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David Chang, Fancy Pants Group
Making Sense of Display Advertising
Let's face it. Nobody likes banner ads. They are the red-headed stepchild of the digital advertising world. In recent years, there have been serious shifts in technology and user behavior. The death of Flash, the rise of mobile, slow loading pages, ad blocking, and programmatic exchanges have all changed the game. When you stop and look around, it's a confusing mess. However, where there's chaos, new opportunities arise to innovate.
Here are some major factors that are transforming display advertising today.
Mobile Behavior Flash ads were about extravagant animations, expanding rich media, and finding clever ways to entice clicks. But with mobile, the screens are small, users scroll through content quickly, and there's minimal time to catch their attention. Getting them to interact with your brand's content is an even taller order.
The Growth of Programmatic When you look at the existing landscape, it makes all the sense in the world that ad inventory transactions should be powered by machines and algorithms. And, clearly, programmatic advertising is growing at a rapid pace. However, most creative executions are not living up to the advantages of programmatic capabilities. These exchanges are littered with unimaginative ads, singular messaging, and static images that aren't optimized for mobile.
Ad Blocking is on the Rise With the release of iOS 9, mobile ad blocking is now possible and used by an increasing number of people. Why? Because most ads suck! Pages take too long to load, and the technology now exists to suppress these terrible ads. Ad blocking's very existence is a testament to how bad it's gotten and how bad the user experience with display advertising has become.
What can brands and agencies do to stand apart and get the most out of their display dollars? The good news is that there's an opportunity to approach creative concepts in more strategic ways than ever before. Here are some suggestions …
Don't Build Sucky Ads Understand the context of the ad. Mobile ads that are simply scaled-down resizes of desktop designs are not likely to be effective. Instead, consider how it will be viewed and how user behavior changes across platforms. Build beautifully designed, elegant, efficient and fast loading ads that work across all platforms and devices.
Get to the Point Nobody is going to read your elaborate 15-second animation no matter how eye catching it is or how engaging the copy. Get to the point, but do so elegantly so the user can absorb your message in an instant. Use animation to capture the user's attention, but avoid being gratuitous.
Invest in Quality Brands that believe in the quality of their product should invest in developing ads that are similarly well crafted. While there are exceptions, the overall quality of display advertising seems to have gotten considerably worse as of late. Many in the industry simply don't care about how that mediocrity positions the brand in the eye of the consumer. It always pains me to see a high-end brand that has released a shoddy execution into the world.
Understand Data Signals With so many data insights available, advertisers have the ability to better understand their audience and to connect with consumers more strategically. Know what data is available and how you might leverage that data to tell a more effective story to the user. How might brand marketers tailor their story if the user has been on that brand's website previously? How might they craft the messaging if they're intersecting a user who is researching a competitive brand? Data is the new secret weapon and should be an integral part of the creative arsenal.
Reach the Right Audience at the Right Time Dynamic Content Optimization (aka DCO) has been around for many years. But now more than ever, marketers can utilize dynamic capabilities to tell a story and serve the proper creative and messaging to the right user. The ads of yesterday were built and served within the context of a static media buy. But with programmatic and DCO capabilities, the ads of today must consider how dynamic creative (powered by data and delivered via programmatic media buys) can tell a smarter, more targeted and ultimately more useful story to the consumer.
Like it or not, the banner isn't going to pack up and disappear any time soon. The landscape is continuing to evolve. Users are getting smarter and savvier than ever before. However, the status quo isn't working when it comes to display advertising. We need to fight mediocrity and do our part to push this facet of the digital marketing industry forward. The overall purpose should be to deliver a better ad experience and provide actual value for the consumer.
About the author:
Since joining Fancy Pants Group in 2011, David has helped the company transform from a production shop into a full-service creative digital studio. He is constantly looking for ways to drive innovation and to improve the craftsmanship of Fancy Pants' digital executions. His attention to detail and his creative approach to technology are distinguishing characteristics of his work in the digital marketing arena. David has worked with a diverse range of clients, including Microsoft, Royal Caribbean and Stride Gum. He previously served as Director of Technology at JWT.
Illustration by Taiwan-based illustrator, Kuocheng Liao


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Description and Features
Style plus functionality – These days, dual-function pieces are all the range. If you're looking a clock, get something that also looks good like the Ambience Mental Clock like the Bulova Clocks.
Gorgeous casing – Encased in a solid wood case, this clock does more than just tell the time. Featuring a Dark Wine finish, this piece can also serve as a beautiful piece of décor that will adorn any plain mantel.
Personalize it – The clock comes with an engraving plate, allowing you etch on it practically anything you want, from your name to a friend's name to a symbol that's meaningful to you.
- Solid wood case, dark wine finish
- Brushed aluminum columns
- Polished chrome inlaid accents and feet
- Spun aluminum dial with raised chrome markers in 12 3 6 9 positions
- Protective glass lens with mirror border
- 2.5" x 0.5" engraving plate included
- 9.5" Height x 10.25" Width x 4" Depth
- Requires 1 x AA Battery (not included)
- Manufacturer's 2-year Warranty
Browse all traditional clocks with a rattan or wood body and no shade
Learn how to choose clocks with our Atomic Clocks
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Spend $49 to get Free Shipping (contiguous USA), or use the shipping calculator above.
Most other items ship for $9 in 1-3 business days (contiguous USA). QuickShip items ship out the next business day.
Depending on your location, shipments take on average 3-5 business days or up to 7-10 from coast to coast. We ship from multiple locations.
You will receive tracking numbers by email when your items ship out, via Fedex, UPS or USPS.
You'll be able to track the packages online or on our site.
You can also talk to our team of experts at any time, or Live Chat.
Enjoy 30 days for easy returns. See our return policy for details.
We replace breakages/damages/missing parts etc for free.
Every item is covered under our extended 1-year warranty.
Guides you might find useful...
In Buyer's Guides: Atomic Clocks
Atomic clocks receive a radio signal from the most accurate clock in the world, the cesium atomic clock, transmitted from the National Institute of Standards in Fort Collins, CO. The radio signal that covers the entire United States will keep your clock accurate to within one second over one million years. You'll never have to reset the clock, even for daylight-saving time!
The US Naval Observatory which operated 70 "atomic clocks" provided the following background information about these clocks: 'A cesium beam atomic clock uses the exact frequency emitted by atoms of the metallic element cesium. Today, cesium clocks measure frequency with an accuracy of one second in 1,400,000 years! It is the most accurate realization of a unit that mankind has yet achieved. A cesium clock operates by exposing cesium atoms to microwaves until they vibrate and counting the corresponding cycles as a measure of time.

subset: commoncrawl


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Get an overview of this hotelLuxury Mecca hotel in Ajyad with 2 restaurants
Collecting Nectar Points
Special Offer: Book by 2 August 2017 and collect double Nectar points!
Simply enter your Nectar card number when you check out. Your points will be credited 35 days after you return from your trip.
*Terms and conditions apply.
Popular property highlights
Located in Ajyad, this luxury hotel is within a 10-minute walk of Kaaba, Great Mosque of Mecca, and Safa and Marwah. Well of Zamzam and Abraj Al-Bait Towers are also within 3 mi (5 km).
Hotel Features
This hotel features 2 restaurants, a coffee shop/café, and 24-hour room service. Free buffet breakfast and free WiFi in public areas are also provided. Additionally, a business center, a meeting room, and concierge services are onsite.
Room Amenities
All 304 rooms provide conveniences like refrigerators and coffee makers, plus free wired Internet and LED TVs with cable channels. Guests will also find 24-hour room service, free bottled water, and hair dryers.Information missing or incorrect? Tell us!Opens in a new window
Elaf Al Mashaer Hotel
Hotel Amenities
Hotel Amenities
Dining is available at one of the hotel's 2 restaurants and guests can grab coffee at the coffee shop/café. A complimentary breakfast is offered each morning. A computer station is located on site and wireless Internet access is complimentary. Business-related amenities at this 5-star property consist of a business center and a meeting room. This Art Deco hotel also offers tour/ticket assistance, concierge services, and complimentary newspapers in the lobby.
- 24-hour front desk
- Total number of rooms - 304
- Number of floors - 16
- Business center
- Coffee shop or café
- Free newspapers in lobby
- Free breakfast
- Concierge services
- Dry cleaning/laundry service
- Elevator/lift
- Tours/ticket assistance
- Free WiFi
- Computer station
- Laundry facilities
- One meeting room
- Parking (limited spaces)
- Porter/bellhop
- Luggage storage
- Year Built 2008
- Number of restaurants - 2
- Number of buildings/towers - 1
Family Friendly Amenities
- Family Hotel
- Free Wi-Fi
- Laundry facilities
- Refrigerator
- Cribs/infant beds (complimentary)
Available in all rooms: Free wired Internet
Available in some public areas: Free WiFi
Parking (limited spaces)
Room Amenities
- Air conditioning
- In-room climate control (air conditioning)
- Free bottled water
- Refrigerator
- Coffee/tea maker
- Daily housekeeping
- Private bathroom
- Free toiletries
- Hair dryer
- Shower only
- Iron/ironing board
- Cable TV service
- Free wired Internet
- In-room safe (laptop compatible)
- Room service (24 hours)
- Connecting/adjoining rooms available
- Free cribs/infant beds
Where to Eat
A complimentary buffet breakfast is served each morning between 6 AM and 9 AM.
Al Multazim - This restaurant specializes in Asian cuisine and serves breakfast, lunch, and dinner.
Al Maqam - This coffee shop specializes in Asian cuisine.
24-hour room service is available.
If you have requests for specific accessibility needs, please note them at check-out when you book your room.
- In-room accessibility
- Roll-in shower
Hotel Policies
Check-in time starts at 2 PM
Check-in time ends at midnight
Minimum check-in age is 18
Special check-in instructions:
Check-out time is noon
Payment types
Children and extra beds
- Children are welcome.
- Kids stay free! Up to 2 children 9 years old and younger stay free when using existing bedding.
- Rollaway/extra beds are not available.
- Free cribs (infant beds)!
- Pets not allowed
You need to know
Extra-person charges may apply and vary depending on property policy.
Hotel Name
- Al Mashaer
- Elaf Al
- Elaf Al Mashaer
- Elaf Al Mashaer Hotel
- Elaf Al Mashaer Hotel Mecca
- Elaf Al Mashaer Mecca
- Elaf Mashaer
- Elaf Mashaer Hotel
- Hotel Elaf Mashaer
- Mashaer
We should mention
Only Muslims are allowed to enter the holy cities of Mecca and Medina. Local laws may restrict unmarried guests from sharing rooms. Guests are responsible for providing proof of marriage, if requested by the property. Couples wishing to share a room must provide proof of marriage. Up to 2 children 9 years old and younger stay free when occupying the parent or guardian's room, using existing bedding. The property has connecting/adjoining rooms, which are subject to availability and can be requested by contacting the property using the number on the booking confirmation. Alcohol is not served or permitted at this property.
Children Stay Free
Book a stay in any room and rate category and there are no room charges for 2 children age 9 years and younger when sharing a room with adults and using existing bedding and receive a 50% discount on meal plans.
Full details at check-in. There are no room charges for children age 9 years and younger when sharing a room with adults and using existing bedding. Offer subject to availability. Offer is not valid with groups/conventions and may not be combined with other promotional offers. Other restrictions and blackout dates may apply.


