses of terms on both the retrieval result set of topic A and the retrieval result set of topic C (Stegman et al. [6]). Their work used measures called centrality and density to evaluate the goodness of term clusters and showed that the promising B terms that link disjoint literature for topics A and C tend to appear in clusters of low centrality and density. Srinivasan's approach to identify promising B terms starts with building two profiles for both topic A and topic C, respectively, from the retrieval result sets of A and C [5]. In her work, the profile of a topic consists of terms that have high frequency in the retrieval result set of that topic and belong to semantic types of interest to the user. Then the intersection of A's profile with C's profile generates the candidate B terms. The process of identifying B terms from given topics A and C is called closed discovery. In her work, Srinivansan also applies the topic profile idea to conduct open discovery, which identifies both B terms and C terms given only topic A. Srinivansan's open discovery algorithm can be simply described as follows: Top-ranking B terms are selected from the profile of topic A. Then, a profile for each selected B term is created from the retrieval result set of that B term. The top-ranking terms in a B term's profile form candidate C terms. If topic A's retrieval result set is disjoint from a candidate C term's retrieval result set, then this candidate C term is reported as having potential relationship with topic A via term B. Slightly different from Srinivansan's topic profile approach, Pratt and Yildiz directly applied association mining on the retrieval result set of topic A to conduct open discovery [4]. In their work, the logical inference based on two association rules A→B, B→C leads to the finding of a candidate C term.
One of the problems that almost all the hypotheses generating approaches face is the large amount of spurious hypotheses generated in the process of automating the Swanson's ABC model. In order to eliminate the spurious hypotheses, different components of the biomedical ontology system, UMLS [12], have been utilized. Weeber et al. [13] used Metathesaurus of the UMLS to extract biomedical phrases and further limited the desired phrases by using the semantic types of the UMLS as an additional filter. Similar strategies are widely used by most of the follow-up research. Zhang et al. [3] and his colleagues used semantic network, another UMLS component that specifies possible relations among different semantic types, in order to restrict the association rules generated from the retrieval result set of topic A in the process of open discovery. Besides utilizing the biomedical ontology system, we envision that cross-repository validation may be another effective addition for eliminating spurious hypotheses.
No matter whether designed for closed discovery or open discovery, the described works are still constrained in the category of automating and refining Swanson's ABC hypothesizing model. Furthermore, all the approaches are based on retrieval result set of one or two initial topics provided by a user, instead of being able to scale up to the whole set of topics within a literature database for the purpose of discovering real, novel and cross-silo biomedical hypotheses.
If we model a biomedical literature repository as a comprehensive network of biomedical concepts belonging to different semantic types, the link discovery techniques may enable large-scale, cross-silo hypotheses discovery that goes beyond information retrieval-based discovery. Link discovery has been extensively studied on social networks such as Facebook, and bibliographic databases such as DBLP in recent years. As an important problem of link mining, link discovery refers to the discovery of future links between objects that are not directly connected in the current snapshot of a given network. In the following, we briefly review those link discovery techniques that are relevant to our work.
In the paper by Faloutsos et al. [14], the author proposed a measure called effective conductance to evaluate the goodness of a connection subgraph. Later, in the paper by Koren et al. [15], an improved measure called cycle free effective conductance was proposed by using only the cycle free paths in computing the proximity. This measure guaranteed that high degree intermediate nodes in the paths do not increase the proximity between two nodes unreasonably. The paper by Liben-Nowell and Kleinberg [16] discussed the problem of link prediction in social networks. It was one of the early works on link prediction that addressed the question of to what extent new collaborations (links) can be predicted by using the toplogy of the network. This work used an unsupervised approach to predict the links based on several network toplogy features in co-authorship networks. The paper by Al Hasan et al. [17] used a supervised learning approach for co-authorship link prediction based on simple neighborhood features, without factoring in any random walk features like effective conductance. Simple neighborhood features have several limitations compared to random walk features: they can not predict connecting paths of length greater than two (Benchettara et al. [18]), nor can they discriminate significant (good) paths from the set of all neighborhood nodes. The paper Benchettara et al. [18] used the bipartite nature of publication networks in a supervised learning framework. The paper Savas et al. [19] addressed the link discovery problem based on the number of paths of different lengths from multiple sources that exist between two nodes. However, this work did not factor in the different degrees of significances that different paths may have. Őzgür and his colleagues [9] applied link discovery technique to generate hypotheses on relationships between genes and vaccines. This work first extracted networks on gene-gene interactions and gene-vaccine interactions from literature with the help of gene and vaccine ontology; then analyzed the networks based upon different centrality measures calculated for each node in the networks. Given its limited focus on gene and vaccine relationships, this work by its nature was not designed for cross-silo biomedical discovery.
