text:
Wayne Dyer
Wayne Dyer, PhD, is an internationally renowned author and speaker in the field of self-development. He's the author of over 30 books, has created many audio programs and videos, and has appeared on thousands of television and radio shows including Oprah and The Tonight Show. He's perhaps best known for his work Your Erroneous Zones, the top-selling book of the 1970s (35 million copies sold).
Much of his earlier work was focused solely on more traditional self-improvement, but he switched in the 1990s to areas of self-actualization. His books Manifest Your Destiny, Wisdom of the Ages, There's a Spiritual Solution to Every Problem, and the New York Times bestsellers 10 Secrets for Success and Inner Peace, The Power of Intention, Inspiration, Change Your Thoughts—Change Your Life, and Excuses Begone have all been featured as National Public Television specials.
Dyer holds a Doctorate in Educational Counseling from Wayne State University and was an associate professor at St. John's University in New York. He is affectionately called the "father of motivation" by his fans. Despite his childhood spent in orphanages and foster homes, Dr. Dyer has overcome many obstacles to make his dreams come true. Today he spends much of his time showing others how to do the same.
When he's not traveling the globe delivering his uplifting message, Wayne is writing from his home in Maui.


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Laurent Polito Wins 2012 PokerStars.com EPT Barcelona €10,000 High Roller
After three days of play in the 2012 PokerStars.com European Poker Tour Barcelona €10,000 High Roller, France's Laurent Polito has emerged victorious. Polito defeated a field of 111 entries to take home the title, the Shamballa Jewels bracelet and €270,229 in prize money.
Season 9 EPT Barcelona €10,000 High Roller Final Table Results
|Laurent Polito
|Alex Bilokur
|Tobias Reinkemeier
|Stanislav Labutkin
|Carlos Mora
|Jean-Noel Thorel
|Bertrand Grospellier
|Joao Vieira
*Denotes two-player deal
On Thursday at noon, the €10,000 High Roller kicked off and a star-studded field of 101 players came out for action. After 10 players opted to use their one-time reload options, the generated prize pool turned out to be €1,087,800. Players such as Team PokerStars Pros Jason Mercier, Jonathan Duhamel, Pius Heinz, Angel Guillen and Bertrand "ElkY" Grospellier were in the mix along with Scott Seiver, Philipp Gruissem and Dan Smith.
After a shortened schedule so that players could jet off to the Barcelona versus Real Madrid soccer match, 76 players remained. Taking the early chip lead was Taylor Paur as he built his starting stack of 50,000 up to 220,400 in chips, proving to be the only player able to finish over the 200,000-chip barrier.
On Friday's Day 2, the field needed to be cut down to the money, meaning it would be a long day. Of those to fall throughout the action were Mike Watson, Bryn Kenney, Andrey Pateychuk and Peter Jetten. Aforementioned Team Pros Mercier and Guillen were also eliminated on Day 2 with Guillen going first and Mercier hitting the rail just a few spots short of the money.
Mercier was eliminated by Andrew Lichtenberger with the blinds at 3,000/6,000/1,000. After Lichtenberger opened to 15,000 from the cutoff seat, Mercier reraised to 37,000 on the button. Lichtenberger four-bet to 62,000 and Mercier moved all-in. Lichtenberger called holding the . Mercier was at risk with . The board ran out and Mercier was out the door.
JC Alvarado was eliminated in 14th place and that put the tournament on the bubble. After a couple of bubble hands, Grzegorz Derkowski found himself all in with the versus the for Polito. A board of spelled the end for Derkowski and the 12 remaining players hit the money and the end of the day.
Finishing on top of the final 12 players was Tobias Reinkemeier with 714,000 in chips. He was joined on Day 3 by Team Pros Grospellier and Duhamel, along with Kristijonas Andrulis, Lichtenberger and PCA $25,000 High Roller champion Alex Bilokur.
Entering Day 3, Polito began third in chips behind Reinkemeier and Bilokur. Together, the three of them made it down to three-handed play before Reinkemeier fell in third place, setting the stage for a heads-up clinic put on by Polito.
When the heads-up match began, Polito was down nearly one million in chips, but that didn't deter him from taking over the match. First, a deal was struck between the two whereby Bilokur would take home €295,451 and Polito €270,229, based on the chip counts. Left to play for was the title, the trophy and the Shamballa bracelet.
Nearly the entire match went Polito's way as he constantly had the upper or a better hand against Bilokur, especially at the showdown. Bilokur just couldn't get anything going and Polito stole the show by winning pot after pot. After taking the chip lead, Polito continued to take chunks from Bilokur before the final hand came up.
The final hand summed up the entire heads-up match altogether, as it was Polito who held the better of it when Bilokur put the money in. Bilokur moved all in with pocket nines, but Polito held two queens. No help came for Bilokur on the flop, turn or river as the board ran out and he was eliminated in second place.
Before winning this event, the largest score of Polito's career was a third-place finish in a €550 Winamax Poker Tour event for €64,000. With over $450,000 in live tournament earnings on his record, Polito now has that distinguishable major tournament victory to help solidify his status.
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subset: commoncrawl


text:
The church in Ephesus had many good qualities, yet they were lacking in one significant area: their love for Jesus Christ. They actively served Him. They taught biblical Truth. They refused to give in to the permissive culture around them. They endured hardships in the name of Christ. They were busy, committed workers for the kingdom.
Jesus commends them for their hard labor and endurance: "I know your deeds, your hard work and your perseverance. I know that you cannot tolerate wicked men, that you have tested those who claim to be apostles but are not, and have found them false. You have persevered and have endured hardships for my name, and have not grown weary" (Revelation 2:2, 3).
Yet, in their sincere efforts to serve Christ, they forgot to spend time loving and adoring Him. They overlooked their prayer time with Him. They no longer worshiped God wholeheartedly. They were so distracted and bogged down by the work of God, they neglected their relationship with Him. Jesus warns them: "Yet I hold this against you: You have forsaken your first love" (Revelation 2:4).
Yes, Jesus wants our obedience. He wants to see us actively serving Him. He wants us to follow the Truth and shun false teachings. He wants us to remain dedicated to Him even in the face of persecution. However, upholding the Truth cannot replace our love for Him.
Throughout the Bible we read of God's love for us—and He wants us to return that love with the same fervor and intensity. When the Pharisees asked Jesus what was the greatest commandment, Jesus replied, "'Love the Lord your God with all your heart and with all your soul and with all your mind.' This is the first and greatest commandment" (Matthew 22:37, 38).
Believing the right things and doing the right things are vital components of our Christian faith—we must continue walking in the Truth. But loving God must be at the root of everything.
How often do you stop to adore Jesus? Do you wait for Sunday worship service because you're just too busy during the week? Do you neglect spending time reading His love letter to humanity because you're consumed with projects and commitments? Are you allowing the busyness of the work of God to supplant your relationship with Him?
If you have let your relationship with Christ fade, follow His words to the Ephesians: "Remember the height from which you have fallen! Repent and do the things you did at first. If you do not repent, I will come to you and remove your lampstand from its place. He who has an ear, let him hear what the Spirit says to the churches. To him who overcomes, I will give the right to eat from the tree of life, which is in the paradise of God" (Revelation 2:5, 7).
If you have ceased to love Him like you did when you first accepted Him as Savior, recalling the time when you were overwhelmed by the love of Christ for you. Recall the time when you were joyous over the forgiveness you received for your sins. Recall how you wanted to spend all of your time adoring and worshiping and thanking Him.
Then identify whatever is robbing you of your ability to love Jesus with all your heart. Reverse your course and return to that time when loving God was your primary focus. Recapture the time when you loved being in the presence of the Lord Jesus. Recapture the time when your love for Jesus occupied your thoughts, your desires, and your decisions.
Pray for the Lord's help to remain in a state of love and adoration for Him. Pray that others will see your continuous love for Christ and they will turn their attention to Him.
"To love him with all your heart, with all your understanding and with all your strength, and to love your neighbor as yourself is more important than all burnt offerings and sacrifices." Mark 12:33


