text:
The Casino Portfolio
In 2004 a British gambler by the name of Ashley Revell sold everything he had, did what he could to raise additional funds and eventually cobbled together a total of $135,000. In a highly publicized gambit, 32-year-old Revell flew to Las Vegas, went downtown to the Plaza Hotel & Casino and bet it all on a single spin of the roulette wheel. He wagered on red. Considering the zero and double-zero, he was getting less than even money and was clearly reckless. The ball spun around the wheel, bumped in and out of a few slots and miraculously landed on a red 7. Revell doubled his money, and—much to the chagrin of management at the Plaza—did not make another bet. Instead, he used the money to finance an online poker site, pokerutd.com. It launched in the U.K. (where online poker is legal) and remains in business today.
Things worked out for Revell. But suggesting that you risk it all on a spin of the wheel is patently bad advice. However, there is reasonably good advice for people who are inclined to gamble and might be looking for an alternative to alternative investing. After all, the stock market has been fairly lackluster for the last dozen years (as this story closes, the S&P 500 hovers around its March 1999 high). Considering that, what would happen if you took the money that you would ordinarily use for speculating the market and instead invested it in forms of advantageous gambling?
With tutelage, discipline and proper money management, it's possible to turn a long-term profit at sports betting, blackjack, poker and fantasy sports. Of course, such an endeavor is not easy and requires work, but for certain people—those who are inclined to gamble anyway—it's not necessarily a bad idea. "Gambling is an interesting way to make money, but it's more of a part-time job than an investment," says Aaron Brown, a risk manager at AQR Capital Management and author of The Poker Face of Wall Street, a memoir that includes details on his days playing high-stakes poker. On the upside, he adds, "The feeling you get from gambling where you have an edge and you are the house is great. It is completely different from playing a game where you expect to lose."
He points out, though, that there are certain personality types who will be likelier to succeed than others. "You want to be the person who gets energized when things go against you and the kind of person who keeps an even keel when things are going well." In other words, once you learn how to beat the gambling game, you have to beat the mental game—the one that can easily derail you from following the proper strategies or making the right bets due to short-term fluctuations. Brown cautions that those who do possess the proper qualities are a limited breed: "I'd say that 80 percent of the people who [attempt to become advantage players] don't do the work to learn the game and they don't have any kind of shot." Of the 20 percent who develop the skills, estimates Brown, 80 percent of them lack the proper money management abilities. "The inevitable downswings are hard to deal with psychologically. You need to have faith in yourself. That requires rare skills. It's why casinos earn money."
But if you can make it work, it's a fabulous way to generate extra income, which can easily surpass the 8 or 9 percent that Wall Street historically returns for investors each year. Besides the money, there is also the lifestyle that comes with being a frequent gambler of elevating stakes. As cheekily put by Nathaniel Tilton, a financial advisor who managed to successfully crack the secrets of blackjack, "One of the major perks of casino gambling is that hosts are always giving you free cigars. Conversely, when you invest on Wall Street, whether you make money or lose it, nobody sends you to shows and buys you dinner."
Nathaniel Tilton, financial advisor by day and casino-crushing card-counter on nights and weekends (he's elusive as to whether or not he remains in the gambling trade), serves as living proof that you can keep a day job, learn to count cards and live the life of a professional gambler when it suits you. Tilton, whose recently published memoir is entitled The Blackjack Life, backs up Brown's belief that gambling part-time cannot be a halfway enterprise. "You need to go all in and be completely devoted to doing everything right," Tilton says. "You need to be perfectly bankrolled and perfect each step of your play. The advantage you strive for is so small that if you give it up on any level, it's hard to win." It should be noted that he and his partner, identified in the book as D.A., started with $15,000 and initially ranged their bets between $30 and $180.
Perfect card-counters play at maybe a 1.25 percent edge on money churned through per hour. So if your average bet is $100 and you can get in 100 hands per hour, your expected return is $125 per hour over a long period of time. In the short run, however, profits and losses can be all over the chart. Therefore, Tilton explains, "You need the fortitude to know that the results will be there as long as you can apply the craft over and over again."
Tilton and D.A. learned to play by taking private lessons from MIT blackjack team superstars Mike Aponte and Semyon Dukach (on separate occasions). Before they played a single hand in the casino, Tilton and D.A. put themselves through rigorous practice sessions and tests. First they learned basic strategy, which is the standard way of playing every possible hand and it brings the casino advantage to below 0.5 percent. Once that was rock-solid, they turned to card counting. (It involves watching all the cards that are dealt and giving values of 1, 0, or negative 1 to each exposed card. This tells you how advantageous the coming hand will be and dictates the amount that you bet.) And finally, they memorized over 100 deviations to basic strategy that are based on the count. For example, you would not normally double down on 9 vs. the dealer's 2. But if the count is 1 or higher, Tilton and his partner would. Perfect all of that, employ techniques to mask that you are card counting (Tilton outlines a variety of these in his book) and you're off to the races.
During their first year, Tilton and D.A. doubled their bankroll. Within three years, supplemented by small infusions of their own money, they built it up to six figures and raised their betting spread along the way. Despite the success and the fact that they could have ramped up the hours and maybe gone fulltime at blackjack, they always maintained their day jobs. Tilton viewed blackjack as a profitable hobby that made life interesting and, thanks to a plethora of casino comps, allowed him to live fatter than he ordinarily could. "You get to go away on weekends, stay in nice hotels, get great fight tickets and challenge your mind," he says. "There is something nice about leaving behind your everyday life on a Friday afternoon and entering a new world at McCarron Airport where a limo driver waits for you. The experience of that is indescribable."
Fantasy sports are popular. So popular that the Fantasy Sports Trade Association estimates 32 million people played in 2010, creating a $3 to $4 billion impact across professional sports. On the surface, fantasy football sounds pretty simple: a dozen or so guys get together, kick in a predetermined amount of money and draft NFL players onto fantasy teams. You get points based on each player's performance. Whoever has the most points at the end of the season wins the money. It doesn't matter whether or not a particular team wins (though that can help); it matters how the individuals that you have drafted perform. The majority of people involved in fantasy football view it as an adjunct to their regular betting and figure that they'll win if they get lucky. Chad Schroeder, who's taken down close to a million dollars over the last five years of playing fantasy sports, views it more like stock investing. He plays in multiple leagues and puts together what he hopes will be a balanced portfolio with some investments that have high-risk/high-reward components built into them.
The first thing he points out is that there are different kinds of leagues and money needs to be allocated in a smart way. Some leagues comprise just 12 participants; they're relatively easy to beat, but your return on investment is limited. Other leagues are composed of hundreds of participants; acing one of these events will require a good team that has been specifically designed to win big or die trying, and a fair bit of luck, but the payoff can be well into the six figures. If, let's say, you have $12,000 allocated for fantasy football, here's how Schroeder suggests investing the money: Spend $2,500 on entering a high-stakes national contest, put $1,050 into three national contests that have buy-ins of $350 each, and $1,500 into one more national contest. The remaining $4,950, he says, should be allocated for 12-man leagues with entry fees that range from $500 to $1,000. He suggests playing via the sites myffpc.com, nffc.stats.com and fulltimefantasy.com.
