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Plans of Concentration
E. Joy Auciello
Bachelor of Arts
ANTHROPOLOGY & ENVIRONMENTAL STUDIES/Communal Sustainability
A study of the relationship between sustainability and community living using ethnographic writing and formal assessment systems.
Sponsors: Carol Hendrickson, Matthew Ollis
Outside Evaluator: Gwendolyn Hallsmith, Director of Planning and Community Development, City of Montpelier
Sam Auciello
Bachelor of Arts
COMPUTER SCIENCE/Programming Languages
A broad study of computer science with a focus on programming languages.
Sponsor: Jim Mahoney
Outside Evaluator: Brandt Kurowski, Logic Branch Productions, LLC
Alexia C. Boggs
Bachelor of Arts
An experiment in analyzing the role of deliberation, focusing on the Supreme Court and the fate of Muammar Gadaffi.
Sponsors: Meg Mott, Lynette Rummel
Outside Evaluator: Scott McLean, Quinnipiac University
Taylor Monique Burrows
Bachelor of Arts
POLITICS & SOCIOLOGY/Conflict Studies
An exploration of the social and political roots of genocide in Yugoslavia and Rwanda, with a subsequent examination of the major impediments to humanitarian intervention at the United Nations.
Sponsors: Lynette Rummel, Dana Howell, Gerald Levy
Outside Evaluator: James Waller, Keene State College
Thea Cabreros
Bachelor of Arts
A comparison of Fyodor Dostoevsky's and Albert Camus' ethical views, and a photographic consideration of unity and paradox.
Sponsors: John Willis, Geraldine Pittman de Batlle
Outside Evaluator: Jonathan Gitelson, Keene State College
Caitlyn Rose Charles
Bachelor of Arts
TESOL Certificate
ENVIRONMENTAL STUDIES/Religion & Psychology
An investigation of the causes and possible solutions to the current environmental crisis through the lenses of psychology, religion and environmental studies.
Sponsors: Amer Latif, Thomas Toleno, Jennifer Ramstetter
Outside Evaluator: Joseph Markowski, Green Mountain College
Zachary Andrew Chilcote
Bachelor of Arts in International Studies
ASIAN STUDIES/Contemporary Chinese Society
A series of papers discussing power dynamics and social mobility in contemporary urban Beijing.
Internship: Beijing, China
Sponsors: Seth Harter, Grant Li
Outside Evaluator: Suzanne Zhang-Gottschang, Smith College
Alexandra Ciampoli
Bachelor of Arts
HISTORY/Medieval Studies & LITERATURE/Medieval Literature
An investigation into the ever-shifting lines between religious orthodoxy, fanaticism and the heretical in the late Medieval period, with a focus on Elizabeth of Spaalbeck, the Beguines and English mystical traditions.
Sponsors: Adam Franklin-Lyons, Richard Glejzer
Outside Evaluator: Susan Wade, Keene State College
Joan Michelle Coles
Bachelor of Arts
An examination of depression, considering diagnosis, therapeutic treatment, and antidepressant medication.
Sponsors: Thomas Toleno, Todd Smith
Outside Evaluator: Nels Kloster, psychiatrist
Emily L. Cox
Bachelor of Arts
THEATER/Music & AMERICAN STUDIES/Musical Theater
An exploration of musical theater as a part of American culture with particular attention given to the role of musical theater in secondary education.
Sponsors: Paul Nelsen, Kathryn Ratcliff
Outside Evaluator: Edward Isser, College of the Holy Cross
Chad Daniels
Bachelor of Science
PHYSICS & COMPUTER SCIENCE/Computational Physics
A study of the principles of statistical and quantum mechanics with a focus on their role in systems of many particles.
Sponsors: Sara Salimbeni, Jim Mahoney
Outside Evaluator: Juan Burciaga, Mount Holyoke College
Morgan Ellyse Donhoff
Bachelor of Arts
A critical analysis of Sylvia Plath's stylized poetics and a series of related artworks.
Sponsors: Heather Clark, Timothy Segar
Outside Evaluator: Joanne Hayes, Greenfield Community College
Isaac Dupree
Bachelor of Arts
COMPUTER SCIENCE/Collaborative Programming
An inquiry into collaborative programming through a series of essays and coding projects.
Sponsors: Jim Mahoney, John Sheehy, Carol Hendrickson
Outside Evaluator: Andrew Cencini, Bennington College
Noah Joseph Farlee
Bachelor of Arts
An exploration of comics and graphic novels as unique art objects that combine literature and visual artistry in equal measure.
Sponsors: Gloria Biamonte, Cathy Osman
Outside Evaluator: Jon Chadurjian, The Center for Cartoon Studies
Shyloh Fawn Favreau
Bachelor of Arts
VISUAL ARTS & BIOLOGY/Apiology
An exploration of bees through visual arts and biology, including bee ecology, conservation, and architecture.
Sponsors: Timothy Segar, Jennifer Ramstetter
Outside Evaluator: Mary Lum, Bennington College
Dane Fredericks
Bachelor of Arts
An examination of the theme of escape in American literature, focusing on Mark Twain, Flannery O'Connor, and Cormac McCarthy.
Sponsors: John Sheehy, Gloria Biamonte
Outside Evaluator: Martha Ackman, Mount Holyoke College
Esperanza Friel
Bachelor of Arts
DANCE/Choreography & WRITING/Short Fiction
A study of narrative in both fiction and dance.
Sponsors: Kristin Horrigan, Brian Mooney
Outside Evaluator: Sara Smith, interdisciplinary artist
Jonathan Hughes Fryer
Bachelor of Arts
A study of evil or monstrous beings of the Western world including the character of the devil in the Abrahamic faiths, the role of demons in late medieval magic and the appearance of demon-like figures in gothic literature.
Sponsors: Amer Latif, Adam Franklin-Lyons
Outside Evaluator: Michael Pittman, Albany College of Pharmacy and Health Sciences
Ashley Gray
Bachelor of Arts
Through painting, works on paper and ceramics, an investigation on how appearances of the ordinary are transformed by the play of light, texture, and focus.
Sponsors: Cathy Osman, Martina Lantin
Outside Evaluator: Elizabeth Myersohn, Smith College
Eva Grimaldi
Bachelor of Arts
A study of visual arts and sociology that compares punk culture in social behavior and art making.
Sponsors: Timothy Segar, Gerald Levy
Outside Evaluator: Elizabeth Alexander, Monserrat College of Art
Nicole Haeger
Bachelor of Arts
An exploration of the relationships between humans and plants in Latin America and Europe, and the role these interactions play in traditional healing methods that could be described as shamanism or witchcraft.
Sponsors: Carol Hendrickson, Adam Franklin-Lyons, Jennifer Ramstetter
Outside Evaluator: Abigail Adams, Central Connecticut State University
Robin Harney
Bachelor of Arts
VISUAL ARTS & HISTORY/Medieval Studies
A body of artwork based on traditional fairy tales incorporating influences from medieval manuscript illumination.
Sponsors: Cathy Osman, Adam Franklin-Lyons
Outside Evaluator: Lauren Mills, independent artist
Nikki Haug
Bachelor of Arts
LITERATURE/History & RELIGION
A study on how the poetry of Robert Browning and Lord Alfred Tennyson critiques materialism, with a focus on the theory of evolution via natural selection.
Sponsors: Richard Glejzer, Amer Latif
Outside Evaluator: Martha Bohrer, North Central College
Raven Peter Hetzler
Bachelor of Arts
An exploration of economic rent-seeking, the methodology of economics and the role that narratives play in economic theorizing.
Sponsor: James Tober
Outside Evaluator: Daniel Barbezat, Amherst College
Alexander Hiam
Bachelor of Arts
COMPUTER SCIENCE/Embedded Systems
A study of classical computer science algorithms and techniques as applied to embedded digital systems through the design and fabrication of open source hardware.
Sponsor: Jim Mahoney
Outside Evaluator: Chuck Malloch, independent engineer
Madelyn Erin Holm
Bachelor of Arts
Certificate in Nonprofit Management
LIBERAL STUDIES/Education and Ceramics
An examination of mandatory community service in primary and secondary education, and the production and analysis of socially engaged ceramics.
Sponsors: Ken Schneck, Martina Lantin
Outside Evaluator: Nicole Nemec, University of Massachusetts Amherst
Aleksandrs Kaspars Jaunzemis
Bachelor of Arts
An examination of Japanese culture and American identity as both are revealed by travel and its aftermath, conducted through historical analysis, photography and creative nonfiction.