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An AirAsia plane traveling from Indonesia to Singapore vanished from air traffic control detection early Sunday morning and officials are scrambling to figure out what happened to the plane.
There are 162 passengers and crew on board the AirAsia QZ 8501 flight, traveling from Surabaya, Indonesia, to Singapore. The plane went missing over the Java Sea, and before it vanished, the pilot asked to change the flight path because of stormy weather.
Accuweather meteorologists said the flight path was hit by thunderstorms. "The storms in the area were capable of producing severe turbulence, strong wind shear, frequent lightning and icing," senior meteorologist Alex Sosnowski said.
The search for flight QZ 8501 was suspended Sunday night local time as darkness fell.
According to the airline, the passengers include 149 people from Indonesia, three from South Korea and one each from Singapore, Malaysia and the United Kingdom. There are six crew members from Indonesia and one from France.
The passengers included 16 children and one infant, AirAsia officials added.
AirAsia released scant information via the airline's Facebook page:
AirAsia Indonesia regrets to confirm that flight QZ8501 from Surabaya to Singapore has lost contact with air traffic control at 07:24hrs this morning.
At the present time we unfortunately have no further information regarding the status of the passengers and crew members on board, but we will keep all parties informed as more information becomes available.
The aircraft was an Airbus A320-200 with the registration number PK-AXC.
At this time, search and rescue operations are in progress and AirAsia is cooperating fully and assisting the rescue service.
AirAsia has established an Emergency Call Centre that is available for family or friends of those who may have been on board the aircraft. The number is: +622129850801.
AirAsia will release further information as soon as it becomes available. Updated information will also be posted on the AirAsia website, www.airasia.com.
RELATED: U.S. Navy Official Says "Pings" Unlikely From Missing Malaysia Jet - CNN
The Airbus 320-200 flying had last undergone maintenance on Nov. 16.
Singapore's aviation authority put out its own release, saying the plane was in Indonesian air traffic control when it went missing. Singaporean authorities learned of the lost contact a half-hour later.
RELATED: Drone Diving To Record Level In Malaysian Plane Search
The situation is already causing deja vu for Twitter users connecting the missing plane to the ongoing mystery disappearance of Malaysia Airlines Flight 370, which disappeared in March and was never recovered.
Some tonight are asking what country is #AirAsia based in? Regret to inform you that once again it's #Malaysia. — Charles Adler (@charlesadler)
December 28, 2014
Can't believe another missing flight! Thoughts for those on #AirAsia #QZ8501 what's going on #Malaysia?! — Sara Jewell (@sjjewell)
December 28, 2014
How on earth can a flight go missing?Malaysia is the common link for the 3rd time this year.Weird? God help the travellers onboard. #AirAsia — Vedant (@LosBlancos9248)
December 28, 2014


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No Tiene La Culpa El Indio (1977)
An Indian from Xochimilco has the power to predict the future, but only when he's looking at the legs of a beautiful woman. An enterprising businessman hears of his powers and decides to make money off them.
Directed By: Miguel M. Delgado
Rated: Unrated
Running Time:
Genre: Documentary, Art House & International


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Est. Reading Time: 3 minutes
Picture this: you're checking out your website data in Google Analytics, and decide to look at your monthly traffic year-over-year. You see a huge spike in traffic on a single day last year, but you aren't quite sure what caused it. Were you running a special that day? Perhaps a new TV commercial aired? Or maybe a direct mail piece dropped? Hmm…you start shuffling through old emails and notes to solve the mystery.
Without knowing exactly what could have affected last year's traffic spike, it's impossible to measure the impact individual circumstances have on your website. Sure, you can keep an Excel spreadsheet with a long list of dates. But what if I told you there was an easy way to keep all those events organized, in one place, and in context? Yep, you can do it right alongside your website data with Google Analytics annotations.
Annotations allow you to note a particular event that could have an impact on your data right on the date that it occurred.
Here are some of the types of things I like to annotate:
- Website downtime
- Sales and special promotions
- Website development changes
- Marketing campaigns (direct mail, TV, radio)
- Content changes
- Press releases or high profile featured content around the web
- And any other time-specific event that could possibly affect website visits and user behavior
Making annotations in GA is incredibly easy. Here's how it's done:
That's it. No, really. It's that simple!! Annotations are indicated by the little text bubbles at the bottom of your chart. To see the details simply click the bubble.
To see a comprehensive list of all annotations for your view, go to the Admin panel and click Annotations.
If I can offer you one final tip for using annotations in GA, it is to be explicit. Trust me when I say, it will save future you a lot of frustration. "Online Sale"…great…but what was on sale? While Google only gives you 160 characters, be as detailed as possible! If your notes are enigmatic, you're wasting your time creating them to begin with.
How do you use GA annotations to help analyze your website data?


text: City of Westminster seeks an experienced, licensed Certified Public Accountant, reporting to the Director of Finance, to develop the City's financial records and reports in compliance with GAAP, GASB, and City requirements. Assists the Director in preparation of annual financial statements and preparing various annual audit analysis sheet summaries; responsible for the continuous improvement and documentation of all finance processes throughout the year. Bachelor's degree required with a minimum of 3 years public accounting, auditing and government experience. MBA preferred. Experience with Tyler Technologies Eden system preferred. This is an exempt position with a starting salary range of $60,242 – $74,090 commensurate with experience and qualifications. Excellent benefits including health, dental, vision, life insurance and retirement plan. Resume must be submitted with application. Apply online at www.westminstermd.gov; or pick up application at 56 W. Main St, Westminster; or call 410-848-5236. The City will accept completed applications for this position until 4:30 p.m., May 8, 2017. EOE