In terms of putting together teams, Schroeder recommends a two-tiered approach. For your 12-man leagues, go the blue-chip route and look for a strong, balanced portfolio of players. That is, he suggests spending your draft allocation (each team starts out with a set amount of theoretical money that can be applied toward buying players, just like in the real NFL) in a quest for value. Just as you shouldn't overpay for Google simply because it's Google, nor should you do the same with an NFL star. "Let's say I like Ryan Matthews," says Schroeder. "And let's say he normally goes in the middle of the second round. Even if I am targeting him, I will not push him up to the beginning of the second round."
When picking players for the big, national contests, Schroeder recognizes that the upside can be tremendous, and for that reason he suggests forgetting about balance. Think of it as playing the World Series of Poker main event, going for a win, and recognizing the need to have an aggressive end game. It's a swing-for-the-fences approach where you increase your likelihood of either flaming out miserably or having a heroic shot for the gold. To achieve that, he says, "Focus on choosing explosive players."
They either soar or fizzle. "Fast players, like Mike Wallace, are the ones who I consider to be explosive; but they're also streaky," says Schroeder. "They run a lot of deep patterns and you never know when they will be connecting." If you go deep in a big fantasy league, "the hope is that they will be connecting in week 14. It gets really competitive, and you need to take the kinds of educated gambles that allow you to keep scoring points. You can enhance that"—but also increase the risk—"by having a wide receiver and quarterback who play together. It's a way of doubling up."
Sometimes, Schroeder points out, the best advice is the most obvious: "Too often, people get hung up on worrying about match ups and how somebody did the week before. I simply go with the players who I think will score the most points and don't worry so much about short-term results or who they're going up against." In other words, he explains, "Avoid over-thinking things and making them more complicated than they are."
On a Friday evening in the Bellagio, card-playing guru David Sklansky sips from a cup of coffee and discusses what it takes for somebody to systematically learn to win at the game of poker. One of the first things he points out is that you need to find a game that you can beat. Often that means looking beyond standbys such as Texas hold'em. The goal, explains Sklansky, would be to get good at a game that hasn't become so popular that everybody else has already figured it out. To that end, he suggests focusing on seven-card stud. It's spread in many casinos but never grew into the media darling that hold'em is. Hence, it lacks the massive following that leads college kids to, as Sklansky puts it, "learn to play the game in a completely unexploitable way."
Luke Kim, who graduated from Wharton and worked for an investment firm before turning to poker full-time, spent years as a winning player who supplemented his 9-to-5 income with proceeds from the game. Though Kim says that good players can still find value in live ring games of Texas hold'em, he went the Sklansky route once he decided to play for a living. It was at a time when online poker was thriving in the United States and, rather than trying the popular six-handed variant, Kim
focused almost exclusively on playing heads up.
He taught himself by getting lessons from the game's most adept practitioners. "I railed the best players online," he says. "I recognized who won the most money and watched how they did it before I played a single hand. I ramped up my aggression and looseness levels to what the best players were doing, even if it seemed unnatural. The style worked for winning players, even when they didn't have hands. I learned that, on the whole, the best players play aggressively. In poker, to be aggressive is good." Kim got his lessons via PokerStars.com, a website that is no longer open to Americans. Those who want to learn by watching can now sign up with smaller sites like CarbonPoker.com, where Americans are can play and observe in order to glean tips from more experienced players.
And while online poker is currently a darker shade of gray than it was two years ago (the biggest potential downside is that you'll deposit money and have a hard time collecting your winnings), Kim advises to keep the deposit as small as $100, play for low stakes, and view the whole thing as a learning experience for live stud games in the casinos or online after it is legalized in America. "When you play online, it's easy to analyze your play and put in a lot of hand volume; it can take just a few months for you to get good," says Kim. And if you don't get your money out? Kim answers with a question: "How many things can you get good at for just $100? Think of how much it would cost for you to get good at golf."
Once you do start playing, think of poker as an entrepreneurial undertaking—one in which you strike out in regions where the competition is soft. And remember the importance of operating within your budget. "I never like to have more than 10-percent of my bankroll at risk at any given time," he says. "You have to remember that no matter how big your edge, you will lose some percentage of the time." But if your luck curve happens to start in the opposite direction, advises Kim, keep it in perspective. "I made $20,000 during my first month of playing $20/$40 poker," he remembers. "I figured that I would make a quarter-million dollars per year, no problem. But then I learned that things don't work that way."
Is it possible to make money by betting sports? Of course. An elite coterie of gamblers earns serious cash by outfoxing bookies around the world. How do they do it? "They devote their lives to it. It's a full-time job," says Anthony Curtis who already has a full-time job in the publishing industry. "Most people, on the other hand, don't know what they're doing, and they bet into what are essentially even money propositions, laying $11 for every $10 that they can win [that 10 percent is the vigorish or betting commission paid to the bookmaker for taking your action]. The number has been hammered into place, to where it is supposed to be. An average guy can't win over time."
But, Curtis points out, he and some friends have found a way to make money without doing a whiff of handicapping. He calls it shopping for numbers. I prefer the catchier scavenger betting. Either way, it's simple to implement, and they did it for much of the last football season. (Due to the time commitment, Curtis is not sure that he'll be doing it in a serious way this year.) "You look for lines that are out of whack with the rest of the marketplace," he says, suggesting that you can use pinnacle.com (an offshore sports betting site that takes no business from Americans but allows us to look at its rock-solid point-spreads) as a benchmark. "If somebody hasn't gotten the memo, you can take advantage of them."
Best of all, you don't need to know anything special about the sport. You're just looking for bookmakers who are either desperate to balance their books or incongruent with the rest of the world. "We'd make bets on things like Swedish handball or Aussie rules football. There was no handicapping or second-guessing," says Curtis. "It was only about finding inefficiencies.
During the NFL play-offs, we'd have six or seven prop bets per game. For example: Will Manning throw over or under a certain number of interceptions?"
Hearing this, I make the mistake of figuring that it would be virtually impossible to predict the number of interceptions thrown. Curtis replies, "You have it backwards. You want to believe that the market as a whole does know, and they can put a really good number on it. Then you find the guy who doesn't have that number. You do a ton of shopping and make a ton of bets, and if you like this sort of thing it's a lot of fun."
Curtis estimates that his edge was 3 to 7 percent, and he figures that a starting bankroll of $10,000 is plenty. Like most gambling, though, this can be swingy, so you need to be willing to withstand losses without altering your strategy.
Additionally, he says, the continual hunt for fresh betting outlets can become a grind, and you want good reasons to believe that you will not get stiffed by the many bookies you must invariably deal with. On the upside, it's a lot slower—and therefore financially safer—than day-trading or casino gambling, and the edge is strong enough that you can make some mistakes and still maintain an advantage. "Plus," he says, "it's a hell of a lot of fun, you get to sweat all these games and you have the best of it. It's the hobbyist's way of beating gambling, and I know it works."
Michael Kaplan is a Cigar Aficionado contributing editor.


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The 1880 census lists my great-grandmother, Elka Rosenbaum, as living in
Wood with her several children. Her husband, Simon, died in NY in 1874. The
youngest child was in utero. We have never been able to identify the
"boarders" and assume a mistake was made identifying them. My ggm died in
1884 and is buried in NY. Some time after that the older brothers opened a
store in Hillsboro and they all moved there. My greatuncle, Isaac, always
claimed he was born in "Lickskillet, Panola County, TX". My grandmother,
Ella, was born in 1872. Thank you for your help.