Hypotheses generation as supervised link discovery on biomedical concept network
We model a biomedical literature as a concept network G, where each node represents a biomedical concept that belongs to certain semantic type, and each edge represents a relationship between two concepts. Each node or each edge is attached with a weight that reflects the significance of the node or the edge. In this work, we use the document frequency of a given node as its weight; use the co-occurrence of the two end nodes as the weight for the corresponding edge. Now, the hypotheses generation problem can be formulated as the process of link discovery on the concept network, i.e., the process of discovering all those pairs of nodes which are not directly connected in the current concept network but will be directly connected in the future. We further model the link discovery on the concept network as a process of supervised learning where a training data set is automatically generated from the concept network without class label assignments by domain subject experts. More specifically, we take two snapshots, namely and , of the concept networks corresponding to two consecutive time durations t f and t s . That is t f is the first time duration and t s is the second time duration. We automatically collect a group of concept pairs that are not directly connected in and labeled each pair as either positive or negative. A concept pair is assigned the class label positive if this pair is directly connected in ; is assigned negative otherwise. For each collected pair, we further extract a set of features from , such that a classification model can be built by using part of the labeled pairs as the training data. Once the classification model is learned, it can be used to predict the appearance of a new edge at a future time between two nodes that are not directly currently connected. The quality of the classification model surely depends on what features we can extract for the labeled pairs. Existing work in link discovery typically uses different types of topological features. We examine two types of topological features, namely random walk based and neighborhood based. Besides topological features, we also propose two semantically-enriched features, namely Semantic CFEC and Author List Jaccard. In the following, we will describe both topological and semantically-enriched features in detail.
Topological features
Given a collected pair of nodes (s, t), we consider the following aspects of topology related to s and t: 1. the neighborhood of s and t; 2. the paths between s and t. To describe the neighborhood of s and t, the following measures are calculated:
Common neighbors:
where τ (s) and τ (t) are the set of neighboring concepts for concepts s and t respectively.
Adamic/Adar: The measure uses the common neighbors between two nodes and weights each of the common neighbors. It gives higher score for nodes with low degree.
Jaccard Co-efficient:
Preferential Attachment:
To describe the paths between s and t, we examine the following features.
Number of paths: more paths between s and t, more likely a future edge between s and t.
Distance between s and t: longer it takes to reach s from t, less likely a future edge between s and t.
From the above equation it is evident that shorter paths are preferred.
Semantically-enriched features
The above measures only evaluate network topology related features. However, each node that represents a biomedical concept is actually associated with rich semantic information. In this work, we consider the following two types of semantic information for a given node, its semantic type and its related author information.
Concept network creation and feature extraction using Map-Reduce framework
In this section, we describe the implementation of the computational model presented in the Hypotheses generation as supervised link discovery on biomedical concept network section. The major challenge to implement such a computational model is related to the need to process a huge amount of data. We use the Map-Reduce framework to implement the following three major components: 1) Extract a comprehensive biomedical concept network from the abstracts of all Medline papers published within 1990-2010; 2) Generate labeled pairs from two consecutive snapshots of the concept network; and 3) For each labeled concept pair, extract all the set of features described in the subsections titled Topological features and Semantically-enriched features.