Sponsors: John Willis, Seth Harter, John Sheehy
Outside Evaluator: Jim Dow, Tufts University, The School of the Museum of Fine Arts, Boston
Kalie Kamara
Bachelor of Arts
POLITICS/African-American Studies
An exploration of the political teachings of Plato, Aristotle, and Machiavelli with respect to the morality of hip-hop music.
Sponsors: Meg Mott, Rosario de Swanson
Outside Evaluator: Etan Nasreddin-Longo, independent scholar
Lila Rose Kole-Berlingieri
Bachelor of Arts
LITERATURE/Visual Arts
An investigation of the role of art and narrative in realizing selfhood through an analysis of works by Marcel Proust and Virginia Woolf, supported by a visual art component.
Sponsors: Geraldine Pittman de Batlle, Cathy Osman
Outside Evaluator: Jules Chametzky, University of Massachusetts Amherst, emeritus
Evan Lamb
Bachelor of Arts
A distillation of the new artistic practices of immersion theater, with a focus on the imagery, narrative structure and performance tactics of Sleep No More.
Sponsor: Paul Nelsen
Outside Evaluator: Edward Isser, College of the Holy Cross
Clare Llewelyn Linsdale-Riley
Bachelor of Arts
An exploration of landscape systems considered through a biological and sculptural lens, with a focus on the ecology of urban spaces and land-based sculpture.
Sponsors: Jennifer Ramstetter, Timothy Segar
Outside Evaluator: Jessica Cuni, Green Mountain College
Cora Marie Lively
Bachelor of Arts
An exploration of the effects of neoliberal economics on U.S. immigration policy and civil rights discourse.
Sponsors: Meg Mott, Katherine Rickenbacker
Outside Evaluator: Sima Shakhsari, Wellesley College
Matthew Paul Llarena
Bachelor of Arts
An analysis of the character of the narrator in The Brothers Karamazov for the sake of accessing another layer of Dostoevsky's authorial discourse.
Sponsor: Geraldine Pittman de Batlle
Outside Evaluator: Melissa Frazier, Sarah Lawrence College
Evan Austin Lorenzen
Bachelor of Arts
VISUAL ARTS/Animation
An investigation of visual and technological appropriation as a means to engage the viewer within the context of animation.
Sponsors: Cathy Osman, Hilary Baker, Carol Hendrickson
Outside Evaluator: Patricia Galuis-Assmus, University of Massachusetts Amherst
Allen Magaña
Bachelor of Arts
MUSIC/Music History & LANGUAGES/Latin American Studies: Spanish
A study of the musical practices in the Franciscan missions in the Alta California territory of New Spain in the late 18th and early 19th centuries.
Sponsors: Stanley Charkey, Rosario de Swanson
Outside Evaluator: Robert Eisenstein, Mount Holyoke College
Cailin Marsden
Bachelor of Arts
ANTHROPOLOGY/Medical Anthropology
A cross-cultural exploration of pregnancy and childbirth with a focus on flows of obstetrical knowledge, ethnographic writing and inter-subjectivity.
Sponsor: Carol Hendrickson
Outside Evaluator: Sienna Craig, Dartmouth College
Daniel P. Mellman
Bachelor of Arts
LITERATURE/Interactive Fiction/Classical Studies & WRITING
An exploration of the genre of interactive fiction, combining literary and historical analysis and incorporating original creative work.
Sponsors: John Sheehy, T. Hunter Wilson, Brian Mooney, Evelyn Richardson
Outside Evaluator: Ethan Gilsdorf, writer
William Pinkney Simons Mikell
Bachelor of Science
BIOLOGY/Human Physiology, Science Writing
A review of the anatomical and physiological effects of running on the human skeleton and an examination of modern running techniques and technologies.
Sponsors: Jaime Tanner, Kyhl Lyndgaard
Outside Evaluator: Joseph Hamill, University of Massachusetts Amherst
Gordon Richard Morse IV
Bachelor of Arts
ASIAN STUDIES/Japanese Education and HISTORY
A study of history and pedagogy focusing on the Japanese educational model through its examination framework, cultural values and history textbooks.
Sponsors: Seth Harter, Adam Franklin-Lyons
Outside Evaluator: Mark Lincicome, College of the Holy Cross
Jesse Mike Nesser
Bachelor of Arts
CULTURAL HISTORY/International and Political Conflict & FILM/VIDEO STUDIES/Documentary Studies
An investigation, through documentary film, academic papers, and a screenplay, of cultural perceptions of political conflict and how these perceptions are influenced and shaped by media.
Sponsors: Dana Howell, Jay Craven, Paul Nelsen
Outside Evaluator: Leonard Berkman, Smith College
Joshua Tyler Petersen
Bachelor of Arts
FILM/VIDEO STUDIES/Visual Aesthetics
An exploration of the myriad branches of feature film and music video through the niche lens of the behind-the-scenes and B-roll content.
Sponsor: Jay Craven
Outside Evaluator: Nora Jacobson, Off the Grid Productions
Charles Vincent Pillette
Bachelor of Arts
A written exploration of the conflicts of meaning in Conrad's Heart of Darkness, the visual language of war monuments, and the perceived and actual experiences of military veterans.
Sponsors: Geraldine Pittman de Batlle, Brian Mooney, John Willis
Outside Evaluator: Madeleine Blais, University of Massachusetts Amherst
Sean Pyles
Bachelor of Arts
A study of radical and normative theories of journalism, including the role played by radical alternative media in periods of democratic tension.
Sponsors: John Sheehy, Lynette Rummel
Outside Evaluator: Mark Roessler, editor-journalist Valley Advocate
Evan Bakewell Sachs
Bachelor of Arts
ENVIRONMENTAL STUDIES/Ecological Design & PHOTOGRAPHY
A study of ecological design and photography, exploring topics of systems thinking, the built environment and humans' relationship to the natural world.
Sponsors: Jennifer Ramstetter, John Willis
Outside Evaluator: Charles Laurel, permaculturist
Elliot Samuel-Lamm
Bachelor of Arts
LIBERAL STUDIES/Brewing Studies & FILM/VIDEO STUDIES/Documentary Film
A holistic approach to the study of brewing, using history, microbiology and chemistry, film and the production of actual beers.
Sponsors: Adam Franklin-Lyons, Jay Craven, Todd Smith
Outside Evaluator: Christopher Jarvis, Hampshire College
Christina Schneider
Bachelor of Arts
An examination of identity, memory and the reconciliation of our lived experience with its external manifestations through philosophical exploration, photographic self-portraiture and collage work.
Sponsors: John Willis, William Edelglass
Outside Evaluator: Amy Montali, Rhode Island College
Aerika Shamlin
Bachelor of Arts
A study of shark genetics that shows how evolution underlies molecular genetics techniques for species identification and studies of genetic diversity.
Sponsors: Todd Smith, Jennifer Ramstetter
Outside Evaluator: Cynthia Hays, Keene State College
Tristan Dirk Stamm
Bachelor of Arts
An exhibition of artwork and an accompanying scientific study connecting ecological processes with personal reflections on the nature of survival.
Sponsors: Cathy Osman, Jennifer Ramstetter
Outside Evaluator: Jessica Cuni, Green Mountain College
Nicholas Lambert Stefan
Bachelor of Arts
ASIAN STUDIES/Chinese Nationalism
A coherent study of Chinese nationalism and patriotism since 1966.
Sponsors: Seth Harter, Grant Li
Outside Evaluator: Orion Lewis, Middlebury College
Jessica Stout
Bachelor of Arts
LITERATURE/British & HISTORY/Women's History
An examination of feminine identity in literature and history with a focus on the works of Jane Austen, George Eliot and Virginia Woolf.
Sponsors: Geraldine Pittman de Batlle, Adam Franklin-Lyons
Outside Evaluator: Jules Chametzky, University of Massachusetts Amherst, emeritus
Peter Sullivan
Bachelor of Arts
POLITICS/Environmental Studies
An investigation of alternatives to conventional environmentalist thinking, focusing on critical perspectives on technology.
Sponsors: Meg Mott, James Tober
Outside Evaluator: Christian Parenti, School for International Training
Kelsa Emmanuelle Summer
Bachelor of Arts
Certificate in Nonprofit Management
ECONOMICS/Development & POLITICS/Development
A study of the economics and politics of development and social change with a focus on organizational forms, incorporating fieldwork from South Africa and Kenya.
Sponsors: James Tober, Lynette Rummel
Outside Evaluator: James Levinson, Tufts University
Melinda Tenenzapf
Bachelor of Arts
A promotional documentary on an American organization supporting African schools, and a written political analysis of development policy and theory during the Cold War.