Paula Zieselman, NYC

subset: commoncrawl


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Hey there everyone!
I thought I'd share a little inspiration you!. Whenever we're prepping for new releases or a show on HSN, I try to take a little time to play around with the products. I always love to mix and match between product lines to see how well they work together. Our goal is always to create the best possible products for you that can stand on their own AND be mixed and matched.
I chose to work with 'Sweatshirt Saturday' from our Saturated Canary line. She is gently watercolored and offset with deep shadows.
The image was die cut using the 'Fancy Squares & Little Things' die set. It is then mounted on another die cut square from the same set. I cut the top of the tag shape with the special half-die from the same die set. TIP OF THE DAY: The half-die is also a great die for easy easel cards!
The gorgeous butterfly banner in the corner is from the 'Dotted Wings' die set. The set comes with extra little butterfly dies that are sized perfectly for use as 3D embellishments on your projects.....just bend the wings in a bit and add a little dot of glue! They're small enough for you to be able to add several to your projects for amazing detail. I added a few to my project and they are just lovely!!!
Both the 'Fancy Squares & Little Things' and 'Dotted Wings' die sets are currently available in kits with SPECIAL PRICING at HSN. Both kits include gorgeous coordinating embossing folders. Quantities are limited so get yours while supplies last!!!
I further accessorized the tag with our delicate 'Gilded Doily' die in the upper right corner, backed by a couple of flags from the 'Basic Stitched Flags' die set.
Lastly, I created a stunning, detailed edge to the tag using a piece cut from the 'Majestic Frames' die set. This die set sold out very quickly on HSN back in March, but we're bringing it back again this summer! Stay tuned to LDRS Creative for new release details!!!!!!
I really hope you've enjoyed my project. I had a lot of fun making it. I hope you can see how easily our products can all be used together to create the most beautiful of projects.
Wishing you all a wonderful day!


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- Methodology article
- Open Access
Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching
© Slawski et al.; licensee BioMed Central Ltd. 2012
- Received: 6 March 2012
- Accepted: 27 October 2012
- Published: 8 November 2012
The robust identification of isotope patterns originating from peptides being analyzed through mass spectrometry (MS) is often significantly hampered by noise artifacts and the interference of overlapping patterns arising e.g. from post-translational modifications. As the classification of the recorded data points into either 'noise' or 'signal' lies at the very root of essentially every proteomic application, the quality of the automated processing of mass spectra can significantly influence the way the data might be interpreted within a given biological context.
We propose non-negative least squares/non-negative least absolute deviation regression to fit a raw spectrum by templates imitating isotope patterns. In a carefully designed validation scheme, we show that the method exhibits excellent performance in pattern picking. It is demonstrated that the method is able to disentangle complicated overlaps of patterns.
We find that regularization is not necessary to prevent overfitting and that thresholding is an effective and user-friendly way to perform feature selection. The proposed method avoids problems inherent in regularization-based approaches, comes with a set of well-interpretable parameters whose default configuration is shown to generalize well without the need for fine-tuning, and is applicable to spectra of different platforms. The R package IPPD implements the method and is available from the Bioconductor platform (http://bioconductor.fhcrc.org/help/bioc-views/devel/bioc/html/IPPD.html).
- Regularization Parameter
- Lasso
- Manual Annotation
- Isotope Pattern
- Cholesky Factorization
Mass spectrometry (MS), often in conjunction with high performance liquid chromatography (HPLC), is the de-facto standard analytical tool to derive important biological knowledge about the protein content of whole cells, organelles, or biomedical samples like tumour or blood plasma. Within a typical experimental setup, purified proteins of the sample under study are digested by an enzyme. Before entering the mass spectrometer, peptides are separated chromatographically according to their physico-chemical properties in order to avoid a massive overlapping of peptide signals within a single scan. Nevertheless, due to the sheer number of peptides present in a sample, interfering patterns still occur frequently, not least because of post-translational modifications such as the deamidation of asparagines or glutamine residues. In order to obtain an unambiguous assignment of the signals, and in particular their isotope patterns, which is a prerequisite for a proper identification and quantification, every data point in m/zdimension is classified either as 'signal' or as 'noise' during the so-called feature detection phase. As this processing lies at the very root of every proteomic application, the quality of feature detection can have dramatic impact on the finally derived results and conclusions. In view of the large amount of data even a single MS experiment can produce, automated analysis is indispensable. However, due to various artifacts arising from electric and chemical noise and baseline trends, the identification of isotope patterns is error-prone and time consuming. In addition, severe overlaps of peptide signals within the same mass spectrometric scan can hamper a straightforward analysis furthermore. In recent years, numerous procedures have been developed to process this data (cf., e.g., [1–8]). Within this paper, we propose a novel method that is demonstrated to perform especially well in challenging situations, characterized e.g. by strong local variations in noise and intensity levels or the presence of isotope patterns of different charges exhibiting overlap, which in many cases may be difficult to resolve even for a human expert by visual inspection. Existing software typically depends on a large set of parameters requiring careful fine-tuning, often being rather sensitive to changes in the measurement process like the change of the platform, which makes a proper parameter choice a laboursome task. In contrast, the proposed method has been designed to depend on a comparatively small set of well-interpretable parameters whose default configuration is shown to be robust, yielding mostly excellent, but at least competitive performance on spectra of different platforms. In a nutshell, our method uses non-negative least squares or non-negative least absolute deviation regression to fit a spectrum s by a large dictionary of templates mimicking isotope patterns; since true positions and charges of isotope patterns in the spectrum are unknown in advance, regions where the signal exceeds a local measure of noise are identified and then a vast set of templates is placed in those regions. In the spirit of sparse recovery, a small subset of the templates, which reasonably explains the observed signal, is selected by applying hard thresholding with a locally adaptive choice of the threshold to the regression coefficients obtained previously. Our method is related to a formerly proposed template-based approach (NITPICK, [3]). As opposed to the present work, NITPICK uses ℓ1-regularized non-negative least squares. Without non-negativity constraints, this procedure is known as the lasso [9]. Reference [10] contains the first application of the lasso to the problem studied in the present paper. Given a dramatic increase in occurrence of high-dimensional datasets in recent years and the resulting need for feature selection, the lasso, due to computationally and theoretically appealing properties, has meanwhile become so popular that it can be regarded as a standard tool of modern data analysis [11]. In this respect, NITPICK follows the usual paradigm suggesting that ℓ1-regularization is the method of choice. In the present paper, we argue for a deviation from that paradigm mainly in view of the following two aspects. First, a major benefit of our fitting+thresholding approach is that parameter choice is more user-friendly, since the threshold can be interpreted in terms of a signal-to-noise ratio. This is unlike the regularization parameter of the lasso, which can in general not be related directly to the signal. In the presence of heterogeneous noise and model misspecifications, the 'right amount' of regularization is notoriously difficult to choose. Second, there is a substantial body of work showing that non-negativity constraints alone may suffice to recover a sparse target. Non-negative least squares + thresholding is analyzed in [12], where it is shown that it can significantly outperform the usual ℓ1-approach with respect to sparse recovery. See Section "Sparse recovery with non-negativity constraints: non-negative least squares + thresholding vs. the non-negative lasso" for a detailed discussion.