Concept network extraction
Each node of the concept network represents a biomedical concept, which is also attached with the following information: semantic type, related authors, and document frequency. Each edge of the concept network represents co-occurrence of the two end nodes in same documents. An edge is attached with the following information: the strength of the edge (i.e., the frequency of co-occurrence of the two end nodes), and the duration of the edge. The concept network is stored by using the following data structures.
Concept-Document Map (CDM): The key of an entry in this map is a concept 'c' and year 'y', and the value of an entry is a set of document ids (PMIDS), where PMID is the ID of the Medline paper that concept c appears and year represents the publication year of this paper. Given a time duration t, we can easily derive a snapshot of CDM for t, denoted as CDM t , by taking a union of all the PMIDs for the keys 〈c, y〉, where the year 'y' is within the given time duration t. To generate this map in Map-Reduce framework each of the mappers processes a subset of the document collection and sends the tuple 〈concept, year〉 as the key and document list as the value to reducers. Reducers aggregate the document set for a given concept and year.
Concept-Concept Matrix (CCM): We compute concept-concept associations from the set of concepts extracted from a PMID. That is, for each concept, we compute the co-occurring concepts within the same document. For each concept-concept association, we compute the co-occurrence frequency occurred in each year. Algorithm 1 describes the implementation of CCM in Map-Reduce framework.
Concept-semantic Type: We extract the semantic type from UMLS Metathesaurus for each of the concepts.
Concept-Author Map (CAM): The key of an entry in this map is a concept 'c' and year 'y', and the value of an entry is a set of authors. This map provides the set of authors who have published a document containing the given concept 'c' in a given year 'y'. Given a time duration t, we can easily derive a snapshot of CAM for t, denoted as CAM t , by taking a union of all the authors for the keys 〈c, y〉, where the year 'y' is within the given time duration t. To generate this map in Map-Reduce framework each of the mappers processes a subset of the document collection and sends the tuple 〈concept, year〉 as the key and author set as the value to reducers. Reducers aggregate the author set for a given concept and year.
Algorithm 1: Generating concept-concept matrix
Data: Document Corpus
Result: Concept-Concept Matrix
initialization CCMis_local matrix;
for each mapper m do
for each document d i in document corpus
of mapper m do
c(i) ← set of concepts extracted from d i ;
y i ← published year of d i ;
for each concept pairs c k , c l of c(i) do
CCM_local[c k , c l , y i ] ← CCM_local[c k , c l , y i ] + 1;
for each entry (c k , c l , y i ) in CCM_local do
key ← (c k , c l , y i ) ;
value ← CCM_local(c k , c l , y i ) ;
return (key, value);
CCM(c k , c l , y i ) ← sum;
for each key (c k , c l , y i ) and do
Given a comprehensive concept network stored in the above data structures, we apply Algorithm 2 to derive a snapshot of the concept network for a given time duration t in Map-Reduce framework. A snapshot of the concept network is stored in a graph data structure.
Automatic generation of class labels for concept pairs
Given two snapshots and of the concept network corresponding to two consecutive time duration t f and t s , we generate a group of labeled pairs based on which a training data set can be formed for the proposed supervised link discovery. The following process describes how we automatically assign class labels to concept pairs without any involvement of subject domain experts.
For a pair of nodes (i, j) that is not directly connected in , we categorize its possible connection situations in as follows:
Connection is strong in : There is an edge between i and j in , namely e ij , and we have e ij .strength ≥ min_support.
Connection is emerging in : There is an edge between i and j in , namely e ij , and we have margin × min_support ≤ e ij .strength <min_support, where 0 <margin < 1.