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Dilma is a very special person, and I also imagine she´s very good engeneer and management professional who is competent a lot. With much pride I endorsement her statement. I beleave in her!
- Abraao Dahis (11/16/08)
Peer Assessment:13
Social Influence:?
dilma balbi
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The Temperance movement of the 1830's was, at its most basic, a precursor or warmup for the time of Prohibition in the early twentieth century. Initially started by fearful wives and children who were fed up with the abuse and neglect that frequent alcohol use brings to a household, the Temperance movement snowballed into an event that had global impacts. Places like Ireland, England, and Scotland held protests and passed legislatures in attempts to forbid the use, trade, and fermentation of hard spirits like whiskey or bourbon. The trouble of alcohol abuse was not the underlying, more important issue. Sure, it was a problem that needed to be dealt with, but the ban on spirits was a tool that paved the winding path to reach the final goal of moral reformation.
Important facts about the Temperance Movement:
-Teetolism- radical anti-alcohol movement committed to ending the use of alcohol. Heavily advocated alcoholic abstinence, going as far as attempting to force the government to pass legislatures that significantly taxed the alcohol industry.
-It officially began in 1826 in the northeastern United States.
-Mainly focused on moral reform and the ban of liquor. Beer and wine were less emphatically opposed.
-American Temperance Society- one of the largest anti- alcohol fellowships, after twelve years boasted nearly 1,500,000 people.
- Caused by angry wives and children fed up with the consequences alcohol abuse by their husbands/ fathers imposed on domestic America.
- Temperance movement effectively halted all liquor, beer, and wine commerce in North America as well as in many European countries.
- The Temperance movement was the culmination of many past events. Spousal abuse, domestic neglect, and chronic unemployment combined together over the course of many years to explode into what is now known as the Temperance movement.
- Important people:
-Benjamin Rush- judged chronic alcohol abuse to be serious health hazards to mind and body.
- Lyman Beecher- minister who warned his listeners against the dangers of liquor.
- Joseph Califano- founded the Center on Addiction and Substance Abuse.
Temperance movement
Anti- Alcohol Propaganda Poster- Primary Source
This is a political cartoon from the Temperance movement era. In plain sight are the offenses that are being blamed on alcohol. For instance, being the cause of both poverty and crime. The hard liquor costs an exorbitant amount of money which causes desperation when funds run out. An alcoholic may choose to turn to a life of crime to support his/her continuous drunken states mind. Other offenses like "robbing women and children" and "being against progress" are equally important to this cartoon: appealing to the sentimental side of the human personality. To a hard man poverty and crime could just be any other day, but to sketch the illusion of women and young children starving without hope because their breadwinner, their head of house decided to buy a bottle of whiskey is enough to soften any previously hard demeanor. Notice in the back the words "waste" and "ruin". The cartoon is drawn in such a way that the bottles of booze have the semblance of an army. From the tight, hard faces to the endless column of bottles, the message is clear: 1) the line of bottles never ends once the first one is picked up and 2) wherever the bottles go, waste and ruin follow shortly after to consume whatever life you started out with.
At the start of the American temperance movement, authorities were ordered to dispose of all alcohol by pouring the barrels full into the city's sewer
The Temperance Movement was largely lead by women and children in 1820's who were upset and fed up with the poor treatment that they were getting by their husbands and fathers that were committing alcohol abuse
The main point of the temperance moment was to keep the American population from falling into a state of decline due to the use of alcohol which was blamed could be blamed for all of the countries weakness. Hoping that through the temperance movement the stability of the nation and individual households could be upheld.


text: 30 April 2007 Top politics from the award-winning New Statesman Sign up for our weekly email * As Tony Blair prepares to step down, we mark the occasion in next week's issue with verdicts from Britain's top writers. David Marquand and Geoff Mulgan set the scene. David Hare looks at war and truth; John Gray detects the hand of a previous premier; and Suzanne Moore pinpoints how Britain has changed. We have exclusive images from the artists Ralph Steadman, Peter Kennard, Robert Del Naja and Stanley Donwood that sum up the Blair years. Our former editors John Lloyd, Ian Hargreaves and Peter Wilby reflect on the NS, Labour and their fraught relationship. On a lighter note, we have haikus from Alex James and Tom Paulin and reminiscences from many more. Led by our political editor, Martin Bright, we will provide incisive coverage of the handover and the start of the Brown era. Over the past year we have set the agenda with a series of exclusives and interviews. As Britain moves into a new era, the NS will continue to lead the debate. John Kampfner, Editor Subscribe from just £1 per issue This article first appeared in the 30 April 2007 issue of the New Statesman, Pakistan: The Taliban takeover More


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Featuring free WiFi and a terrace, B&B Il Giovine offers pet-friendly accommodation in Campello sul Clitunno. Guests can enjoy the on-site restaurant. Free private parking is available on site.A flat-screen TV, as well as a laptop are featured. Rooms are fitted with a private bathroom equipped with a bidet. For your comfort, you will find bath robes, free toiletries and a hair dryer.There is room service at the property.The nearest airport is Perugia San Francesco d'Assisi Airport, 36 km from B&B Il Giovine.
Photos Of
Facilities of Hotel B&B Il Giovine
- Pets allowed
Room facilities
- Air conditioning


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EDP Renováveis Annual Shareholders' Meeting
EDP Renováveis, S.A. ("EDPR") informs that at the Annual General Shareholders' Meeting held today, the Shareholders have approved the following resolutions:
- Appointment, for the term of three years, of Mr. José António de Melo Pinto Ribeiro as Chairperson of the General Shareholders' Meeting.
- Review and approval of the individual annual accounts of EDP Renováveis, S.A., as well as those consolidated with its subsidiaries, for the fiscal year ended onDecember 31st, 2013.
- Review and approval of the application of results for the fiscal year ended on December 31st, 2013 as proposed by the Board of Directors, as follows:
Legal Reserve …………………………. €5,699,882.39
Dividends* …………………………… €34,892,326.48
Voluntary Reserve …………………. €16,406,614.99
(*which corresponds to a gross dividend of €0.04 per share)
- Review and approval of the Individual Management Report of EDP Renováveis, S.A., the Consolidated Management Report with its subsidiaries, and its Corporate Governance Report, for the fiscal year ended December 31st, 2013.
- Review and approval of the management and performance by the Board of Directors and its Executive Committee during the fiscal year ended December 31st, 2013.
- Establishment a maximum limit to the Directors' variable remuneration according to Article 26.4 of the Articles of Association.
- Approval of the remuneration policy for the members of the Board of Directors of the company.
- Re-election, as Auditor of EDP Renováveis S.A., of KPMG Auditores, S.L. recorded in the Official Register of Auditors under number S0702 and with Tax Identification Number B-78510153, for the year 2014.
- Delegation of powers to the Chairperson of the Board of Directors Mr. Antonio Luis Guerra Nunes Mexia, to the Vice-Chairperson of the Board of Directors and Chief Executive Officer Mr. João Manuel Manso Neto, and to the Secretary of the Board of Directors Mr. Emilio García-Conde Noriega, to the full extent that is necessary under law, to implement all the resolutions adopted by the General Shareholders' Meeting and for that purpose, to develop, clarify, specify, interpret, supplement and rectify said resolutions or those derived from deeds or documents issued in execution of the same and, in particular, any omissions, defects or errors of content or form, that could prevent registration of said resolutions and their implications with the Commercial Register.
EDP Renováveis, S.A.


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Fondant, Gumpaste, Tylose? What To Use To Make Figurines...
Decorating By proudmommaof3 Updated 2 Dec 2009 , 5:46pm by cylstrial
I am soooo new and have never taken a class (which I really need to) and have soooo many questions. My daughter wants me to make a "favorite things" cake for her 9th b-day (duck, rainbow, mermaid, ice skates, ballet slippers, etc. LORD HELP ME) Do I use fondant or gumpaste and what exactly is tylose?
Can someone please explain my best ways of going about this task? Thank you.
I am no expert in this, but when I make my figures, I mainly use fondant by itself. My understanding is that when fondant and gumpaste are mixed or when you add tylose, it makes the fondant mixture dry quicker. I don't work fast enough, so I use fondant by itself so it allows me more time to tweek what I don't like. I just make sure hat I make things several days in advance so there is enough drying time. I hope that helps.
For figurines, I use 1/2 fondant - 1/2 gumpaste. It will dry harder than fondant so that it doesn't squish when you touch it and such. And it does dry faster than regular fondant as well.
Tylose is a powder that you add to fondant. It makes the fondant dry faster and harder. Fondant with tylose is equivalent to gumpaste.


text:
Apr 15 2013
Filed under:
Digital marketing
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We have just hit send to another big email broadcast to over sixty thousand (yes 60,000) anglers throughout the UK for Fishing Wales and the Environment Agency. The email was a quick visual email to announce the new season announce the new another issue to the ever successful to the e-zine we design and built. The online analytics showed yet again a huge hit. Here's a link to the latest e-zine: www.wildfishingwales.com/
- Email & email newsletter design and broadcast
- Digital marketing &SEO
- Web analytics, audits and customer insight
- Digital illustration
- Strategy
- Viral