text:
Mercurial > dropbear
view dropbear.8 @ 875:6c7a15668d5a
Log when generating a hostkey
|Matt Johnston <email@example.com>
|Tue, 03 Dec 2013 21:13:58 +0800
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.TH dropbear 8 .SH NAME dropbear \- lightweight SSH server .SH SYNOPSIS .B dropbear [\-RFEmwsgjki] [\-b .I banner\fR] [\-r .I hostkeyfile\fR] [\-p .IR [address:]port ] .SH DESCRIPTION .B dropbear is a SSH server designed to be small enough to be used in small memory environments, while still being functional and secure enough for general use. .SH OPTIONS .TP .B \-b \fIbanner bannerfile. Display the contents of the file .I banner before user login (default: none). .TP .B \-r \fIhostkey Use the contents of the file .I hostkey for the SSH hostkey. This file is generated with .BR dropbearkey (1) or automatically with the '-R' option. See "Host Key Files" below. .TP .B \-R Generate hostkeys automatically. See "Host Key Files" below. .TP .B \-F Don't fork into background. .TP .B \-E Log to standard error rather than syslog. .TP .B \-m Don't display the message of the day on login. .TP .B \-w Disallow root logins. .TP .B \-s Disable password logins. .TP .B \-g Disable password logins for root. .TP .B \-j Disable local port forwarding. .TP .B \-k Disable remote port forwarding. .TP .B \-p \fI[address:]port Listen on specified .I address and TCP .I port. If just a port is given listen on all addresses. up to 10 can be specified (default 22 if none specified). .TP .B \-i Service program mode. Use this option to run .B dropbear under TCP/IP servers like inetd, tcpsvd, or tcpserver. In program mode the \-F option is implied, and \-p options are ignored. .TP .B \-P \fIpidfile Specify a pidfile to create when running as a daemon. If not specified, the default is /var/run/dropbear.pid .TP .B \-a Allow remote hosts to connect to forwarded ports. .TP .B \-W \fIwindowsize Specify the per-channel receive window buffer size. Increasing this may improve network performance at the expense of memory use. Use -h to see the default buffer size. .TP .B \-K \fItimeout_seconds Ensure that traffic is transmitted at a certain interval in seconds. This is useful for working around firewalls or routers that drop connections after a certain period of inactivity. The trade-off is that a session may be closed if there is a temporary lapse of network connectivity. A setting if 0 disables keepalives. .TP .B \-I \fIidle_timeout Disconnect the session if no traffic is transmitted or received for \fIidle_timeout\fR seconds. .SH FILES .TP Authorized Keys ~/.ssh/authorized_keys can be set up to allow remote login with a RSA or DSS key. Each line is of the form .TP [restrictions] ssh-rsa AAAAB3NzaC1yc2EAAAABIwAAAIgAsp... [comment] and can be extracted from a Dropbear private host key with "dropbearkey -y". This is the same format as used by OpenSSH, though the restrictions are a subset (keys with unknown restrictions are ignored). Restrictions are comma separated, with double quotes around spaces in arguments. Available restrictions are: .TP .B no-port-forwarding Don't allow port forwarding for this connection .TP .B no-agent-forwarding Don't allow agent forwarding for this connection .TP .B no-X11-forwarding Don't allow X11 forwarding for this connection .TP .B no-pty Disable PTY allocation. Note that a user can still obtain most of the same functionality with other means even if no-pty is set. .TP .B command="\fIforced_command\fR" Disregard the command provided by the user and always run \fIforced_command\fR. The authorized_keys file and its containing ~/.ssh directory must only be writable by the user, otherwise Dropbear will not allow a login using public key authentication. .TP Host Key Files Host key files are read at startup from a standard location, by default /etc/dropbear/dropbear_dss_host_key, /etc/dropbear/dropbear_rsa_host_key, and /etc/dropbear/dropbear-ecdsa_host_key or specified on the commandline with -r. These are of the form generated by dropbearkey. The -R option can be used to automatically generate keys in the default location - keys will be generated after startup when the first connection is established. This had the benefit that the system /dev/urandom random number source has a better chance of being securely seeded. .TP Message Of The Day By default the file /etc/motd will be printed for any login shell (unless disabled at compile-time). This can also be disabled per-user by creating a file ~/.hushlogin . .SH ENVIRONMENT VARIABLES Dropbear sets the standard variables USER, LOGNAME, HOME, SHELL, PATH, and TERM. The variables below are set for sessions as appropriate. .TP .B SSH_TTY This is set to the allocated TTY if a PTY was used. .TP .B SSH_CONNECTION Contains "<remote_ip> <remote_port> <local_ip> <local_port>". .TP .B DISPLAY Set X11 forwarding is used. .TP .B SSH_ORIGINAL_COMMAND If a 'command=' authorized_keys option was used, the original command is specified in this variable. If a shell was requested this is set to an empty value. .TP .B SSH_AUTH_SOCK Set to a forwarded ssh-agent connection. .SH NOTES Dropbear only supports SSH protocol version 2. .SH AUTHOR Matt Johnston ([email protected]). .br Gerrit Pape ([email protected]) wrote this manual page. .SH SEE ALSO dropbearkey(1), dbclient(1), dropbearconvert(1) .P https://matt.ucc.asn.au/dropbear/dropbear.html


text:
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text:
Optimal for heavy industrial, high cycle applications. These operators employ lubricated gears and include a floor level chain hoist to ease manual operation in an emergency or power outage.
Optimal for heavy industrial high cycle applications. These operators are for use on
sectional overhead doors and employ lubricated gears.
We've Met with Emergency Response Professionals to Discuss Emergency Vehicle Access Needs.
In response to those needs, we've created solutions specifically for you. LiftMaster® understands your biggest concerns are the potential for theft of expensive life-saving equipment and vandalism while you're away from the facility. In addition, costly repairs and re-certifications after driving through a door create unplanned expenditures and time consuming paperwork.
After two major door repairs, The Albemarle Fire Department of Albemarle, North Carolina recently had the opportunity to make much needed safety upgrades to their main firehouse door systems. Chief Shawn Oke was thrilled with the results:
Our LiftMaster door enhancements are providing a safer workplace for our firefighters, have eliminated costly door repairs and are allowing our units to respond to emergencies faster.Chief Shawn Oke
LiftMaster's Firehouse Solutions are integrated packages, assuring seamless integration of a motorized commercial door system. The combination of advanced technology, features and accessories provided by LiftMaster, simplifies the process of determining the best solution to meet the needs of your firehouse.


text:
Indians let 9th-inning lead slip away vs White Sox
CHICAGO – The Cleveland Indians looked poised to salvage a four-game split.
One pitch from John Axford and one big swing by Alexei Ramirez changed that.
Ramirez hit a two-run homer off Axford in the ninth inning, rallying the Chicago White Sox over the Indians 4-3 on a rainy Sunday.
The Indians scored twice in the top of the ninth, getting the go-ahead run on a wild pitch by closer Matt Lindstrom.
Axford (0-1) came in seeking his fifth save in as many chances. He issued a leadoff walk to Jordan Danks, who stole second.
One out later, Ramirez homered over the left field fence.
"Nobody in this game is perfect," David Murphy, whose single had tied the game in the ninth, said of Axford. "But everybody in here has confidence in him and knows that he's going to get it done pretty much every day."
The White Sox won a series against Cleveland for the first time since October 2012. Chicago took three of four in this set after dropping 14 straight to the Indians.
"Everybody knows me as an aggressive hitter," Ramirez, who has a hit in all 13 games, said through a translator. "I'm going to go looking for a good pitch to hit and when I get it, I'm going to swing at it. And that's what happened, I got a good pitch."
"(Axford is) definitely a really good pitcher and he just left me a pitch up there and I was able to put a good swing on it," he said.
The game was delayed 1 hour, 15 minutes because of rain in the area. There was another 45-minute delay in the fourth.
Cleveland trailed 2-1 going into the ninth. Michael Brantley reached on first baseman Jose Abreu's error, and singles by Yan Gomes and Murphy tied it.
"Try to have a solid at-bat. There's a lot of things you can do to be productive right there, at least get that runner at second to third," Murphy said.
With two outs, Lindstrom (1-1) threw a wild pitch to Nick Swisher that put the Indians ahead.
Marcus Semien hit a solo home with one out in the eighth to give Chicago a 2-1 lead and chase Corey Kluber.
Kluber, who had his third straight start affected by weather, gave up eight hits and two runs over 7 1-3 innings. He struck out six without a walk.
"Oh, man, he was so good, even with the delay," manager Terry Francona said. "He used all his pitches, worked ahead. He just basically really pitched a really good ballgame."
Brantley gave Cleveland a 1-0 lead with two out in the fourth when he homered off Jose Quintana.
NOTES: Cleveland is off Monday before beginning a three-game series at Detroit on Tuesday with right-hander Zach McAllister (1-0, 2.31 ERA) scheduled to face Anibal Sanchez (0-0, 3.00 ERA).


text:
The Alabaster is painstakingly ground by hand to optimum thickness for maximum light transmission, colour and strength. It is then polished to a smooth finish inside and out. Photographs of these chandeliers do not do them justice, they are very beautiful and will grace any room.
Like all our chandeliers we can make these any size and in any available shade of Alabaster, so if you don't see exactly what you want here, please contact us to discuss your needs.
Please Note: the stated height does not include the chain which will vary according to your ceiling height.
|Is Empty