Algorithm 2: Generating the snapshot of the concept network, G t , for a time duration t
Data: Concept-Concept Matrix CCM, Concept-Document Map CDM, time duration t
Result: Snapshot of the Concept Network for a time duration t
initialization Create CDM t : the snapshot of CDM for the time duration t;
for each 〈key(c i , c j , y k ),value(val)〉 in CCM do
if y k ∈ t then
if no node exists for c i then
create a node v i for c j ; v i .name = c i ;
v i .frequency = (CDM t .get(c i )).size();
if no node exists for c j then
create a node v j for c j ; v j .name = c j ;
v j .frequency = (CDM t .get(c j )).size();
if no edge links for 〈v i , v j 〉 then
create an edge e ij between v i and v j
e ij .strength = 0;
e ij .strength = e ij .strength + val
Connection is weak in : There is an edge between i and j in , namely e ij , e ij .strength <margin × min_support, where 0 <margin < 1.
No direct connection in : There is no edge between i and j in .
Given a pair of nodes that has no direct connection in , we assign the class label positive to it if this pair's connection is strong in ; assign the class label negative to it if this pair's connection is weak in or there is no direct connection in . If this pair's connection in is emerging, its class label should be emerging, however, we don't consider this class in this work. The major challenging issue of generating labeled pairs is that there would be a huge number of pairs that are not directly connected in . In order to address this issue, we use the following procedure to generate labeled pairs.
For each pair whose connection is strong in , if it has no direct connection in , assign positive to this pair.
For each pair whose connection is weak in , if it has no direct connection in , assign negative to this pair.
Select a random sample of the nodes in and generate concept pairs from the selected random sample. If a pair has no connection in both and , assign negative to it.
The number of labeled pairs generated from a large-scale concept pairs can be huge. Furthermore, the number of positive pairs and negative pairs can be highly unbalanced. To address these issues, we randomly select certain portion of positive and negative pairs to form a training data set.
Feature extraction
- 1.
Trim such that it only contains edges with strength greater than or equal to the minimum support. Store the trimmed in each of the mapper's main memory. After trimming, is much smaller, so it is feasible to store it in memory.
- 2.
Distribute the labeled pairs among the mappers. Each mapper extracts the features for a subset of concept pairs using the trimmed .
Experimental results
- 1.
The performance of the proposed supervised link discovery approach. More specifically, we evaluate whether the proposed approach is able to conduct reasonable predictions on concept links that are currently weak or non-existing but may become strong in the future. Since predictions are carried out based on a classification model that is built upon a training data set extracted from two consecutive snapshots of the concept network, the performance of link discovery can be evaluated by measures such as classification accuracy, recall, and precision as results of n-fold cross validation on the training data.
- 2.
The effect of the parameters min-support and margin on the performance of link discovery. These two parameters are used in generating class labels for concept pairs of the training data.
- 3.
The effect of the proposed features for each concept pair, such as CFEC, Semantic-CFEC and Author-Jaccard, on the performance of link discovery.
- 4.
The effect of using different snapshots of the concept network to generate training data. For this purpose, we first take three consecutive snapshots of the concept network, each of which spans a 5-year period; then generate the first training data set from the first two snapshots and the second training data set from the last two snapshots. Accordingly, we compare the performance of classification models built on these two training sets.
- 5.
The effects of different supervised learning methods on the performance of link discovery. For this purpose, we experiment with two typical supervised learning methods, one is C4.5 decision tree and the other is Support Vector Machine(SVM). Decision tree generates results that are easy to interpret, whereas SVM is well received due to its outstanding performance in various applications.
Experimental setting
Statistics of the generated concept network
Total number of concept pairs
Total number of documents
Total number of concepts
Number of instances
Test support value
+Ve instances
-Ve instances
We first applied C4.5 Decision Tree on the training data set generated from and to study the effects of parameters and proposed features on the performance of the proposed approach; then studied the performance of C4.5 Decision Tree built on both training data sets; finally compared the performance of C4.5 Decision tree and SVM based on both training data sets. A 10-fold cross validation was used to evaluate classification accuracy, recall, precision and F-Measure in all experiments.