text:
Studies concerning the physiological significance of Ca2+ sparks often depend on the detection and measurement of large populations of events in noisy microscopy images. Automated detection methods have been developed to quickly and objectively distinguish potential sparks from noise artifacts. However, previously described algorithms are not suited to the reliable detection of sparks in images where the local baseline fluorescence and noise properties can vary significantly, and risk introducing additional bias when applied to such data sets. Here, we describe a new, conceptually straightforward approach to spark detection in linescans that addresses this issue by combining variance stabilization with local baseline subtraction. We also show that in addition to greatly increasing the range of images in which sparks can be automatically detected, the use of a more accurate noise model enables our algorithm to achieve similar detection sensitivities with fewer false positives than previous approaches when applied both to synthetic and experimental data sets. We propose, therefore, that it might be a useful tool for improving the reliability and objectivity of spark analysis in general, and describe how it might be further optimized for specific applications.
- calcium sparks
- spark analysis
- myocytes
- confocal microscopy
- image processing
the study of brief, localized Ca2+ release events, such as Ca2+ sparks, using fluorescent indicators and confocal microscopy has afforded new insights into Ca2+ signaling in cardiac, skeletal, and smooth muscle (5). The task of analyzing these images is hampered, however, by the high degree of photon (shot) noise normally present. This is a consequence of the low number of photons collected per pixel at the high scan rates required.
Spark detection algorithms seek to speed up analysis and reduce the risk of bias inherent in manually distinguishing Ca2+ release events from noise, while identifying release events too small to be seen by eye (6, 12, 22). However, current algorithms were designed primarily only for images with a stable baseline intracellular Ca2+ concentration ([Ca2+]i). Here, we consider the effects of baseline variability on detection, and introduce a more general method of spark detection. We focus on linescan images, because these have received more attention in the literature and are better suited than full-frame (XYT) videos for the systematic investigation of the algorithm's detection capabilities. Nevertheless, the similarity of the problems encountered in processing both types of data sets means that the same methods can often be used with minor modification (e.g., Refs. 4, 7, 12, and 23).
Three distinct approaches that completely automate all steps of spark detection in linescans have been detailed in the literature. The most common, referred to as the "conventional algorithm" (4, 22), is most fully described by Cheng et al. (6). This combines median and mean filtering operations to improve the signal-to-noise ratio (SNR) in the image, before excising pixels >1.5 SD above the mean as potential sparks. Resting fluorescence and the noise standard deviation are estimated from the remaining pixels, followed by normalization and hysteresis thresholding to detect the sparks. This strategy is fast, capable of efficient analysis of large data sets, and has been made available both in an IDL implementation (6, 17) and as an ImageJ plugin (15). One important shortcoming is that the noise estimate is not robust: if, for example, no sparks are present, then the removal of the brightest pixels from the computation will lead to underestimating the noise (and potentially more false positive detections), whereas a high true spark density may have the opposite effect. This problem is somewhat overcome by the "à trous wavelet algorithm," which uses the isotropic undecimated wavelet transform (IUWT) for robust noise estimation, denoising, and final detection, and thereby improves the sensitivity and reliability with which small sparks may be identified (22). The IUWT step combines filtering with subtraction to iteratively smooth an image and extract the difference between consecutive smoothed versions. The original image can be represented by the sum of a single highly smoothed representation and several wavelet (or detail) levels. Larger features are visible with greater contrast on the most highly smoothed wavelet levels. One can effectively isolate objects with properties that resemble sparks by thresholding appropriate wavelet levels. The two-dimensional (2D) IUWT is appropriate when the sparks appear approximately isotropic (rotationally symmetric) in the image space, which implies that the spatial and temporal spread is similar in terms of pixels (22). If this is not the case, the 1D IUWT might be more appropriate. This has previously been used to detect prolonged Ca2+ release events dubbed Ca2+-embers (20). Finally, if the properties of the expected sparks are known approximately, the "matched filter detection algorithm" can be used to compute correlations between the image and a template spark (the matched filter) (12). By comparing the correlation coefficients with those obtained by correlating the template with noise generated by randomly reordering the pixels in the original linescan, appropriate thresholds can be determined.
Although each of these algorithms has been extensively tested and validated using synthetic images in which sparks are superimposed on constant backgrounds, none are well suited for processing more complex images in which the baseline fluorescence underlying sparks can change, for example, images acquired under voltage clamp (4, 6). This results from the assumption that the noise can be treated as signal-independent and characterized by the same standard deviation value everywhere. While it has been proposed to apply baseline subtraction before using the conventional algorithm for detecting sparks in such cases (4, 6), this does not take into consideration the signal-dependent nature of the photon noise present in fluorescence confocal images (13). This can cause the noise standard deviation to be several times larger in brighter, as compared with darker, parts of the image. Failing to take this variability into consideration is likely to reduce either the specificity or the sensitivity of spark detection, if not both.
The current study grew out of a need to efficiently and objectively analyze linescans of multiple smooth muscle cells in retinal arterioles. In these images, sparks may arise from the baseline or be superimposed on prolonged [Ca2+]i elevations, termed Ca2+-oscillations (8). This represents a challenging test of spark detection in the presence of baseline and noise variability. After outlining and validating an appropriate general noise model, we introduce a new algorithm that extends the range of linescan images in which sparks can be automatically detected to include such data sets, while still being suitable for simpler images with constant baselines. By combining variance stabilization with local baseline subtraction, our algorithm effectively adapts detection thresholds automatically to local baseline and noise. This approach reduced false positives without compromising sensitivity. We propose, therefore, that the benefits of this strategy are not limited to complex cases but may extend to spark detection in any linescan.
Test Images
Experimental images.
Linescan images in our laboratory were recorded using a confocal laser-scanning microscope (μRadiance, Bio-Rad) on an inverted microscope (Nikon Eclipse TE300; ×60 oil immersion objective, numerical aperture 1.4). Test solutions and tissues were excited in linescan mode (500 s−1) at 488 nm; emitted light was filtered (530–560 nm) and detected using a photomultiplier tube (PMT). Data acquisition was controlled with Timecourse software (Lasersharp, Bio-Rad). Output images were 8-bit.
To verify the validity of our noise model and acquire spark-free images, we imaged different concentrations of Ca2+ (10 nM, 100 nM, 200 nM, 300 nM) and Fluo-4 (5 μM, 10 μM) in a solution containing 100 mM KCl, 10 mM MOPS, and 1 mM EGTA with pH 7.2 at 22°C for various permutations of iris size, gain setting, and laser power. This resulted in 96 linescan stacks, each consisting of 512 × 512 × 8 pixel frames, which were concatenated to form 512 × 4,096 pixel images.
We also obtained linescans of retinal arteriolar smooth muscle from Sprague-Dawley rats loaded with Fluo-4 AM (Molecular Probes). Microvessel segments between 0.2 and 2.0 mm in length, and with an outer diameter of 20–40 μm, were anchored with tungsten wire slips and superfused with bath solution (37°C) containing (in mM) 140 NaCl, 5 KCl, 5 d-glucose; 2 CaCl2, 1.3 MgCl2, and 10 HEPES, pH 7.4 with NaOH. The recording bath was rotated to align the long axis of the arteriole to the x-axis of the microscope, so that multiple cells were visible in each linescan. Animals were killed for tissue extraction under UK Home Office Certificate of Designation no. 5012. More detailed descriptions of the protocol are provided in Refs. 8, 16, and 21.
Synthetic images.
The algorithm's ability was tested using sparks of known properties embedded in synthetic noisy images (6, 12, 20, 22). Synthetic sparks had an exponential rise and decay and Gaussian spatial profile (11) with a rise time of 10 ms, half-time to decay of 20 ms, and full width at half-maximum (FWHM) of 2 μm (pixel size = 0.15 μm × 2 ms), as described for cardiac sparks (5). Amplitudes, defined as ΔF/F0 (F0 = the baseline fluorescence), were scaled at 0.05 intervals in the range 0.05–0.6, and randomly positioned in 512 × 2,048 pixel images under the condition that no sparks overlapped with one another or occurred at the image border. Poisson noise was added, and image sets were produced for SNRs of 2, 3, and 4, where the SNR is defined as the square root of F0 (12). For each combination of SNR and spark amplitude, five images were generated for spark frequencies of 20, 40, 60, and 80 sparks per image. This resulted in a total of 720 test images, including 36,000 sparks.
In addition, we generated a synthetic linescan resembling arteriolar smooth muscle by superimposing sparks on top of a varying background, which was computed by evaluating a tensor product spline.
Noise in Confocal Linescans
Poisson and Gaussian noise models.
In fluorescence confocal imaging, the "true signal" λi can be viewed as the mean number of photons that would be counted at a certain pixel given sufficient repeated observations, while the recorded signal fi is based on a single observation. The primary source of noise in the recorded image is expected to be photon noise, which is due to the inherent randomness of photon emission by the source and follows a Poisson distribution (6, 13, 14). A simple model of the signal is then (1) where Xi ∼Pois.(λi) is a Poisson distributed random variable, α is a conversion factor (related to the gain of the imaging system), and μ is a constant offset (black level) that may be added to every pixel before digitization. A property of Poisson noise is that the noise variance is equal to the true signal, and the SNR can be defined as the square root of this value (13). However, it is important to note that pixel values in an image do not typically directly equate to photon counts, so that the standard deviation of the noise described by Eq. 1 is αλi1/2; i.e., it is a function of the unknown true signal and the (often) unknown conversion factor introduced during acquisition.
A consequence of this is that the noise varies along with the signal, meaning that the detectability of a spark is a function of the local baseline. Previously, Gaussian noise models have been widely used for spark detection (4, 6, 15, 22), in which the noise is treated as signal independent and characterized by a single standard deviation value. This simplifies detection by allowing the same tests to be applied everywhere and is justifiable when both the SNR is high (greater than about 3.5) and the underlying baseline is constant. However, the Poisson and Gaussian distributions differ appreciably when the SNR is low or if the baseline varies throughout the image.
Variance stabilization.
Variance stabilization transforms (VSTs) may be used to convert Poisson data into a form in which the noise may be treated as Gaussian (1). If one has access to the photon counts Xi (i.e., one has corrected for α and μ in Eq. 1), a simple stabilization method is to compute the square root of each count, after which the noise is asymptotically Gaussian with a standard deviation of 1/2 (2). The convergence of the noise can be slightly improved by first adding 3/8 to each count, resulting in the Anscombe transform (1). However, both of these methods become less reliable when the SNR < 5 (18), while the SNR of images used for spark detection is often in the range 2–4 (22). If one first smoothes the data using a filter h with a nonzero mean before computing square roots, it has been shown that effective variance stabilization of Poisson data may be achieved at much lower SNRs (around 0.5), assuming that the signal is constant under the support of the filter (24). Both optimal offset values (analogous to the 3/8 in the Anscombe transform) and the final asymptotic noise variance can be calculated from the filter coefficients (24).
This suggests that variance stabilization of filtered data is appropriate for confocal images of sparks. However, one seldom has access to the true photon counts. Determining the effects of gain, digitization, and other noise sources with sufficient accuracy to convert pixel values can be prohibitively difficult in practice. We therefore suggest a simplified form of the transform: (2) where the square root transform of the image is computed after filtering and subtracting the offset only. Note that inaccurate offset correction reduces the rate of convergence of the noise to a Gaussian distribution, so that some signal dependence may still be evident at low SNRs, although in practice we observed that estimating μ as the lowest valued pixel within the image (assuming the presence of a background region) or from dark images recorded without excitation was sufficient for even the lowest SNRs we encountered in experimental images. Alternative strategies for estimating the offset in the absence of dark images have been presented elsewhere (e.g., Refs. 3 and 25). The stabilized noise standard deviation is then determined directly from the data, as described below.
Validation of the noise model and variance stabilization technique.
Because a valid understanding of the noise is fundamental to detection, we explored the sufficiency of Eq. 1 using confocal linescan images of Ca2+ solutions. The mean of all pixels in each linescan was taken as a measure of the signal, and the standard deviation of the difference between adjacent pixels, normalized by division by 21/2, was used as a measure of the noise. This was preferred over simply computing the standard deviation of all pixels because any small unevenness in illumination would inflate the raw standard deviation. To compare results across images, we corrected for μ and α by subtracting a black level value of 4.8 from all pixels, before dividing the result by 1/3 of the value of the gain setting used during image acquisition; these settings were appropriate for our system, for which varying the gain setting has a largely linear effect upon the recorded pixel intensities. A plot of the results is then shown in Fig. 1A, from which one can discern a clear relationship between signal and noise. The line y = x1/2 is also superimposed on the plot. This indicates where the data points would be expected to fall if the noise is Poisson and has a standard deviation equal to the square root of the signal.
Figure 1B then shows the effect of computing the square root transform after applying a 1D Gaussian filter (FWHM = 12 pixels). The horizontal scale is roughly equivalent to the SNR, since it is the square root of the mean normalized pixel value. Most points in Fig. 1B lie on or close to a horizontal line, indicating that the noise has been effectively stabilized, i.e., is independent of the signal. There is some variability, particularly at very low SNRs, probably due to imperfections in estimating μ and the influence of other noise components being more significant when the true signal is very low. However, the stabilization is effective when the SNR is greater than about 2. We therefore conclude that Eq. 1 is a suitable model that describes the majority of the noise apparent in experimental images, and the square root transform of filtered data is appropriate for noise stabilization at the SNRs of interest in images containing Ca2+ sparks, as illustrated for a sample linescan from adjacent myocytes in a retinal arteriole (Fig. 2).