Sponsors: Jay Craven, Lynette Rummel
Outside Evaluator: Bess O'Brien, independent artist
Scott David Weaver
Bachelor of Arts
LIBERAL STUDIES/Latin American Studies
An interdisciplinary examination of the origins of violence in contemporary Mexican governance and public life.
Sponsors: Carol Hendrickson, Rosario de Swanson
Outside Evaluator: Michael Clancy, University of Hartford
Edward Regan White
Bachelor of Arts
A collection of experiments examining the ghostly forces that shape and move private and political bodies, using political theory and photography.
Sponsors: Meg Mott, John Willis
Outside Evaluator: Jim Dow, Tufts University, The School of the Museum of Fine Arts, Boston
Henry C. White
Bachelor of Arts
FILM/VIDEO STUDIES/Medievalism in modern cinema & HISTORY/Medieval Studies
An examination of both historic and pop culture representations of the medieval world, focusing on medieval chronicles and modern cinema.
Sponsors: Paul Nelsen, Adam Franklin-Lyons
Outside Evaluator: Carl James Grindley, Hostos Community College, City University of New York
Margaret Ann Hutslar Wood
Bachelor of Arts
Certificate in Nonprofit Management
LIBERAL STUDIES/Education and Gender Studies
A study of sexuality education and how it relates to social inequality through a comparison of education provided in public schools to that provided in community-based organizations.
Sponsors: Ken Schneck, Renee Byrd
Outside Evaluator: Gary Kelly, Clarkson University
Anna Claire Woodward
Bachelor of Arts
PSYCHOLOGY/Education & Child Development
An exploration of early childhood education by examining the ideals of Waldorf pedagogy and the significance of play in child development.
Sponsor: Thomas Toleno
Outside Evaluator: Laura Tucker, New England Youth Theatre
Elias Zeidan
Bachelor of Arts
COMPUTER SCIENCE & LANGUAGES/Computational Linguistics
An investigation of natural language processing and machine translation.
Sponsors: Jim Mahoney, Grant Li
Outside Evaluator: Jason Naradowsky, University of Massachusetts


text:
Community Excavation: Dunbeath Broch
25 August 2018
Join AOC Archaeology Group in undertaking consolidation work and excavations at Dunbeath Broch, Caithness. No previous experience is necessary and tools and training will be provided. Visitors are most welcome throughout; do come and see how our work is progressing.
Participation is free and open to all but places are limited, so please book in advance. Please contact Charlotte at AOC for more information: firstname.lastname@example.com

subset: commoncrawl


text:
Tools and Perks
As if our revolutionary products and business opportunity weren't enough, joining the Zija family also gives you access to the tools and perks you need to succeed.
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Zija's luxury car program puts you behind the wheel like never before. If you "Qualify for the Ride", you'll get a cash bonus to put toward buying or leasing your own Mercedes Benz. You can earn from $400 - $1,000 per Period, based on Pay Rank.
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text: New Hampshire was a key to the Democratic resurgence from 2004 to 2008. John Kerry won the state in the 2004 presidential election, the same year Democrat John Lynch defeated a Republican incumbent to become New Hampshire's governor. In 2006, Democrats Paul Hodes and Carol Shea-Porter defeated incumbent Republicans to win New Hampshire's two congressional seats. In November 2008, Barack Obama won New Hampshire, while Democrat Jeanne Shaheen defeated incumbent Republican U.S. Sen. John Sununu.


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By Fr. Frank Pavone
International Director, Priests for Life
The foundation of all that can be accomplished in the pro-life movement is prayer.
"Without me," Christ said, "you can do nothing" (Jn. 15:5). Jesus does
not merely "help" us, as if we were working on our own, and He came along to
ease the burden. Rather, we cannot even begin a good work without His saving action within
us. All we do is His gift. We need to pray more, and with greater fervor.
But be careful not to misuse a good thing. Even prayer can become an excuse, a refuge
from our responsibility to take action to stop abortion. The forms of action are many and
varied. We are not speaking here of any one kind. But we are called to action.
For the very same reason we are called to pray! Does God really need us to pray? Does
He need to be reminded of His duties, or told to do His job? Of course not. Yet He calls
us to pray, because He wants us to be involved in what He is doing. If, therefore, He
calls us to pray, even when He could act without our prayers, it is reasonable that He
calls us to action, even when He could act without our works. God calls us not because He
needs us but because He chooses to use us.
Prayer is not just asking God to do something. That's part of it, but it's more. Prayer
is union with God. Prayer means we open ourselves so wide to God that He comes in and does
something through us! Prayer and action are not two separate options, but rather two
aspects of the same reality: union with God.
When we come to prayer, we come to the living God, a consuming fire, the source of all
activity. When we come away from prayer we should not feel rested but restless. We should
not feel that we've done our duty, but that we've been given our duty.
Be careful when you ask God to end abortion. His response may very well be to reach
down from heaven, lift you up by the back of the neck, and throw you into the battle! God
is not going to rip open the sky, come down, and tell our nation to stop abortion.
Instead, He is going to put conviction in your heart and words on your lips and command
you to speak and act!
Let us never use prayer to escape from action. Instead, let us immerse ourselves in
true prayer, which enables us to act in union with God, who destroys death and restores
Priests for Life Brochures
en español]


text:
Don't see your question? Visit our help section.
Get access to great in-store deals and local pick-up
Sign up for our email deals newsletter!
Move into a living, breathing world... and invite friends to visit from anywhere on earth. Days and seasons pass in real time, so whether you want to decorate your home, catch bugs or fish, or just chat with the characters in your village, there's always something to do.


text:
At the Candy Lady shop in Albuquerque, customers can chose from 20 flavors of fudge, a wall of licorice, handmade chocolates and bags of blue-tinged crystals made of rock candy.
Owner Debbie Ball calls it "Breaking Bad Candy" after the show that, until recently, was a fixture in her community.
Ball supplied 100 pounds of the candy as a crystal meth prop for the AMC series "Breaking Bad," about a chemistry teacher turned methamphetamine maker. When the show stopped needing her services, Ball decided to sell the crystals in her store. In the last year, she has sold 30,000 bags for $1 each.
Fans of "Breaking Bad" aren't the only ones lamenting the pending end of the series, which wrapped filming in late April and premieres the first of its final eight episodes Sunday. The production spent an estimated $1 million per episode in Albuquerque, providing an economic boon to many local companies that supplied goods and services and capitalized on the show's growing fan base, pitching everything from location tours to bath salts that promise to "relax away the Bad."
"We're all disappointed because it's given Albuquerque so much publicity, whether it's good or bad, and still brings people into the store," Ball said. "We have people who interrupt their vacations just to visit the locations and buy the candy."
For $65, fans can take a 3 1/2-hour tour of some of the popular locations featured in "Breaking Bad," including such unglamorous sites as a carwash, a restaurant called Twisters (which stands in for the fictional Los Pollos Hermanos), a law office, the Crossroads Motel and the homes of characters on the show.
"We've had customers from Germany, UK, Philippines, France, Singapore, Australia and New Zealand," said Jesse Herron, co-owner of ABQ Trolley Co. "We ask them what brings them to Albuquerque and 9 times out of 10 it's because of the show. It's crazy."
"Breaking Bad" has been a long-running success story for New Mexico's film industry, which has seen its ups and downs in recent years. The state became a hotbed for film production, especially under the administration of former Democratic Gov. Bill Richardson, but filming dropped off two years ago when newly elected Republican Gov. Susana Martinez called for a big cut in the state's film rebate program.
Martinez then changed course and recently approved legislation that increased the state's rebate to 30% from 25% for TV series shooting at least six episodes in the state.
The new incentive was dubbed "The Breaking Bad Bill" in recognition of the series' contributions to New Mexico.
Since production began in 2007, "Breaking Bad" has spent an estimated $60 million to $70 million on goods, services and wages for crew members in New Mexico — not counting wages paid to actors, writers and directors — said a source close to the show who was not authorized to speak publicly.
The production employed a cast and crew of 120 people over 62 episodes, 90% of whom were New Mexico residents.
The show's stars Bryan Cranston (who plays Walter White) and Aaron Paul (his partner, Jesse Pinkman) bought homes in Albuquerque and lived in the city during filming.
The series helped establish New Mexico as a key hub for movies such as "The Avengers" and "The Lone Ranger" and for other television shows, said Nick Maniatis, director of the New Mexico Film Office.
"It allowed for other producers and studios to say, 'Hey, they can shoot quality work in a state with quality crews and facilities,'" Maniatis said. The A&E series "Longmire" also films in New Mexico and two undisclosed TV series are in preproduction, film industry officials said.