Support and margin
We generated the labeled pairs by using the procedure described in the Automatic generation of class labels for concept pairs section with different values for the variable min_support and for the variable margin. The number of positive instances and the negative instances generated for training purpose is highly unbalanced. Table 2 shows the number of positive and negative examples for different values of min_support. Given the fact that unbalanced data sets are difficult to train on, we performed an under-sampling of the majority class.
Semantically-enriched features
Two different training data sets
C4.5 decision tree vs. SVM
A case study
If we consider the time duration from 1991 to 1995, there exists no Medline record in this time duration that mentioned both of "Prostatic Neoplasms" and "NF-κ B inhibitor alpha". Document frequency of "Prostatic Neoplasms" in this time duration is 6807, whereas document frequency of "NF-κ B inhibitor alpha" is 91. However, the co-occurence frequencies of this concept pair are 15 and 42 corresponding to the MEDLINE corpus in the time durations 1996-2000 and 2001-2005, respectively. It is worthwhile to study if the supervised learning model built from the first training data set is able to predict the strong connection between these two concepts after 1995.
Recall that, in our experimental study, the first training data set was formed by randomly selecting 10% of labeled pairs generated from concept network snapshots Gt 1= 1991-1995 and Gt 2= 1996-2000. We first made sure that the pair "Prostatic Neoplasms" and "NF-κ B inhibitor alpha" is not part of the first training data set. Then we run the supervised learning model built on the first training data set to make a prediction for this pair. The model successfully predicted the strong connection between these two concepts after 1995 by assigning a positive class label to this pair.
Significant paths using cycle free effective conductance feature
Prostatic Neoplasms →
Tumor Necrosis Factor-alpha →
NF-κ B inhibitor alpha
Prostatic Neoplasms →
RNA, Messenger →
NF-κ B inhibitor alpha
Prostatic Neoplasms →
Adenosine Triphosphate →
NF-κ B inhibitor alpha
Prostatic Neoplasms →
Oligopeptides →
NF-κ B inhibitor alpha
Prostatic Neoplasms →
Tetradecanoylphorbol Acetate →
NF-κ B inhibitor alpha
Prostatic Neoplasms →
Cycloheximide →
NF-κ B inhibitor alpha
Modeling a biomedical literature repository as a comprehensive network of biomedical concepts and viewing hypotheses generation as a process of automated link discovery on the concept network representing the literature repository, opens the door for performing large-scale cross-silo biomedical hypotheses discovery. We have presented the methods to generate a concept network and concept-author map from large-scale literature repositories using Map-Reduce framework. The link discovery on the concept network was further modeled as a classification problem and we proposed a framework to automatically generate the labeled instances of concept pairs for supervised link discovery. Our method also extracts multiple heterogeneous features for labeled concept pairs. These features include path based features such as cycle free effective conductance (CFEC), neighborhood features such as preferential attachment. In addition, we proposed a new feature based on CFEC namely semantic-CFEC, which utilizes the semantic type of the nodes in the path. Another important contribution of work is the use of author information. To the best of our knowledge, this is the first work that exploited the connecting two concepts via author links associated with those concepts for hypotheses discovery. Through experimental results, we showed an improvement of 7-9% in classification accuracy of link discovery obtained due to the addition of semantic type and author based features.
As part of the future work, we will explore using ensemble methods such as gradient descent boosted decision trees for classification. We will also explore the prediction of emerging connections between concepts in addition to the prediction of strong connections. A web service that generates biomedical hypotheses based on the proposed method will be built and published.
This article has been published as part of BMC Genomics Volume 13 Supplement 3, 2012: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2011: Genomics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenomics/supplements/13/S3.