Additional noise features.
Two additional localized features of the noise were identified: 1) isolated bright pixels occur with a frequency greater than would be expected based purely on the Poisson distribution (20), and 2) there is some correlation between the values of spatially adjacent pixels, i.e., pixels scanned consecutively, even when no Ca2+ release event is present.
These observations are linked since several very bright pixels were often seen clustered along the direction of the scan line, as has been observed by others using PMTs (10). If necessary, extreme values may be removed quite easily using 3 × 3 median filtering. To not unduly affect the noise distribution, the median values should only be inserted into the image where bright pixels are found. These locations can be identified, for example, by computing the difference between the raw and median filtered images, and looking for pixels with values > 5× the standard deviation of the difference image.
The correlation between adjacent (consecutively scanned) pixels is a more insidious problem, which may not be initially apparent. Its origins are unclear, but such a relationship may arise as a consequence of the dark noise, minor laser fluctuations, or diffusion of fluorescent molecules when the signal is very weak. We confirmed its existence by calculating Pearson's linear correlation coefficients between adjacent rows and columns respectively of a frame of a representative Ca2+ concentration image (selected as having the SNR closest to the median). We found a correlation along the spatial dimension of 0.119 ± 0.046 (means ± SD), which was much greater than along the temporal dimension (0.002 ± 0.043). We conclude then that even when there is only noise present in the image, there can exist a correlation between the pixels adjacent to one another along the direction of scanning. This relationship is sufficiently small that it can normally be disregarded, but becomes important when using sophisticated techniques that assume the full statistical independence of the noise at each pixel in an image, because one risks underestimating the noise, increasing false positives. For this reason, noise is estimated by our algorithm while assuming the independence of the noise at adjacent pixels along the temporal dimension only.
Spark Detection Using Variance Stabilization
Our spark detection algorithm combines the information from multiple spatial locations by means of a filtering operation, before stabilizing the noise variance using the square root transform and looking for brief fluorescence increases along the temporal dimension. Any background offset (μ in Eq. 1) should be subtracted from every pixel before application of the algorithm.
Preprocessing and variance stabilization.
Filtering is initially applied along the spatial dimension only, using a 1D Gaussian filter hs. When the FWHM of the filter equals that of sparks, this is effectively a matched filter because of the approximately Gaussian spatial profile of sparks (11). If sparks are close together, or if the baseline varies appreciably at different spatial locations (e.g., because of the imaging of multiple cells), a smaller filter FWHM may be preferable to improve variance stabilization or avoid merging events. Each pixel is then replaced by the square root of its filtered value.
Noise estimation.
The noise distribution may now be considered Gaussian. Its standard deviation σ0 can be robustly estimated by taking the median of the absolute difference between all pairs of temporally adjacent pixels in the variance stabilized image, and dividing the result by 0.6745 × 21/2. This is equivalent to using the Haar filters and applying the median absolute deviation of wavelet coefficients estimation strategy described by Donoho (9).
Temporal filtering and baseline subtraction.
The baseline should now be subtracted before detection. A constant background value might be identified as the median of the data, or the iterative spline-fitting approach described by Bray et al. (4) could be used. However, to deal with the possibility of varying baselines while avoiding the high computational requirements of spline fitting, we propose instead to apply two 1D filters along the time dimension of the image: a smaller filter to reduce noise, and a larger filter to suppress brief release events (i.e., sparks) and approximate the baseline. The difference between both filtered images can then be thresholded for detection.
While Gaussian filters might again be used for this purpose, a more efficient algorithm can be defined in terms of the filters commonly used for the 1D IUWT (19). Applied to a signal c0 = f, increasingly smoothed versions are calculated iteratively by convolution with a filter h↑j, i.e., where h = [1, 4, 6, 4, 1]/16 is derived from the cubic B-spline, and h↑j is the upsampled filter obtained by inserting 2j − 1 zeros between each pair of adjacent coefficients of h. The smoothed image cj is equivalent to the result of filtering f using a single, larger filter hj, which may also be calculated iteratively using given that h0 = 1. The iterative algorithm is, however, much faster, because each iteration requires only a filter with 5 non-zero coefficients, whereas hj rapidly becomes large and dense as j increases. Also, for practical purposes, one must then only choose a number of filtering iterations m to apply for noise suppression, and a number of iterations n for baseline generation (n > m). The final image used for detection is the difference between both images, i.e., If n = m + 1, then d is equivalent to a single set of wavelet coefficients generated using the 1D IUWT. Again, there exists a filter ht that could be applied to f to generate d directly, which is (3)
The mean of d is 0, and all non-zero values are either the result of noise or the result of actual variations in fluorescence. If a pixel in d has a sufficiently large absolute value relative to the noise, this indicates that the underlying signal is neither constant nor linear under the support of ht. Because of the use only of linear filters thus far, the noise distribution remains Gaussian. To determine an appropriate threshold to apply, it is desirable then to know the standard deviation σ of the values expected if only noise is present. This is estimated based on σ0 and the filter ht using the equation (4) Significant regions, likely to correspond to sparks, are first identified as connected pixels above a threshold of τ × σ, where τ is an adjustable threshold parameter. In some cases, particularly when using small filters to detect out-of-focus sparks, several regions might exceed the threshold but relate to the same underlying event. Also, above-threshold regions do not necessarily always contain the peak of the event to which they belong. Therefore it is desirable to expand these regions. This is accomplished in the conventional algorithm by increasing the size of each detected region until it is surrounded by pixels below a low threshold, set at 2 estimated noise standard deviations. This approach could also be used here, but we found that applied globally it could cause very large, in-focus sparks to produce excessively large regions. Therefore, we instead defined a lower threshold separately for each potential spark, so that each above-threshold region expanded until surrounded by pixels less than a user-definable percentage of its amplitude (where the amplitude is defined as the maximum pixel value found within the region). For the results here we used 40%, which ensured that detected regions corresponding to sparks were large enough to contain the peaks. These secondary thresholds were also clipped so that none could be higher than τ.
Implementation and Tests
We implemented our algorithm and tests using MATLAB (R2010a, The Mathworks, Natick, MA) and its associated Image Processing Toolbox V7.0 (source code is provided in the Supplemental Material, which is available online at the Journal website). For ease of comparison, we also reimplemented the conventional and à trous algorithms in MATLAB, following the original descriptions and source code given by Cheng et al. (6) and v Wegner et al. (22). The conventional algorithm was used with threshold parameter Cri = 3.8, while the wavelet algorithm used denoising and threshold parameters δ = 4 and τ = 3.75, respectively, with the detection applied to wavelet levels 2–5. These values had been used in the original papers for similarly sized sparks.
Because all tested algorithms output binary images containing spark regions, a detection was defined as a "true positive" if it contained the peak of a real spark and a "false positive" otherwise; if multiple peaks occurred within the region, only one true detection was counted. This necessitated one additional change to be made to the à trous algorithm. A feature of this method is that multiple binary images are produced by thresholding each wavelet level, and previously this information had been combined to give spark regions either by an intersection (i.e., a pixel must be above the threshold on all wavelet levels) (22) or union operation (i.e., a pixel must be above the threshold on any wavelet level) (20). The former approach tends to cause the subdivision of detected sparks into multiple regions—most of which would be considered false positives by our metric—while the reduced requirements of the latter make false detections elsewhere more likely. To improve the fairness of the test, we therefore combined both approaches: spark regions were finally identified using a union operation, but kept only if they contained any pixels that were above the threshold on all wavelet levels. For all algorithms, any detected regions that overlapped image boundaries (where filtered values are less reliable) were always removed first.
Algorithm Sensitivity and Positive Predictive Value
To test algorithm performance on synthetic images (stable baseline), we compared the conventional and à trous detection algorithms with two versions of our approach.
With the VST1 variant, spatial filter FWHM = 8 pixels, smoothing iterations = 3, baseline iterations = 5, and τ = 4.5.
With the VST2 variant, spatial filter FWHM = 12 pixels, smoothing iterations = 4, baseline iterations = 8, and τ = 4.8.
These variations were chosen to show the effects of using relatively conservative filter sizes, such as may be used when the baseline varies considerably (VST 1), and also larger filters, which are more able to optimize detection when the baseline is approximately constant (VST 2). An example image is shown in Fig. 3, alongside the output from different detection algorithms.
Following previous work, we report the sensitivity and positive predictive value (PPV) in each case for different spark amplitudes at different (fixed) SNRs (4, 6, 15, 17, 20, 22). The sensitivity is defined as the probability that an event was detected, calculated as the ratio of the correctly identified events to the total number of sparks present in the images. The PPV gives the probability that a detection corresponds to a true spark, i.e., the number of true positive detections divided by the total number of detections. The ideal value is therefore 1 in each case.
Figure 4 shows the results, in which curves shifted to the left indicate better performance. From this, one can see that the VST 1 algorithm achieves a sensitivity slightly improved over that of the conventional algorithm, along with a more substantially improved PPV. In fact, the VST 1 leads to fewer false positives than both the conventional and à trous algorithms, but the higher sensitivity of the à trous algorithm also raises its PPV. The overall performance of VST 1 in this test may therefore be considered to fall between that of both previous methods.
The parameters used with the VST 2 variant led to clear improvements in both sensitivity and PPV. Indeed, only 9 false positives were produced by the VST 2 in these tests, compared with a total of 25,710 true detections; the low PPV value when the SNR = 2 and amplitude = 0.05 is a result of a single false detection and no true detections in this particular image set. Apart from this outlier, the PPV is then close to one using VST 2 for all other data points. While the sensitivity of the à trous algorithm is slightly higher than VST 2 for very low spark amplitudes, this coincides with a reduction in PPV, so that the greater number of true detections comes at a cost of false positives (in total, the à trous algorithm produced 25,579 true and 336 false detections). The sensitivity curve of VST 2 shows a steeper ascent, however, and reaches its maximum more quickly, which leads to a slightly higher number of detections. The steeper curve of VST 2 may be considered desirable inasmuch as it indicates a sharper cutoff between detectable and undetectable events, and so a greater consistency of detection for events with fixed properties.
Spark Detection at Low SNRs
To assess its performance at low SNRs, we retested the VST algorithm using simulated images with a fixed spark amplitude but varying SNRs, allowing direct comparison with the published performance of the matched filter detection algorithm (see Fig. 3 in Ref. 12). A synthetic spark was generated using the IDL code, and default parameters were provided with the matched filter algorithm. Our algorithm was applied using the VST 1 and VST 2 parameters, except that the FWHM of the Gaussian spatial filters was set to 5 pixels. This corresponded more closely with the FWHM of the much narrower synthetic sparks used in this test, allowing a fairer comparison with the matched filter.
Results for sensitivity and PPV for our algorithm are plotted in Fig. 5. SEN50 and PPV50 are the SNRs at which the algorithm gives half of its maximum sensitivity and PPV, respectively; smaller values are therefore preferable, since this indicates that the algorithm performs better in noisier images. For the conventional algorithm, SEN50 and PPV50 are reported to be ∼1.2 and ∼1.35, respectively, whereas for the matched filter detection algorithm, these are ∼0.8 and ∼0.5, demonstrating significant improvements in both sensitivity and PPV (12). Using VST 1, SEN50 and PPV50 were approximately 1.2 and 0.5, indicating similar sensitivity to the conventional algorithm and similar PPV to the matched filter detection algorithm. However, for VST 2 the SEN50 is ∼0.7, and no meaningful value for PPV50 could be found because the PPV never dips below 50% at any tested SNR. This suggests that the VST algorithm is no less capable of detections at low SNRs compared with the matched filter algorithm when the baseline is constant, while offering a further substantial reduction in the likelihood of false positives.
False Positive Detections in Experimental Images
We wished to test the robustness of the false positive estimates calculated using simulated images, in case the additional sources of noise in experimental images (see above) might degrade performance. Table 1 shows the result of applying the detection algorithms (with the same settings as above) to the Ca2+ concentration images with estimated SNR ≥ 2. These represent experimental linescans in which all detected events are known to be false positives. In all cases, prefiltering (using the simple median-replacing algorithm outlined above) to remove isolated bright pixels helps reduce false detections. It can be seen that the conventional algorithm produces an average of almost one false detection per 512 × 512 pixels, while the à trous algorithm gives slightly more. The VST algorithm is much more robust, with ∼0.1 false positive detections per image when using smaller filters and τ = 4.5, which falls to 0.03 with larger filters and higher threshold τ = 4.8.
Application to Ca2+-Linescan Images From Vascular Smooth Muscle