text:
- Research
- Open Access
- Published:
New feature subset selection procedures for classification of expression profiles
Genome Biology volume 3, Article number: research0017.1 (2002)
16k Accesses
124 Citations
3 Altmetric
Methods for extracting useful information from the datasets produced by microarray experiments are at present of much interest. Here we present new methods for finding gene sets that are well suited for distinguishing experiment classes, such as healthy versus diseased tissues. Our methods are based on evaluating genes in pairs and evaluating how well a pair in combination distinguishes two experiment classes. We tested the ability of our pair-based methods to select gene sets that generalize the differences between experiment classes and compared the performance relative to two standard methods. To assess the ability to generalize class differences, we studied how well the gene sets we select are suited for learning a classifier.
We show that the gene sets selected by our methods outperform the standard methods, in some cases by a large margin, in terms of cross-validation prediction accuracy of the learned classifier. We show that on two public datasets, accurate diagnoses can be made using only 15-30 genes. Our results have implications for how to select marker genes and how many gene measurements are needed for diagnostic purposes.
When looking for differential expression between experiment classes, it may not be sufficient to look at each gene in a separate universe. Evaluating combinations of genes reveals interesting information that will not be discovered otherwise. Our results show that class prediction can be improved by taking advantage of this extra information.
Microarrays can be used to obtain simultaneous measures of transcript abundance for thousands of genes. A number of projects have applied this technology to study differences between diseased and healthy tissue (for example [1]) and differences between different types and subtypes of diseases (for example [2,3,4]). The aim is to help improve the understanding of the diseases at a molecular level and to develop new diagnostic and prognostic tools. Microarray experiments can also help identify marker genes.
Typically, such experiments have taken on the order of 50-100 samples of different patients and used microarrays to measure the abundance (or relative abundance) of 5,000-10,000 genes. In some cases the samples are labeled with information about disease (for example, healthy/diseased, disease type). In other cases, such labels are not given and the aim can be to discover groupings of the samples, and clustering and class-discovery methods can be applied. When labels are given, the aim is to find sets of genes that distinguish well between samples with different labels. The identity of such genes can help understand disease mechanisms. Classifiers employing expression data for the identified genes can be used for diagnostic and prognostic applications, that is, class prediction.
Finding sets of genes with expression values that allow class separation may be achieved by the use of supervised or unsupervised methods. The most common practice is to apply a direct approach, where the class labels of the samples are used in the search for separating genes (supervised). However, there exist methods where partitions of the samples are found in an unsupervised manner together with gene sets that support these partitions, for instance by clustering [5]. If any of the partitions correlate sufficiently with known class labels, the gene set that supports this partitioning maybe reported as relevant for separating the sample classes.
Developing class predictors is in machine-learning terminology a 'supervised learning problem'. The expression data constitute a training set of labeled examples where each example is the expression profile for one sample. The aim is to develop a model that correctly predicts the labels of new examples. In order to test models it is standard practice to not include all examples in the training set so that the resulting models can be tested on the examples not used to build the model (the test set).
The models to be built will use some features of the examples. One problem with gene expression data is that each example has too many features, and many of them are noisy and irrelevant for the learning problem at hand. This is a common problem in machine learning and pattern recognition and a number of approaches have been proposed to select a subset of the features to be used. The problem of finding the best subset is commonly referred to as the feature subset selection (FSS) problem.
One approach is to consider each feature individually and see whether it distinguishes examples with different class labels. Let us restrict ourselves to the problem where we have only two classes (for example, healthy/diseased). A perfect feature would, for example, have high values for class 1 and low values for class 2, and could be used by itself to predict class membership of new examples. Such features rarely exist, and more commonly one needs to find a set of features that makes it possible to make a decision boundary that separates most class 1 examples from most class 2 examples.
A few groups have published results obtained using different FSS procedures on microarray data. These methods evaluate each feature (gene) with respect to how well it distinguishes between class 1 and class 2. Then they rank all genes according to the result and select the top K genes as the feature subset to be used. Some also employ a method to remove redundancy in the selected gene set; for example, some genes may behave very similarly and effectively provide the same information [6]. Other groups have reduced the dimensionality (number of features) by singular value decomposition (SVD), also referred to as principal component analysis (PCA), and used, for example, the first ten principal components as the feature subset [4,7].
The methods considering each gene separately potentially miss sets of genes that together allow good separation between the classes while each of the genes individually does not. We interpreted the results obtained by Xiong et al. [7] to indicate that this may indeed be the case. They obtained significantly higher accuracy using the seven first principal components than using 42 genes that each separate well between the classes.
Here we investigate new FSS methods that analyze pairs of genes, making it possible to find pairs that distinguish well between sample classes. Additionally we investigate the so-called forward selection method for FSS, where a good feature set is constructed by a 'greedy' selection procedure [8]. The results of these procedures are compared to results for previously reported FSS methods and the results show that our new FSS method gives more stable and better classification accuracy than methods evaluating each gene separately. The prediction accuracies achieved with our pair-based methods are also comparable to the best results reported in other papers.
We also list the genes chosen by our FSS methods and study the differences compared with sets chosen by other methods. Furthermore, we seek an explanation for the difference in classification accuracy achieved.
Finally, we discuss the implications of our findings. Apparently our FSS procedure provides an approach to finding gene sets that allows good separation between different classes and reveals better prediction results than other methods. Further research in this direction is required - considering wrapped feature selection systems, for example. The importance of considering combinations of genes in the feature selection process may contribute to new approaches to understanding diseases. Additionally, the importance of gene combinations may inspire new ways of designing microarray experiments for diagnostic and prognostic purposes.
To evaluate our methods we applied them to two public datasets ([9,10]; see also Materials and methods) and used two standard methods for comparison. The comparison is indirect, meaning that we use the average error rate of a learned classifier as a quality measure on the feature selection procedure. Here we describe our methods and the results we have achieved using them. We also show that our methods reveal better results than two standard methods.
We apply a novel FSS procedure for ranking genes based on relevance for separating two experiment classes. Rather than evaluating each gene independently of the other genes, we consider gene pairs. Each gene pair is evaluated by how well it separates two classes, assigning a separation score to the pair. For a comparison study we use a greedy method from machine learning called 'forward selection' and a gene-ranking method based on evaluating each gene separately. We will subsequently refer to the latter as 'individual ranking'.
Gene pair ranking
We propose a filter method for evaluating pairs of genes by how well they separate two classes. We give each pair of genes a score reflecting how well the pair in combination distinguishes two experiment classes. Figure 1 shows an example of a pair of genes separating two classes well, along with the diagonal linear discriminant (DLD) axis and the decision boundary given by the axis (for an introduction to DLD, see [11]). We evaluate a gene pair by computing the projected coordinates of each experiment on the DLD axis using only these two genes. We then take the two sample t-statistic on the projected points as the pair score. We propose two alternative methods for selecting a feature subset based on pair scores, one exhaustive method called 'all pairs' and one faster method called 'greedy pairs'.
The all-pairs procedure considers all pairs of genes. Given pair scores for all pairs we select the top-ranked disjoint pairs in a greedy manner. First, the pair with highest pair score is selected, then all pairs containing any of these two genes are removed from the list. Then the highest-scoring pair from the remaining list is chosen, and so on.
By removing the already selected genes from the gene set, we do not take the risk that one exceptionally high-scoring gene can drag along several 'bad' companion genes. If such a high-scoring gene was left in the gene set, it would probably be responsible for many of the top-ranked pairs. By removing selected genes from the gene set, a high-scoring gene will only cause its best available companion to join it in the set of selected genes.
As an alternative, less computationally expensive, method we try an approach evaluating only a subset of all gene pairs (greedy pairs). The greedy-pairs approach first ranks all genes on the basis of individual t-score. Subsequently, this procedure first selects the best gene g i ranked by gene t-score, then finds the gene g j that together with g i , maximizes the pair t-score. These two genes are then removed from the gene set and the procedure is repeated on the remaining set until we have selected the desired number of genes. This approach is computationally much faster than all pairs, as only a subset of all gene pairs will be evaluated, but it may miss some pairs with high score.
Prediction accuracy results
For testing our methods we used two public datasets, one representing colon (tumor/normal) tissues [10], and the other representing acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) [9] (see also Materials and methods). For both these datasets we perform cross-validation tests on the two-class problem, distinguishing between ALL and AML in the ALL/AML dataset and normal and tumor in the colon dataset. By dividing the data into a training set and a test set several times, we compare the average performance of different prediction methods on the test set using four different feature subset selection (FSS) procedures on the training set. In this study we use two linear discriminant methods: diagonal linear discriminant (DLD) and Fisher's linear discriminant (FLD), and one local method; k nearest neighbors (kNN) (for kNN and FLD, see [12]). The FSS procedures we use are all pairs, greedy pairs, forward selection and individual ranking. The application of several prediction methods is to see if the differences between the FSS procedures are specific to a particular prediction method rather than more general. Instead of comparing the different prediction methods we compare the ability of the different FSS procedures to find feature subsets that generalize the class differences. A comparison is done for feature subsets of size 2,4, 6,..., m, where m is the number of experiments in the dataset. We also leave out different portions from the training data (1, 33%, 50%), to see what effect this has on prediction accuracy.
The results are summed up in plots like the one shown in Figure 2, with the number of genes along the x-axis and the prediction accuracy along the y-axis. The curves in Figure 2 show performance using the different FSS procedures and DLD prediction. Plots in Figure 2a,2b illustrate prediction performance on the colon dataset when one example is left out at each round (often referred to as leave-one-out cross-validation or LOOCV; Figure 2a) and when leaving out 31 examples (50%; Figure 2b). Plots in Figure 2c,2d show prediction performance for the ALL/AML dataset leaving out one (Figure 2c) and 36 (50%; Figure 2d) of the examples from the training set. The complete set of plots, showing results for all four FSS procedures and all three prediction methods, are available as Additional data files.
For the colon dataset, the pair-based methods achieve much better results than individual ranking and forward selection. When leaving out only one example from the training set there is no clear difference in prediction accuracy, except that forward selection gives worse results than the other methods (Figure 2a). Leaving out larger portions of the data, the pair-based methods give superior prediction accuracy, compared to both individual ranking and forward selection (Figure 2b). For this dataset, the all-pairs ranking gives slightly better results than the greedy-pairs ranked genes.
Using FLD and 3NN (kNN where k = 3) prediction, the tendency is also that pair-based methods give the best prediction results, so the good results achieved with the pair-based methods are not just specific for DLD prediction. Comparing our results to those of Xiong et al. [7], we achieve results comparable to what they achieved using the first seven principal components and FLD prediction. Using FLD prediction and the 20 top-ranked genes, selected by all-pairs ranking, we achieve a prediction accuracy of 87.8% leaving out 32% (20 examples) and 85.9% leaving out 50% (31 examples) from the training data. The corresponding results achieved by Xiong et al. were 87.0% and 85.7%. Weston et al. [13] report an average error rate of 12.8% on the colon data when leaving out 12 examples at each cross-validation iteration using the top 15 genes from their own feature selection method and a support vector machine [14] for prediction. Using the top 14 genes from all-pairs ranking and DLD prediction we achieve an error rate of 12.2%.
For the ALL/AML dataset there is smaller difference in results using the pair-based methods (all pairs, greedy pairs) and individual ranking, but it is still in favor of the pair based methods. On this dataset, forward selection gives prediction results far worse than the other methods. When leaving out 1, 24 (33%) or 36 (50%) of the examples from the training set, the pair-based methods give slightly better results compared to individual ranking using all three prediction methods. Generally the prediction accuracy rises a bit faster with the number of features using ranking by gene t-score than using pair-based ranking. However, the pair-based methods give slightly better prediction accuracy when the number of genes in the feature subset increases. In all cases except one (LOOCV and FLD prediction), all pairs or greedy pairs has the best maximum prediction accuracy. For LOOCV and FLD prediction, individual ranking, all pairs and greedy pairs had the same maximum prediction accuracy.
Selected gene sets
Given the all-pairs ranking method, we study which genes show up as highly relevant in pairs. For this study we use the data for all experiments in each of the colon and ALL/AML datasets. We ranked all pairs of genes for each dataset and list the top-ranked 25 disjoint pairs in Tables 1 and 2.
For the colon dataset it is interesting that, taking all the available data into account, the top-ranked gene pairs contain several genes that are not among the top genes when ranked individually. In fact, only 24 out of the top 50 genes ranked by pair t-score are among the top 50 genes ranked by gene t-score. Only 32 out of 50 genes are in the top 100 ranked by gene t-score.
For the ALL/AML dataset, 31 out of 50 genes, ranked by pair t-score, are among the top 50 ranked by gene t-score. Forty-two out of 50 were among the top 100 ranked by gene t-score. The difference from the colon data set was that the all-pairs ranking method selects far more genes not from the top individually ranked genes. This could account for why we do not see a larger difference in prediction accuracies for this dataset.
The recurring question when working with diseases and gene-expression profiles is which genes are involved and which genes are suitable as marker genes. The focus of this paper is on methods for finding gene sets that are suitable for discriminating between experiment classes, like disease/normal or between different subclasses of a disease.
We propose a conditional gene relevance measure, the pair t-score, where genes are scored by how well they separate experiment classes in the context of some other gene. This approach may inspire some insight into the biological mechanisms behind a disease as we consider genes in combination with others. We believe our approach is a step in the right direction to find the genes that are interesting for separating experiment classes but do not show up as interesting on an individual basis. Considering combinations of genes in the feature selection process may contribute to new approaches to understanding disease. Additionally, the importance of gene combinations may inspire new ways of designing microarray experiments for diagnostic and prognostic purposes. Once a set of marker genes has been identified, this makes cheap production of diagnostic microarrays possible, as such a microarray needs only a relatively small number of marker genes spotted on it. Alternatively, other methods of measuring gene expression can be used, as the number of genes to be monitored is rather small. For the ALL/AML and colon datasets we demonstrate that quite accurate diagnoses can be achieved using only the gene-expression levels of 20-30 and 15-20 genes, respectively.
We do not claim that our pair-based methods will find all interesting genes, as there may be relevant genes that are significant by themselves but may not appear in any of the high-scoring pairs. However, we demonstrate that looking at gene pairs will reveal some extra information about class differences. Our methods are not meant as a substitute for single gene evaluation, but rather as a supplement to already existing methods.
An interesting point regarding cross-validation is that when leaving one example out during training, the performance curves shows a different picture from the one we get when leaving out larger portions of the data. Leaving out larger portions from the training data gives smoother performance curves, showing a clearer picture of differences in prediction performance. It is also interesting to study how much the prediction accuracy drops when leaving out more than one example from the training set. The all-pairs and greedy-pairs methods are here shown to be more robust than individual ranking and forward selection in terms of loss of prediction accuracy, as shown in the plots in Figure 2.
Further work will include integrating our methods into the J-Express software package [15], to make them available to the biological community. Other methods for evaluating combinations of genes will be investigated, and we will consider other prediction methods, like support vector machines and artificial neural networks [12], testing whether better prediction accuracy can be achieved. We will also consider feature redundancy, which might lead to even smaller feature sets than we currently achieve. In these studies we will also include additional datasets.
We propose a new method for evaluating combinations of genes. Comparing the top-ranked genes ranked by gene-pair evaluation to the gene sets selected by two standard methods, we demonstrate that the gene sets selected by the pair-based approaches are more robust in terms of their ability to distinguish experiment classes. These results indicate that in evaluating each gene independently one risks overlooking some interesting genes. The improved prediction accuracy indicates that the genes we find might be at least as interesting to study as the top-ranked genes found by an independent evaluation of each gene. Looking at gene pairs, we find some genes that are not obviously good discriminators alone, but discriminate well when used in pairs with other genes. Thus, we discover some interesting genes that will not be discovered unless one looks at gene combinations.
We expect that some high-scoring pairs can occur just by chance because of the vast number of pairs evaluated. But if the pairs we select are high scoring by chance, we do not expect an improvement in prediction accuracy. Thus, by demonstrating a better ability to generalize class differences when using high-scoring pairs, we also show that these pairs give some extra information on the experiment classes.
Materials and methods
For testing purposes we use the datasets published by Golub et al. [9] and Alon et al. [10]. The dataset published by Golub et al. [9] consists of 72 samples, of which 47 were acute lymphoblastic leukemia (ALL) and 25 samples were acute myeloid leukemia (AML). Gene-expression levels were measured using Affymetrix high-density oligonucleotide arrays containing 7,129 genes. The Alon et al. data [10] consists of 62 samples, of which 22 are normal and 40 are colon tumor tissue. Gene-expression levels were measured using Affymetrix oligonucleotide arrays complementary to more than 6,500 genes. Published along with the Alon et al. paper was a dataset [10] containing expression levels for the 2,000 genes with highest minimal intensity across the samples, and this is the dataset we study in this paper. We refer to the Golub et al. dataset [9] as 'ALL/AML' and the Alon et al. dataset [10] as 'colon'. Before analysis, we carried out the following preprocessing steps on both datasets: base 10 logarithmic transformation; and for each gene, subtract the mean and divide by the standard deviation.
Because the ALL/AML dataset contains a lot of negative intensity values, we first use the following preprocessing steps (similar to those proposed by Dudoit et al. [16]) on the ALL/AML dataset: thresholding with a floor of 1; and filtering by excluding genes with max/min ≤ 5 and (max - min) ≤ 500. This leaves us with a dataset of 3,934 genes.
Prediction methods
We use three prediction methods: one local method, k nearest neighbors (kNN), and two linear discriminant methods, diagonal linear discriminant (DLD) and Fisher's linear discriminant (FLD). We consider an expression profile x = (x1, x2, ..., xn) to which we want to assign a class label (1 or 2). Formally we wish to estimate the function f(x) → {1,2} based on the available training data with as small error rate as possible. There are many approaches to estimate f, making different assumptions about the distribution of the classes. kNN only considers the neighborhood around x, whereas DLD and FLD aim to find a best possible linear separating rule between classes based on all the available training data.
Many variants of the kNN algorithm exist. We choose to find the k nearest neighbors by Euclidean distance, using the selected feature subset, and let each of the k neighbors get a vote of weight 1. We then predict x to be of the class getting the majority of the votes. Thus if k = 3 and there were two examples of class 1 and one example of class 2 among the three nearest neighbors of x, we would predict that x belongs to class 1. kNN is a local method as it only considers the neighborhood of the experiment x, and does not consider the information in the rest of the training data. We use a fixed value of k = 3 in our tests.
DLD and FLD are two discriminant methods for which a discriminant axis a is computed on the basis of the available training data. The prediction using a is class 1 if
and class 2 otherwise. The parameters
1 and 2 are here the mean expression profile of classes 1 and 2, respectively. The difference between DLD and FLD is in how the discriminant axis is computed. The DLD axis is computed as
a = S-1 (1 - 2) (2)
where S is the diagonal variance matrix whose elements are the common variance estimate
To compute the FLD axis, S is substituted by the covariance matrix in equation 2. S and will have the same values on the diagonal, while will also contain covariances between genes.
When only using a subset G = {gf1,...,g fl } of the genes (l <n), some modifications to the equations above have to be done. For DLD it is sufficient to define a feature subset matrix F = Diag({0, 1}n), where F ii = 1 if and only if g i ∈ G. Equation 1 can then be replaced with
This equation corresponds to setting every element a i where g i ∉ G to 0.
For FLD the matrix
-1 will contain information on gene redundancy in the whole gene set. This is not the case for S-1, which is a diagonal matrix. It would therefore not be correct to do the same modifications to the equations as for DLD. For a feature subset of l genes, an l × l covariance matrix ' has to be computed, considering only the genes in G. Furthermore will the vectors a','1 and '2 be l × 1 instead of n × 1 vectors, x' is now the vector containing only the expression values of the genes in G. The equations stay the same as before, except that , a,12 and x are now substituted with ', a','1, '2 and x'.
Feature selection
When considering which feature (gene) subset to select for class prediction or for study in the wet lab, we need some method of eliminating the least interesting and highlight the most interesting before a choice is made. A natural choice is to rank the features after some relevance measure and then select some of the features with highest score for further use.
As the main focus of this paper is class prediction, we will discuss feature selection in this context. In the machine-learning literature, Kohavi and John [8] divide features into two main categories: irrelevant and relevant, of which the relevant genes can be separated into strongly relevant and weakly relevant. Strongly relevant features are the obvious ones, distinguishing well between classes independently of other features. Weakly relevant features, on the other hand, are not that obvious, and may not be relevant except in the context of other features. These features might be difficult to discover, as combinations of genes have to be evaluated in some manner. Furthermore there may be redundancy among the features. The machine-learning feature selection problem is to select the minimum optimal feature set, meaning that we want to find the smallest subset of features that gives optimal prediction accuracy. No algorithm exists for solving this problem except trying all feature subsets and choosing the best, which is not feasible if there are more than a trivial number of features. For microarrays, where the number of features is in the thousands, this is not an alternative at all. The approach normally used is filter methods or wrapper methods. Filter methods evaluate each feature independently of the prediction method to be used, whereas wrapper methods evaluate the feature set relative to the prediction method.
Standard methods
A large number of measures have been proposed for scoring genes, starting with Golub et al. [2] that proposed using . Other gene measures in the literature include both non-parametric measures like the TNoM score of Ben-Dor et al. [17] and information gain (proposed by Xing et al. [6]), and parametric measures like t-score [7], Fisher score [13], naive Bayes global relevance [18] and between- to within-variance ratio [16]. These methods reward genes that allow (approximate) separation of experiment classes on the basis of the gene-expression levels. For instance, the measure proposed by Golub et al. [2] rewards genes where the mean expression levels in the two classes are far apart but at the same time the standard deviation in each class is small.
Instead of comparing different relevance measures, we choose to use t-score as our relevance measure, as the focus of this paper is on FSS methods rather than relevance measures. Given the DLD axis, we try the greedy feature-selection method called forward selection. The greedy forward selection first selects the best gene, meaning the gene with highest t-score, and in the subsequent steps adds the gene that leads to the highest two-sample t-statistic score. The t-score at step i is computed on the projected data points on the DLD-axis using only the expression values of the i selected genes.
Evaluating feature subset selection (FSS) performance
To evaluate the robustness of our pair-based approaches, we evaluate their performance compared to individual ranking and forward selection by cross-validation. To rank genes on an individual basis, we use the two-sample t-statistic on the expression values of the gene across the experiments.
Given a training set, we study how well the different FSS procedures do in finding feature subsets that generalize the differences between two experiment classes. Success is measured by how accurately a prediction method predicts the correct class labels of a set of labeled experiments (the test set) on the basis of the selected feature subset only. The FSS procedures are compared by studying the performance of a prediction method in the context of which FSS method is used to select feature subsets. Thus, if we use the same prediction method, but we get varying prediction accuracy results using alternative FSS procedures, we can draw conclusions about how 'good' each of the FSS methods is. It is important to note that the training set and test set should have no common examples. Otherwise, the learning algorithm (either the FSS procedure or the prediction method) could take advantage of information on the examples of which it is going to predict the class labels during the learning process. The procedure for cross-validation is as follows.
Repeat a number of times the following procedure:
Partition the examples randomly into a training set and a test set, including x% of the examples in the training set;
Use the FSS procedure on the training set;
Train a classifier using the training set and only the selected features;
Use the classifier to predict the class labels of each example in the test set and count the number of correct and false predictions.
Output the percentage of correct predictions.
Using different FSS procedures in the feature selection step, we plot the performance of the three classifiers kNN, DLD and FLD for feature subsets of size 2, 4, 6,..., m. The result is plots like the ones shown in Figure 2, showing a comparison of the prediction accuracy using the four different FSS procedures. We also perform tests for different values for x, typically 50% and 66%. In addition we do LOOCV tests.
Cross-validation procedure
The prediction accuracy estimate
is the average performance over all iterations performed in (A). Thus is a random variable drawn from a distribution with mean p, the true prediction accuracy, and a standard deviation σ. The more iterations of the above cross-validation procedure used to compute the smaller σ will become and the better will estimate the true value of p. Empirical studies showed us that taking a randomly chosen test set at each iteration produced curves that sometimes had large deviations from the true p curve, even when taking 62 iterations for the colon dataset as done by Xiong et al. [7]. We also tried an approximation to another cross-validation procedure called k-fold cross-validation. In k-fold cross-validation the dataset is split randomly into k equally large subsets. Then k iterations of cross-validation is carried out, at each round taking one of the subsets as the test set, and the rest of the data as the training set. As the datasets we used could not always be split into equally large subsets, that is, m is not a multiple of the training set size s, we used a modified version of the k-fold approach. We solve this by making the test sets one by one, randomly drawing s experiments out for a test set at each iteration. In addition, we keep a count on how many times each experiment has been left out so far. By not allowing any experiments to be left out, the i + 1th time before all experiments has been left out i times, we ensure that all experiments are left out the same number of times in the long run. Compared with the other approach, we found that using the approximate k-fold cross-validation, converged much faster towards p, giving better estimates using the same number of iterations. Nonetheless, we chose to run 5m iterations using the approximate k-fold cross-validation approach, such that the prediction accuracy estimates should show a trustworthy comparison of the different FSS procedures.
Additional data files
Additional data files are available giving plots of average prediction accuracy performance, using three prediction methods and holding back different portions of the data from the training set. L-24-OCV stands for leave-24-out cross validation, and so on. Each curve shows the performance for feature sets of varying size selected by four feature selection methods. These files are also available at our website [19].
ALL/AML dataset plots using: 3NN prediction and LOOCV; 3NN prediction and L-24-OCV; 3NN prediction and L-36-OCV; DLD prediction and LOOCV; DLD prediction and L-24-OCV; DLD prediction and L-36-OCV; FLD prediction and LOOCV; FLD prediction and L-24-OCV; FLD prediction and L-36-OCV.
Colon dataset plots using: 3NN prediction and LOOCV; 3NN prediction and L-20-OCV; 3NN prediction and L-31-OCV; DLD prediction and LOOCV; DLD prediction and L-20-OCV; DLD prediction and L-31-OCV; FLD prediction and LOOCV; FLD prediction and L-20-OCV; FLD prediction and L-31-OCV.
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Supplementary datasets and prediction results for Golub et al. 'Molecular classification of cancer: class discovery and class prediction by gene expression monitoring'. [http://www-genome.wi.mit.edu/MPR/data_set_ALL_AML.html]
Data pertaining to the article 'Broad patterns of gene expression revealed by clustering of tumor and normal colon tissues probed by oligonucleotide arrays'. [http://microarray.princeton.edu/oncology/affydata/index.html]
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Vapnik V: Statistical Learning Theory. New York: John Wiley and Sons,. 1999
Dysvik B, Jonassen I: J-Express: exploring gene expression data using Java. Bioinformatics. 2001, 17: 369-370. 10.1093/bioinformatics/17.4.369.
Dudoit S, Fridlyand J, Speed T: Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. Technical report 576. Berkeley, CA: Department of Statistics, University of California,. 2000, [http://www.stat.berkeley.edu/~sandrine/tecrep/576.pdf]
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I.J. has been supported by grants from the Norwegian research council. We thank Bjarte Dysvik and Karl-Henning Kalland for helpful discussion.
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Bø, T.H., Jonassen, I. New feature subset selection procedures for classification of expression profiles. Genome Biol 3, research0017.1 (2002). https://doi.org/10.1186/gb-2002-3-4-research0017
DOI: https://doi.org/10.1186/gb-2002-3-4-research0017
- Prediction Accuracy
- Additional Data File
- Feature Subset
- Individual Ranking
- Feature Subset Selection