Local film industry veterans also remain optimistic that Sony could film a "Breaking Bad" spinoff series centered on the lawyer Saul Goodman (Bob Odenkirk) at Albuquerque Studios, where "Breaking Bad" was based.
A spokeswoman for Sony Pictures Television declined to comment.
Although initially hesitant to promote the show because of its depiction of criminal activity, the Albuquerque Convention and Visitors Bureau has made "Breaking Bad" a centerpiece of a film tourism campaign. The bureau provides a self-guided walking tour on its website listing the locations used in the series.
"It's been great for the city and given us new avenues to promote the city along with our film tourism program," bureau spokeswoman Megan Mayo Ryan said.


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U.S. Equal Employment
Opportunity Commission
Veteran Civil Rights Attorney to Oversee Agency Litigation in Florida, Puerto Rico
WASHINGTON--Robert E. Weisberg, a veteran civil rights attorney in Miami, was appointed Regional Attorney of the U.S Equal Employment Opportunity Commission's (EEOC) Miami District Office which includes offices in Tampa, Florida and San Juan, Puerto Rico.
"The EEOC is fortunate to have been able to hire someone of Bob's caliber as the head of our litigation program in the Miami District Office," said EEOC General Counsel P. David Lopez. "His broad and deep experience litigating civil rights cases throughout Florida will be a great asset to the agency."
"I feel very honored by General Counsel Lopez's confidence in selecting me for this important position," Weisberg said. "My private practice for over 25 years has focused on representing individuals and groups whose civil rights have been violated. I look forward to becoming part of the EEOC's efforts to enforce civil rights laws and fulfill its mission of eradicating employment discrimination."
Weisberg has previously litigated many types of civil rights cases in Miami, including representing hundreds of men and women who were discriminated against in employment based on race, gender, color, national origin, religion, age and disability. Weisberg has also prosecuted civil rights actions based on racial discrimination in denial of public accommodations and to enforce violations of voting rights and racial discrimination in the provision of governmental services.
Weisberg is board certified by the Florida Bar in the area of Labor and Employment Law and has been recognized in Best Lawyers in America in an area of practice including employment law, and has also been recognized for his legal work by a number of other publications. He is a frequent lecturer on areas of employment law and has served as an Adjunct Professor of Law at the University of Miami, teaching classes on employment law.
The EEOC enforces federal laws prohibiting employment discrimination. Further information about the EEOC is available on its website at www.eeoc.gov.


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Retail Price:
The Equipe Dash recalls the beauty of classic automobile dashboards that offered vital engine information. Dash watches offer time keeping information in a sporty, original look that is exceptionally easy-to-read. These bold machines feature cushion-shaped cases indicative of the 1960's.
- Battery quartz movement
- 10atm water resistant
- Single timezones
- Case is 13mm thick in the center, 13mm near edge
- Case is 48mm wide with crown (44mm without)
- Flat stainless steel silver other case
- Blue analog dial with silver markers and numbers
- Silver hands with luminous hands
- Black leather band, 22mm wide near case, 20mm near buckle with strap buckle clasp
- Date Display
The Equipe Dash recalls the beauty of classic automobile dashboards that offered vital engine information Dash watches offer time keeping information in a sporty original look that is exceptionally easy-to-read These bold machines feature cushion-shaped cases indicative of the 1960 s
Equipe Watches is all about the unbridled spirit and ravishing beauty of automobiles Each Equipe watch recalls some high-option auto feature from the engine to the exhaust from the interior to the exterior from the tiniest ball joint to the most obvious paint color Equipe embodies identifiable parts of the car gaskets pistons timing chains gauges and other sleek touches with enviable reality and daring design With 15 series such as Dash Big Block Hemi and Balljoint the edgy styles are a masterpiece in watch making design Made according to the strictest standards of excellence each Equipe line has its own unique body style and is crafted of the finest materials All watches utilize high-grade 316L surgical stainless steel Miyota Citizen movements hardened non-glare mineral crystals screw-down crowns screw-in case backs and luminous hands markers or dials All are water resistant to 100 meters and backed by a two-year limited warranty Equipe Watches retail for 299 to 599 with the top-of-the-line Balljoint selling for 999 due to its extremely high-tech 360-degree moveable lugs


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Well, found the problem. It's a tiny spring that pushes the ratchet in (see attached pic. There should be another spring end pushing that lever back). The e d had snapped off. Unfortunately, it doesn't look replaceable, and since it's partly responsible for making sure the handbrake stays on, I might have to replace the whole brake :-( I'd I can get the handbrake assemy out, I might see if there's a creative solution to be had. If so, I'll post here.
just guessing really. I've never been a "car" person, but I used to be a cycle mechanic, and in general am quite handy. I will take it apart tonight and have a look, fingers crossed! I really don't believe the ratchet could have worn in such a short time - if it has I'm going to bet it's snapped, not worn. Interestingly, a bit of googling suggests that Ford recommend the button is only for releasing the brake, not applying it, and I read somewhere that it puts more pressure on the cable that goes to the button if you press the button as you apply the brake, which can cause that to fail sooner (the cause of the "brake won't release" problem that seems more common).
strange that it went so suddenly. You'd think if the teeth were wearing, you'd feel it getting worse. The car's only done 51k. My (uneducated!) guess is that it's something like the spring gone in the ratchet, so it's not springing back and biting the teeth, but without taking it apart, no way to know. The handbrake mechanism is a big lump, and seems to cost a lot, so I really hope I don't need to replace it.
why? It's got to be something related to the ratchet, surely that doesn't require an entirely new handbrake?
pull the handbrake up, and the lever drops again. Putting something under the lever (3 cd boxes right now!) stops the lever dropping, and so the car is not moving (I gave it a push to check!)
'09 Mechanical Handbrake Ratchet doesn't hold
lon posted a topic in Ford S-Max ClubHi all, I've searched the forum (and Google), and only found the opposite problem to mine... Driving into work today, my handbrake ratchet stopped holding. I just tried using it at a junction, and it didn't hold. The handbrake itself works, and so at work I've parked it in gear, and put a few CD's under the handle to stop it dropping back down again. I've seen plenty of issues where handbrakes are stuck on, but not like mine. Anyone else had this, and found a solution? Or anyone who knows the handbrake mechanism well enough to hazard a guess as to how it might be fixed? thanks Leon
Bluetooth Music Streaming On Factory Satnav (09 S-Max)
lon replied to lon's topic in Audio & ElectronicsSorry Supertomy - I saw your PM, but didn't get back. Can't seem to use this from my phone... Anyway, I did not ever get an answer, but to clarify, I have a standard USB socket in my glove box, no jacks, mini usb or anything. I'd love to find out if there's an answer. I also tried hooking up my old iPod, but it doesn't work. Only seems to work with never iPods and iPhones (tried with my Brother-in-Laws iPhone, and my wife's Ipod Nano (1st gen) which work, but my old clock wheel iPod doesn't.) Leon
can anyone tell me if the current behaviour is normal? My wife is complaining about it - I've sort of got used to it. Best I can describe it, if you hit a bump in the road that would deflect the steering, it's effectively amplified by the power steering. Not so much that it would be dangerous, but it's a bit unnatural. certainly my old focus didn't feel like this, but I'm sure something I have driven before has felt like this.
Bluetooth Music Streaming On Factory Satnav (09 S-Max)
lon replied to lon's topic in Audio & Electronicsjust like the one you can see in the vid here: http://www.ford-mobile-connectivity.com/downloadsUpdates when I press cd/aux, i get a "line in" screen, and I can select devices, but bluetooth is not listed.