Authors' Affiliations
- Swanson DR: Raynaud's syndrome and undiscovered public knowledge. Perspectives in Biology and Medicine. 1984, John Hopkins University Press, 30 (1): 7-18.Google Scholar
- Bekhuis T: Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy. Biomed Digit Libr. 2006, 3: 2-10.1186/1742-5581-3-2.PubMed CentralView ArticlePubMedGoogle Scholar
- Hu X, Zhang X, Yoo I, Wang X, Feng J: Mining hidden connections among biomedical concepts from disjoint biomedical literature sets through semantic-based association rule. International Journal of Intelligent Systems - Granular Computing: Models and Applications. 2010, 25: 207-223.Google Scholar
- Pratt W, Yildiz MY: Capturing connections across the biomedical literature. Proceedings of the 2nd International Conference on Knowledge Capture; Sanibel Island, FL, USA. 2003, ACM, 105-112.Google Scholar
- Srinivasan P: Text mining: generating hypotheses from Medline. Journal of American Society for Information Science and Technology. 2004, 55 (5): 396-413. 10.1002/asi.10389.View ArticleGoogle Scholar
- Stegmann J, Grohmann G: Hypothesis generation guided by co-word clustering. Scientometrics. 2003, 56 (1): 111-135. 10.1023/A:1021954808804.View ArticleGoogle Scholar
- Swanson DR: Somatomedin C and arginine: implicit connection between mutually-isolated literatures. Perspectives in Biology and Medicine. 1990, John Hopkins University Press, 33: 157-186.Google Scholar
- Xie Y, Katukuri JR, Raghavan VV, Presti T: Conceptual biology research supporting platform: current design and future issues. Applications of Computational Intelligence in Bioinformatics and Biomedicine: Current Trends and Open Problems. 2008, Berlin, Heidelberg: Springer-Verlag, 307-324.Google Scholar
- Őzgür A, Vu T, Ergan G, Radev DR: Identifying gene-disease associations using centrality on a literature mined gene-interaction network. Bioinformatics. 2008, 24 (13): i277-i285. 10.1093/bioinformatics/btn182.PubMed CentralView ArticlePubMedGoogle Scholar
- MEDLINE®/PubMed®Resources Guide. [http://www.nlm.nih.gov/bsd/pmresources.html]
- Swanson DR: Medical literature as a potential source of new knowledge. Bull Med Libr Assoc. 1990, 78: 29-37.PubMed CentralPubMedGoogle Scholar
- Unified Medical Language System® (UMLS®). [http://www.nlm.nih.gov/research/umls/]
- Weeber M, Klein H, Aronson AR, Mork J, de Jong-van den Berg LTW, Vos R: Text-based discovery in biomedicine: the architecture of the DAD-system. Proceedings of the AMIA Annual Fall Symposium; Philadelphia, PA, USA. 2000, ACM, 903-907.Google Scholar
- Faloutsos C, McCurley KS, Tomkins A: Fast discovery of connection subgraphs. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Seattle, WA, USA. 2004, 118-127.Google Scholar
- Koren Y, North SC, Volinsky C: Measuring and extracting proximity in networks. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Philadelphia, PA, USA. 2006, 245-255.Google Scholar
- Liben-Nowell D, Kleinberg J: The link-prediction problem for social networks. Proceedings of the Twelfth International Conference on Information and Knowledge Management; New Orleans, LA, USA. 2003, 556-559.Google Scholar
- Hasan MA, Chaoji V, Salem S, Zaki M: Link prediction using supervised learning. Proc of SDM 06 Workshop on Link Analysis, Counterterrorism and Security. 2006Google Scholar
- Benchettara N, Kanawati R, Rouveirol C: A supervised machine learning link prediction approach for academic collaboration recommendation. Proceedings of the Fourth ACM Conference on Recommender Systems; Barcelona, Spain. 2010, 253-256.View ArticleGoogle Scholar
- Lu Z, Savas B, Tang W, Dhillon I: Supervised link prediction using multiple sources. Proceedings of the 2010 IEEE International Conference on Data Mining. 2010, IEEE Computer Society, 923-928.View ArticleGoogle Scholar
- Support Vector Machine. [http://www.csie.ntu.edu.tw/~cjlin/libsvm/]
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


text:
Breach - Breaching a Barricade Gameplay Movie
A player gets hit by an ambush.
A player gets hit by an ambush.
Two teams battle to control a choke point.