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(CNSNews.com) – The man who shot a security guard at the Family Research Council (FRC) on Wednesday was "given a license" to do so because of groups such as the Southern Poverty Law Center (SPLC) that have labeled the FRC a "hate group," said FRC President Tony Perkins.
The SPLC has posted what it calls a "Hate Map" on its website that points to the FRC as a "hate" group located in Washington, D.C..
On Wednesday, 28-year-old Floyd Lee Corkins of Herndon, Va., allegedly entered the lobby of the Family Research Council and shot the security guard. According to the FBI, Corkins had a 9mm handgun, two magazines of ammunition, 50 rounds of additional ammo and 15 Chick-fil-A sandwiches in his backpack. According to the FBI, he "stated words to the effect of 'I don't like your politics.'"
(Chick-fil-A was recently castigated by homosexual activists and some Democratic lawmakers because its CEO expressed the belief that marriage is the union of one man and one woman.)
At a press conference on Thursday outside the FRC headquarters in downtown Washington, FRC President Perkins said, "I want to express my appreciation to the groups and organizations that we do not agree with on many public policy issues who have also expressed their outrage at what took place here yesterday. For that, I appreciate it. I appreciate them making those statements."
"But I would ask them to go a step further and to join us in calling for an end to the reckless rhetoric that I believe led to yesterday's incident that took place right behind me," he said.
"And that does bring me to my final point," said Perkins. "Let me be clear, that Floyd Corkins was responsible for firing the shot yesterday that wounded one of our colleagues and our friend Leo Johnson. But Corkins was given a license to shoot an unarmed man by organizations like the Southern Poverty Law Center that have been reckless in labeling organizations hate groups because they disagree with them on public policy."
"And I believe the Southern Poverty Law Center should be held accountable for their reckless use of terminology that is leading to the intimidation and what the FBI here has categorized as an act of domestic terrorism," he said. "There's no room for that in a society such as ours that works through differences that we have on issues in public policy through a peaceful means."
The suspect, Floyd Corkins, had been volunteering for the last six months at The DC Center for the LGBT Community. The DC Center released a statement condemning the violence, which was signed by 40 other local and national LGBT groups.
"We were saddened to hear news of the shooting this morning at the offices of the Family Research Council," read the statement. "Our hearts go out to the shooting victim, his family, and his co-workers."
During the press conference outside of the FRC building, Perkins said designating organizations as "hate groups" for disagreeing with public policy positions is harmful.
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CNSNews.com on Thursday left repeated messages with the Southern Poverty Law Center seeking an interview to discuss the Family Research Council and its designation on the SPLC's "Hate Map." The SPLC, however, did not respond before this story was posted.