A spectrum is understood as a sequence of pairs , where x i =m i /z i is a mass (m i , measured in Dalton Da) to charge (z i ), and y i is the intensity, i.e. the abundance of a particular mass (modulo charge state), observed at x i , i=1,…,n, which are assumed to be ordered increasingly.
Template model
Parameter estimation
The parameters θc,j=(αc,jσc,jμc,j)⊤of the peaks (3) are unknown in practice. Following a central paradigm of our framework, which is to relieve the user of performing laboursome fine-tuning of parameters, we have developed a systematic procedure automatically providing estimates of these parameters, which is considerably more efficient and flexible than a grid search. For instance, the parameters may additionally depend on the m/z-position. Our framework for parameter estimation extends a conceptually similar approach in [18] designed for a Gaussian peak shape.
We refrain from using least squares regression to determine the parameters in (5) due to its sensitivity to possible outliers, which arise from poorly resolved, wiggly or overlapping isotope patterns, which may affect the quality of the estimates . Therefore, the linear model is fitted in a robust way by using least absolute deviation regression. Given the resulting estimates of the parameters {νl,m}, m/z-specific estimates for the parameters in (3) are obtained by evaluating (5).
Template fitting
The optimization problem (7) is a quadratic (q=2) or linear (q=1) program and is solved using interior point methods (e.g. [20]). The details are relegated to Appendix "Fitting with non-negativity constraints" section. As far as the choice of q is concerned, we point out that q=1 yields a robust fit that can deal better with deviations from model assumptions, i.e. deviations from the averagine model or from the peak model. However, in general, we are unable to provide any recommendation about how to choose q. Therefore, in our validation, both are evaluated.
Comparison with pepex
In prior work [21], subsequently referred to as 'pepex', non-negative least squares fitting is used as well. An important difference to our approach is that the matrix Φ is not constructed from the convolution of isotope distributions and peak shapes as described in Section "Template model". Instead, peak detection is applied first to reduce the raw intensity data to peak clusters, a step that is usually referred to as centroiding. At the second stage, called de-isotoping, peak clusters are fitted by a design matrix containing isotope distributions themselves, not convolved versions. While the approach is computationally more attractive and avoids estimation of peak shape parameters (cf. Section "Parameter estimation"), the division into centroiding and de-isotoping may lead to poor performance for low resolution and noisy data, or in the presence of overlapping patterns. In these cases, peak detection is little reliable. In our template-based approach, there is no separation of centroiding and de-isotoping. It performs much better in the aforementioned cases, since it operates directly on the data and is hence less affected if single peaks of a pattern are difficult to detect. This reasoning is supported by our evaluation in Section "Results and discussion" as well as that in [3]. At the same time, our approach can in principle be applied to centroided spectra as well. In this case, the columns of the matrix Φ directly represent isotope distributions instead of isotopic patterns.
Postprocessing and thresholding
- 1.
Separately for each c, divide the sets into groups of 'adjacent' positions. Positions are said to be adjacent if their distance on the m/z scale is below a certain tolerance ppm specified in parts-per-million, cf. Section "Finding a set of default parameters". In the context of 'peak splitting', the templates at locations sharing the same group are assumed to fit precisely one true underlying peak.
- 2.With the notation of Eq. (2), for each c=1,…,C, and for g=1,…,G c , we solve the following optimization problem.(8)
- 3.
One ends up with sets and coefficients .
The additional benefit of solving (8) in step two as compared to the selection of the template with the largest coefficient within each group as proposed in [3] is that we are able to determine the location of the pattern even more accurately as predetermined by a limited sampling rate, since in (8) we optimize the location over a continuum. The optimization problem (8) can be solved fast and accurately by sampling the integrand on a fine grid of points and then solving a nonlinear least squares problem with optimization variables mc,g and βc,g.
The idea underlying this procedure is that in noise regions, the fit to the data will be poor, and consequently, the size of the residuals is expected to be large relative to the signal, hence leading to a low goodness-of-fit statistic. The truncation at 0.5 limits the influence of this correction. A final list is generated by checking whether the signal-to-noise ratios (9) exceed a 'significance threshold' t specified by the user. We do not give a general guideline for choosing t, because a reasonable choice is very specific to experimental conditions, e.g. the platform used and the composition of the spectrum. It is important to note that while t itself is constant, we take into account that the noise level is heterogeneous, since thresholding is based on the ratios (9), where the local noise level enters.
Finding a set of default parameters
Apart from the signal-to-noise threshold t, we have introduced the parameters window, i.e. the width h of the sliding window required for the computation of the local noise level (6), the template placement parameter factor.place and the parts-per-million tolerance ppm within which peaks are considered to be merged by the postprocessing procedure. With the exception of the threshold t, we have fixed all parameters to a default setting which we expect to give reasonable (albeit potentially suboptimal) results on spectra different from the ones analyzed here, without the need of manual tuning. In order to find such a default setting, we performed a grid search using only one selected spectrum of those described in Section "Datasets" below. While our default setting, which can be found in the HTML manual of the R package IPPD, already performs well, we recommend to do such a calibration to optimize the performance of our method.
Sparse recovery with non-negativity constraints: non-negative least squares + thresholding vs. the non-negative lasso
We believe that our preference for the first alternative is a major methodological contribution that has potential to impact related problems where non-negativity problems come into play. In the present section, we provide, at a high level, a series of arguments rooting in the statistics and signal processing literature that clarify our contribution and support our preference.
Linear models and usual paradigms in statistics
which corresponds to model (1), where '≈' is used instead of '=' to account for stochastic noise or model misspecifications. Linear models of the form (10) have been and continue to be objects of central interest in statistical modelling.
Classical work in statistics shows that under mild conditions if the number of sample n grows at a faster rate than the number of features p, the ordinary least squares estimator (in probability) as n→∞.
Since many contemporary datasets, like the MS datasets of the present paper, are characterized by a large p, which is of the same order as n or even larger, the first bullet has considerably lost relevance. Translated to MS datasets, it provides a statement about the case where the resolution tends to infinity. Therefore, modern statistical theory studies regimes in which p is allowed to grow at a faster rate than n, with a focus on results that hold for finite sample sizes. These results hinge on some sort of sparsity assumption on β∗, the simplest being that β∗is zero except for some index set (support) of small cardinality. In this context, a multitude of results has been proved (see e.g. [23] for an overview) indicating that the lasso estimate is a statistically optimal procedure in the sense that if the regularization parameter is chosen in the right way, the squared Euclidean distance is nearly of the same order as that of an estimator one could construct if the non-zeroes of β∗were known.
The second bullet provides quite some justification for NITPICK, which is based on the lasso. However, as detailed below, the italicized part can be critical. On the other hand, there are several results that support our approach.
The power of non-negativity constraints
It turns out that the non-negativity constraint β≥0imposed in non-negative least squares (NNLS) may lead to a drastically better performance than that of the ordinary least squares estimator in 'large p' situations provided Φsatisfies additional conditions. Roughly speaking, it is shown in [12] that if Φhas non-negative entries, which is fulfilled for the template matching problem of Section "Template model", the NNLS estimator does not overfit and is unique even in the singular case (p>n). These results indicate that NNLS may behave surprisingly well in a high-dimensional setup, without using ℓ1-regularization, which is often propagated in the literature as basically the only option ([24], Section 16.2.2).