Figure 6 shows the application of spark detection to a linescan of retinal arteriolar smooth muscle. This represents a more complex type of image than those for which previous spark detection algorithms were designed, depicting multiple cells in which sparks occur both from baseline and superimposed on prolonged Ca2+-oscillations (8). Sparks that have been manually identified by both of two experienced independent observers (T. M. Curtis and J. G. McGeown) are shown in Fig. 6B. The VST algorithm (using similar settings to VST 1, but with spatial filter FWHM = 12 pixels to better correspond to the FWHM of sparks in these images) detected all nine events that were identified manually, in addition to identifying eight further potential sparks. In the final filtered and baseline subtracted image used for detection (Fig. 6C), each pixel has been divided by the estimated noise standard deviation so that it may be effectively seen as a z-score. The detection of an event requires pixels in this image to exceed the threshold, so that from this one can directly ascertain the maximum threshold that would still be low enough to detect any given event. In this case, all manually identified sparks would have been found using τ = 7.5, while all the additional events would have been missed. Given the very low number of false positives expected when τ = 4.5, this suggests that the algorithm is capable of considerably more sensitive detection than is possible by eye, and the additional detections are likely to correspond to true Ca2+ release events. Nevertheless, higher thresholds could be used to increase confidence in the detections yet further, while still maintaining better detection that can be achieved manually.
As previously mentioned, the development of our algorithm was motivated by a need for reliable detection when the baseline varies substantially (spatially and temporally) within an image. We generated a simulated image of arteriolar smooth muscle in which 23 synthetic sparks were added to a varying baseline (Fig. 7A). The sparks all had a fixed absolute amplitude, but the varying baseline changed the relative amplitude, and thus the ease of detection, so that some sparks were difficult to identify even by eye. None of the previously described algorithms produce acceptable results. The changing baseline causes high correlations with the matched filter even when no sparks are present and there is no effective control of false positives, while the thresholding of the conventional and wavelet algorithms are unable to identify the sparks. By contrast, the VST algorithm was capable of detecting all the events, and yielded no false positives (Fig. 7E).
Finally, the results of applying the VST detection to an experimental linescan are shown in Fig. 8. Sparks are detected throughout the image in different cells, despite differences in resting fluorescence within each cell. In the selected traces, one can also see large sparks summating to form longer [Ca2+] elevations, and further release events can be resolved and detected during the decaying phases. Note that some of the increased variability seen during elevated fluorescence (visible particularly in Fig. 7C) is a consequence of the increased noise standard deviation of Poisson noise, and spatiotemporal filtering used to reduce noise might cause these noise variations to resemble potential sparks. For this reason, a detection algorithm that takes into consideration the true noise distribution is required to ensure that only regions in which the fluorescence exceeds this elevated local noise level are detected. The same algorithm parameters were used for Figs. 6–8. Experimental images were preprocessed using the approach described above to remove extreme pixels prior to detection.
Comparison With Other Algorithms
In both the current and conventional algorithms the aim is to reduce noise before thresholding the resultant image to detect sparks. We have attempted to retain the principal advantages of the conventional algorithm while improving its performance and generality. This was achieved by replacing nonlinear normalization and filtering, which can be unduly affected by baseline variability and differences in spark density, with more predictable variance stabilization and linear filtering operations, based on a more robust noise estimate.
Our proposed detection method also shares some similarities with the à trous algorithm (inasmuch as both are based on separable, Gaussian-like filtering) (22) and the matched filter detection algorithm (in that filter sizes should be appropriate for the size of events to be detected) (12). However, in addition to differences in implementation, one may appreciate a more fundamental distinction in these approaches by considering the surface plots of the filters shown in Fig. 9. The matched filter naturally looks most "sparklike" (Fig. 9A). When the filtering is applied, this leads to high correlations with any similar sparks found within the image. However, the shape and relatively large, flat background of the filter mean that it also correlates with non-sparklike changes in fluorescence. This makes it insufficiently selective to cope with background changes. By contrast, the filters associated with the IUWT produce zero correlations not only when the signal is constant under the support of the filter but also if it is linear, and so IUWT filters of an appropriate size for spark detection are less prone to give non-zero correlations because of a more slowly varying baseline. This means that any correlation significantly higher than that expected from noise alone is much more likely to indicate a spark, rather than a baseline change. The à trous algorithm uses the 2D IUWT, which effectively convolves an image with a filter that has a positive, Gaussian-like central component, surrounded by negative values (Fig. 9B). The filter associated with the VST algorithm is based on the 1D IUWT (albeit extended into 2D by the separate Gaussian spatial filter). This has a similar positive central component but is surrounded by negative values only along the temporal dimension (Fig. 9C). In both cases, a significant correlation occurs whenever the weighted sum of all the pixels under the positive central region is sufficiently large relative to the weighted sum of all other surrounding pixels under the filter support. This means that a fluorescence increase along any dimension could potentially lead to a significant correlation using the 2D IUWT, whereas the increase must occur along the time dimension using the 1D IUWT. Consequently, if the baseline is known to be flat, then the 2D IUWT can be preferable because it makes use of the fact that a spark peak is completely surrounded by lower values, but the 1D IUWT is needed to process images such as ours to prevent cell boundaries (e.g., Fig. 6A) from being misclassified as [Ca2+] release events. Nevertheless, we have shown that appropriate settings allow the VST algorithm to achieve similar sensitivity to the à trous algorithm when applied to constant baseline images, while retaining an improved PPV (Fig. 4).
Event detection using our algorithm then results from a rejection of the hypothesis that the (square root of the) signal is either locally constant, or increasing or decreasing linearly along the temporal dimension under the filter support. Clearly, as filter sizes increase this criterion becomes harder to meet if the baseline is not truly constant or many other sparks occur within the filter support, so that when using very large filters there is an increased risk of detecting non-spark-like fluorescence increases (such as the peaks of more prolonged waves or oscillations). Consequently, a balance must be struck between sensitivity and specificity. This means that smaller filters are to be preferred whenever they are applied to images that are very complex, while larger filters can improve sensitivity in simpler images. In any case, the need for a locally constant or linear baseline is clearly less stringent than the requirement of the conventional and matched filter algorithms for a constant baseline everywhere.
Algorithm Parameters and Refinements
Suitable parameters to use with the VST algorithm can be inferred from knowledge of the image and can generally be kept constant for a set of similar linescans. The threshold parameter τ is the simplest to determine. At τ = 5, the likelihood of false positive detections is already extremely low, and so higher values are only really suitable if one observes other phenomena in the image (such as brief, subtle variations in fluorescence or pattern noise) that are smaller than one wishes to detect. The full-width at half maximum (FWHM) of the spatial filter should be less than or equal to the FWHM of a typical spark. The most difficult choice is then the selection of temporal filters, for which several combinations may need to be tried, although the parameter space using our proposed iterative algorithm is rather small. We found that no more than five smoothing iterations should be applied to approximate the baseline for microvascular images, because more resulted in the misclassification of the peaks of longer events as sparks. Although we have used no fewer than three iterations for noise suppression here so as to improve the detection sensitivity of very small events in very noisy images, in practice, two iterations may be preferable for images in which sparks may be seen occurring very close to one another in time. For example, in Fig. 8D a separate, brief rise in fluorescence can be distinguished immediately before the second detected spark, but this was not recognized as a separate event because it was merged with the much larger, subsequent spark during detection. By using only two smoothing iterations instead of three, the events would have been separated.
A benefit of the strategy we have outlined is that it results in a single image to be thresholded for detection, which can be viewed and one can determine by visual inspection whether appropriate settings have been used to detect the type of events that are of interest (see Fig. 3, E and F, and Fig. 6C). However, improved detection of events with more widely varying properties might be achieved by replacing the temporal filtering step with a discrete undecimated or continuous wavelet transform, which effectively allows the detection to be applied using different levels of filtering. Because a 1D transform is applied along the time dimension, one is not limited to the use of the IUWT, the benefits of which are most apparent for the detection of isotropic objects in higher dimensions. The primary cost of this would be an increase in the complexity of the algorithm both in terms of its implementation and use, because multiple images would be used for detection and the information must somehow be recombined to give the final result.
The Control of False Positives
The results in Table 1 show that, when applied to real linescans, one can be more confident that a detected region given by the VST algorithm corresponds to a real spark when compared with a region output by the conventional and à trous algorithms, with the number of false positives reduced by ∼90% or more when using the VST approach. One explanation is that the VST algorithm is more tractable. Because it involves no nonlinear operations other than the square root transform for variance stabilization, the eventual threshold is more simply defined in terms of true noise standard deviations. The algorithm assumes that the noise is essentially Poisson in nature and that the signal is constant (or linear) under the support of the filters used when no event is observed. Under these conditions, event detection requires a pixel to have a value greater than 4.5–5 times the noise standard deviation, which is expected to be a very rare event. On the other hand, the nonlinear normalization and median filtering steps used by the other algorithms, in addition to possible wavelet denoising, mean that the eventual noise distribution is less predictable and the threshold less interpretable.
The high numbers of false positives reported here when using the conventional and à trous algorithm can be reduced in practice by subsequently filtering detections based upon measurements (22). This has not been used here primarily because the outcome of such postprocessing is therefore dependent on the measurement strategy employed [e.g., filtering and threshold crossing (6, 22), or Gaussian and exponential fitting (11)], and, in any case, such postprocessing may be applied to the output of any detection algorithm, including the VST, to improve its performance. Because measurement strategies do not generally take into consideration the noise properties, we consider the goal of the detection algorithm to be to identify regions in which an increase in signal is present, such that the null hypothesis that it is due to noise is rejected. The limitations of low SNRs mean it is likely that events that are too small to accurately measure using current methods can nonetheless be reliably detected.
Detection Bias and Variance Stabilization
Previously, it has been suggested that one may apply spark detection in complex images using global thresholds after baseline subtraction (4, 6). The definition of a global threshold in terms of spark amplitude (ΔF/F0), rather than noise, was justified on the grounds that widely different criteria would be used to identify sparks within the same image if the threshold were adapted to local noise (6). However, using an absolute threshold during detection not only implies that many events can be missed when the baseline fluorescence is low (since a high threshold is required to be suitable when the baseline is elevated), but it affords room to introduce more bias. This occurs because the probability of obtaining a false positive is dependent on the size of the threshold criterion relative to the noise, and so also increases with the baseline. Consequently, one potentially underestimates and overestimates spark frequencies for low and high baselines respectively. Using variance stabilization to effectively adapt thresholds greatly reduces this risk, so that at least the likelihood of false positives remains similar everywhere.
Furthermore, although we have used a straightforward Poisson noise model for simplicity, the method of variance stabilization employed here has been shown to be suitable when an additional source of Gaussian noise is also present in an image (25). Therefore, our algorithm is able to deal with additional read noise, which may occur when using PMTs, but which is more commonly associated with full-frame imaging using conventional charge-coupled device cameras (14). Nevertheless, if one is confident that the noise in an image is similar everywhere, the variance stabilization step can be easily omitted from our algorithm by simply not computing square roots. This may even lead to a slightly improved sensitivity in some cases (since spark profiles are modified by the transform, which may have an effect on their ease of detection). One should, however, apply caution, and possibly raise thresholds accordingly, since the very good PPV reported here is assisted by the variance stabilization, and so any apparent increase in detection numbers from omitting this step should be carefully checked for false positives.
Here, we have described in some detail the issues encountered in automating spark detection, and demonstrated that improvements in detection sensitivity, reliability, and generality do not necessarily require a complex algorithm. We have introduced a detection algorithm that foregoes nonlinear normalization and filtering in favor of a strategy based on separable linear filtering and variance stabilization. By using both synthetic and experimentally acquired images, we have shown that our algorithm allows one not only to achieve similar or better sensitivity to current automated methods, but also to extend the range of images that can be processed automatically while reducing the risk of false positive detections in all cases.
This work was supported by the European Social Fund (to P. Bankhead).
Present addresses: P. Bankhead, Nikon Imaging Center, Bioquant BQ 0004, Im Neuenheimer Feld 267, D-69120 Heidelberg, Germany; C. Norman, Department of Physiology, Naresuan University, Phitsanulok 65000, Thailand.
No conflicts of interest, financial or otherwise, are declared by the author(s).
- Copyright © 2011 the American Physiological Society