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So last week, Jen went naughty.
But this week? She goes plain old criminal.
As a friend? I'd just like to be the first to extend the olive branch and say, if you need to … like … talk … we're all here for you. Because there are healthy choices, and unhealthy choices, and, well… it's really just a small short spiral down to …
Sorry, what?
Oh. Right. We're just talking music.
My bad.
This week, Jen Kehl, our maestro and our musical blog hoppy tour guide for Twisted Mixed Tape Tuesday is looking for: song that coulda sent me to jail. Check out my co-conspirators here:
Hmm. I'm about as honest and law abiding as they come. So I was momentarily stumped.
But then it hit me. I was, like, TOTALLY, a teenager once. So here's five songs from my formative years that tested my moral fortitude while being, like, really catchy.
OffSpring – Come out and Play (1994)
So the thing with this song? The lyric that stuck at 17? "Your under 18 you won't be doing any time. Hey! Come out and Play!"
Yeah! If I ever had the mind to be? I could have done some damage with zero repercussions, right? There's the message I got as a teenager. I remember thinking that it was wrong, but, meh! Life.
Then I listened to the song about ten years later and actually LISTENED to the lyrics. Smart commentary.
It goes down the same as the thousand before;
No one's getting smarter
No one's learning the score.
Your never ending spree of death and violence and hate;
Is gonna tie your own rope
Tie your own rope
Tie your own
Hey! man you talkin' back to me?
Take him out!
You gotta keep'em separated.
Everyone loses. Including you. Seriously, in the world of easier said than done? Sort your shit out and just be happy.
Ah! But in the Offspring did not begin my never actually started considerations of truancy!
For my next choice, I give you Roxette's Joyride from 1991! This came out before I hit my teens, but late enough for me to understand that joyriding was something in the "bad" column of behaviours. As for things in this video that could send you to jail? Abuse of the blue screen and horrible spandex pants spring to mind. And I suspect playing guitar on the hood of your car while speeding down the highway might not fly with the local authorities either. Even if you explain the whole "we totally faked it with a blue screen" thing. That said, I have a soft spot for Roxette given when they arrived in my musical awakening. And I think their absence to date on this hop is a total crime. Rather like cowbell, the world needs more Roxette! So here they are:
But we should get firmly back into my teens. You know? Where developing morals meet bad influence and hang out for a while? As the mother of two preschool girls I'm not panicking (quite) yet. Really.
Next up I give you Cypress Hill's Hits From the Bong from their 1993 album Black Sunday. I know many of you on this hop are Tarantino fans and I almost cringe to inform you that whenever "Son of a Preacher Man" starts and ISN'T "Hits From the Bong", a small part of 15-year-old me dies. It's pure "time and place" music. I remember a specific summer holiday and the group I hung out with who played this album ad infinitum. Even owning the album made me feel bad-ass. The song is still on my IPod. And, coincidentally, is right up there with Paul Simon's "Slip Sliding Away" for songs I hope don't come up during my treadmill workouts.
As far as edgy suggestively criminal songs from my era goes, I don't think my list would be complete without the Beastie Boys. So here's Sabotage from 1994's Ill Communication. Which I guess is more about cops being cool than the other way around, but they are pretty 1970s retro cool in the video. This was a favorite from the team I lifeguarded with at Westboro Beach on the Ottawa River when I was seventeen. We had these jackets that read "Rescue/Sauvetage" – Sauvetage being rescue in French. So a number of the guys thought that worked with the Beastie Boys song, and much running around packing rescue torpedos like "heat" ensued. After hours, of course. I look at that with my last pick and am suddenly curious about the blending of the 15-year-old crowd and the 17-year-old crowd based on musical choice. I think that would have been an … interesting party.
So that brings us to closing time. My final choice is a song that hit big in my first year of university. The song was cool. The video was fun. And, hey, if I was going to date someone of questionable moral character? I think it might have been someone like one of these guys. Because, really? What girl of still maturing impulse control wouldn't want a dude who can a) sing and play guitar in a totally cool club and b) probably also pay for her drinks given his, um, entrepreneurial side gig. Here's Fun Lovin' Criminals with Scooby Snacks. Also – another round of warm fuzzies to all the Tarantino fans out there what with the shout out at the beginning of the song 🙂
So that's all I've got for you this week! Until next time!