There are several recent papers [25–27] in the sparse recovery literature in which it is shown that a sparse, non-negative vector can be recovered from few linear measurements n≪p. In [12], these results are extended to a noisy setup. More specifically, it is shown that NNLS + thresholding can consistently recover β∗and its support. Very recently, using similar conditions as in [12], Meinshausen [28] has established several guarantees of NNLS in a high-dimensional setup.
One should bear in mind that the non-negativity constraints are essential for our approach. Thresholding the unconstrained ordinary least squares estimator in general leads to poor results in the 'large p' situation.
Shortcomings of ℓ1-regularization in theory
In [12], it is not only shown that NNLS + thresholding is a sound strategy to perform sparse recovery of a non-negative target, but also examples are given where the non-negative lasso is outperformed even if its regularization parameter is set to match theoretical results and regardless of whether subsequent thresholding as advocated in [29, 30] is used or not. In particular, inferiority of the lasso arises in the presence of small, yet significantly non-zero entries in β∗. These are specifically affected by the non-negligible bias of ℓ1-regularization [31]. It is important to note that the comparison in [12] does not contradict prior comparisons of the lasso (aka soft thresholding) and (hard) thresholding for orthonormal designs (Φ⊤Φ=I) in [32, 33], where both approaches perform similarly well and non-negativity constraints are not particularly important. Orthonormal designs, which lead to greatly simplified estimation problem are not of interest in the context of the paper, since the template matrix Φis far from being orthonormal.
Shortcomings of ℓ1-regularization in practice
The study in [12] is of more theoretical nature, since all constants of the problem, in particular the noise level, are known, so that the regularization parameter can be set in an optimal fashion. This can realistically not be accomplished in practice. Likewise, the information-theoretic criterion employed in [3] as well as the data-splitting approach of [34] rely on knowledge of the noise level, or a consistent estimate thereof, which is hard to obtain in the 'large p' situation [35]. In any case, the regularization parameter remains a quantity that is hard to grasp and hence hard to set for a practitioner, since it cannot be related directly to the signal. In contrast, the threshold t admits a straightforward interpretation.
Moreover, when using ℓ1-regularization, data fitting and model selection are coupled. While this is often regarded as advantage, since model selection is performed automatically, we think that it is preferable to have a clear separation between data fitting and model selection, which is a feature of our approach. Prior to thresholding, the output of our fitting approach gives rise to a ranking which we obtain without the necessity to specify any parameter. Selection is completely based on a single fit simply by letting the the threshold vary. On the contrary, if one wants to reduce the number of features selected by the lasso, one resets the regularization parameter and solves a new optimization problem. Note that it is in general not possible to compute the entire solution path of the lasso [22] for the MS datasets used for the present paper, where the dimension of Φ is in the ten thousands so that the active set algorithm of [22] is prohibitively slow. In this regard, model selection by thresholding is computationally more attractive.
Results and discussion
For the assessment of the pattern picking performance, in total eight spectra generated by two different ionization methods, matrix assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI), respectively, form the basis of the evaluation. While MALDI has been coupled to a time-of-flight (TOF) mass analyzer, ESI MS spectra have been recorded on both a linear ion trap (LTQ) and an Orbitrap mass analyzer. In addition, a series of spectra were prepared with the aim of investigating in detail the method's performance in the presence of overlapping peptides.
For MALDI mass spectra (Additional file 1), time of flight mass analysis was performed; spectra were recorded on an ABI MALDI-TOF/TOF 4800 instrument in positive ion mode using α-cyano-4-hydroxy-cinnamic acid (CHCA) as matrix. Nanospray ESI spectra (Additional file 2) were measured in positive ion mode on a Thermo LTQ Orbitrap Velos MS; both high resolution measurements using the Orbitrap mass analyzer (referred to as 'Orbitrap') and, alternatively, low resolution linear ion trap (IT) measurements were performed with this setup. This experiment has been chosen in order to demonstrate the utility of our method at different concentration levels, that it is robust with respect to changes in the data-generating process and that the method is capable of handling singly charged ions, the main form generated by MALDI MS, as well as higher charged ions formed in ESI MS. Tryptic digests (performed in 40 mM ammonium bicarbonate) of model proteins were used as analytes: bovine myoglobin and chicken egg lysozyme (10 and 500 fmol each) for MALDI-TOF experiments, and lysozyme (250 and 1000 fmol) for ESI experiments. Disulfide bonds were reduced with dithiothreitol (DTT) prior to alkylation, free cysteine residues were alkylated by iodacetamide. No further sample pretreatment was performed prior to MS analysis. When referring to these spectra, we omit that tryptic digests are given: e.g., the term 'MALDI-TOF myoglobin spectrum (500 fmol)' means the respective tryptic digest.
To demonstrate explicitly the method's ability to separate strongly overlapping patterns even in the case of badly resolved signals, 22 additional spectra have been generated in positive ion mode on a Bruker Daltonics HCT Ultra Ion Trap MS with an electrospray ion source. Three synthetic peptides (cf. Section "Unmixing of overlaps" for details) with sequences corresponding to tryptic peptides from bovine serum albumin (BSA) were used as analytes. In each measurement two out of three peptides were mixed in different ratios to get overlapping peptide signals, also with different charge states. Two different concentrations (500 fmol/μ l and 1000 fmol/μ l) were injected into the mass spectrometer via a Cole-Parmer syringe pump.
Validation strategy
Validation of pattern picking is notoriously difficult, because a gold standard which is satisfactory from both statistical and biological points of view is missing. In this context, a major problem one has to account for is that spectra frequently contain patterns whose shape is not distinguishable from those of peptides, but which are in fact various artifacts resulting e.g. from impurities during sample preparation and measurement. These artifacts do not constitute biologically relevant information and are, in this sense, 'false positives'. An important instance are signals derived from the matrix (or from matrix-clusters) frequently observed in MALDI MS. The pattern of these signals is similar to that of peptides; nevertheless, due to their molecular composition, which differs significantly from that of an average peptide, the exact masses can be used to exclude these signals from the data analysis. On the other hand, from a statistical perspective which judges a method according to how well it is able to detect specific patterns in a given dataset, a qualification as 'true positive' is justified. With the aim to unify these aspects, we have worked out a dual validation scheme. In order to reduce the number of artifacts, all automatically generated lists of candidates for peptide masses as well as the lists of a human expert (see below) are postprocessed by a peptide mass filter [36]: only peptides whose monoisotopic mass deviated less than 200 ppm from the closest peptide mass centera are used for subsequent evaluation.
Comparison with manual annotation
The first part investigates how well a method is able to support a human expert who annotates the spectra manually. More specifically, the automatically generated lists are matched to the manual annotation such that an entry of the list (potential peptide mass) is declared 'true positive' whenever there is a corresponding mass in the manual annotation deviating by no more than Δ ppm. Otherwise, it is declared 'false positive'. In order to adapt Δppm to the resolution of the different mass lines, we used the following strategy: assuming that most of the peptides will have a mass larger than 700 Da, we determined the spacing Δm/z between neighboring data points in m/zdirection for each mass spectrum in the lower mass range. If we further assume that a simple manual annotation by visual inspection can result in a mass deviation from the 'correct' mass position of at most Δm/z, we can derive the following tolerance values: Δ=100 ppm for ion trap recordings, Δ=50 ppm in the case of MALDI-TOF recordingsb and Δ=20ppm for Orbitrap data.