A player battles to stop a convoy.
A player keeps the enemy's paws off his vehicles.
This week we take a look at demos for Crysis 2 and Bulletstorm, as well as Breach.
A player demolishes a barrier under fire.
Players make a successful canister grab despite dying.
A squad escorts a convoy through a tunnel.
This video divulges the real gadgets and equipment elite operatives use on missions both in the game and in real life.
Check out this quick gameplay video for Breach.
Today on the Spot! We have an awesome demo of Sam & Max: The Devil's Playhouse, a fresh new interview for Breach, and the AU team chips in with a ...
Peter Tamte talks about destruction, cover systems, and multiplayer in this interview for Breach.
Use your keyboard!
Log in to comment


text:
Google's Thrust on Nutrition
Having a problem with managing nutrition? Don't you worry for Google is setting its sights on your waistline and is aiming to help you with that. Google has just launched a new program last Thursday, May 30, that enables users t o have easy access to nutrition information for more than 1,000 foods. This program will be made available throughout the United States within 10 days.
"Figuring out how to make smart choices about some of our favorite foods can often be a cumbersome and daunting process. So we're hoping we can make those choices a little bit easier: starting today you will be able to quickly and easily find extensive nutrition information for over 1,000 fruits, vegetables, meats and meals in search.", nutrition project product manages Ilya Mezheritsky had said.
The list includes basics such as meat and vegetables and some of the more uncommon variety like the chow mein. So whether you simply want to know how much salt is in a glass of tomato juice or you want to know how much fat is in a burrito, Google can provide you with the answers quick and easy.
This program was made possible by Google's Knowledge Graph which basically brings together all kinds of information from across the web that is not exactly accessible. The graph then helps connect things that are not related. For example, if you are looking for information on "summer squash carbs", "zucchini" is included in the relevant searches because zucchini s a type of summer squash.
As I have already mentioned, this program is still in progress. However, Google has posted some examples of what the detailed search result will look like when this feature is fully implemented.
It could be said that public health is one of the issues that Google tries to address. In fact, this new feature is actually reminiscent of Google's 2011 Google Launch of the Body Browser. The company even has a feature that directs suicide-related searches to suicide prevention resources.
Aside from Google, I think it is worthy to note that since 2010, Bing has been featuring some nutrition facts in its search results as well though it's not as effective as Google is expected to be. The data is also not available through Bing's natural language queries.


text:
0 items
Please enter your details so that we can contact you soon.
Apart from being one of the most significant center of major cultural activities in Kolkata , Birla Academy of Art and Culture maintains a continuous liaison with foreign offices to organize exhibitions of major International relevance. To promote and proliferate both diverse and sustained cultural activities it extends financial assistance to other agencies in organizing programmes on art and culture.
108-109,Southern Ave, Kolkata, West Bengal 700029
2466-6802, 2466-2843, 4008-9489
All Rights Reserved © Birla Academy of Art and Culture, 2014.


text:
GREENWOOD, S.C. – Despite 20-point efforts from Shanteona Keys (Roswell, Ga.) and Enisha Donley (Loganville, Ga.) the Georgia College women's basketball team (12-13, 8-10 PBC) lost 77-67 at Lander University (9-16, 6-12 PBC) Thursday night.
Aarika Judge wowed the home crowd with seven threes en route to 31 points, adding 10 rebounds for a double-double. The Bobcats led early, but Judge powered a Bearcat comeback from which Georgia College never recovered.
Keys went for 25 points, going 5-for-5 from downtown and pulling in seven rebounds. She now has seven 20-point games this season. Donley did her damage inside the arc, going 10-for-19 without attempting a three. She also added six rebounds, as she netted her second 20-point effort of the season.
The Bobcats out-shot Lander 47 percent (26-55) to 44 percent (26-59). The difference came in threes, as the Bearcats made nearly twice as many (12-7). Lander also controlled the rebounding 40-29.