Bluetooth Music Streaming On Factory Satnav (09 S-Max)
lon posted a topic in Audio & ElectronicsHi all, I've seen a lot of discussions, but none seem to hit the mark, so apologies if I'm asking a well known question. Please give me a link instead of a snark (actually, both is fine ;) ) though if there is an answer out there! I have a bluetoooth voice control satnav stereo unit on my recently purchased 09 s-max. It has a USB port in the glove box, and a cd multichanger under the driving seat (in case it's relavent). I connected my Android phone, and the phone functions seem to work, but I can't find anyway to play music from the handset on the car stereo. I thought maybe it was a s/w issue, so I updated the unit software using the usb pendrive, but same result. If I read the manual correctly, it seems like it should be possible, but I can't find anything as helpful as instructions! So, my conclusion is that either: my phone isn't compatible (can anyone propose a compatible handset? I work in mobile so I have access to most that I could test with) I misunderstood the feature set, and it's just not possible the menu is so complicated, I just haven't stumbled on the right part yet. Any help would be greatly appreciated. Leon
thanks jeebowhite - sounds a bit involved for me! I'll see if I get used to it first before I start mucking about with stuff like that. Interesting in the diesel v. petrol on here. My old car was a 1.8 petrol, but I just assumed that I'd seen the setting when test driving a diesel and forgotten about it. I think I set it when I first got the car (about 4 years ago), and forgot all about it until i got the new s-max. Old car was written off, so no way to check it now.
ok, thanks! That will stop me cycling through every setting again
Tuning The Feel Of Power Steering
lon posted a topic in Ford S-Max ClubHi all, Just joined, as I just bought an '09 S-max Titanium, 2.0L petrol. One thing I noticed, is how "active" the power steering feels. I seem to remember this when I first bought a car with PS, so I'm not super concerned. I previously had a Focus Zetec, and that had a setting for PS, between Sport, Normal and Comfort, IIRC. I was looking around the menus, but couldn't find anything similair on the S-Max. Can anyone confirm if I should, or shouldn't have this, and where to find it? Thanks, Leon


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Scratch Audio: GarageBand for the Web!
There were some excellent presentations at the most recent AZ Disruptors meetup at Axosoft headquarters. I'm someone who attended the first TechCrunch 40 event; I was recently at Jason Calacanis' LAUNCH event -- and I've gotta say, we have some amazing local talent. At the AZ Disruptors lunches, I'm seeing products, presentations, and founders that easily rival the best of what I've seen in the Bay Area.
Scratch Audio is a top-notch example of this, as is Sean Tierney, Scratch Audio's co-founder.
The product is designed to enable collaboration amongst musicians. Think GarageBand for the web. Singer in London, guitarist in New York, synth in Chicago, rythm section in L.A.? No problem. Float the ideas straight from your imagination to the musical zeroes and ones of Scratch Audio.
Pass it around. Collaborate. Add. Subtract. Enhance. All you need is a browser and some basic equipment like a microphone.
This product really piqued my interest because I'm a regular user of GarageBand, even though I'm not a musician. I use it for recording the spoken word -- for podcasting. And this actually opens up an entirely interesting set of new paradigms for the podcasting world, too.
Check out Sean's great presentation:
This article was written by Angelo Coppola. He is a software industry veteran who currently serves as Vice President of Marketing for Axosoft. Angelo will provide AZ Disruptor classes with mentorship in the areas of marketing, design, and user experience. You can be a mentor, too.


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Nanoelectronic Modeling: Multimillion Atom Simulations, Transport, and HPC Scaling to 23,000 Processors
Published on
Dr. Gerhard Klimeck is a Professor of Electrical and Computer Engineering at Purdue University and serves as the Associate Director for Technologies of the National Science Foundation Network for Computational Nanotechnology since Dec. 2003. For the NCN he has been directing the replacement of web-form driven online simulation on www.nanoHUB.org by fully interactive simulations. The change in technology resulted in a six-fold growth of simulation user numbers in just over 2 years to over 5,900 annual users. His research interest is in the quantum mechanical modeling of electron transport through nanoelectronic devices, parallel computing, and genetic algorithms. This interest drove the development of NEMO 1-D and NEMO3-D. Dr. Klimeck received his Ph.D. in 1994 from Purdue University and his German electrical engineering degree in 1990 from Ruhr-University Bochum.
This work supported by the Semiconductor Research Corporation, the National Science Foundation, and the Army Research Office.
Cite this work
Researchers should cite this work as follows:
Lawson 3102, Purdue University, West Lafayette, IN


text:
Bordeaux Thursdays....
More posts
- A refreshing change
- Very good with lamb
- Smooth blend. Coats the mouth.
- Sauvignon Blanc at Bistro Moulin in Monterey
- Hints of chocolate and cassis on the nose. Needs to breath. Aroma is fun to work with on the glass. Chocolate on the late after taste. Easy Bordeaux. Great first wine when starting with French.


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- Methodology article
- Open Access
Eigengene networks for studying the relationships between co-expression modules
- Peter Langfelder1 and
- Steve Horvath2Email author
© Langfelder and Horvath; licensee BioMed Central Ltd. 2007
- Received: 08 May 2007
- Accepted: 21 November 2007
- Published: 21 November 2007
There is evidence that genes and their protein products are organized into functional modules according to cellular processes and pathways. Gene co-expression networks have been used to describe the relationships between gene transcripts. Ample literature exists on how to detect biologically meaningful modules in networks but there is a need for methods that allow one to study the relationships between modules.
We show that network methods can also be used to describe the relationships between co-expression modules and present the following methodology. First, we describe several methods for detecting modules that are shared by two or more networks (referred to as consensus modules). We represent the gene expression profiles of each module by an eigengene. Second, we propose a method for constructing an eigengene network, where the edges are undirected but maintain information on the sign of the co-expression information. Third, we propose methods for differential eigengene network analysis that allow one to assess the preservation of network properties across different data sets. We illustrate the value of eigengene networks in studying the relationships between consensus modules in human and chimpanzee brains; the relationships between consensus modules in brain, muscle, liver, and adipose mouse tissues; and the relationships between male-female mouse consensus modules and clinical traits. In some applications, we find that module eigengenes can be organized into higher level clusters which we refer to as meta-modules.
Eigengene networks can be effective and biologically meaningful tools for studying the relationships between modules of a gene co-expression network. The proposed methods may reveal a higher order organization of the transcriptome. R software tutorials, the data, and supplementary material can be found at the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/EigengeneNetwork.
- Module Membership
- Black Module
- Clinical Trait
- Consensus Network
- Consensus Module
Gene co-expression networks constructed from gene expression microarray data capture the relationships between transcripts [1–7]. From the point of view of individual genes ('from below'), modules are groups of highly interconnected genes that may form a biological pathway. From the point of view of systems biology ('from above'), functional modules bridge the gap between individual genes and emergent global properties [8–10]. Here we view modules as basic system components (i.e., nodes of a network) and describe their relationships using network language. We find that co-expression modules may form a biologically meaningful meta-network that reveals a higher-order organization of the transcriptome. We refer to modules in a meta-network of modules as meta-modules.
Our analysis can be viewed as a network reduction scheme that reduces a gene co-expression network involving thousands of genes to an orders of magnitude smaller meta-network involving module representatives (one eigengene per module). We refer to the resulting network as eigengene network. Using eigengene neworks, we will show that the information captured by co-expression modules is far richer than a catalogue of module membership.
As a motivating example, consider the comparison between gene co-expression networks in human and chimpanzee brains. Using gene expression microarray data corresponding to different brain regions, Oldham et al [11] found relatively large modules that are preserved between human and chimpanzee brains. Only one human brain module (corresponding to genes expressed in the cortex) was not preserved in chimpanzee brains. The original analysis focused on human modules and assessed their preservation in a corresponding chimpanzee co-expression network. We refer to such an analysis as a standard marginal module analysis since it simply determines whether a set of modules can be found in another network. Here we pursue a more comprehensive analysis that not only quantifies module preservation but also determines inter modular preservation. We refer to modules that are preserved among data sets as consensus modules. In our applications, we show that two consensus modules may be highly related to each other in one data set but unrelated in another. Inter-modular relationships are biologically interesting because changes in pathway dependencies may reflect biological perturbations.
In this work we present methods a) for finding consensus modules across multiple networks, b) for describing the relationship between consensus modules (eigengene networks), and c) for assessing whether the relationship between consensus modules is preserved across different networks (differential eigengene network analysis).
Eigengene networks
where N denotes the number of module eigengenes. Note that the scaled connectivity C I (A Eigen ) is close to 1 if the I-th eigengene has a high positive correlation with most other eigengenes.
The density D(A Eigen ) is close to 1 if most eigengenes have high positive correlations with each other.
Meta-modules in a single eigengene network
Since eigengenes form a network, one can use a module detection procedure to identify modules comprised of eigengenes. We refer to modules in an eigengene network as meta-modules. Meta-modules may reveal a higher order organization among gene co-expression modules. We use average linkage hierarchical clustering to define meta-modules as branches of the resulting cluster tree (Methods, Eq. 21). The resulting meta-modules are sets of positively correlated eigengenes.
Differential eigengene network analysis
Larger values of D(Preserv(1,2)) indicate stronger correlation preservation between all pairs of eigengenes across the two networks. Measures (5, 6) are intuitive, descriptive measures for assessing the extent of preservation between networks. To arrive at a statistical significance level (p-value), one can use a permutation test (described in Methods). Many statistical tests have been proposed to test for differences between correlations, e.g., [14–16].