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Question: Xara Stores in the United States imports the designer inspired clothes
Xara Stores in the United States imports the designer-inspired clothes it sells from suppliers in China and Brazil. Xara estimates that it will have 45 orders in a year, and it must arrange to transport orders (in less-than-full containers) by container ship with shippers in Hong Kong and Buenos Aires. The shippers Xara uses have a travel time of 32 days from Buenos Aires and 14 days from Hong Kong, and Xara wants its orders to have an average travel time of no more than 21 days. About 10% of the annual orders from the shipper in Hong Kong are damaged, and the shipper in Buenos Aires damages about 4% of all orders annually. Xara wants to receive no more than 6 damaged orders each year. Xara does not want to be dependent on suppliers from just one country, so it wants to receive at least 25% of its orders from each country. It costs $3,700 per order from China and $5,100 per order to ship from Brazil. Xara wants to know how many orders it should ship from each port in order to minimize shipping costs.
a. Formulate a linear programming model for this problem.
b. Solve this model by using graphical analysis.
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a. Formulate a linear programming model for this problem.
b. Solve this model by using graphical analysis.
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Android Tablet
Android Tablet News
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Knock knock
Who's there?
Amos who?
The whole Joke archive. Tons of jokes!
Knock knock
Who's there?
Amos who?
When her son turned eight, the mom knew he would soon be questioning the existence of Santa Claus. One day, the boy looked at his mom and said, "I know something about Santa Claus, the Easter Bunny, and the Tooth Fairy."
Taking a deep breath, his mom responded, "Oh, what is that?"
"They're all nocturnal."
Q: What did the salt say to the pepper?
A: "Season's greetings!"
A teenager needed to wash a sweatshirt. Unsure of the proper water temperature and setting, he hollered to his mom, "What setting do I use on the washing machine?"
"It depends," his mother replied. "What does it say on your shirt?"
"University of Texas."
A man took his six-year-old son to his first football game. Afterward, he asked the boy what he thought of the game.
"It was exciting," he replied. "But I don't understand why they were killing each other for twenty-five cents."
"What do you mean?" the dad asked.
"Well, everyone kept yelling, 'Get the quarter back!'"
Q: Why did the boy take his baseball bat to the library?
A: Because his teacher told him to hit the books.
Q: It can be cracked; it can be made. It can be told; it can be played. What is it?
A: A joke.
Q: What kind of coat can only be put on when wet?
A: A coat of paint.
Q: It is weightless, but it can be seen. Put it in a bucket, and the bucket will be lighter. What is it?
A: A hole.
Q: What can run but never walks, has a mouth but never talks, has a head but never weeps, has a bed but never sleeps?
A: A river.
Q: A woman gave birth to two sons who were born in the same hour of the same day of the same year, but they were not twins. How is this possible?
A: They were two of triplets.
A man entered an ice cream shop and asked, "What flavors of ice cream do you have?"
"Vanilla, chocolate, strawberry," the girl wheezed as she spoke, patted her throat, and seemed unable to continue.
"Do you have laryngitis?" the man asked sympathetically.
"No," the girl whispered. "Just vanilla, chocolate, and strawberry."
Q: What's big, red, and flies?
A: A fire truck.
You kno the expression make it rain? It means to throw money on ho's. I made my own expression. Make it hail. I throw change at sluts. Theyll be like " Stop, ow. wahy r u doin this! Wat r these nickels?!? Why r u doing this?!?" Ima b like "Cause ima baller on a budget bitch!!!"
There were three blondes on an island and they didn't know how to go home. A genie came along and granted each of them a wish.
The first blonde said: "I want to be smart enough to get off the island." So she swam back home.
The second blonde said: "I want to be smarter than the first blonde!" So she built a boat and went home.
Then, when the genie asked what the third blonde wanted, she said: "I want to be smarter than all of them!"
So she walked across the bridge!