Judge finished 7-for-11 in threes and 10-of-17 overall. She was flanked by Lea Saunders at 15 points.
The loss puts Georgia College at 8-10 in the Peach Belt Conference (PBC) listed one game behind the University of North Georgia for the fourth and final playoff spot in the PBC West Division.
The Bobcats are back in action Saturday, March 2 at 1:30 p.m., hosting Armstrong (12-13, 7-11 PBC) for Senior Day. The Pirates most recently beat Francis Marion University 75-61 Monday.
The Georgia College Department of Athletics, two-time winners of the PBC Commissioner's Cup, sponsors 11 varsity athletic programs at the NCAA Division II level. As a Division II program, Bobcat Athletics prides itself on balancing the life of the student-athlete, evidenced by the teams' multiple appearances in post-season competition as well as documented academic success and community-service involvement. Sign up at www.twitter.com/BobcatSports and www.facebook.com/gcbobcats for up-to-the-minute reports, and visit www.GCBobcats.com for more information, and to nominate your favorite former Bobcats and Colonials to the GC Athletics Hall of Fame.


text:
Shania Twain, Jennifer Nettles + More to Present at 2013 Billboard Music Awards
Vegas will once again be going country, but this time it's for the 2013 Billboard Music Awards. The superstar-filled event will be bursting at the seams with country artists this year -- it's just been announced that Jennifer Nettles of Sugarland, Shania Twain and others will present awards during the event.
It will be great to see the spunky Nettles, since she's kept quiet ever since her bundle of joy, Magnus, was born. Twain is certainly no stranger to these awards shows, having been a standout in the country music scene for years and years.
Also taking the stage to present at the 2013 Billboard Music Awards will be newcomer favorites Florida Georgia Line, plus country crossover Kid Rock and television country star Hayden Panettiere of ABC's 'Nashville.' Non-country acts presenting include Miley Cyrus, Celine Dion, Ke$ha and Carly Rae Jepsen, among others.
It's going to be a great night for the genre, as several country acts have been nominated for Billboard Awards, as well, including Taylor Swift, Luke Bryan and Carrie Underwood. In fact, the '22' hitmaker has 11 nominations in all, including the prestigious Top Artist, Top Billboard 200 Artist and Top Social Artist.
Swift will also show off her crossover success by performing at the red carpet event, as will the 'Better Dig Two' sibling trio the Band Perry. After they rocked the stage at the 2013 ACMs, fans of the group are eagerly anticipating what type of performance they'll unveil this time around.
The 2013 Billboard Music Awards will air this Sunday, May 19, on ABC at 8PM ET, live from Las Vegas' MGM Grand Garden Arena.


text:
The Mobile Literacy Arts Bus (MLAB) is an artist-run, renovated recreational vehicle that exists as a flexible space open to community members' proposals for alternative educational and cultural programming.
MLAB is the collaborative effort of the 2007-2008 Social Sculpture class at Syracuse University, comprised of 10 art and architecture students and lead by artist and Director of Community Initiatives in the Visual Arts of Syracuse University, Marion Wilson. Our mission was to transform a used, 1984 Recreational Vehicle Bus into a Mobile Literacy and Arts Bus for use by the Syracuse City School District and the greater Syracuse Community. MLAB serves as a physical manifestation of Syracuse University's Scholarship in Action initiative, by pairing University resources with community needs in an attempt to address the staggering drop out rates in the Syracuse City School District High Schools. Through the School of Education at Syracuse University, incredible curricula that bridge photography, poetry and literacy currently exist within the public schools-- however due to a crisis of space, the schools don't always have the space or resources to house it. MLAB is this space. The bus serves as a mobile classroom, digital photo lab, gallery space, and community center. As a team, we did it all: demolition, design, and construction.
MLAB is made possible from the generous support of the School of Education at Syracuse University and Entitiative.
MLAB is made possible from the generous support of the School of Education at Syracuse University and Entitiative.
Thursday, December 11, 2008
Subscribe to: Posts (Atom)