Application 1: Differential eigengene network analysis of human and chimpanzee brain expression data
Here we report results of our differential eigengene network analysis of human and chimpanzee microarray brain data. The microarray data were originally published in [17]. A gene co-expression analysis of these data is reported in [11]. To facilitate a comparison with the original marginal module analysis, we used the genes selected by that work. The data, R code, and more details of this analysis can be found in Additional File 1 and on our webpage.
To find consensus modules, we used the consensus dissimilarity measure (Eq. 22) and average linkage hierarchical clustering. Genes of a given consensus module were assigned the same color, while unassigned genes were labeled grey. We found 7 consensus modules, shown in Fig. 2A: black (41 genes), blue (40 genes), brown (294 genes), pink (41 genes), red (78 genes), turquoise (884 genes), and yellow (151 genes). The functional enrichment analysis of these consensus modules is described below. For each data set, we represented the consensus modules by their corresponding module eigengenes and constructed an eigengene network between them (Eq. 1).
The differential eigengene network analysis yields two main novel findings that could not have been obtained using a standard marginal method. First, we find that the relationships between the module eigengenes are highly preserved. Figs. 2E and 2H show the eigengene networks AEigen,humanand AEigen,chimp, respectively. It is clear that the human and chimp eigengene networks of consensus modules are highly preserved. As described in Eq. (4), we defined a preservation network Preservehuman,chimp= Preserv(AEigen,human, AEigen,chimp) between the 7 consensus eigengenes.
For each individual eigengene, we find that its relationships with the other eigengenes is highly preserved as reflected by a high connectivity in the preservation network (Eq. 5): C red (Preservehuman,chimp) = 0.94, C black = 0.95, C yellow = 0.92, C turquoise = 0.95, C pink = 0.91, C blue = 0.91, C brown = 0.94. We find a high overall preservation (Eq. 6) between the two networks as reflected by a high density of the preservation network D(Preservehuman,chimp) = 0.93. Figs. 2F,G summarize our findings about the relationships of the consensus modules.
The second novel finding is that the consensus eigengenes in the human data set fall into three branches (meta-modules), see Fig. 2C. The first meta-module consists of the red, black, and yellow eigengenes; the second meta-module contains the turquoise eigengene; and the third meta-module contains the pink, blue and brown eigengenes. Remarkably, these 3 meta-modules can also be detected in the chimp data, see Fig. 2D. While the definition of consensus modules trivially implies that they are preserved between the two data sets, it is a non-trivial result that in this application the meta-modules are preserved as well.
To understand the biological meaning of the consensus modules, we studied differential expression of the consensus module eigengenes across the brain areas from which the microarray samples were taken. The results are summarized in Fig. 2 which shows the t-test p-values of differential expression of module eigengenes in the various brain regions from which samples were taken. Clearly, eigengenes can be characterized by their differential expression patterns in different brain regions. Furthermore, this analysis allows a biologically meaningful characterization of the meta-modules. The first meta-module (comprised of the black, yellow, and red module eigengenes) represents 270 genes that tend to be differentially expressed in the caudate nucleus. The second meta-module (comprised only of the turquoise eigengene) represents 884 genes that tend to be differentially expressed in cerebellum. The third meta-module (comprised of the pink, blue, and brown module eigengenes) represents 375 genes that are differentially expressed in the cortical samples. Thus, the meta-modules of this application correspond to biologically meaningful super-sets of modules and genes.
Given the strong relationships between modules in each meta-module, it is natural to ask whether the consensus modules are truly distinct. For example, the black and red modules show very similar levels of differential expression, see Fig. 2B. In this case, gene ontology information suggests that the two modules are indeed distinct. The black module is enriched with white matter related genes while no such enrichment can be found for the red module [11]. Likewise, gene ontology suggests that the yellow and black modules are distinct even though their module eigengenes are correlated.
In summary, the eigengene network analysis reveals a higher order organization of the consensus modules in the transcriptome.
Comparing our findings to a standard marginal module analysis
A standard approach for comparing the modules between several network is to identify modules in a 'reference' network and to study the preservation of the module assignment in the other networks [7]. In the original analysis, Oldham et al chose the human gene co-expression network as reference network since both preservation and non-preservation of human modules was of interest. This marginal module analysis is appropriate when the modules of one data set are the focus of the analysis but it is not designed to identify consensus modules that form the focus of our article. To compare differential eigengene network analysis analysis to the standard marginal module method, we compared our consensus modules to the 7 human modules found in [11]. We used a pairwise Fisher exact test to determine whether there is significant overlap between the consensus and the human modules. The results are summarized in Additional File 2. Overall, we find good agreement between consensus modules and human specific modules, which reflects the fact that most human modules are preserved in chimpanzees. Most of the human modules can be assigned to a consensus module and vice-versa, except for the human blue (360 genes) and green (126) modules which mostly disappeared from the consensus. Interestingly, small remnants (24 and 12 genes, respectively) of the two modules form the majority of the only consensus module (labeled pink, 41 genes) that does not have a clear human counterpart. Another small remnant (33 genes) of the human blue module forms most of the consensus blue module (40 genes).
The green and blue human modules were found to represent mostly cortical samples (and cerebellum for the green module) and were the least preserved in chimpanzees [11]. This is congruent with our finding of their lack of conservation using the consensus module method. One possible explanation for the absence of these modules in chimpanzees is that they largely reflect gene expression in the cerebral cortex, a brain region that has expanded dramatically in the human lineage. The standard marginal differential network analysis also identified several genes – LDOC1, EYA1, LECT1, PGAM2 – whose connectivities (Eq. 8) were significantly lower in the chimp network. None of these genes are present in our consensus modules, providing additional evidence of the method's agreement with the results of [11].
By definition, the consensus module detection is designed to find modules that are shared between data sets. Obviously, there will be many applications where data set specific modules are of interest. In such applications a standard marginal module detection analysis will be preferable.
Application 2: Differential eigengene network analysis of four mouse tissues
Figures 3F,K,P, and 3U show the eigengene networks AEigen,brain, AEigen,muscle, AEigen,liver, and A Eigen, adipose , respectively. To assess the preservation of consensus modules across pairs of tissues, we defined preservation networks (Eq. 15), e.g., Preservmuscle,adipose= Preserv(AEigen,muscle, AEigen,adipose). We find the following overall preservation values between the eigengene networks: D(Preservbrain,muscle) = 0.93, Dbrain,liver= 0.88, Dbrain,adipose= 0.85, Dmuscle,liver= 0.88, Dmuscle,adipose= 0.85, Dliver,adipose= 0.87. Hence, at the level of tissues, we observe good preservation between the consensus eigengene networks with highest preservation between the brain and muscle tissues. Interestingly, these two data sets also show the strongest relationships between the eigengenes in each data set (strongest red and green patterns in the heatmap plots). This can be measured by the density of the absolute values of ME correlations, Dcor ≡ D(|cor(E I , E J )|). For the muscle and brain network we find Dcor,muscle= 0.45 and Dcor,brain= 0.45. The eigengenes in liver show, as a data set, relationships somewhat similar to those of brain and muscle, though the patterns in the heatmap plot are not as strong, Dcor,liver= 0.37. The adipose tissue shows the weakest relationships between the module eigengenes, Dcor,adipose= 0.31. The eigengene preservations, e.g., C red (Preservemuscle,adipose) can be found in Fig. 3, in the upper triangle of the matrix of plots F-U.
As an aside, we mention that pairwise network preservation measures are directly comparable only when the compared preservation networks involve the same set of consensus eigengenes, as is the case in this four-tissue application.
We find that the eigengene networks contain meta-modules, i.e., groups of highly correlated eigengenes (Figs. 3B–E). As an example, we focus on the meta-modules in the brain eigengene network. As can be seen from Fig. 3, the consensus eigengenes in brain tissue form 3 meta-modules that are partially preserved in the other tissues. Specifically, the first brain meta-module consists of the black, blue, magenta, and red consensus eigengenes. It is highly preserved in muscle and adipose but less so in liver. The second brain meta-module consists of the green-yellow, pink and yellow consensus eigengenes. This meta-module is highly preserved in muscle and liver but less so in adipose. The third brain meta-module consists of the turquoise, green and purple eigengenes. It is highly preserved in liver and adipose but less so in muscle. These results show that meta-modules may or may not be preserved across the different eigengene networks.