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Hullo, everyone! This is a little piece that came out of my frustration for all the new story ideas I keep getting. All stories mentioned are real works-in-progress or new ideas. Enjoy!
I'm literally banging my head on my desk when Benedict comes into my office. He coughs, and I look up, rubbing my now sore forehead. "Yes?" I grumble.
Benedict hands me an envelope. "Another one, from Shadow."
I let the envelope fall onto the desk, rolling my eyes at the "Confidential" stamp on it. Shadow is the guy who dishes up ideas for me. Right now he is the person I am quite angry with. Emphasis on angry.
"Would you like me to read it for you?" Benedict offers. He's so helpful, bless his heart. If he weren't here, I probably would have thrown my computer out the third story window by now.
I sigh and push the envelope across the desk to him. "Does he really think I need this many ideas? This is, like, the fourth one this week! Honestly! I mean, I really loved that twin idea, and the ideas for Chloe were super helpful, but I can't write all of these at once!"
Benedict's eyes widen as he reads, and he grins. "This one's good."
I groan. "I don't think I want to hear it."
He dangles the paper in front of me. "Contemporary Robin Hood story with the possibility of becoming a Cinderella crossover… Oh, and it even has a pretty nice first line."
I snatch the paper away and read it excitedly. "Oooh! I love it!" I hug the paper to my chest, then moan. "But I have so many other stories…"
Benedict pats my shoulder. "You'll be fine, Bri. Just set it aside for now and write down any ideas that come to mind. You can make it your reward for getting Chloe's story done."
I gaze longingly at the story idea. "Benedict, if you get any more envelopes from Shadow, do me a favor and hide them, okay? I've already got two possibilities for NaNoWriMo projects… I don't think I need any more options."
Benedict smiled. "Of course."
Well, what about you? Do you have too many story ideas, or not enough? What's your favorite not-yet-written idea? Comment below!


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Innovative laptops and tablet PCs with snazzy OLED screens took CES by storm back in January. The sleek devices promised sharper colors while providing longer battery life.
Five months later, that early momentum seems to have come to a screeching halt. OLED screens aren't reaching laptops and tablets as quickly as they are reaching large-screen TVs, mobile devices, and wearables.
Shipment issues have also plagued laptops and 2-in-1s with OLEDs, and display makers have shown little interest in pursuing OLED panels for PC devices.
Dell's Alienware 13 gaming laptop with an OLED screen, which was supposed to ship in April, is finally available starting at $1,299. Delays have hit Lenovo's 14-inch ThinkPad X1 Yoga, which is listed as "coming soon." Lenovo did not provide a reason for the delay.
HP recently started shipping its Spectre x360 Convertible Laptop with a 13.3-inch OLED screen, at the end of the "spring" time period when the company said it would ship it. Samsung's Galaxy TabPro S was the first device shown at CES out of the gate, with the 13.3-inch OLED 2-in-1 shipping in March.
Since CES, no PC maker has announced a significant product with an OLED screen. Most laptops and tablets screens deliver adequate performance with LED backlighting, and there are issues with OLEDs that need to be resolved, said David Hsieh, senior director of displays at IHS DisplaySearch.
Like in TVs, OLED is expected to ultimately replace LED in laptops and tablets. OLEDs don't have lighting back-panels, which makes them thinner and more power efficient than LEDs. A thin layer of organic material emits light when current passes through, while LED is a voltage-driven display with a backlight.
The OLED panels are thinner, which makes devices sleeker. OLED offers better response times and contrast ratios.
The current OLED laptop market is aimed at gamers and multimedia workers.
"These markets care a lot about the design form factor and specifications. It takes a longer time for the PC [makers] to work on it," Hsieh said.
HP, for one, doesn't have immediate plans to introduce OLED screens across a wide range of products unless there's an application for it, Mike Nash, the company's vice president of the customer experience and portfolio strategy, said back in a January interview.
Despite the power and size advantages, some issues need to be resolved before OLED appeals to PC makers. OLED PCs are selling at a premium as the panels are still not being mass-produced, Hsieh said.
An OLED PC isn't worth a premium, however, if used only for Web surfing. The OLED benefits kick in when brighter colors are shown, which is when it uses less energy than LED. The power benefits of OLED over LED are negligible in static color backgrounds.
Installing OLED panels in laptops also isn't as easy as installing LED screens, which are well tuned to current PC making techniques.
"The basic principle of the signal-driving schemes are different. So it takes some special consideration when designing OLED into a laptop system," Hsieh said.
There are also OLED burn-in issues that display makers are trying to resolve, Hsieh said. Images tend to stick on the screen for a little while after it is turned off, which could hurt the life of a display.
There's also little interest from top display makers Samsung and LG in building OLED for laptops and hybrids. Samsung is the only OLED supplier to PC makers and is making a small number of panels tuned for PC devices. The company is expected to start making OLED laptop panels in larger volumes in the third quarter, Hsieh said.
A Samsung spokeswoman said the company "is consistently focusing on all types of applications for its OLED displays including laptops."
LG is betting its future on OLED and is investing millions of dollars in factories. But OLED is a bigger priority for big-screen TVs, and small-screen devices like wearables and mobile devices.
OLED is not a priority for laptops or hybrids because there isn't a viable market yet, the company said in an earnings call in late April. LG will start making panels depending on the market response, but didn't provide a timeline.