As the performance of our as well as those of all competing methods depends on a threshold-like parameter governing, crudely speaking, the trade-off between precision and recall, we explore the performance for a range of reasonable parameter values, instead of fixing an (arbitrary) value, which we believe to be little meaningful. The results are then visualized as ROC curve, in which each point in the (Recall, Precision)-plane corresponds to a specific choice of the parameter. Formally, we introduce binary variables {B i (t)} for each mass i contained in the list of cardinality when setting the threshold equal to t, where B i (t)equals 1 if the mass is matched and 0 otherwise, and denote by L the number of masses of the manual annotation. The true positive rate (recall, R), and the positive predictive value (precision, P) associated with threshold t are then defined by , . An ROC curve results from a sequence of pairs {R(t),P(t)}for varying t.
Database query
The second part evaluates the lists in terms of a query to the Mascot search engine [37], version 2.2.04. In particular, we account for a major problem of a manual annotation, namely that peptides yielding weak MS signals might easily be overlooked, but might be detected by methods designed to extract those weak signals. Since we are especially interested in demonstrating the method's ability to separate overlapping patterns, we adapted the standard search parameters of Mascot's peptide mass fingerprint routine to allow two missed cleavage sites and to incorporate the following (variable) post-translational modifications: 'Oxidation (M)', 'Carbamidomethyl (C)', 'Amidated (Protein C-term)', 'Deamidated (NQ)'. In particular, the latter two modifications will frequently trigger MS signals interleaving with the pattern of their unmodified counterpart: in the case of a deamidation the modified ion shows a mass of approx. 0.98 Da more compared to the amidated peptide. The same mass tolerances as for the manual annotation are used. As for the comparison with the manual annotation, we evaluate several lists corresponding to different choices of the threshold. Instead of an ROC curve, which turned out to be visually unpleasant, we display the statistics (score, coverage and fraction of hits) of two lists per method, namely of those achieving the best score and the best coverage, respectively. The complete set of results as well as further details of our evaluation like the manual annotation are contained in Additional file 3.
Competing methods
We compare our method in its two variants depending on the choice of the fitting criterion (cf. Eq. (7)), labelled l1(q=1) and l2(q=2), respectively, with the following competing methods.
where W is a diagonal matrix with entries wc,j=LNL+ (mc,j), j=1,…,p c , c=1,…,C, whose purpose is to re-scale the amount of ℓ1-regularization according to the local noise level. The columns of the template matrix Φ in (8)). The parameter λhere plays the role of the threshold t, cf. Section "Validation strategy".
As discussed in Section "Template fitting", pepex performs centroiding and de-isotoping separately. De-isotoping is based on non-negative least squares. Since pepex is limited to detect patterns of charge state one, its performance is only assessed for MALDI-TOF spectra. Accordingly, when comparing the ouptput of pepex with the manual annotation, the few patterns of charge state two are excluded. The parameters nm, pft, mincd, maxcd and nsam were set to optimize performance with respect to manual annotation. The ROC curves are based on peaklists resulting from ten different choices of the signal-to-noise parameter snr.
Isotope wavelet
As opposed to our method, this approach is not able to handle overlaps. On the other hand, it typically shows strong performance in noisy and low intensity regions or on datasets with extremely low concentrations [39, 40]. While the isotope wavelet is not limited to charge one, it is run in charge one only mode for the MALDI-TOF spectra, to achieve more competitive performance. For the sake of fair of comparison, the result of the isotope wavelet on the MALDI-TOF spectra are evaluated in the same way as those of pepex.
The parameter setting for the ABI MALDI-TOF/TOF MS software was as follows: Local Noise Width (m/z) 250, Min Peak Width at FWHM 2.9. The Cluster Area Optimization S/N threshold has been dynamically adapted to about three times the S/N threshold as suggested by the ABI documentation. Since the vendor software is limited to charge one, its outputs are evaluated in the same way as those of pepex. Given the disproportionally high effort needed to find an optimal parameter setting of the vendor software for ESI spectra, its performance is not assessed.
Manual annotation vs. database query
Mascot results
MALDI Myo 500 fmol
l 1
l 2
MALDI Lys 500 fmol
l 1
l 2
MALDI Myo 10 fmol
l 1
l 2
MALDI Lys 10 fmol
l 1
l 2
Orbi Lys 1000 fmol
l 1
l 2
IT Lys 1000 fmol
l 1
l 2
Orbi Lys 250 fmol
l 1
l 2
IT Lys 250fmol
l 1
l 2
Figure 4 and Table 1 reveal an excellent performance of our methods (l1 and l2) throughout all MALDI-TOF spectra under consideration. For the myoglobin spectra high sequence coverages are attained that clearly stand above those of competing methods. For the spectra at 10 fmol, only the performance of lasso is competetive with that of our methods in terms of the Mascot score; all other competitors, including the vendor software which has been tailored to process these spectra, are significantly weaker. In particular, the strikingly high proportion of 'hits' (≥94%) indicates that even at moderate concentration levels, our methods still distinguish well between signal and noise. This observation is strongly supported by the ROC curves in Figure 4, where the precision drops comparatively slowly with increasing recall. In this regard, our methodology clearly contrasts with approaches like the isotope wavelet that aim at achieving high protein sequence coverage. The latter often requires the selection of extremely lowly abundant peptide signals hidden in noise at the expense of reduced specificity.
For MALDI-TOF spectra at high concentration levels, pepex achieves the best scores and is competitive with respect to sequence coverage. However, the performance of pepex degrades dramatically at lower concentration levels, as it is unambiguously shown by both parts of the evaluation. In particular, the database scores are the worst among all methods compared. This provides some support for our reasoning at the end of Section "Template fitting".
For the ESI spectra, our methods in total fall a bit short of the lasso (particularly for the ion trap spectra), but perform convincingly as well, thereby demonstrating that they can deal well with multiple charge states. This is an important finding, since the presence of multiple charges makes the sparse recovery problem as formulated in model (1) much more challenging, because the number of parameters to be estimated as well as the correlations across templates are increased. In spite of these difficulties, Figure 5 and Table 1 suggest that the performance of our pure fitting approach (7) does not appear to be affected. Using a more difficult set of spectra, the capability to process ESI data with impressive success is additionally shown in the next section.
Additional remarks
In Figure 4, the area under the curve (AUC) of our methods attained for myoglobin is higher for lower concentration. At first glance, this may seem contradictory since an increase in concentration should lead to a simplified problem. However, a direct comparison of the AUCs is problematic, since the number of true positives (17 at 10 fmol, 106 at 500 fmol) is rather different. For instance, there are choices of the threshold that yield 18 true positives and not a single false positive for both of our methods at 500 fmol, yet the AUC is lower.
The fact that some of the ROCs start in the lower left corner results from outputs containing only false positives.
Unmixing of overlaps
One of the main advantages of our method over more simplistic pattern picking methods is the ability to disentangle isotope patterns of overlapping peptide signals, whose presence may lead to a significantly more challening pattern picking problem as e.g. discussed in [41] in the slightly different context of intact protein mass spectra. Therefore, a potential application for our approach will be the analysis of a certain class of posttranslational modifications, the deamidation of amino acid residues containing a carboxamide side chain functionality. The deamidation of asparagine (Asn) or glutamine (Gln) residues, yielding aspartic acid (Asp) or glutamic acid (Glu) residues, respectively, is an important posttranslational modification, which can have immense effects on the structure of peptides [42] and is of great relevance in a number of pathophysiological events [43]. During the deamidation, the side chain carboxamide is hydrolysed, which is accompanied by a mass increase of 0.98 Da. Thus, in a spectrum of a mixture of the amidated and deamidated form, a direct overlap of both signals can be observed. It has to be noted that additionally to the amidated/deamidated forms, in case of Asn deamidation, a second product containing an iso-peptide bond is formed, too, which has the same molecular behaviour; these two forms can be identified solely by their differential MS/MS behavior.