To understand the biological meaning of the consensus modules, we used functional enrichment analysis using gene ontology information [20]. The detailed results including alternative methods for adjusting for multiple comparisons can be found in the functional enrichment table presented in Additional File 4. Overall, we find that most modules are significantly enriched with known gene ontologies. Specifically, the black module is highly enriched with ribosomal genes (Bonferroni-corrected Fisher's exact p-value p = 8 × 10-10); the blue module with immune/stimulus/defense response (p < 3 × 10-17 for each of the three terms); brown with translation regulator activity (p = 4 × 10-3) and nucleotide binding (p = 5 × 10-3); magenta with stimulus/defense response (p < 2 × 10-6) and signal pathways (p < 2 × 10-3); red with cell cycle (p = 1.4 × 10-19) as well as nucleotide/ATP binding (p < 10-8); turquoise with protein binding (p = 6 × 10-3); yellow with carbohydrate metabolism (p = 3 × 10-4); pink and green-yellow with protein localization (p = 0.003 and p = 0.004), and green with alternative splicing/intracellular organelles (p = 4 × 10-4).
Our method detected two protein transport and localization modules (pink and green-yellow) and one may ask whether these modules are truly distinct. The two modules are closely related in 3 of the 4 data sets, but in the adipose tissue they have a weak (and negative) correlation of -0.24. Hence, from the consensus point of view, they are two distinct modules. Further, note that the green and black modules are very close on the consensus dendrogram, and their module eigengene (ME) correlation is high in absolute value but negative. The functional enrichment analysis suggests that the modules are different, although some terms are related (ribosomes for the black module and intracellular organelle for the green); this is an indication that the sign of the correlation of eigengenes is biologically meaningful.
While a standard marginal module analysis would succeed in studying preservation of individual data set modules, the consensus eigengene module analysis allows us to find shared modules and to study higher-order relationships between the consensus modules. Meta-modules in the brain tissues indicate the following relationships: the first (black, blue, magenta, red) suggests a relationship among ribosomal, immune/defense/stimulus response and cell cycle pathways; the second (green-yellow, pink, yellow) between protein localization and carbohydrate metabolism; the third (turquoise, green, purple) among protein binding and alternative splicing/intracellular organelle pathways.
The data also include clinical trait information on the mice (e.g., cholesterol and insulin levels, body weight, etc.), and one can ask whether some of the consensus modules (or more precisely, their eigengenes) relate significantly to any of the traits. We find no significant correlation between consensus module eigengenes and the traits. In application 3, we report significant relationships between consensus modules and clinical traits.
Permutation test of consensus module membership
Application 3: Consensus modules across female and male mouse liver tissues
The experimental data include clinical traits such as mouse body weight, cholesterol levels, etc. As detailed in Additional File 5, we selected 7 potentially interesting traits. Figs. 5H,I present the correlations and corresponding p-values for relating the clinical traits to the module eigengenes. We find that the turquoise module (605 genes) is highly significantly correlated with weight in both the female (r = 0.5, p = 5 × 10-8) and male samples (r = 0.47, p = 3.1 × 10-8). The greenyellow module (82 genes) relates to weight with comparable correlations, r = -0.44 (p = 8 × 10-8) and r = -0.50 (p = 4 × 10-9) in females and males, respectively. The yellow module is significantly related to insulin levels in both the female and male data sets, r = 0.38 (p = 5 × 10-6) and r = 0.35 (p = 7 × 10-5), respectively. The correlation between the eigengenes of the consensus turquoise and greenyellow modules are -0.68 and -0.74 in the female and male samples, respectively; the module eigengenes are relatively close by absolute value of the correlation, but the sign difference suggests that they distinct. This result is another motivation to use signed networks (Eq. 1) to describe the relationships between eigengenes.
Given that the female and male networks appear similar but not the same, one may ask whether the consensus module analysis provides an indication of how they differ. For this purpose we compared the female liver module assignment as reported in [18] to our consensus module assignment, see Additional File 6. Using the same parameters for the clustering and branch detection, we found that two of the 12 modules (labeled by salmon and light-yellow color) in that work are not represented in the consensus modules. Investigating the function of these two modules is beyond the scope of this work.
Simulation studies of consensus modules
To assess the performance of the consensus module detection method, we performed a simulation study involving two simulated gene expression data sets. The two data sets contained both shared and non-shared modules. The actual simulation procedure is described in more detail in Additional File 7 and the R code can be found on our webpage.
Briefly, each simulated module is built around a chosen seed profile (referred to as the true module eigengene) by adding gene expression profiles with increasing amount of noise. We studied the performance of consensus module detection under varying levels of added noise. The sensitivity and specificity are determined from the numbers of true and false positives (n TP and n FP ) and true and false negatives (n TN and n FN ) as Sensitivity = n TP /(n TP + n FN ), Specificity = n TN /(n TN + n FP ). To measure the fidelity of the calculated module eigengenes to the true module eigengenes, we report the proportion P0.95 of the detected modules whose eigengene has a correlation greater than 0.95 with the true module eigengene, i.e., Fidelity = P0.95. Results of the simulation are summarized in Table 1. We found that when noise is low and modules are very clearly defined, the sensitivity, specificity, and fidelity are 100%. It is worth noting that for low and moderate noise levels, the fidelity does not vary substantially with changes in the branch cut height, indicating that module eigengenes are robust to inclusion/exclusion of moderate numbers of genes in the module. As the noise increases, sensitivity, specificity, and fidelity decrease. We note that the specificity and sensitivity depend on the choice of cutting parameters for the cluster trees. We have not performed an exhaustive search to identify parameter values that would give optimal performance. Our default settings perform well across a range of different simulation models.
We propose the use of eigengene networks to study the relationships between co-expression modules. Eigengene networks will be useful for any module detection method that leads to modules of highly correlated genes. While eigengene networks can easily be adapted to other co-expression module detection methods, we define them within the framework of weighted gene co-expression network analysis since this framework preserves the continuous nature of the co-expression information and leads to robust results [4, 7]. Our empirical applications illustrate the kind of novel questions that can be addressed with eigengene networks. We find that modules can be organized into meta-modules that can be biologically meaningful and interesting.
Eigengene networks can naturally be integrated with other types of quantitative data. For example a microarray sample trait T (such as body weight or survival time) can be included as an additional node of the eigengene network. The adjacency between an eigengene E I and the sample trait T can be defined as aEigen,I,T= (1 + cor(E I , T))/2, generalizing Eq. (24) (Methods). Eigengenes that are adjacent to a clinical trait may correspond to pathways (modules) that are associated with the clinical trait. We illustrate this point in our third application involving female and male mouse liver data, in which we analyze the relationship between consensus modules and clinical traits.
Eigengene networks can be used for describing module relationships in a single data set (single eigengene network analysis) or they can be used to compare module relationships across different data sets (differential eigengene network analysis). To facilitate differential eigengene network analysis, we propose methods for finding consensus modules. Our approach for detecting consensus modules relies on a consensus dissimilarity measure (Methods, Eq. 22) that compares topological overlap matrices of different data sets. In our applications, consensus implies that the modules are present in all data sets (networks). In other applications, it may be preferable to relax this stringent requirement and look for 'common' modules instead. For example, if the number of studied data sets is large (say more than 5), the robustness can be increased by replacing the minimum by a suitably chosen quantile (e.g., the median). Details of such a generalization are presented in Methods.
Since we define the consensus topological overlap as a minimum (Methods, Eq. 18), a bias will result if the topological overlap of one network tends to be higher (or lower) than that of the other data sets because of non-biologic reasons including different microarray platforms, gene expression normalization methods, or different sample sizes. To address this potential bias, one can scale the individual topological overlap matrices or adjacency matrices. Alternatively, we describe a highly robust but less sensitive method for defining consensus modules in the Methods (Eq. 37). In brief, this robust method defines modules in each of the individual data sets and defines consensus modules by keeping track of shared module membership. Module detection depends on several parameters choices, e.g., how to cut off branches of a hierarchical cluster tree. In practice, it is advisable to carry out a robustness analysis with regard to the module definition. For example, the reader can use the R code published on our web page to verify that our findings are relatively robust. Since the module eigengene (first principal component) represents a suitably defined average gene expression profile, it is highly robust with regard to moderate changes in module membership. We find that the consensus eigengene network construction is highly robust and it has high sensitivity and specificity in our simulation studies.
We find that eigengene network methods lead to mathematically robust and biologically meaningful results. We provide three microarray data applications illustrating that eigengene networks effectively represent module relationships. Studies of inter-modular relationships may reveal changes in pathway dependencies due biological perturbations.
Network adjacency matrices and connectivity
Note that 0 ≤ D(A) ≤ 1. The density equals the average adjacency (connection strength) between the genes.