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David Ortiz is hitting well, and Dan Shaughnessy wrote a stupid, awful column. Stop me if you've heard these ones before.
When Shaughnessy committed 1,000 words of attempted character assassination, Ortiz was hitting .426 with four home runs and 17 RBI in just 15 games. There are many ways to write that story. You write about Ortiz's veteran leadership on a young, unexpectedly successful Boston team. Or about Ortiz's determined comeback from an Achilles injury. Or about how Ortiz, at age 37, has once again responded to reports of his demise with another incredible start.
Here's the very first paragraph of Shaughnessy's story in the Boston Globe:
Hitting is not this easy. Athletes do not get better as they mature into their late 30s. Baseball has been peppered with performance-enhancing drugs for the last 20 years. The cheaters are always ahead of the testers. A number of players from the Dominican Republic have tested positive for steroids. Injuries to the Achilles' tendon are consistent with steroid use. It is not natural for a guy to hit .426 out of the gate without the benefit of any spring training.
Except, this isn't a column accusing David Ortiz of PED usage. It's a column about David Ortiz denying accusations of PED usage. Except there are no active accusations, so Shaughnessy has to create them, confront Ortiz with them, and then run his denials. The truly incredible part is that at some point, this logic ran through Shaughnessy's brain and he decided it would be a good idea for a column.
You want more?
This is an uncomfortable topic, but it's preferable to question a man face-to-face than to tarnish him by whisper and innuendo. I went to Ortiz Tuesday afternoon in the Sox clubhouse and put some hard questions to him. I told him he looks dirty.
Because what you are doing looks too good to be true.
"They test me all the time," he said. "They make you pee and they test your blood, too. This year I would say I've probably been tested five times, peeing. Blood, just once. That was in spring training. They don't warn you. They just show up.''
What it is like to be suspected?
"I don't think I have been," he said. "Nobody comes to me and tells me, 'They suspect you are using steroids.' ''
But you fit all the models. You are from the Dominican Republic. You are an older player. Older players don't get better. You've had injuries consistent with steroid use. You showed up on the list from 2003. You fit all the formulas.
Remember when a blogger was crucified for questioning Raul Ibanez's resurgence in 2009? Jerod Morris was just a few years early—roid-baiting is now the stuff of major metropolitan newspapers.
Ortiz responded yesterday, in an interview with ESPN Deportes. He took issue with Shaughnessy citing his Dominican nationality as a checkmark in the "he's probably doping" column, even though more Americans than Dominicans have been suspended. But he really gets to the heart of the matter when he notes this is a media thing, and slightly more specifically, a Boston media thing:
"I have spent many years in Boston and still do not know the right way to do things: Do it right or do it wrong," Ortiz said. "If you do it wrong, they'll finish you. If you're doing well, they'll finish you, too."
The Red Sox have been in first place for most of a season in which they're supposed to finish last. If there's not enough there for a local sports columnist without having to resort to sorry-I'm-not-sorry defamation after a month, I feel for Dan Shaughnessy's shriveled black writer's soul. But Boston's lost three in a row, seven of 10, and Ortiz is hitless in his last 13 at-bats. Shank can happily go back to blasting the team for being bad, which is a lot easier and more straightforward than this tortured bit of "I'm just asking questions."


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...and stay in touch.
The cry of "Viva la Fiesta" has been reverberating through the streets of Old Santa Fe every autumn for 306 years. (more…) Read More
Established in 1712, this year we proudly celebrate 306 years of community and a spirit of inclusiveness. (more…) Read More
Please check our calendar to see up-coming events and schedule information. Read More
This year Santa Fe Fiesta festivities will take place between September 1st through the 9th. Our most popular events are:
For more information on how you can help please visit our contact page.
Welcome to the official website of the Santa Fe Fiesta Council. The Santa Fe Fiesta Council is a volunteer organization created by the Official Fiesta Proclamation of our city forefathers on September 16, 1712.
Today the Santa Fe Fiesta Council is comprised of 100 regular members and 20 representatives of civic organizations and businesses. This group of people has a common interest to promote, manage and perpetuate the annual Fiesta de Santa Fe for the benefit of the City of Santa Fe, its citizens and their descendants.
The Fiesta Council enlists the cooperation of the local citizenry, the Catholic Church, the local business community and both the City and County of Santa Fe officials.
History of Santa Fe Fiesta The cry of "Viva la Fiesta" has been reverberating through the streets of Old Santa Fe every autumn for 306 years. The sound generates a curious blend of thanksgiving, revelry and pride in the hearts of Santa Feans who celebrate Fiesta annually to commemorate Don Diego De Vargas' peaceful reoccupation of the City of Holy Faith in 1692.
The historic capital is one of the oldest in the United States. It was established by Don Juan de Oñate at San Gabriel in 1598 and moved over 30 miles south to the foot of the Sangre de Cristo Mountains where Santa Fe was founded in 1610. In 1680 the Indians revolted, burned the city and drove out the Spanish colonists, who fled to Guadalupe del Paso, now Juarez, Mexico. They rescued from the burning church the 29-inch wood carved Marian statue, La Conquistadora, originally brought to Santa Fe in 1625 by the missionary, Fray Alonso de Benavides. More…