Peptides mixed together
sequence residueno. in BSA
monoisotopic mass(protonated) / charge
351.20437 / +2
351.48816 / +3
352.20850 / +2
Unmixing of overlaps
We have proposed a template matching approach for feature extraction in proteomic mass spectra. The main methodological innovation is a framework for sparse recovery in which sparsity is not promoted explicitly by a regularization term, as it is usually done and was done in previous work. We fully exploit the strength of non-negativity constraints, which permits us to circumvent the delicate choice of a 'proper' amount of regularization, an ever-lasting problem in statistics, and to work with thresholding instead. The latter is not only computationally attractive, because one does not have to repeatedly solve the same optimization problem for different choices of the regularization parameter, but also increases user-friendliness, since the threshold is directly related to the signal-to-noise ratio, the quantity domain experts are interested in. The replacement of a regularization parameter by a threshold is a cornerstone in our conceptual design guided by the principle to relieve the user from laboursome fine tuning of parameters. We believe that a small set of well-interpretable parameters with suitable defaults additionally improves robustness and reproducibility of results. In this context, we would like to emphasize again that apart from the threshold, the user does not have to specify any parameters before running our software.
In a comprehensive experimental study involving instruments of varying resolution and spectra of varying concentration levels, where we comparatively assess the performance of our approach on the basis of an elaborate dual validation scheme, it is demonstrated that the performance for pattern picking is excellent for MALDI-TOF spectra and outstands due to its specificity in selecting signal and only little noise. A major strength of the method is its ability to unmix overlapping peptide signals as shown for a series of ESI spectra. In total, we demonstrate that our approach is broadly applicable to a variety of spectra. While our approach is guided by a concrete application in proteomics, the framework is general enough to be of much of use for related deconvolution problems emerging in other fields − only the templates have to be adjusted according to the specific application.
While in this paper, we have focused on single spectra, the approach can be extended to process whole LC-MS runs, as it has already been implemented in our R package IPPD. More precisely, the sweep line scheme of [44] is used to agglomerate the results from single spectra. To apply our methods on a routine basis, an improved implementation, notably parallelization, is required, since e.g. processing a single spectrum of the Maxquant datasets [2] takes 10s on average on a Unix system equipped with an Intel(R) Core(TM)2 Duo CPU T9400 (2.53GHz) and 4 GB main memory. There is much room for an improvement, since our implementation is based on interpreted R code.
Concerning future directions of research, a question we have not yet answered in a satisfactory way is the choice of the fitting criterion. While both criteria (least squares and least absolute deviation) employed in this paper perform well, their implicit assumption of additive noise might be questionable [45]. It is worth investigating whether a multiplicative noise model could even yield better results. Second, one might ask whether the performance could be further improved when it is used jointly with the isotope wavelet, which is affected by overlaps, but has the potential to achieve higher protein sequence coverage.
aMonoisotopic peptide mass centers are modelled by: 1.000485·m n + 0.029, where m n denotes the nominal mass.bFor the MALDI-TOF lysozyme datasets an extended search tolerance of 100ppm was applied due to experimental miscalibration of the MS.
Fitting with non-negativity constraints
In the following, we provide the details concerning optimization problem (7). In view of the special structure of Φ, (7) is computationally tractable even if n and the number of templates are in the ten thousands. We exploit the sparsity of the problem arising from templates which are highly localized, i.e. the domain on which they are numerically different from zero covers only a small part of the whole m/z range of the spectrum. As a consequence both Φand the Gram matrix Φ⊤Φ, which is crucial in the computation, can conveniently be handled by using software for sparse matrices. For R, such software is available in the Matrix package [46].
Non-negative least squares
Solution of linear systems of this structure constitutes the main computational effort to be made. Fast solutions are obtained by using CHOLMOD[47], which offers an efficient implementation for computing the Cholesky factorization of sparse symmetric, positive definite matrices. Since the diagonal of changes from one Newton iteration to the next, one Cholesky factorization has to be performed per Newton step. Once we have solved (14) for a specific γ, we solve a new problem of the type (14) for γ·M, M>1. This is repeated until γexceeds a predefined maximum value. For a thorough account on the log-barrier method, we refer to [20].
Complexity analysis of non-negative least squares
We here provide the order of magnitude of floating points operations (flops) required per update (i.e. per Newton step) for the specific non-negative least squares problems considered for this paper. In our implementation, we exploit that the templates contained in the matrix Φare highly localized. As a result, after a suitable column permutation, the matrix Φ⊤Φ is roughly a band matrix with bandwidth k no larger than only few hundreds. The dominant operation is solving the linear system with the help of the Cholesky factorization, which can be done in O(p k2)flops (e.g. [20], p.670). Our algorithm terminates after usually no more than one hundred Newton steps.
Non-negative least absolute deviation
In order to solve the linear system, we proceed as for non-negative least squares. The computational cost of this operation is roughly the same, since the sparse structure of Φ⊤Φcan still be exploited. For non-negative least squares, re-computation of the Hessian only involves a diagonal update, an operation of negligible computational cost. However, for non-negative least absolute deviation, computation involves the matrix multiplication (Φ⊤([Ξ+ ]−2 + [Ξ−]−2)Φ), i.e. essentially a repeated computation of a scaled Gram matrix. In spite of the special structure of Φ⊤Φ, the computational cost is of the same order as the solution of the linear system even when using a self-written routine for matrix multiplication tailored to the specific structure.
The authors would like to thank Markus Martin for setting up the Bruker Daltonics HCT Ultra Ion Trap MS and Bart van den Berg for measuring the LC-MS datasets used in the vignette of the R package IPPD. We thank Fredrik Levander and Thorsteinn Rognvaldsson for providing us the pepex implementation. We thank the reviewers and an associate editor whose comments and suggestions led to a substantial improvement over previous drafts.
Clusters of Excellence 'Multimodal Computing and Interaction' (to M.S., R.H. and B.G.), 'Inflammation@Interfaces' (to A.T. and T.J.) within the Excellence Initiative of the German Federal Government; DFG (grants BIZ4:1-4 to R.H. and A.H.).