Transformations of the adjacency matrix
Note that Power(A, β) also satisfies the conditions of an adjacency matrix (Eq. 7). By choosing a power β > 1 the power transformation can be used to emphasize large adjacencies at the expense of low ones, i.e., the power transformation can be used for 'soft-thresholding' [4]. We use this approach for defining weighted gene co-expression networks.
Preservation Network
The preservation transformation Preserv(A(1), A(2),...) can be used to determine whether adjacencies are preserved between given networks A(1), A(2),.... Specifically,
Preserv ij (A(1), A(2),...) = 1 - [Max ij (A(1), A(2),...) - Min ij (A(1), A(2),...)].
is an aggregate measure of adjacency preservation between networks A(1) and A(2).
Consensus networks
We now introduce the notion of a consensus network for given input adjacency matrices A(1), A(2),.... Intuitively, two nodes should be connected in a consensus network only if all of the input networks 'agree' on that connection. This naturally suggest to define
Consensus ij (A(1), A(2),...) = Min ij (A(1), A(2),...).
The Consensus transformation is related to Preserv (Eq. 15): if the Max(A(1), A(2),...) network is dense, that is if Max ij (A(1), A(2),...) ≈ 1 for all pairs of nodes i, j, we find Preserv(A(1), A(2),...) ≈ Consensus(A(1), A(2),...). On the other hand, if the Max network is sparse with most adjacencies close to zero, Preserv and Consensus differ.
We use the definition (18) in all our applications, but generalizations are of interest as well. Our definition of the Consensus adjacency may be too stringent when dealing with more than a handful of networks. To address this, we use the quantile transformation (Eq. 12) to define a more robust consensus network as follows
Consensusq,ij(A(1), A(2),...) = Quantq,ij(A(1), A(2),...).
Note that our Consensus network (Eq. 18) is a special case of Eq. (19) with q = 0. For q = 0.25 and q = 0.5, the resulting consensus network is defined as the first quartile and the median, respectively, of the input adjacencies.
Dissimilarity transformation for module detection
The dissimilarity transformation Dissim(A) turns an adjacency matrix (which is a measure of similarity) into a measure of dissimilarity by subtracting it from 1, i.e.,
Dissim ij (A) ≡ 1 - a ij .
This transformation is useful for defining module detection procedures. As an aside, we mention that Dissim(A) does not satisfy our definition of an adjacency matrix since its diagonal elements equal 0.
Module detection using hierarchical clustering
This dissimilarity is used as input to average linkage hierarchical clustering. Branches in the resulting cluster tree (dendrogram) are referred to as modules. As detailed in our R tutorials, we use two different branch cutting techniques: the constant-height cut method and the dynamic tree cut method [19]. This module detection approach has been successfully used in several studies [6, 7, 11, 18, 22, 30].
Consensus modules
Consensus modules are defined as modules in the consensus network (Eq. 18). Analogously to the single network case (Eq. 21), we define a consensus gene dissimilarity
Dissim(Consensus(TOM(A(1)), TOM(A(2)),...)),
and use it as input to average linkage hierarchical clustering. Consensus modules are defined as branches of the resulting clustering tree. By definition, consensus modules consist of genes that are closely related in all networks A(1), A(2),...; in other words, the modules are present in all networks.
Weighted gene co-expression network construction and module detection
Denote the i-th gene expression profile (where i = 1...n) across m microarrays as x i . Thus, x i is a vector with m components. We use two different measures of co-expression similarity to compare a pair of gene expression profiles x i and x j . The first measure S = (s ij ) is the absolute value of the Pearson correlation coefficient, i.e.,
s ij = |cor(x i , x j )|
Note that ssigned,ijequals 1, 1/2, and 0 if the correlation equals 1, 0, and -1, respectively.
The power transformation with β > 1 allows one to suppress low co-expression similarities that may be spurious while at the same time preserving the continuous nature of co-expression information.
In our applications, we use the unsigned adjacency (Eq. 25) to define gene co-expression networks. To choose the power β, we use the scale free topology criterion [4]. We define eigengene networks using the signed adjacency (Eq. 26) because we find it useful to preserve the sign of the co-expression information between module eigengenes. The scaled connectivity C i (A signed ) is close to 1 and 0 if the correlations between x i and other network genes tend to be positive and negative, respectively.
An important step in network analysis is to identify modules of co-expressed genes. As detailed above, we define modules as clusters of genes with high topological overlap (Eq. 11) since this yields relatively large and robust modules [4, 6, 7, 22, 24].
Module eigengenes
To define the module eigengene of a module, we use the Singular Value Decomposition (SVD) of the module expression matrix [31]. The gene expression matrix of the I-th module is denoted by X(I)= (), where the index i = 1, 2,...,n I corresponds to the module genes and the index l = 1, 2,...,m corresponds to the microarray samples. We assume that each gene expression profiles , i.e. each row of X(I), has been standardized to mean 0 and variance 1. The singular value decomposition of X(I)is denoted by
An equivalent definition can be given in terms of principal component analysis where the module eigengene is defined as the first principal component. Since the orientation (i.e., sign) of each singular vector is undefined, we fix the orientation of each eigengene by constraining it to have a positive correlation with the average gene expression across module genes. In practice, we find that the module eigengene explains typically more than 50 percent of the variance of the module expressions.
Relating genes within a module to the module eigengene
The closer is to 1 or -1, the stronger the evidence that the j-th gene is part of the I-th module.
Definition of meta-modules in eigengene networks
Using this dissimilarity as input to average linkage hierarchical clustering leads to a cluster tree of modules (represented by eigengenes). The branches of this tree correspond to meta-modules in our applications.
Consensus meta-modules
This dissimilarity is used to define and detect consensus meta-modules, that is meta-modules present in all input eigengene networks. A small consensus dissimilarity between two eigengenes is an indication that the modules are closely related in all studied data sets. This may be due to biological reasons, namely when corresponding modules represent distinct but interacting pathways. On the other hand, the corresponding modules could also be non-distinct and should be merged. For example, the module detection method may have been too sensitive, which results in many but related modules. To decide whether close modules should be merged, we suggest to use external information, e.g., gene ontology information, or to study module preservation in an independent data set.
Generalized consensus networks
A limitation of consensus networks defined by Eq. (18) (as well as by Eq. 19) and the consensus dissimilarity (22) is that the direct comparison of networks is only meaningful if the corresponding adjacencies have similar distributions. This need not be the case: differences in sample sizes, array platforms, or gene expression normalization methods may seriously bias the results of the Quantile transformation (Eq. 12). To address this, we propose a robust approach to defining consensus modules. The idea is to replace the TOM(1), TOM(2),... matrices in (22) by 'compressed' adjacency matrices , defined using the following steps. First, module detection is performed in each dataset separately, and corresponding module eigengenes are calculated. In data set s, denote by Module(s)(i) the index of the module that gene i belongs to. Module(s)(i) may encode the original module membership or it can be defined using the module eigengene based connectivity measure (Eq. 30): each gene is assigned to the module for which it has the maximum module eigengene based connectivity:
Module(s)(i) = argmax J (||).
As an aside, we mention that one could also use the quantile consensus Consensus q method (Eqs. 36, 37) instead of our minimum based consensus transformation.
Our definition (Eq. 37) is quite intuitive: the consensus dissimilarity is zero for two genes that belong to the same module in every individual set; the consensus dissimilarity will be small if the two genes belong to closely related modules in each data set; and for a gene outside any properly defined module (colored in grey in our applications), the dissimilarity with any other gene will attain its maximum value of 1. A potential major advantage of definition (Eq. 37) is that the individual dataset modules need not be obtained using the same module detection procedure. This allows finding consensus modules in datasets whose properties differ substantially. The differences can be countered by using appropriate data set specific module detection methods. However, this freedom of choice greatly increases the parameters used in the consensus module detection, and as a result, increases the danger of over-fitting. In contrast, our minimum-based consensus TOM method, Eq (22) involves only one clustering tree and hence involves far fewer parameters.
Permutation tests
Simulation studies of consensus module detection.
Noise level
Branch cut
Consensus module detection
Availability and requirements
Project name: Consensus Eigengene Networks
Project home page: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/EigengeneNetwork/
Operating system(s): Platform independent
Programming language: R
Licence: GNU GPL
We would like to thank Mike Oldham, Dan Geschwind and Winden Kellen for discussions about the human-chimp data; Jake Lusis, Tom Drake, Atila Van Nas for discussion about the mouse data; Tova Fuller, Anja Presson, Jun Dong, Wen Lin, Paul Mischel, Stan Nelson, and Lin Wang for helpful discussions. The work was supported in parts by grant 1U19AI063603 01.
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