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This article was originally published in the March/April 1998 issue of Home Energy Magazine. Some formatting inconsistencies may be evident in older archive content.
| Back to Contents Page | Home Energy Index | About Home Energy |
Home Energy Magazine Online March/April 1998
by Deborah Rider Allen
After more than 30 years of energy efficiency regulations and standards, the United States still carries a heavier energy load per capita than any other industrialized country. But aren't consumers buying more energy-efficient products for their homes, and aren't homes more efficient also? The numbers say yes.
According to the 1997 Consumer New Home Survey conducted by NFO Research Incorporated for Professional Builder magazine (which surveyed 60,000 U.S. households), 66% of respondents said they would spend an extra $2,000 for energy-efficient products or features during the construction of a new home. Another 15% would spend up to $3,000. But this willingness to spend extra money comes only with the condition that the builder save them at least $250 annually on heating and cooling costs. Even with that savings, the remaining 19% of respondents would still invest in the efficient house or product, saying that it would take too long to recoup the expenditure (see Figure 1).
To the detriment of the so-called greenwashed (fake green) companies , the majority of consumers will no longer accept just a promise of energy savings for the products or services they choose for their home. To determine the credibility of claimed energy-saving features, 83% of the survey respondents want some sort of guarantee from their builder or the manufacturer of the product before they would purchase it for their home. Only 13% would simply take the builder's or seller's word for it. The final 4% said they were not concerned about credibility. And more than 50% also wanted an independent evaluation, such as blower door or Duct Blaster test conducted by an energy service company.
It is an entirely different marketplace and the whole industry is changing by demand, says John J. Tooley, senior building science specialist with Advanced Energy in Raleigh, North Carolina. In order to meet the demand for energy efficiency, product and service performance has to be guaranteed.
So what products are consumers purchasing to make their homes more energy efficient? Figures from the Air-Conditioning and Refrigeration Institute (ARI) show that consumers are buying central air conditioners and heat pumps in ever-increasing numbers. According to ARI, just 26 years ago only 10% of the housing stock, or 7.3 million homes, in the United States had central air. Today that number is significantly higher. Seventy-two percent or 68 million homes today have air conditioning, with 44% of those having central air or heat pumps and 27% having one or more window units.
In the new family home market, four out of every five homes built today will include central air conditioning. And the figures also show a significant difference between geographical regions. About 61% of new homes in the Northeast are built with central air conditioners and heat pumps, while in the South, these items can be found in 98% of new homes.
We have seen a very dramatic increase in the number of shipments of units in 1997, and we appear to be headed for the second best year in our history, says Edward W. Dooley, vice president of communications for ARI. The units that are being shipped today are much more energy efficient, so energy efficiency is definitely playing a role in purchasing.HVAC: Better Numbers Central air conditioners and heat pumps have become more energy efficient over the past 20 years. The average SEER (seasonal energy efficiency ratio) of units shipped by manufacturers in the United States 15 years ago was around 7. But from 1976 to 1996, ARI reports that SEER ratings increased 52% on air conditioners (one or more-piece units designed to be used together, like central air conditioning with an indoor coil and outdoor condensing unit) and 60% on heat pump units. Though this is due in part to government regulations in 1991 and 1992, mandating that all new residential air conditioning equipment have a SEER of at least 10, figures show that consumers are not only buying more energy efficient units, but they are choosing units that are above mandated levels. The 1997 Consumer New Home Survey reports that 82% of respondents were willing to pay more for a home where a builder exceeded, rather than met, mandated building codes. SEER averages show a parallel tendency. In 1996, SEER averages were 10.68 for central air-conditioners and 11 for central heat pumps.
In Richmond, Virginia, consumers now regularly purchase equipment above the minimum 10 SEER standard. More than 80% of the customers in the Virginia Power Energy Efficiency Financing Program (VPEEFP--a demand-side management program that assists home buyers in purchasing upgraded heating and cooling systems), are buying 12 SEER units for their homes. What we are seeing is that the consumers are much more conscious of buying more efficient heating and cooling equipment, says Ralph T. Savage, director of energy efficiency programs for Virginia Power. They are willing to pay a little more up front to improve energy efficiency, and this has been steadily occurring for the last three years.
VPEEFP has also seen consumers purchasing more products related to home performance and improved indoor air quality. Savage says there has been a marked increase in the number of consumers spending additional dollars to purchase air filtration products such as electronic air filters and high-efficiency particulate air (HEPA) filters. In the 1997 Consumer New Home Survey, 16% of respondents ranked healthy-home features as something they definitely wanted. Another 50% said they would really like to have those types of features. In terms of finances, 32% would spend an additional $1,000-$1,999, while 14% would spend more than $3,000 for healthy home features.
Consumers are also buying more efficient heating equipment, but this is probably due to more rigorous standards for those appliances. Statistics from the Gas Appliance Manufacturers Association (GAMA) show that in 1986, only 31% of the total shipments of central air gas furnaces had annual fuel utilization efficiency (AFUE) ratings of 80% or more. Just ten years later, that number had grown to 85% of the market. (Minimum allowable AFUE rating is 78.8%. Most of these increases are due to the improvements in required efficiency standards.) Central air oil furnaces show a similar trend. In 1986, GAMA reported that 53% of total shipments were for products with AFUEs of 80% or more. By 1996 that figure had risen to 98% of the market. These GAMA reports tracked shipments of upflow, downflow, horizontal, and multipurpose units. The trend continues annually, but only minor improvements show in comparing figures from 1995 and 1996 (see Tables 1 and 2).Filling Up On Fluff Consumers are also spending more money to put higher R-value levels of insulation into their homes today. Michael J. Herbert, vice president of sales and marketing for Insulation Consulting and Supply Corporation, a company that distributes and applies all types of insulation in more than 25 states, says the amount of insulation being installed per house has increased significantly over the past several years. Though contributing factors include the increased size of today's homes and the further adoption of the CABO model energy code in many states, consumer awareness has incrased with insulation R-values.
Greater use of insulation is accompanied by more sealing and tightening of the building envelope. This has spun off into more energy-efficient framing techniques, and more care taken for any plumbing or electrical work that goes through outer walls. It does not matter what type of insulation product material is used; if it is not used in conjunction with a good sealing package for the thermal envelope, the R-value of the product is insignificant, says Herbert. While consumers are purchasing more energy-efficient products for their homes, the home building industry has had a wake up call in both sales and marketing and building performance levels. With a majority of consumers demanding guarantees for products and services, there has been a domino effect in the demand for quality in the home building industry. The increased performance levels of products and services are driving those who are not performing out of the picture, says Tooley. It is driving each part and piece of building a house and raising its quality and value. New products are also coming up to meet these needs.
Marketing products as more energy-efficient has become an integral part of the home building strategy. If you don't market your product as energy-efficient and everyone else does, then you lose, says energy conservation consultant Charles B. Bowles, president of the Energy Consortium in Richmond, Virginia. You have to have it in your marketing whether you are building them super energy-efficient or just doing standard techniques with a few energy-saving bells and whistles. Fortunately for consumers, builders, and manufacturers who are paying more than lip service to a desire for efficiency, the trend toward guarantees is increasing.
In a marketing survey for a builder in Texas, Bill Zoeller of Steven Winters Associates recently interviewed ten new home buyers about their perceptions of their recent purchase, including questions about energy use. Most of the home buyers knew what kind of HVAC system they had in their new home, and most of them found the system to be adequate. In the cooling climate of Houston, average summer cooling costs are between $150 and $390 per month (for one-story and a two-story house respectively).
Five of the home buyers said they would accept a less conventional HVAC system with a $500 per year savings. The respondents who wouldn't accept the system were concerned about reliability and serviceability. Despite this, eight respondents said they would have paid $1,500 more in the sales price to save $500 per year in energy costs. Nine respondents said that they use window treatments to control heat gain, saying they would pay between $500 and $3,000 for solar coatings and insulated glass resulting in a $500 per year savings in energy costs. Most of the respondents were concerned about indoor air quality, and six said they would have paid $500-$3,000 more for a system to remove irritants in the indoor air.
Zoeller's conclusions from the survey suggested that the developer should offer a high-efficiency, relatively conventional HVAC system, offer solar-coated or insulated windows as an optional upgrade, and maintain at least one model home with an air purification system.
But while the numbers reflect a growing consumer trend to make energy efficiency part of the equation when purchasing a home, the 1997 Consumer New Home Survey shows that only 7% of respondents ranked energy efficiency as their number one reason for buying a different home. A larger home, a newer home, and better location ranked as the top three reasons. Interestingly enough, retirees were more likely that the general population to rate higher energy efficiency as the primary reason for buying a different home (13% compared with 7%).
More than half of the respondents (70%) also went so far as to say that they definitely want or would really like to have an intelligent house energy management feature installed in their house. Of those, 28% would be willing to pay less than $500 for such a feature, 26% would pay between $599-$999 and 28% would pay $1,000-$1,999. Keep an eye out for our upcoming special report on energy efficiency and home automation in the May/June issue of Home Energy.
There are a minority of home consumers whose first priority is energy efficiency and who consider energy efficiency as a major concern. Today's consumer is still more interested in carpets and paint and crown molding, says Bowles. Thanks mostly to government efficiency standards and utility rebates, impressive improvements have been made in appliance efficiency, but overall home performance is still not a high priority for most consumers.
Deborah Rider Allen is a freelance writer in Richmond, Virginia.
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Sanyo Electric has unveiled a short-throw laser projector that can display a 100-inch high-definition image that's brighter and has more vivid colors than anything else on the market, it said this week.
The prototype projector builds on Sanyo's ultra-short focus technology that makes use of a large diameter aspheric lens and high-precision aspheric mirror to project a large image from a short distance. The company already has a short-throw projector on the market, the LP-XL51, but that uses a conventional lamp as its light source and manages an 80-inch image over just 8 centimeters with a brightness of 2,700 lumens.
Compared to these specifications, the prototype is in an altogether different class.
Sanyo has switched from a bulb to a laser light source resulting in a much brighter and richer picture. Efficiencies in light usage result in a projected image with brightness of 7,000 lumens, which is a record for laser projectors, said Reina Mito, a Sanyo spokeswoman.
The use of a laser and an improved image processing engine also means the projected image has a color range of about 170 percent that of a standard NTSC image. That means colors are about twice as rich as could be expected from an LCD television, Sanyo said.
It can also project bigger images managing a full high-def 100-inch image over 63 centimeters or a 150-inch image over 94 centimeters. These combine to give the projector the shortest throw ratio yet accomplished with such a projector, said Mito.
Compared to the commercial model its also bigger—much bigger. The prototype is an impressive 2.4 meters long by 51 centimeters by 63 centimeters, which puts it into the same league as a large piece of furniture. Part of the size is due to its nature, prototypes are rarely small and compact, and partly from the use of a laser light source and the new image processing that has gone inside.
The size will be reduced as Sanyo takes the technology closer to commercial production. Development continues and while there are plans to commercialize the technology, there's no set timeframe for doing so at present.
Sanyo is initially aiming the technology at use in places like conference rooms and halls and for digital cinema or digital signage applications.


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Revel shutdown OKd as prayer service nears
ATLANTIC CITY – Hours before hundreds of workers were to hold a prayer rally as three casinos are set to close, New Jersey officials issued a final closure order for another one.
The state Gaming Enforcement Division gave final approval to Revel's shutdown on Sept. 1 and 2, signing the order on Tuesday night and releasing it on Wednesday.
Employees of Revel, the Showboat and Trump Plaza will attend a prayer service at a church near the casinos on Wednesday.
Showboat and Revel are due to close over Labor Day weekend, with Trump Plaza shutting down on Sept. 16.
The shutdowns are part of a rapid unraveling of Atlantic City's gambling market, which began the year with 12 casinos, but will have eight before summer ends.
The Atlantic Club closed in January.