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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Ratings & Reviews
Judy Holliday re-creates her Broadway role of flibbertigibbet telephone operator Ella Peterson in Bells are Ringing. Ella works for Susanswerphone, a hole-in-the-wall answering service run by her cousin Sue (Jean Stapleton). Our girl Ella can't help but become involved in the lives of her customers, which brings her to the attention of a dimwitted police detective, Barnes (Dort Clark), who suspects that Susanswerphone is a front for a house of ill repute. The cop is so obtuse that he never notices the story's genuine criminal, a flamboyant German bookie (Eddie Foy Jr.) who poses as a record executive and uses the names of composers as code for the various racetracks around the country. To avoid Barnes' wiretapping, Ella goes around New York in person to minister to the needs of her clients--most notably playwright Jeffrey Moss (Dean Martin), who is in danger of becoming an alcoholic if he can't come up with a good idea for a play. Assuming a false identity, Ella prattles on about some of her other clients, notably a dentist (Bernie West) who composes pop songs on his air hose. Moss is inspired by Ella, and eventually falls in love with her. Because she will not reveal who she really is to Jeffrey, Ella decides that her relationship is founded on lies, and walks out of his life. But Moss, together with the other Susanswerphone customers who have been "rescued" by Ella, show up at Ella's doorstep for a happy ending. Bells are Ringing is not an example of MGM's Arthur Freed unit at its best, but Judy Holliday is luminescent in this, her last screen role (incidentally, Holliday's "blind date" in one scene is played by her then boyfriend, jazz musician Gerry Mulligan). The film's songs, by Betty Comden, Adolph Green and Jule Styne, include the hit numbers "Just in Time" and "The Party's Over". ~ Hal Erickson, Rovi
Cast & Crew
- Judy Holliday - Ella Peterson
- Dean Martin - Jeffrey Moss
- Fred Clark - Larry Hastings
- Eddie Foy, Jr. - J. Otto Prantz
- Jean Stapleton - Sue


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OziRacer needed a new banner ad design and created a contest on 99designs.
A winner was selected from 45 designs submitted by 10 freelance designers.
Flinders View Estate
I own a development company that specialises in developing property in growth markets around Australia. We need an advert designed to highlight our latest development, Flinders View Estate located in Port Augusta in South Australia
This is a 26 lot sub division that offers a house and land package with unique 'Rural Lifestyle Lots'... These lots are '10 times bigger' than a normal house lot, so this is very important to promote in the new advert.
The people we are targeting are young couples about to buy their first home or existing families looking to 'up-size' from their existing homes for more room for their growing families.
For the first time buyers we want to highlight that for the same price as a house lot in town they can buy one of ours thats 10 times bigger only 5 minutes drive from the town centre.
For the up-sizers we want to promote that we have House & Landpackages ready to go that offer them a cost effective housing solution. Our houses included everything from large sheds, water tanks, landscaping, so its a complete solution.
We want the main theme to be the fact that we are offering 'rural lifestyle' lots as a better alternative to buying a town lot.
Our development is unique where it offers large 1 hectare lots to buyers that are 10 times bigger than a in town lots. The development is also located only 5 minutes outside the Port Augusta CBD, so in regional terms its down the road..
The advert will be a havl page advert in the local newspaper and I want it to be practical showing the site plan, as well as examples of the houses we are building.
I also want all the fluffy shots to get potential buyers dreaming about owning one (Kids with horses, happy young couple, ect)
I also want to promote that we have a builder and promote that we have a complete house & land package available. I can provide high def images of their houses.
I want the site plan to feature in the advert ( email email@example.com to receive this)... I also want to show what lots are sold.
- I have a mocked up logo, please re-create this to suit your design
- I want headings to capture interest like: 'Complete House & Land packages starting as low as $330,000' or 'Buy land only and build your dream home
- I will email you a version of the artwork that will give you an idea on what we are after.


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Come to Drakeshire for New Years Eve!
Still don't have any plans for New Year's Eve? If you're looking for something the whole family can enjoy during the day or somewhere to hang out after dark and watch the ball drop, Drakeshire Lanes has you covered on both ends of the spectrum!
Bring the family in on Tuesday, December 31st, as we will be hosting a New Year's Eve party from 11:00 am until 1:00 pm. The party costs $13 per person and for the price of one ticket you will get to enjoy:
- Two hours of unlimited bowling (bowling shoes included)
- Pizza and pop
- Party favors
- Prizes
Five people will be accepted per lane. All money must be paid in full by the day before the party – December 30th.
Later on in the evening for the adults, we will be hosting our New Years Eve Rock & Glow Bowl Party! Tickets cost $80 per couple. Once you check in at 7:00 pm, the entertainment for the night includes:
- Dinner at 7:30 pm
- Three games of no-tap bowling starting at 9:00 pm
- Mystery games
- A champagne toast at midnight
- Party favors
A grand prize of $300 will be awarded to the couple that finishes with the highest combined bowling score (based on 40 couples). There is a $40 non-refundable deposit due at the time of reservation. The balance is due by December 29th.
Don't just sit at home on New Year's Eve, come out to Drakeshire Lanes at 35000 Grand River Ave. in Farmington Hills for our two exciting parties! Contact us today or call us at 248-478-2230 to reserve your spot!


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So I recieved my MAC Antiqutease goodies today – good good, I am still waiting for the pigment sets to become availble online and the sparkle eyeliners.
I have a look on the US website and find the set of 5 pencils cost….tah dah….$29.50.
On the UK website it costs……..£24.50.
Oh hello. Only yesterday I am sure they said on the News that the pound is stronger than ever against the dollar, and is currently at $2.11 to £1.
So if I were to fly to the US this set would cost me £13.97! Uh-huh. There is something like a £10 difference there. I find it sort of outrageous – yeah yeah, things balance out but still, I do think it is a rip off when you charge so much more in the UK than you do everywhere else.
Oh nothing really, just a little moan. And no, buying from the US can also be costly, cos shipping and dreaded customs charges who these days have eagle eyes. Oh yeah, I get charged on an item which is over £18, even by 10p and the worst thing is that the customs charge may be fair, but the handling fee of £10 is just unacceaptable.
Time to get a best buddy in the USA I say!


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Pidgin is an instant messaging program for Windows, Linux, BSD, and other Unixes. You can talk to your friends using AIM, ICQ, Jabber/XMPP, MSN Messenger, Yahoo!, Bonjour, Gadu-Gadu, IRC, Novell GroupWise Messenger, QQ, Lotus Sametime, SILC, SIMPLE, and Zephyr.
Pidgin can log in to multiple accounts on multiple IM networks simultaneously. This means that you can be chatting with friends on AIM, talking to a friend on Yahoo Messenger, and sitting in an IRC channel all at the same time.
Pidgin 2.10.3
Pidgin homepage


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Skip Left Navigation
County Home
Code Enforcement Home
Yuba County Code Enforcement awards all abatement
contracts through a competitive bidding process.
If you are interested in
submitting a bid for any upcoming abatements please contact our office
for more details.
contact CE staff
CDSA General Comments/Contact
©2007 County of Yuba, CA


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(c) Pocumtuck Valley Memorial Association, Deerfield MA. All rights reserved.
Contact us for information about using this image.
The Temperance Movement began in the early 1800s and by 1833 there were 6,000 local societies in the United States. The town of Rowe, Massachusetts, held a meeting of the Temperance Society on July 4, 1835. This article reports that there are few, if any, intemperate people in town owing to the fact that there had been no tavern there for the past 30 years. Greenfield also held a meeting on July 4. Many temperance speakers, like the Rev. T. Strong, were ministers. The Greenfield Gazette and Franklin Herald was the newspaper in Greenfield, Massachusetts, from June 26, 1827 to June 27, 1837. It changed its name to the Gazette & Mercury.
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See Also...
"Popery-Temperance" article from the Greenfield Gazette and Franklin Herald newspaper
"5 Men Shot in the Streets of Monson!!!"
The Constitution of the Deerfield Temperance Society
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