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Firsts To Look Forward To!
What are you looking forward to most, firsts wise? ;)
Crawling, first words, standing, walking (and falling over), eating new foods, first summer holiday, anything else?
I'm just glad to be off work until at least summer so I get to see all Michaela's. I think I missed out on a lot with Daniel going back to work when he was 4 months old.
Re: Firsts To Look Forward To!
First words!
Going to the cottage and seeing him play in the water, go in the boat, and general stuff like that that I did as a kid.
Not looking forward to him being mobile! Right now I can put him down and walk away to go pee. Not sure how this is going to work after!
Re: Firsts To Look Forward To!
Kisses. I loved the first time Nathan decided to kiss us.
Re: Firsts To Look Forward To!
Looking forward to his first of everything. His first roll over was so exciting. He looks ready to crawl now. So, let's see!
Re: Firsts To Look Forward To!
First words! (Better be mama lol)
I'm also super excited to get her in the pool! And even out on a beach in the sand :)
Call me Debbie downer but honestly none of it, lol. I loveeeee the newborn phase so each time he hits a new milestone I'm sad and wish it would all slow down. He's sitting up unassisted and I'm like begging him to let me cradle hold him like a little tiny baby. Haha.
All of them. From his first word to his first steps. I want to see them all.
Sent from my DROID BIONIC using Forum Runner
Re: Firsts To Look Forward To!
Looking forward to each and every one, probably most eager about his first words. :D
Re: Firsts To Look Forward To!
All of them, but right now I'm especially looking forward to first words!
She rolled for the first time today!
Re: Firsts To Look Forward To!
First words especially and I cannot wait to see how she reacts to everything at Walt Disney World.
Re: Firsts To Look Forward To!
With my other 2 I hated the crawling phase!
Cetch will already say "Mama". I don't think he knows what it means yet but he does know it gets a reaction. Cetch lOVES to be in the kitchen if people are in there. He is afraid he will miss something if he is in the living room. So if DH is holding him and sitting down in the living room and I'm in the kitchen Cetch will yell "mama". He knows this will either get me to come back to him or DH will bring him to the kitchen area.
Re: Firsts To Look Forward To!
First words for obvious reasons considering my first has never spoken and my second didn't talk till she was two. Really hoping Paxton talks earlier than that. After that I'd say sitting up. I love love love it when they sit and study/play with toys. I think it's the cutest thing ever.
Re: Firsts To Look Forward To!
First words!
Re: Firsts To Look Forward To!
First car ride withOUT her screaming her head off! :glare: :lol:
I'm torn too, she is my last baby so I will never get to experience baby things again but I am looking forward to her sitting up and her first steps.
Re: Firsts To Look Forward To!
for Micah to say mama and know what it means
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Currently only 3% of all the money in circulation is created by the Bank of England. This 3% consists of coins and banknotes. The remaining 97% is numbers in bank accounts. These bank deposits are created by commercial (i.e. high-street) banks, through some simple accounting, when they make loans. Find out how banks create money here.
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Angela Bofill
|Funk R&B / Soul
|1970s 1980s 1990s 2000s
|discogs official homepage VIAF wikidata wikipedia
1954, West Bronx, New York City, New York, USA, Bofill began writing songs at the age of 12. She formed her first group while still at high school, the Puerto Rican Supremes, who performed in church and at local school dances. Her father, a Latin music singer, had once sung with Cuban band leader Machito. After graduating from the Manhattan School of Music, she toured with Ricardo Morrero and recorded her first single, My Friend, which earned her a nomination as best Latin female vocalist from Latin New York magazine. Encouraged by this success, she embarked on a solo career, writing and performing the jazz suite Under The Moon And Over The Sky in conjunction with the Brooklyn Academy of Music. She then became lead vocalist with the Dance Theater of Harlem Chorus, and performed alongside Stan Getz and Benny Goodman at Madison Square Garden.
Following an introduction by Dave Valentin, she was signed as a soloist with...


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Question: The famous Geico gecko commercial humanized a gecko by using
The famous Geico gecko commercial humanized a gecko by using personification. The gecko was given a cockney British accent and explained how cheap Geico insurance would be. Explain how this ad illustrates allegorical presentation in advertising.
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Answer to relevant QuestionsWhen discussing exposure as the initial stage of information processing, it was claimed that gaining exposure is a necessary but insufficient condition for success. Explain. There are three general strategies for changing attitudes. Explain each, using, for illustration, consumers' attitudes toward a fast-food chain of your choice (McDonald's, Burger King, KFC, etc.). Applying the principles of the Integrated Information Response Model (see Figure 8.3), which response pattern did they use—high involvement, low involvement, or continual switching? Present arguments for and against using advertising agencies. Are there lessons to learn from the experiences of Alex Bogusky (CP+B), featured in the Marcom Insight? Select two advertising campaigns that have been on television for some time. Describe in detail what you think their creative message styles are.
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